Reviewerly/exHarmony huggingface dataset:
Training of a technical specialist for teaching activities

The article presents an analysis of modern higher education system, examines the features of the functioning of higher educational institutions. It substantiates the modern requirements for a university professor. The professional competencies are considered, which are: deep knowledge of the subject of study, pedagogical skills, a broad scientific outlook, readiness for innovation and scientific creativity, knowledge of scientific research methods, innovative mobility. The article presents a comparative characteristic of the elements of professional competence and components of the creative potential of a university professor. It substantiates the integration of scientific, technical and pedagogical knowledge, methods of action and abilities that reveal the creative potential of a professor of a technical university. The article analyzes the characteristics of the technical and pedagogical activities of a specialist in a technical university. The need for the formation of pedagogical competences is substantiated, including the components - to know, be able and possess; the ability to integrate technical knowledge, skills and knowledge in teaching activities; the ability to provide the scientific community with the results of technical and pedagogical research. The authors in the article consider the requirements necessary for a specialist with a technical education for the implementation of pedagogical activities in a technical university and propose a number of activities that contribute to the preparation of technical specialists for teaching.
Consumer research based on the consumption of IP virtual derivatives

In the context of industrial development, IP can be understood as "copyright", i.e., ownership of cultural products with a wide audience base and commercial exploitation value. IP virtual derivatives are a special form of IP derivatives in the midstream of the IP licensing and development industry chain. Users and consumers in the downstream of IP virtual derivatives industry chain are one of the most important parts of the industry chain, and industry development needs to understand users' preferences and needs in order to develop more special and irreplaceable products in the competitive market, harvest users' attention and traffic, and finally complete product realization.
Motion Planning &amp;amp; Obstacle Avoidance of Mobile Robot in a Stochastic Environment using Reinforcement Learning

-Path planning in unstructured environments by avoiding obstacles and navigating in the shortest path is a basic functionality of autonomous mobile robots. Even though many efficient algorithms can provide optimal paths when navigating from starting to the goal position, there are still numerous technical challenges when it comes to finding optimal collision avoidance paths in non-deterministic conditions. In recent years, Reinforcement Learning (RL) algorithms have significantly contributed to optimizing paths with less computational time for mobile robots. This paper discusses the implementation of RL algorithms to solve the problem of navigation of mobile robots in stochastic environments. Three RL algorithms, Q-Learning, SARSA, and Dyna-Q were considered for path planning. The results of the simulation were used to compare the relative performance of these algorithms to learn the optimal path for different cases of stochasticity.
Performance Evaluation of Non-Linear State Estimation Filter in Presence of Non Gaussian Underwater Environment

This research paper mainly focusing on assessing the functioning of filter in Non-Gaussian noise (Non-GN) in underwater target tracking environment. Here the measurements coming from the target like bearings and elevation measurements are consider to be corrupted with Gaussian noise(GN) and non-Gaussian noises(Non-GN). Gaussian mixture (GM) and short noise (SN) are consider in Non-GN and assuming that in phase sampled measurements are generated spontaneously with some specific time, and increases the burden of filter. Calculating the efficiency of Modified gain extended kalman filter (MGEKF) and cubature kalman filter (CKF), by elevating plenty of scenarios. Comparing those results with Cramer-Rao Lower bound (CRLB).
String Abstract Domains and Their Combination

We survey recent developments in string static analysis, with an emphasis on how string abstract domains can be combined. The paper has formed the basis for an invited presentation given to LOPSTR 2021 and PPDP 2021.
Filtering the cavity field as well as the spin of electron from noise

The aim of this work is to estimate the electro-magnetic field at a given spatial point within the cavity as well as the spin of the electron bound to its nucleus placed within the cavity. We calculate it using the Hudson-Parthasarathy Schr&#x00F6;dinger quantum stochastic differential equation (HPS-QSDE), Plot Noise-to-Signal ratio (NSR) for cavity field observable and spin of electron which validate the effectiveness of the Belavkin quantum filter using MATLAB.
Contragan-Acl:Contrastive Learning for Image-to-Image Translation with Adversarial Comsistency Loss

Generative adversarial networks (GANs) are a hot research topic recently. So unsupervised image-to-image translation become a central task in computer vision. In this paper, we propose contrastive learning training strategy that obtain more significant features of true and false samples to improve the ability of distinguishing true and false samples. Then we design adversarial consistency loss that the generator and discriminator are added to the reconstruction process to further deal with the details and texture of the generated image to stabilize training strategy. Experiments on CelebA-HQ and a AFHQ,the FID score about reduce by more than 10% and LPIPS score increase by 10%.
Applications of Blockchain Technology in Smart Microgrids

