Reviewerly/exHarmony huggingface dataset:
Arnold Transform Based Medical Image Scrambling and Reconstruction Technique with Improved PSNR Parameter for Increasing the Robustness of Digital Watermarking Algorithms in Comparison with Random Shuffling Method

In this work, a new transform-based technique to secure the data in watermarking algorithm is proposed. Materials and methods: In this research, Arnold transform-based image scrambling method developed and compared with random shuffling method and 30 is the sample size of each group. All medical images are collected from standard medical database. Results: The execution of image scrambling algorithm is calculated using peak signal to noise ratio (PSNR) and universal index quality measure (UIQI) parameters. Low values of PSNR and UIQI represent higher scrambling. Arnold transform provides mean PSNR value of 7.1314db and UIQI is 69.0437%. Random shuffling provides mean PSNR value is 9.2890db, and UIQI is 75.3633%. Conclusion: Based on the experimental and the statistical analysis by using the independent sample T test, the Arnold transform-based image scrambling method performs significantly better than random shuffling method with PSNR (P=0.01) and UIQI (P=0.21).
Evaluation of Seating Position During Wheelchair Transfer using Body-pressure and Proximity Sensors with Conductive Texture

Patient transfer during nursing care imposes a heavy burden on long-term caregivers, frequently causing back pain. It is therefore necessary to improve nursing skills in order to reduce the burden on caregivers so as to prevent accidents. In this study, we fabricated capacitive body-pressure and proximity sensors that use conductive texture for detection, in order to evaluate nursing care movements. To investigate the response of the sensor, an experiment was conducted in which a stainless block was brought close to and then placed in contact with a simulated living body. The results show that the sensor can detect the distance to a living body, in addition to the applied pressure. Based on these response characteristics, a sitting experiment was conducted in which 10-channel body-pressure and proximity sensors were installed. The results indicated that the proposed sensor can be used to analyze the seating position of patients in a wheelchair based on the pressure on the seat surface.
14. Interdisciplinary Approach to Assessing the Acoustic Quality of Violins

Abstract The paper presents the interdisciplinary approach to assessing the acoustic quality of violins through a survey addressed to specialists in the field of music - instrumentalists, teachers, artists. Violins with modified geometric parameters in intermediate stages from a technological point of view were investigated, respectively unfinished violins (in white). The aim of the study was to classify the musical instruments heard according to five acoustic criteria considered relevant for violinists. Criteria such as: bright tone, clarity of sounds, warm/silky sound, amplitude of sounds and equal sound on all 4 strings were also established through a survey of artistic impressions among specialists, in a stage prior to the study presented in this article.
Reflecting the trends in the academic landscape of special education using probabilistic dynamic topic modeling

Purpose Special education is the education segment that deals with the students facing hurdles in the traditional education system. Research data have evolved in the domain of special education due to scientific advances. The present study aims to employ text mining to extract the latent patterns from the scientific data. Design/methodology/approach This study examined the 12,781 Scopus-indexed titles, abstracts and keywords published from 1987 to 2021 through an integrated text-mining and topic modeling approach. It combines dynamic topic models with highly cited reviews of this domain. It facilitates the extraction of topic clusters and communities in the topic network. Findings This methodology discovered children’s communication and speech using gaming techniques, mental retardation, cost effect on infant birth, involvement of special education children and their families, assistive technology information for special education, syndrome epilepsy and the impact of group study on skill development peers or self as the hottest topic of research in this domain. In addition to finding research hotspots, it further explores annual topic proportion trends, topic correlations and intertopic research areas. Originality/value The results provide a comprehensive summary of the popularity of research topics in special education in the past 34 years, and the results can provide useful insights and implications, and it could be used as a guide for contributors in special education form a structured view of past research and plan future research directions.
Cryptocurrencies, Electronic Securities, Security Token Offerings, Non Fungible Tokens: New Legal Regulations for “Crypto Securities” and Implications for Issuers and Investor and Consumer Protection

In the current era of information age, the knowledge and digital economy has become part of the national economy as a result of the Fourth Industrial Revolution. Nevertheless, there have long been several unanswered questions in business practice about the proper use of Cryptocurrencies, Electronic Securities, Security Token in business. This is a topic which, due to the lack of interest of theorists, represents an almost unresearched area, while the answers to the resulting ambiguities and problems are relatively difficult to find. The aim of the scientific study is to examine the current legislative options and related problems of electronic commerce in world business. This goal was formulated according to current needs and emerging practical problems in business practice. Due to the nature of the researched topic, we primarily applied the analysis of legal regulations when processing the issue. However, we also use scientific literature, case law and the analogy of law. With our contribution we provide qualified answers to the pitfalls of business economic practice. The benefit of our contribution is to critically point out selected application problems in a broader context and to suggest ways of improvement.
Emerging Optical Interconnects for AI Systems

