Reviewerly/exHarmony huggingface dataset:
M. Clarke, F. Dufeu and P. Manning, Inside Computer Music. New York: Oxford University Press, 2020. ISBN 9780190659653 (paperback).

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A survey on detecting healthcare concept drift in AI/ML models from a finance perspective

Data is incredibly significant in today's digital age because data represents facts and numbers from our regular life transactions. Data is no longer arriving in a static form; it is now arriving in a streaming fashion. Data streams are the arrival of limitless, continuous, and rapid data. The healthcare industry is a major generator of data streams. Processing data streams is extremely complex due to factors such as volume, pace, and variety. Data stream classification is difficult owing to idea drift. Concept drift occurs in supervised learning when the statistical properties of the target variable that the model predicts change unexpectedly. We focused on solving various forms of concept drift problems in healthcare data streams in this research, and we outlined the existing statistical and machine learning methodologies for dealing with concept drift. It also emphasizes the use of deep learning algorithms for concept drift detection and describes the various healthcare datasets utilized for concept drift detection in data stream categorization.
Plug-and-Play Regulators for Image-Text Matching

Exploiting fine-grained correspondence and visual-semantic alignments has shown great potential in image-text matching. Generally, recent approaches first employ a cross-modal attention unit to capture latent region-word interactions, and then integrate all the alignments to obtain the final similarity. However, most of them adopt one-time forward association or aggregation strategies with complex architectures or additional information, while ignoring the regulation ability of network feedback. In this paper, we develop two simple but quite effective regulators which efficiently encode the message output to automatically contextualize and aggregate cross-modal representations. Specifically, we propose (i) a Recurrent Correspondence Regulator (RCR) which facilitates the cross-modal attention unit progressively with adaptive attention factors to capture more flexible correspondence, and (ii) a Recurrent Aggregation Regulator (RAR) which adjusts the aggregation weights repeatedly to increasingly emphasize important alignments and dilute unimportant ones. Besides, it is interesting that RCR and RAR are plug-and-play: both of them can be incorporated into many frameworks based on cross-modal interaction to obtain significant benefits, and their cooperation achieves further improvements. Extensive experiments on MSCOCO and Flickr30K datasets validate that they can bring an impressive and consistent R@1 gain on multiple models, confirming the general effectiveness and generalization ability of the proposed methods. Code and pre-trained models are available at: https://github.com/Paranioar/RCAR.
Finding the area of localization of oncopathology according to the parameters of the patient's blood

A mathematical approach is described that allows, according to the blood test of a cancer patient, to locate the tumor. A statistical classifier developed by the authors is used, based on polynomial regression and having probabilistic estimates. The classifier was trained on the bases of peripheral blood of oncological patients in five different body systems.
Application of Decision Tree Algorithm in Human Resource Management

The management is to find the best combination of variables or attributes that can be used to predict the future performance of employees. This prediction is done by using a set of rules called decision trees. Decision trees, also known as decision analysis, are used to predict future behavior or performance based on past behavior and attributes. This process includes finding the best combination of attributes (variables) to produce better prediction results than other combinations. This paper studies mining in human resource market analysis. Base mining technology, this paper uses the concept, principle, content, algorithm and mode of data mining to build a human resources data mining model; This paper analyzes the ID3 algorithm of decision tree method, studies the application of data mining in human resource, and human resource management decision support.
Automatic English Essay Scoring Algorithm Based on Machine Learning

With the development of natural language processing(NLP) technology and machine learning, the research task of automatic English scoring(AES) is becoming clearer and clearer, and the research difficulties arise due to the mutual constraints of research methods and annotation data. How to build a perfect and reliable scoring system has become a great challenge under today's research. In this paper, we designed an English AES system, and verified the effectiveness of RF on English scoring model by analyzing the prediction effect of RF on non-text features and text features, and then compared the Pearson correlation coefficients(PCC) of RF(RF), GBDT, and XGBoost, and the study showed that the performance of RF algorithm is higher than the other two composition scoring methods.
Design and Performance Evaluation of an FPGA based EOG Signal Preprocessor

