Synop

synop

A brand new research and development center for (EVs) and EV chargers has opened in Brooklyn, New York. electric vehicles The Synop Charge Lab, an interactive facility which will serve as an epicenter for interoperability testing spanning all chargers, telematics, and electric vehicles, was announced on October 10 by , a software platform for EVs. Synop According to the company, they chose Brooklyn for the site of the new R&D center for the abundance of n the New York City metropolitan area, and the opportunity to collaborate with local companies in the thriving regional ecosystem. tech talent i The new space will also serve as a headquarters for Synop’s operations in New York. Accessible to both Synop’s team and collaborative partners, the lab offers an environment conducive to rapid product prototyping and live simulations of EV charging in authentic scenarios. “Opening the Charge Lab is an exciting new chapter in the company’s history,” said Synop CEO Gagan Dhillon. “The research and development that we can do hands-on with partners at this facility represents a significant step forward in our commitment to providing enterprise software for the EV fleet industry.” Synop works with commercial vehicle fleets, such as school buses or shipping trucks, to help them quickly adopt EVs into their fleets. The company serves as a bridge between commercial fleet operators and local energy companies, helping both better manage infrastructure for electric vehicles. New York State has to transition to zero-emission vehicles by 2035, and New York City saw a 44% increase in EV registrations from 2021 to 2022, according to the Department of Transportation, as by . However, just 2% of the city’s 1.9 million registered vehicles are electric, so there is still a long way to go to achieve the State’s goals. committed reported City Limits Mayor Eric Adams in January the addition of 1,000 new electric vans to replace gasoline-powered vehicles in the city’s fleet. announced Synop’s leaders see the initiative as a means to help push the city toward a more electric future. “We envision the Synop Charge Lab becoming an integral part of the Brooklyn community,” said Synop Chief Commercial Officer Mark Braby. This article was originally published by in Stiven Cartagena The Sociable.

New Electric Vehicle Research & Development Center Launches in Brooklyn, NYC

Funding trends and performance YTD Disclaimer : Article inspired from InWara . This is not financial advice. The gaming sector is expected to grow from $108.9 billion in 2017 to $128.5 billion in 2020 according to Statista.com. The adoption levels are increasing, and the sector has jumped to rank 5 in terms of the number of ICOs. Despite this, many projects who have already adapted blockchain in the gaming industry, are facing challenges. Gaming sector jumps to number 5 in terms of number of ICOs in Q3, 2018 Despite the general slowdown, Gaming sector has seen growth in the number of ICOs from Q2, 2018 to Q3, 2018. In Q2, gaming was at the Xth position from where it has frogged to 5th. Source: InWara’s Q3 report Geographical Outlook Source: InWara’s ICO database USA retains the lead Gaming ICOs have seen some serious traction in the United States with Refereum alone raising $33 million in their main sale in 2018. As a percentage of total ICOs, USA dominated the gaming sector with 23% of all ICOs. China: Challenges at 2nd Despite the rumors of China cracking down on the nascent crypto market, ICOs in the gaming sector have managed to raise $53 million collectively in China. Estonia: Surprising entrant in the top 5 Surprisingly, Estonia made its way to the top 5 countries by not only drawing venture capitalists but also individual investors to successfully close 7 ICOs in the gaming sector. Active VCs in this space Bridgelink Capital Bridgelink Capital is a UK-based investment firm which focuses on quality early startup investment, to be the leading investment company globally Genesia Ventures Inc Genesia Ventures, Inc. is a Japan based venture capital firm focusing on seed to pre-series stage investment. Winter Capital Winter Capital is a private equity and venture capital firm specializing in startup and growth capital investments operating mainly in Russia and Europe. Block VC BlockVC is one of the leading blockchain and digital asset management firm with an exclusive focus on projects, tokens, and ventures related to blockchain technologies. 8 Decimal capital 8 Decimal Capital operates as a venture capital investment focused in the United States. Challenges faced by the Gaming industry Creating a token is easy. Listing it is the challenge Source: InWara’s ICO database Listing has been one of the prime challenges adversely impacting the sector. The listing rate in the gaming sector was 33% in 2017 and dipped to 30% in 2018. While this rate is better than other sectors such as Healthcare, this still bodes unfavorably to individual investors and liquidity remains the primary concern. However, the potential use of those tokens as casino tokens can alienate pressure, if adoption is strong. Returns on Investment: Gaming sector returns were not favoring the odds Source: InWara’s ICO database Although the Gaming sector was successful in terms of number of ICOs, the returns in Gaming sector ICOs were not good. Only 3 of the 36 listed ICOs delivered positive RoI, and what’s worse, they declined steadily over 2018. The average RoI of Gaming sector after witnessing an all time high in early 2018, have steadily slid downwards. Pundits have been betting on the overall Gaming sector to thrive and thus drive growth in the performance of the ICOs in the sector. Top Fundraisers - Gaming Sector Source: InWara’s ICO database Top 5 ICOs raised 60+% of total funds in the sector Game Credits tops the list by raising $106 million and Mediarex Enterprise raised $65 million — both in 2017. It is interesting to note that a majority of these rounds are from and blockchain/digital asset accumulators and not specific VCs as virtual money has been a part of gaming for the last 10 years. Refereum was the most successful with $33 million raised in 2018. The sector is expected to have a higher community support. Gamers have traditionally used fiat currency (i.e. USD) to purchase gaming items and this is expected to be conducive to the adoption of digital currency. GameCredits GameCredits: Founded in 2014. The GameCredits Mobile Store aims to be an alternative choice and the first one built around cryptocurrency to solve issues in the larger gaming industry. After two years of development with gaming industry leaders, Datcroft LTD., Game credits are close to completion of development of the first crypto mobile gaming store. MobileGo tokens will be used to gamify the mobile platform and to incentivize gamers for loyalty and participation through rewards. Mediarex Enterprises Ltd Mediarex Sports & Entertainment: Founded: 2012 Mediarex runs ChiliZ, a tokenized voting platform that empowers fans to crowd-manage sport and e-sports organizations. Headquartered in Gzira, Malta, Mediarex Sports & Entertainment (S&E) is a gaming event, statistics & ranking company. ENJIN Pte Ltd Enjin: Founded: 2008 Enjin is a decentralized gaming platform that aims to provide a solution for the existing issues in the gaming industry. Enjin is a gaming platform launched in 2009, based in Singapore. Enjin provides community management services (CMS) platform for gamers, developers, clan leaders, server owners, etc. They have 250,000 gaming communities across thousands of games and 18.7 million registered gamers. REFEREUM Refereum: Founded in 2017 The RFR Utility Product Token is a digital token on the Ethereum blockchain with an ability to execute code it will be held in a digital wallet. Refereum, Inc. was incorporated in September 2017 and its Management team comprises specialists in the game industry. DMarket Dmarket: Founded in 2017 DMarket aims to provide an opportunity for every gamer to perform easy, one-click buy/sell, exchange, and evaluation of every possible game asset, item, skin, or another game object. DMarket, a blockchain-based marketplace, helps connect all games and platforms together. Where is this data coming from? To leverage InWara’s data, schedule a free expert walkthrough here To get a detailed investment analysis on all ICOs and Private funding rounds, visit InWara’s ICO database and Private funding database

