Memfault is the first IoT reliability platform that empowers teams to build more robust devices with software at scale

Memfault

memfault

Kuma Meshes Head-On - Everything You Need to Know

কুমা মেশেস হেড-অন - আপনার যা জানা দরকার

Kuma Meshes Frontal – Alles, was Sie wissen müssen

Kuma se enfrenta de frente: todo lo que necesitas saber

Kuma Meshes de front – Tout ce que vous devez savoir

कुमा मेशेस हेड-ऑन - वह सब कुछ जो आपको जानना चाहिए

クマが正面からメッシュ化 - 知っておくべきことすべて

Kuma Meshes 정면 대결 - 알아야 할 모든 것

Kuma Meshes Head-On - Tudo o que você precisa saber

Kuma Meses в лоб – все, что вам нужно знать

Kuma Kafa Başına Meshes - Bilmeniz Gereken Her Şey

Kuma Meshes Head-On - Mọi thứ bạn cần biết

Kuma Meshes 迎面而来——您需要了解的一切

The Human Side of Animals: Chapter 7 - The language of Animals

Over the past few weeks, Facebook officially transferred control of Parse to the open source community. If you’re using Parse today, then there should be little change to you. If you’re a developer tracking these repos, please update your branches to . Our home is now at , and all docs have relocated to https://github.com/parse-community http://parseplatform.org http://docs.parseplatform.org The Parse project has been fully community run for at least the past three months. Ever since Facebook announced the open source version of Parse server, we’ve added long awaited and powerful features such as: - , supported across all major client SDKs.- support, starting with in addition to MongoDB.- models and no longer restricted to Facebook and Twitter.- New Live Queries Multiple Database Postgres Flexible authentication Class Level Permissions . And so much more; Parse is stronger open than closed. We’ve seen a trove of companies offering hosting services around the open source project and we encourage the less tech savvy users to explore those if they aren’t comfortable with hosting their own solution. The adaptable model implemented in Parse Server makes it easy to extend it, allowing the most experienced developers to tailor its behaviour to their needs. With adapters for file storage, authentication, databases, caching, etc., Parse Server is designed to adapt your needs as you scale and grow. We’re commited to expand that support in the future and remove bottlenecks that prevent you and your team to grow and scale. We’re grateful to , , and the entire Facebook Parse team for this gift to the open source community. Kevin Lacker Fosco Marotto I would like to also personally thank all contributors, on every repositories and in particular (but no particular order), , , , , , and many more. They have pulled a tremendous amount of work and dedication to the success of the project. This transition would not have been possible without them. Arthur Cinader Tyler Brock Natan Rolnik Steven Shipton Roger Hu Michael Mimeault What’s next? Over the next few weeks and months, we’ll develop a proper roadmap with the help of the community to understand your most pressing issues and assess what features you want to see in the long run implemented in the parse platform. We’ll update all the repositories READMEs with the dedicated maintainers, or go-to persons to reach out if you have issues or questions. Parse is a complex ecosystem, with many open source components, and can be difficult to maintain. We always welcome new maintainers. If you feel you’re up to the task and the challenge, feel free to reach out to or open an issue on the repository to which you’d like to contribute. community@parseplatform.org We’re more commited to Parse than ever and now it’s yours. Florent Vilmart, principal maintainer. is how hackers start their afternoons. We’re a part of the family. We are now and happy to opportunities. Hacker Noon @AMI accepting submissions discuss advertising & sponsorship To learn more, , , or simply, read our about page like/message us on Facebook tweet/DM @HackerNoon. If you enjoyed this story, we recommend reading our and . Until next time, don’t take the realities of the world for granted! latest tech stories trending tech stories

