Just last month at Embedded World 2024, Qualcomm announced its RB3 Gen 2 Platform based on the QCS6490 processor with Cortex-A78 and A55 processing cores

Avnet AI Vision Development Kit features Qualcomm QCS6490 SoC, dual camera, GbE, and USB-C PD - CNX Software

<strong>Avnet</strong> rolls out a new development kit enabling design engineers to create a spectrum of simple to sophisticated FPGA applications.

Avnet Releases AUBoard 15P Development Kit

- $51.4 million cash on hand sufficient to fund current operations, including multiple value driving milestones, through the end of 2025

enVVeno Medical Reports Third Quarter 2023 Financial Results and Provides Corporate Update

Oct 26 (Reuters) - South Africa's state-owned logistics company Transnet has requested an unspecified cash injection from the government as it seeks to reduce debt and return to profitability, it said on Thursday. "Transnet has requested that national ...

S.Africa's Transnet seeks government bailout as debt mounts

Eutelsat Communications (Euronext Paris / London Stock exchange: ETL) announces today the successful entry into service of its state-of-the-art EUTELSAT KONNECT VHTS Very High Throughput Satellite, at the 2.7°E orbital position.

Successful Entry Into Service of the EUTELSAT KONNECT VHTS Satellite, Driving Connectivity Growth

ALNET Technologies partners with AllCloud to revolutionise loan management in South Africa Issued by Blain Communications for Alnet Technologies Johannesburg, 25 Oct 2023 Visit our press office Read time 2min 50sec In this article Comments (0) Gary Govender,

ALNET Technologies partners with AllCloud to revolutionise loan management in South Africa

Niclas Elvgren, Head of the Professional Solutions Business Unit at Imint, considers how new video enhancement software is poised to unlock the full potential of remote assistance devices used within the oil and gas sector by addressing video quality issues.

Revolutionising remote assistance within the oil and gas sector with video enhancement technology

For complete information about, and access to, our official publications and services, go to About the Federal Register on NARA's archives.gov. The OFR/GPO partnership is committed to presenting accurate and reliable regulatory information on ...

Federal Acquisition Regulation: Cyber Threat and Incident Reporting and Information Sharing

Summary Avid Bioservices' investment has decreased due to disappointing near-term guidance, but the long-term outlook remains positive. The company is at a pivot point and expected to generate significant free cash flow in the future.

Laughing Water Capital - Avid Bioservices: Worth Stomaching The Volatility

In a significant move towards a more modular future, Avnet and Arduino have announced a global partnership. This partnership is a strategic alliance that

Arduino and Avnet partner for global distribution

Avnet

avnet

Augmented reality and virtual reality appear to be having a moment -- for real this time. After numerous fits and starts in recent years, it now seems that these technologies are finally coming into their own. In the early months of 2021, Facebook reportedly had -- one-fifth of its workforce -- working on AR and VR. ARHT Media had also in September 2020 featuring holograms of three different people from three different countries, which, if nothing else, is a promising development for those of us suffering from Zoom fatigue during the coronavirus pandemic. And in March 2021, , offering users of that company’s AR HoloLens and VR Windows Mixed Reality platform a shared space. 10,000 people staged a virtual meeting Microsoft Mesh made its debut In other words, AR and VR, having long ago proven their worth beyond gaming, appear to be veering ever closer to the mainstream. And the expectation is that that will continue. Prescient & Strategic Intelligence predicts that the market size for these technologies, valued at $37 billion in 2019, will be by 2030, a robust compound annual growth rate (CAGR) of 42.9 percent over the course of this decade. $1.274 trillion AR/VR’s momentum is very much tied to that of 5G, which these technologies require -- and which is having a moment of its own. T-Mobile, declared by Craig Moffett of MoffettNathanson Research, has made particularly promising strides, launching the in August 2020 and showing a to those of its competitors, while working toward its goal of providing a nationwide 5G network by the end of 2021. provides the faster speed and lower latency “the next big thing” first standalone network superior downlink speed Sol Rogers, CEO/founder for the immersive content studio REWIND, sees this being the last great hurdle for AR and VR. As he in 2019, "5G will usher in the next era of immersive and cloud-connected experiences. Faster, more uniform data rates, lower latency and lower cost per bit will ensure it." wrote for Forbes The caveat is that we have been fooled before when it comes to AR/VR. The hype, in fact, reached a crescendo in 2017, when in the wake of Facebook’s $3 billion purchase of Oculus three years earlier. But Google subsequently ditched its VR platform. VR headsets, found in of American households in 2017, were present in just eight percent two years later. VR enterprises multiplied 13 percent The rebound, however, appears to have come. For one thing, the collaborative possibilities of AR/VR have been buoyed by recent social distancing norms. Spatial is one company that was , empowering users to employ avatars -- which are created from users’ headshots -- to move about a virtual office space, a technology now available on Oculus Quest, Quest 2, and Nreal. addressing the issue even before the pandemic As for ARHT, it worked with the global healthcare company Novartis to enable academics from Australia, Greece and Germany to last September entitled “Asthma Trends and the Digital Health Solves.” And as ARHT CEO Larry O’Reilly told AVNetwork, such an approach was “way more engaging” than a Zoom call, as all three appeared to be in the same room, to the point of even having a panel discussion. appear together for a symposium That same AVNetwork piece noted that anatomy students at Cleveland’s Case Western Reserve University use the HoloLens in classes in combination with HoloAnatomy software developed internally, to learn about the human body. It has proven to be so successful that those students engaged in remote learning during the pandemic have been shipped headsets, enabling lessons to continue. The HoloLens can also be used as a according to Tom McGuiness, Microsoft’s executive vice president for healthcare, as evidenced by the fact that three doctors in Brazil helped walk a surgeon in France through a delicate shoulder procedure via the AR headset. Moreover, immersive video platforms can be used to prepare surgeons before they ever head into the operating room, courtesy of holographics like those used at Case Western. They can even introduce medical students to an OR ahead of time. “GPS for the surgeon,” There is no shortage of other uses for AR/VR. These technologies can be used in shopping, for example, to picture how shoes might look on your feet. They can be used for workouts. They can even be used to relieve pain and anxiety. Cost remains an issue to AR and VR becoming mainstream, as headsets alone can run , and users might require other gadgetry as well. There is also some concern about the headsets can take -- temporary symptoms such as eyestrain, headache, etc. So let the buyer beware on both those counts. as much as $800 physical toll Still, there is no denying that these technologies finally appear to be having their moment — thanks, in part, to the progress of 5G, the many practical uses of AR and VR, and the unique challenges caused by the pandemic.