Gaming ICOs, poised for growth in 2019? | Complete Synopsis | Gaming sector ICOs analysis

From the development of modern humans, we as people have collaborated towards achieving common goals. Our modern corporate and political governance structures have evolved accordingly, and over time, we’ve seen major shifts in the way we govern large corporations; the industrial revolution vastly changed those structures, and again we saw similar adjustments with the birth of the internet. We’re on the cusp of seeing an even more substantial shake-up with the invention of the Decentralized Autonomous Organization (DAO). DAO’s provide the ability to run an organization with no centralized leadership. This means no appointed Board of Directors, no CEO, and no Manager. Each and every person is considered a “contributor”. A DAO runs by executing smart contracts on the , which in turn, solidifies the rules of the organization and carries out the functions of the business. Team members within the organization are incentivized to work on projects by receiving reputation and tokens that hold value on the blockchain. These tokens provide workers with an income structure, and their reputation gives them decision making power within the organization. As a contributor you’re allowed to propose certain tasks, vote on proposals, and influence the direction of the company. Those who contribute the most have the most influence within the organization, and rightfully so. blockchain A relevant example of a DAO company is Dash (Digital Cash). The development of any project and its funding are publicly proposed and decided on by the Dash community. Those who have contributed to Dash since the beginning have special roles in these votings. This assumes that the ones most invested (time/energy/coins) will have the most incentive to make the project a success. Their interests are proven by their reputation and contributions to Dash over time. With the DAO model decisions that often take classic organizations months or even years, can be made within a relatively short amount of time. DAOstack DAOstack is a fairly new player in the DAO field, evolving from Matan Field’s Backfeed project. Field states that DAO companies have lacked a solid framework to support decentralized self-governing organizations thus far. DAOstack aims to fix that. DAOstack classifies itself as an operating system for the DAO. Just as Wordpress allows for the creation of websites, DAOstack allows for the creation of decentralized web companies. In their own words, “It’s the Wordpress for the DAO. It does for the blockchain what Wordpress has done for the web.” It offers the ability to use a plug and play model to start a new DAO, configure its governance structure, distribute tokens and reputation, find collaborators and offer rewards to workers. DAOstack introduces users to building DAO companies in a way that’s easy to use. They’ve created what they call their “DAO stack”. This provides the necessary framework needed to bring DAO’s to life. The following becomes the internal product layers making up their stack. is the framework that runs the operations and governs the agencies within DAOstack. This makes it easy for agencies to create, configure, deploy, and upgrade from the library of governance modules and templates. Arc is the decentralized application (DApp) built by DAOstack that provides the user interface for Arc. This is how users interact with the platform. Alchemy is a javascript library that makes it easy for front-end developers to create applications on top of Arc with little to no blockchain experience. Arc.js are a shared set of registries within DAOstack. Including the registry of all agencies and DAO’s, the registry of pre-built governance modules, and a registry of offers and requests. This helps third-party DApps with each other, resulting in further collaboration. The ArcHives network Key Benefits The modular design of Arc means every new DAO automatically has a menu of pre-built governance modules at its fingertips. Users have the ability to pick and choose the modules that best fit the specific needs of their organization, thus making development cheaper and more efficient by employing pre-built blocks that are easily accessible. This saves time, storage, and operation costs. As with any organization, growth welcomes new challenges. It often becomes evident that the old way of doing things doesn’t make sense as companies grow. As DAOstack’s governance modules are plug and play, they can be easily changed and upgraded to make new rules in an ever-evolving organization. However, changing these governance modules comes with its own challenges. There are guidelines that come with changing a set of rules within a DAO. DAOstack’s open framework enhances an ecosystem of true collaboration. This ecosystem is made of a multitude of distinct but interoperable DAO’s that interact with one another. This allows users to form large-scale, open-source networks and business agreements in fractions of the time and costs than it has traditionally. Over time, contributions from independent developers and new DAO companies built within DAOstack will create a database of templates, governance modules, and applications that the community will continually benefit from. The incentives that developers are given to continue contributing to the community will allow them the freedom to experiment with an increasingly large number of governance protocols. What sets them apart DAOstack is doing something different than other players in the DAO marketplace. What sets them apart is the ability to mix and match certain governance modules and rules that make the most sense for your organization. Where __________ might work for __________, it doesn’t make sense for the __________ community to govern by those same set of rules. This is important because DAOstack now appeals to larger organizations. Offering complete customization to fit any project, no matter what the size. Competitors have limitations on flexibility; applications on the DAOstack don’t have such limitations. Another DAO company, Aragon, offers a framework that is more suitable for startups but is not as applicable as organizations scale. DAOstack modules provide a scalable product, allowing the ability to make changes quickly and with ease by removing outdated governance modules and adding new ones. The open framework allows the user to leverage the knowledge base, at a fraction of the cost. This will allow companies to form faster, requiring less capital to scale and upgrade on the fly using an established product that has been vetted by the DAOstack community. Conclusion Convincing large existing corporations to adopt this model will be the greatest obstacle to the success and development of a DAOstack community. Since the decision makers already have vested interests in keeping themselves and those they’ve appointed in power, why make a shift that would essentially put them on an even playing field? I could see smaller internal projects being operated through a DAO to take advantage of outside talent (similar to Microsoft leveraging ideagoras on the internet), but shifting their current company’s governance structure is a tall task, to say the least. It’s too early to tell, but my thought is that real adoption will come with the formation of completely new DAO companies that take over old inefficient markets. Similar to how Netflix put Blockbuster out of business when they utilized the benefits of the internet. DAO’s will expose the inefficiencies of the archaic hierarchy model, and fix the problems associated with getting large groups of people to work together. It will give rise to new major organizations that will change the way we govern big business, and DAOstack is creating the first framework to make that possible.

