Reviewerly/exHarmony huggingface dataset:
Communication-Efficient Local Stochastic Gradient Descent for Scalable Deep Learning

Synchronous Stochastic Gradient Descent (SGD) with data parallelism, the most popular parallel training strategy for deep learning, suffers from expensive gradient communications. Local SGD with periodic model averaging is a promising alternative to synchronous SGD. The algorithm allows each worker to locally update its own model, and periodically averages the model parameters across all the workers. While this algorithm enjoys less frequent communications, the convergence rate is strongly affected by the number of workers. In order to scale up the local SGD training without losing accuracy, the number of workers should be sufficiently small so that the model converges reasonably fast. In this paper, we discuss how to exploit the degree of parallelism in local SGD while maintaining model accuracy. Our training strategy employs multiple groups of processes and each group trains a local model based on data parallelism. The local models are periodically averaged across all the groups. Based on this hierarchical parallelism, we design a model averaging algorithm that has a cheaper communication cost than allreduce-based approach. We also propose a practical metric for finding the maximum number of workers that does not cause a significant accuracy loss. Our experimental results demonstrate that our proposed training strategy provides a significantly improved scalability while achieving a comparable model accuracy to synchronous SGD.
Blockchain Technology as a Method Based on Organizing Big Data to Build Smart Cities: The Dubai Experience

A number of specialists and experts emphasized the importance of keeping abreast of the global development of the use of blockchain technology, which emerged in 2008 and was created as an encrypted series, with various transactions recorded, to be decentralized so that transactions between users are allowed directly without the need for third parties or intermediaries, stressing the need for banks to adopt the Block Qin technology. This Block Qin technology will enhance the performance of banking services and upgrading, and has caused and make a real breakthrough in the world of the digital economy, in addition to sectors such as e-commerce and remittances, smart city infrastructure, real estate, trade, education, health, and other fields.
Dark Web

There is a pressing need for understanding blockchain, cybercrime, and dark web-based fraud. As the world continues to turn digital, uses of cryptocurrencies are becoming mainstream. With this technological adoption becoming a reality, crime is adapting to the times. “Click here for free Bitcoin,” “set up an account and earn 100BTC instantly” are merely anecdotal examples of the ways the act of fraud is innovating. Deeper into this proliferation of technology lies the dark web, where your social security and identity may be offered for a small sum as we speak. Blockchain technology fueled dark web marketplaces' enormous growth, which facilitated identity fraud and many other cybercrimes taking place as we speak. This chapter and its authors aim to provide a thorough yet simplified explanation of these technologies while expressing current trends and theories surrounding dark web fraud trading of fraud guides and the use of social engineering. This chapter aims to explain all aspects of this area of cybercrime for all to understand.
OSD: An Occlusion Skeleton Dataset for Action Recognition

Currently available 2D skeleton datasets for action recognition mostly contain nonoccluded skeleton samples. Models trained on such datasets lack generalization ability in occlusion situations. In this paper we propose an occlusion projection method, which projects a 3D occlusion object into 2D plane to generate a 2D occluded area. Based on this method, we build a 2D occlusion skeleton dataset named OSD with 56,800 occluded skeleton samples and 60 distinct classes. Experimental results show that the model trained on OSD has better generalization ability in occlusion situations compared with the model trained on datasets with nonoccluded samples, which proves the effectiveness of OSD.
Extraction of Product Defects and Opinions from Customer Reviews by Using Text Clustering and Sentiment Analysis

The development of e-commerce has created new shopping trends of customers. In online shopping environments, product reviews play a critical role in the choice of customers. Online reviews are additionally valuable for the manufacturers and the vendors by providing easily accessible feedback to them. In this study, a text analysis method is proposed to find the defective features of the products by detecting features with negative opinion tendency in the clustered customer reviews. The output of the proposed model, the extracted defects, may provide a strong source of guidance both for consumers in purchase decisions and for producers in product improvement.
Developing a framework for self-isolation app: a case study of Jakarta’s COVID-19 mitigation

