Reviewerly/exHarmony huggingface dataset:
Some psychological aspects of distant teaching

The article presents a short review of works dealing with distant teaching problems and the results of students and lecturers’ survey at a higher educational institution about their attitude to distant teaching and psychological problems arising in the process of implementation. The experiment conducted reveals the major part of students and lecturers’ positive attitude towards the form of distant teaching which is being applied at the present period of time. As the main drawback of distance teaching, the surveyed students pointed out the lack of direct contact with a lecturer (45% of students who rated distance education in a negative way). The revealed important negative aspect of distance teaching is that some students began to spend more time getting ready for classes (21%). 55% of lectures are positive about distance education, and 45% are neutral. There are no people who rated in a negative way or very positive. Among those who gave a positive estimation of distance teaching, 33% of respondents, as well as students, indicate a high structure of educational material. Those lecturers, who have a satisfactory degree of distance teaching skills, but indicate that this work requires a lot of effort, turned out to be 90%.
Arabizi Chat Alphabet Transliteration to Algerian Dialect

In the last decade, social media have become a necessary tool for communication in the daily life of Internet users. They use various platforms to share their views on a variety of topics or to target opinions and comments on the satisfaction of a given product. Companies and large distributions surface are increasingly affected by new technology and present in social media in order to improve the quality of their services. Internet users in general and customers of the Algerian Operator Telephone Ooredoo, in particular, are increasingly using social media to communicate with each other and comment on different pages, in order to express their opinions and views on a product or brand given. Most of the messages and comments used by the Operator’s customers are written in informal chat language, spelled in Latin characters, also known as Arabizi. In this paper, we designed a rule-based converter for transliterating customer’s comment from Arabizi to Algerian dialect on the official Ooredoo Telephone Operator page. The obtained transliteration is used for sentiment analysis purpose, in order to improve the quality of the operator’s services and therefore customer satisfaction. Transliteration is performed at the word and character level of all the comments used. The results, thus obtained, show a character error rate of 10.07%.
Concept evaluation of a new aircraft passenger privacy bubble using virtual prototyping: A Human-Centered Design framework

BACKGROUND: The Human-Centered Design methodology advocates VR prototyping, as an effective tool to evaluate concepts in a cost-efficient, time-saving way. It is the question of whether it works in the development of a product intended to increase privacy while flying. OBJECTIVE: The current study aims at the application of virtual reality on the evaluation of a new privacy bubble called PRIVA for the passenger cabin. METHODS: An interactive VR was created and aligned with the HTC VIVE headset. 40 participants took part in the experiment as well as in the post-experiment survey. RESULTS: The concept was in overall, desirable as it was perceived to be more private, comfortable, satisfactory, effective, and appealing to participants compared to the current seat experiences. It was also perceived as more satisfactory with regards to the activities. CONCLUSIONS: The VR was effective, although there are limitations, the product seems promising and should be developed further.
Study on Optimizing Block Ciphers (AES, CHAM) on Graphic Processing Units

With the development of IoT and cloud technologies, it is necessary to efficiently encrypt a large amount of data. GPUs (Graphic Processing Units) are considered as a proper candidate for a crypto accelerator in these situations. In this paper, we study how to optimize AES, a representative standard block cipher algorithm, and CHAM, a recently proposed lightweight block cipher algorithm, in the GPU environment. As a result of applying optimization techniques that make full use of algorithmic properties and computational characteristics in the GPU environment, we could achieve performance improvement up to a dozen times than classical CPU implementation on a 64-bit operating system on AMD RYZEN 5 3600 CPU and NVIDIA RTX 2070 SUPER GPU.
EMS: An Extensible and Modular Staking Architecture for Proof-of-Stake Systems

Staking is an essential component in Proof-of-Stake (PoS) based blockchain systems. While a host of PoS blockchains have staking schemes in place, the implementations of those mechanisms are highly customized to meet the needs of specific blockchains and vary in terms of the offered functionalities. In this paper, we present EMS, an extensible and modular staking architecture for PoS systems. EMS specifies a generic and modular staking implementation framework by applying a novel bucket-based data structure across different system components. In particular, EMS is able to accommodate a variety of design requirements for staking in PoS systems by manipulating the optional fields in the bucket-based data structure, thereby providing great flexibility and extensibility. Our instantiation of EMS on the IoTeX blockchain further demonstrates its viability and effectiveness in practice.
Comparison of Approaches for UAV Dynamics Consideration in Sampling Based Path Planning Methods