The Reality is AR and VR are Edging Toward the Mainstream

2019 is the year of adoption. We have weathered the storm of early 2018, lived through the ensuing drought and we can see glimpses of the promised land on the horizon. At , we are strongly passionate about overall adoption of blockchain technology as we believe it can benefit many people and industries. As such, we are always on the look-out for news of increasing adoption and collaboration. aelf Last year I wrote an article with a as I found it very difficult to find a single source for this information online. Comprehensive List of Banks using Blockchain tech Although there was a strong interest in blockchain tech over the last 24 months, due to a mixture of issues, including the volatile ‘unproven’ market and rampant ICO scams, many companies avoided public exposure linking them to the blockchain industry. Many would agree we have seen a shift in this mindset as more high profile individuals and companies are now being more open about their association with blockchain. Take for example, the #1 YouTuber, . Pewdiepie, who has recently partnered with Dlive In fact, according to a by Gemalto, adoption of blockchain to help secure IoT doubled in 2018. report Deloitte recently released survey results in regards to . The companies represented by these executives all have an annual revenue of over $100M USD. One point to note, was that over 75% of respondents believe they will lose a competitive advantage if they do not adopt blockchain. Did someone say FOMO? executive’s views on blockchain adoption Also, according to the survey, over 90% of respondents are already participating in or planning to within 12 months, a blockchain consortium or alliance. According to the , global spending on blockchain development will almost double to $2.9 Billion this year, and increase exponentially to reach $12.4 billion by 2022. International Data Corporation The financial sector will lead the charge with an estimated of $1.1 Billion to be spent in this sector alone. The Manufacturing and resources sector will see the fastest growth this year with a growth rate of 77.6%. Notably, of (Statisca.com), over 50% are included in this list; and in particular, 9 out of the top 10 with a combined market value of over $5.6 trillion USD in 2018. the 100 largest companies in the world by market value Surprisingly, the largest industry in this list is Transport, Shipping & Logistics. There could be a few reasons due to this, firstly, there were a few large alliances that have been created specifically to aid in blockchain adoption with members in the hundreds. Secondly, this is one of the biggest industries in which there is a high degree of interdependence and cross company reliance. The finance industry was always going to be in the top of this list, but another exciting development is the high rank that government received with many countries showing a clear positive attitude towards the technology. In light of all this, it is safe to say, blockchain is here to stay and 2019 is the Year of Adoption. So let’s take a look at the enterprises who have already taken the leap. This list is focusing on active enterprises in 2019. For those that are wondering, this list has almost 1,000 Enterprises in it. *There may be early adopters that are not in this list due to having little activity in the first quarter of this year. If there are ones that are active and not on this list, comment below with the links and I’ll aim to add them in **Accomodation** Cheval Blanc The Dolder Grand **Agriculture** Bayer Crop Science Bumble Bee FoodsCargill **Auto Industry** Hyundai BMW Ford General Motors Genuine Parts CompanyGreat DaneHummer Jaguar Karamba Security Volkswagen Waymo Data **Automated Retail Companies**AAEONAR SystemsFastcorp VendingGlobal Vending GroupGreenbox RoboticsInvendaIVMIVSRetail Automated ConceptsSandenVendoThe-Venders and Wemp **Communication** Deutsche Telekom NEC Europe Smart Communications Solace Corporation Swisscomm Broadridge China Mobile Comcast FarEasTone Kakao OrbcommOrange SoftBank Sprint Telefónica **Consultancy**21 Consulting GmbHAccentureAdaptive Financial Consulting Bain & CompanyBCS ConsultingBerkeley Research Group CH & CoCOMPELLIO DNV GL Business Assurance Group Everis InfosysJDX Post Oak LabsProtivitiRaoof Consultancy Limited Scrypt.MediaSynechronSynpulse XLNTEC Thomas ReutersThoughtWorksTigerRiskTomorrow Labs AizoonAONAsset Balance Solutions Cambridge Associates Deloitte enVista EY J.J. Keller & Associates Inc KPMG RevUnit ThreatAdvice Tata Consultancy Services **Data Managemen**Accuity CommodDT EverisInterxion RoyaltyRange Ansonia Credit DataApplya Connected Holdings Docusign Nielsen Ocean Protocol **Digital Security**CrossVerifyGemaltoGuardtimeObjectTechSLIBSovrinUtimacoYoti **Education** RMIT Duke University Harvard University Swinburne University The Air Force Institute of TechnologyThe University of TennesseeUniversity of Central ArkansasUniversity of Denver University of Colorado Boulder University of Memphis University of Michigan University of Nevada Yale University **Finance**ABN AMROAIBAl Rajhi Banking & Investment CorporationAlawwal BankAlfa Bank Allied Irish Bank ANZ Banking GroupATB FinancialB3Banca Intesa SanpaoloBanca MediolanumBanca MifelBanco BICEBanco BisaBanco Bradesco Banco de Crédito del PerúBanco de Investment GlobalBanco de SabadellBanco de Galicia y Buenos AiresBanco IndustrialBanco Mercantil del NorteBanco Mercantil Santa CruzBanco Nacional de BoliviaBanco Popular DominicanoBanco RegionalBanco SantanderBangkok BankBank ABCBank Al Habib LimitedBank BusanBank Frick & CoBank of America | Merrill LynchBank of CyprusBank of Montreal BankinterBanorteBanreservasBanque Internationale de Commerce — BRED (Suisse)Banque ThalerBarclays BCIBladex BNY Mellon Bolsa de MadridBPCE BRAC Bank LimitedBradescoCBH Compagnie Bancaire HelvétiqueChina Banking AssociationChina Merchant BankChina CITIC Bank International LimitedChina Guangfa BankChong Hing Bank LimitedCIBCCIM Banque S.A GeneveCitiBankCLS GroupCommercial Bank of Africa LimitedCommerzBankCommonwealth Bank of AustraliaCooperatieve RabobankCredicorp BankCredit Agricole S.A.Credit Libanais SAL CTBC BankDaiwa Securities GroupDanske BankDavivienda DenizBank A.S. Deutsche BörseDigiledgeDianrongDNB Ernst & Young European Central Securities Depositories Association First America BankFulton Financial CorporationGazprombank Habib Metropolitan Bank LimitedHo Chi Minh City Development JS CommercialHQLA ICICI Bank LimitedIndovina Bank Ltd Intesa SanpaoloItaú Joint Stock Commercial Bank for Investment and Development of VietnamJoint-Stock Company BCS-Investment BankJPMorgan ChaseJS Bank Limited KASIKORNBANK Public Company LimitedKB Financial GroupKEB Hana BankKuveyt Turk Katilim Bankasi Macquarie Bank MauBank LimitedMizuho Bank National Bank of CanadaNational Bank of EgyptNational Bank of Kenya LimitedNational Bank of KuwaitNatixis Norddeutsche Landesbank GirozentraleNordeaNorthern TrustOmidyar NetworkOP Financial Group Paris Europlace PKO Bank PolskiPortal FinancePrime Bank Ltd.Privatbank IHAG Zurich PT Bank Central Asia TbkPT Bank CIMBNiaga TbkQiwiRaiffeisen BankRBCRBSRCBCRCI Bank & ServicesResona Bank, Ltd. Reyl & Cie SA Satoshi Systems ScopusSEB Group Shinhan BankShuttle HoldingsSIA SMBCSnapCheck SRI Capital Stock Exchange of ThailandSuMi TRUSTSunTrust Synchrony Bank Temasek Holdings The First International Bank of Israel Ltd The Toronto-Dominion Bank TradeCloudTradeIXTradeWindTurkiye Finans Katilim BankasiUBSUniCreditUnion Bank Of The PhilippinesUSBancorpVietnam Joint Stock Commercial Bank for Industry and Trade Volksbank Mittweida Wells FargoWestpac Woori Bank Alipay Ant Financial Banco BTG Pactual Bank MellatBank Melli IranBank Pasargad BBVA Bloomberg BNP Paribas Boerse Stuttgart Digital Ventures BP PLC Credit Suisse DBS Deutsche Bank DTCC Eastern Caribbean Central Bank Employees’ Retirement System eToro Fidelity Investments Goldman Sachs HSBC ING International Chamber of Commerce International Securities Services Association Jamica Stock Exchange Julius Baer Linx Partners London Metal Exchange London Stock Exchange Luxembourg Stock Exchange Mastercard Morgan Creek MUFG NASDAQ Nomura Pakistani Central Bank Paypal Proof of Capital Revolut Santander SBI Holdings SGX Signature Bank Silvergate Bank SIX Group Société Générale SoFi Square Standard Chartered Bank Swissquote SWIFT Telenor Microfinance Bank Thai Stock Exchange The National Payments Corporation of India Thomson Reuters Eikon Tora Virginia’s Police Officer’s Retirement System Visa WEG Bank Western Union **Government** Caisse des dépôts et consignations CSIRO United NationsU.S. Department of Agriculture Australian Federal Government AUSTRAC Brazil’s Ministry of Planning, Budget & Management Canadian Municipality of Innisfil City of Denver Colorado State Government Colorado Water Institute Commerce Committee of Connecticut General Assembly County Auditors’ Association of Ohio Department of Homeland Security Government of Kyrgyzstan Government of Rwanda Illinois Department of Financial & Professional Regulation Illinois Department of Transportation Luxembourg Government Malta Financial Services Authority Ministry of Land & Natural Resources, ZambiaMunicipal Government of Tulum, Mexico Municipal Government of Tsukuba, Japan New York City Economic Development Corporation Pennsylvania’s Economic Development Office (Venture Capital Arm) Seoul Digital Foundation Singapore Government South Korea’s Defence Acquisition Program Administration South Korea Internet & Security Agency Thailand’s Ministry of Science & Technology UK Parliament U.S. Department of Energy U.S. Department of State U.S. House of Representatives West Virginia State Government Wyoming State Government **Insurance** MetLife Ping An Insurance Aegon AIA Allianz AXA Generali Health Care Service Corporation MAPFRE RE Munich Re State Farm Swiss ReTokia Marine TrueNorth XL Catlin ZhongAn Zurich **Internet** Google Twitter Baidu Digital Garage Facebook Opera **IT Solutions**7CommAKonceptsAmazon Web ServicesAOS SASAppwayAt.cashaXpireBillon Group LtdBlanc LabsBoleroBRQ Digital SolutionsCalypso TechnologyCanonical Česká fintech asociace, z.s. CIeNETCMACOCUS AGCodeITCognizantcoIB Eclipse FoundationEPAMEvernym FinastraFinFabrik GFT TechnologiesGiant MachinesGoogle CloudGuildOne Hewlett Packard EnterpriseIBM Inacta AGInfosys Finacle iSiDLG CNSMagiaDigitalMicrosoftNec NTT Data PCCW SolutionsRedhat Vyoma SoftwareWiproZirconTech Capgemini Cass Information Systems Inc. CISCO Collabrian EXL Fujitsu Technology Solutions HCL Imaginnovate Intel Neuro Red Oracle Salesforce SAP SE VMWare **Legal**ACCORD ProjectCOALA National Council of Clerks (France) Linklaters Orrick Taylor Associates **Medical** COCIR European Federation of Nurses Associations Abbvie AnthemBig Blue Cardinal Health Ciox Health CVS Health Genentech HumanaMultiplan NMC Healthcare Optum Pfizer Quest DiagnosticsUnited Healthcare **Media**Les Échos _CBS_Fox Business Network **Mining** BHP Billiton GoldcorpKutcho CopperLG CorpOcean Partners USAWheaton Precious Metals **Non-Profit** Oxfam Freshwater Trust UNICEF **Online Marketplace** Moonlighting **Pharmaceuticals**Amgen MerckPfizer Sanofi Boehringer Ingelheim Roche **Real Estate** Convexity Properties Emaar Group Greenbrier Management Growth Capital ServicesICP Medici Land Governance St. Regis Aspen Resort **Retail & Manufacturing** Acqua di Parma Ardbeg BelvedereBenefit CosmeticsBerlutiBodega Numanthia BulgariCaffe-Pasticceria CovaCape Mentelle CelineChandonCharles & KeithChâteau Cheval BlancChâteau d’YquemChaumetCloudy BayDFSDiorDom PerignonEmilio PucciFendiFenty Beauty by Rihanna FREDFresh (Cosmetics & FragrancesGivenchyGlenmorangie GuerlainHennessyHouse of BijanHublotKalaKat Von D BeautyKenzo KrugLa Grande Epicerie LoeweLoro pianaL’Oreal Maison Francis KurkdjianMarc JacobsMercierMoet & ChandonMoynat Naja Newton VineyardNicholas Kirkwood RimowaRuinartSephoraStarboard Cruise ServicesTAG Heuer Terrazas de los AndesThomas Pink Veuve ClicquotVolcan di mi tierra WenjunZenith A. Favre & Fils AB InBev Anheuser-Busch InBev Albertsons Companies BASF Bridgestone Carrefour Foxconn Golden State Foods Kroger Levi Strauss Louis Vuitton Naked Brand Group Nestle Nike Overstock Parfums Christian Dior Rakuten Target Unilever Walmart **Sports Teams** U.S. LA Kings (Ice Hockey) **Technology** Dell HuaweiLenovo T-Mobile Aidant Technologies AMD Apple Avnet Bosch Foxconn Fujitsu Hewlett PackardHitachi HTC LG NASA Nvidia PitneyBowes RCS Global Samsung Sapphire Technology Seagate TechnologySiemens Sony SweetSense Tencent TSMC **Transport, Logistics & Shipping** CMA CGM ERTICO Feadship Penske 10–4360dataABAQ.USAble FreightAdventAGForceAITAgistixAltriaAmaltoAmerican Global LogisticsAmerican Short Line & Regional Railroad AssociationAMTRAMXAnhueser-BuschApex APL ArcBest CorporationAriaLogisticsAriesArrive LogisticsArrowATSAxle LogisticsBCGBest TransportBlueGraceBlue HorseshoeBourque LogisticsBP BNSF Railway BoyleBoxLockBreakthrough FuelBucklandBurris LogisticsC.H. RobinsonC & K TruckingC.R. EnglandCargoChainCarrier DirectCDL MarketingCenturyCerasisChopTankCitizens ReserveClearDestinationCJ Logistics CNColumbia TransportConvoyCovenant TransportCowan SystemsCoyoteCRST InternationalCSXCTSI GlobalCustoms DirectDaimlerDAMCODasekeDATDDCDecker Truck Line Inc.DeringerDescartesDexFreightDicomDleveredDLT LabsDMTBDreamOrbitDriverReachEagle TransportationEast Coast TransporteCapitalEchoEdge LogisticsEnergy Transportation GroupEnvio360Eroad eShipGlobaleVansEXPAK Logistics FedExFirstFleetFleetCompleteFleetPalFLSFMIFontinalis PartnersForward AirFourKitesFR8StarFraightFreight RoverFreight SourceFullbayGateHouse LogisticsGebruder WeissGE TransportationGlobalTranzGT SolutionsHill Bros Hyundai Merchant Marine IMCInnovativosIntelligent AuditIrvingITLIts LogisticsJ.B. HuntJD LogisticsKamionKansas City Southern LinesKencoKingsgate LogisticsKion GroupKleinschmidtKochKonexial Kuehne & Nagel LabelmasterLandStarLanehubLazer SpotLDILiquid FreightLivingstonLoad OneLoadblockLoadDeliveredLogiflexLogistics+Logistyx TechnologiesLotte Global LogisticsLyncAmericaMach1 Maersk Manhattan AssociatesMatch FactorsMaven MachinesMaverickMcLeod SoftwareMedallion Transport & LogisticsMercerMercuryGateMMM ExpressMode TransportationMorpheus.NetworkMSTSNascentNavistarNBTGNFINGLNorfolk SouthernNT LogisticsNTGNVision GlobalOAROmnitracsOpenPortOOIDAOrigintrailP & GP & S TransportationP.B. IndustriesPackygePAMPanalpinaPantos LogisticsPaschall Truck LinesPavestone CapitalPegasus TransTechPeir Phillips Connect TechnologiesPhoenix Capital GroupPilot Freight ServicesPitt OhioPlatform SciencePLMPolarisPrecisionPrime3Project44ProTransPTIPurolatorPWCQITR & R ExpressRaider ExpressRail IncRay-Mont LogisticsRedwoodReliance PartnersRIMRoadrunner Transportation SystemsRoadSyncRouteiqueRSI LogisticsRTWAirRUANRyderSAIASaint John Capital Schneider Trucking SCLogicScopelitisSeaboard MarineSharelogisShawSheerShipwellSimpleLogisticsSinglePoint CapitalSkyBitzSmartHopSMC3SolarisSouthEastern Freight LinesSource Alliance NetworkSpireonSuddathSunsetSupplyOnSyfan LogisticsTadmoreTAFSTeam WorldwideThe Home DepotThird Coast LogisticsTideworks TechnologyTMCTMW SystemsTRAC IntermodalTraffixTransCardTransfixTransfloTransLytixTransPlaceTransportationInsightTransProTranzlogixTranzportTrinity LogisticsTrinium TechnologiesTriumphPayTrucker PathTruckstopTruck ToolsUber FreightUNISUnion Pacific UPS U.S. XpressUSA TruckVelocity TransportVHI TransportVitesseVnomicsWataco Supply Chain ServicesWELWernerWhirlpool CorporationWhite ArrowWiseTech GlobalWolf DirectWOLTWorldwide ExpressWWS TradingXLNTECYRC WorldwideZoof-It **Utilities** ENGIE Repsol WASSHA (DIGITAL GRID Corporation) Concentric Gazprom Petroteq TEPCO Wien Energie **Noteworthy Individuals** — Russian Opposition Leader — U.S. Presidential Candidate — South Korean Actor — Inventor of World Wide Web — Founder SpaceX & Tesla — French President — CEO Twitter & Square — Retired NBA Basketball Player — №1 Youtube Channel — LinkedIn co-founder — Professional Tennis Player — CEO Berkshire Hathaway & Billionaire investor Alexei Navalny Andrew Yang Bae Yong-joon Tim Berners-Lee Elon Musk Emmanuel Macron Jack Dorsey Kobe Bryant Pewdiepie Reid Hoffman Serena Williams Warren Buffet <a href="https://medium.com/media/3c851dac986ab6dbb2d1aaa91205a8eb/href">https://medium.com/media/3c851dac986ab6dbb2d1aaa91205a8eb/href</a>