Abstract Self-isolation for COVID-19 suspects plays a crucial role due to a lack of medical personnel and hospital capacity in Jakarta. The ongoing COVID-19 pandemic has taken an unprecedented toll on health services because the number of infections continues to climb and is thought to be far higher because many people have not been tested. This phenomenon opens an opportunity to support innovative collaborations between government and city co-creators (e.g. start-up companies) to develop a digital application for monitoring the self-isolation activity of COVID-19 suspects. This paper proposes a conceptual framework for developing a self-isolation app by conducting in-depth interviews with 20 participants from the Jakarta Health Office and mobile health app start-up companies. The result of this paper figures out the key features of the app, such as suspects database, daily monitoring form, geotagging, chatroom, test scheduling, panic button, and daily notification. In this paper, Jakarta was selected as a case study since it is stated as a collaboration city which initiates the opportunity to business sectors for increasing access and public service quality. This paper uses design thinking methods as an evidence base to develop a self-isolation app. The conceptual framework provides the foundation for the input-process-output model and insights into what an appropriate approach for the requirements of the self-isolation app development. This paper provides a better understanding of common elements in building the app. Its conceptual framework fits in any government sector. Also, it can inspire other cities to consider the model in developing a similar application with structural and systematic phases in every development step. Further research should be carried out to build an extension model of the design thinking approach to get specific quantitative and qualitative results.
Investigative Techniques for the De-Anonymization of Hidden Services

Anonymity networks, also referred to as anonymous networks or darknets are an example of privacy-enhancing technology, the historical goal of which is to avoid censorship, preserve user privacy, and promote freedom of speech. The Tor network, undoubtedly the most popular anonymization technology at the time of this writing, according to its website, has several categories of users: individuals who wish to safeguard their online activity, businesses wanting to keep data confidential or conducting competitive analysis, journalists willing to protect anonymous sources and sensitive research, and activists who want to report abuses from danger zones.
Utility-Based Differentially Private Recommendation System

The Recommendation system relies on feedback and personal information collected from users for effective recommendation. The success of a recommendation system is highly dependent on storing and managing sensitive customer information. Users refrain from using the application if there is a threat to user privacy. Several works that were performed to protect user privacy have paid little attention to utility. Hence, there is a need for a robust recommendation system with high accuracy and privacy. Model-based approaches are more prevalent and commonly used in recommendation. The proposed work improvises the existing private model-based collaborative filtering algorithm with high privacy and utility. We identified that data sparsity is the primary reason for most of the threats in a recommender framework through an extensive literature survey. Hence, our approach combines the l injection for imputing the missing ratings, which are deemed low, with differential privacy. We additionally introduce a random differential privacy approach to alternating least square (ALS) for improved utility. Experimental results on benchmarked datasets confirm that the performance of our private noisy Random ALS algorithm outperforms the non-noisy ALS for all datasets.
Adapting a solar photovoltaic panel experiment for blind students

Abstract Renewable energy is a trending topic due to its importance in today’s society especially because of global warming. Because of its importance, this topic should be discussed at schools in an inclusive education environment. Experimental work is a very good activity to provide students effective conceptual discussions, but it is necessary to improve the teaching–learning process for an inclusive education paradigm. In this paper we propose a low-cost experimental activity using Arduino that allows regular students and blind students to explore together and understand features and properties about solar energy and a photovoltaic panel, such as how the electric power generated by the photocell depends on the angle of incidence of solar light and the load resistance of the circuit.
Semi-Automated Clinical Lexicon Induction and Its Use in Cohort Selection from Clinical Notes

Special purpose lexicons are invaluable in biomedical natural language processing. They are especially crucial for a task such as the 13-criteria based cohort identification from clinical notes, process in N2C2 2018 Track 1 Challenge. While manually developed lexicons helped us achieve high performance, the process was ad hoc and nonreproducible. This paper presents a semi-automated lexicon induction method, using Logistic Regression (LR) and word embeddings, which brings rigor to the process. The key idea was to use n-grams in the training corpus as features of LR and identify those features (n-grams) with the most impact on the outcome as the lexicon. The semi-automatically generated lexicons achieved overall F measure of 0.9166 versus 0.9003 with manually generated lexicons. Therefore, this study shows that lexicons generated using a rigorous, semi-automated approach can retain performance while bringing rigor to the process.
Privacy Preserving Distributed Summation in a Connected Graph

Most decentralized algorithms for multi-agent systems used in control, signal processing and machine learning for example, are designed to fit the problem where agents can only communicate with immediate neighbors in the network. For instance, decentralized and distributed optimization algorithms are based on the fact that every agent in a network will be able to influence every other agent in the network even if each agent only communicates with its immediate neighbors (given that the network is connected). That is, a distributed optimization problem can be solved in a decentralized manner by letting the agents exchange messages with their neighbors iteratively. In many algorithms that solve this kind of problem, agents in the network does not need individual values from their neighbors, rather they need a function of the values from its neighbors. This observation makes it interesting to consider privacy preservation in such algorithms. By privacy preservation, we mean that raw data from individual agents will not be exposed at any time during calculations. This paper is concerned with decentralized algorithms, where each agent must learn the sum of its neighbors values, and we propose a privacy preserving method to compute this sum. Employing this method in corresponding decentralized algorithms makes the whole algorithm privacy preserving. The only restriction we make on the graph topology of the network is that each agent must have at least two neighbors. We provide simulations of the proposed method, which illustrates the scalability of it.
A trust management model based on mutual trust and a reward‐with‐punishment mechanism for cloud environments