Electrooculogram (EOG) is an electrophysiological signal produced around the eyes due to eyeball motion. This signal can be utilized to study eye movements which is bene-ficial in many medical and bio-electrical applications such as controlling human-computer interfaces and diagnosing different ocular diseases. However, the EOG is often contaminated with high-frequency motion artifacts, 50/60 Hz grid interference, and baseline wander. Hence, the collected signals are required to be preprocessed before finally being used in applications. This paper proposes an efficient FPGA-based EOG processor for fast and real-time processing of EOG signals, especially for medical diagnosis. To the best of our knowledge, this is the first work to implement EOG serial preprocessing by FIR and IIR filters on FPGA. MATLAB's FDA tool is used for mathematical validation and primary simulation. The proposed system was implemented on the Xilinx Zynq-7000 FPGA by hardware/software co-design. By statistical analysis, the software and hardware results were found to have the Pearson Correlation Coefficient of 0.99 and a Mean Root Squared Error in the 10– <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> range. The resource utilization and power consumption are presented. The on-chip power consumption for this design is 0.271 watts where dynamic power is 0.163 watts (60%), and static power is 0.108 watts (40%). Performance evaluation and comparative study of the software-hardware results revealed the efficacy of the designed EOG preprocessor.
The impact of corporate governance and firm performance on waste and effluent disclosure: Evidence from polluting industries in Indonesia

This study examined the influence of corporate governance – including the presence of board gender diversity, board independence, board attention, and audit committee – and firm performance on waste and effluent disclosure in polluting industry companies in Indonesia. This study used secondary data obtained from the companies’ annual and sustainability reports during the period 2017 to 2021. The waste and effluent disclosure was measured using a scoring method based on Global Reporting Initiative (GRI) Standard 306: Waste and Effluent. This study focused on companies in five polluting industries, including Pulp and Paper, Chemicals, Oil and Gas, Metals and Mining, and Infrastructure, Utilities, and Transportation, listed on the Indonesia Stock Exchange. The results of the study show that corporate governance, including the presence of board gender diversity, board independence, board attention, and audit committee, has a significant influence on waste and effluent disclosure. In contrast, firm performance, which was proxied by return on assets (ROA), has no significant influence on the waste and effluent disclosure of companies in polluting industries listed on the Indonesia Stock Exchange.
Specifics of Transformation Projects in Universities and the Work of the Project Team

The article deals with the practices and specifics of the implementation of transformation projects in Russian universities. Particular attention is focused on the direct performers – members of project teams. The specifics of their formation are considered, the criteria necessary for employees to be included in a project team, as well as the peculiarities of such team functioning and development for more effective and productive activities are studied. The peculiarities of exclusion and withdrawal of employees from the project team – what are the reasons for it and what actions are taken by the head of the team to replace the withdrawn participant (selection of a new or redistribution of functions of the withdrawn to the remaining employees or another option) are considered separately. The conclusions presented in the article are based on the materials of an empirical sociological study conducted during the year (in the period from December 2021 to December 2022) by the method of indepth semi-formalized interviews with 78 representatives of administrative and managerial staff, scientific and pedagogical workers, heads of departments and deans of universities in three regional classical universities (in Siberian and Volga Federal Districts of the Russian Federation). Based on the results of the empirical study, the authors come to several conclusions. The transformation projects implemented in universities are associated with many diverse problems, key among which were: the destruction of agreements due to geopolitical risks; lack or complete absence of funding; varying financial success of projects; disunity of the team and lack of understanding of common goals and objectives of the project; lack of practical implementation of the project, etc. The success of the transformation project is signaled by both classical criteria called in the theory and practice of project management, for example, compliance with project deadlines, project execution within the allocated resources, and criteria less frequently mentioned in research, for example, the development of the project team, the formation of project values and norms of project culture, satisfaction of stakeholders and others. Transformation project teams are often formed spontaneously, the basis is often the potential candidate’s belonging to the required position or function, the competencies of candidates are rarely assessed, and their future competence and role statuses are planned.
MGV Obstacle Avoidance Trajectory Generation Considering Vehicle Shape

This study investigates the application of obstacle avoidance trajectory generation considering the vehicle shape of a micro ground vehicle by successive convexification and state-triggered constraints. The avoidance trajectory is generated by numerical computation and path-following experiments are conducted to assess the generated trajectory. The numerical computation results indicate that the trajectory obtained by the algorithm successfully avoids obstacles considering the vehicle shape and satisfies the constraints. The experiment includes the model predictive control to follow the generated trajectory. Numerical computations and experiments confirm the usefulness of the trajectory generation algorithm.
Adaptive Double-Spending Attacks on PoW-based Blockchains