DAOstack — Synopsis For The Average Consumer

New York City plans to support electric vehicle (EV) adoption with a $15 million initiative to install 600 new curbside chargers. Can the city’s current grid infrastructure keep pace? New York City is ramping up its efforts to support electric vehicle (EV) adoption as it plans to install 600 new curbside EV chargers. Mayor Eric Adams recently announced that the initiative is part of a $15 million federal grant being awarded to the city to bolster its EV charging infrastructure — with a focus on underserved and low-income communities. “We’re building a cleaner, greener, and more affordable city for all New Yorkers, and through initiatives like this, we’re making sure that no New Yorkers are left behind,” wrote Mayor Adams in a blog post. “This $15 million grant will let us build the nation’s largest EV charging program, focused on low-income and disadvantaged areas so people living in all five boroughs can afford to drive and charge electric vehicles.” By 2050, New York City aims to get 20% of all new vehicles registered as electric. Thanks to incentives like the Inflation Reduction Act tax credits and Drive Clean rebates, a growing number of NYC drivers are switching to EVs. Despite these efforts, the city's grid is already struggling to keep up with the power and infrastructural requirements of current EV owners. Can NYC’s Electrical Grid Keep Up with the Growing Demand for EV Charging? NYC is currently grappling with two key challenges to scale its charging infrastructure — space and available power. For commercial transportation, charging hubs require significant space for infrastructure and larger electric trucks. In dense urban areas, especially NYC, real estate is limited and expensive, making it difficult to find suitable locations to accommodate charging stations and vehicles without disrupting city traffic. “Most of the federal EV infrastructure grants are for chargers deployed along highways (not buildings), limiting monetary incentives available to building developers deploying EV chargers,” Akshay Singh, Industrial and Automotive Industries Principal at PwC Strategy&, told me. “City planners need to work closely with utilities to make sure upgrades are being planned & performed timely, especially to supply dense urban areas with the highest EV adoption.” New York’s grid is already strained as the city attempts to electrify not only its transportation but also its buildings. Singh asserts that developers often do not understand the level of grid upgrades required due to a lack of available information. Moreover, these challenges are expected to become more prevalent with charging sites located in high-rise buildings that have not had capacity upgrades in years. “Major challenges with EV charging today are around how to make the economics of operating EV chargers feasible. DC fast chargers that operate at 100kW+ power output (to charge EVs within 30-45 mins) can cost upwards of $50k per unit which makes upfront costs of a multi-unit charging site very expensive,” added Singh. Electric Vehicles as Untapped Grid Assets? As New York pushes toward its ambitious goal of having 400,000 EVs on the road by 2030 as part of the "Green Rides" initiative, it’s clear that the city needs more fast-charging sites. Conglomerates like Revel are already making strides with its "superhub" in Brooklyn, boasting 25 high-speed chargers. But even with their success, the company faces a significant challenge: finding enough grid power to keep these large fast-charging stations running smoothly. While the city grapples with its grid limitations, some startups are stepping in to offer innovative solutions through A.I. and edge computing. Brooklyn-based Synop, for instance, aims to help stabilize the grid by integrating data from grid operators, EV charging stations, and EV fleet operators. “We focus on edge computing to enhance charging management,” Gagan Dhillon, CEO of Synop, told me. By bringing cloud-based functionality closer to the charging site, the company says it can increase reliability and reduce latency, “which is critical in areas with constrained grids or complex microgrid setups.” Dhillon added that one overlooked aspect of EV adoption is that electric vehicles are essentially batteries on wheels, and with the right infrastructure, they can serve as valuable assets rather than just power consumers. “EVs can act as backup power sources during times of high grid demand, providing energy back to the grid or even powering homes when needed,” he said. “With the integration of smart charging technology, EVs can be programmed to charge during off-peak times when energy is more abundant. This flexibility makes them less of a strain on the grid than often imagined.” With more people driving EVs and a grid under a lot of strain, NYC's EV goal success will hinge on how open it is to new technologies, speeding up infrastructure projects, and investing in sustainable energy solutions. “There will be a need for higher powered EV chargers that will further increase the grid upgrade requirements. Developers need to work together with local utilities from the start to successfully deploy EV projects,” Singh told me. “Along with that, additional [capital expenditure] needed to sustain the grid for more demand needs to be laid out and discussions on who will bear the cost burden.” New York City plans to support electric vehicle (EV) adoption with a $15 million initiative to install 600 new curbside chargers. Can the city’s current grid infrastructure keep pace? New York City plans to support electric vehicle (EV) adoption with a $15 million initiative to install 600 new curbside chargers. Can the city’s current grid infrastructure keep pace? New York City plans to support electric vehicle (EV) adoption with a $15 million initiative to install 600 new curbside chargers. Can the city’s current grid infrastructure keep pace? New York City is ramping up its efforts to support electric vehicle (EV) adoption as it plans to install 600 new curbside EV chargers. Mayor Eric Adams recently announced that the initiative is part of a $15 million federal grant being awarded to the city to bolster its EV charging infrastructure — with a focus on underserved and low-income communities. “We’re building a cleaner, greener, and more affordable city for all New Yorkers, and through initiatives like this, we’re making sure that no New Yorkers are left behind,” wrote Mayor Adams in a blog post . “This $15 million grant will let us build the nation’s largest EV charging program, focused on low-income and disadvantaged areas so people living in all five boroughs can afford to drive and charge electric vehicles.” By 2050, New York City aims to get 20% of all new vehicles registered as electric. Thanks to incentives like the Inflation Reduction Act tax credits and Drive Clean rebates, a growing number of NYC drivers are switching to EVs. Despite these efforts, the city's grid is already struggling to keep up with the power and infrastructural requirements of current EV owners. blog post blog post Inflation Reduction Act Inflation Reduction Act switching switching Can NYC’s Electrical Grid Keep Up with the Growing Demand for EV Charging? NYC is currently grappling with two key challenges to scale its charging infrastructure — space and available power. For commercial transportation, charging hubs require significant space for infrastructure and larger electric trucks. In dense urban areas, especially NYC, real estate is limited and expensive, making it difficult to find suitable locations to accommodate charging stations and vehicles without disrupting city traffic. “Most of the federal EV infrastructure grants are for chargers deployed along highways (not buildings), limiting monetary incentives available to building developers deploying EV chargers,” Akshay Singh , Industrial and Automotive Industries Principal at PwC Strategy&, told me. “City planners need to work closely with utilities to make sure upgrades are being planned & performed timely, especially to supply dense urban areas with the highest EV adoption.” Akshay Singh Akshay Singh New York’s grid is already strained as the city attempts to electrify not only its transportation but also its buildings. Singh asserts that developers often do not understand the level of grid upgrades required due to a lack of available information. Moreover, these challenges are expected to become more prevalent with charging sites located in high-rise buildings that have not had capacity upgrades in years. “Major challenges with EV charging today are around how to make the economics of operating EV chargers feasible. DC fast chargers that operate at 100kW+ power output (to charge EVs within 30-45 mins) can cost upwards of $50k per unit which makes upfront costs of a multi-unit charging site very expensive,” added Singh. Electric Vehicles as Untapped Grid Assets? As New York pushes toward its ambitious goal of having 400,000 EVs on the road by 2030 as part of the " Green Rides " initiative, it’s clear that the city needs more fast-charging sites. Conglomerates like Revel are already making strides with its " superhub " in Brooklyn, boasting 25 high-speed chargers. But even with their success, the company faces a significant challenge: finding enough grid power to keep these large fast-charging stations running smoothly. Green Rides Green Rides superhub superhub While the city grapples with its grid limitations, some startups are stepping in to offer innovative solutions through A.I. and edge computing. Brooklyn-based Synop, for instance, aims to help stabilize the grid by integrating data from grid operators, EV charging stations, and EV fleet operators. “We focus on edge computing to enhance charging management,” Gagan Dhillon , CEO of Synop, told me. By bringing cloud-based functionality closer to the charging site, the company says it can increase reliability and reduce latency, “which is critical in areas with constrained grids or complex microgrid setups.” Gagan Dhillon Gagan Dhillon Dhillon added that one overlooked aspect of EV adoption is that electric vehicles are essentially batteries on wheels, and with the right infrastructure, they can serve as valuable assets rather than just power consumers. “EVs can act as backup power sources during times of high grid demand, providing energy back to the grid or even powering homes when needed,” he said. “With the integration of smart charging technology, EVs can be programmed to charge during off-peak times when energy is more abundant. This flexibility makes them less of a strain on the grid than often imagined.” With more people driving EVs and a grid under a lot of strain, NYC's EV goal success will hinge on how open it is to new technologies, speeding up infrastructure projects, and investing in sustainable energy solutions. “There will be a need for higher powered EV chargers that will further increase the grid upgrade requirements. Developers need to work together with local utilities from the start to successfully deploy EV projects,” Singh told me. “Along with that, additional [capital expenditure] needed to sustain the grid for more demand needs to be laid out and discussions on who will bear the cost burden.”

