Reviewerly/exHarmony huggingface dataset:
A User-centered Decision-aiding Process for Selecting Power Analysis Tools

A priori power analyses have become increasingly popular in scientific communities, but the practice has not been widely discussed by HFE researchers. Given the complexity of conducting such analyses, software tools are essential. We review the emergence and current state of power analysis software tools, and use the concept of User-Centered Design as an analytical lens for examining the relationship between researchers’ needs and power analysis tools’ characteristics. Centering on users’ wants and needs, we propose a decision-aiding process to assist researchers in identifying appropriate power analysis tools. This process consists of five user-centered considerations – two functional criteria (software input; software output) and three usability heuristics (learnability; ease of use; accessibility) – which are organized into a workflow. These criteria and heuristics are translated into questions that researchers should go through during the decision process. We illustrate use of the decision-aiding process with three example scenarios and lay out implications for researchers.
Development of online-systems for preparing for the EGE

An online system for preparing applicants for the Unified state exam is presented. In contrast to existing systems, the presented project is a combination of functions inherent in social networks and traditional online learning tools. It is expected that this format of interaction between students and teachers can have a positive impact on the quality of preparation for the exam. The paper considers the functional features of the system, describes the user roles, presents the stack of implementation technologies and key interfaces. The developed system is available online and undergoing beta testing.
Puyo Puyo AI using Monte Carlo tree search

Puyo Puyo is a kind of falling block puzzle game. The purpose of the game is to operate the falling blocks and to form a chain, but it is classified as an incomplete information game because the information about the blocks that will fall in the future is not disclosed to the players. As a Puyo Puyo AI, Mayah was proposed. However, Mayah is based on exhaustive search. Furthermore, due to the short thinking time per move, we must develop more efficient estimation method. To improve the search speed, in this paper, we propose Puyo Puyo AI using Monte Carlo tree search.
Bayesian Matrix Factorization for Visual Tracking

Visual tracking is an open issue in computer vision. Designing an robust tracking algorithm is a challenging problem due to complicated appearance variations, such as partial occlusion, illumination variations, fast motion and out-of-plane rotation. Some tracking algorithms use a set of templates to represent a target candidate. Such representations are not robust to drastic appearance variations. In order to alleviate the influence of appearance variations and outliers, we propose a novel tracking algorithm based on a robust Bayesian matrix factorization, where sample data is decomposed into a basis matrix and a coefficient matrix. The sample data is consist of previous tracking results. A column vector is stacked by a tracking result in a frame. A target candidate is represented by a linear combinations of the columns in the basis matrix. The matrix factorization can capture the correlation between different features in sample data. Also the appearance variations in tracking processing can be learnt in matrix factorization. Experimental results demonstrate the appearance models is effective and the proposed tracker in a particle filter tracking framework is robust to outliers and complicated appearance variations.
Designing user interface of web-based e-commerce application

Abstract This study aims to provide guidance and examples in designing web interface of e-commerce applications that are good and easy to use by the users. This research used descriptive method by making a systematic description of the facts that refer to the object of research. Data collecting was carried out through literature study from journals, learning materials, and curriculum data. The result is that if users find it difficult to operate an application, the application will immediately be abandoned. In conclusion, this design has fulfilled the basic requirement of comfortable design and practibility.
Understanding the Insecurity of Processor Caches Due to Cache Timing-Based Vulnerabilities

This article discusses a recently developed test suite for checking timingbased vulnerabilities in processor caches, which has revealed the insecurity of today's processor caches. The susceptibility of caches to these vulnerabilities calls for more research on secure processor caches.
Statistical Image Segmentation and Region Classification Approaches for Automotive Radar

This paper discusses statistical approaches currently being investigated to perform image segmentation and region classification in high resolution automotive radar imagery in the complex urban environment. The purpose is to identify the free traversable space ahead of the vehicle which would ultimately provide input into autonomous vehicle path planning algorithms. Three general methodologies are described, all based on the image region pixel intensity statistics which vary depending on the features within the imaged scene. Results show the promising potential for segmentation with these methods and some examples of segmentation and identification have been demonstrated for chosen road scene region types of asphalt, grass, shadow and objects/other.
Rotational Equivariance for Object Classification Using xView

