Reviewerly/exHarmony huggingface dataset:
Integrating and Assessing the Use of a “Makerspace” in a Russian Cultural Studies Course: Utilizing Immersive Virtual Reality and 3D Printing for Project-Based Learning

This article discusses the development of an immersive virtual reality (VR)- infused Makerspace for experiential learning. Students in an advanced-level Russian course used a Makerspace to complete a three-part project aimed at language and cultural learning through art and presentational speaking. Participants completed a survey about their experience at the end of the project, thus providing some data by which to assess its success. Students used Oculus Rift headsets to view 360-degree target-culture images and engaged in a handson sculpting activity that resulted in printing 3D models. Learners also used iPads for a painting activity, which was then compared with the VR sculpting task. The survey results showed that the Makerspace, and using VR in particular, was a success. Positive outcomes included facilitating task motivation, fostering speaking and artistic creativity, enabling deeper learning and focus on the task, assisting with cultural development, and enjoying learning by doing/making. Suggestions for future Makerspace projects in language labs are discussed.
HyPR: Hybrid Page Ranking on Evolving Graphs

PageRank (PR) is the standard metric used by the Google search engine to compute the importance of a web page via modeling the entire web as a first order Markov chain. The challenge of computing PR efficiently and quickly has been already addressed by several works previously who have shown innovations in both algorithms and in the use of parallel computing. The standard method of computing PR is handled by modelling the web as a graph. The fast growing internet adds several new web pages everyday and hence more nodes (representing the web pages) and edges (the hyperlinks) are added to this graph in an incremental fashion. Computing PR on this evolving graph is now an emerging challenge since computations from scratch on the massive graph is time consuming and unscalable. In this work, we propose Hybrid Page Rank (HyPR), which computes PR on evolving graphs using collaborative executions on muti-core CPUs and massively parallel GPUs. We exploit data parallelism via efficiently partitioning the graph into different regions that are affected and unaffected by the new updates. The different partitions are then processed in an overlapped manner for PR updates. The novelty of our technique is in utilizing the hybrid platform to scale the solution to massive graphs. The technique also provides high performance through parallel processing of every batch of updates using a parallel algorithm. HyPR efficiently executes on a NVIDIA V100 GPU hosted on a 6th Gen Intel Xeon CPU and is able to update a graph with 640M edges with a single batch of 100,000 edges in 12 ms. HyPR outperforms other state of the art techniques for computing PR on evolving graphs [1] by 4.8x. Additionally HyPR provides 1.2x speedup over GPU only executions, and 95x speedup over CPU only parallel executions.
Vehicular Movement Tracking by Fuzzy C-means Clustering of Optical Flow Vectors

Vehicle Tracking and Detection has many useful applications, ranging from Automated Parking Management, to a huge-scale Automated Traffic Violator Apprehension System. For vehicle tracking which this study focuses on, it proposes the use of optical flow for identifying movement of particles, as well as Fuzzy C-means clustering on optical flow output, in order to separate disconnected moving vehicles from one another. By the end of the study, the proposed algorithm made use of the previous vehicle count from the detection algorithm and was able to successfully identify the location of the different vehicles within the frame.
Architecture and Interface for Collaborating with a Group of Agents in an Adversarial Game

Despite the impressive advances in artificial intelligence (AI), close collaboration between people and AI systems is still hard to achieve. To overcome this problem, we have designed an architecture consisting of the Contract Net Protocol, the Behavior Tree, and a human–multiple agent interface that we call human-swarm interaction. Its effectiveness and feasibility were evaluated using team play in a simulated Tail Tag game. Matches with up to 19 AI agents and 1 person on one team and 20 people on the other team were played. The results indicate that our system is scalable and that there is room for improvement. We identified two key features for AI agents to achieve collaboration with people in a serious game as represented by Tail Tag game: the ability to at least remain alive in any situation, and active declaration of roles being attempted as well as sharing of roles.
Improving the Effect of Exercise through Comparison of Real and Imitated Motion Images Produced by Artificial Intelligence

We present an artificial intelligence application system to improve the skills of excise and human health. Captured video images of human exercise are processed so that the images imitates the trainer's motion. We call such images "imitated motion images". We employed imitated motion images of a person to make it easier to imitate trainer's exercises skills by the person. As an experiment for the evaluation of imitated motion images, we performed experiments with our collaborators. Then, we asked the collaborators for their opinions about real and imitated motion images. The results suggest that the imitated motion images are more effective. Because manual processing to produce imitated motion images is too much work, the existing technology, Pose2Pose, is used. When we tested multiple motion images of multiple person, we confirmed that about half of the images could be processed automatically, and it was also confirmed that the effect on the improvement of skills was achieved.
Web 2.0 applications as the tools of motivation in secondary physics education