NYC's $15 Million EV Charger Initiative: Can the City Grid Handle Rising Energy Demands?

Summarizing Large Datasets of Customer Feedback Using Retrieval-Augmented Generation (RAG)

38 Lifehacks From "Tools of Titans" by Tim Ferriss

Authors:
(1) Pinelopi Papalampidi, Institute for Language, Cognition and Computation, School of Informatics, University of Edinburgh;
(2) Frank Keller, Institute for Language, Cognition and Computation, School of Informatics, University of Edinburgh;
(3) Mirella Lapata, Institute for Language, Cognition and Computation, School of Informatics, University of Edinburgh. Table of Links Abstract and Intro
Related Work
Problem Formulation
Experimental Setup
Results and Analysis
Conclusions and References
A. Model Details
B. Implementation Details
C. Results: Ablation Studies A. Model Details In this section we provide details about the various modeling components of our approach. We begin by providing details of the GRAPHTRAILER architecture (Section A.1), then move to discuss how the TP identification network is trained (Section A.2), and finally give technical details about pre-training on screenplays (A.3), and the sentiment flow used for graph traversal (A.4). A.1. GRAPHTRAILER We address discontinuities in our model (i.e., top-k sampling, neighborhood size selection) by utilising the StraightThrough Estimator [7]. During the backward pass we compute the gradients with the Gumbel-softmax reparametrization trick [25, 32]. The same procedure is followed for constructing and sparsifying scene-level graphs in the auxiliary screenplay-based network. A.2. Training on TP Identification Section 3 presents our training regime for the video- and screenplay-based model assuming TP labels for scenes are available (i.e., binary labels indicating whether a scene acts as a TP in a movie). Given such labels, our model is trained with a binary cross-entropy loss (BCE) objective between the few-hot gold labels and the network’s TP predictions. However, in practice, our training set contains silverstandard labels for scenes. The latter are released together with the TRIPOD [41] dataset and were created automatically. Specifically, TRIPOD provides gold-standard TP annotations for synopses (not screenplays), under the assumption that synopsis sentences are representative of TPs. And sentence-level annotations are projected to scenes with a matching model trained with teacher forcing [41] to create silver-standard labels. A.3. Self-supervised Pre-training A.4. Sentiment Flow in GRAPHTRAILER One of the criteria for selecting the next shot in our graph traversal algorithm (Section 3.1) is the sentiment flow of the trailer generated so far. Specifically, we adopt the hypothesis[9] that trailers are segmented into three sections based on sentiment intensity. The first section has medium intensity for attracting viewers, the second section has low intensity for delivering key information about the movie and finally the third section displays progressively higher intensity for creating cliffhangers and excitement for the movie. Accordingly, given a budget of L trailer shots, we expect the first L/3 ones to have medium intensity without large variations within the section (e.g., we want shots with average absolute intensity close to 0.7, where all scores are normalized to a range from -1 to 1). In the second part of the trailer (i.e., the next L/3 shots) we expect a sharp drop in intensity and shots within this section to maintain more or less neutral sentiment (i.e., 0 intensity). Finally, for the third section (i.e., the final L/3 shots) we expect intensity to steadily increase. In practice, we expect the intensity of the first shot to be 0.7 (i.e., medium intensity), increasing by 0.1 with each subsequent shot until we reach a peak at the final shot. This paper is available on arxiv under CC BY-SA 4.0 DEED license. [9] https : / / www . derek - lieu . com / blog / 2017 / 9 / 10 / the - matrix - is - a - trailer - editors-dream Authors: (1) Pinelopi Papalampidi, Institute for Language, Cognition and Computation, School of Informatics, University of Edinburgh; (2) Frank Keller, Institute for Language, Cognition and Computation, School of Informatics, University of Edinburgh; (3) Mirella Lapata, Institute for Language, Cognition and Computation, School of Informatics, University of Edinburgh. Authors: Authors: (1) Pinelopi Papalampidi, Institute for Language, Cognition and Computation, School of Informatics, University of Edinburgh; (2) Frank Keller, Institute for Language, Cognition and Computation, School of Informatics, University of Edinburgh; (3) Mirella Lapata, Institute for Language, Cognition and Computation, School of Informatics, University of Edinburgh. Table of Links Abstract and Intro Related Work Problem Formulation Experimental Setup Results and Analysis Conclusions and References A. Model Details B. Implementation Details C. Results: Ablation Studies Abstract and Intro Abstract and Intro Related Work Related Work Problem Formulation Problem Formulation Experimental Setup Experimental Setup Results and Analysis Results and Analysis Conclusions and References Conclusions and References A. Model Details A. Model Details B. Implementation Details B. Implementation Details C. Results: Ablation Studies C. Results: Ablation Studies A. Model Details In this section we provide details about the various modeling components of our approach. We begin by providing details of the GRAPHTRAILER architecture (Section A.1), then move to discuss how the TP identification network is trained (Section A.2), and finally give technical details about pre-training on screenplays (A.3), and the sentiment flow used for graph traversal (A.4). A.1. GRAPHTRAILER We address discontinuities in our model (i.e., top-k sampling, neighborhood size selection) by utilising the StraightThrough Estimator [7]. During the backward pass we compute the gradients with the Gumbel-softmax reparametrization trick [25, 32]. The same procedure is followed for constructing and sparsifying scene-level graphs in the auxiliary screenplay-based network. A.2. Training on TP Identification Section 3 presents our training regime for the video- and screenplay-based model assuming TP labels for scenes are available (i.e., binary labels indicating whether a scene acts as a TP in a movie). Given such labels, our model is trained with a binary cross-entropy loss (BCE) objective between the few-hot gold labels and the network’s TP predictions. However, in practice, our training set contains silverstandard labels for scenes. The latter are released together with the TRIPOD [41] dataset and were created automatically. Specifically, TRIPOD provides gold-standard TP annotations for synopses (not screenplays), under the assumption that synopsis sentences are representative of TPs. And sentence-level annotations are projected to scenes with a matching model trained with teacher forcing [41] to create silver-standard labels. A.3. Self-supervised Pre-training A.4. Sentiment Flow in GRAPHTRAILER One of the criteria for selecting the next shot in our graph traversal algorithm (Section 3.1) is the sentiment flow of the trailer generated so far. Specifically, we adopt the hypothesis[9] that trailers are segmented into three sections based on sentiment intensity. The first section has medium intensity for attracting viewers, the second section has low intensity for delivering key information about the movie and finally the third section displays progressively higher intensity for creating cliffhangers and excitement for the movie. Accordingly, given a budget of L trailer shots, we expect the first L/3 ones to have medium intensity without large variations within the section (e.g., we want shots with average absolute intensity close to 0.7, where all scores are normalized to a range from -1 to 1). In the second part of the trailer (i.e., the next L/3 shots) we expect a sharp drop in intensity and shots within this section to maintain more or less neutral sentiment (i.e., 0 intensity). Finally, for the third section (i.e., the final L/3 shots) we expect intensity to steadily increase. In practice, we expect the intensity of the first shot to be 0.7 (i.e., medium intensity), increasing by 0.1 with each subsequent shot until we reach a peak at the final shot. This paper is available on arxiv under CC BY-SA 4.0 DEED license. This paper is available on arxiv under CC BY-SA 4.0 DEED license. available on arxiv [9] https : / / www . derek - lieu . com / blog / 2017 / 9 / 10 / the - matrix - is - a - trailer - editors-dream

