Reviewerly/exHarmony huggingface dataset:
THE ROLE OF STUDENTS' RESEARCH WORK IN THE EDUCATIONAL PROCESS

The article examines the issues of students' research work, components and functions of research work. The types and forms of research work in academic and extracurricular time are distinguished.&#x0D; Of particular practical importance is the specialist's ability to adequately perceive complex life situations, correctly assess them, quickly adapt to new cognitive situations, purposefully process available information, search for and supplement the missing information, know the patterns of its optimal use, predict the results of activities, using one's intellectual and creative potential .&#x0D; At each level of education, it is necessary to develop students' creative thinking and research skills, without which it is difficult both to continue their education and to be realized in the labor market.&#x0D; In this connection, scientific research activity is gaining more and more importance and is turning into one of the main components of the professional training of the future specialist.&#x0D; Scientific and research activity improves not only the professional level, but also forms specific skills and habits, the appropriate composition of thinking and communication. In connection with the new requirements for the quality of the training of specialists, meeting the needs of the accelerated scientific, technical and socio-economic development of the country, the scientific and research activity of students is considered as an important factor in improving the entire system of training specialists for various branches of the national economy. It is scientific and research activity that allows deepening the professional direction of education, educating specialists with high creative potential and influences the formation of social and professional maturity of future specialists.&#x0D; Thus, in order to successfully manage the research work of students, the dynamics of its constituent elements, the teacher must clearly imagine its main tasks: which research object to choose and which research methods to apply. Therefore, the strategic task in this direction is to foresee the following conditions, means: forms and content of activities, which are the most favorable and optimal for the development and improvement of scientific research work.&#x0D; Accordingly, scientific and research activities should be organized so that students, after graduating from the educational institution, strive for constant improvement of their professional level. A person must constantly learn, correlating his knowledge with new requirements, his needs, the needs of society, the labor market. The personality itself is its professional toolkit.
Learning from Highly Imbalanced Big Data with Label Noise

This study explores the effects of class label noise on detecting fraud within three highly imbalanced healthcare fraud data sets containing millions of claims and minority class sizes as small as 0.1%. For each data set, 29 noise distributions are simulated by varying the level of class noise and the distribution of noise between the fraudulent and non-fraudulent classes. Four popular machine learning algorithms are evaluated on each noise distribution using six rounds of five-fold cross-validation. Performance is measured using the area under the precision-recall curve (AUPRC), true positive rate (TPR), and true negative rate (TNR) in order to understand the effect of the noise level, noise distribution, and their interactions. AUPRC results show that negative class noise, i.e. fraudulent samples incorrectly labeled as non-fraudulent, is the most detrimental to model performance. TPR and TNR results show that there are significant trade-offs in class-wise performance as noise transitions between the positive and the negative class. Finally, results reveal how overfitting negatively impacts the classification performance of some learners, and how simple regularization can be used to combat this overfitting and improve classification performance across all noise distributions.
Advanced Ray Tracing GPU

IMAGINATION DEBUTS POWERVR PHOTON RAY TRACING ARCHITECTURE
Researchers Find Solution to OverHeating Mobile Phones

Study uncovers way to encode computational information without using electrical current.
Record Robotic Sales

New figures show a surge in the sales of service robots, but those figures don’t show the impact that this technology is having on workers
PsiQuantum and GF to Build Full-Scale Quantum Computer

COMPANIES ARE MANUFACTURING THE COMPONENTS NECESSARY FOR A COMMERCIALLY VIABLE QUANTUM COMPUTER.
Implementation of Hybrid Median Filtering using Modified Data Comparator

Filters are used to remove the different types of noises including salt and pepper, gaussian, and random noises from image. Therefore, the VLSI oriented hardware implementation of filters plays the crucial role in real time applications. However, the conventional hardware-based filters are failed to reduce the look-up-table (LUT)s, path delays, and power consumption. Therefore, this work is focused on implementation of Hybrid Median Filter (HMF) using Data Comparator (DC) logic. Initially, the multiplexer selection logic-based data comparato r is used to identify the high and low values from two numbers. Then, data comparator is repeated for multiple number of times for nine pixels combinations, which identifies the median value from nine pixels. The subjective and objective evaluation shows that the proposed HMF-DC resulted in superior performance in terms reduced noise, hardware metrics like LUTs, delay, and power consumption as compared to state of art approaches.
Hardware Implementation of an Automatic Color Equalization Algorithm for Real-time Image Enhancement

