Reviewerly/exHarmony huggingface dataset:
Selection of Digital Learning Platforms for Future Education 4.0 in India

Due to Industry 4.0, new jobs are on trend, which needs the implementation of concepts. Industries require new skills, such as digital skills. There is a need to change new learning methodologies and educational techniques in all educational institutions. Digital Learning Platforms will provide focus on a broader overview, as there is a need for systematic and interdisciplinary thinking in individuals. The main aim of the research is to identify areas in which digital learning platforms or e-Learning platforms are selected by individuals to start self-learning through these platforms in terms of supporting Education 4.0 and Industry 4.0. As research methods, a questionnaire survey was conducted with statistical methods. The survey was done by random selection; more than 1000 individuals, including students and faculties of different educational institutions across India, were contacted for the study. Out of them, 350 responses were received. The research outcomes predict that the selection of digital learning tools or e-learning tools is based on their effectiveness, personalized experience, ease of use, user-friendliness, and user experience in support of Education 4.0 and Industry 4.0.
Transformer with Layer Fusion and Interaction

Neural machine translation models, such as Recurrent Neural Networks, Long Short-Term Memory networks and the Transformer, are widely used in many translation tasks. Neural machine translation models often use the deep encoder and decoder as multiple layers to capture more semantic information. However, these models leverage the encoder top layer and decoder only when they process a sequence, which omits other layers’ useful information. This paper, we propose an approach that includes two strategies. First, we propose a strategy called layer fusion to make full use of all layers. Then, we make the hierarchical interaction between the encoder and the decoder by using the output of each encoder layer as the part input of the corresponding decoder layer. We take the Transformer as an example in this work since it has achieved the best performance. The experimental results on the English and German translation data of WMT14 demonstrate the effectiveness of the proposed approach, and it is also applicable to other models.
Online Examination and Evaluation System

Currently, online test systems have adapted easily to today's technologically advanced world. Examinations are an intrinsic part of the educational process. Even though the test are conducted online the teacher has to do manual evaluation. The examinations can be classified into two main types of evaluation, objective answer and subjective answer. As of now, online evaluation is available for the objective questions, hence the manual assessment of the theory answer, is a tedious task for the teacher. The teacher checks the answer manually and gives the marks. In this paper, the literature survey of existing solution is analyzed.
Deep Multi-Levels Edge-Guided Network for Super-Resolution

Image super-resolution (SR) task based on deep learning has made good progress, but the information of different frequencies is still lost when generating high resolution images. This is because the low frequency information in the image is rich, but the high frequency information accounts for less. In order to improve the utilization of edge information, we propose a deep multi-levels edge-guided network (DMEGN) to focus on the edges of the image. We first pre-trained an additional network branch to learn the edge using the same network structure with SR branch, and proposed an Edge Guide Adaptive Module (EGAM) based on the idea of bilateral filtering. Then, in the training stage, EGAM was used to transfer the features of different levels of the edge branch to the main branch, making it more sensitive to the edge information. Experiments show that our method can improve both the performance of mainstream data set and human eye perception.
Small Object Detection Algorithm Based on Context Information and Attention Mechanism

As a special scene of object detection task, small object detection has been applied to some remote shooting scenes, especially in the field of unmanned aerial vehicle (UAV) object location and tracking. Because the small object (smaller than 32*32 pixels) only covers a small area, lacks diversity in location, and is usually accompanied by medium size (32*32 pixels to 96*96 pixels) objects, the universality of the small object detection task becomes very difficult. Aiming to deal with these difficulties, this paper proposes CGA-YOLO, a small object detection algorithm based on context information extraction and global attention mechanism. Based on YOLOv5, we use the window-based self-attention mechanism based on swin transformer to extract context information. Then, we also integrate the global attention mechanism to find the global information in the scene with dense objects. In the experimental results, CGA-YOLO improves by about 8% compared to YOLOv5 on the VisDrone2019 dataset, and the performance is improved significantly in the case of dense arrangement, which shows that the proposed strategy is effective for small object detection and recognition.
Vecma: An advance chess engine