Film Trailer Generation via Task Decomposition: Model Details

টাস্ক ডিকম্পোজিশনের মাধ্যমে ফিল্ম ট্রেলার জেনারেশন: মডেলের বিবরণ

Filmtrailer-Generierung durch Aufgabenzerlegung: Modelldetails

Generación de avances de películas mediante descomposición de tareas: detalles del modelo

Génération de bandes-annonces de film via la décomposition de tâches : détails du modèle

टास्क डिकंपोज़िशन के माध्यम से फ़िल्म ट्रेलर निर्माण: मॉडल विवरण

タスク分解による映画予告編生成: モデルの詳細

작업 분해를 통한 영화 예고편 생성: 모델 세부정보

Geração de trailer de filme por meio de decomposição de tarefas: detalhes do modelo

Создание трейлера к фильму посредством декомпозиции задач: детали модели

Görev Ayrıştırma Yoluyla Film Fragmanı Oluşturma: Model Ayrıntıları

Tạo đoạn giới thiệu phim thông qua phân tách nhiệm vụ: Chi tiết mô hình

通过任务分解生成电影预告片：模型细节

Four Thousand Years of Blood, Snot, and Tears

চার হাজার বছরের রক্ত, স্নোট এবং অশ্রু

Viertausend Jahre Blut, Rotz und Tränen

Cuatro mil años de sangre, mocos y lágrimas

Quatre mille ans de sang, de morve et de larmes

चार हज़ार साल का खून, बलगम और आँसू

4000年にわたる血と鼻水と涙

4천년의 피와 콧물과 눈물

Quatro mil anos de sangue, ranho e lágrimas

Четыре тысячи лет крови, соплей и слез

Dört Bin Yıllık Kan, Sümük ve Gözyaşı

Bốn ngàn năm máu, nước mũi và nước mắt

四千年的血、鼻、泪

Authors:
(1) Dinesh Kumar Vishwakarma, Biometric Research Laboratory, Department of Information Technology, Delhi Technological University, Delhi, India;
(2) Mayank Jindal, Biometric Research Laboratory, Department of Information Technology, Delhi Technological University, Delhi, India
(3) Ayush Mittal, Biometric Research Laboratory, Department of Information Technology, Delhi Technological University, Delhi, India
(4) Aditya Sharma, Biometric Research Laboratory, Department of Information Technology, Delhi Technological University, Delhi, India. Table of Links Abstract and Intro
Background and Related Work
EMTD Dataset
Proposed Methodology
Experiments
Conclusion and References 2. Background and Related Work This section discusses the past methodologies for movie genre classification and the motivations behind our study. Video content is majorly partitioned to (1) Video frames (Images) and (2) Audio (Speech {dialogues} + Non speech {vocals}). To analyze the video content, various studies are done in the past, focusing chiefly upon cognitive [3]–[7] or affective [8] levels individually. For a more effective study, both levels need to be taken into account to perform better in a genre classification task. In the past studies, many cognition-based approaches have been proposed based upon low-level features, including visual disturbances, average shot length, gradual change in light intensity in video frames, and peaks in audio waveform [3], to capture scene components [4]. Other features used for cognitive classification include RGB colors in frames [6], film shots [7], shot length [9], type of background in scenes (dark/non-dark) [6], etc. Similarly, some approaches are proposed for only affective analysis [8]. A movie can have multiple genres depicting a lot of information to viewers thus also serve as a task to recommend a movie to a viewer. Jain et al. [5] used 4 video features (shot length, motion, color dominance, lighting key) and 5 audio features to classify movie clips using only 200 training samples. They used complete movie clips to predict genres. However, the study uses only 200 training samples for training their model. Accordingly, the accuracy reported by them might be due to over-fitting. Also, the study focused only on single-label classification. Huang et al. [4] proposed the Self Adaptive Harmony Search algorithm with 7 stacked SVM’s that used both audio and visual features (about 277 features in total) on a 223 sized dataset. Ertugrul et al. [10] used low-level features, including the movies' plot, by breaking the plot into sentences and classifying sentences into genres and taking the final genre to be one with maximum occurrence. Pais et al. [11] proposed to fuse image-text features by relying on some important words from the overall synopsis and performed movie genre classification based on those features. The model was tested on a set of 107 movie trailers. Shahin et al. [12] used movie plots and quotes and proposed Hierarchical attention networks to classify genres. Similarly, Kumar et al. [13] proposed to use movie plots to classify genre using hash vectorization by focusing on reducing overall time complexity. The above-mentioned studies rely on low-level features and do not capture any high-level features from movie trailers, thus cannot be relied upon for a good level recognition system. From more recent studies, many researchers used deep networks for movie genre classification tasks. Shambharkar et al. [14] proposed a single label 3D CNN-based architecture to seize the spatial and temporal features. Though spatial and temporal features are captured in this, the model is not robust due to single-label classification. Some researchers have worked on movie posters to classify movie genres. Chu et al. [15] formulated a deep neural network to facilitate object detection and visual appearances. Though work captured a lot of information from posters, the poster itself is not enough to completely describe a movie. Simoes et al. [16] proposed a CNN-Motion that included scene histograms provided by the unsupervised clustering algorithm, weighted genre predictions for each trailer, along with some low-level video features. This provided a major group of features from a video but lacked some affective and cognitive-based features to classify genre. Thus, from the past literature, it is evident that major information should be extracted from the video trailers for cognitive as well as affective study. So, our motivation behind the work is to device an approach relying on both levels of video content analysis as in [1]. We believe that the proposed architecture and the model are novel and robust and can be used in the future for various research perspectives. This paper is available on arxiv under CC BY-NC-SA 4.0 DEED license. Authors: (1) Dinesh Kumar Vishwakarma, Biometric Research Laboratory, Department of Information Technology, Delhi Technological University, Delhi, India; (2) Mayank Jindal, Biometric Research Laboratory, Department of Information Technology, Delhi Technological University, Delhi, India (3) Ayush Mittal, Biometric Research Laboratory, Department of Information Technology, Delhi Technological University, Delhi, India (4) Aditya Sharma, Biometric Research Laboratory, Department of Information Technology, Delhi Technological University, Delhi, India. Authors: Authors: (1) Dinesh Kumar Vishwakarma, Biometric Research Laboratory, Department of Information Technology, Delhi Technological University, Delhi, India; (2) Mayank Jindal, Biometric Research Laboratory, Department of Information Technology, Delhi Technological University, Delhi, India (3) Ayush Mittal, Biometric Research Laboratory, Department of Information Technology, Delhi Technological University, Delhi, India (4) Aditya Sharma, Biometric Research Laboratory, Department of Information Technology, Delhi Technological University, Delhi, India. Table of Links Abstract and Intro Background and Related Work EMTD Dataset Proposed Methodology Experiments Conclusion and References Abstract and Intro Abstract and Intro Background and Related Work Background and Related Work EMTD Dataset EMTD Dataset Proposed Methodology Proposed Methodology Experiments Experiments Conclusion and References Conclusion and References 2. Background and Related Work This section discusses the past methodologies for movie