Long Live Parse

Protect People From Themselves: Reducing Default Risks in P2P Lending

Authors:
(1) Omid Davoodi, Carleton University, School of Computer Science;
(2) Shayan Mohammadizadehsamakosh, Sharif University of Technology, Department of Computer Engineering;
(3) Majid Komeili, Carleton University, School of Computer Science. Table of Links Abstract and Intro Background Information Methodology Prototype Interpretability Prototype-query Similarity Interpretability of the Decision-Making Process The Effects of Low Prototype Counts Discussions ABSTRACT Part-prototype networks have recently become methods of interest as an interpretable alternative to many of the current black-box image classifiers. However, the interpretability of these methods from the perspective of human users has not been sufficiently explored. In this work, we have devised a framework for evaluating the interpretability of part-prototype-based models from a human perspective. The proposed framework consists of three actionable metrics and experiments. To demonstrate the usefulness of our framework, we performed an extensive set of experiments using Amazon Mechanical Turk. They not only show the capability of our framework in assessing the interpretability of various part-prototype-based models, but they also are, to the best of our knowledge, the most comprehensive work on evaluating such methods in a unified framework. Introduction As Artificial Intelligence and Machine Learning have become more ubiquitous in many parts of the society and economy, the need for transparency, fairness, and trust increases. Many of the state-of-the-art methods and algorithms are black boxes where the decision-making process is opaque to humans. Interpretable and Explainable Artificial Intelligence aims to address this issue by offering methods that either explain the decisions of black-box models or are inherently interpretable themselves. Prototype-based classifiers are a category of inherently interpretable methods that use prototypical examples to make their decisions. It is assumed that as long as the prototypes themselves are understandable by a human, the decision itself is interpretable[1]. Prototype-based classifiers are not new inventions. Many existed long before the need for interpretability became so urgent[2–6]. In recent years, newer methods have been proposed that combine the power and expressablility of neural networks with the decision-making process of a prototype based classifier to create prototypical neural nets[7], [8], reaching results competitive with the state of the art while being inherently interpretable in the process. A newer subcategory of prototype-based classifiers is part-prototype networks. These networks, usually operating in the domain of image classification use regions of a query sample, as opposed to the entire query image, to make their decisions. ProtoPNet[9] is the first of such methods that offered fine-grained explanations for image classification while offering state-of-the-art accuracy. Figure 1 shows an example of how a part-prototype method makes its decisions. The explanations given by these methods can be very different from each other. Even when the general layout of the explanation is similar, the part-prototypes themselves can be vastly different. It is unusual to assume that they offer the same level of interpretability. Therefore, the evaluation of their interpretability is necessary. While many of these methods evaluate the performance of their models and compare them to the state of the art, few analyze the interpretability of their methods. Most of the analysis in this regard seems to be focused on automatic metrics for assessing interpretability[10]. Such automatic metrics, while useful, are not a replacement for human evaluation of interpretability. Others have worked on human-assisted debugging[11] but have not extended that to a full evaluation of method interpretability. Kim et al. offered a method for evaluating visual concepts by humans and even performed experiments on ProtoPNet and ProtoTree[12], but their evaluation suffers from a number of issues. The scale of the experiments in Kim et al. is small, with only two part-prototype methods evaluated using only a single dataset. The experimental design of that work also relies on fine-grained ratings by human annotators. This type of design can be an unreliable way of measuring human opinion when there is no consensus on what each option means[13]. It used the class label to measure the quality of the prototypes in the CUB dataset even though there was no indication that the human users were familiar with the minutiae of the distinctions between 200 classes of birds. Lastly, it used the default rectangular representation of prototypes from ProtoPNet and ProtoTree. These representations are prone to being overly broad and misleading to the human user compared to the actual activation heatmap. As a result, we propose a human-centric analysis consisting of a set of experiments to assess the interpretability of part-prototype methods. Goals The interpretability of a part-prototype system is not a well-defined concept. In this work, we focus on three properties that such systems should have in order to be interpretable. • Interpretability of the prototype itself: The concept a prototype is referring to should be recognizable and understandable to a human. Figure 2 (a) shows an example of a prototype that is not interpretable because it points to an irrelevant background region. Machine learning methods and neural networks, in particular, can make correct decisions based on feature combinations in the data that a human might not understand. In addition, the presentation of such features is very important. A prototype might refer to a very unusual concept but its presentation might lead a human to wrongfully believe that they understand the reasoning behind a decision. • The similarity of a prototype to the corresponding region in the query sample: Even if the prototype itself is easily understood by a human, its activation on the query sample might not show the same concept as the prototype. Figure 2 (b) shows an example of this problem. This is important because it shows that the structural similarity in the embedding space that the prototypes reside in is not compatible with human understanding of similarity. This is a problem that has been reported in previous literature[14]. • The interpretability of the decision-making process itself is also an important aspect of prototype-based methods. Even if the prototypes and their similarity to the activated patches of the query sample are understood by humans, the final decision might not be. For example, a model might select and use unrelated prototypes to correctly classify a sample. The main novelty of this work is a more robust framework for evaluating the interpretability of part-prototype-based networks using human annotators. Some previous methods have tried to do such evaluations based on automatic metrics[10], and some other works have worked on human-based evaluation of interpretability for other types of explainable AI methods[15], [16]. The closest work is HIVE[12] which suffers from a number of issues that are addressed in our approach. More on this will follow in the next section. Another novelty of this work is the proposal of three actionable metrics and experiments for evaluating the interpretability of part-prototype-based classifiers. We believe that if a model fails these tests, it would not be a good interpretable model. These can assist future researchers in providing evidence rather than just making assumptions about the interpretability of their approaches. Finally, our extensive set of experiments using Amazon Mechanical Turk includes comparisons of six related methods on three datasets. To the best of our knowledge, this is the most comprehensive work on evaluating the interpretability of such methods in a unified framework. This paper is available on arxiv under CC 4.0 license. Authors: (1) Omid Davoodi, Carleton University, School of Computer Science; (2) Shayan Mohammadizadehsamakosh, Sharif University of Technology, Department of Computer Engineering; (3) Majid Komeili, Carleton University, School of Computer Science. Authors: Authors: (1) Omid Davoodi, Carleton University, School of Computer Science; (2) Shayan Mohammadizadehsamakosh, Sharif University of Technology, Department of Computer Engineering; (3) Majid Komeili, Carleton University, School of Computer Science. Table of Links Abstract and Intro Abstract and Intro Background Information Background Information Methodology Methodology Prototype Interpretability Prototype Interpretability Prototype-query Similarity Prototype-query Similarity Interpretability of the Decision-Making Process Interpretability of the Decision-Making Process The Effects of Low Prototype Counts The Effects of Low Prototype Counts Discussions Discussions ABSTRACT Part-prototype networks have recently become methods of interest as an interpretable alternative to many of the current black-box image classifiers. However, the interpretability of these methods from the perspective of human users has not been sufficiently explored. In this work, we have devised a framework for evaluating the interpretability of part-prototype-based models from a human perspective. The proposed framework consists of three actionable metrics and experiments. To demonstrate the usefulness of our framework, we performed an extensive set of experiments using Amazon Mechanical Turk. They not only show the capability of our framework in assessing the interpretability of various part-prototype-based models, but they also are, to the best of our knowledge, the most comprehensive work on evaluating such methods in a unified framework. Introduction As Artificial Intelligence and Machine Learning have become more ubiquitous in many parts of the society and economy, the need for transparency, fairness, and trust increases. Many of the state-of-the-art methods and algorithms are black boxes where the decision-making process is opaque to humans. Interpretable and Explainable Artificial Intelligence aims to address this issue by offering methods that either explain the decisions of black-box models or are inherently interpretable themselves. Prototype-based classifiers are a category of inherently interpretable methods that use prototypical examples to make their decisions. It is assumed that as long as the prototypes themselves are understandable by a human, the decision itself is interpretable[1]. Prototype-based classifiers are not new inventions. Many existed long before the need for interpretability became so urgent[2–6]. In recent years, newer methods have been proposed that combine the power and expressablility of neural networks with the decision-making process of a prototype based classifier to create prototypical neural nets[7], [8], reaching results competitive with the state of the art while being inherently interpretable in the process. A newer subcategory of prototype-based classifiers is part-prototype networks. These networks, usually operating in the domain of image classification use regions of a query sample, as opposed to the entire query image, to make their decisions. ProtoPNet[9] is the first of such methods that offered fine-grained explanations for image classification while offering state-of-the-art accuracy. Figure 1 shows an example of how a part-prototype method makes its decisions. The explanations given by these methods can be very different from each other. Even when the general layout of the explanation is similar, the part-prototypes themselves can be vastly different. It is unusual to assume that they offer the same level of interpretability. Therefore, the evaluation of their interpretability is necessary. While many of these methods evaluate the performance of their models and compare them to the state of the art, few analyze the interpretability of their methods. Most of the analysis in this regard seems to be focused on automatic metrics for assessing interpretability[10]. Such automatic metrics, while useful, are not a replacement for human evaluation of interpretability. Others have worked on human-assisted debugging[11] but have not extended that to a full evaluation of method interpretability. Kim et al. offered a method for evaluating visual concepts by humans and even performed experiments on ProtoPNet and ProtoTree[12], but their evaluation suffers from a number of issues. The scale of the experiments in Kim et al. is small, with only two part-prototype methods evaluated using only a single dataset. The experimental design of that work also relies on fine-grained ratings by human annotators. This type of design can be an unreliable way of measuring human opinion when there is no consensus on what each option means[13]. It used the class label to measure the quality of the prototypes in the CUB dataset even though there was no indication that the human users were familiar with the minutiae of the distinctions between 200 classes of birds. Lastly, it used the default rectangular representation of prototypes from ProtoPNet and ProtoTree. These representations are prone to being overly broad and misleading to the human user compared to the actual activation heatmap. As a result, we propose a human-centric analysis consisting of a set of experiments to assess the interpretability of part-prototype methods. Goals The interpretability of a part-prototype system is not a well-defined concept. In this work, we focus on three properties that such systems should have in order to be interpretable. • Interpretability of the prototype itself: The concept a prototype is referring to should be recognizable and understandable to a human. Figure 2 (a) shows an example of a prototype that is not interpretable because it points to an irrelevant background region. Machine learning methods and neural networks, in particular, can make correct decisions based on feature combinations in the data that a human might not understand. In addition, the presentation of such features is very important. A prototype might refer to a very unusual concept but its presentation might lead a human to wrongfully believe that they understand the reasoning behind a decision. • The similarity of a prototype to the corresponding region in the query sample: Even if the prototype itself is easily understood by a human, its activation on the query sample might not show the same concept as the prototype. Figure 2 (b) shows an example of this problem. This is important because it shows that the structural similarity in the embedding space that the prototypes reside in is not compatible with human understanding of similarity. This is a problem that has been reported in previous literature[14]. • The interpretability of the decision-making process itself is also an important aspect of prototype-based methods. Even if the prototypes and their similarity to the activated patches of the query sample are understood by humans, the final decision might not be. For example, a model might select and use unrelated prototypes to correctly classify a sample. The main novelty of this work is a more robust framework for evaluating the interpretability of part-prototype-based networks using human annotators. Some previous methods have tried to do such evaluations based on automatic metrics[10], and some other works have worked on human-based evaluation of interpretability for other types of explainable AI methods[15], [16]. The closest work is HIVE[12] which suffers from a number of issues that are addressed in our approach. More on this will follow in the next section. Another novelty of this work is the proposal of three actionable metrics and experiments for evaluating the interpretability of part-prototype-based classifiers. We believe that if a model fails these tests, it would not be a good interpretable model. These can assist future researchers in providing evidence rather than just making assumptions about the interpretability of their approaches. Finally, our extensive set of experiments using Amazon Mechanical Turk includes comparisons of six related methods on three datasets. To the best of our knowledge, this is the most comprehensive work on evaluating the interpretability of such methods in a unified framework. This paper is available on arxiv under CC 4.0 license. This paper is available on arxiv under CC 4.0 license. available on arxiv

On the Interpretability of Part-Prototype Based Classifiers: A Human Centric Analysis

