Reviewerly/exHarmony huggingface dataset:
Performance of Two Approaches of Embedded Recommender Systems

Nowadays, highly portable and low-energy computing environments require programming applications able to satisfy computing time and energy constraints. Furthermore, collaborative filtering based recommender systems are intelligent systems that use large databases and perform extensive matrix arithmetic calculations. In this research, we present an optimized algorithm and a parallel hardware implementation as good approach for running embedded collaborative filtering applications. To this end, we have considered high-level synthesis programming for reconfigurable hardware technology. The design was tested under environments where usual parameters and real-world datasets were applied, and compared to usual microprocessors running similar implementations. The performance results obtained by the different implementations were analyzed in computing time and energy consumption terms. The main conclusion is that the optimized algorithm is competitive in embedded applications when considering large datasets and parallel implementations based on reconfigurable hardware.
Surabaya Government Performance Evaluation Using Tweet Analysis

The purpose of this research is to determine the various positive attributes appreciated by the public, and the negative things that need to be improved by the Surabaya government. The sentiment analysis methods, including the Naïve Bayes Classifier, Support Vector Machine, and Logistic Regression, are employed to classify the pros and cons of the Surabaya government. The comparison of the three methods demonstrated that SVM gives the best classification accuracy compared to others. Police performance is the highlighted word in the positive category, while traffic congestion is in the negative.
Blind Image Deconvolution Algorithm Based on Sparse Optimization with an Adaptive Blur Kernel Estimation

Image blurs are a major source of degradation in an imaging system. There are various blur types, such as motion blur and defocus blur, which reduce image quality significantly. Therefore, it is essential to develop methods for recovering approximated latent images from blurry ones to increase the performance of the imaging system. In this paper, an image blur removal technique based on sparse optimization is proposed. Most existing methods use different image priors to estimate the blur kernel but are unable to fully exploit local image information. The proposed method adopts an image prior based on nonzero measurement in the image gradient domain and introduces an analytical solution, which converges quickly without additional searching iterations during the optimization. First, a blur kernel is accurately estimated from a single input image with an alternating scheme and a half-quadratic optimization algorithm. Subsequently, the latent sharp image is revealed by a non-blind deconvolution algorithm with the hyper-Laplacian distribution-based priors. Additionally, we analyze and discuss its solutions for different prior parameters. According to the tests we conducted, our method outperforms similar methods and could be suitable for dealing with image blurs in real-life applications.
Foreign accent classification using deep neural nets

Speech analysis for extracting attributes such as the speaker, gender, accent and like has been a field of great interest and has been widely studied. The paper presents a novel architecture for accent identification by using a cascade of two deep-learning architecture. We design and test our propo sed architecture on common voice dataset. The architecture consists of a cascade of Convolutional Neural Network (CNN) and Convolutional Recurrent Neural Network (CRNN). It is trained on Mel-spectrogram of the audios. We consider five of the most popular English accents groups namely India, Australia, US, England, Canada in this study. The proposed model has an accuracy of 78.48% using CNN and 83.21% using CRNN.
Online mobile teaching methods based on Android in the 5G environment

Aiming at the problem of long delay response of online mobile teaching platform, a design method of online mobile teaching platform based on Android 5G environment is proposed. Firstly, the hardware of the online learning platform is designed, and the Android mobile terminal is taken as the hardware core. In the software part of the system, AIMD algorithm is used to reduce the transmission volume. By controlling the speed of multimedia data flow on the platform, the network congestion of the platform is alleviated. DB-KAD algorithm is used to select the optimised network delay parameters to meet the requirements of multimedia data transmission. The performance test results show that the maximum response delay of this method is 0.07 s, the maximum buffer delay of video is 1.4 s, the number of video pauses varies in the range of 0-1.0, and the response delay requirement of online mobile teaching platform.
Biometric-Based Unimodal and Multimodal Person Identification with CNN Using Optimal Filter Set

The convolution neural network (CNN) has brought about a drastic change in the field of image processing and pattern recognition. The filters of CNN model correspond to the activation maps that extract features from the input images. Thus, the number of filters and filter size are of significant importance to learning and recognition accuracy of CNN model-based systems such as the biometric-based person authentication system. The present paper proposes to analyze the impact of varying the number of filters of CNN models on the accuracy of the biometric-based single classifiers using human face, fingerprint and iris for person identification and also biometric-based super classification using both bagging and programming-based boosting methods. The present paper gives an insight into the optimal set of filters in CNN model that gives the maximum overall accuracy of the classifier system.
Research on the innovation of modern network distance education models based on the web