Deployment of autonomous UAVs in an indoor environment puts higher requirements for path planning algorithms due to the more complex nature of the environment (narrow crossings, absence of GNSS, smaller manoeuvring space, etc. ...). Due to the manoeuvring of UAV between obstacles, it is required to consider its dynamics in the path planning process. Basic versions of sampling-based path planning methods such as RRT (rapidly exploring random trees) or PRM (probabilistic roadmaps) don't consider the dynamics of the object we are planning a path for. In the past several approaches for path planning with dynamic constraints were developed. The first approach, randomized kino-dynamic planning, was presented for the first time. The main goal of this approach is to incrementally search state space as configuration space and find control input, that results in a collision-free trajectory. The second approach, presented in, utilizes differential flatness of UAV's (quadrotor) equations of motions. Thus, it is possible to precisely follow the path previously generated by the path planning algorithm. The main task of this paper is to compare the mentioned approaches for path planning with kino-dynamic constraints and decide which one of them is more suitable for the path planning module of autonomous UAV that is currently being developed at the Institute of Robotics at the Slovak University of Technology.
Multi-resolution Collaborative Representation for Face Recognition

Sparse representation, collaborative representation, and other kinds of representation based classifiers have been extensively applied to face recognition. Specially, lots of experiments demonstrate that collaborative representation exhibits great potential. These existing classifiers generally focus on the single resolution. They do not work well for multiple resolution issues. However, images taken by different cameras in the real world have different resolutions. To deal with multi-resolution issues, this paper proposes a multi-resolution collaborative representation method. It builds multi-resolution training sample matrices and combines the collaborative representation to solve the multi-resolution recognition problem. Comparison experiments show that the proposed method exhibits the best comprehensive performance between all the tested methods.
Determining the angles of head rotation on a selective set of facial keypoints

Using a reduced set of facial keypoints improves the accuracy of calculating the angles of rotation of the head. In this paper, we propose methods for evaluating the accuracy of calculating angles and an algorithm for selecting a subset of facial keypoints. This approach has improved the accuracy of angle calculation by more than 50%.
Fault Transient Signal Analysis of UHV Transmission Line Based on Wavelet Transform and Prony Algorithm

In this paper, the Mallat signal analysis of wavelet transform is used to segment the signal, and then Prony algorithm is used to fit the segmentation algorithm. This method has the advantages of clear section distinction, strong anti-interference, fast calculation response and strong robustness. In the verification stage, the anti-interference robustness of the wavelet is tested by adding noise signals with different signal-to-noise ratio, and the ideal segmentation results are obtained by using wavelet analysis. Prony algorithm can be used to analyze and calculate each sub segment according to the segmentation.
Disaster Survival Training Using Virtual Reality

All over the world, there are various types of unpredictable and devastating natural disasters that form unexpected and extreme threats with harmful impacts on humans. So, disaster survival training introduces several precautions and response activities occurring earlier, during, and after the disaster. Training for disaster survival uses virtual reality that makes people feel like the reality of the different disastrous scenarios. This training simulates the effect of an earthquake, in which the player learns, understands, and gets a better knowledge of the disaster survival techniques to survive in a disastrous situation. After completion of survival training, illiterate people, adults, and youngsters learn the actions to take in case an earthquake is occurring. In this training, at last, the survivability score is generated. This score evaluates the efficiency of the user's survivability.
Exposing Students to New Terminologies While Collecting Browsing Search Data (Best Technical Paper)

Information overload is a well-known problem that generally occurs when searching for information online. To reduce this effect having prior knowledge on the domain and also a searching strategy is critical. Obtaining such qualities can be challenging for students since they are still learning about various domains and might not be familiar with the domain-specific keywords. In this paper, we are proposing a framework that aims to assist students to have a richer list of keyphrases that are pertinent to a domain under study and provide a mechanism for lectures to understand what search strategies their students are adopting. The proposed framework includes a Google Chrome Extension, a background and a remote server. The Google Chrome Extension is utilized to collect, process browsing data and generate reports containing keyphrases searched by students. The results of the user evaluation were compared with a similar framework (TextRank). The results indicate that our framework performed better in terms of accuracy of keyphrases and response time.
Information Search Trail Recommendation Based on Markov Chain Model and Case-based Reasoning