Automatic color equalization (ACE) algorithm is an effective method for color image enhancement, but its computational complexity is extremely high. In this paper, we first modify the ACE algorithm to reduce the computational complexity and realization cost while maintaining good visual quality. Subsequently, an efficient VLSI architecture for the hardware-friendly ACE algorithm is proposed to meet the requirement of real-time image enhancement. FPGA (Field Programmable Gate Arrays) implementation result shows that the proposed architecture can operate at 120MHz and achieve a throughput of 60 frame/s for 256×256 resolution images using about 1.15k and 1.78k of FPGA's logic (LUT) and register resources, respectively. Compared with the existing design, the proposed architecture can achieve higher performance with fewer hardware resources and comparable visual quality.
Utility maximization for asynchronous streaming of bufferable information flows

We consider optimizing the streaming of bufferable information flows to multiple clients sharing a network. The information flow to each client can be broken down into time segments where each segment is associated with a possibly varying quality/distortion-rate trade-off which can be adapted to network resources available and allocated to the client. The segments are downloaded, buffered and consumed sequentially by each client, and this proceeds in an asynchronous manner across the clients. Such settings are relevant to streaming of video and audio, and potentially to streaming of augmented reality and virtual reality content. We focus on jointly optimizing the network’s resource allocation and clients’ quality adaptation across segments so as to fairly optimize clients’ Quality of Experience (QoE), while incorporating clients’ sensitivity to rebuffering events caused when a client’s buffer empties. We consider QoE models capturing trade-offs between clients’ mean quality and temporal variability in quality. We present a simple asymptotically optimal online algorithm to solve the problem. It distributes the tasks of resource allocation to the network and quality adaptation to the respective clients. Further, it is asynchronous and is lightweight in terms of implementation overheads.
The Unsmashable Guitar

Sandvik has created a smash-proof, 3D printed guitar and challenged rock legend Yngwie Malmsteen (and Tom Austin-Morgan) to smash it.
Study on centroid type-reduction of general type-2 fuzzy logic systems with sensible beginning weighted enhanced Karnik-Mendel algorithms

Abstract General type-2 fuzzy logic systems have received wide concerns in current academic subject. Type-reduction is the kernel module for the systems. This paper shows the interpretations for the beginning of Karnik-Mendel (KM) algorithms. According to the famous numerical integration technique, the weighting approaches of enhanced Karnik-Mendel (EKM) algorithms are put forward. Then the sensible beginning weighted enhanced Karnik-Mendel (SBWEKM) algorithms are put forward to perform the centroid type-reduction. Compared with the EKM algorithms, WEKM algorithms and SBEKM algorithms, this approach helps to improve both the absolute errors and convergence speeds as shown in four computer simulation experiments.
Video Segmentation and Compression

With the advent of overdriving technologies, the problems faced by computer vision are rectified with the working process like segmentation, compression, etc. The segmentation process is done for the video analysis. In this, based on the coherence between the regions involved in the video, the information is clustered and segregated for analyzing the required part from the video. The capability of video segmentation is to divide and abstract the required frames, recognize the activity, detect the object and enhance the video quality. Video compression is an out-bouncing technique to reduce the number of bits that zip the video file without loss to store and process in a compact space. It gets uplifted with maximum efficacy using artificial intelligence, machine learning and deep learning algorithms. The proposed model is designed for social distance prediction on the basis of the COVID-19 scenario. In the proposed model, a clipped video is taken and segmented into several numbers. With the segments, the region of interest is extracted, and it is applied to the convolutional neural network for further classification to indicate whether social distancing is maintained in that recorded area.
Hot Public Appeal Extraction and Visual Analysis Combined BERT and Spatio-Temporal Location

Analyzing the temporal and spatial distributions of public appeal can contribute to understanding issues of public concern. However, how to find hidden and valuable for early warning information and intelligence clues in large-scale text data remains a challenging task, especially with the explosion of public appeals. This paper proposes an intelligent extraction model of hot public appeal based on BERT and Spatio-Temporal location. For the unstructured appeals and its address, the fusion model is trained after extracting the keywords by word segmentation. Taking Baibuting Community as an example, experimental results show that the text representation model and text clustering method proposed in this paper have better topic detection effect than traditional methods. Combining spatial and temporal information with the classification results, We analyze the spatial and temporal distribution characteristics of hot topics in different regions. It will assist the grass-roots level in the rapid discovery, rapid response and coordinated disposal of the main problems of public appeal, and improve the refinement and intelligence level of grass-roots social governance.
The graph embedded topic model