In view of the low degree of visualisation of the current network distance education model, this paper proposes an innovative research on the modern network distance education model based on web. Firstly, the physical structure of the network distance education model is elaborated and the architecture of the network distance education model is constructed. Taking the examination of network question bank under the mode of distance education as an example, this paper designs the implementation process of the examination of network question bank, in order to realise the innovative research on the mode of network distance education. The experimental data show that the CPU time of this method is about 0.1 hours shorter than that of the traditional method and the flexibility coefficient is close to 1 when the number of users is increased by 200 at a time. This shows that the method is more flexible and intelligent.
Overall Understanding of Indoor Scenes by Fusing Multiframe Local RGB-D Data Based on Conditional Random Fields

Indoor mobile robots normally cannot capture the whole information of a scene by a single frame of perceptive data due to the limited sensor scope. The category of the current scene may be misjudged by robotics due to incomplete scene information, which leads to operation error. To address this problem, we propose an approach that leverages conditional random fields (CRFs) to fuse multiframe RGB and depth (RGB-D) visual data corresponding to the same scene. This method takes full advantage of prior knowledge that object categories significantly relate to the scene attributes. As a new image arrives, we incrementally integrate the current object detection results to update scene understanding by identifying duplicate objects between images, ranking available objects in terms of their relevance to the scene, and fusing new information with the existing CRF. With this approach, scene classification can be solved with higher precision based on multiview images than on single image frames sampled in the same places. Additionally, a configuration map of a scene is incrementally built into the above framework. The map includes identities of the recognized objects and various relations between them. This kind of map would not only benefit common robotic tasks but also offer a novel channel for human-robot interaction. We test the efficiency of our method on image sequences extracted from the NYU v2 dataset. The results show that our approach achieves the best performance against state-of-the-art baselines.
Background subtraction with shadow removal using hue and texture model for moving object detection

This paper proposes a background subtraction method with shadow removal for moving object detection. The proposed method is based on existing ViBe algorithm and shadow is removed for the foreground detected by ViBe. Shadow removal uses luminance, color hue, and texture model.
Escalating Linguistic Violence

Microaggressions are stereotype-based slights against members of marginalized groups. People who are the frequent targets of linguistic microaggressions recognize the messages and the harm caused by a constant stream of subtle mistreatment: small slights can accumulate into serious harms, especially when the same hidden messages are repeated over a long period of time and/or from a variety of sources. In certain circumstances, the harms of hate speech may also not be immediately apparent and may require repetition before they accumulate into noticeable violence. However, in Mari Matsuda’s discussion, it remains unclear precisely how the cultural pattern contributes to the harmfulness of hate speech. Matsuda expanded Richard Delgado’s argument to include other types of hate speech and to foreground the threat it poses to the autonomy of both targets and bystanders. The similarity between hate speech and microaggressions is the threat they pose to dignity.
Towards a non-functional requirements discovery approach for persuasive systems

A number of software systems that attempt to help people achieve behavior change have been proposed in various domains such as health and wellness. However, sometimes, such systems have failed to provide a satisfactory or sustainable User Experience (UX), as it is observed when users may be reluctant to respond to the activation of the systems' changing demands. Moreover, a negative User Experience (UX) can be exposed by Behavior Change Support Systems (BCSS) if designers do not have clear understanding of the requirements that factually help changing the user behavior that accomplishes a sustainability goal. We first explored the Persuasive System Design (PSD) model that should be considered in UX assessment of BCSSs. Then, we propose a requirements discovery process that can be considered to redesign a software interactive system based on negative UX.
An Improved Bytewise Approximate Matching Algorithm Suitable for Files of Dissimilar Sizes