পার্ট-প্রোটোটাইপ ভিত্তিক ক্লাসিফায়ারগুলির ব্যাখ্যাযোগ্যতা: একটি মানবকেন্দ্রিক বিশ্লেষণ

Zur Interpretierbarkeit von auf Teilprototypen basierenden Klassifikatoren: Eine menschenzentrierte Analyse

Sobre la interpretabilidad de clasificadores basados en prototipos parciales: un análisis centrado en el ser humano

Sur l'interprétabilité des classificateurs basés sur des pièces-prototypes : une analyse centrée sur l'humain

पार्ट-प्रोटोटाइप आधारित क्लासिफायर की व्याख्या पर: एक मानव केंद्रित विश्लेषण

部分プロトタイプベースの分類器の解釈可能性について: 人間中心の分析

부분 프로토타입 기반 분류기의 해석 가능성: 인간 중심 분석

Sobre a interpretabilidade de classificadores baseados em protótipos de peças: uma análise centrada no ser humano

Об интерпретируемости классификаторов, основанных на прототипах деталей: человекоцентричный анализ

Parça Prototip Tabanlı Sınıflandırıcıların Yorumlanabilirliği Üzerine: İnsan Odaklı Bir Analiz

Về khả năng giải thích của các bộ phân loại dựa trên nguyên mẫu từng phần: Phân tích lấy con người làm trung tâm

基于部分原型的分类器的可解释性：以人为中心的分析

Why Monitoring a Distributed Database is More Complex Than You Might Expect

Fortify Your Digital Defenses: Top Home Cybersecurity Strategies for Personal Protection

We’re living in a pandemic of data breaches. More than 17 billion personal records were exposed last year, and 2024 has begun with a string of several high-profile back-to-back breaches in the first quarter, including data exposure of millions of AT&T account holders. Big Tech companies are in the crosshairs, too, facing mounting criticism from government officials calling for urgent reform. Even the open-source software supply chain is under threat from social engineering campaigns looking to compromise maintainer projects. ‘Social engineering’ is the key term here because it’s not software vulnerabilities that enterprises should be worried about. Those only make up 5% of breaches, while 68% involve the human element. How can this be? And what should companies do about it? The doors we open for hackers – and how to close them Exotic and sophisticated zero-day attacks and vulnerability exploits may garner the bulk of attention from the press, CISOs, and security teams, but the reality is that today’s cybersecurity tools are becoming exceedingly efficient at preventing and remediating these attacks. Hackers know this. They’re not going after software vulnerabilities. They’re attacking identity because it’s far cheaper and easier to exploit or force human error. So, they target credentials such as passwords, browser cookies, and private API keys. The data says it all, really: a Verizon report proves these credentials show up in 86% of security breaches related to web-based applications and platforms. Obtaining these credentials is how attackers do the most damage with minimal ease. Sometimes, these credentials are shared a little too freely. Other times, a shocking amount goes unused. The point is that organizations have lost control of their credentials, and the result is a growing fragmentation of identities across increasingly complex technology stacks. Those stacks are filled with layers that manage security in different ways, from Kubernetes clusters to Linux servers, copious amounts of specialized databases, IoT, cloud APIs, etc. It gets even more convoluted on a granular level once you’re inside a cloud account. If you have three Windows servers and seven Linux servers, each of those also governs its own login credentials. If you’ve got a multi-cloud setup with both AWS and Azure, those companies also track identity in different ways. In the quest for greater efficiency, DevOps engineers are even splitting cloud-native applications into smaller micro-services, each needing its own identity. Each of these layers opens yet another door for attackers to enter. If you’re an enterprise operating with more DevOps and cloud infrastructure, you’re inevitably going to add even more layers to the stack. More layers mean more doors. In other words, the mere presence of secrets, such as private keys, API keys, passwords, or even browser cookies, should be seen as a vulnerability waiting to be exploited in an identity attack. Stop using secrets for access purposes The problem isn’t the technology itself, though, but human behavior. Any time you create more doors, you’re increasing the probability of human error. So, how do you fix these data breaches? The first step is to consolidate all identities. All identities - including people and machines like servers, laptops, bots, databases, microservices, etc. – must be united into an inventory that provides a single source of truth. The second step is to make these identities impossible to impersonate or “steal”, effectively making the infrastructure security immune to human mistakes. To do this, minimize or eliminate the reliance on secrets for authentication purposes. As a rule, your identity should also never ever be presented as information. The only way to prevent data theft on a catastrophic scale is to remove all static credentials and standing privileges and instead deploy strong authentication with cryptographic identity. In practice, this means basing identity on three-point criteria that include 1) a user’s specific device, 2) a biometric marker, and 3) a personal identification number (PIN). In theory, this is exactly how the iPhone works: the biometric marker is facial recognition, the personal identification number is your PIN code, and the Trusted Platform Module (TPM) chip inside the phone is what governs the ‘machine identity.’ But credentials – or doors, if you will – are just half the story. Observability isn’t enough The thing about human error is that it’s blind to the quality of your cybersecurity and observability tools. It only takes one human to make a mistake by granting access to the wrong people. This already happens. As we learned earlier this year, Google Kubernetes Engine admins are not immune to misunderstanding the significance and scope of default user groups and assigning dangerous permissions to them. The cybersecurity industry has spent a disproportionate amount of time and money on observability. Companies are acquiring and maintaining tools to respond to threats rather than fix enforcement to proactively protect infrastructure. One reason companies tend to be put off by fixing enforcement is the aforementioned complexity of modern technology stacks. The natural assumption is you need competent teams who can configure every single layer with its own authentication, its own authorization, its own encryption, and its own audit. The smarter solution here would be for the cybersecurity industry to embrace a new paradigm for modernizing secure access to infrastructure. This paradigm has to be built on embracing mechanisms that unify all access within a single control point for authentication and authorization. Most enterprises sorely need (and yet lack) this mechanism as a front-end to all of their disparate infrastructure. Access should only ever be granted based on tasks, and only the minimum required privileges should ever be granted. These should then be de-provisioned immediately afterward. This concept of on-demand least-privileged access then needs to be applied to both human and non-human machine and service accounts. In other words, every company should be able to easily enforce the policy that developers should never have access to production data. However, without unified access controls, this will remain a sci-fi concept for many cybersecurity professionals. Unifying access controls isn’t just helpful in the proactive sense. If a data breach does eventually occur, a centralized view of all the access relationships attributed to a user or resource is essential for purposes like quickly revoking unwanted access or auditing. This is how enforcement and observability can be combined for agility. And if you’ve already assigned cryptographic identity beforehand to each individual user or resource, you’ve now associated the actual human user with each action. This means assigning the right permissions to the right people becomes a much easier task. Defend against human behavior There are, of course, positive signs of technology leaders in the industry taking data breaches very seriously. Microsoft has pledged to shore cloud vulnerabilities, prevent credentials from being stolen, and automatically enforce multi-factor authentication for employees. Any efforts to embrace stronger authentication and  cryptographic measures should be commended. If cybersecurity is going to ‘win,’ though, enterprises have to understand that stopping data breaches isn’t about defending against viruses or ultra-sophisticated hacking techniques. It’s about defending against human behavior that exposes infrastructure to data leaks. That’s the mindset that needs to change. Unified access control, paired with zero trust, must be the foundation for modernizing secure access. We’re living in a pandemic of data breaches. More than 17 billion personal records were exposed last year , and 2024 has begun with a string of several high-profile back-to-back breaches in the first quarter, including data exposure of millions of AT&T account holders. 17 billion personal records were exposed last year Big Tech companies are in the crosshairs , too, facing mounting criticism from government officials calling for urgent reform. Even the open-source software supply chain is under threat from social engineering campaigns looking to compromise maintainer projects. Big Tech companies are in the crosshairs Even the open-source software supply chain is under threat ‘Social engineering’ is the key term here because it’s not software vulnerabilities that enterprises should be worried about. Those only make up 5% of breaches , while 68% involve the human element. How can this be? And what should companies do about it? make up 5% of breaches The doors we open for hackers – and how to close them The doors we open for hackers – and how to close them Exotic and sophisticated zero-day attacks and vulnerability exploits may garner the bulk of attention from the press, CISOs, and security teams, but the reality is that today’s cybersecurity tools are becoming exceedingly efficient at preventing and remediating these attacks. Hackers know this. They’re not going after software vulnerabilities. They’re attacking identity because it’s far cheaper and easier to exploit or force human error. So, they target credentials such as passwords, browser cookies, and private API keys. The data says it all, really: a Verizon report proves these credentials show up in 86% of security breaches related to web-based applications and platforms. 86% of security breaches Obtaining these credentials is how attackers do the most damage with minimal ease. Sometimes, these credentials are shared a little too freely. Other times, a shocking amount goes unused . The point is that organizations have lost control of their credentials, and the result is a growing fragmentation of identities across increasingly complex technology stacks. Those stacks are filled with layers that manage security in different ways, from Kubernetes clusters to Linux servers, copious amounts of specialized databases, IoT, cloud APIs, etc. a shocking amount goes unused It gets even more convoluted on a granular level once you’re inside a cloud account. If you have three Windows servers and seven Linux servers, each of those also governs its own login credentials. If you’ve got a multi-cloud setup with both AWS and Azure, those companies also track identity in different ways. In the quest for greater efficiency, DevOps engineers are even splitting cloud-native applications into smaller micro-services, each needing its own identity. Each of these layers opens yet another door for attackers to enter. If you’re an enterprise operating with more DevOps and cloud infrastructure, you’re inevitably going to add even more layers to the stack. More layers mean more doors. In other words, the mere presence of secrets, such as private keys, API keys, passwords, or even browser cookies, should be seen as a vulnerability waiting to be exploited in an identity attack. Stop using secrets for access purposes Stop using secrets for access purposes The problem isn’t the technology itself, though, but human behavior. Any time you create more doors, you’re increasing the probability of human error. So, how do you fix these data breaches? The first step is to consolidate all identities. All identities - including people and machines like servers, laptops, bots, databases, microservices, etc. – must be united into an inventory that provides a single source of truth. The second step is to make these identities impossible to impersonate or “steal”, effectively making the infrastructure security immune to human mistakes. To do this, minimize or eliminate the reliance on secrets for authentication purposes. As a rule, your identity should also never ever be presented as information. The only way to prevent data theft on a catastrophic scale is to remove all static credentials and standing privileges and instead deploy strong authentication with cryptographic identity. In practice, this means basing identity on three-point criteria that include 1) a user’s specific device, 2) a biometric marker, and 3) a personal identification number (PIN). In theory, this is exactly how the iPhone works: the biometric marker is facial recognition, the personal identification number is your PIN code, and the Trusted Platform Module (TPM) chip inside the phone is what governs the ‘machine identity.’ But credentials – or doors, if you will – are just half the story. Observability isn’t enough Observability isn’t enough The thing about human error is that it’s blind to the quality of your cybersecurity and observability tools. It only takes one human to make a mistake by granting access to the wrong people. This already happens. As we learned earlier this year, Google Kubernetes Engine admins are not immune to misunderstanding the significance and scope of default user groups and assigning dangerous permissions to them . misunderstanding the significance and scope of default user groups and assigning dangerous permissions to them The cybersecurity industry has spent a disproportionate amount of time and money on observability. Companies are acquiring and maintaining tools to respond to threats rather than fix enforcement to proactively protect infrastructure. One reason companies tend to be put off by fixing enforcement is the aforementioned complexity of modern technology stacks. The natural assumption is you need competent teams who can configure every single layer with its own authentication, its own authorization, its own encryption, and its own audit. The smarter solution here would be for the cybersecurity industry to embrace a new paradigm for modernizing secure access to infrastructure. This paradigm has to be built on embracing mechanisms that unify all access within a single control point for authentication and authorization. Most enterprises sorely need (and yet lack) this mechanism as a front-end to all of their disparate infrastructure. Access should only ever be granted based on tasks, and only the minimum required privileges should ever be granted. These should then be de-provisioned immediately afterward. This concept of on-demand least-privileged access then needs to be applied to both human and non-human machine and service accounts. In other words, every company should be able to easily enforce the policy that developers should never have access to production data. However, without unified access controls, this will remain a sci-fi concept for many cybersecurity professionals. Unifying access controls isn’t just helpful in the proactive sense. If a data breach does eventually occur, a centralized view of all the access relationships attributed to a user or resource is essential for purposes like quickly revoking unwanted access or auditing. This is how enforcement and observability can be combined for agility. And if you’ve already assigned cryptographic identity beforehand to each individual user or resource, you’ve now associated the actual human user with each action. This means assigning the right permissions to the right people becomes a much easier task. Defend against human behavior Defend against human behavior There are, of course, positive signs of technology leaders in the industry taking data breaches very seriously. Microsoft has pledged to shore cloud vulnerabilities, prevent credentials from being stolen, and automatically enforce multi-factor authentication for employees. Any efforts to embrace stronger authentication and  cryptographic measures should be commended. If cybersecurity is going to ‘win,’ though, enterprises have to understand that stopping data breaches isn’t about defending against viruses or ultra-sophisticated hacking techniques. It’s about defending against human behavior that exposes infrastructure to data leaks. That’s the mindset that needs to change. Unified access control, paired with zero trust, must be the foundation for modernizing secure access.