An information search trail recommendation method based on the Markov chain model and case-based reasoning is proposed. A laboratory user experiment was designed to evaluate the proposed method. The experimental results demonstrated that novice searchers have a positive attitude toward the search trail recommendation and a willingness to use the recommendation. Importantly, this study found that the search trail recommendation could effectively improve novice searchers' search performance. This finding is mainly reflected in the diversity of information sources and the integrity of the information content of the search results. The proposed search trail recommendation method extends the application scope of information recommendations and provides insights to improve the organization and management of online information resources.
Characteristics of dental note taking: a material based themed analysis of Swedish dental students

Abstract Background The transition from upper secondary to higher education and from higher education to professional practice requires that students adapt to new literacy practices, academic and professional. However, there is a gap of knowledge regarding literacy practices in dental education. Therefore, the aim of this study was to identify what characterizes dental students’ notetaking and secondarily to determine what dental students express regarding their notetaking. Methods To analyze students’ perspectives about the purposes of notetaking and to examine their written notes in depth, three volunteer students, out of the 24 students that voluntarily and anonymously handed in their notes, were interviewed. The three undergraduate dental students that participated in this material-based, semi-structured interview study, framed within a New Literacy Studies approach, were on their third year (6th semester). The focus of these material-based interviews was on each student’s notes. Questions prepared for semi-structured interviews were open-ended and allowed for individual follow-up questions related to the interviewee’s answer. To analyze the outcome of the interviews a thematic analysis was used. Results From the material-based interviews eight themes that relate to what , how and for what purpose students write were discerned. These eight themes include professional vocabulary, core content as well as clinical examples that belong to what students read and write; multimodal accentuation as well as synthesis that belong to how students read and write; and mnemonic strategies, academic purposes, and professional purposes that belong to for what purpose students read and write. Conclusions Findings from the interviews indicate that the digital development, offering a variety of available tools, has expanded the notion of notetaking. This study identified that dental students’ notetaking has changed during their education from initially being synchronous, to also include multimodal and asynchronous writing, making notetaking more of a writing practice. Further, students’ writing practices seem to be motivated by their knowledge formation in relation to a subject matter, but also in relation to their experiences during clinical training. Although, our hypothesis was that the main purpose of notetaking and writing was to pass their course examinations, this study showed that students that were half-way through their dental education, are aware that literacy practices are for learning for their future profession, and not only for passing their exams.
Visual Question Answering: Methodologies and Challenges

Given an image and a question in natural language based on the contents of the image, a Visual Question Answering system should model and produce an answer in natural language inferred from the information within the image. Visual question answering is a problem with increasing significance in the field of Artificial Intelligence as it lies in the crucial intersection between computer vision and natural language processing. Various methodologies have been proposed for obtaining a natural language answer to a user inputted question based on a given image. The purpose of this paper is to review various contemporary techniques for visual question answering. The advantages and limitations of these approaches are compared in this review. In addition, the areas for improvement within these approaches are discussed in this paper.
CMC and MALL unite

There is no doubt that Computer Mediated Communication (CMC) and mobile mediated communication are linked as technology continues to transform the way we communicate with each other. Campbell (2019) analyzed how mobile communication evolved into portable devices to form a complete system of mobile media, reshaping the fabric of our social lives via ‘sociality’ and ‘spatiality’. In this short paper, we would like to offer a brief overview of the diverse oral presentations which took place in the joint CMC and MALL (Mobile Assisted Language Learning) Special Interest Group (SIG) symposium at the online conference this year. This short paper will introduce various online apps which are available for free in both computer-based and mobile versions and can be adapted to foreign language learning in various ways.
Bio-inspired Machine Learning Mechanism for Detecting Malicious URL Through Passive DNS in Big Data Platform

Malicious links are used as a source by the distribution channels to broadcast malware all over the Web. These links become instrumental in giving partial or full system control to the attackers. To overcome these issues, researchers have applied machine learning techniques for malicious URL detection. However, these techniques fall to identify distinguishable generic features that are able to define the maliciousness of a given domain. Generally, well-crafted URL’s features contribute considerably to the success of machine learning approaches, and on the contrary, poor features may ruin even good detection algorithms. In addition, the complex relationships between features are not easy to spot. The work presented in this paper explores how to detect malicious Web sites from passive DNS based features. This problem lends itself naturally to modern algorithms for selecting discriminative features in the continuously evolving distribution of malicious URLs. So, the suggested model adapts a bio-inspired feature selection technique to choose an optimal feature set in order to reduce the cost and running time of a given system, as well as achieving an acceptably high recognition rate. Moreover, a two-step artificial bee colony (ABC) algorithm is utilized for efficient data clustering. The two approaches are incorporated within a unified framework that operates on the top of Hadoop infrastructure to deal with large samples of URLs. Both the experimental and statistical analyses show that improvements in the hybrid model have an advantage over some conventional algorithms for detecting malicious URL attacks. The results demonstrated that the suggested model capable to scale 10 million query answer pairs with more than 96.6% accuracy.
Decision Support System For Determining The Provision Of Single Tuition Relief Using KNN and SAW Methods