A Comprehensive List of Enterprises Partnering with blockchain in 2019

has transformed the way businesses operate. It has allowed organizations to quickly scale up or down, improving agility and reducing costs. However, while the cloud offers many benefits, it can also be easy to overspend on cloud resources if not managed properly. Cloud computing In the world of cloud computing, has become a leading provider of infrastructure-as-a-service (IaaS) solutions. With AWS, businesses can leverage scalable, flexible, and cost-effective computing resources to support their operations. However, as the complexity and scale of AWS deployments grow, so do the associated costs. Amazon Web Services (AWS) To manage these costs, AWS cost optimization has become a critical aspect of AWS management. In this blog, we will discuss how to maximize your cloud investments through . AWS cost optimization Importance of cost optimization in AWS Businesses may face difficulties managing their AWS costs as they deploy and scale their AWS workloads. Therefore, cost optimization is essential for businesses that want to avoid overspending and maximize their ROI. The cloud computing model allows businesses to pay only for the resources and services they use. However, if not managed correctly, this can result in unnecessary or inefficient resource usage, leading to unnecessary costs. Businesses can reduce cloud costs and increase return on investment through effective cost optimization. A few reasons why cost optimization in AWS is essential are: Cost reduction: This can be achieved by monitoring and optimizing usage patterns, choosing the right pricing models and selecting the appropriate instance types. Resource optimization: By identifying unused or underutilized resources, organizations can reduce their AWS spending and ensure that resources are used optimally. Better budgeting and forecasting: By monitoring and analyzing their AWS usage data, companies can better budget and forecast their AWS spending. This allows them to plan for future growth and avoid unexpected bills. Improved performance: Businesses can ensure that their applications run successfully and efficiently by choosing the right instance types and optimizing resource usage. Businesses can reinvest their savings from cost optimization in other aspects of their operations, such as innovation, R&D, and employee training. By leveraging AWS cost optimization tools and strategies, businesses can optimize their cloud computing usage, achieve increased efficiency and remain competitive in today’s fast-paced digital marketplace. Overview of AWS cost optimization tools AWS Cost Explorer Cost Explorer is a web-based tool that provides insights into your AWS costs. It allows you to view and analyze your AWS usage and costs over time and provides recommendations for cost optimization. AWS Trusted Advisor Trusted Advisor provides automated suggestions to improve your AWS setup in several areas, including cost reduction, security and performance. AWS Budgets Budgets is a tool that allows you to set custom cost and usage budgets for your AWS resources. It sends alerts when you exceed your budget, helping you to avoid unexpected costs. AWS Cost Anomaly Detection This tool uses machine learning algorithms to detect anomalies in your AWS usage and spending. It alerts you when it identifies unusual spending patterns, enabling you to investigate and take corrective action. AWS Compute Optimizer Compute Optimizer analyzes your EC2 instances and recommends optimal instance types and sizes based on your usage patterns. This helps you reduce costs by ensuring that you are only using the resources you need. Amazon S3 Amazon S3 provides features like transfer acceleration and direct connect. This can help optimize data transfer costs by reducing the amount of data that needs to be transferred over the internet. AWS Reserved Instance (RI) Reports RI Reports optimize RI usage by providing usage insights and cost reduction opportunities. AWS Savings Plans Commit to usage level for discounts with Savings Plans. This also allows you to significantly reduce costs compared to on-demand pricing. AWS CloudWatch Monitor AWS resources and applications in real-time as well as streamline your infrastructure and application maintenance with AWS CloudWatch. Leverage these tools and implement cost optimization best practices. This will reduce your AWS expenses and improve the efficiency of your AWS environment. Best practices for AWS cost optimization With the right optimization strategies in place, businesses can analyze and adjust their AWS usage to match their actual needs. This allows companies to reduce wasted capacity and select the most cost-effective pricing models and options for their workloads. Use the right-sized resources Use resources that are appropriately sized for the workload. Oversized resources can lead to unnecessary costs, while undersized resources can lead to performance issues. Regularly monitor usage and adjust resources accordingly. Use Spot Instances Spot Instances are a cost-effective option for non-critical workloads. They provide compute capacity at significantly lower costs compared to On-Demand Instances. However, you must be able to handle interruptions as these instances can be reclaimed by AWS. Use Reserved Instances (RIs) and Savings Plans RI and Savings Plans offer significant discounts on compute resources. Analyze your workload patterns to identify which instances are likely to be used for an extended period and commit to RI or Savings Plan. Use Auto Scaling Automatically add or remove instances based on demand. This ensures that you only pay for resources when they are needed. It can help you reduce costs by scaling down when demand is low. Use AWS cost management tools AWS provides a range of cost management tools, including Cost Explorer, Trusted Advisor, and Budgets. These tools help you monitor and optimize your AWS usage and costs. Use a serverless architecture Serverless architectures allow you to pay only for the resources you consume and scale automatically based on demand. This helps reduce costs by eliminating the need to manage and pay for idle resources. Use CloudFront and CDN Use Amazon CloudFront and CDN to reduce bandwidth usage and increase the performance of your application. These services can help reduce your data transfer costs and increase the speed of your application. Use AWS cost allocation tags Use AWS Cost Allocation Tags to categorize your resources based on purpose, owner, or environment. This will help you identify cost drivers, optimize resource usage and allocate costs effectively. Things to consider for better cost optimization Analyze resource usage patterns and adjust capacity to match actual needs, which can help reduce costs and improve performance. Choose the most cost-effective pricing model based on workload needs and usage patterns. AWS resources can automatically adjust capacity in response to changes in demand. This helps organizations scale resources up or down as needed, without over-provisioning or underutilizing capacity. Choose the best storage options according to data usage patterns and access needs. Review storage usage regularly to remove unnecessary or redundant data. Establish processes and tools for monitoring and controlling costs, setting budgets and cost optimization goals. Select the most cost-effective pricing models as well as options for different resources and services. Leverage the benefits of AWS cost optimization AWS cost optimization aims to reduce unnecessary costs. At the same time, it enables businesses to get the most out of their computing resources. The components of this service include: Cost-effective resources Matching supply and demand Optimizing over time Cost-aware architecture Managing expenditure AWS cost optimization is essential for businesses that want to reduce their AWS expenses without sacrificing performance, security, or reliability. Businesses can optimize their AWS usage by following best practices and leveraging AWS cost optimization tools and services. This will improve their return on investment and free up resources to invest in other areas of their operations. How Softweb Solutions can help with AWS cost optimization Being one of the top AWS Cloud consulting services providers, Softweb Solutions holds a strong bench of AWS-certified experts, including certified solution architects, certified cloud practitioners, and certified developers. etc. We follow industry best practices and ensure that you receive the best AWS solutions including cost optimization. Our continue to monitor your AWS usage and adjust as required to ensure ongoing cost optimization. We take a comprehensive approach to AWS cloud cost optimization by analyzing current usage, identifying areas for optimization, implementing cost optimization strategies, and monitoring and refining the approach over time. Contact our AWS experts to get a better understanding of how you can leverage AWS cost optimization services. AWS consultants Originally published at . s softwebsoultion