The ever-growing demand for accurate machine learning models resulted in an increase in dataset and model sizes of deep neural networks. This paper discusses reconfigurable optical networks as the key enabler for scaling AI systems.
The Role of Blockchain Technology in Enhancing Security Management in the Supply Chain

This study aims to analyze the role of blockchain technology in enhancing security management in supply-chain management. The information was collected from various sources, organized into two sets of variables for privacy and security cases, and analyzed using EViews software. Data analysis was done by performing three tests: correlation, cross-correlation, and Granger’s causality test. The findings revealed a correlation between the use of blockchain technology in the supply chain and security and privacy complaints among organizations and individuals. It also revealed a unidirectional causality between security concerns and the degree of application of blockchain technology in the supply chain. The paper represents one of the very few empirical studies that have been done about the subject. The paper is expected to change the public’s understanding of blockchain technology related to security and privacy, two of the essential aspects of supply-chain management in contemporary corporate society. The main limitation of this study comes in the form of a lack of readily available data. Several data sources had to be used, including existing literature and statistics from reputed sites like Statista, to ensure sufficient information was collected to facilitate the study’s key objectives successfully.
Fuzzy Clustering Algorithm for Lithofacies Classification of Ordovician Unconventional Tight Sand Reservoir from Well-Logs Data (Algerian Sahara)

In this paper, we present an approach based on a fuzzy clustering algorithm applied for lithofacies classification in an unconventional tight sand reservoir from well-logs data. In some cases, these kinds of reservoirs are radioactive due to the presence of non-clayey radioactive minerals. Thus, conventional lithology classification methods can provide poor results. For that, artificial intelligence, such as Fuzzy logic, can be suitable to solve the problem. Fuzzy clustering is an unsupervised machine learning technique where a given set of data is classified. It is a more general logic then classical logic because it does not ignore uncertainties and accepts the implicit consideration of the inherited error associated with any physical measurement. This technique has been applied to real data of one well in an unconventional tight sand reservoir in the Algerian Sahara. Predicted results are compared to lithofacies obtained from conventional methods and spectral mineralogical well-logs data.
A Study on the Impact of Multiview Distributed Feature Coding on a Multicamera Vehicle Tracking System at Roundabouts

Visual sensor networks are one potential enabler for the evolution of the Internet of things.Due to their limited resources in terms of energy and bandwidth, it is crucial to identify appropriate approaches that take into considerations such constraints and reduce the amount of data transmitted to the gathering point (sink).In this context, this paper describes the impact of a distributed smart-camera system that exploits an analyze-then-compress strategy, on a multi-view vehicle tracking at roundabouts application.In the tested system, part of the processing is shifted to the smart cameras, i.e., the object detection/classification and feature extraction, so that only the extracted features describing moving vehicles are transmitted instead of the whole image/video.Features are further compacted by using a state-of-the-art distributed coding technique, based upon an efficient clustering method that exploits the temporal and spatial (multiple views) correlations between features.The system is tested on a real-data scenario, by evaluating the bit-rate reduction capabilities in dependence of the channel conditions, as well as the matching accuracy of the reconstructed descriptors in the specific tracking application.Both feature-wise and object-wise matching are investigated.For the chosen application scenario, a bit-rate reduction of 30 -35% is proved to be achievable in non-ideal channel conditions.Even more interestingly, such reduction is proved not to harm the matching accuracy (i.e., it is coherent with the target application), for which an F-score up to 0.923 is guaranteed.
PercolationDF: A percolation-based medical diagnosis framework

Goal: With the continuing shortage and unequal distribution of medical resources, our objective is to develop a general diagnosis framework that utilizes a smaller amount of electronic medical records (EMRs) to alleviate the problem that the data volume requirement of prevailing models is too vast for medical institutions to afford. Methods: The framework proposed contains network construction, network expansion, and disease diagnosis methods. In the first two stages above, the knowledge extracted from EMRs is utilized to build and expense an EMR-based medical knowledge network (EMKN) to model and represent the medical knowledge. Then, percolation theory is modified to diagnose EMKN. Result: Facing the lack of data, our framework outperforms naïve Bayes networks, neural networks and logistic regression, especially in the top-10 recall. Out of 207 test cases, 51.7% achieved 100% in the top-10 recall, 21% better than what was achieved in one of our previous studies. Conclusion: The experimental results show that the proposed framework may be useful for medical knowledge representation and diagnosis. The framework effectively alleviates the lack of data volume by inferring the knowledge modeled in EMKN. Significance: The proposed framework not only has applications for diagnosis but also may be extended to other domains to represent and model the knowledge and inference on the representation.
An efficient algorithm of fuzzy reinstatement labelling