Social Engineering is the Biggest Cyber Threat

4 Elasticsearch Performance Challenges and How to Solve Them

4 ইলাস্টিক সার্চ পারফরম্যান্স চ্যালেঞ্জ এবং কীভাবে সেগুলি সমাধান করা যায়

4 Leistungsprobleme bei Elasticsearch und wie man sie löst

4 desafíos de rendimiento de Elasticsearch y cómo resolverlos

4 défis de performances Elasticsearch et comment les résoudre

4 इलास्टिकसर्च प्रदर्शन चुनौतियाँ और उन्हें कैसे हल करें

Elasticsearch のパフォーマンスに関する 4 つの課題とその解決方法

4가지 Elasticsearch 성능 과제 및 해결 방법

4 desafios de desempenho do Elasticsearch e como resolvê-los

4 проблемы производительности Elasticsearch и способы их решения

4 Elasticsearch Performans Zorlukları ve Bunların Çözümü

4 thách thức về hiệu suất của Elaticsearch và cách giải quyết chúng

4 个 Elasticsearch 性能挑战及其解决方法

Intro In today's world, where cybercrimes are becoming increasingly widespread, it is important to have access to effective tools for investigation and counteraction against these threats. One such tool is the CSI Linux operating system, specially designed for the needs of cybersecurity and cyber investigations. CSI Linux aims to provide users with tools for fast and accurate detection of digital traces and evidence that can be used for criminal investigations or for ensuring cybersecurity in organizations. The developers of this operating system claim that it simplifies the process of collecting, analyzing, and interpreting digital data, making it an important tool for professionals in the field of cybersecurity and cyber investigations. But is it really so? Let's find out... Basic information To begin with, I would like to provide some basic information about this distribution. Official website: https://csilinux.com/ As of the writing of this article, CSI Linux uses Ubuntu 22.04 LTS as its base platform. CSI Linux is an open-source project, so anyone interested can contribute to making it better. The installation process is quite simple, and there are many videos on this topic, such as this one. Although the process has changed slightly now, after downloading the disk image, you simply need to add (Machine -> Add) the virtual disk file in Vbox format to VirtualBox for it to run. To log into the system by default, use the username "CSI" and the password "CSI." There is also a whole book dedicated to working with CSI Linux, which can be purchased here. The terminology, list of all available software in CSI Linux, and other documentation are available at this link. Previously, CSI Linux had three separate packages on the CSI Linux Investigator platform, but now CSI Linux Gateway and CSI Linux Analyst are merged into one package, while CSI Linux SIEM is available as a separate image on the official website. All distributions can also be downloaded from the official website. For a better understanding, let me describe the three components of CSI Linux mentioned above: CSI Linux SIEM: This is a virtual machine included in the CSI Linux Investigator distribution. In fact, it is an Ubuntu distribution that includes a configured Zeek IDS and ELK Stack (Elasticsearch, Logstash, and Kibana). It is used as an intrusion detection system to protect other virtual machines (CSI Linux Analyst and CSI Linux Gateway), process logs, and display data on the management panels of CSI Linux Analyst. CSI Linux Gateway: This is a TOR user gateway that operates in a "sandbox" using utilities such as AppArmor, Jailbreak, and Shorewall Firewall. When using CSI Linux Analyst + CSI Linux Gateway, all traffic will pass through the TOR node. CSI Linux Analyst: This is the "core" of this distribution. It is a virtual machine running Ubuntu with a large amount of pre-installed software grouped by categories. We will discuss the list of categories later. It's worth noting that in the Incident Response menu, there is a section for CSI SIEM, but it is not active and displays "CSI SIEM Installer and Launch utility coming soon…" indicating a likely intention to integrate it with the third distribution. However, in any case, you can install and run SIEM separately using virtualization in VirtualBox, allowing you to have the full set of packages on the same server. Functionality review Now, let's get a bit closer to the mentioned operating system. To start working, we create a case (click on the "Start a Case" icon on the desktop or go to Menu -> CSI Linux Tools -> Start a Case). Then, all subsequent operations can be performed within the created case, where the results will be stored. Upon creation, you are immediately prompted to start work, and there are several options in the CSI Case Management Menu. It's important to know that most of the software is not pre-installed, and an automatic installation script is launched when selecting a program. Now, let's quickly go through the list of programs (both pre-installed and not) in each available category: CSI Linux Tools: A set of programs for system updates, management of CSI Tor VPN and Whonix Gateway for traffic routing, working with API resources used in CSI Linux, downloading videos, creating screenshots from various online resources, and much more. Secure Comms: This category includes "secure" online messengers. Encryption: Everything for decrypting hashes, password cracking, storing passwords, and personal data. OSINT/Online Investigations: A fairly large category where everyone can find the necessary tools for OSINT tasks. Among the most interesting are social media searches, phone and email searches. Dark Web: If the investigator encounters connection problems, this category is suitable. 'TOR > VPN,’ 'WHONIX'’ and so on, all for pleasant surfing. Incident Response: Everything for incident investigation, malware scanners, network analysis (Wireshark), and more. Computer Forensics: Computer forensics (data recovery and file analysis). Mobile Forensics: Mobile forensics tools for analysis and work with mobile phones. Vehicle Forensics: A set of utilities for working with the CAN (Controller Area Network) protocol. CAN is a network technology widely used in automation, embedded devices, and the automotive industry. Malware Analysis and Reverse Engineering: For the analysis of malicious scripts and programs, reverse engineering. SIGINT: A set of programs for radio channel analysis, hacking, and programming (FM, WiFi, etc.). Virtualization: A set of managers for virtualization. Threat Intelligence: Threat reconnaissance, cybercrime maps worldwide, and more. Practice Below are some screenshots of CSI Linux in action, demonstrating several tools and functionalities. You'll find attempts to search for individuals by username and email, showcasing the system's capabilities in gathering information from various sources. Conclusion After a basic introduction to the system and conducting several tests searching by phone, email, and username, I can say that CSI Linux partially works out of the box, but in most cases, you need to configure the software you're trying to investigate. There's an issue with blocking bots, through which criminalization occurs, as Tor VPN IP addresses have a poor reputation, or maybe sites identify it as bot activity and quickly block connections. The system is noticeably geared towards working with the world's most famous resources and social networks, but it lacks "out-of-the-box" support for local ones, which may be the most significant in a particular country. The set of programs is quite interesting, although not everything works right away, as already noted. For example, when I searched for data using my email, Buster found an article from 15 years ago on "Economic Truth" mentioning my address and Skype account. However, a simple Google search can find the same and even more. Furthermore, a search by username produced a bunch of irrelevant and unreliable information, which theoretically could be useful if filtered manually. Now I would like to add a little comparison with a competing system to CSI Linux and a more well-known operating system. Kali Linux, a Linux distribution, is popular in the cybersecurity community and is a basic tool for many cybersecurity specialists. However, comparing these two systems, we can say that the software offered by default in each of them is quite different. It's essential to understand that all the software presented in CSI Linux can be installed on Kali Linux and vice versa, as both are based on Ubuntu/Debian and are essentially extensions of the base. As the developers themselves claim, Kali Linux, with its set of programs, is more geared towards tasks such as penetration testing, security research, computer forensics, and reverse engineering. As soon as you log in and look at the main menu, you'll find mostly software for testing websites, social networks, or servers for vulnerabilities or hacking. There are many different bots, attackers, network analysis programs, and so on. So, these two systems are based on the same foundation but are created and configured for different tasks, although they can replace each other. CSI Linux has the right to exist, and it is definitely improving, but much needs to be "fine-tuned" to fit its needs, and one should be prepared for that. The "breakthrough" through the set of programs exists, but it lacks a wow effect and has quite a few inconsistencies. I hope you found this article interesting. By the way, I should mention that the CSI community has its own academy where you can