Abstract The aim of this study is to introduce some effective methods of integrating educational web 2.0 apps into Physics lessons, to share some experience and ideas how they can be used to support learning and make students more active in the learning process. This collection of apps focuses on the most popular activities of the students (using social media, playing online games etc.) A short insight will be given into the procedure how these modern techniques can foster the students’ creativity, develop their collaboration skills, give opportunity to deal with differentiated learning problems and, last but not least, change the students’ attitude towards learning Physics.
File Slack Handling Tool

File slack is the storage space that occurs when a user creates a file that does not occupy over all the space allocated by the file system. It is the product of the file system design. From a digital investigation perspective, this left data may contain forensically relevant information, since the unused space has not overwritten the old data.This document presents the theory behind the file slack and showcases a thorough example of the slack space. An overview of the features of a developed tool is presented. This tool can extract information available in file slack associated with each regular file within the New Technology File System (NTFS) formatted partition. Moreover, it can dump the file slacks in a RAW format and comes with hashing capabilities using MD5 and SHA1 algorithms.
Analysis of Darknet Market Activity as a Country-Specific, Socio-Economic and Technological Phenomenon

The technological peculiarities of the Darknet as well as the availability of illicit items on the embedded market-places have raised heated debates in the media and keen interest by law enforcement and academics. In prior work, researchers have already investigated the infrastructure of Darknet platforms and the global distribution of Darknet market activity.In our work, we take a broader perspective by studying the Darknet as a regional, socio-economic and technological phenomenon. Our starting assumption is that there exist cross-country indicators that are related to Darknet market activity. We identify relevant indicators, and discuss their relationship to cybercrime from a theoretical perspective. We apply regression modelling and conduct a qualitative comparative analysis (QCA) to study the impact of the identified indicators on the number of items offered on the Darknet. We find that GDP per capita, the number of Bitcoin downloads per capita, the number of Tor relay users per capita and an education index correlate with market activity on Darknet platforms.
Spam Detection Using Machine Learning

Emails are essential in present century communication however spam emails have contributed negatively to the success of such communication.Studies have been conducted to classify messages in an effort to distinguish between ham and spam email by building an efficient and sensitive classification model with high accuracy and low false positive rate.Regular rule-based classifiers have been overwhelmed and less effective by the geometric growth in spam messages, hence the need to develop a more reliable and robust model.Classification methods employed includes SVM (support vector machine), Bayesian, Naïve Bayes, Bayesian with Adaboost, Naïve Bayes with Adaboost.However, for this project, the Bayesian was employed using Python programming language to develop a classification model.
Firefly Algorithm Based on Euclidean Metric and Dimensional Mutation

Firefly algorithm is a meta-heuristic stochastic search algorithm with strong robustness and easy implementation. However, it also has some shortcomings, such as the "oscillation" phenomenon caused by too many attractions, which makes the convergence speed is too slow or premature. In the original FA, the full attraction model makes the algorithm consume a lot of evaluation times, and the time complexity is high. Therefore, In this paper, a novel firefly algorithm (EMDmFA) based on Euclidean metric (EM) and dimensional mutation (DM) is proposed. The EM strategy makes the firefly learn from its nearest neighbors. When the firefly is better than its neighbors, it learns from the best individuals in the population. It improves the FA attraction model and dramatically reduces the computational time complexity. At the same time, DM strategy improves the ability of the algorithm to jump out of the local optimum. The experimental results show that the proposed EMDmFA significantly improves the accuracy of the solution and better than most state-of-the-art FA variants.
Infographics as a Creative Design Method for Foreign Language Teaching

This paper considers the conceptual design scopes of infographics. The particular emphasis is on the integration of infographics into the training process of foreign language teachers-to-be. A creative potential of infographics for developing a communicative competence is analyzed. During the research study aimed at identifying awareness of bachelor students of Herzen state pedagogical university about the impact and application of infographics to enhance foreign language teaching. The article focuses on the competence-based approach, and the components of foreign language communicative competence are delineated. It has been revealed that using of infographics in the context of the competence approach makes the process of learning foreign languages practice-oriented and promotes the formation of the necessary skills for foreign language communication. The giving example of infographics provides with formation of a receptive type of speech activity. The data collected via quantitative methodology with a set of questionnaire as the main instrument which has undergone the content validity. The results of survey demonstrate students’ opinions about creation a rich environment to foreign language learning through infographics. The results obtained allow making a conclusion about methodological validity and technological relevance of infographics and it can be considered a means of enhancement the pedagogical excellence of foreign language teachers.
Deep Learning-Based Face Hallucination: A Survey