The present electrical power system is centralized and provides one-way communication from producer to consumer. However, a centralized perspective in this regard is not competent in directing distinctive problems like reliability, seclusion, system transactions, etc., due to the interaction's difficulty, restrictions, and dynamism. The above concerned problems can be overcome by introducing blockchain technology in the power systems. To notice the power system's true capability, a disseminated per view that takes into consideration of these attributes is needed. With the increasing local energy generation from renewable energy sources, the decentralized peer-peer local energy market is becoming popular. Of late, blockchain-based answers have been put forward to directing power system problems by enhancing its security.
Single Image Dehazing with Color Correction Transform Dark Channel Prior

Image acquisition in poor weather conditions, such as haze, can have a negative impact on image quality and data processing. Object detection, which is critical in computer vision applications, is harmed by the subsequent image deterioration. The precise identification and positioning of objects require the restoration of hazy images. Color Correction Transform Dark Channel Prior (CCTDCP) is a new prior based dehazing technique proposed and implemented in this paper. The CCTDCP strategy is intended to consider the brightening impact of haze and blue shading cast of hazy pictures. For preserving the image's originality, the proposed technique employs white balance color correction transform, dark channel prior, and gamma correction. The experimental results on benchmark datasets exhibit superior performance of CCTDCP algorithm over the state-of-the-art methods in terms of PSNR and SSIM.
Aspect level sentiment classification with multi-scale information

Mining the sentiment polarity of different aspects of products becomes more and more important for companies, as the rapid development of e-commerce. Therefore, aspect-level sentiment analysis technology has attracted widespread attention from researchers. Most of the existing aspect-level sentiment analysis methods are based on feature engineering or neural networks to capture the key points in the sentence, i.e., semantic information. However, most of existing methods mainly pay attention to the impact of a single word in the sentence and simply use individual sentiment words as the basis for the judgement of sentiment polarity, while ignore the importance of the local joint information (i.e., multi-scale information) for a sentence. In order to deal with this problem, we present a novel Aspect-based Multi-scale information Graph Convolutional Network (AMGCN) for aspect-level sentiment analysis, which captures the multi-scale information from the sentence via a series of convolutional neural networks and then incorporate aspect information by a graph convolutional neural network and an attention mechanism. Experimental results over five benchmark datasets demonstrate that the proposed AMGCN can achieve some superior performance in aspect-level sentiment classification when compared to existing methods.
Improved Reliable Deep Face Recognition Method Using Separated Components

Face recognition is used as one of the most successful biometric methods due to the availability of advanced resources such as faster processors and higher memory and providing intelligent methods based on the power of these resources. Nevertheless, there are still many challenges in this area. The face plays an important role in the transmission of emotions and carries the characteristics hidden in it, the identity of individuals. Face recognition has been added to some control devices, security, welfare, criminal identification, and many other areas, which is the main motivation for research in this field. In this paper, the DCSFR method is presented to pay attention to the main features of the face such as eyes, lips, mouth, and nose, which is the main novelty of this work, to get higher accuracy or speed than the previously existing methods. In this approach, instead of using general information in face recognition, facial components such as eyes, nose, mouth are separated into another image, and face classification operations (deep learning by convolution neural network) are performed on separated components. The results show that the computational cost with the proposed method is reduced by about 70%. Also, it can be achieved that CNN does not perform as well as the complete picture of the disassembled components.
Interoperability vs. Tradition: Benefits and Challenges of Web of Things in Building Automation

Building automation (BA) enables energy-optimized operation of buildings and eases the monitoring and maintenance of their utilities. The heterogeneous and dynamic environment of a building creates a challenge for BA to support flexible deployment where programs should be able to semantically reason about the physical process and controls. Towards addressing this challenge, we looked at W3C’s Web of Things (WoT) recommendations which address the problem of interoperability in the IoT domain by using machine-understandable descriptions of protocol bindings and interactions, along with architectural patterns that are scalable to different deployment scenarios. We introduced WoT concepts in our BA products in incremental implementation steps, which were deployed in real-life building installations. Our findings based on the evaluation of a successful product deployment show that the WoT architecture along with its core concept of Thing Descriptions may serve as a holistic mechanism for capturing knowledge and unifying interactions in BA. As a contribution to both BA industry and research, we demonstrate how the W3C WoT can unify control function descriptions and process data in a non-intrusive and brownfield-compatible way, which is highly relevant for BA and similar industries. In our work we substantiate how the unifying effect of WoT fits naturally to traditional BA system to establish interoperability, and further, opens up the possibility to make it truly autonomous.
Teaching Linked Open Data using Bibliographic Metadata