take a course on various aspects of the project. Details are available at the CSI Linux Academy. Intro In today's world, where cybercrimes are becoming increasingly widespread, it is important to have access to effective tools for investigation and counteraction against these threats. One such tool is the CSI Linux operating system, specially designed for the needs of cybersecurity and cyber investigations. CSI Linux aims to provide users with tools for fast and accurate detection of digital traces and evidence that can be used for criminal investigations or for ensuring cybersecurity in organizations. The developers of this operating system claim that it simplifies the process of collecting, analyzing, and interpreting digital data, making it an important tool for professionals in the field of cybersecurity and cyber investigations. But is it really so? Let's find out... Basic information To begin with, I would like to provide some basic information about this distribution. Official website: https://csilinux.com/ As of the writing of this article, CSI Linux uses Ubuntu 22.04 LTS as its base platform. CSI Linux is an open-source project, so anyone interested can contribute to making it better. The installation process is quite simple, and there are many videos on this topic, such as this one. this Although the process has changed slightly now, after downloading the disk image, you simply need to add (Machine -> Add) the virtual disk file in Vbox format to VirtualBox for it to run. To log into the system by default, use the username "CSI" and the password "CSI." There is also a whole book dedicated to working with CSI Linux, which can be purchased here . here The terminology, list of all available software in CSI Linux, and other documentation are available at this link . link Previously, CSI Linux had three separate packages on the CSI Linux Investigator platform, but now CSI Linux Gateway and CSI Linux Analyst are merged into one package, while CSI Linux SIEM is available as a separate image on the official website. All distributions can also be downloaded from the official website. For a better understanding, let me describe the three components of CSI Linux mentioned above: CSI Linux SIEM: This is a virtual machine included in the CSI Linux Investigator distribution. In fact, it is an Ubuntu distribution that includes a configured Zeek IDS and ELK Stack (Elasticsearch, Logstash, and Kibana). It is used as an intrusion detection system to protect other virtual machines (CSI Linux Analyst and CSI Linux Gateway), process logs, and display data on the management panels of CSI Linux Analyst. CSI Linux SIEM: CSI Linux Gateway: This is a TOR user gateway that operates in a "sandbox" using utilities such as AppArmor, Jailbreak, and Shorewall Firewall. When using CSI Linux Analyst + CSI Linux Gateway, all traffic will pass through the TOR node. CSI Linux Gateway: CSI Linux Analyst: This is the "core" of this distribution. It is a virtual machine running Ubuntu with a large amount of pre-installed software grouped by categories. We will discuss the list of categories later. CSI Linux Analyst: It's worth noting that in the Incident Response menu, there is a section for CSI SIEM, but it is not active and displays "CSI SIEM Installer and Launch utility coming soon…" indicating a likely intention to integrate it with the third distribution. However, in any case, you can install and run SIEM separately using virtualization in VirtualBox, allowing you to have the full set of packages on the same server. Functionality review Now, let's get a bit closer to the mentioned operating system. To start working, we create a case (click on the "Start a Case" icon on the desktop or go to Menu -> CSI Linux Tools -> Start a Case). Then, all subsequent operations can be performed within the created case, where the results will be stored. Upon creation, you are immediately prompted to start work, and there are several options in the CSI Case Management Menu. It's important to know that most of the software is not pre-installed, and an automatic installation script is launched when selecting a program. Now, let's quickly go through the list of programs (both pre-installed and not) in each available category: CSI Linux Tools: CSI Linux Tools: CSI Linux Tools: A set of programs for system updates, management of CSI Tor VPN and Whonix Gateway for traffic routing, working with API resources used in CSI Linux, downloading videos, creating screenshots from various online resources, and much more. Secure Comms: Secure Comms: Secure Comms: This category includes "secure" online messengers. Encryption: Encryption: Encryption: Everything for decrypting hashes, password cracking, storing passwords, and personal data. OSINT/Online Investigations: OSINT/Online Investigations: OSINT/Online Investigations: A fairly large category where everyone can find the necessary tools for OSINT tasks. Among the most interesting are social media searches, phone and email searches. Dark Web: Dark Web: Dark Web: If the investigator encounters connection problems, this category is suitable. 'TOR > VPN,’ 'WHONIX'’ and so on, all for pleasant surfing. Incident Response: Incident Response: Incident Response: Everything for incident investigation, malware scanners, network analysis (Wireshark), and more. Computer Forensics: Computer Forensics: Computer Forensics: Computer forensics (data recovery and file analysis). Mobile Forensics: Mobile Forensics: Mobile Forensics: Mobile forensics tools for analysis and work with mobile phones. Vehicle Forensics: Vehicle Forensics: Vehicle Forensics: A set of utilities for working with the CAN (Controller Area Network) protocol. CAN is a network technology widely used in automation, embedded devices, and the automotive industry. Malware Analysis and Reverse Engineering: Malware Analysis and Reverse Engineering: Malware Analysis and Reverse Engineering: For the analysis of malicious scripts and programs, reverse engineering. SIGINT: SIGINT: SIGINT: A set of programs for radio channel analysis, hacking, and programming (FM, WiFi, etc.). Virtualization: Virtualization: Virtualization: A set of managers for virtualization. Threat Intelligence: Threat Intelligence: Threat Intelligence: Threat reconnaissance, cybercrime maps worldwide, and more. Practice Below are some screenshots of CSI Linux in action, demonstrating several tools and functionalities. You'll find attempts to search for individuals by username and email, showcasing the system's capabilities in gathering information from various sources. Conclusion After a basic introduction to the system and conducting several tests searching by phone, email, and username, I can say that CSI Linux partially works out of the box, but in most cases, you need to configure the software you're trying to investigate. There's an issue with blocking bots, through which criminalization occurs, as Tor VPN IP addresses have a poor reputation, or maybe sites identify it as bot activity and quickly block connections. The system is noticeably geared towards working with the world's most famous resources and social networks, but it lacks "out-of-the-box" support for local ones, which may be the most significant in a particular country. The set of programs is quite interesting, although not everything works right away, as already noted. For example, when I searched for data using my email, Buster found an article from 15 years ago on "Economic Truth" mentioning my address and Skype account. However, a simple Google search can find the same and even more. Furthermore, a search by username produced a bunch of irrelevant and unreliable information, which theoretically could be useful if filtered manually. Now I would like to add a little comparison with a competing system to CSI Linux and a more well-known operating system. Kali Linux , a Linux distribution, is popular in the cybersecurity community and is a basic tool for many cybersecurity specialists. However, comparing these two systems, we can say that the software offered by default in each of them is quite different. It's essential to understand that all the software presented in CSI Linux can be installed on Kali Linux and vice versa, as both are based on Ubuntu/Debian and are essentially extensions of the base. As the developers themselves claim, Kali Linux, with its set of programs, is more geared towards tasks such as penetration testing, security research, computer forensics, and reverse engineering. As soon as you log in and look at the main menu, you'll find mostly software for testing websites, social networks, or servers for vulnerabilities or hacking. There are many different bots, attackers, network analysis programs, and so on. Kali Linux So, these two systems are based on the same foundation but are created and configured for different tasks, although they can replace each other. CSI Linux has the right to exist, and it is definitely improving, but much needs to be "fine-tuned" to fit its needs, and one should be prepared for that. The "breakthrough" through the set of programs exists, but it lacks a wow effect and has quite a few inconsistencies. I hope you found this article interesting. By the way, I should mention that the CSI community has its own academy where you can take a course on various aspects of the project. Details are available at the CSI Linux Academy . CSI Linux Academy