The COVID-19 pandemic has caused a decline in the economic level of the people. This requires universities to quickly take concrete steps in helping their students. The purpose of this study is to assist the tertiary institution in determining who are eligible students to get a single tuition relief (UKT) using the KNN and SAW methods. The results of the UKT relief test show that this decision support system produces the same output as a manual calculation performed with Microsoft Excel. A further development that can be done is the addition of criteria by the situation and conditions of the relevant agencies.
BSE-MAML: Model Agnostic Meta-Reinforcement Learning via Bayesian Structured Exploration

Deep reinforcement learning (RL) is playing an increasingly important role in web services such as news recommendation, vulnerability detection, and personalized services. Exploration is a key component of RL, which determines whether these RL-based applications could find effective solutions eventually. In this paper, we propose a novel gradient-based fast adaptation approach for model agnostic meta-reinforcement learning via Bayesian structure exploration (BSE-MAML). BSE-MAML could effectively learn exploration strategies from prior experience by updating policy with embedding latent space via a Bayesian mechanism. Coherent stochasticity injected by latent space are more efficient than random noise, and can produce exploration strategies to perform well in novel environment. We have conducted extensive experiments to evaluate BSE-MAML. Experimental results show that BSE-MAML achieves better performance in exploration in realistic environments with sparse rewards, compared to state-of-the-art meta-RL algorithms, RL methods without learning exploration strategies, and task-agnostic exploration approaches.
Self-supervised Multi-view Synchronization Learning for 3D Pose Estimation

Current state-of-the-art methods cast monocular 3D human pose estimation as a learning problem by training neural networks on large data sets of images and corresponding skeleton poses. In contrast, we propose an approach that can exploit small annotated data sets by fine-tuning networks pre-trained via self-supervised learning on (large) unlabeled data sets. To drive such networks towards supporting 3D pose estimation during the pre-training step, we introduce a novel self-supervised feature learning task designed to focus on the 3D structure in an image. We exploit images extracted from videos captured with a multi-view camera system. The task is to classify whether two images depict two views of the same scene up to a rigid transformation. In a multi-view data set, where objects deform in a non-rigid manner, a rigid transformation occurs only between two views taken at the exact same time, i.e., when they are synchronized. We demonstrate the effectiveness of the synchronization task on the Human3.6M data set and achieve state-of-the-art results in 3D human pose estimation.
Development of an AI Recommender System to Recommend Concerts Based on Microservice Architecture Using Collaborative and Content-Based Filtering Methods

Recommender system is a complex software system primarily intended to select the most relevant content based on user’s personal preferences. In order to achieve the set goal, a number of tasks must be completed, including: track user actions across various devices; get product data from a number of sources and maintaining their currency; consolidate the data; create user and product profiles bases on big data in a real-time mode; select recommendations in cold start and highly sparse data environment; assess the quality of the recommender system. Completion of each specific task must not extend the time to complete other tasks. Users must instantly get the relevant content even if the system is heavily loaded, for example, due to a popular event announcement. A workaround may be to divide the system into independent components with the ability to scale specific services. Microservice architecture examined in this article intends to ensure required flexibility due to asynchronous message exchange via a data bus and other principles offered by SOA concept. Apart from interaction between the components, the article also introduces the results of development of each specific service from asynchronous user action tracker to recommender engine based on the hybrid approach that includes collaborative and content-based filtering methods, and the knowledge-based approach using Artificial Intelligence techniques. Special attention is paid to a subject category with a number of aspects that prevent applying generic approaches to building recommender systems.
Highlight-Video Generation System for Baseball Games

Highlight videos are designed to help people understand the key content without having to watch the original long videos in their entirety. Notably, highlight videos of sports are in considerable demand from viewers. We propose a highlight-video generation system for baseball games. The original video, along with the game log, is used to rapidly and accurately extract trimmed event video using simple computer vision algorithms. Several types of highlight videos can be generated by extracting all the trimmed event videos and combining them in various manners. Experimental results show that the proposed method has 98.18% accuracy in trimmed event video extraction. Moreover, it is possible to generate highlight videos within an average of 2 min after receiving the input video.