With the recent addition of large, curated and labeled data sets to the remote sensing discipline, deep learning models have largely surpassed the performance of classical techniques. These deep models, typically Convolutional Neural Networks, are invariant to translation through the use of successive convolution layers which are themselves equivariant to translation. Further, the combination of multiple convolution and pooling layers means that in practice, the model is also approximately invariant to translation. However, until recently these models could only approach rotational invariance through data augmentation. Here we propose using a new model formulation which achieves rotational equaivariance without data augmentation for overhead imagery classification. We utilize the popular xView data set to compare the rotational equivariance formalization against a regular CNN and CNN with rotational data augmentation for the task of image classification.
Yapay Zeka Teknikleri İle Gelen E-Postaların Ayrıştırılması

Technological developments are making individuals and organizations ever more dependent on e-mail to communicate and share information.The increasing use of e-mail as an essential and popular communication method poses potentially severe threats to the Internet and society.Spam e-mails cause security problems for internet users and waste storage, bandwidth, and productivity resources.The increase in the volume of spam e-mails has created an intense need to develop more reliable and robust antispam filters.Therefore, it has become necessary to recommend adaptive spam detection models.In this paper, an intelligent system for the detection and filtering of spam e-mails is described.Machine learning methods aim to create the best models using the available data and analyze new data most accurately, with the help of the model created using previous data.In this study, spam detection was carried out using machine learning methods.In this study, K-nearest neighbors, support vector machine, and decision trees were used in the classification stage.The classification achieved an accuracy of 98.2% in spam detection.
Efficient Gs-IBE for Collusion Resistance on Multi-handler Cloud Environment Using Attribute-Modulator (AM)

In the digital world, adequate data has been stored and accessed under a cloud by using various users. The system emanates beneath the user management to resolve the security and privacy risks. In multi-user setup, the attendance to add operations carried out to sign-up and user cancellation from cloud to yield the risk overload. Further to avoid this problem, the paper castoff a Generalized-signcrypt identity-based encryption (Gs-IBE) with a random modulator that has been experimented. The conditional variable Am(r) been castoff to recognize the collusion in the random value. The Am(r) perform the distinct job in the random bit generation of each random element selection.
Deep learning for biomedical applications

The types of data most commonly used for machine learning in biomedical research, including electronic health records, imaging, -omics, sensor data, and medical text, are complex, heterogeneous, poorly annotated, and generally unstructured. However, gaining knowledge and actionable insights from all these data sources is a key challenge in implementing personalized medicine and next-generation healthcare. Deep learning, which describes a class of machine learning algorithms based on neural networks, provides effective opportunities to model, represent, and learn from such complex and heterogeneous sources. Here, we review how this paradigm has already affected healthcare, we note limitations and needs for improved methods and applications, and we discuss the challenges to implement and deploy augmented human intelligence based on deep learning in the clinical domain.
Role of RNNs for Non-sequential Tasks in The Question Answering Context

Current state-of-the-art neural network-based Question Answering (QA) systems consist of both Recurrent Neural Networks (RNNs) and Feedforward Neural Networks (FFNNs). They generally performed well on 19 of the 20 tasks in the benchmark bAbI dataset. The only task that they failed badly is a task involving inductive reasoning where the order of the facts is not important in producing the correct answer. In this paper, we removed the RNNs from DMN+ QA system to form the ff-DMN system. The results demonstrate that ff-DMN improves the accuracy of the induction task significantly. Further experiments reveal that using RNNs is important if intra-sentence reasoning is required while it may adversely affect the performance if inter-sentence reasoning is involved. Finally, by incorporating ff-DMN and DMN+ our ensemble model outperforms the other QA systems on all the 20 tasks.
Personal Wearable Light Space

This paper introduces a doctoral project on personal wearable light spaces. The topic combines textile material design with somaesthetic interaction design, investigating possible socio-cultural implications when humans become light sources. The project applies material speculation to explore future scenarios through physical prototypes. Objectives and the research question are stated, and the methodological framework is described. The project timeline and expected research contributions are listed.
A Light Spot on the Role of Artificial Intelligence and Deep Learning in Social Networks

Artificial Intelligence (AI) is the broad science that concerned with teaching machines to “think” or perform tasks like humans. Recently, AI is driving the progress in various up-to-date applications in natural language processing, computer vision, speech recognition and expert systems. The area of the smart applications in our daily life is predicted to expand in the future due to the remarkable evolution of AI. Deep learning, the most advanced form of AI, has developed hand in hand with the digital era, casting its shadow on various areas such as entertainment, finance, healthcare and social networks. With the explosion of data as well as the progressive progress in hardware field, deep learning can be considered as the future of AI. This chapter provides a brief history review of AI as well as a condensed explanation of deep learning. Furthermore, it sheds a light around the usage of deep learning algorithms in social networks.
Categorization of events