genre classification and the motivations behind our study. Video content is majorly partitioned to (1) Video frames (Images) and (2) Audio (Speech {dialogues} + Non speech {vocals}). To analyze the video content, various studies are done in the past, focusing chiefly upon cognitive [3]–[7] or affective [8] levels individually. For a more effective study, both levels need to be taken into account to perform better in a genre classification task. In the past studies, many cognition-based approaches have been proposed based upon low-level features, including visual disturbances, average shot length, gradual change in light intensity in video frames, and peaks in audio waveform [3], to capture scene components [4]. Other features used for cognitive classification include RGB colors in frames [6], film shots [7], shot length [9], type of background in scenes (dark/non-dark) [6], etc. Similarly, some approaches are proposed for only affective analysis [8]. A movie can have multiple genres depicting a lot of information to viewers thus also serve as a task to recommend a movie to a viewer. Jain et al. [5] used 4 video features (shot length, motion, color dominance, lighting key) and 5 audio features to classify movie clips using only 200 training samples. They used complete movie clips to predict genres. However, the study uses only 200 training samples for training their model. Accordingly, the accuracy reported by them might be due to over-fitting. Also, the study focused only on single-label classification. Huang et al. [4] proposed the Self Adaptive Harmony Search algorithm with 7 stacked SVM’s that used both audio and visual features (about 277 features in total) on a 223 sized dataset. Ertugrul et al. [10] used low-level features, including the movies' plot, by breaking the plot into sentences and classifying sentences into genres and taking the final genre to be one with maximum occurrence. Pais et al. [11] proposed to fuse image-text features by relying on some important words from the overall synopsis and performed movie genre classification based on those features. The model was tested on a set of 107 movie trailers. Shahin et al. [12] used movie plots and quotes and proposed Hierarchical attention networks to classify genres. Similarly, Kumar et al. [13] proposed to use movie plots to classify genre using hash vectorization by focusing on reducing overall time complexity. The above-mentioned studies rely on low-level features and do not capture any high-level features from movie trailers, thus cannot be relied upon for a good level recognition system. From more recent studies, many researchers used deep networks for movie genre classification tasks. Shambharkar et al. [14] proposed a single label 3D CNN-based architecture to seize the spatial and temporal features. Though spatial and temporal features are captured in this, the model is not robust due to single-label classification. Some researchers have worked on movie posters to classify movie genres. Chu et al. [15] formulated a deep neural network to facilitate object detection and visual appearances. Though work captured a lot of information from posters, the poster itself is not enough to completely describe a movie. Simoes et al. [16] proposed a CNN-Motion that included scene histograms provided by the unsupervised clustering algorithm, weighted genre predictions for each trailer, along with some low-level video features. This provided a major group of features from a video but lacked some affective and cognitive-based features to classify genre. Thus, from the past literature, it is evident that major information should be extracted from the video trailers for cognitive as well as affective study. So, our motivation behind the work is to device an approach relying on both levels of video content analysis as in [1]. We believe that the proposed architecture and the model are novel and robust and can be used in the future for various research perspectives. This paper is available on arxiv under CC BY-NC-SA 4.0 DEED license. This paper is available on arxiv under CC BY-NC-SA 4.0 DEED license. available on arxiv

Multilevel Profiling of Situation and Dialogue-based Deep Networks: Background and Related Work

পরিস্থিতি এবং সংলাপ-ভিত্তিক গভীর নেটওয়ার্কের মাল্টিলেভেল প্রোফাইলিং: পটভূমি এবং সম্পর্কিত কাজ

Multilevel-Profiling situations- und dialogbasierter Deep Networks: Hintergrund und verwandte Arbeiten

Elaboración de perfiles multinivel de redes profundas basadas en situaciones y diálogos: antecedentes y trabajos relacionados

Profilage multiniveau de la situation et des réseaux profonds basés sur le dialogue : contexte et travaux connexes

स्थिति और संवाद-आधारित गहरे नेटवर्क की बहुस्तरीय रूपरेखा: पृष्ठभूमि और संबंधित कार्य

状況と対話に基づくディープネットワークのマルチレベルプロファイリング：背景と関連研究

상황 및 대화 기반 심층 네트워크의 다단계 프로파일링: 배경 및 관련 연구

Perfil multinível de redes profundas baseadas em situação e diálogo: histórico e trabalho relacionado

Многоуровневое профилирование глубоких сетей, основанных на ситуациях и диалоге: предыстория и сопутствующая работа

Durum ve Diyaloğa Dayalı Derin Ağların Çok Düzeyli Profilinin Oluşturulması: Arka Plan ve İlgili Çalışmalar

Lập hồ sơ đa cấp về tình huống và mạng lưới sâu dựa trên đối thoại: Bối cảnh và công việc liên quan

情境和基于对话的深度网络的多层次剖析：背景和相关工作

Authors:
(1) Dinesh Kumar Vishwakarma, Biometric Research Laboratory, Department of Information Technology, Delhi Technological University, Delhi, India;
(2) Mayank Jindal, Biometric Research Laboratory, Department of Information Technology, Delhi Technological University, Delhi, India
(3) Ayush Mittal, Biometric Research Laboratory, Department of Information Technology, Delhi Technological University, Delhi, India
(4) Aditya Sharma, Biometric Research Laboratory, Department of Information Technology, Delhi Technological University, Delhi, India. Table of Links Abstract and Intro
Background and Related Work
EMTD Dataset
Proposed Methodology
Experiments
Conclusion and References 5. Experiments In this part, we will examine various model architectures on different modalities and prefeature fused models. Later, we verify our work by validating it on the standard LMTD-9 dataset as well as on our proposed dataset. Finally, a comparative study is discussed to explore our model robustness. All the experiments are performed on GPU workstations with 128 GB DDR4 RAM and Nvidia Titan RTX (24 GB) GPU configuration. 5.1. Datasets To verify our framework, we utilize our proposed dataset and standard LMTD-9 [2] dataset. Comprehensive details are mentioned as follows: 5.1.1. English movie trailer dataset (EMTD) EMTD: Our proposed dataset contains a separate training set of 1700 unique trailers and a validation set of 300 unique trailers, all taken from IMDB, as mentioned in Section 3. 5.1.2. Labeled movie trailer dataset (LMTD-9) LMTD [16], [20] is a multi-label large-scale movie trailer dataset including trailer link, trailer metadata, plot/summary, unique trailer id consisting of around 9k movie trailers belonging to 22 distinct labels/genres. For verification purposes, a validation set (subpart) of LMTD-9 [2] is used that only includes the Hollywood trailers released after 1980 and trailers specific to our genre list. The dataset contains varying length trailers with different video quality and aspect ratios. 5.2. Classification results on different models In this section, we will discuss our experiments with different framework variations. We experimented with 3 different frameworks based on separate modalities and pre-fused features. MS (Video frames analysis): Model considering the only Situation based features from video frames.