Most of existing graph neural networks (GNNs) developed for the prevalent text-rich networks typically treat texts as node attributes. This kind of approach unavoidably results in the loss of important semantic structures and restricts the representational power of GNNs. In this work, we introduce a document similarity-based graph convolutional network (DS-GCN) encoder to combine graph topologies and document semantics for text-rich network representation. Then, a graph decoder, based on the latent position model, is used to reconstruct the graph while preserving the network topology. The document matrix is rebuilt by a document decoder, based on the embedded topic model, which considers both topic and word embeddings. By including a cluster membership variable for each node in the network, we thus develop an end-to-end clustering technique relying on a new deep probabilistic model called the graph embedded topic model (GETM). Numerical experiments on three simulated scenarios emphasize the ability of GETM in fusing the graph topology structure and the document embeddings, and highlight its node clustering performance. Moreover, an application on the Cora-enrich citation network is conducted to demonstrate the effectiveness and interest of GETM in practice.
Watermarking in Frequency Domain Using Magic Transform

In this age of information, multimedia security becomes extremely important. Digital watermarking plays a vital role in multimedia security (text, video, audio). Researchers have been working on different watermarking techniques over the decades. In this chapter, before embedding the watermark into the host image, Magic transform is used for encryption of the watermark. Wavelet transform is used for obtaining the different frequency regions, and the Daubechies filter is used to obtain different frequencies. The algorithm is tested under different attacks: median filter, salt and pepper and compression. While extracting the watermark, inverse Magic transform is used, this provides more security to the technique.
Text Information Extraction from Digital Image Documents Using Optical Character Recognition

In a digital world, different images are generated from various sources and used in multiple applications. Text present in these images contains meaningful evidence for explanation and structuring of images and the semantic understanding of the images. But text extraction is challenging due to variations of text patterns (image types, mode of capture, text position, etc.). Thus, text information extraction from digital documents is an emerging area of research. Various systems have been reviewed for the detection and extraction of text from images. This chapter is concerned with developing optical character recognition based text information extraction from digital image documents. The optical character recognition system involves several algorithms utilized for text detection and identification, text localization, text segmentation, and categorization of extracted features. Tesseract is currently the most accurate optical character recognition engine applied to design the proposed system and implemented using the C/C++ programming paradigm in the Linux platform. The experimental results demonstrated a significant performance for standard document images and scanned pages from books/magazines.
Blockchain architecture and methodologies for internet of things environment

SECURITY AND PRIVACYVolume 6, Issue 2 e299 EDITORIAL Blockchain architecture and methodologies for internet of things environment Alireza Souri, Corresponding Author Alireza Souri [email protected] Department of Software Engineering, Haliç University, İstanbul, Turkey Correspondence Alireza Souri, Department of Software Engineering, Haliç University, İstanbul, Turkey. Email: [email protected]Search for more papers by this authorEyhab Al-Masri, Eyhab Al-Masri School of Engineering and Technology, University of Washington Tacoma, Tacoma, Washington, USASearch for more papers by this authorSaru Kumari, Saru Kumari Department of Mathematics, Chaudhary Charan Singh University, Meerut, IndiaSearch for more papers by this author Alireza Souri, Corresponding Author Alireza Souri [email protected] Department of Software Engineering, Haliç University, İstanbul, Turkey Correspondence Alireza Souri, Department of Software Engineering, Haliç University, İstanbul, Turkey. Email: [email protected]Search for more papers by this authorEyhab Al-Masri, Eyhab Al-Masri School of Engineering and Technology, University of Washington Tacoma, Tacoma, Washington, USASearch for more papers by this authorSaru Kumari, Saru Kumari Department of Mathematics, Chaudhary Charan Singh University, Meerut, IndiaSearch for more papers by this author First published: 17 January 2023 https://doi.org/10.1002/spy2.299Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL No abstract is available for this article. Volume6, Issue2Special Issue: Blockchain Architecture and Methodologies for Internet of Things EnvironmentMarch/April 2023e299 RelatedInformation
Feature-Based Opinion Classification Using the KPCA Technique: Concept and Performance Evaluation

Over the last several years, a widespread trend on the internet has been the proliferation of online evaluations written by people with whom they share their ideas, interests, experiences, and opinions. Opinion mining, also known as sentiment analysis, is the process of classifying pieces of text written in a natural language on a subject into positive, negative, or neutral categories according to the human emotions, views, and feelings that are communicated in that text. The field of sentiment analysis has progressed to the point that it can now analyse internet evaluations and provide significant information to people as well as corporations, which may assist these parties in the decision-making process. In the proposed model, feature extraction extracts the collection of features that are both semantically and statistically significant using the kernel principal component analysis (KPCA) method. According to the findings of the simulations, the suggested model performs better than other existing models.
Deep Learning for User Behaviour Prediction Using Streaming Analytics