With the rapid development of personal computers and the widespread use of the world wide web, the Internet has emerge as an information carrier and has gradually replaced the traditional media such as newspapers and television, becoming the main source of information. Thus effecting the popularity of traditional games like chess. As, various chess website and app appear to be on web, but there is a gap between players and Ai bots as various challenges occurs like efficiency, intelligence of the engine and engine level to mimic different player's depth. This paper propose a unique implementation on the previously based chess AI engines which increases the efficiency of computation of every move of the game tree. Vecma engine is unique Negamax implementation of game tree along with the Ideal vector value map. Proposed model focuses on parameters which are usually excluded from the other AI engines like positional king safety, optimal Queen positional advantage and the complex pawn advancement with the vector map integration for every piece. This engine also keeps a check on heuristic functional values of the future checkmate or stalemate probabilities. We have also develop a GUI which can be used as a base for developing other AI variations. With both Vector map and Negamax tree the chess engine is far more computationally efficient as compare to other chess engines.
Pixel-Level Feature Clustering Learning for Image Anomaly Detection and Localization

Image anomaly detection and localization not only need to provide image-level anomaly judgment but also need to locate pixel-level anomaly areas. This paper proposes a model named pixelAD, which builds an end-to-end network through pixel- level feature clustering learning to solve this problem. The normal prototype is obtained during training by clustering the normal pixel-level features. We generate pixel-level cluster labels of normal samples according to the prototypes, which guide the model to update parameters by calculating the assignment loss. For inference, pixelAD directly outputs the pixel-level anomaly score end-to-end. The experimental results of the real industrial dataset MVTecAD show that PixelAD has an excellent performance in anomaly detection and anomaly localization.
A Retriever-Reasoner Method for Multi-Document Reading Comprehension

Machine reading comprehension (MRC) is one of the main research of question answering system tasks. In real-world scenarios, due to the large scale of document data, and many questions that need to be answered urgently, traditional extractive MRC method faces low efficiency, low accuracy, and has poor reasoning ability. In order to efficiently and accurately extract knowledge from massive unstructured text data, we propose a multi-document MRC model based on a two-stage approach. Through document retrieval and answer reasoning, the model can hierarchically match the relevant passages from coarse-to-fine. Experimental results show that our method outperforms baseline and improves Rouge-L and BLEU-4 by 2.5% and 1.9%, respectively. In addition, the reasoning process is interpretable and can provide support for information understanding.
Privacy-Preserving and Models Intrusion Detection Federated Deep Learning Challenges, Schemas and Future Trajectories

Deep learning has made remarkable research advancements and wide-ranging applications in the domains of computer vision, multimodal, natural language processing, additionally, other areas. This has caused the academic community to pay increasingly close attention to the attack and defense technology in its training and testing phases, among which the federal deep learning has produced positive results. Federated deep learning models are prone to memorizing private and sensitive terminal participants' data, model parameters, when combined with the model's inherent vulnerability, they will result in privacy leakage, poisoning attack, model inference attack, adversarial attack. We briefly discuss the conception of federated deep learning as well as security challenges and open questions in this paper. In order to facilitate the understanding of these challenges and problems, we further propose a security system model. We also provide an overview and deduce the attack and mitigation approaches to the most sophisticated privacy-preserving and intrusion detection models. in the last two years. To tackle these challenges and enlighten further encryption techniques researches, finally, we discuss and describe current prospects and future trajectories of federated deep learning.
Image-Text Matching With Shared Semantic Concepts

Image-text matching is a fundamental vision-language task with massive applications. It aims to retrieve textual descriptions directly from query images and vice versa. However, textual semantic concepts, including objects, actions, and scenes, are apparent and structured. Directly correlating them with pixel-level visual representations hinders the ability of the retrieval model to reason about high-level relationships between two modalities. Therefore, it is challenging to align complex visual information with ambiguous descriptions. To tackle this issue, a shared semantic concepts fusion network is proposed to identify potential semantic relationships between image and text, and to narrow the cross-modal gap. Specifically, the semantic concepts are extracted in a shared space by a plug-and-play network that exploits inner knowledge and latent relationships. Afterwards, the visual and textual semantic concepts and enhanced representations are incorporated by a multi-feature fusion module. Experimental results are conducted on two benchmark datasets, and our model outperforms the state-of-the-art methods by 0.8% and 1.4% on R@1 for text and image retrieval of Flickr30K.
Non-Autoregressive Machine Translation with a Novel Masked Language Model