MD (Dialogues-metadata analysis): Model considering dialogues from audio and descriptions from metadata as features.


MSD (Multi-modality analysis): Model considering situation-based features from video frames, dialogues from audio and descriptions from metadata as features. The architecture proposed in Section 4.2.3 with pre-fused features is used for MSD. However, the input corpus is slightly modified. The corpus defined in Section 4.4 is used for MSD. Precision, Recall, and F1-score for MSD on LMTD-9 and EMTD is depicted in Table 5. However, the AU (PRC) comparison of MSD with MS and MD is discussed in the upcoming section. Some variation can be seen among the performance of different genres. Most of the trailers belonging to major genres are being classified precisely (with an F1 score of 0.84 and above), which shows that the proposed model is performing well. The action genre was the best performing genre among five with an F1-score of 0.88 and 0.89 on EMTD and LMTD-9 respectively. The romance genre was seen to be the least performing genre among all genres in terms of the F1-score. It is observed that many romance genre trailers are being misclassified into comedy as both these genres are dominated by similar words like happy, smile, laugh, etc. 5.3. AU (PRC) Comparison The AU (PRC) i.e., area under the precision-recall curve, is calculated to compare our classification results, as we are dealing with the multi-label classification problem. AU (PRC) measure helps to compare the actual performance of our model, compensating for the noise effect due to class imbalance in the multi-label dataset. The AU (PRC) curves are created for all 3 models on both datasets as depicted in Fig. 5, Fig. 6, and Fig. 7. On the validation set of EMTD, we found almost similar AU (PRC) values 92%, 91%, 88% on MSD, MD, and MS, respectively. However, we found that our MSD gives the 82% AU (PRC) values on the LMTD9 dataset, which is greater than the other two models i.e., 72% and 80% AU (PRC) of MD and MS respectively as in Table 6. However, for overall comparison with some other models that we experimented with within our study, we mention their results in Table 6. For choosing the best architecture, the models are compared in terms of AU (PRC) on both of the validation datasets. The implementation of Features Model Dataset EMTD LMTD-9 Dialogue (MD) E-Bi LSTM 0.87 0.66 ECnet 0.91 0.72 Situation (MS) ECnet 0.86 0.75 TFAnet 0.88 0.80 Fused Features (MSD) ECnet 0.92 0.82 all the mentioned models helps us in deciding the best model for the fused features. Although MD has comparable AU (PRC) values with MSD on EMTD but on LMTD-9, MSD outperformed MD. Similar is the case with MS on LMTD-9. While MSD performed simultaneously well on both datasets, which is not true in the case of MS and MD individually. So, by cross dataset validation MSD proves to be a more robust one. We conclude that the proposed MSD is the best performing model. 5.4. Baseline comparison In this section, we validate the performance of our proposed model by performing the state of art comparison with the previous approaches for movie genre classification using the AU (PRC) metric for each genre separately as depicted in Table 7. All the results mentioned in Table 7 are shown up to two decimal places and are based on the standard LMTD-9 dataset except for Fish et. al. [22], whose results are based on MMX trailer-20 dataset. It does not consider the romance genre in its study. However, for the other genres, the difference in the AU (PRC) values of Fish et. al [22] and MSD is worth noting. MSD outperforms it by 20% on average. Low-level visual features based classification [23] is based on 24 low-level visual features, SAS-MC-v2 [24] uses only the synopsis for trailer classification, Fish et. al. [22] and CTT-MMC-TN [25] are based on high-level features. Comparing to low-level feature approaches [23], [24], MSD on an average outperforms by 10%, and by comparing from approaches using high-level features [22], [25], it outperforms by 8% on an average for each genre. It is also observed comedy genre performed well in most works as compared to the other four genres while science-fiction has relatively lower AU (PRC) values. This could be due to the unavailability of proper distinction in the science-fiction genre, as its features overlap with some other similar genres (like action). The comparative study demonstrates that the proposed model is robust as it outperforms existing approaches and gives excellent results. The better performance is due to the reason that the architecture proposed includes both cognitive and affective features, helping the model to learn substantial characteristics of each genre, hence predicting genres more precisely. This paper is available on arxiv under CC BY-NC-SA 4.0 DEED license. Authors: (1) Dinesh Kumar Vishwakarma, Biometric Research Laboratory, Department of Information Technology, Delhi Technological University, Delhi, India; (2) Mayank Jindal, Biometric Research Laboratory, Department of Information Technology, Delhi Technological University, Delhi, India (3) Ayush Mittal, Biometric Research Laboratory, Department of Information Technology, Delhi Technological University, Delhi, India (4) Aditya Sharma, Biometric Research Laboratory, Department of Information Technology, Delhi Technological University, Delhi, India. Authors: Authors: (1) Dinesh Kumar Vishwakarma, Biometric Research Laboratory, Department of Information Technology, Delhi Technological University, Delhi, India; (2) Mayank Jindal, Biometric Research Laboratory, Department of Information Technology, Delhi Technological University, Delhi, India (3) Ayush Mittal, Biometric Research Laboratory, Department of Information Technology, Delhi Technological University, Delhi, India (4) Aditya Sharma, Biometric Research Laboratory, Department of Information Technology, Delhi Technological University, Delhi, India. Table of Links Abstract and Intro Background and Related Work EMTD Dataset Proposed Methodology Experiments Conclusion and References Abstract and Intro Abstract and Intro Background and Related Work Background and Related Work EMTD Dataset EMTD Dataset Proposed Methodology Proposed Methodology Experiments Experiments Conclusion and References Conclusion and References 5. Experiments In this part, we will examine various model architectures on different modalities and prefeature fused models. Later, we verify our work by validating it on the standard LMTD-9 dataset as well as on our proposed dataset. Finally, a comparative study is discussed to explore our model robustness. All the experiments are performed on GPU workstations with 128 GB DDR4 RAM and Nvidia Titan RTX (24 GB) GPU configuration. 5.1. Datasets To verify our framework, we utilize our proposed dataset and standard LMTD-9 [2] dataset. Comprehensive details are mentioned as follows: 5.1.1. English movie trailer dataset (EMTD) EMTD: Our proposed dataset contains a separate training set of 1700 unique trailers and a validation set of 300 unique trailers, all taken from IMDB, as mentioned in Section 3. 