This chapter presents several categorizations relevant to the events described in the book. Both international and national categorizations are discussed and compared. The categorization used in this book is presented: it is based on actual facts as reported in the technical literature. Finally the chapter expands on the challenges and limitations inherent to any form of categorization.
Policy Adaptive Multi-agent Deep Deterministic Policy Gradient

We propose a novel approach to address one aspect of the non-stationarity problem in multi-agent reinforcement learning (RL), where the other agents may alter their policies due to environment changes during execution. This violates the Markov assumption that governs most single-agent RL methods and is one of the key challenges in multi-agent RL. To tackle this, we propose to train multiple policies for each agent and postpone the selection of the best policy at execution time. Specifically, we model the environment non-stationarity with a finite set of scenarios and train policies fitting each scenario. In addition to multiple policies, each agent also learns a policy predictor to determine which policy is the best with its local information. By doing so, each agent is able to adapt its policy when the environment changes and consequentially the other agents alter their policies during execution. We empirically evaluated our method on a variety of common benchmark problems proposed for multi-agent deep RL in the literature. Our experimental results show that the agents trained by our algorithm have better adaptiveness in changing environments and outperform the state-of-the-art methods in all the tested environments.
A novel deep neural design and efficient Pipeline architecture for Person Re-Identification in high resolution Video

The primary objective of person re-identification (Re-ID) is to retrieve a person of interest across different nonintersecting cameras for managing in distributed surveillance systems. This has added to its increasing popularity on account of its widespread use, applications and research significance. In this study, we have proposed a novel pipelined deep learning architecture which acts as a robust feature extractor and also helps in reducing down the search space by generating feature embeddings followed by executing a distance metric measurement for finding the similar neighbourhood embeddings and subsequently sorting the cluster centroids of the matching embeddings for finding a set of the nearest match before passing down to a siamese network for similarity checking in the reidentification process. Our experiments reported to achieve a performance accuracy of 85~90% with a model size of 288 MB executing at 30 fps in real-time.
A Study of the Students’ Reading Competency in the Junior High School Based on the 2013 Curriculum

Students’ reading abilities were below of minimum mastery criteria. This study was conducted to describe and analyze the students’ reading competency at junior high school level. The significance was to design and develop reading modular instructions with explicit reading strategies. This study was designed quantitatively. The students’ reading competency was measured quantitatively using a reading competency test. The test consisted of 25 multiple choice items with four options. The obtained data were analyzed quantitatively in two steps, namely: descriptive and inferential analysis. The mean scores of the students’ reading competency based on the reading indicators. The research findings were the mean score of the students’ reading competency in class D was 63.20 and in class H was 36.80. The range score between class D and H was same for descriptive text. However, for recount text, the score in class D was larger than class H. Comparing to class H, the score variability in class D was larger for descriptive text and lower for recount text. The EFL teachers are suggested to use the appropriate reading strategies and materials which can encourage the students for following fun reading activity in the classroom.
Embedding into an Emerging Money System: The Case of Bitcoin

As global economic crises place the issue of money at the forefront of media attention, a growing minority are turning to “cryptocurrencies” as an emerging digital alternative to state-issued currencies. Bitcoin, the most popular version of this emerging medium, is a useful proxy to answer a key question to the sociology of money: how do people embed themselves into an emerging money system, and what role does value play in this process? Drawing on 23 interviews with Bitcoin adopters, I find that embedding into Bitcoin is closely tied to personal experience and temporal contexts. This study demonstrates that the adoption of Bitcoin follows a distinct process. First adopters discover the value Bitcoin on their own terms. Next, they reflexively overcome challenges to these initial perceptions of value. Finally, they reaffirm their embeddedness in the system through rituals of commitment. This finding has implications for the sociology of money and economic sociology by distilling the connection between fictional expectations that are used to anchor value systems and the social construction of monetary utilities and group identities. Additionally, this connection helps to unpack how Bitcoin continues to mature as a money system despite being characterized by diverse adopters that often engage in economically inefficient activities.
Utilizing The Edmodo Application As A Learning Media In SD Alwasliyah 26 Medan