Previous state-of-the-art studies have proposed various analytical models to understand the double-spending attacks (DSA) occurred in Proof-of-Work (PoW) based Blockchains. Although many insights behind the double-spending attacks have been disclosed, we still believe that advanced versions of DSA can be developed to create new threats for the PoW-based blockchains such as the Bitcoin blockchain. In this paper, we present two new types of double-spending attacks in the context of the PoW-based blockchain and discuss the insights behind them. By considering more practical network parameters, such as the number of confirmation blocks, the hashpower of the double-spending attacker, the amount of coins in the target transaction, and the network-status parameter, we first analyze the success probability of the conventional double-spending attack, named Naive DSA. Based on Naive DSA, we create two new adaptive DSA, i.e., the Adaptive DSA and the Reinforcement Adaptive DSA (RA-DSA). In our analytical models, a double-spending attack is converted into a Markov Decision Process. We then exploit the Stochastic Dynamic Programming (SDP) approach to obtain the optimal attack strategies under Adaptive DSA and RA-DSA. Numerical simulation results demonstrate the correlations between each critical network parameter and the expected attacker's reward. Through the proposed analytical models, we aim to alert the PoW-based blockchain ecosystem that the threat of double-spending attacks is still at a dangerous level. For example, our findings show that the attacker can launch a successful attack with a small hashpower proportion much lower than 51% under RA-DSA.
Heuristic Potential of Library Bibliographic Classification

In the era when knowledge and information have become a strategic resource, theoretical and practical issues of building classification systems have seen an increased interest. The study of various library classifications, as well as their comparative analysis, allows us to identify local (and/or global) trends in the development of theory and practice of library and information activities. The multidimensional vision of heuristic potential as a complex of possibilities for finding effective new tools and techniques for solving complex problems has determined the scope and features of the research method. The purpose of the article is to determine the methodological mechanisms which can help expanding the capabilities of Library Bibliographical Classification (LBC) in solving cognitive, research and creative tasks. Realization of this goal was carried out on the basis of historical and methodological reconstruction method, rarely used at present. The object of historical and methodological reconstruction is the technology of knowledge organization. Subject scientific interest is focused on the study of a set of gnoseological tools and techniques that facilitate and simplify the solution of problems of library classification. Basing on the scientific publications of the staff of the Research Centre for the Development of LBC of the Russian State Library, as well as texts of Medium Schedules (2001—2019, issues 1—8), Abridged Schedules (2021) and Schedules for children’s and school libraries (2016), the authors present the original methodological base for updating the LBC of the Russian State Library. The paper reveals that the core of this methodological base is the method of scientific conjugation. The authors test the hypothesis of transformation of the LBC from the system of organizing the existing knowledge into a system of producing new knowledge with the expanded functionality. In addition to the traditional applied functions of the LBC — organizational, reference, information, methodological — classification also performs cognitive, enlightening, educational and research functions. The heuristic potential of the LBC is revealed through the knowledge and experience in different subject areas.
Induce a Blink of the Eye: Evaluating Techniques for Triggering Eye Blinks in Virtual Reality

As more and more virtual reality (VR) headsets support eye tracking, recent techniques started to use eye blinks to induce unnoticeable manipulations to the virtual environment, e.g., to redirect users' actions. However, to exploit their full potential, more control over users' blinking behavior in VR is required. To this end, we propose a set of reflex-based blink triggers that are suited specifically for VR. In accordance with blink-based techniques for redirection, we formulate (i) effectiveness, (ii) efficiency, (iii) reliability, and (iv) unobtrusiveness as central requirements for successful triggers. We implement the soft- and hardware-based methods and compare the four most promising approaches in a user study. Our results highlight the pros and cons of the tested triggers, and show those based on the menace, corneal, and dazzle reflexes to perform best. From these results, we derive recommendations that help choosing suitable blink triggers for VR applications.
WordGesture-GAN: Modeling Word-Gesture Movement with Generative Adversarial Network

Word-gesture production models that can synthesize word-gestures are critical to the training and evaluation of word-gesture keyboard decoders. We propose WordGesture-GAN, a conditional generative adversarial network that takes arbitrary text as input to generate realistic word-gesture movements in both spatial (i.e., (x, y) coordinates of touch points) and temporal (i.e., timestamps of touch points) dimensions. WordGesture-GAN introduces a Variational Auto-Encoder to extract and embed variations of user-drawn gestures into a Gaussian distribution which can be sampled to control variation in generated gestures. Our experiments on a dataset with 38k gesture samples show that WordGesture-GAN outperforms existing gesture production models including the minimum jerk model [37] and the style-transfer GAN [31, 32] in generating realistic gestures. Overall, our research demonstrates that the proposed GAN structure can learn variations in user-drawn gestures, and the resulting WordGesture-GAN can generate word-gesture movement and predict the distribution of gestures. WordGesture-GAN can serve as a valuable tool for designing and evaluating gestural input systems.
Half a century of digital renewal of the Russian school in the light of the researcher’s biography. To the 80th anniversary of A. Yu. Uvarov