Abstract Aiming at overcoming problems such as malicious entities and trust crises in cloud environments, a trust management model based on mutual trust and a reward‐with‐punishment mechanism is proposed in this paper. First, according to reputation values of entities and trust relationships among entities, we calculate the comprehensive trust values of a service request user to several candidate service providers and probe these providers with high comprehensive trust values. Second, we select a trade provider according to mutual trust. Finally, after the trade, we update the reputation values and other data of the request user, trade provider, and recommenders based on the final trade result to reward or punish them to various degrees, thus ultimately reducing malicious or dishonest behavior. The experimental results show that our model can effectively identify malicious entities to increase the trade success rate.
Above All Made by Themselves: The Visual Rhetoric of W. E. B. Du Bois’s Data Visualizations

In this article, I examine and contextualize a selection of award-winning data visualizations created by W. E. B. Du Bois and his team for the 1900 World’s Fair in Paris, France. I show that Du Bois’s success with these data visualizations is partially attributable to the ways in which he merged artistic creativity with statistical empiricism to overcome the practical and ideological constraints of his rhetorical situation, namely a need to be seen amongst the fair’s larger spectacle and a refutation of the “scientific” racism that pervaded academia at the time. The research presented confirms Du Bois as an important but previously unrecognized progenitor of data visualization and therefore deserving of much more recognition in the fields of technical and professional communication (TPC) and data visualization than he currently receives. Ultimately, I argue that his achievement recommends useful lessons for contemporary scholars, practitioners, and pedagogues of TPC and data design.
Analyses of Tabular AlphaZero on NoGo

The AlphaZero algorithm has been shown to achieve superhuman levels of plays in chess, shogi, and Go. This paper presents analytic investigations of the algorithm on NoGo, a variant of Go that players cannot capture the opponents' stones. More specifically, lookup tables are employed for learning instead of deep neural networks, referred to as tabular AlphaZero. One goal of this work is to investigate how the algorithm is influenced by hyper-parameters. Another goal is to investigate whether the optimal plays and theoretical values can be learned. One of the hyper-parameters is thoroughly analyzed in the experiments. The results show that the tabular AlphaZero can learn the theoretical values and optimal plays in many settings of the hyper-parameter. Also, NoGo on different board sizes is compared, and the learning difficulty is shown to relate to the game complexity.
Developing Blitz Games and Using Them in Teaching Engineering Disciplines

Modern system of vocational education is focused on enhancing its quality and increasing the competitiveness and investment attractiveness of secondary vocational education. Russian National Project “Education” poses a challenge of modernizing this level of education, including by implementing adaptive, practice-focused and flexible educational programs. Russian educators recognize the importance of expanding the range of active learning methods used in studying engineering subjects. Implementing them into teaching/learning process allows students not to be passive narratees; moreover, it activates their thinking and develops independence in making their decisions. This helps increase their motivation and interest in their studies and ensures more efficiently forming their professional and general cultural competences. One of simple but efficient learning methods are blitz games that are interactive by their nature and presuppose free exchange of opinions regarding the ways and techniques of solving certain problem situations among those involved in educational activities. Blitz game makes learning exciting and interesting for both the teacher and the students.
Optimizing Stochastic Gradient Descent Using the Angle Between Gradients

In the field of machine learning, Stochastic Gradient Descent has been proven an effective method to shorten the time spent on minimizing the output cost. Due to the fact that the data pattern of each mini-batch could be somewhat varied from the full dataset, most existing optimization algorithms attempt to alleviate this variance via computing certain calibration terms associated with the previous gradient. Since the previous gradient is computed from the past Stochastic Gradient Descent state, it can adversely affect the calibration terms introducing deviation that result in an inaccurate new gradient. To resolve this problem, we propose a method that reduces the aforementioned deviation via applying a preprocessing technique to the previous gradient prior to its usage. The technique uses the angle between the previous and the current gradients to improve the precision of the calibration terms, reducing the effects of the deviations. Empirical results are obtained from incorporating the proposed method with a fully-connected vanilla neural network. The proposed technique is evaluated using the MNIST dataset against 10 previously proposed optimization algorithms. The experiment shows the benefits in reducing the cost result from adopting the proposed preprocessing technique to improve the gradient derivation.
Using a Raspberry Pi to prevent an intoxicated driver from operating a motor vehicle