The use of e-learning in learning is very much needed in this century. The use of learning media in SD Alwasliyah 26 Medan is very less varied. Due to the lack of knowledge about technology and information as well as the use and use of the internet for teachers at SD Alwasliyah 26 Medan. The purpose of implementing this community service activity is to provide an understanding of the varied learning media, namely Edmodo, to increase the skills of teaching teachers to support teacher performance in utilizing technology in the school. The method used is to carry out direct practice by holding training by making Edmodo learning media guided by presenters from the community service implementation team so that the process is known from start to finish. The object of this activity is the teachers of SD Alwasliyah 26 Medan
CMA-EV: A Context Management Architecture Extended by Event and Variability Management

Nowadays software systems must adapt themselves to suit the specific context in which they operate. Context-awareness concepts are suitable for systems that must be optimally configured during execution. Because they allow systems to interact with real world by deciding the degree of adaptation the environment requires. In this sense, this paper presents our approach that consists of a CMA-EV architecture for the development of context-aware event-based systems, from the modeling phase through a context management architecture extended by event processing and variability management. Also, a discussion about the importance and challenges in designing context-aware and event-based applications is presented. An essential aspect that is often neglected in context modeling and also triggers adaptation is the context variability. This paper puts the light on this notion and proposes context feature model that would help the designer in programming adaptation for context-aware systems. All crucial these notions (context, event, and variability) are combined in our proposed CMA-EV architecture for the development of context aware and event-based system extended by variability management. Moreover, a Smart Tourism Recommender System (STRS), that respect personal preferences and capture usage, personal, social and environmental contextual parameters, is implemented to validate our approach.
Feature Extraction of Color Hierarchical Structure of Indoor Space Based on Virtual Reality Technology

The traditional color hierarchical structure feature extraction method needs to establish the spectrum of each color, and the processing structure is highly affected by color resolution, which affects the accuracy of feature extraction. Aiming at the above problems, research on the feature extraction method of indoor spatial color hierarchy based on virtual reality technology. The parameters of virtual space modeling are obtained from the high color resolution images, and the indoor space modeling is carried out by using virtual reality technology. Carry on spatial color correction to the established model, and detect the edge of color hierarchy, and extract the characteristics of indoor spatial color hierarchy according to the principle of clustering. The experimental results show that the feature extraction method based on virtual reality technology has the advantages of short processing time, good extraction effect and reliability.
The concept of “success” in modern Russian and English languages

This article discusses the meaning of the important to any culture concept of &amp;ldquo;success&amp;rdquo;. Etymology of the lexeme &amp;ldquo;success&amp;rdquo; in Russian and English languages and its lexical meaning according to most reputable Russian and English explanatory dictionaries are analyzed. Based on the conducted analysis, the authors outline the common meanings, such as achievement and implementation of the set goal, ability to achieve successful results, certain career achievements. The article provides the results of survey carried out by private Strayer University, USA, according to which success is the realization of personal potential, good relationships, happiness, aspirations and achievement of the set goals. In order to compare the attitude towards success in the Russian Federation and the United States, polling was conducted in the universities of Moscow Region (Academy of Social Management, Moscow Region State University), which revealed that 15% of respondents believe that success is good relationships and happiness. Despite the detected differences, the authors conclude that the concept of &amp;ldquo;success&amp;rdquo; has a broader meaning than just financial wealth, luck and power, and thus, support the researchers from Strayer University in amending the dictionary entry on the concept of &amp;ldquo;success&amp;rdquo; not only in English, but in Russian language as well.
Cyber Security: Threats and Challenges in India

Cyber security plays an important role in Information Technology. Securing the information becomes a challenge in present days. The main purpose of cyber security is to protect all organizational assets from threats as well as disruptions caused due to natural disasters. Various governments and companies are taking necessary measurements in order to prevent cybercrimes. Cyber security deals with the “Securing the future- securing the tomorrow’s world”.
Using a Binary Diffractive Optical Element to Increase the Imaging System Depth of Field in UAV Remote Sensing Tasks

Using an example of a real-world data set, it is shown that the accuracy of the image detector based on a YOLOv3 neural network does not deteriorate when using only one nonblurred color channel. The binary diffractive optical element was calculated, which allows increasing the imaging system depth of field by several times. This is achieved by using different color channels for various defocus values. A comparison of the MTF curves of the original and apodized imaging systems for a given minimum acceptable value of image contrast is presented. This approach allows us to create novel remote sensing imaging systems with an increased depth of field.