CSI Linux: Linux Distribution for Cyber and OSINT Investigation

Hacking Backward and Forward-Compatible Application Releases

How to Streamline CI/CD With Octopus and TeamCity

অক্টোপাস এবং টিমসিটির সাথে কীভাবে সিআই/সিডি স্ট্রিমলাইন করবেন

So optimieren Sie CI/CD mit Octopus und TeamCity

Cómo optimizar CI/CD con Octopus y TeamCity

Comment rationaliser le CI/CD avec Octopus et TeamCity

ऑक्टोपस और टीमसिटी के साथ CI/CD को कैसे सुव्यवस्थित करें

Octopus と TeamCity で CI/CD を効率化する方法

Octopus와 TeamCity를 사용하여 CI/CD를 간소화하는 방법

Como simplificar CI/CD com Octopus e TeamCity

Как оптимизировать CI/CD с помощью Octopus и TeamCity

Octopus ve TeamCity ile CI/CD Nasıl Kolaylaştırılır

Cách hợp lý hóa CI/CD với Octopus và TeamCity

如何使用 Octopus 和 TeamCity 简化 CI/CD

When It’s Time to Give REST a Rest

যখন আরেস্টকে বিশ্রাম দেওয়ার সময় হয়

Wenn es Zeit ist, sich auszuruhen

Cuando es hora de darle un descanso al DESCANSO

Quand il est temps de se reposer

जब आराम करने का समय हो

RESTを休ませる時が来たら

REST에 휴식을 주어야 할 때

Quando é hora de dar um descanso ao REST

Когда пришло время дать REST отдохнуть

REST'e biraz dinlenme zamanı geldiğinde

Khi đến lúc cần cho REST nghỉ ngơi

何时该让 REST 休息一下

Dependency Inversion Principle in Go: What It Is and How to Use It

ডিপেনডেন্সি ইনভার্সন প্রিন্সিপল ইন গো: এটি কী এবং কীভাবে এটি ব্যবহার করবেন

Prinzip der Abhängigkeitsumkehrung in Go: Was es ist und wie man es anwendet

Principio de inversión de dependencia en Go: qué es y cómo usarlo

Principe d'inversion de dépendance dans Go : qu'est-ce que c'est et comment l'utiliser

गो में निर्भरता व्युत्क्रम सिद्धांत: यह क्या है और इसका उपयोग कैसे करें

Go における依存性逆転の原則: その内容と使い方

Go의 종속성 반전 원리: 정의 및 사용 방법

Princípio de inversão de dependência em Go: o que é e como usá-lo

Принцип инверсии зависимостей в Go: что это такое и как его использовать

Go'da Bağımlılığı Tersine Çevirme Prensibi: Nedir ve Nasıl Kullanılır?