There are various real world applications that make use of the facial images obtained from cameras, but these images often carry several disturbances with it and such images are commonly referred as low- resolution images. These images show major impact on various face image related applications. To make use of such low-resolution images, there are many face hallucination methods developed. The existing face hallucination methods can be classified into two types: reconstruction- and learning-based. Learning-based methods have achieved better performance on various applications. The most commonly used learning-based method is using position-patch. In recent times, based face hallucination methods are also shown much more importance. Hence this survey mainly focuses on various position-patch and Convolution Neural Network based methods and their outcomes in brief. Along with this, it also presents some research directions for further improvements on face hallucination.
Rethinking the ranks of visual channels

Data can be visually represented using visual channels like position, length or luminance. An existing ranking of these visual channels is based on how accurately participants could report the ratio between two depicted values. There is an assumption that this ranking should hold for different tasks and for different numbers of marks. However, there is surprisingly little existing work that tests this assumption, especially given that visually computing ratios is relatively unimportant in real-world visualizations, compared to seeing, remembering, and comparing trends and motifs, across displays that almost universally depict more than two values.To simulate the information extracted from a glance at a visualization, we instead asked participants to immediately reproduce a set of values from memory after they were shown the visualization. These values could be shown in a bar graph (position (bar)), line graph (position (line)), heat map (luminance), bubble chart (area), misaligned bar graph (length), or `wind map' (angle). With a Bayesian multilevel modeling approach, we show how the rank positions of visual channels shift across different numbers of marks (2, 4 or 8) and for bias, precision, and error measures. The ranking did not hold, even for reproductions of only 2 marks, and the new probabilistic ranking was highly inconsistent for reproductions of different numbers of marks. Other factors besides channel choice had an order of magnitude more influence on performance, such as the number of values in the series (e.g., more marks led to larger errors), or the value of each mark (e.g., small values were systematically overestimated). Every visual channel was worse for displays with 8 marks than 4, consistent with established limits on visual memory. These results point to the need for a body of empirical studies that move beyond two-value ratio judgments as a baseline for reliably ranking the quality of a visual channel, including testing new tasks (detection of trends or motifs), timescales (immediate computation, or later comparison), and the number of values (from a handful, to thousands)
Special Issue on The Workshop on MAthematical performance Modeling and Analysis (MAMA 2021)

The complexity of computer systems, networks and applications, as well as the advancements in computer technology, continue to grow at a rapid pace. Mathematical analysis, modeling and optimization have been playing, and continue to play, an important role in research studies to investigate fundamental issues and tradeoffs at the core of performance problems in the design and implementation of complex computer systems, networks and applications.
Face Encryption based on Feature Extraction Supported by Canny Edge Detector

Individual privacy protection is regarding a subject in surveillance and security areas. Protection of people's faces in image in linking with complex info, abused cases, involvement, and others on public broadcasting media and social networks are very important. Partial encryption human face is selected to reduce the computational requirements for huge volumes of image data and reducing the computational processing time required to conduct the entire image. In this paper, a new fast algorithm is proposed to encrypt partially human face, at first human face is detected using vole and jones algorithm, features points are extracted from face region to be encrypted, these features points are determined by using Canny edge detector since Canny edge algorithm considers the best edge detector with decent localization properties marked edges areas similar to the edges in the actual image as possible, i.e. that it is an essential feature. Furthermore, the importance of the key for the encryption process and its significant role in increasing and improving security, a new method for generating key is proposed making them resistant to attackers. The suggested encryption algorithms produced good results in the encryption process, since it has a shorter encrypting period and a greater encrypting effect, according to experimental results.
Efficiency Improvement in IoT Use for Elderly NCD Patients in Malaysia: Extending the Practical Implementation of Theory of Planned Behavior