This paper describes LD4DH, the <em>Linked Data for Digital Humanities: Publishing, Querying, and Linking on the Semantic Web</em> workshop at the Digital Humanities Oxford Summer School. It includes a description of the general structure of the workshop, how it has changed over the course of the last seven years, between 2015 and 2021, and evaluates the differences between in-person delivery in 2018–2019 and the online mode in 2020–2021. Discussion is centred on the description of the data as well as the illustration of the processes, methods, and software used throughout the workshop. The paper concludes with a summary of participant evaluation, and reflects on the opportunities and challenges of teaching Linked Open Data to a mixed cohort of predominantly Humanities researchers and professionals from the cultural heritage sector.
Recommender Systems: When Memory Matters

In this paper, we study the effect of non-stationarities and memory in the learnability of a sequential recommender system that exploits user's implicit feedback. We propose an algorithm, where model parameters are updated user per user by minimizing a ranking loss over blocks of items constituted by a sequence of unclicked items followed by a clicked one. We illustrate through empirical evaluations on four large-scale benchmarks that removing non-stationarities, through an empirical estimation of the memory properties, in user's behaviour interactions allows to gain in performance with respect to MAP and NDCG.
A Federated Learning System for Target Recognition over 5G MEC Networks

In the scenario of electricity substation and other construction sites, it is necessary to monitor workers’ wearing of helmets through image recognition technologies for safety production. However, fast data training and target recognition usually require high communication and computation capacities. To relieve the burden of networks and data centers caused by large amount of data uploading and processing, this paper combines the 5G MEC network with the federated learning framework. An adaptive compression mode selection mechanism is proposed to efficiently transmit training models over time-variant bandwidth, and a federated learning system is designed for real-time target recognition. The accuracy of our proposed model is 6.73% higher than that of a traditional non-cooperative method. In terms of communication delay, the training delay is reduced by 24.5% compared with centralized processing method.
Software Effort Estimation for Successful Software Application Development

The recent advancements in information and communication technology (ICT) have inspired all the operational domains of both public and private sector enterprise to endorse this technology. Software development plays a crucial role in supporting ICT. Software effort estimation serves as a critical factor in software application development, and it helps application development teams to complete the development process on time and within budget. Many developmental approaches have been used for software effort estimation, but most of them were conventional software methods and therefore failed to produce accurate results when it came to web or mobile effort estimation. This chapter explains different types of software applications, software estimation models, the importance of software effort estimation, and challenges faced in software effort estimation.
Emotional Quotient and Licensure Examination Performance of Liceo De Cagayan University Teacher Education Graduates Of 2014 to 2016

Licensure Examination for Teachers which is being administered by the Professional Regulation Commission is a type of exam intended for the professionalization of teachers in the Philippines. The passing score for both BSED and BEED examinees is 75%. LET passing percentage of the examinees is a major concern for teacher education institutions because CHED requires TEIs to have a LET passing percentage higher than the national passing percentage. The study aimed to determine the LET performance of BSE and BEED graduates of Liceo de Cagayan University. It also looked into the relationship of the EQ Conditions and the LET results in general education, professional education and the LET ratings. A survey questionnaire was employed to determine the EQ conditions of the LET examinees whereas the LET results were obtained from an official PRC LET Results documents. The results revealed that the BEED examinees have better results in professional but struggled in general education whereas the BSED examinees have performed well in general education and struggled in the major subjects. The study also found out that EQ conditions played an important role in the LET performance of the examinees. With these, the researchers recommended that the teacher education institutions should provide more review sessions on general education for the BEED examinees, more review sessions for major subjects for BSED and activities that will further enhance the EQ conditions of the LET examinees.
Preparation Status Analysis on Professional Basic Courses Examination for Academy Graduates Upgrading Undergraduates of Electronic Information Category from Perspective of Course Fundamentals of Electric Circuits