Optimizing Cloud Investments with AWS Cost Optimization

Optimización de las inversiones en la nube con AWS Cost Optimization

Optimisation des investissements dans le cloud avec AWS Cost Optimization

एडब्ल्यूएस लागत अनुकूलन के साथ क्लाउड निवेश का अनुकूलन

AWS コスト最適化によるクラウド投資の最適化

Otimizando investimentos em nuvem com o AWS Cost Optimization

Tối ưu hóa các khoản đầu tư vào đám mây với Tối ưu hóa chi phí AWS

使用 AWS 成本优化优化云投资

**LONDON, United Kingdom, October 22nd, 2024/Chainwire/--**Aventus, a leading provider of enterprise blockchain solutions and parachain on Polkadot, today confirms the launch of Aventus 2.0, an evolution of the Aventus Network aimed at establishing a stronger foundation for long-term growth and value capture. The update introduces several strategic initiatives designed to enhance network performance and stakeholder utility, including increasing transaction volume and overall network usage, expanding the ecosystem through successful partnerships with Layer 3 appchains, enhancing token holder engagement via a liquidity mining program, and reducing token supply via a burn mechanism. The vision for Aventus 2.0 was developed by MVP Workshop, a Blockchain Product Research & Development Studio who designed Polygon Edge and Astar Network, in collaboration with Scytale Digital and the Aventus Services team. Following the approval of a community governance proposal in which AVT token holders voted in favour of executing this vision, the Aventus Services team will implement the Aventus 2.0 plan over the next four months. This process underscores Aventus’s commitment to stakeholder-driven decision-making, ensuring that major network decisions are made through community consensus. Aventus 2.0 comprises three main components: A Layer 3 appchain model, whereby enterprises are able to operate on the Aventus Network and benefit from the security, scalability, interoperability and decentralised infrastructure offered by the Aventus ecosystem.
A liquidity mining program on Uniswap, including a custom user-friendly dApp, to enhance token holder engagement. Appchains are also able to request grants from Aventus community treasury and launch their own liquidity mining programs to drive liquidity for their own tokens, contributing a portion of their token supply and network fees to the Aventus treasury to support the broader Aventus ecosystem and its development.
A new collator rewards mechanism to ensure a secure and efficient Aventus Network, alongside an automatic burn mechanism for a portion of gas fees to reduce the overall supply of AVT tokens in circulation. Alan Vey, Founder at Aventus, commented: “Aventus 2.0 builds on important learnings from existing Aventus Network clients as well as the invaluable expertise of our partners at MVP Workshop & Scytale Digital, and represents a significant milestone in our journey to enhance the Aventus Network’s capabilities and deliver greater value to stakeholders.” The appchain model is already seeing traction, with existing users of the Aventus Network having recently launched their own Aventus Layer 3 appchains. Barry Helfrich, CIO at Enigmatic Smile, adds: “We needed the Voucher Ledger solution to be secure, fast and stable enough to process the discounts collected by hundreds of millions of users in our rewards ecosystem — no small feat, but Aventus has helped us build such a solution. The team has been helpful, professional and responsive throughout the process. We’re looking forward to continuing our long-standing relationship with them.” The updated network will provide enhanced functionality and improved user experiences, positioning the Aventus Network as a trusted leader in enterprise blockchain solutions and key contributor to enterprise use cases within the Polkadot ecosystem. About Aventus Aventus transforms how customers create trust and unlock growth, crafting pioneering Web3 solutions for brands, from creating more connected, integrated experiences to enhancing traceability, transparency, and product authentication. Founded in 2020, Aventus is the only trusted digital product extension platform that provides a secure and reliable Web3 environment for customers to launch market-leading programs and product activations. With deep industry expertise and a strong understanding of enterprise needs, Aventus delivers one the best feature sets of Web3 with the familiarity of Web2, driving significant brand reputation, trust, and enterprise growth for its customers. Its production-ready, end-to-end Blockchain-as-a-Service software is modular, scalable, and interoperable, giving clients the flexibility they need to respond to rapidly-evolving market opportunities. For more information, users can visit: www.aventus.io, and also their X, LinkedIn and Telegram. Contact Head of Marketing Ellie Hyman Aventus ellie.hyman@aventus.io This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here. **LONDON, United Kingdom, October 22nd, 2024/Chainwire/--**Aventus, a leading provider of enterprise blockchain solutions and parachain on Polkadot, today confirms the launch of Aventus 2.0, an evolution of the Aventus Network aimed at establishing a stronger foundation for long-term growth and value capture. The update introduces several strategic initiatives designed to enhance network performance and stakeholder utility, including increasing transaction volume and overall network usage, expanding the ecosystem through successful partnerships with Layer 3 appchains, enhancing token holder engagement via a liquidity mining program, and reducing token supply via a burn mechanism. The vision for Aventus 2.0 was developed by MVP Workshop, a Blockchain Product Research & Development Studio who designed Polygon Edge and Astar Network, in collaboration with Scytale Digital and the Aventus Services team. Following the approval of a community governance proposal in which AVT token holders voted in favour of executing this vision, the Aventus Services team will implement the Aventus 2.0 plan over the next four months. This process underscores Aventus’s commitment to stakeholder-driven decision-making, ensuring that major network decisions are made through community consensus. Aventus 2.0 comprises three main components: A Layer 3 appchain model, whereby enterprises are able to operate on the Aventus Network and benefit from the security, scalability, interoperability and decentralised infrastructure offered by the Aventus ecosystem. A liquidity mining program on Uniswap, including a custom user-friendly dApp, to enhance token holder engagement. Appchains are also able to request grants from Aventus community treasury and launch their own liquidity mining programs to drive liquidity for their own tokens, contributing a portion of their token supply and network fees to the Aventus treasury to support the broader Aventus ecosystem and its development. A new collator rewards mechanism to ensure a secure and efficient Aventus Network, alongside an automatic burn mechanism for a portion of gas fees to reduce the overall supply of AVT tokens in circulation. A Layer 3 appchain model, whereby enterprises are able to operate on the Aventus Network and benefit from the security, scalability, interoperability and decentralised infrastructure offered by the Aventus ecosystem. A liquidity mining program on Uniswap, including a custom user-friendly dApp, to enhance token holder engagement. Appchains are also able to request grants from Aventus community treasury and launch their own liquidity mining programs to drive liquidity for their own tokens, contributing a portion of their token supply and network fees to the Aventus treasury to support the broader Aventus ecosystem and its development. A new collator rewards mechanism to ensure a secure and efficient Aventus Network, alongside an automatic burn mechanism for a portion of gas fees to reduce the overall supply of AVT tokens in circulation. Alan Vey, Founder at Aventus, commented: “Aventus 2.0 builds on important learnings from existing Aventus Network clients as well as the invaluable expertise of our partners at MVP Workshop & Scytale Digital, and represents a significant milestone in our journey to enhance the Aventus Network’s capabilities and deliver greater value to stakeholders.” Alan Vey, Founder at Aventus, commented: “Aventus 2.0 builds on important learnings from existing Aventus Network clients as well as the invaluable expertise of our partners at MVP Workshop & Scytale Digital, and represents a significant milestone in our journey to enhance the Aventus Network’s capabilities and deliver greater value to stakeholders.” The appchain model is already seeing traction, with existing users of the Aventus Network having recently launched their own Aventus Layer 3 appchains. Barry Helfrich, CIO at Enigmatic Smile, adds: “We needed the Voucher Ledger solution to be secure, fast and stable enough to process the discounts collected by hundreds of millions of users in our rewards ecosystem — no small feat, but Aventus has helped us build such a solution. The team has been helpful, professional and responsive throughout the process. We’re looking forward to continuing our long-standing relationship with them.” Barry Helfrich, CIO at Enigmatic Smile, adds: “We needed the Voucher Ledger solution to be secure, fast and stable enough to process the discounts collected by hundreds of millions of users in our rewards ecosystem — no small feat, but Aventus has helped us build such a solution. The team has been helpful, professional and responsive throughout the process. We’re looking forward to continuing our long-standing relationship with them.” The updated network will provide enhanced functionality and improved user experiences, positioning the Aventus Network as a trusted leader in enterprise blockchain solutions and key contributor to enterprise use cases within the Polkadot ecosystem. About Aventus Aventus transforms how customers create trust and unlock growth, crafting pioneering Web3 solutions for brands, from creating more connected, integrated experiences to enhancing traceability, transparency, and product authentication. Founded in 2020, Aventus is the only trusted digital product extension platform that provides a secure and reliable Web3 environment for customers to launch market-leading programs and product activations. Aventus Aventus With deep industry expertise and a strong understanding of enterprise needs, Aventus delivers one the best feature sets of Web3 with the familiarity of Web2, driving significant brand reputation, trust, and enterprise growth for its customers. Its production-ready, end-to-end Blockchain-as-a-Service software is modular, scalable, and interoperable, giving clients the flexibility they need to respond to rapidly-evolving market opportunities. For more information, users can visit: www.aventus.io , and also their X , LinkedIn and Telegram . www.aventus.io www.aventus.io X X LinkedIn LinkedIn Telegram Telegram Contact Head of Marketing Ellie Hyman Aventus ellie.hyman@aventus.io This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here. This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here. here . here

Aventus Network Confirms Launch Of Aventus 2.0, Key To Driving Enterprise Use Cases To Polkadot

Aventus Network confirma el lanzamiento de Aventus 2.0, clave para impulsar casos de uso empresariales en Polkadot

A Rede Aventus confirma o lanzamento de Aventus 2.0, clave para impulsar casos de uso empresarial para Polkadot

एवेंटस नेटवर्क ने एवेंटस 2.0 के लॉन्च की पुष्टि की, जो पोलकाडॉट के लिए एंटरप्राइज़ उपयोग के मामलों को आगे बढ़ाने की कुंजी है

Aventus Network potvrđuje lansiranje Aventusa 2.0, ključnog za usmjeravanje poslovnih slučajeva na Polkadot

Aventus Network-ը հաստատում է Aventus 2.0-ի գործարկումը, որի բանալին ձեռնարկությունների օգտագործման դեպքերն է Polkadot-ի համար:

Aventus Network が、Polkadot へのエンタープライズ ユース ケースを推進する鍵となる Aventus 2.0 のリリースを発表

Réseau Aventus Confirme Lancement Ya Aventus 2.0, Clé Pona Ko Conduire Ba Cas Ya Utilisation Ya Entreprise Na Polkadot

Ny Aventus Network dia nanamafy ny fandefasana ny Aventus 2.0, fanalahidin'ny fitantanana ny raharaha fampiasana orinasa amin'ny Polkadot

Авентусын сүлжээ нь Авентус 2.0-ийг нэвтрүүлж эхэлснийг баталлаа

د Aventus شبکه د Aventus 2.0 لانچ تاییدوي، د پولکاډوټ ته د تصدۍ کارولو قضیې چلولو کلیدي

Aventus Network potvrdzuje spustenie Aventusu 2.0, kľúča k podnecovaniu podnikových prípadov použitia až po Polkadot

ایوینٹس نیٹ ورک نے ایوینٹس 2.0 کے آغاز کی تصدیق کی، پولکاڈٹ کے لیے انٹرپرائز استعمال کے کیسز چلانے کی کلید

LONDON, United Kingdom, August 7th, 2024/Chainwire/--Blockchain-based solution drives significant improvements in data visibility, accuracy and operational efficiency within cargo handling sector, according to research conducted by Aventus at Heathrow Airport. Airlines can benefit from overall cost savings in their cargo handling operations by leveraging Web3-based solutions, according to a study conducted by Aventus & Airport Perishables Handling (APH) at Heathrow Airport. The existing toolset used for global ULD management has remained unchanged since the 1990s and relies heavily on manual data inputs, resulting in critical challenges with data visibility and accuracy, secure information sharing, and costly ULD losses, damages and delays – costing airlines north of $1.6 billion annually. Web3, through blockchain technology, offers a robust solution to the challenges of traditional ULD management, by: Enabling immutable, tamper-proof records that provide a single source of truth, eliminating disputes and improving regulatory compliance;
Reducing administrative overhead and minimize errors by automating manual processes via self-executing smart contracts;
Enabling real-time visibility into ULD location, custodianship and condition, and streamlining data sharing to enable airlines to optimize operations. In a pilot study conducted at Heathrow Airport, Aventus, an industry-leading provider of end-to-end Web3 solutions for enterprises and parachain on Polkadot, found that its end-to-end blockchain-based cargo handling solution drives improvements in data visibility, accuracy and operational efficiency within the cargo handling sector. 90% reduction in communication and error incidents as a result of digitized data capture;
83% reduction in manual documentation time;
81% reduction in time between ULD stock updates from 3-4 hours to just 30 minutes, enabling real-time decision-making; and
28% reduction in loading times as a result of optimized ULD loading workloads. Alan Vey, Founder at Aventus, comments: “These results are truly remarkable, underscoring the transformative potential of blockchain for not only the aviation industry but for supply chains globally. We are proud to be empowering enterprises to enhance data accuracy, reduce operational inefficiencies, and achieve greater transparency. As we expand our partnerships across North America, Europe, the Middle East and Asia, we anticipate these results to only improve via network effects.”
Michelle Roosevelt, Director at Aviation Perishables Handling, adds: “Aventus' technology is fast and responsive, which is key in our busy airport environment. We've seen huge improvements in productivity. The app is more than a tool – it's reshaped how we manage and track our aircraft containers, and the Aventus team's support and expertise in meeting our needs has been outstanding.” Aventus’ aviation solution brings partnerships with major enterprises to the Polkadot ecosystem, including Aviation Perishables Handling at Heathrow Airport, Vodafone’s Digital Asset Broker, as well as other major airlines across Asia and the Middle East. For more information on the study, users can download the full report here. About Aventus Aventus transforms how customers create trust and unlock growth, crafting pioneering Web3 solutions for brands, from creating more connected, integrated experiences to enhancing traceability, transparency, and product authentication. Founded in 2020, Aventus is the only trusted digital product extension platform that provides a secure and reliable Web3 environment for customers to launch market-leading programs and product activations. With deep industry expertise and a strong understanding of enterprise needs, Aventus delivers the best feature sets of Web3 with the familiarity of Web2, driving significant brand reputation, trust, and enterprise growth for its customers. Its production-ready, end-to-end Blockchain-as-a-Service software is modular, scalable, and interoperable, giving clients the flexibility they need to respond to rapidly-evolving market opportunities. Contact Head of Marketing Ellie Hyman Aventus ellie.hyman@aventus.io This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here. LONDON, United Kingdom, August 7th, 2024/Chainwire/--Blockchain-based solution drives significant improvements in data visibility, accuracy and operational efficiency within cargo handling sector, according to research conducted by Aventus at Heathrow Airport. Airlines can benefit from overall cost savings in their cargo handling operations by leveraging Web3-based solutions, according to a study conducted by Aventus & Airport Perishables Handling (APH) at Heathrow Airport. Aventus Aventus The existing toolset used for global ULD management has remained unchanged since the 1990s and relies heavily on manual data inputs, resulting in critical challenges with data visibility and accuracy, secure information sharing, and costly ULD losses, damages and delays – costing airlines north of $1.6 billion annually. Web3, through blockchain technology, offers a robust solution to the challenges of traditional ULD management, by: Enabling immutable, tamper-proof records that provide a single source of truth, eliminating disputes and improving regulatory compliance; Reducing administrative overhead and minimize errors by automating manual processes via self-executing smart contracts; Enabling real-time visibility into ULD location, custodianship and condition, and streamlining data sharing to enable airlines to optimize operations. Enabling immutable, tamper-proof records that provide a single source of truth, eliminating disputes and improving regulatory compliance; Reducing administrative overhead and minimize errors by automating manual processes via self-executing smart contracts; Enabling real-time visibility into ULD location, custodianship and condition, and streamlining data sharing to enable airlines to optimize operations. In a pilot study conducted at Heathrow Airport, Aventus, an industry-leading provider of end-to-end Web3 solutions for enterprises and parachain on Polkadot, found that its end-to-end blockchain-based cargo handling solution drives improvements in data visibility, accuracy and operational efficiency within the cargo handling sector. 90% reduction in communication and error incidents as a result of digitized data capture; 83% reduction in manual documentation time; 81% reduction in time between ULD stock updates from 3-4 hours to just 30 minutes, enabling real-time decision-making; and 28% reduction in loading times as a result of optimized ULD loading workloads. 90% reduction in communication and error incidents as a result of digitized data capture; 83% reduction in manual documentation time; 81% reduction in time between ULD stock updates from 3-4 hours to just 30 minutes, enabling real-time decision-making; and 28% reduction in loading times as a result of optimized ULD loading workloads. Alan Vey, Founder at Aventus, comments: “These results are truly remarkable, underscoring the transformative potential of blockchain for not only the aviation industry but for supply chains globally. We are proud to be empowering enterprises to enhance data accuracy, reduce operational inefficiencies, and achieve greater transparency. As we expand our partnerships across North America, Europe, the Middle East and Asia, we anticipate these results to only improve via network effects.” Michelle Roosevelt, Director at Aviation Perishables Handling, adds: “Aventus' technology is fast and responsive, which is key in our busy airport environment. We've seen huge improvements in productivity. The app is more than a tool – it's reshaped how we manage and track our aircraft containers, and the Aventus team's support and expertise in meeting our needs has been outstanding.” Alan Vey, Founder at Aventus, comments: “These results are truly remarkable, underscoring the transformative potential of blockchain for not only the aviation industry but for supply chains globally. We are proud to be empowering enterprises to enhance data accuracy, reduce operational inefficiencies, and achieve greater transparency. As we expand our partnerships across North America, Europe, the Middle East and Asia, we anticipate these results to only improve via network effects.” Michelle Roosevelt, Director at Aviation Perishables Handling, adds: “Aventus' technology is fast and responsive, which is key in our busy airport environment. We've seen huge improvements in productivity. The app is more than a tool – it's reshaped how we manage and track our aircraft containers, and the Aventus team's support and expertise in meeting our needs has been outstanding.” Aventus’ aviation solution brings partnerships with major enterprises to the Polkadot ecosystem, including Aviation Perishables Handling at Heathrow Airport, Vodafone’s Digital Asset Broker, as well as other major airlines across Asia and the Middle East. For more information on the study, users can download the full report here . here here About Aventus Aventus transforms how customers create trust and unlock growth, crafting pioneering Web3 solutions for brands, from creating more connected, integrated experiences to enhancing traceability, transparency, and product authentication. Founded in 2020, Aventus is the only trusted digital product extension platform that provides a secure and reliable Web3 environment for customers to launch market-leading programs and product activations. With deep industry expertise and a strong understanding of enterprise needs, Aventus delivers the best feature sets of Web3 with the familiarity of Web2, driving significant brand reputation, trust, and enterprise growth for its customers. Its production-ready, end-to-end Blockchain-as-a-Service software is modular, scalable, and interoperable, giving clients the flexibility they need to respond to rapidly-evolving market opportunities. Contact Head of Marketing Ellie Hyman Aventus ellie.hyman@aventus.io This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here. This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here . here here