Nguyên tắc đảo ngược phụ thuộc trong Go: Nó là gì và cách sử dụng nó

Go 中的依赖倒置原则：它是什么以及如何使用它

Failure is Required, So Embrace It: Understanding Fail-Safe and Fail-Fast Strategies In Software

ব্যর্থতা প্রয়োজন, তাই এটিকে আলিঙ্গন করুন: সফ্টওয়্যারে ব্যর্থ-নিরাপদ এবং ব্যর্থ-দ্রুত কৌশলগুলি বোঝা

Fehler sind unvermeidlich, also akzeptieren Sie sie: Verstehen von Fail-Safe- und Fail-Fast-Strategien in der Software

El fracaso es necesario, así que acéptelo: comprensión de las estrategias de software a prueba de fallos y rápido

L'échec est nécessaire, alors acceptez-le : comprendre les stratégies de sécurité et d'échec rapide dans les logiciels

विफलता आवश्यक है, इसलिए इसे स्वीकार करें: सॉफ्टवेयर में विफलता-सुरक्षित और विफलता-त्वरित रणनीतियों को समझना

失敗は必要だから受け入れよう: ソフトウェアにおけるフェイルセーフとフェイルファスト戦略を理解する

실패는 필수이므로 이를 수용하세요: 소프트웨어의 오류 방지 및 오류 방지 전략 이해

A falha é necessária, então aceite-a: entendendo estratégias à prova de falhas e rápidas em software

Провал обязателен, поэтому примите его: понимание отказоустойчивых и отказоустойчивых стратегий в программном обеспечении

Başarısızlık Gereklidir, O halde Onu Kucaklayın: Yazılımda Arızaya Karşı Korumalı ve Arızayı Hızlandırma Stratejilerini Anlamak

Thất bại là bắt buộc, vì vậy hãy chấp nhận nó: Tìm hiểu các chiến lược an toàn và thất bại nhanh trong phần mềm

失败是必然的，所以要接受它：理解软件中的故障安全和快速失败策略

Functions are a fundamental concept in JavaScript, allowing developers to encapsulate code for reuse, organization, and abstraction. In this guide, we’ll explore various aspects of functions in JavaScript, including their declaration, parameters, return statements, function expressions, and arrow functions. 1. Declaration of Functions In JavaScript, functions can be declared using the function keyword followed by the function name and a pair of parentheses () containing optional parameters. Here's a basic example: function greet(name) {
return Hello, ${name}!;
}

console.log(greet('sadanand gadwal')); // Output: Hello, sadanand gadwal! The greet function is declared using the function keyword. It takes a parameter name and returns a greeting message using string interpolation. Parameters
Functions can accept parameters, which are variables that hold the values passed to the function when it is called. Parameters are declared within the parentheses following the function name.
Here's an example function add(a, b) {
return a + b;
}

console.log(add(5, 3)); // Output: 8 The add function takes two parameters a and b and returns their sum.
The subtract function takes two parameters a and b and returns the result of a - b. 3. Return Statements Functions can use the return statement to send a value back to the code that called the function. If a function doesn't explicitly return a value, it implicitly returns undefined. Here's an example: function subtract(a, b) {
 return a - b;
}

console.log(subtract(10, 4)); // Output: 6 Both add and subtract functions use the return statement to return the result of the arithmetic operation. 4. Function Expressions Function expressions define functions as part of an expression rather than as a declaration. They can be named or anonymous and are often used to assign functions to variables. Here’s an example of a named function expression: const multiply = function multiply(a, b) {
    return a * b;
};

console.log(multiply(7, 8)); // Output: 56 And here’s an example of an anonymous function expression: const divide = function(a, b) {
    return a / b;
};

console.log(divide(100, 5)); // Output: 20 The multiply function is defined using a named function expression. The function is assigned to the variable multiply.
The divide function is defined using an anonymous function expression. The function is assigned to the variable divide. 5. Arrow Functions Arrow functions are a more concise way to write functions in JavaScript, introduced in ES6. They have a more compact syntax and automatically bind this to the surrounding code's context. Here's an example: const square = (x) => {
    return x * x;
};

console.log(square(4)); // Output: 16 For simple functions that have only one expression in the body, the curly braces and return keyword can be omitted: const cube = (x) => x * x * x;

console.log(cube(3)); // Output: 27 The square function is defined using an arrow function. It takes a parameter x and returns the square of x.
The cube function is also defined using an arrow function but with a more concise syntax since it has only one expression in its body. 6. Example: Using Functions function calculate(operation, a, b) {
    switch (operation) {
        case 'add':
            return add(a, b);
        case 'subtract':
            return subtract(a, b);
        case 'multiply':
            return multiply(a, b);
        case 'divide':
            return divide(a, b);
        default:
            return 'Invalid operation';
    }
}

console.log(calculate('add', 5, 3)); // Output: 8
console.log(calculate('multiply', 4, 6)); // Output: 24
console.log(calculate('divide', 10, 2)); // Output: 5
console.log(calculate('power', 2, 3)); // Output: Invalid operation The calculate function takes three parameters: operation, a, and b. It uses a switch statement to determine which operation to perform (add, subtract, multiply, divide) and calls the corresponding function with the given arguments.
The switch statement also handles the case when an invalid operation is provided, returning an error message. Conclusion Functions are a powerful feature in JavaScript, allowing developers to write modular and reusable code. Understanding the different ways to declare and use functions is essential for any JavaScript developer. Bonus: Complete code: // Declaration of Functions
function greet(name) {
    return `Hello, ${name}!`;
}

// Parameters
function add(a, b) {
    return a + b;
}

// Return Statements
function subtract(a, b) {
    return a - b;
}

// Function Expressions
const multiply = function multiply(a, b) {
    return a * b;
};

const divide = function(a, b) {
    return a / b;
};

// Arrow Functions
const square = (x) => {
    return x * x;
};

const cube = (x) => x * x * x;

// Example: Using Functions
function calculate(operation, a, b) {
    switch (operation) {
        case 'add':
            return add(a, b);
        case 'subtract':
            return subtract(a, b);
        case 'multiply':
            return multiply(a, b);
        case 'divide':
            return divide(a, b);
        default:
            return 'Invalid operation';
    }
}

console.log(greet('sadanand gadwal')); // Output: Hello, sadanand gadwal!
console.log(add(5, 3)); // Output: 8
console.log(subtract(10, 4)); // Output: 6
console.log(multiply(7, 8)); // Output: 56
console.log(divide(100, 5)); // Output: 20
console.log(square(4)); // Output: 16
console.log(cube(3)); // Output: 27
console.log(calculate('add', 5, 3)); // Output: 8
console.log(calculate('multiply', 4, 6)); // Output: 24
console.log(calculate('divide', 10, 2)); // Output: 5
console.log(calculate('power', 2, 3)); // Output: Invalid operation 🌟 Stay Connected! 🌟 Hey there, awesome reader! 👋 Want to stay updated with my latest insights? Follow me on social media! 🐦 📸 📘 💻 🌐 Functions are a fundamental concept in JavaScript, allowing developers to encapsulate code for reuse, organization, and abstraction. In this guide, we’ll explore various aspects of functions in JavaScript, including their declaration, parameters, return statements, function expressions, and arrow functions. 1. Declaration of Functions Declaration of Functions In JavaScript, functions can be declared using the function keyword followed by the function name and a pair of parentheses () containing optional parameters. function () Here's a basic example: function greet(name) {
return Hello, ${name}!;
}

console.log(greet('sadanand gadwal')); // Output: Hello, sadanand gadwal! function greet(name) {
return Hello, ${name}!;
}

console.log(greet('sadanand gadwal')); // Output: Hello, sadanand gadwal! The greet function is declared using the function keyword. It takes a parameter name and returns a greeting message using string interpolation. The greet function is declared using the function keyword. It takes a parameter name and returns a greeting message using string interpolation. Parameters
Functions can accept parameters, which are variables that hold the values passed to the function when it is called. Parameters are declared within the parentheses following the function name.
Here's an example Parameters Functions can accept parameters, which are variables that hold the values passed to the function when it is called. Parameters are declared within the parentheses following the function name. Here's an example Parameters Parameters Functions can accept parameters, which are variables that hold the values passed to the function when it is called. Parameters are declared within the parentheses following the function name. Here's an example function add(a, b) {
return a + b;
}