&lt;abstract&gt;&lt;p&gt;The fuzzy reinstatement labelling ($ FRL $) puts forward a reasonable method to rewind the acceptable degrees of arguments in fuzzy argumentation frameworks. The fuzzy labelling algorithm ($ FLAlg $) computes the $ FRL $ by infinitely approximating the limits of an iteration sequence. However, the $ FLAlg $ is unable to provide an exact $ FRL $, and its computation complexity depends on not only the number of arguments but also the accuracy. This brings a quick increase in complexity when higher accuracy is acquired. In this paper, through the in-depth study of the $ FLAlg $, we introduce an effective algorithm for decomposing $ FRL $ by strongly connected components. For simple fuzzy frameworks in the form of trees, odd cycles, and even cycles, the new algorithm provides an exact value of the limit. Therefore, by avoiding the infinite approximation process, it is independent of accuracy. And for complex frames, the new algorithm outputs an approximate value to the $ FLAlg $. It is more efficient because the number of arguments in the approximation process is usually reduced.&lt;/p&gt;&lt;/abstract&gt;
An Automated Approach for Analysing Students Feedback Using Sentiment Analysis Techniques

Conducting and evaluating continuous student feedback is essential for any quality enhancement cell (QEC) within an education institution. Students’ feedback based on their personal opinions can play a vital role in ensuring quality education. However, students’ subjective opinions are often ignored due to time constraints or a lack of adequate analysis strategies. Therefore, to automate the quality assurance process, two classification models (i.e., based on Monkey learn API and SentiWord using TextBlob) are proposed to analyze students’ feedback data. The results shows that the model employing MonkeyLearn performs nearly 22% points better than the Textblob on the Albanian language dataset obtained from 114 students’ responses, achieving 72.12% accuracy.
CHAMP: Coherent Hardware-Aware Magnitude Pruning of Integrated Photonic Neural Networks

We propose a novel hardware-aware magnitude pruning technique for coherent photonic neural networks. The proposed technique can prune 99.45% of network parameters and reduce the static power consumption by 98.23% with a negligible accuracy loss.
Adaptive Bat Optimization Algorithm for Efficient Load Balancing in Cloud Computing Environment

Cloud computing is the new development technology where it permit users as much as needed with its greater performance. Where the cloud computing is a heterogeneous method where it contains huge number of various system data. With this system tasking few data else calculating with greater systems, where it accepts the computing that the forwarding and accessing time is very difficult with system. In cloud environment, the load managing is the important function to get performed. Where the load balancing permits to share various workloads through the various nodes and it verifies by checking that there is no individual node gets over loaded. In previous applications, where the response time is high and then the communication cost is maximum and thereby to get out of previous declined problem, with the discussed methods, and for capable load balancing, the adaptive bat optimization algorithm (ABOA) is discussed. The discussed various servers huge balancing and depending upon the independent and dependent function. The task gets at the time of the run time through the server with different approximate gap with different load specifications. Variation inequality (VI) issues are gets eliminated then there is a Nash equilibrium method fixes through the calculated game. After discussing, an iterative proximal (IP) algorithm through ABOA to get calculated a Nash equilibrium result through the various servers huge managing one. By the outcome, the discussed ABOA gives good task than that of the previous system.
The Detection and Following of Human Legs Based on Feature Optimized HDBSCAN for Mobile Robot

Abstract Accurate target recognition and stable follow in real clutter environment are important tasks for following a random robot. In this paper, a scheme of detecting and tracking human legs using a single lidar is proposed. The HDBSCAN algorithm is optimized by human leg characteristics, and the leg line features are fused on the basis of the density characteristics of the leg laser radar data, and the leg information of the target is clustered and its location is obtained. Based on the relative shift angle of target moving and the lock-in algorithm of tracking distance, the single target detection and tracking in unstructured environment can be realized. The lidar dataset in multiple scenes verifies that the clustering algorithm based on feature optimization improves the accuracy of target legs detection by more than 10% compared with the original algorithm. The scheme performs follow-up experiments on a self-built solid robot. The experimental results show that the robot can achieve accurate and stable human target follow in clutter environment.
$$M^3$$-IB: A Memory-Augment Multi-modal Information Bottleneck Model for Next-Item Recommendation