Aventus Supply Chain Solution Demonstrates Polkadot's Impact On Aviation Industry

এভেন্টাস সাপ্লাই চেইন সলিউশন এভিয়েশন ইন্ডাস্ট্রিতে পোলকাডটের প্রভাব প্রদর্শন করে

Aventus Supply Chain Solution demonstriert den Einfluss von Polkadot auf die Luftfahrtindustrie

La solución de cadena de suministro de Aventus demuestra el impacto de Polkadot en la industria de la aviación

La solution de chaîne d'approvisionnement d'Aventus démontre l'impact de Polkadot sur l'industrie aéronautique

एवेंटस सप्लाई चेन सॉल्यूशन एविएशन इंडस्ट्री पर पोलकाडॉट के प्रभाव को प्रदर्शित करता है

Aventus サプライチェーン ソリューションが Polkadot の航空産業への影響を実証

Aventus 공급망 솔루션은 항공 산업에 대한 Polkadot의 영향을 보여줍니다.

A solução de cadeia de suprimentos da Aventus demonstra o impacto da Polkadot na indústria da aviação

Решение Aventus для цепочки поставок демонстрирует влияние Polkadot на авиационную отрасль

Aventus Tedarik Zinciri Çözümü Polkadot'un Havacılık Endüstrisindeki Etkisini Gösteriyor

Giải pháp chuỗi cung ứng Aventus chứng minh tác động của Polkadot đối với ngành hàng không

Aventus 供应链解决方案展示了 Polkadot 对航空业的影响

Problem If you did not get the chance, please check out , where we explain how we see the event ticketing industry, why its broken, and how we can change it. In a nutshell, Aventus aims to solve the following 3 problems: our first blog post “The Event Ticketing Industry is Broken and in Need of Disruption” 1. The resale of bulk bought tickets at inflated prices on secondary markets. Secondary ticket markets are controversial because they enable touts to buy up tickets from primary markets quickly and re-sell them on average 49% above face value with margins often exceeding 1000% , prohibiting price-sensitive fans from attending an event. Adele and other major artists have protested, banning the resale of their tickets after prices have appeared at up to £9000 on Stubhub . Additionally, in the U.K. it is illegal for football tickets to be resold without the club’s permission . However, such attempts at regulation have been futile, since ticket black markets continue to form anyway . [1] [2] [3] [4] 2. Counterfeit tickets. The U.K. Metropolitan Police’s 2 year study of ticket fraud associated with the London 2012 Olympic games elucidated that organised crime networks were able to generate over £40m per year through the creation of counterfeit tickets . Furthermore, an investigation by the Telegraph showed that Brits wasted over £5m on counterfeit tickets advertised on social media in 2015 . [5] [6] 3. Maximising ticket sales to the event’s target audience. According to Sean Moriarty, CEO of TicketMaster, “nearly 35% of ticket inventory goes unsold, and if you ask fans why they didn’t go to shows, one of the most popular reasons is ’I didn’t know about it’” . Furthermore, 60% of 16–24 year old concert and festival goers stated that they would go more regularly if tickets were less expensive . This indicates that promoters have difficulty reaching target audiences and that this problem is further aggravated by inflated prices. [7] [7] Solution Overview Aventus is a blockchain-based protocol that creates a more fair, secure, and transparent event ticketing industry, practically eliminating counterfeit tickets and unfair touting. Organisers can create, manage and promote their events and associated tickets with dramatically reduced platform costs, and can even control secondary markets. It solves the aforementioned problems in the following manner: Secondary Market Control At the core of the Aventus solution is a model where off-blockchain transactions of tickets are prevented. By ensuring that sales occur through the platform, event organisers can reliably enforce resale price minima and maxima, and even receive a portion of secondary sales revenue. The solution is twofold: This can be the owner’s face, identity document or credit card and is added at the point of purchase. Changes to the identity can only occur if the ticket is resold through a secondary market utilising the Aventus solution. The physical item photographed will be checked at the event and compared to that on the blockchain to grant entry. Tickets have an associated photo with them: Tickets are put up for sale and all network participants can register an intent to purchase. Matching is then performed by network participants every so often to allocated tickets to interested buyers in a pseudo-random fashion. Owners reselling their tickets do not know who they are selling to: Blockchain Security Each ticket has a unique identifier and due to the blockchain’s immutable properties, once the event organisers have set up tickets no counterfeits can be created. Due to the transparency of the Blockchain, any user can check the validity of a ticket immediately. If the event itself is fraudulent, Aventus’ economic model incentivises network participants to report it and subsequently determine its validity using a decentralised consensus mechanism. Promotion and Platform Cost Reduction There are two key ways in which Aventus will cause events to reach a wider and more targeted audience: (unless forbidden by the organisers), forming a global, open pool of events whose tickets can be sold by any application, incentivised via a commission on each sale. This destroys the current siloed nature of the industry and encourages multiple participation to drive more sales. All events listed on the Aventus protocol will be accessible to any application using it Note, promoters can be listing apps and/or individuals who help make ticket sales via these apps. Their commissions can be as dynamic and complex as desired, since each promoter can receive a different commission based calculations of reputation. Event organisers can reward promoters with a commission on ticket sales they make. The competition between listing apps and reduced fees due to blockchain micro-transactions and state channels should make overall ticket prices lower. The Aventus Ecosystem Structure The Aventus ecosystem is comprised of 3 main layers, as depicted in figure 1: the protocol, the service layer, and the application layer. The Aventus stack. Protocol Layer This layer is entirely composed of Ethereum smart contracts that allow for the creation and validation of events, the issuance and sale of tickets in primary and secondary ticket markets (customisable by the event organisers), and the distribution of ticket sale revenue and market/event fees between the event organisers, ticket promoters, market matchers, attendees, etc. There will be no fees for using these contracts so that the protocol can remain competitive and enjoy maximal adoption. Service Layer Services available here are aimed to make using the protocol as efficient and easy to use as possible, especially for developers looking to build mainstream ticketing applications. Tools will include: An Ethereum account management platform where users with minimal cryptocurrency knowledge can have their accounts, funds, and transactions managed on their behalf. A fiat to AVT conversion mechanism so that users can buy tickets easily, and so event organisers and app developers can cash out quickly if necessary to avoid currency risk. A ticket and ID validation service where attendees’ faces will be scanned and compared to photos uploaded at point of purchase using machine vision. This will reduce entry fraud at events and allow organisers to track the performance of bouncers. Template user interfaces and integration tools that further lowers the barrier to entry to creating a customised ticketing interface for event organisers (e.g. a football club) or promoters (e.g. an influencer). Others worth mentioning include in-event advertising tools and reputation calculations for promoters with respect to sales generated in previous, similar events. More services will be added over time depending on demand. Although developers using these services will incur an additional cost, such tools will make building ticketing applications significantly easier especially with respect to onboarding mainstream users. Applications Layer The final level consists of applications that build on top of the services layer (or in some cases just the protocol layer). The Aventus system creates a global, open pool of events that can be accessed by any application using it, and any application can receive a commission for selling tickets for a given event. Hence, more apps built on the protocol ultimately benefits the event organisers since it results in more ticket sales. Since the Aventus solution fixes sources of fraud prevalent across the entire event ticketing industry, we expect apps in a variety of sectors including cinema, theatre, music/festivals, and sports. There could be apps targeting even more specific sectors than these, such as Premiere League Football, that provide tailored benefits to attendees and hosts based on characteristics of their niche communities. Further, we will encourage existing event-hosting companies such as sports teams and music festivals to leverage tools in the Aventus system to create their own secure ticketing store-fronts, bypassing the inflated 15–20% per-ticket fees that existing industry leaders charge. We expect these applications to be primarily front-ends with minimal custom backend functionality, and to charge additional fees or use other revenue models such as advertising. The Aventus team will create and launch the first ticket sales application in the sports and music industry. It will allow for the creation and management of events and associated tickets, along with the creation of promotional schemes for encouraging more ticket sales. Further, it will enable ticket buyers to receive rewards and find new events to attend easily. It should also be noted that Aventus will concentrate efforts on working with third party developers to support the creation of other applications on top of the services layer. Conclusion This post briefly highlights the problems in the event ticketing industry, along with the solution we at Aventus have attempted to tackle the industry with. We will be releasing our technical white paper in less than a week, so stay tuned. Please join our or if you are interested in what we’re doing or have any questions! All feedback is appreciated. Slack channel follow us on Twitter