Recently, the stipulation has been made that the professional basic courses will be added to the selection examination of Academy Graduates Upgrading Undergraduates in Jiangsu Province. For the category of electronic information major, these courses include fundamentals of electric circuits, analog electronic technology, and digital electronic technology. First, the paper introduces the test objectives to the course of fundamentals of electric circuits prescribed by the selection examination document, and fostering goals of the three majors that offered the above mentioned courses, namely electronic information technology, applied electronic technology, and electromechanical integration technology. Then the development of optional course has been explicitly presented, which is set to help students prepare the selection exam on fundamentals of electric circuits. The innovation of teaching method is made on the difficult point in teaching, interconversion between the expressionsof alternating current. By issuing the questionnaires to the students of 2019 and 2020 graders in May 2021, the data were collected in June 2021 and analyzed subsequently, the paper has uncovered the relationships of assessment results of optional course with student enrollment ways, professional teaching hours and textbook, attendance rates of optional course, providing the reference information for the system construction and teaching reform of Academy Graduates Upgrading Undergraduates in the future.
Ranking Social Media News Feeds: A Comparative Study of Personalized and Non-personalized Prediction Models

Ranking news feed updates by relevance has been proposed to help social media users catch up with the content they may find interesting. For this matter, a single non-personalized model has been used to predict the relevance for all users. However, as user interests and preferences are different, we believe that using a personalized model for each user is crucial to refine the ranking. In this work, to predict the relevance of news feed updates and improve user experience, we use the random forest algorithm to train and introduce a personalized prediction model for each user. Then, we compare personalized and non-personalized models according to six criteria: (1) the overall prediction performance; (2) the amount of data in the training set; (3) the cold-start problem; (4) the incorporation of user preferences over time; (5) the model fine-tuning; and (6) the personalization of feature importance for users. Experimental results on Twitter show that a single non-personalized model for all users is easy to manage and fine-tune, is less likely to overfit, and it addresses the problem of cold-start and inactive users. On the other hand, the personalized models we introduce allow personalized feature importance, take into consideration the preferences of each user, and allow to track changes in user preferences over time. Furthermore, personalized models give a higher prediction accuracy than non-personalized models.
Bi-GRU-Attention Enhanced Unsupervised Network for Skeleton-Based Action Recognition

Action recognition is widely used in human-computer interaction, intelligent monitoring and other applications. Recently, more and more researcher pay attention to it. While, skeleton-based action recognition is more and more popular because of its low cost and robustness. Current good performance methods based on skeleton is supervised mostly, which needs large labeled datasets for train. But for some special task, such as the pedestrian action recognition of unmanned driving in real traffic scene, it is difficult to gain the labeled action data because the large cost of time, money and energy. Therefore, we proposed a Bi-GRU-Attention Enhanced Unsupervised Network (BGAEUN) for action recognition based on skeleton sequence. BGAEUN adopts an encoder-decoder network, and adds an attention mechanism in the encode, learns the weights of the hidden state, and obtains the weights of different skeleton nodes, so that it can better characterize actions and provide better skeleton features. For the decoder, using fixed weight and fixed state strategies to weaken the decoder, calculate the loss of the output and input of the decoder, minimize the loss, make it similar to the input, and minimize the reconstruction loss in the training process. BGAEUN exploits the last layer feature of encoder to classify by k-nearest neighbors algorithm. Experiments on large-scale action dataset NTU-RGB + D show BGAEUN achieves a higher recognition accuracy than current most unsupervised skeleton action recognition methods.
Human–Computer Interactions Through Multi-agent Systems: Design and Implementations

In computing, multi-agent systems are a relatively new subfield made up of several interacting computer elements known as agents. This interface is part of a global system representing software architecture for robot control to help the human operator. The implementation of human–computer interaction (HCI) using a multi-agent system is evolving day by day. We examined different past research works and attempted to cover various works step by step and furthermore as per different various advancements and applications. Various technologies, which are covered here, are some recent applications in this domain like tele-health, air traffic control, mixed reality, learning from demonstrationLearning from Demonstration (LfD) and psychological aspects of agents with HCI. Later, we introduced the musical scaleMusical Scale recommendation and explained its importance in the musical world. Music is evergreen and popular worldwide. And in music, musical scaleMusical Scale determination is one of the most complex things. We implement it in respect of this proposed topic. Here, we discussed how HCI using multi-agent systems can help the singers get the scale recommendation.