console.log(add(5, 3)); // Output: 8 function add(a, b) {
return a + b;
}

console.log(add(5, 3)); // Output: 8 The add function takes two parameters a and b and returns their sum. The subtract function takes two parameters a and b and returns the result of a - b. The add function takes two parameters a and b and returns their sum. add a b The subtract function takes two parameters a and b and returns the result of a - b . subtract a b a - b 3. Return Statements Return Statements Functions can use the return statement to send a value back to the code that called the function. If a function doesn't explicitly return a value, it implicitly returns undefined . return undefined Here's an example: function subtract(a, b) {
 return a - b;
}

console.log(subtract(10, 4)); // Output: 6 function subtract(a, b) {
 return a - b;
}

console.log(subtract(10, 4)); // Output: 6 Both add and subtract functions use the return statement to return the result of the arithmetic operation. Both add and subtract functions use the return statement to return the result of the arithmetic operation. add subtract return 4. Function Expressions Function Expressions Function expressions define functions as part of an expression rather than as a declaration. They can be named or anonymous and are often used to assign functions to variables. Here’s an example of a named function expression: const multiply = function multiply(a, b) {
    return a * b;
};

console.log(multiply(7, 8)); // Output: 56 const multiply = function multiply(a, b) {
    return a * b;
};

console.log(multiply(7, 8)); // Output: 56 And here’s an example of an anonymous function expression: anonymous function expression: const divide = function(a, b) {
    return a / b;
};

console.log(divide(100, 5)); // Output: 20 const divide = function(a, b) {
    return a / b;
};

console.log(divide(100, 5)); // Output: 20 The multiply function is defined using a named function expression. The function is assigned to the variable multiply. The divide function is defined using an anonymous function expression. The function is assigned to the variable divide. The multiply function is defined using a named function expression. The function is assigned to the variable multiply . multiply multiply The divide function is defined using an anonymous function expression. The function is assigned to the variable divide . divide divide 5. Arrow Functions Arrow Functions Arrow functions are a more concise way to write functions in JavaScript, introduced in ES6. They have a more compact syntax and automatically bind this to the surrounding code's context. Here's an example: this const square = (x) => {
    return x * x;
};

console.log(square(4)); // Output: 16 const square = (x) => {
    return x * x;
};

console.log(square(4)); // Output: 16 For simple functions that have only one expression in the body, the curly braces and return keyword can be omitted: one expression return const cube = (x) => x * x * x;

console.log(cube(3)); // Output: 27 const cube = (x) => x * x * x;

console.log(cube(3)); // Output: 27 The square function is defined using an arrow function. It takes a parameter x and returns the square of x. The cube function is also defined using an arrow function but with a more concise syntax since it has only one expression in its body. The square function is defined using an arrow function. It takes a parameter x and returns the square of x . square x x The cube function is also defined using an arrow function but with a more concise syntax since it has only one expression in its body. cube 6. Example: Using Functions 6. Example: Using Functions function calculate(operation, a, b) {
    switch (operation) {
        case 'add':
            return add(a, b);
        case 'subtract':
            return subtract(a, b);
        case 'multiply':
            return multiply(a, b);
        case 'divide':
            return divide(a, b);
        default:
            return 'Invalid operation';
    }
}

console.log(calculate('add', 5, 3)); // Output: 8
console.log(calculate('multiply', 4, 6)); // Output: 24
console.log(calculate('divide', 10, 2)); // Output: 5
console.log(calculate('power', 2, 3)); // Output: Invalid operation function calculate(operation, a, b) {
    switch (operation) {
        case 'add':
            return add(a, b);
        case 'subtract':
            return subtract(a, b);
        case 'multiply':
            return multiply(a, b);
        case 'divide':
            return divide(a, b);
        default:
            return 'Invalid operation';
    }
}

console.log(calculate('add', 5, 3)); // Output: 8
console.log(calculate('multiply', 4, 6)); // Output: 24
console.log(calculate('divide', 10, 2)); // Output: 5
console.log(calculate('power', 2, 3)); // Output: Invalid operation The calculate function takes three parameters: operation, a, and b. It uses a switch statement to determine which operation to perform (add, subtract, multiply, divide) and calls the corresponding function with the given arguments. The switch statement also handles the case when an invalid operation is provided, returning an error message. The calculate function takes three parameters: operation , a , and b . It uses a switch statement to determine which operation to perform ( add , subtract , multiply , divide ) and calls the corresponding function with the given arguments. calculate operation a b switch add subtract multiply divide The switch statement also handles the case when an invalid operation is provided, returning an error message. switch Conclusion Conclusion Functions are a powerful feature in JavaScript, allowing developers to write modular and reusable code. Understanding the different ways to declare and use functions is essential for any JavaScript developer. Bonus: Complete code: Bonus: Complete code: // Declaration of Functions
function greet(name) {
    return `Hello, ${name}!`;
}

// Parameters
function add(a, b) {
    return a + b;
}

// Return Statements
function subtract(a, b) {
    return a - b;
}

// Function Expressions
const multiply = function multiply(a, b) {
    return a * b;
};

const divide = function(a, b) {
    return a / b;
};

// Arrow Functions
const square = (x) => {
    return x * x;
};

const cube = (x) => x * x * x;

// Example: Using Functions
function calculate(operation, a, b) {
    switch (operation) {
        case 'add':
            return add(a, b);
        case 'subtract':
            return subtract(a, b);
        case 'multiply':
            return multiply(a, b);
        case 'divide':
            return divide(a, b);
        default:
            return 'Invalid operation';
    }
}

console.log(greet('sadanand gadwal')); // Output: Hello, sadanand gadwal!
console.log(add(5, 3)); // Output: 8
console.log(subtract(10, 4)); // Output: 6
console.log(multiply(7, 8)); // Output: 56
console.log(divide(100, 5)); // Output: 20
console.log(square(4)); // Output: 16
console.log(cube(3)); // Output: 27
console.log(calculate('add', 5, 3)); // Output: 8
console.log(calculate('multiply', 4, 6)); // Output: 24
console.log(calculate('divide', 10, 2)); // Output: 5
console.log(calculate('power', 2, 3)); // Output: Invalid operation // Declaration of Functions
function greet(name) {
    return `Hello, ${name}!`;
}

// Parameters
function add(a, b) {
    return a + b;
}

// Return Statements
function subtract(a, b) {
    return a - b;
}

// Function Expressions
const multiply = function multiply(a, b) {
    return a * b;
};

const divide = function(a, b) {
    return a / b;
};

// Arrow Functions
const square = (x) => {
    return x * x;
};

const cube = (x) => x * x * x;

// Example: Using Functions
function calculate(operation, a, b) {
    switch (operation) {
        case 'add':
            return add(a, b);
        case 'subtract':
            return subtract(a, b);
        case 'multiply':
            return multiply(a, b);
        case 'divide':
            return divide(a, b);
        default:
            return 'Invalid operation';
    }
}

console.log(greet('sadanand gadwal')); // Output: Hello, sadanand gadwal!
console.log(add(5, 3)); // Output: 8
console.log(subtract(10, 4)); // Output: 6
console.log(multiply(7, 8)); // Output: 56
console.log(divide(100, 5)); // Output: 20
console.log(square(4)); // Output: 16
console.log(cube(3)); // Output: 27
console.log(calculate('add', 5, 3)); // Output: 8
console.log(calculate('multiply', 4, 6)); // Output: 24
console.log(calculate('divide', 10, 2)); // Output: 5
console.log(calculate('power', 2, 3)); // Output: Invalid operation 🌟 Stay Connected! 🌟 🌟 Stay Connected! 🌟 Hey there, awesome reader! 👋 Want to stay updated with my latest insights? Follow me on social media! 🐦 📸 📘 💻 🌐 🐦 📸 📘 💻 🌐

Getting Started With Functions in JavaScript: Declarations, Parameters, and More

A Developer’s Guide: How to Automate a Financial Statement Analysis for Microfinance

Beyond the Smile: Decoding Emoji Lengths in JavaScript

Stories, News & Mentions about MEMFAULT

Memfault’s Stories on HackerNoon