Modeling of users and items is essential for accurate recommendations. Traditional methods focused only on users’ behavior data for recommendation. Several recent methods attempted to use multi-modal data (e.g. items’ attributes and visual features) to better model users and items. However, these methods fail to model users’ dynamic and personalized preferences on different modalities. In addition, besides useful information for recommendation, the multi-modal data also contains a great deal of irrelevant and redundant information that may mislead the learning of recommendation models. To solve these problems, we propose a Memory-augment Multi-Modal Information Bottleneck method, named $$M^3$$ -IB, for next item recommendation. First, we design a memory network framework to maintain modality-specific knowledge and capture users’ dynamic modality-specific preferences. Second, we propose to model and fuse users’ personalized preferences on different modalities with a multi-modal probabilistic graph. Then, to filter out irrelevant and redundant information in multi-modal data, we extend the information bottleneck theory from single-modal to multi-modal scenario and design a multi-modal information bottleneck (M2IB) model. Finally, we provide a variational approximation and a flexible implementation of the M2IB model for next item recommendation. Experiential results on five real-world data sets demonstrate the promise of the proposed method.
Modeling of the Educational Process Based on Smart Technologies

The relevance of the article is caused by the rapidly growing importance of qualitative higher education. Higher levels of scientific and technological processes lead to the ever-growing demand for highly-qualified specialists where low-skilled workers can lose their jobs without any stop in the trend to change the usual style of life. Specialists should be able to possess a very important skill, i.e. the skill to adapt to high-tech changes in the working environment. Changing requirements to workers’ professional competencies cause changing the direction of educational systems development forcing them to be modified. One of the new fundamental directions in this modification is the emergence and improvement of educational smart technologies. However, due to the specific character of smart education, the task to assess its quality becomes more complicated creating difficulties primarily for employers. The article offers a new approach to assess the quality of modern education based on the mathematical models proposed by the authors. Methods to build and analyze differential equations as well as methods of the mathematical theory of fuzzy sets are used allowing us to transfer qualitative educational criteria into quantitative ones and further perform their analysis. The model combines key advantages of existing methods to assess the quality of education. The model offered is flexible as it can be adapted to any number of various criteria and factors and can be used as the basis for the system of education quality indicators applicable both for job seekers and employers.
Extractive Summarization of Indian Legal Documents

A legal document is generally very long and possess hierarchical structure which makes it very difficult to get a quick understanding of these documents. Legal practitioners generally engage legal experts for making summaries of lengthy documents which is a very costly and time-consuming process. Recently, automatic summarization systems have been proposed to deal with this problem, which has the potential to be much more efficient at simplifying complicated legal documents. In this work, web scrapping is used for extraction of legal documents. Then, a comparative analysis is done on these documents using various extractive summarization approaches. From the experimental results, it has been found that the graph-based approaches performed well in general. Another important finding is that, in addition to the frequency of words, other information such as context information, lexical information is also equally important for summarization and can improve the performance of summarization systems.
Efficient realization of quantum primitives for Shor’s algorithm using PennyLane library

Efficient realization of quantum algorithms is among main challenges on the way towards practical quantum computing. Various libraries and frameworks for quantum software engineering have been developed. Here we present a software package containing implementations of various quantum gates and well-known quantum algorithms using PennyLane library. Additoinally, we used a simplified technique for decomposition of algorithms into a set of gates which are native for trapped-ion quantum processor and realized this technique using PennyLane library. The decomposition is used to analyze resources required for an execution of Shor's algorithm on the level of native operations of trapped-ion quantum computer. Our original contribution is the derivation of coefficients needed for implementation of the decomposition. Templates within the package include all required elements from the quantum part of Shor's algorithm, specifically, efficient modular exponentiation and quantum Fourier transform that can be realized for an arbitrary number of qubits specified by a user. All the qubit operations are decomposed into elementary gates realized in PennyLane library. Templates from the developed package can be used as qubit-operations when defining a QNode.
s-LIME: Reconciling Locality and Fidelity in Linear Explanations

The benefit of locality is one of the major premises of LIME, one of the most prominent methods to explain black-box machine learning models. This emphasis relies on the postulate that the more locally we look at the vicinity of an instance, the simpler the black-box model becomes, and the more accurately we can mimic it with a linear surrogate. As logical as this seems, our findings suggest that, with the current design of LIME, the surrogate model may degenerate when the explanation is too local, namely, when the bandwidth parameter $$\sigma $$ tends to zero. Based on this observation, the contribution of this paper is twofold. Firstly, we study the impact of both the bandwidth and the training vicinity on the fidelity and semantics of LIME explanations. Secondly, and based on our findings, we propose s-LIME, an extension of LIME that reconciles fidelity and locality.