The goal of digital forensics is to recover and investigate pieces of data found on digital devices, analysing in the process their relationship with other fragments of data from the same device or from different ones. Approximate matching functions, also called similarity preserving or fuzzy hashing functions, try to achieve that goal by comparing files and determining their resemblance. In this regard, ssdeep, sdhash, and LZJD are nowadays some of the best-known functions dealing with this problem. However, even though those applications are useful and trustworthy, they also have important limitations (mainly, the inability to compare files of very different sizes in the case of ssdeep and LZJD, the excessive size of sdhash and LZJD signatures, and the occasional scarce relationship between the comparison score obtained and the actual content of the files when using the three applications). In this article, we propose a new signature generation procedure and an algorithm for comparing two files through their digital signatures. Although our design is based on ssdeep, it improves some of its limitations and satisfies the requirements that approximate matching applications should fulfil. Through a set of ad-hoc and standard tests based on the FRASH framework, it is possible to state that the proposed algorithm presents remarkable overall detection strengths and is suitable for comparing files of very different sizes. A full description of the multi-thread implementation of the algorithm is included, along with all the tests employed for comparing this proposal with ssdeep, sdhash, and LZJD.
SCOTT: Shape-Location Combined Tracking with Optimal Transport

Optimal transport (OT) is a prominent framework for point set registration, that is, to align points in two sets. Point set registration becomes particularly difficult when points are organized into objects and the correspondence among the objects is to be established. The registration of pixels must maintain consistency at the object level despite the possibility of object division, merging, and substantial alteration in size and shape over time. Existing approaches in OT exploit either similarity in shape for the entire set of points or spatial closeness of individual points, but not the two simultaneously. We propose a new weighted Gromov--Wasserstein distance (WGWD) to combine both sources of information. Importantly, we use a bipartite graph partitioning strategy to regularize OT in order to achieve object-level consistency and to enhance computational efficiency. We apply the method to cell tracking, specifically, the task of associating biological cells in consecutive image frames from time-lapse image sequences. We call the system SCOTT Shape-Location COmbined Tracking with Optimal Transport). By establishing a pixel-to-pixel correspondence, our method can effectively detect intricate scenarios including cell division and merging (overlapping). Experiments show that our method achieves high accuracy in tracking the movements of cells and outperforms existing methods in the detection of cell division and merging. Location information is shown to be more useful than shape information, while the combination of the two achieves optimal results.
Image recognition and diagnosis for vibration characteristics of cone valve core

Research on vibration characteristics of hydraulic valves is helpful to improve the performance of the hydraulic valve. In this article, a visualization experimental method is designed to capture the cone valve core vibration, and the image sequence of the vibration is obtained. And the least-squares contour fitting of the valve core is proposed to analyze the vibration characteristics of the valve core, which could eliminate noise in the vibration signal and improve accuracy. The difference of the least-squares variance between adjacent data points obtained by least-squares contour fitting is one order of magnitude smaller than corner detection method, while the signal-to-noise ratio is more than twice the corner detection. Furthermore, the frequency spectrum and amplitude of the valve core vibration are also discussed. The deviation of the valve core from the coordinate origin will increase with increasing of the pressure difference and decreasing of the pre-compression of the spring and the amplitude of vibration increases too. The vibration signal of the valve core under different conditions has similar frequency spectrum, which is mainly composed of the vibration signal spectrum caused by the fluctuation of oil pressure and turbulence of the liquid system.
The Need for Machine-Processable Agreements in Health Data Management

Data processing agreements in health data management are laid out by organisations in monolithic “Terms and Conditions” documents written in natural legal language. These top-down policies usually protect the interest of the service providers, rather than the data owners. They are coarse-grained and do not allow for more than a few opt-in or opt-out options for individuals to express their consent on personal data processing, and these options often do not transfer to software as they were intended to. In this paper, we study the problem of health data sharing and we advocate the need for individuals to describe their personal contract of data usage in a formal, machine-processable language. We develop an application for sharing patient genomic information and test results, and use interactions with patients and clinicians in order to identify the particular peculiarities a privacy/policy/consent language should offer in this complicated domain. We present how Semantic Web technologies can have a central role in this approach by providing the formal tools and features required in such a language. We present our ongoing approach to construct an ontology-based framework and a policy language that allows patients and clinicians to express fine-grained consent, preferences or suggestions on sharing medical information. Our language offers unique features such as multi-party ownership of data or data sharing dependencies. We evaluate the landscape of policy languages from different areas, and show how they are lacking major requirements needed in health data management. In addition to enabling patients, our approach helps organisations increase technological capabilities, abide by legal requirements, and save resources.
ON THE ISSUE OF THE READINESS OF SCIENTIFIC AND PEDAGOGICAL WORKERS OF THE UNIVERSITY TO IMPLEMENT INCLUSIVE EDUCATION