Aventus — A touting and counterfeit solution for the event ticketing industry

Authors:
(1) Halil Yigit Oksuz, Control Systems Group at Technische Universitat Berlin, Germany and Exzellenzcluster Science of Intelligence, Technische Universitat Berlin, Marchstr. 23, 10587, Berlin, Germany;
(2) Fabio Molinari, Control Systems Group at Technische Universitat Berlin, Germany;
(3) Henning Sprekeler, Exzellenzcluster Science of Intelligence, Technische Universit¨at Berlin, Marchstr. 23, 10587, Berlin, Germany and Modelling Cognitive Processes Group at Technische Universit¨at Berlin, Germany;
(4) Jorg Raisch, Control Systems Group at Technische Universitat Berlin, Germany and Exzellenzcluster Science of Intelligence, Technische Universitat Berlin, Marchstr. 23, 10587, Berlin, Germany. Table of Links Abstract and Introduction Problem Setup Federated fair over-the-air learning (FedAir) Algorithm Convergence Properties Numerical Example Conclusion and References VI. CONCLUSION In this paper, we have introduced the FedFAir algorithm which uses Over-the-Air Computation to carry out efficient decentralized learning while providing fairness and improved performance. We have shown that the FedFAir algorithm converges to an optimal solution of the minimax problem. Furthermore, we have also illustrated our theoretical findings with a numerical example. Future research will include the development of resilient federated learning algorithms when there are malicious agents in the system. REFERENCES [1] B. McMahan, E. Moore, D. Ramage, S. Hampson, and B. A. y Arcas, “Communication-efficient learning of deep networks from decentralized data,” in Artificial intelligence and statistics. PMLR, 2017, pp. 1273–1282. [2] V. Smith, C.-K. Chiang, M. Sanjabi, and A. S. Talwalkar, “Federated multi-task learning,” Advances in neural information processing systems, vol. 30, 2017. [3] K. Bonawitz, H. Eichner, W. Grieskamp, D. Huba, A. Ingerman, V. Ivanov, C. Kiddon, J. Konecny, S. Mazzocchi, B. McMahan et al., “Towards federated learning at scale: System design,” Proceedings of Machine Learning and Systems, vol. 1, pp. 374–388, 2019. [4] Q. Yang, Y. Liu, T. Chen, and Y. Tong, “Federated machine learning: Concept and applications,” ACM Transactions on Intelligent Systems and Technology (TIST), vol. 10, no. 2, pp. 1–19, 2019. [5] A. Nedic, “Distributed gradient methods for convex machine learning problems in networks: Distributed optimization,” IEEE Signal Processing Magazine, vol. 37, no. 3, pp. 92–101, 2020. [6] V. P. Chellapandi, A. Upadhyay, A. Hashemi, and S. H. Zak, “On ˙ the convergence of decentralized federated learning under imperfect information sharing,” IEEE Control Systems Letters, 2023. [7] T. Omori and K. Kashima, “Combinatorial optimization approach to client scheduling for federated learning,” IEEE Control Systems Letters, 2023. [8] T. Sery, N. Shlezinger, K. Cohen, and Y. C. Eldar, “Over-the-air federated learning from heterogeneous data,” IEEE Transactions on Signal Processing, vol. 69, pp. 3796–3811, 2021. [9] T. Gafni, N. Shlezinger, K. Cohen, Y. C. Eldar, and H. V. Poor, “Federated learning: A signal processing perspective,” IEEE Signal Processing Magazine, vol. 39, no. 3, pp. 14–41, 2022. [10] M. Ye, X. Fang, B. Du, P. C. Yuen, and D. Tao, “Heterogeneous federated learning: State-of-the-art and research challenges,” ACM Computing Surveys, vol. 56, no. 3, pp. 1–44, 2023. [11] J. Koneˇcn `y, H. B. McMahan, F. X. Yu, P. Richt´arik, A. T. Suresh, and D. Bacon, “Federated learning: Strategies for improving communication efficiency,” arXiv preprint arXiv:1610.05492, 2016. [12] T. Li, A. K. Sahu, A. Talwalkar, and V. Smith, “Federated learning: Challenges, methods, and future directions,” IEEE Signal Processing Magazine, vol. 37, no. 3, pp. 50–60, 2020. [13] P. Kairouz, H. B. McMahan, B. Avent, A. Bellet, M. Bennis, A. N. Bhagoji, K. Bonawitz, Z. Charles, G. Cormode, R. Cummings et al., “Advances and open problems in federated learning,” Foundations and Trends® in Machine Learning, vol. 14, no. 1–2, pp. 1–210, 2021. [14] H. Y. Oksuz, F. Molinari, H. Sprekeler, and J. Raisch, “Federated learning in wireless networks via over-the-air computations,” in 2023 62nd IEEE Conference on Decision and Control (CDC), 2023, pp. 4379–4386. [15] F. Molinari, N. Agrawal, S. Sta´nczak, and J. Raisch, “Max-consensus over fading wireless channels,” IEEE Transactions on Control of Network Systems, vol. 8, no. 2, pp. 791–802, 2021. [16] M. Frey, I. Bjelakovi´c, and S. Sta´nczak, “Over-the-air computation in correlated channels,” IEEE Transactions on Signal Processing, vol. 69, pp. 5739–5755, 2021. [17] K. Yang, T. Jiang, Y. Shi, and Z. Ding, “Federated learning via overthe-air computation,” IEEE Transactions on Wireless Communications, vol. 19, no. 3, pp. 2022–2035, 2020. [18] F. Molinari and J. Raisch, “Exploiting wireless interference for distributively solving linear equations,” IFAC-PapersOnLine, vol. 53, no. 2, pp. 2999–3006, 2020. [19] D. Bertsekas, A. Nedic, and A. Ozdaglar, Convex analysis and optimization. Athena Scientific, 2003, vol. 1. [20] M. Mohri, G. Sivek, and A. T. Suresh, “Agnostic federated learning,” in International Conference on Machine Learning. PMLR, 2019, pp. 4615–4625. [21] Z. Hu, K. Shaloudegi, G. Zhang, and Y. Yu, “Federated learning meets multi-objective optimization,” IEEE Transactions on Network Science and Engineering, vol. 9, no. 4, pp. 2039–2051, 2022. [22] D. P. Bertsekas, “Necessary and sufficient conditions for a penalty method to be exact,” Mathematical programming, vol. 9, no. 1, pp. 87–99, 1975. [23] K. Srivastava, A. Nedi´c, and D. Stipanovi´c, “Distributed min-max optimization in networks,” in 2011 17th International Conference on Digital Signal Processing (DSP). IEEE, 2011, pp. 1–8. [24] R. Ahlswede, “Multi-way communication channels,” in Second International Symposium on Information Theory: Tsahkadsor, Armenia, USSR, Sept. 2-8, 1971, 1973. [25] A. Giridhar and P. Kumar, “Toward a theory of in-network computation in wireless sensor networks,” IEEE Communications Magazine, vol. 44, no. 4, pp. 98–107, apr 2006. [26] F. Molinari, N. Agrawal, S. Sta´nczak, and J. Raisch, “Over-the-air max-consensus in clustered networks adopting half-duplex communication technology,” IEEE Transactions on Control of Network Systems, 2022. [27] W. Rudin et al., Principles of mathematical analysis. McGraw-hill New York, 1976, vol. 3. [28] D. Tse and P. Viswanath, Fundamentals of Wireless Communication. Cambridge University Press, 2012. [29] A. F. Molisch, Wireless communications. John Wiley & Sons, 2012. [30] B. Sklar, “Rayleigh fading channels in mobile digital communication systems. i. characterization,” IEEE Communications magazine, vol. 35, no. 7, pp. 90–100, 1997. [31] R. G. Bartle and D. R. Sherbert, Introduction to real analysis. Wiley New York, 2000. [32] B. T. Polyak, Introduction to optimization. optimization software. Inc., Publications Division, New York, 1987. [33] Y. I. Alber, A. N. Iusem, and M. V. Solodov, “On the projected subgradient method for nonsmooth convex optimization in a hilbert space,” Mathematical Programming, vol. 81, pp. 23–35, 1998. This paper is available on arxiv under CC BY 4.0 DEED license. Authors: (1) Halil Yigit Oksuz, Control Systems Group at Technische Universitat Berlin, Germany and Exzellenzcluster Science of Intelligence, Technische Universitat Berlin, Marchstr. 23, 10587, Berlin, Germany; (2) Fabio Molinari, Control Systems Group at Technische Universitat Berlin, Germany; (3) Henning Sprekeler, Exzellenzcluster Science of Intelligence, Technische Universit¨at Berlin, Marchstr. 23, 10587, Berlin, Germany and Modelling Cognitive Processes Group at Technische Universit¨at Berlin, Germany; (4) Jorg Raisch, Control Systems Group at Technische Universitat Berlin, Germany and Exzellenzcluster Science of Intelligence, Technische Universitat Berlin, Marchstr. 23, 10587, Berlin, Germany. Authors: Authors: (1) Halil Yigit Oksuz, Control Systems Group at Technische Universitat Berlin, Germany and Exzellenzcluster Science of Intelligence, Technische Universitat Berlin, Marchstr. 23, 10587, Berlin, Germany; (2) Fabio Molinari, Control Systems Group at Technische Universitat Berlin, Germany; (3) Henning Sprekeler, Exzellenzcluster Science of Intelligence, Technische Universit¨at Berlin, Marchstr. 23, 10587, Berlin, Germany and Modelling Cognitive Processes Group at Technische Universit¨at Berlin, Germany; (4) Jorg Raisch, Control Systems Group at Technische Universitat Berlin, Germany and Exzellenzcluster Science of Intelligence, Technische Universitat Berlin, Marchstr. 23, 10587, Berlin, Germany. Table of Links Abstract and Introduction Abstract and Introduction Problem Setup Problem Setup Federated fair over-the-air learning (FedAir) Algorithm Federated fair over-the-air learning (FedAir) Algorithm Convergence Properties Convergence Properties Numerical Example Numerical Example Conclusion and References Conclusion and References VI. CONCLUSION In this paper, we have introduced the FedFAir algorithm which uses Over-the-Air Computation to carry out efficient decentralized learning while providing fairness and improved performance. We have shown that the FedFAir algorithm converges to an optimal solution of the minimax problem. Furthermore, we have also illustrated our theoretical findings with a numerical example. Future research will include the development of resilient federated learning algorithms when there are malicious agents in the system. REFERENCES [1] B. McMahan, E. Moore, D. Ramage, S. Hampson, and B. A. y Arcas, “Communication-efficient learning of deep networks from decentralized data,” in Artificial intelligence and statistics. PMLR, 2017, pp. 1273–1282. [2] V. Smith, C.-K. Chiang, M. Sanjabi, and A. S. Talwalkar, “Federated multi-task learning,” Advances in neural information processing systems, vol. 30, 2017. [3] K. Bonawitz, H. Eichner, W. Grieskamp, D. Huba, A. Ingerman, V. Ivanov, C. Kiddon, J. Konecny, S. Mazzocchi, B. McMahan et al., “Towards federated learning at scale: System design,” Proceedings of Machine Learning and Systems, vol. 1, pp. 374–388, 2019. [4] Q. Yang, Y. Liu, T. Chen, and Y. Tong, “Federated machine learning: Concept and applications,” ACM Transactions on Intelligent Systems and Technology (TIST), vol. 10, no. 2, pp. 1–19, 2019. [5] A. Nedic, “Distributed gradient methods for convex machine learning problems in networks: Distributed optimization,” IEEE Signal Processing Magazine, vol. 37, no. 3, pp. 92–101, 2020. [6] V. P. Chellapandi, A. Upadhyay, A. Hashemi, and S. H. Zak, “On ˙ the convergence of decentralized federated learning under imperfect information sharing,” IEEE Control Systems Letters, 2023. [7] T. Omori and K. Kashima, “Combinatorial optimization approach to client scheduling for federated learning,” IEEE Control Systems Letters, 2023. [8] T. Sery, N. Shlezinger, K. Cohen, and Y. C. Eldar, “Over-the-air federated learning from heterogeneous data,” IEEE Transactions on Signal Processing, vol. 69, pp. 3796–3811, 2021. [9] T. Gafni, N. Shlezinger, K. Cohen, Y. C. Eldar, and H. V. Poor, “Federated learning: A signal processing perspective,” IEEE Signal Processing Magazine, vol. 39, no. 3, pp. 14–41, 2022. [10] M. Ye, X. Fang, B. Du, P. C. Yuen, and D. Tao, “Heterogeneous federated learning: State-of-the-art and research challenges,” ACM Computing Surveys, vol. 56, no. 3, pp. 1–44, 2023. [11] J. Koneˇcn `y, H. B. McMahan, F. X. Yu, P. Richt´arik, A. T. Suresh, and D. Bacon, “Federated learning: Strategies for improving communication efficiency,” arXiv preprint arXiv:1610.05492, 2016. [12] T. Li, A. K. Sahu, A. Talwalkar, and V. Smith, “Federated learning: Challenges, methods, and future directions,” IEEE Signal Processing Magazine, vol. 37, no. 3, pp. 50–60, 2020. [13] P. Kairouz, H. B. McMahan, B. Avent, A. Bellet, M. Bennis, A. N. Bhagoji, K. Bonawitz, Z. Charles, G. Cormode, R. Cummings et al., “Advances and open problems in federated learning,” Foundations and Trends® in Machine Learning, vol. 14, no. 1–2, pp. 1–210, 2021. [14] H. Y. Oksuz, F. Molinari, H. Sprekeler, and J. Raisch, “Federated learning in wireless networks via over-the-air computations,” in 2023 62nd IEEE Conference on Decision and Control (CDC), 2023, pp. 4379–4386. [15] F. Molinari, N. Agrawal, S. Sta´nczak, and J. Raisch, “Max-consensus over fading wireless channels,” IEEE Transactions on Control of Network Systems, vol. 8, no. 2, pp. 791–802, 2021. [16] M. Frey, I. Bjelakovi´c, and S. Sta´nczak, “Over-the-air computation in correlated channels,” IEEE Transactions on Signal Processing, vol. 69, pp. 5739–5755, 2021. [17] K. Yang, T. Jiang, Y. Shi, and Z. Ding, “Federated learning via overthe-air computation,” IEEE Transactions on Wireless Communications, vol. 19, no. 3, pp. 2022–2035, 2020. [18] F. Molinari and J. Raisch, “Exploiting wireless interference for distributively solving linear equations,” IFAC-PapersOnLine, vol. 53, no. 2, pp. 2999–3006, 2020. [19] D. Bertsekas, A. Nedic, and A. Ozdaglar, Convex analysis and optimization. Athena Scientific, 2003, vol. 1. [20] M. Mohri, G. Sivek, and A. T. Suresh, “Agnostic federated learning,” in International Conference on Machine Learning. PMLR, 2019, pp. 4615–4625. [21] Z. Hu, K. Shaloudegi, G. Zhang, and Y. Yu, “Federated learning meets multi-objective optimization,” IEEE Transactions on Network Science and Engineering, vol. 9, no. 4, pp. 2039–2051, 2022. [22] D. P. Bertsekas, “Necessary and sufficient conditions for a penalty method to be exact,” Mathematical programming, vol. 9, no. 1, pp. 87–99, 1975. [23] K. Srivastava, A. Nedi´c, and D. Stipanovi´c, “Distributed min-max optimization in networks,” in 2011 17th International Conference on Digital Signal Processing (DSP). IEEE, 2011, pp. 1–8. [24] R. Ahlswede, “Multi-way communication channels,” in Second International Symposium on Information Theory: Tsahkadsor, Armenia, USSR, Sept. 2-8, 1971, 1973. [25] A. Giridhar and P. Kumar, “Toward a theory of in-network computation in wireless sensor networks,” IEEE Communications Magazine, vol. 44, no. 4, pp. 98–107, apr 2006. [26] F. Molinari, N. Agrawal, S. Sta´nczak, and J. Raisch, “Over-the-air max-consensus in clustered networks adopting half-duplex communication technology,” IEEE Transactions on Control of Network Systems, 2022. [27] W. Rudin et al., Principles of mathematical analysis. McGraw-hill New York, 1976, vol. 3. [28] D. Tse and P. Viswanath, Fundamentals of Wireless Communication. Cambridge University Press, 2012. [29] A. F. Molisch, Wireless communications. John Wiley & Sons, 2012. [30] B. Sklar, “Rayleigh fading channels in mobile digital communication systems. i. characterization,” IEEE Communications magazine, vol. 35, no. 7, pp. 90–100, 1997. [31] R. G. Bartle and D. R. Sherbert, Introduction to real analysis. Wiley New York, 2000. [32] B. T. Polyak, Introduction to optimization. optimization software. Inc., Publications Division, New York, 1987. [33] Y. I. Alber, A. N. Iusem, and M. V. Solodov, “On the projected subgradient method for nonsmooth convex optimization in a hilbert space,” Mathematical Programming, vol. 81, pp. 23–35, 1998. This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv

Boosting Fairness and Robustness in Over-the-Air Federated Learning: Conclusion and References

Gamified Surveys and Cognitive Load Detection in mHealth: Conclusion, Acknowledgment, and References

Authors:
(1) Michal K. Grzeszczyk, Sano Centre for Computational Medicine, Cracow, Poland and Warsaw University of Technology, Warsaw, Poland;
(2) M.Sc.; Paulina Adamczyk, Sano Centre for Computational Medicine, Cracow, Poland and AGH University of Science and Technology, Cracow, Poland;
(3) B.Sc.; Sylwia Marek, Sano Centre for Computational Medicine, Cracow, Poland and AGH University of Science and Technology, Cracow, Poland;
(4) B.Sc.; Ryszard Pręcikowski, Sano Centre for Computational Medicine, Cracow, Poland and AGH University of Science and Technology, Cracow, Poland;
(5) B.Sc.; Maciej Kuś, Sano Centre for Computational Medicine, Cracow, Poland and AGH University of Science and Technology, Cracow, Poland;
(6) B.Sc.; M. Patrycja Lelujko, Sano Centre for Computational Medicine, Cracow, Poland;
(7) B.Sc.; Rosmary Blanco, Sano Centre for Computational Medicine, Cracow, Poland;
(8) M.Sc.; Tomasz Trzciński, Warsaw University of Technology, Warsaw, Poland, IDEAS NCBR, Warsaw, Poland andTooploox, Wroclaw, Poland;
(9) D.Sc.; Arkadiusz Sitek, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA;
(10) PhD; Maciej Malawski, Sano Centre for Computational Medicine, Cracow, Poland and AGH University of Science and Technology, Cracow, Poland;
(11) D.Sc.; Aneta Lisowska, Sano Centre for Computational Medicine, Cracow, Poland and Poznań University of Technology, Poznań, Poland;
(12) EngD. Table of Links Abstract and Introduction Related Work Methods Results and Discussion Limitations Conclusion, Acknowledgment, and References Limitations The main limitation of this work is the small sample size of participants used for both developing the cognitive load detector and evaluating the cognitive effort required to complete the two survey versions. To mitigate this issue, we pre-trained the detector on a similar task, but further improvements in its performance may be possible with a larger sample size. The generalizability of our method can also be impacted by the homogeneous nature of the study participants. The volunteers were healthy individuals from a similar age group. To ensure the robustness of our method, future studies should include a more diverse and bigger sample size. Finally, we conducted experiments in controlled laboratory settings. In the future, we plan to conduct similar studies in real-world conditions[7]. In this feasibility study, we focused solely on developing automatic detection of high cognitive load, which is the first step towards estimating cognitive effort in the real world. This may not be sufficient to distinguish more subtle differences in cognitive demand caused by the mobile application. To identify the game element or combination of such elements that is the most helpful in reducing cognitive burden we would like to conduct an ablation study in the context of well-being questionnaires in our future work. We would also like to include the self-reported cognitive load after survey completion for a better understanding of survey difficulty. Moreover, it would be interesting to collect data during semi-demanding tasks, such as text reading, in order to train a more finely-graded classifier. Finally, we would like to include other tasks requiring a high cognitive load to ensure the efficiency of our method across various cognitively demanding activities. This paper is available on arxiv under CC BY 4.0 DEED license. Authors: (1) Michal K. Grzeszczyk, Sano Centre for Computational Medicine, Cracow, Poland and Warsaw University of Technology, Warsaw, Poland; (2) M.Sc.; Paulina Adamczyk, Sano Centre for Computational Medicine, Cracow, Poland and AGH University of Science and Technology, Cracow, Poland; (3) B.Sc.; Sylwia Marek, Sano Centre for Computational Medicine, Cracow, Poland and AGH University of Science and Technology, Cracow, Poland; (4) B.Sc.; Ryszard Pręcikowski, Sano Centre for Computational Medicine, Cracow, Poland and AGH University of Science and Technology, Cracow, Poland; (5) B.Sc.; Maciej Kuś, Sano Centre for Computational Medicine, Cracow, Poland and AGH University of Science and Technology, Cracow, Poland; (6) B.Sc.; M. Patrycja Lelujko, Sano Centre for Computational Medicine, Cracow, Poland; (7) B.Sc.; Rosmary Blanco, Sano Centre for Computational Medicine, Cracow, Poland; (8) M.Sc.; Tomasz Trzciński, Warsaw University of Technology, Warsaw, Poland, IDEAS NCBR, Warsaw, Poland andTooploox, Wroclaw, Poland; (9) D.Sc.; Arkadiusz Sitek, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA; (10) PhD; Maciej Malawski, Sano Centre for Computational Medicine, Cracow, Poland and AGH University of Science and Technology, Cracow, Poland; (11) D.Sc.; Aneta Lisowska, Sano Centre for Computational Medicine, Cracow, Poland and Poznań University of Technology, Poznań, Poland; (12) EngD. Authors: Authors: (1) Michal K. Grzeszczyk, Sano Centre for Computational Medicine, Cracow, Poland and Warsaw University of Technology, Warsaw, Poland; (2) M.Sc.; Paulina Adamczyk, Sano Centre for Computational Medicine, Cracow, Poland and AGH University of Science and Technology, Cracow, Poland; (3) B.Sc.; Sylwia Marek, Sano Centre for Computational Medicine, Cracow, Poland and AGH University of Science and Technology, Cracow, Poland; (4) B.Sc.; Ryszard Pręcikowski, Sano Centre for Computational Medicine, Cracow, Poland and AGH University of Science and Technology, Cracow, Poland; (5) B.Sc.; Maciej Kuś, Sano Centre for Computational Medicine, Cracow, Poland and AGH University of Science and Technology, Cracow, Poland; (6) B.Sc.; M. Patrycja Lelujko, Sano Centre for Computational Medicine, Cracow, Poland; (7) B.Sc.; Rosmary Blanco, Sano Centre for Computational Medicine, Cracow, Poland; (8) M.Sc.; Tomasz Trzciński, Warsaw University of Technology, Warsaw, Poland, IDEAS NCBR, Warsaw, Poland andTooploox, Wroclaw, Poland; (9) D.Sc.; Arkadiusz Sitek, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA; (10) PhD; Maciej Malawski, Sano Centre for Computational Medicine, Cracow, Poland and AGH University of Science and Technology, Cracow, Poland; (11) D.Sc.; Aneta Lisowska, Sano Centre for Computational Medicine, Cracow, Poland and Poznań University of Technology, Poznań, Poland; (12) EngD. Table of Links Abstract and Introduction Abstract and Introduction Related Work Related Work Methods Methods Results and Discussion Results and Discussion Limitations Limitations Conclusion, Acknowledgment, and References Conclusion, Acknowledgment, and References Limitations The main limitation of this work is the small sample size of participants used for both developing the cognitive load detector and evaluating the cognitive effort required to complete the two survey versions. To mitigate this issue, we pre-trained the detector on a similar task, but further improvements in its performance may be possible with a larger sample size. The generalizability of our method can also be impacted by the homogeneous nature of the study participants. The volunteers were healthy individuals from a similar age group. To ensure the robustness of our method, future studies should include a more diverse and bigger sample size. Finally, we conducted experiments in controlled laboratory settings. In the future, we plan to conduct similar studies in real-world conditions[7]. In this feasibility study, we focused solely on developing automatic detection of high cognitive load, which is the first step towards estimating cognitive effort in the real world. This may not be sufficient to distinguish more subtle differences in cognitive demand caused by the mobile application. To identify the game element or combination of such elements that is the most helpful in reducing cognitive burden we would like to conduct an ablation study in the context of well-being questionnaires in our future work. We would also like to include the self-reported cognitive load after survey completion for a better understanding of survey difficulty. Moreover, it would be interesting to collect data during semi-demanding tasks, such as text reading, in order to train a more finely-graded classifier. Finally, we would like to include other tasks requiring a high cognitive load to ensure the efficiency of our method across various cognitively demanding activities. This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv

Gamified Surveys and Cognitive Load Detection in mHealth: Limitations

Authors:
(1) Michal K. Grzeszczyk, Sano Centre for Computational Medicine, Cracow, Poland and Warsaw University of Technology, Warsaw, Poland;
(2) M.Sc.; Paulina Adamczyk, Sano Centre for Computational Medicine, Cracow, Poland and AGH University of Science and Technology, Cracow, Poland;
(3) B.Sc.; Sylwia Marek, Sano Centre for Computational Medicine, Cracow, Poland and AGH University of Science and Technology, Cracow, Poland;
(4) B.Sc.; Ryszard Pręcikowski, Sano Centre for Computational Medicine, Cracow, Poland and AGH University of Science and Technology, Cracow, Poland;
(5) B.Sc.; Maciej Kuś, Sano Centre for Computational Medicine, Cracow, Poland and AGH University of Science and Technology, Cracow, Poland;
(6) B.Sc.; M. Patrycja Lelujko, Sano Centre for Computational Medicine, Cracow, Poland;
(7) B.Sc.; Rosmary Blanco, Sano Centre for Computational Medicine, Cracow, Poland;
(8) M.Sc.; Tomasz Trzciński, Warsaw University of Technology, Warsaw, Poland, IDEAS NCBR, Warsaw, Poland andTooploox, Wroclaw, Poland;
(9) D.Sc.; Arkadiusz Sitek, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA;
(10) PhD; Maciej Malawski, Sano Centre for Computational Medicine, Cracow, Poland and AGH University of Science and Technology, Cracow, Poland;
(11) D.Sc.; Aneta Lisowska, Sano Centre for Computational Medicine, Cracow, Poland and Poznań University of Technology, Poznań, Poland;
(12) EngD. Table of Links Abstract and Introduction Related Work Methods Results and Discussion Limitations Conclusion, Acknowledgment, and References Results and Discussion Cognitive Load Detection. To understand how to obtain a reliable cognitive load detection model we investigate different approaches to training protocols including varying lengths of the signal input and model pretraining as described above. Due to a small number of data, the model performance was varying between runs and therefore we run each training session 40 times and report the mean and standard deviation of all runs. We find that the number of data samples was insufficient to train the model from scratch on the pilot cognitive load dataset only (see Vanilla in Table 1). Model pre-training on WESAD dataset has shown to be helpful for cognitive load detection (see WESAD pre-trained in Table 1). The length of the signal is also important, in general, the 10 seconds window seems to be insufficient to obtain promising cognitive load detection performance even when the model is pretrained. We expected that the better the performance of the stress detector on the pretraining task the better will be the performance on the fine-tuning task. However, the link between source and target task performance is not that apparent. There is a weak correlation between source training data stress detection and fine-tuned cognitive load detector performance (r=0.18 p=0.04), a slightly stronger correlation between source validation data stress detection and target cognitive load detection (r=0.25, p=0.005), and no correlation between the test subject stress detection and target dataset cognitive load score (see Figure 3). Further, to check if our cognitive load detector is not just detecting stress, we run the best-performing stress detector with a 30s window on all participants from the pilot dataset and check % of samples for each participant from each condition (baseline and cognitive load) that are classified as stressed. We find that signal from participants 1, 3, 4, 7 and 8 had 0 samples classified as stressed, participants 0, 6, 9 and 10 had 9-22% samples classified as stressed and only participants 2 and 5 showed higher levels of stress with 59% and 55% of signal classified as stressed respectively. This suggests that improvements obtained via pre-training are not due to the physiological signal similarity in both conditions (cognitive load is not classified as stress) but rather due to learning of general feature extraction from the 1D signal. Response Time. On average participants spent less time responding to questions in gamified condition than in Non-gamified condition, with 5.5s and 6s per question respectively. However, interestingly, in gamified condition participants spent more time at the start and end questions which potentially could be due to the introduction of the gamified components which could capture respondents' attention (see Figure 4). Note that time spent on the very last question in each survey has been not included in this analysis as they mostly capture the time needed to move between questionnaires. Cognitive Burden. To ensure that the cognitive load detector can distinguish between high and low cognitive load for each participant in the survey dataset we first applied all 440 models (from 40 runs and 11 folds) trained with a 30s window on the pilot dataset to every participant in the survey dataset in cognitive load and baseline conditions and for each participant, we selected a model showing the best performance for this participant. In other words, we matched the models to participants using a small calibration dataset obtained from participants prior to filling the survey application. The performance of the selected best model is reported in Table 2 for each session. We have excluded the prediction of cognitive load during survey completion for those participants for whom we could not get a model with performance above 0.7 F1 score (marked with X in Table 2). Encouragingly, for 10 out of 13 participants, we were able to find a model which could reliably distinguish between their high and low cognitive load. The time spent in the high cognitive load and stress does not differ between gamified and non-gamified survey conditions. Potentially to understand if there is a difference between cognitive burden we would need a classifier trained on at least three different tasks with cognitive load levels: high (demanding task), medium (e.g. reading task) and low. Participants Experience. Only half of the participants noticed the difference in survey application appearance between the sessions. Participants who noted the difference reported preferring the gamified version more. Potentially the gamification was too simplistic to modulate the level of engagement or enjoyment. It is nontrivial to introduce game components in the surveys to reduce the burden but not affect the responses. This paper is available on arxiv under CC BY 4.0 DEED license. Authors: (1) Michal K. Grzeszczyk, Sano Centre for Computational Medicine, Cracow, Poland and Warsaw University of Technology, Warsaw, Poland; (2) M.Sc.; Paulina Adamczyk, Sano Centre for Computational Medicine, Cracow, Poland and AGH University of Science and Technology, Cracow, Poland; (3) B.Sc.; Sylwia Marek, Sano Centre for Computational Medicine, Cracow, Poland and AGH University of Science and Technology, Cracow, Poland; (4) B.Sc.; Ryszard Pręcikowski, Sano Centre for Computational Medicine, Cracow, Poland and AGH University of Science and Technology, Cracow, Poland; (5) B.Sc.; Maciej Kuś, Sano Centre for Computational Medicine, Cracow, Poland and AGH University of Science and Technology, Cracow, Poland; (6) B.Sc.; M. Patrycja Lelujko, Sano Centre for Computational Medicine, Cracow, Poland; (7) B.Sc.; Rosmary Blanco, Sano Centre for Computational Medicine, Cracow, Poland; (8) M.Sc.; Tomasz Trzciński, Warsaw University of Technology, Warsaw, Poland, IDEAS NCBR, Warsaw, Poland andTooploox, Wroclaw, Poland; (9) D.Sc.; Arkadiusz Sitek, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA; (10) PhD; Maciej Malawski, Sano Centre for Computational Medicine, Cracow, Poland and AGH University of Science and Technology, Cracow, Poland; (11) D.Sc.; Aneta Lisowska, Sano Centre for Computational Medicine, Cracow, Poland and Poznań University of Technology, Poznań, Poland; (12) EngD. Authors: Authors: (1) Michal K. Grzeszczyk, Sano Centre for Computational Medicine, Cracow, Poland and Warsaw University of Technology, Warsaw, Poland; (2) M.Sc.; Paulina Adamczyk, Sano Centre for Computational Medicine, Cracow, Poland and AGH University of Science and Technology, Cracow, Poland; (3) B.Sc.; Sylwia Marek, Sano Centre for Computational Medicine, Cracow, Poland and AGH University of Science and Technology, Cracow, Poland; (4) B.Sc.; Ryszard Pręcikowski, Sano Centre for Computational Medicine, Cracow, Poland and AGH University of Science and Technology, Cracow, Poland; (5) B.Sc.; Maciej Kuś, Sano Centre for Computational Medicine, Cracow, Poland and AGH University of Science and Technology, Cracow, Poland; (6) B.Sc.; M. Patrycja Lelujko, Sano Centre for Computational Medicine, Cracow, Poland; (7) B.Sc.; Rosmary Blanco, Sano Centre for Computational Medicine, Cracow, Poland; (8) M.Sc.; Tomasz Trzciński, Warsaw University of Technology, Warsaw, Poland, IDEAS NCBR, Warsaw, Poland andTooploox, Wroclaw, Poland; (9) D.Sc.; Arkadiusz Sitek, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA; (10) PhD; Maciej Malawski, Sano Centre for Computational Medicine, Cracow, Poland and AGH University of Science and Technology, Cracow, Poland; (11) D.Sc.; Aneta Lisowska, Sano Centre for Computational Medicine, Cracow, Poland and Poznań University of Technology, Poznań, Poland; (12) EngD. Table of Links Abstract and Introduction Abstract and Introduction Related Work Related Work Methods Methods Results and Discussion Results and Discussion Limitations Limitations Conclusion, Acknowledgment, and References Conclusion, Acknowledgment, and References Results and Discussion Cognitive Load Detection. To understand how to obtain a reliable cognitive load detection model we investigate different approaches to training protocols including varying lengths of the signal input and model pretraining as described above. Due to a small number of data, the model performance was varying between runs and therefore we run each training session 40 times and report the mean and standard deviation of all runs. We find that the number of data samples was insufficient to train the model from scratch on the pilot cognitive load dataset only (see Vanilla in Table 1). Model pre-training on WESAD dataset has shown to be helpful for cognitive load detection (see WESAD pre-trained in Table 1). The length of the signal is also important, in general, the 10 seconds window seems to be insufficient to obtain promising cognitive load detection performance even when the model is pretrained. Cognitive Load Detection. Cognitive Load Detection. Vanilla WESAD pre-trained We expected that the better the performance of the stress detector on the pretraining task the better will be the performance on the fine-tuning task. However, the link between source and target task performance is not that apparent. There is a weak correlation between source training data stress detection and fine-tuned cognitive load detector performance (r=0.18 p=0.04), a slightly stronger correlation between source validation data stress detection and target cognitive load detection (r=0.25, p=0.005), and no correlation between the test subject stress detection and target dataset cognitive load score (see Figure 3). Further, to check if our cognitive load detector is not just detecting stress, we run the best-performing stress detector with a 30s window on all participants from the pilot dataset and check % of samples for each participant from each condition (baseline and cognitive load) that are classified as stressed. We find that signal from participants 1, 3, 4, 7 and 8 had 0 samples classified as stressed, participants 0, 6, 9 and 10 had 9-22% samples classified as stressed and only participants 2 and 5 showed higher levels of stress with 59% and 55% of signal classified as stressed respectively. This suggests that improvements obtained via pre-training are not due to the physiological signal similarity in both conditions (cognitive load is not classified as stress) but rather due to learning of general feature extraction from the 1D signal. Response Time. On average participants spent less time responding to questions in gamified condition than in Non-gamified condition, with 5.5s and 6s per question respectively. However, interestingly, in gamified condition participants spent more time at the start and end questions which potentially could be due to the introduction of the gamified components which could capture respondents' attention (see Figure 4). Note that time spent on the very last question in each survey has been not included in this analysis as they mostly capture the time needed to move between questionnaires. Response Time. Response Time. Cognitive Burden. To ensure that the cognitive load detector can distinguish between high and low cognitive load for each participant in the survey dataset we first applied all 440 models (from 40 runs and 11 folds) trained with a 30s window on the pilot dataset to every participant in the survey dataset in cognitive load and baseline conditions and for each participant, we selected a model showing the best performance for this participant. In other words, we matched the models to participants using a small calibration dataset obtained from participants prior to filling the survey application. The performance of the selected best model is reported in Table 2 for each session. We have excluded the prediction of cognitive load during survey completion for those participants for whom we could not get a model with performance above 0.7 F1 score (marked with X in Table 2). Encouragingly, for 10 out of 13 participants, we were able to find a model which could reliably distinguish between their high and low cognitive load. The time spent in the high cognitive load and stress does not differ between gamified and non-gamified survey conditions. Potentially to understand if there is a difference between cognitive burden we would need a classifier trained on at least three different tasks with cognitive load levels: high (demanding task), medium (e.g. reading task) and low. Cognitive Burden. Cognitive Burden. Participants Experience. Only half of the participants noticed the difference in survey application appearance between the sessions. Participants who noted the difference reported preferring the gamified version more. Potentially the gamification was too simplistic to modulate the level of engagement or enjoyment. It is nontrivial to introduce game components in the surveys to reduce the burden but not affect the responses. Participants Experience. Participants Experience. This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv

Stories, News & Mentions about AVNET

Avnet’s Stories on HackerNoon

Avnet's News & Mentions