Drink driving or driving under the influence of alcohol is major problem throughout the world. In Ireland, drink driving has increased in its occurrences and the amount of people who still take the chance and drink and drive is high. According to reports released from the Road Safety Authority, the first two months of 2019 alone in Ireland, there has been a 17% increase in people being caught while driving under the influence since the preceding first two months of 2018. It was found that the main method in Ireland at least of catching intoxicated drivers was the use of checkpoints. Although, due to manpower and rural areas, it is difficult for police to be in the right place at the right time. This paper explores the option of using a Raspberry Pi as a device that could be used to prevent or stop a driver under the influence of alcohol from operating their vehicle. In order to test the sensor, a series of sober breath samples and a series of alcohol-potent products were used. The results have proven that the Raspberry Pi along with a MQ-3 gas sensor can accurately evaluate Blood Alcohol Concentration.
A Triangle Design Framework for Functional Footwear for Chinese Older Adults

Well-designed functional footwear can reduce health hazards in various environments. Considering the particular needs of older adults, the design of footwear must address multifunctional demands, which requires the use of the latest technology and extensive research. Focusing on Chinese older adults, this paper proposed a triangle design framework to single out critical research functions of design. It contributes to user-centered design literature by mapping out a logical, comprehensive design flow that integrates a service platform in the framework, in addition to the foci on designers and older users. The main functions of the service platform included surveying and analyzing demand, creating an empathic experience for the designers, capturing the perceptual cognition of older adults, and evaluating the product. The users’ requirements are classified according to the physiological function of older adults as well as usage scenarios. The suggested design flow is user-centered and demand-oriented, and the empathy concept is incorporated into all aspects of design. Both physical and mental health are expected to be improved to promote active aging for older adults as a result of implementing this design framework.
Next Generation Blockchain-Based Financial Services

This paper explores the transition towards a paradigm in which centralization and decentralization systems coexist in the provision of financial services. The blockchain technology application to the financial industry is giving birth to Decentralized Finance (DeFi). The transition is studied through a cross-chain analysis that allows to compare different blockchain ecosystems characterized by diverse evolution courses. The results show a path dependency linked to the first-mover advantage of the Ethereum blockchain. The analysis also highlights the emergence of new players that propose higher scalability opportunities (e.g., Eos, Tezos) and different design choices in terms of governance. This exploratory study also emphasizes the potential complementarity between the standard financial system and DeFi, discussing the main differences among the financial services provided on-chain and off-chain.
Affordance as general value function: a computational model

General value functions (GVFs) in the reinforcement learning (RL) literature are long-term predictive summaries of the outcomes of agents following specific policies in the environment. Affordances as perceived action possibilities with specific valence may be cast into predicted policy-relative goodness and modeled as GVFs. A systematic explication of this connection shows that GVFs and especially their deep-learning embodiments (1) realize affordance prediction as a form of direct perception, (2) illuminate the fundamental connection between action and perception in affordance, and (3) offer a scalable way to learn affordances using RL methods. Through an extensive review of existing literature on GVF applications and representative affordance research in robotics, we demonstrate that GVFs provide the right framework for learning affordances in real-world applications. In addition, we highlight a few new avenues of research opened up by the perspective of “affordance as GVF,” including using GVFs for orchestrating complex behaviors.
Analysis of Ensemble Learning Models for Identifying Spam over Social Networks using Recursive Feature Elimination

Social Networking platforms are regarded as being the reliable and valued communication medium for transferring information and communicating, used by the millions throughout the world. Users' reliance on these social networking sites is growing to seek perspectives, updates, alerts, news etc. While it is evident that the online social networks have become a way for information sharing, at the same instant they have rapidly become a medium for spreading misinformation, rumors, unsolicited messages, propaganda, fake news, and so on. It can indeed be said that a social networking platform consists of the two types of users, namely Spammers and Non-Spammers. Spammers typically spread misinformation or share undesirable content on social networking websites, out of malicious intents. In this work, a model is proposed to identify Spammers in Twitter network. This work is based on the user behavior-based and content-basedfeatures like Hashtags, URLs, Mentions, Replies, and Retweets. In this work, the Recursive Feature Elimination is used along with Support Vector Machine, Random Forest, Logistic Regression, Adaptive Boosting, and XGBoost. For data pre-processing, Weka is used, and implemented the five classifiers mentioned with Recursive Feature Elimination using sklearn in Python. Performance measures such as TP Rate, FP Rate, Precision, F-Measure and Accuracy are used for evaluating the performance of the proposed model