Non-autoregressive translation (NAT) has become a hot direction for its acceleration on decoding. Conditional masked language model (CMLM) performs excellently in NAT models. We review and extend the CMLM in some strategy: (1) N-gram mask strategy, which can help model to learn coarse sematic information of target language; (2) top-k decoding strategy, our model generates the top-k probability words in each step so that it can generate the final sentence in constant number steps. Extensive experiments demonstrate that our method is progressive compared with CMLM and some other NAT models. Specially, on the dataset WMT 14 EN-DE, our approach can achieve 27.24 BLEU score with only 0.1 BLEU sacrifice compared with the autoregressive counterpart base Transformer while speeding up 3 times on decoding.
Phishing Attack Detection on Text Messages Using Machine Learning Techniques

Phishing is the exceedingly prevalent type of social engineering attack which attempts to manipulate or exploit computer users. By performing phishing especially on text messages, attackers try to get information about someone or something. Since such phishing attacks on text messages are evolving continuously, it is essential to design an effective mechanism for the detection of the same. This paper presents a phishing attack detection system for text messages (PADSTM) which concentrates on detection of phishing attacks in text messages using Machine Learning (ML). It makes use of ML techniques which include Naive Bayes' Classifier, Support Vector Classification, Random Forest Classifier, and KNearest Neighbor Algorithm (KNN) to detect the phished messages. PADSTM focuses on the blacklist of URLs and various customized keywords in the text messages for efficient detection of phishing attack. Experimental results show that the performance of Random Forest Classifier is superior to the other ML techniques in respect to accuracy and F1-score in detecting the phished messages.
Improving boosting methods with a stable loss function handling outliers

In classification problems, the occurrence of abnormal observations is often encountered. How to obtain a stable model to deal with outliers has always been a subject of widespread concern. In this article, we draw on the ideas of the AdaBoosting algorithm and propose a asymptotically linear loss function, which makes the output function more stable for contaminated samples, and two boosting algorithms were designed based on two different way of updating, to handle outliers. In addition, a skill for overcoming the instability of Newton’s method when dealing with weak convexity is introduced. Several samples, where outliers were artificially added, show that the Discrete L-AdaBoost and Real L-AdaBoost Algorithms find the boundary of each category consistently under the condition where data is contaminated. Extensive real-world dataset experiments are used to test the robustness of the proposed algorithm to noise.
A Deep Reinforcement Learning Approach for Cooperative Target Defense

This paper considers a new variant of the pursuit-evasion problem, called the cooperative target defense problem with three agents (attacker, targeter, and defender) in a 3D space. The targeter tries to fly as quickly as possible from a starting point to the terminal, while the defender seeks to protect it from the attacker. The problem is difficult to solve under traditional game theory methods, while deep reinforcement learning (DRL) has shown strong adaptability in these complex and higher-dimensional tasks. Inspired by the successful applications of Proximal Policy Optimization (PPO), this paper proposes a PPO-based algorithm for the problem, intending to derive the optimal behavioral policies for both sides. We design the corresponding state space, action space, and rewards of the agents. Three kinds of reward functions are proposed for the attacker and compared by experimental results. Our study provides a good foundation for the cooperative target defense problem.
Campus Globalization Initiatives Aimed at Developing Engineers Capable of Working on the World Stage

NIT, Ube College has been selected for the Global Engineer Project and has been implementing global education, especially for the lower grades of the first through third year, since the 2019 academic year. However, NIT, Ube College has already been conducting mutual exchanges of faculty members and students through academic exchange agreements with overseas educational institutions for more than a decade. In 2018, before the coronavirus pandemic broke out, there were 105 students who received language training and specialized experiments and practical training at overseas partner schools during summer and spring long vacations, and 59 short-term international students from those schools. However, there are a significant number of students who are not active in activities conducted both on and off campus, and the challenge is how to involve such students. What we have tried to do is to globalize the campus, and I would like to introduce three initiatives that we have implemented during the past two years of pandemic. The first was to provide online language training and overseas training, and position it as an official course for which credit can be granted. In addition, online English conversation classes were made available to students who wished to take them. The second is the “Global Engineer Lecture Series,” in which alumni with overseas work experience give lectures online. In FY2021, five lectures were held. The third is the implementation of after-school English conversation classes utilizing foreign faculty members and long-term international student tutors, in which many students in lower grades participate, leading to an increase in the level of globalization on campus.
Global Engineering Education in KOSEN During the COVID-19 Pandemic: Turning Crisis Into Opportunities

Although various international exchange programs are available at Hachinohe KOSEN, many students are hesitant to participate in these programs because they lack confidence in speaking English and feel uncomfortable communicating with people of different nationalities and backgrounds. When the COVID-19 pandemic caused overseas travel restrictions, the international exchange program introduced some new events, to lessen language barrier of the students and increase their motivation to become global engineers. This paper presents examples of various international related activities and curriculum provided at Hachinohe KOSEN during the COVID-19 pandemic.
Choosing between remote and face-to-face features in a test setting: methodology used in two usability and user experience case studies