5.1.2. Labeled movie trailer dataset (LMTD-9) LMTD [16], [20] is a multi-label large-scale movie trailer dataset including trailer link, trailer metadata, plot/summary, unique trailer id consisting of around 9k movie trailers belonging to 22 distinct labels/genres. For verification purposes, a validation set (subpart) of LMTD-9 [2] is used that only includes the Hollywood trailers released after 1980 and trailers specific to our genre list. The dataset contains varying length trailers with different video quality and aspect ratios. 5.2. Classification results on different models In this section, we will discuss our experiments with different framework variations. We experimented with 3 different frameworks based on separate modalities and pre-fused features. MS (Video frames analysis): Model considering the only Situation based features from video frames. MD (Dialogues-metadata analysis): Model considering dialogues from audio and descriptions from metadata as features. MSD (Multi-modality analysis): Model considering situation-based features from video frames, dialogues from audio and descriptions from metadata as features. MS (Video frames analysis): Model considering the only Situation based features from video frames. MS (Video frames analysis): Model considering the only Situation based features from video frames. MS (Video frames analysis): MD (Dialogues-metadata analysis): Model considering dialogues from audio and descriptions from metadata as features. MD (Dialogues-metadata analysis): Model considering dialogues from audio and descriptions from metadata as features. MD (Dialogues-metadata analysis): MSD (Multi-modality analysis): Model considering situation-based features from video frames, dialogues from audio and descriptions from metadata as features. MSD (Multi-modality analysis): Model considering situation-based features from video frames, dialogues from audio and descriptions from metadata as features. MSD (Multi-modality analysis): The architecture proposed in Section 4.2.3 with pre-fused features is used for MSD. However, the input corpus is slightly modified. The corpus defined in Section 4.4 is used for MSD. Precision, Recall, and F1-score for MSD on LMTD-9 and EMTD is depicted in Table 5. However, the AU (PRC) comparison of MSD with MS and MD is discussed in the upcoming section. Some variation can be seen among the performance of different genres. Most of the trailers belonging to major genres are being classified precisely (with an F1 score of 0.84 and above), which shows that the proposed model is performing well. The action genre was the best performing genre among five with an F1-score of 0.88 and 0.89 on EMTD and LMTD-9 respectively. The romance genre was seen to be the least performing genre among all genres in terms of the F1-score. It is observed that many romance genre trailers are being misclassified into comedy as both these genres are dominated by similar words like happy, smile, laugh, etc. 5.3. AU (PRC) Comparison The AU (PRC) i.e., area under the precision-recall curve, is calculated to compare our classification results, as we are dealing with the multi-label classification problem. AU (PRC) measure helps to compare the actual performance of our model, compensating for the noise effect due to class imbalance in the multi-label dataset. The AU (PRC) curves are created for all 3 models on both datasets as depicted in Fig. 5, Fig. 6, and Fig. 7. On the validation set of EMTD, we found almost similar AU (PRC) values 92%, 91%, 88% on MSD, MD, and MS, respectively. However, we found that our MSD gives the 82% AU (PRC) values on the LMTD9 dataset, which is greater than the other two models i.e., 72% and 80% AU (PRC) of MD and MS respectively as in Table 6. However, for overall comparison with some other models that we experimented with within our study, we mention their results in Table 6. For choosing the best architecture, the models are compared in terms of AU (PRC) on both of the validation datasets. The implementation of Features Model Dataset EMTD LMTD-9 Dialogue (MD) E-Bi LSTM 0.87 0.66 ECnet 0.91 0.72 Situation (MS) ECnet 0.86 0.75 TFAnet 0.88 0.80 Fused Features (MSD) ECnet 0.92 0.82 all the mentioned models helps us in deciding the best model for the fused features. Although MD has comparable AU (PRC) values with MSD on EMTD but on LMTD-9, MSD outperformed MD. Similar is the case with MS on LMTD-9. While MSD performed simultaneously well on both datasets, which is not true in the case of MS and MD individually. So, by cross dataset validation MSD proves to be a more robust one. We conclude that the proposed MSD is the best performing model. 5.4. Baseline comparison In this section, we validate the performance of our proposed model by performing the state of art comparison with the previous approaches for movie genre classification using the AU (PRC) metric for each genre separately as depicted in Table 7. All the results mentioned in Table 7 are shown up to two decimal places and are based on the standard LMTD-9 dataset except for Fish et. al. [22], whose results are based on MMX trailer-20 dataset. It does not consider the romance genre in its study. However, for the other genres, the difference in the AU (PRC) values of Fish et. al [22] and MSD is worth noting. MSD outperforms it by 20% on average. Low-level visual features based classification [23] is based on 24 low-level visual features, SAS-MC-v2 [24] uses only the synopsis for trailer classification, Fish et. al. [22] and CTT-MMC-TN [25] are based on high-level features. Comparing to low-level feature approaches [23], [24], MSD on an average outperforms by 10%, and by comparing from approaches using high-level features [22], [25], it outperforms by 8% on an average for each genre. It is also observed comedy genre performed well in most works as compared to the other four genres while science-fiction has relatively lower AU (PRC) values. This could be due to the unavailability of proper distinction in the science-fiction genre, as its features overlap with some other similar genres (like action). The comparative study demonstrates that the proposed model is robust as it outperforms existing approaches and gives excellent results. The better performance is due to the reason that the architecture proposed includes both cognitive and affective features, helping the model to learn substantial characteristics of each genre, hence predicting genres more precisely. This paper is available on arxiv under CC BY-NC-SA 4.0 DEED license. This paper is available on arxiv under CC BY-NC-SA 4.0 DEED license. available on arxiv

Multilevel Profiling of Situation and Dialogue-based Deep Networks: Experiments

পরিস্থিতি এবং সংলাপ-ভিত্তিক গভীর নেটওয়ার্কগুলির মাল্টিলেভেল প্রোফাইলিং: পরীক্ষা-নিরীক্ষা

Multilevel-Profiling situations- und dialogbasierter Deep Networks: Experimente

Elaboración de perfiles multinivel de redes profundas basadas en situaciones y diálogos: experimentos

Profilage multiniveau de réseaux profonds basés sur la situation et le dialogue : expériences

स्थिति और संवाद-आधारित गहरे नेटवर्क की बहुस्तरीय प्रोफाइलिंग: प्रयोग

状況と対話に基づくディープネットワークのマルチレベルプロファイリング：実験

상황 및 대화 기반 심층 네트워크의 다단계 프로파일링: 실험

Perfil multinível de redes profundas baseadas em situação e diálogo: experimentos

Многоуровневое профилирование ситуационных и диалоговых глубоких сетей: эксперименты

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Lập hồ sơ đa cấp về Mạng lưới sâu dựa trên đối thoại và tình huống: Thử nghiệm

情境和基于对话的深度网络的多层次剖析：实验

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