Introduction. The article is devoted to the development of inclusive education in higher educational institutions. As the authors of the article show, at the present stage of development of education in world and domestic pedagogy, they are increasingly turning to the idea of inclusive education. The driving force behind the development of inclusive education, including inclusive higher education in many countries, including the Russian Federation, has been the ratification of the UN Convention on the Rights of Persons with Disabilities, which is the leading international document that defines strategies, programs and recommendations on the right to education. Particular attention is paid to the development of higher inclusive education, considered as one of the basic and inalienable conditions for the professional self-realization of disabled people and people with disabilities, improving their social status, ensuring their full participation in society. Materials and methods. The article defines the peculiarities of the regulatory and organizational conditions that ensure inclusive education in educational institutions of higher education. The features of the organization of inclusive educational activities at the university are highlighted. Results. The article discusses the monitoring of teacher readiness for inclusive education and a description of the general picture of the readiness of Russian teachers for it, using qualitative and quantitative analysis. Needs for further participation of teachers in continuing education programs are identified. A survey of university teachers was conducted. Based on the results of the monitoring of the readiness of the faculty to work in inclusive groups, the conclusion was substantiated. The effective integration of modern information and technological technologies in the processes of further education is substantiated, which can become a necessary tool for solving one of the main problems, which consists in increasing the level of readiness of scientific and pedagogical workers of higher education to implement inclusive education. Discussion and Conclusions. The article discusses the role of the development of higher inclusive education, considered as one of the main and inalienable conditions for the professional selfrealization of disabled people and people with disabilities. The role of further education in the advanced training of university teachers was considered. The system of training scientific and pedagogical personnel for work in an inclusive education environment is defined. The components of the introduction of information technology education, occurring through a change in the types of information activities.
Traditional Learning Technologies and Ensuring the Quality of Economic Education in the Era of Digital Transformation

The article considers the challenges of the Fourth Industrial Revolution and the resulting new requirements for the quality of education. There is an increase in the importance of information technologies in socio-economic processes, in particular in education. At the same time, the authors prove that the main thing in education is its result, not the educational technologies used. High-quality economic education requires the active use of not only new “fashion” technologies, but also traditional educational technologies.
Machine learning-based technique for big data sentiments extraction

A huge amount of data is generated every minute for social networking and content sharing via Social media sites that can be in a form of structured, unstructured or semi-structured data. One of the largest used social media sites is Twitter, where each and every day millions of data generated in the form of unstructured tweets. Tweets or opinions of the people can be used to extract sentiments of the people. Sentiment analysis is beneficial for organizations to improve their products and make required changes on demand to increase their profit. In this paper, three machine learning algorithms Support Vector Machine (SVM), Decision Trees (DT), and Naive Bayes (NB) for classifying sentiments of twitters data. The purpose of this research is to compare the outcomes of these algorithms to identify best machine learning method which gives most accurate and efficient results for classifying twitter data. Our experimental result shows that same preprocessing methods on a different dataset affect similarly the classifiers performance. After analyzing the results it is observed that SVM provides 64.96%, 71.26% and 91.25% precision which is better than other two algorithms. Also, overall Recall and F-measure rate of SVM is greater than NB and DT for three datasets. However, it is important to further study current available preprocessing techniques that help us to improve results of various classifiers.
Evaluating the Presence of Heavy Metals in the Vicinity of an Industrial Complex

Heavy metals occur naturally in the earth's crust, but anthropogenic activities such as mining can release them in large quantities into the environment.Mines are important industrial complexes for economic development, but may have major environmental consequences if the relevant standards are not met.In the Mitrovica region of Kosovo there is a Pb and Zn mine known as Trepça.This mine was built decades ago, and is a major source of environmental pollution, with a considerable amount of sterile material that remains after the mine's flotation.This results in the contamination of water, soil and sludge.This research focuses on the pollution containing heavy metal waste that comes from the Trepça mine generated from lead and zinc mining activities.In order to evaluate the environmental pollution in water, soil and sludge, we measured heavy metals (Pb, Zn, Cu, Cd, Ni and Fe) using inductively coupled plasma optical emission spectrometry (ICP AES).Our results indicate that the presence of metal contamination in the study area is strongly influenced by mining activities.The results presented here can serve national agencies in taking measures to implement monitoring programs for heavy metals in areas surrounding the Trepça mining complex.