The penetration of computer technology in general education in our country started more than half a century ago. Alexander Yurievich Uvarov was among those who started the work in the field of digital renewal of education. Today he is the Doctor of Sciences (Education), Professor at the Institute of Education of the National Research University Higher School of Economics, leading researcher at the Axel Berg Institute of Cybernetics and Educational Computing of the Federal Research Center “Computer Science and Control” of the Russian Academy of Sciences, laureate of the RF Government Prize in Education, long-time member of the editorial board of Informatics and Education journal. A. Yu. Uvarov made a significant contribution to the development of school informatics. His organizational activities and research largely determined the reality and future prospects for the digitalization of the national school. It is necessary to emphasize the importance and scientific significance of the research carried out by A. Yu. Uvarov. In January 2023, he turned eighty years old. The biography of this researcher, well known in Russia and abroad, reflects, as in a mirror, the half-century history of the digital renewal of the Russian school.
Bounded Model Checking for Asynchronous Hyperproperties

Abstract Many types of attacks on confidentiality stem from the nondeterministic nature of the environment that computer programs operate in. We focus on verification of confidentiality in nondeterministic environments by reasoning about asynchronous hyperproperties . We generalize the temporal logic to allow nested trajectory quantification, where a trajectory determines how different execution traces may advance and stutter. We propose a bounded model checking algorithm for based on QBF-solving for a fragment of and evaluate it by various case studies on concurrent programs, scheduling attacks, compiler optimization, speculative execution, and cache timing attacks. We also rigorously analyze the complexity of model checking .
STIF: Intuitionistic fuzzy Gaussian membership function with statistical transformation weight of evidence and information value for private information preservation

Data sharing to the multiple organizations are essential for analysis in many situations. The shared data contains the individual's private and sensitive information and results in privacy breach. To overcome the privacy challenges, privacy preserving data mining (PPDM) has progressed as a solution. This work addresses the problem of PPDM by proposing statistical transformation with intuitionistic fuzzy (STIF) algorithm for data perturbation. The STIF algorithm contains statistical methods weight of evidence, information value and intuitionistic fuzzy Gaussian membership function. The STIF algorithm is applied on three benchmark datasets adult income, bank marketing and lung cancer. The classifier models decision tree, random forest, extreme gradient boost and support vector machines are used for accuracy and performance analysis. The results show that the STIF algorithm achieves 99% of accuracy for adult income dataset and 100% accuracy for both bank marketing and lung cancer datasets. Further, the results highlights that the STIF algorithm outperforms in data perturbation capacity and privacy preserving capacity than the state-of-art algorithms without any information loss on both numerical and categorical data.
Research on Water Garbage Detection Algorithm Based on GFL Network

Aiming at the problem that existing water surface garbage detection algorithms cannot meet the tradeoff between real-time and accuracy in natural scenes, we propose a GFL (Generalized Focal Loss) based GFL_ HAM algorithm to improve the accuracy of water surface garbage detection, and better apply to water surface garbage detection application scenarios. We have added the HAM (Hybrid Attention Model) module to the feature extraction module (resnet50) of the GFL network to improve the feature extraction capability of the backbone network and the network representation capability. And use the self-constructed garbage detection datasets under natural scenes for training and testing. After analyzing the training process and test results, our network has higher reliability compared to the current mainstream single stage network. Our network can achieve 60.12% mAP on the water surface garbage detection datasets, which is 1.09%, 1.36%, and 1.61% higher than YOLOv3, SSD, and GFL, respectively.
Music Lessons in Distance Learning and a Presentation of Musical Objectives in the Affective, Psychomotor and Cognitive Domains

The aim of the study was to determine the distribution of planned music objectives in the affective, psychomotor and cognitive domain in the associated taxonomic categories in the internship of student teachers (full-time students of primary education in the academic year 2019/20). The research sample included 84 third-year undergraduate students who conducted distance music lessons in pairs (n = 42) via video explanation, and 55 first-year Master of Elementary Education students who independently taught distance music lessons (n = 55) at the elementary level during a condensed two-week internship under the supervision of a general education teacher. This study utilised a descriptive method of empirical pedagogical research. Data were obtained by analysing 97 lesson plans. Using a t-test, some statistically significant differences between the two groups of student teachers were found. The conditions that were found and the differences can be attributed to several factors, such as the difference in the amount of acquired musical-professional and didactic competences between the two groups of students, the particularities of distance learning, and the fact that the 3rd year students had their practicum under the guidance of professional didacticians and the master’s students under the guidance of mentors at the elementary school.