Remote test settings have become more common due to COVID-19. This paper presents two user tests focusing on the usability and user experience of an augmented reality-related solution. We describe the proceeding of the tests from the perspective of what has been conducted face-to-face and remotely. Thereafter, the appropriateness of the used test methodology is evaluated based on (i) the acquired results, (ii) the ease of using and understanding the methods and (iii) the test atmosphere. The physical presence of a person providing technical support to the test participant proved vital for augmented reality-related testing; the location of other test organisers appears more indifferent. Finally, we present the perspectives to contemplate when choosing between face-to-face and remote features in the test setting.
Development of software for calculation of optimal direction of solar panels during «time of use» in regions with complex topography

The object of current research is efficient usage of solar energy via optimal panel orientation. Using the mathematical methods of finding extremal value of function, the software was developed to calculate the daily optimal direction of PV panel. The knowledge of daily optimal direction of PV panels enables to determine the optimal panel direction for any time of use which allows maximizing the amount of harvested solar energy. The proper functioning of this software has been confirmed by field measurements. It was found that panel optimal direction can be determined by considering only the direct solar radiation, since taking into account diffuse solar radiation of sky practically doesn’t affect optimal PV panel direction. The essential factors affecting the optimal direction of PV panel are the topography of the considered site and the significant changes of optical thickness, for example, before and after noon. The calculations also show that the partial cloudiness doesn’t remarkably affect the optimal PV panel direction, but the regular presence of clouds in same part of the sky does affect it. This software is applied to the Ukrainian site Verkhovyna for which the optimal daily, seasonal and annual solar panel directions are calculated and presented. The formula to calculate the optimal direction for any time of use via daily optimal tilt angle and daily direct solar radiation falling onto the panel is given. The interface of the software has been designed in a user-friendly way and can be easily used by wide range of consumers. This software will especially be useful for quick calculation of optimal direction of solar panels in tourist sites located in mountainous regions.
Implementing associative memories by Echo State Network for the applications of natural language processing

This paper presents Echo State Network (ESN) based associative memories and their applications to English words. Among the papers describing ESN applications in general, some papers deal with natural language processing (NLP). Those NLP papers utilize an ESN's property of temporal signal learning to translate an English sentence into its corresponding predicate logic formula. In practical NLP applications, input sentences often include misspelled or undefined words. To cope with such problems, we constructed a training algorithm to realize an ESN-based associative memory. This algorithm can be used in two ways: auto-associative one when the input and output patterns are the same in each pair of the training data, and hetero-associative one otherwise. In this paper, we firstly show the basic performance of an ESN-based auto-associative memory by applying it to two-dimensional images. We made sure that the performance is improved by averaging the result of multiple runs. Secondly, we describe the performances of ESN-based auto- and hetero-associative memories when being applied to English words. The former is to recall correct English words from incomplete spelling ones, and the latter is to chain two different English words of an input and its corresponding output. The former performance in recalling 26 incomplete words can be improved by averaging the outputs of multiple runs, and the improved success rate of correct words is around 65%. The latter performance in chaining 26 words attains the success rate of 83.4%.
A neuroscientific emoji

Many scientific and medical emojis (ie, small digital images or icons used to express an idea or emotion) are now in use. For instance, the anatomical heart and lung emojis were released in 2020. 1 Burge J First look: new emojis coming to iOS in 2020. https://blog.emojipedia.org/first-look-new-emojis-coming-to-ios-in-2020/Date: July 16, 2020 Date accessed: September 16, 2021 Google Scholar Some health professionals have proposed the use of emojis in clinical or research settings to assess various preferences, such as choice of food. 2 Sick J Monteleone E Pierguidi L et al. The meaning of emoji to describe food experiences in pre-adolescents. Foods. 2020; 91307 Crossref PubMed Scopus (24) Google Scholar The use of emojis in science is only just being explored, but expansion of emojis to scientific and medical contexts could be useful in research settings by the biomedical community.
Retracted: A Novel Structure of Blockchain Applied in Vaccine Quality Control: Double-Chain Structured Blockchain System for Vaccine Anticounterfeiting and Traceability

[This retracts the article DOI: 10.1155/2021/6660102.].