Streams of web user interactions reflect behaviour of customers or users of a web application through which a company is being operated online. The interactions may be in the form of visits to web components and even purchases made by users in case of e-Commerce applications. Modelling user behaviour can help the organizations to ascertain patterns of user behaviours and improve their products and services to meet their needs besides making promotional schemes. There are many existing methods for modelling user behaviour. However, of late, deep learning models are found to be more accurate and useful. In this paper a deep learning based framework is proposed for predicting web user behaviour from streams of user interactions. The framework is based on the mechanisms that exploit Recurrent Neural Network (RNN), one of the deep learning approaches, to learn from low-level features of sequential and streaming data. The mechanisms are used to model user interactions and predict the user behaviour with respect to purchasing items in future. In presence of plenty of items, item embeddings is explored for better results. In addition to this, attention mechanisms are employed to achieve RNN model interoperability. The empirical study revealed that the proposed framework is useful besides helping to evaluate different variants of attention mechanisms and item embeddings.
A Survey on Resource Allocation Techniques in Cloud Computing

Cloud is an important and emerging technology utilized by various fields for storing, processing and retrieving of data anywhere and anytime without any interruption. Cloud is now acting as a platform for many companies for storing and other computational purposes to reduce infrastructure and maintenance cost similarly they can utilize their application widely based on pay per use. To make available of data to all cloud users Resource Allocation (RA) is mandatory process. In cloud hardware, software and platform are the resources utilized to satisfy user needs hence sharing these resources according to users need is a difficult task. Cloud service provider and cloud service consumer plays the major role in RA. The parameters under resource allocation, its issues and challenges are needed to be analyzed deeply before implementing any optimizing approach in RA. Hence in this work various resource allocation methods have been studied and issues in it is analyzed and presented as a survey. This work is useful for both cloud users and researchers in overcoming the challenges faced in RA.
Model of Pollution Information Based on Collaborative Filtering Algorithm

Due to intrinsic qualities of pollution, it is frequently difficult for those seeking information on pollution to locate what they are looking for among abundance of knowledge readily available. It might be difficult for information providers on pollution to produce original content for vast audience of people that are interested. To resolve this paradox, The most important tool is suggestion system. The UFTB algorithm analyzes sorts of pollution information and evaluation data that users have viewed by developing a model for understanding user preferences. The recommended model for analyzing pollution data using co-filter technique is created using modified cosine similarity, and it is believed that adopting default value will enhance procedure. And to avoid over-optimization, we should use information about excluding user-unfavorable types of pollution, optimize default value prediction matrix, incorporate similarity data into suggested formula to obtain value and use sort, identify N users who are most similar to target user, and suggest target user’s pollution information in accordance with their preferences.
Correction

This article refers to:Machine learning predictions of concentration-specific aggregate hazard scores of inorganic nanomaterials in embryonic zebrafish
Application of Virtual Sample Generation and Screening in Process Parameter Optimization of Botanical Medicinal Materials

Background: The small sample problem widely exists in the fields of the chemical in-dustry, chemistry, biology, medicine, and food industry. It has been a problem in process modeling and system optimization. The aim of this study is to focus on the problems of small sample size in modeling, the process parameters in the ultrasonic extraction of botanical medicinal materials can be obtained by optimizing the extraction rate model. However, difficulty in data acquisition results in problem of small sample size in modeling, which eventually reduces the accuracy of modeling prediction. Methods: A virtual sample generation method based on full factorial design (FFD) is proposed to solve the problem ofa small sample size. The experiments are first conducted according to the Box-Behnken design (BBD) to obtain small-size samples, and the response surface function is establis-hed accordingly. Then, virtual sample inputs are obtained by the FFD, and the corresponding virtual sample outputs are calculated by the response surface function. Furthermore, a screening method of virtual samples is proposed based on an extreme learning machine (ELM). The connection weights of ELM are used for further optimization and screening of the generated virtual samples. Result: The results show that virtual sample data can effectively expand the sample size. The preci-sion of the model trained on semi-synthetic samples (small-size experimental simples and virtual samples) is higher than the model trained merely on small-size experimental samples. Conclusion: The virtual sample generation and screening methods proposed in this paper can effec-tively solve the modeling problem of small samples. The reliable process parameters can be ob-tained by optimizing the model trained by the semi-synthetic samples.