Background: The rapidly aging population in Malaysia is no more a surprise especially due to the sudden increasing rate of infertility and greater life expectancy creating an impact on the overall mortality percentage. Nearly half or 40% of healthcare expenses are expected to yield from the growing number of elderly people. Purpose: Theory of Planned Behavior (TPB) which analyses the behavioral intention, is incorporated into this study on the use of IoT among elderly NCD-patients. According to TPB particular “cognitive-emotional” and “attitudinal” factors determine the behavioral intention of consumer or user. It is assumed that intention is required for a particular behavior in any context. This study also intend to explain the intention of elderly NCD-patients in Malaysia, while promoting a healthy lifestyle. Design/methodology/approach: The conceptual framework developed in this study will evaluate the current attitude of elderly patients in incorporating IoT-technology, societal and environmental factors influencing them to adopt IoT-technology, along with the constraints – leading the intension/behavior among the population. This, in turn, is expected to render developing efficient communication to all the stakeholders in increasing awareness among the targeted elderly NCD patients. Findings: The outcomes of this study are expected to produce meaningful insights for regulators, healthcare institutions and IT technology enterprises to collaborate in designing meaningful campaigns disseminating true benefits of IoT devices, by aiming to change the attitudes of elderly patients, improve their confidence Research limitations: The study was undertaken in a single country (Malaysia), which is a constraint on generalizability. This study only applied a quantitative method Originality/value: Analyzing the relational impacts of the constructs in the framework for this study will ultimately enhance the intention of IoT technology’s use among elderly patients in Malaysia.
A Novel Stance based Sampling for Imbalanced Data

While the world is suffering from coronavirus pandemic (COVID-19), a parallel battle with Infodemic, the proliferation of fake news online is also taking place.The spread of fake news during this global pandemic COVID-19 has dangerous consequences.This is the driving force behind this study.Relying on incorrect information obtained from the internet or social media can be fatal.According to a World Health Organization survey, at least 800 people have lost their lives because of COVID-19 misinformation during this time, highlighting the accurate automated classification of fake news.However, the data at disposal for classification is imbalanced.The Internet has a vast repository of authentic healthcare news, whereas Fake News on COVID-19 healthcare is not abundant.This imbalance leads to incorrect classification.The paper studies alternative approaches to text sampling.In this paper, we propose a stance based sampling method for balancing news data.The disparity between the title and content of news items is utilized to sample data points selectively and rectify the imbalance.The key findings are that the proposed stance-based sampling strategies enhance categorisation task performance consistently for varying degrees of imbalance.The proposed techniques can better detect misleading news in the health care sector.
Cascading Deep Learning Approach for Identifying Facial Expression YOLO Method

Face detection is one of the biggest tasks to find things. Identification is usually the first stage of facial recognition. and identity verification. In recent years, in-depth learning algorithms have changed dramatically in object acquisition. These algorithms can usually be divided into two groups, namely two-phase machines like Faster R-CNN or single-phase machines like YOLO. While YOLO and its variants are less accurate than the two-phase detection systems, they outperform other components with wider genes. When faced with standard-sized objects, YOLO works well, but can't get smaller objects. A face recognition system that uses AI (Artificial Intelligence) separates or verifies a person's identity by analyzing their faces. In this project, a single neural network predicts binding boxes and class opportunities directly from the full images in a single test.
Siamese Network with Transfer Learning for Similar Query Retrieval in Online Health Community Forums

In health community forums, patients seek information, acquire knowledge and support each other. They find help by asking questions about their illness. Finding similar people with similar issues to obtain correct information is often a great challenge for them. Hence, this paper proposes a query similarity model to retrieve the similar query that is already discussed. The model is implemented with a Siamese neural network to assess the semantic similarity between the queries. The presence of several medical terms imposed great challenge to the model to generalize the query well. Hence, the model is fine-tuned with a transfer learning technique on a medical question–answer dataset. Several variants of the model are compared, and the best model could achieve an F1-score of 82.4%. The result showed that the proposed method is very effective for natural language understanding in medical domain.
Algorithmic Regulation: An Introduction

As the power and sophistication of 'big data' and predictive analytics has continued to expand, so too has policy and public concern about the use of algorithms in contemporary life. This is hardly surprising given our increasing reliance on algorithms in our everyday experience, touching policy sectors from healthcare, transport, finance, retail, manufacturing, education, employment through to public service provision and the operation of the criminal justice system. This has prompted concerns about the need and importance of holding algorithmic power to account, yet it is far from clear that existing legal and other oversight mechanisms are up to the task. This is the introductory chapter to a collection of essays, edited by the authors, offering a critical exploration of 'algorithmic regulation', understood as both a means of coordinating and regulating social action and decision-making, as well as the need for institutional mechanisms through which the power of algorithms and algorithmic systems might themselves be regulated.