Reviewerly/exHarmony huggingface dataset:
Sub-defect of product of <i>I</i>×<i>I</i> finite sub-defect matrices

For a non-empty set I, the sub-defect of an I×I doubly substochastic matrix A=[aij]i,j∈I, denoted by sd(A), is the smallest cardinal number α for which there is a set J with card(J)=α, I∩J=∅, and there exists a doubly stochastic matrix D=[dij]i,j∈I∪J which contains A as a sub-matrix. In this paper, we show the set of all finite sub-defect matrices is closed under multiplication. We also show that the inequality max{sd(A),sd(B)}≤sd(AB)≤min{n,sd(A)+sd(B)} which is obtained by Lei Cao et al. remains valid for all finite sub-defect matrices.
METHODOLOGICAL APPROACHES TO THE MANAGEMENT OF VOCATIONAL AND TECHNICAL EDUCATION INSTITUTIONS ON THE BACKGROUND OF INFORMATIZATION

The purpose of this research is to find strategies for vocational education to improve the quality of talent training under the current new economic state characterized by mobile Internet, cloud technology, big data, new energy, robot and artificial intelligence technology, so as to provide better and stronger human resources support for economic development. This research uses survey method, literature research method and functional analysis method in order to arrive at the core management strategies of vocational education management in the context of informationization. Through the analysis of the current situation of information-based teaching in vocational schools, this paper discusses the key working elements of Vocational Education Management under the background of information, and analyzes the existing problems. According to the existing problems, this paper puts forward four strategies to establish a correct concept of information-based teaching, continuously improve teachers' information-based teaching ability, pay attention to the construction of information-based environment and guide students to play a good role as learning subjects. Through the research on the current situation of information-based teaching in vocational schools, this study summarizes the management elements in the information-based process of vocational education, points out the existing problems in information-based teaching, and creatively puts forward the strategies and methods to solve the existing problems. This study points out the management points that need to be paid attention to in the process of vocational education informatization, and puts forward management strategies according to the problems existing in the current vocational education informatization teaching, which will contribute to improving the effect of vocational education informatization teaching, so that vocational education can cultivate more excellent technical and skilled talents for social and economic development.
Vision-Autocorrect: A Self-Adapting Approach towards Relieving Eye-Strain Using Facial-Expression Recognition

The last two years have seen a rapid rise in the duration of time that both adults and children spend on screens, driven by the recent COVID-19 health pandemic. A key adverse effect is digital eye strain (DES). Recent trends in human-computer interaction and user experience have proposed voice or gesture-guided designs that present more effective and less intrusive automated solutions. These approaches inspired the design of a solution that uses facial expression recognition (FER) techniques to detect DES and autonomously adapt the application to enhance the user’s experience. This study sourced and adapted popular open FER datasets for DES studies, trained convolutional neural network models for DES expression recognition, and designed a self-adaptive solution as a proof of concept. Initial experimental results yielded a model with an accuracy of 77% and resulted in the adaptation of the user application based on the FER classification results. We also provide the developed application, model source code, and adapted dataset used for further improvements in the area. Future work should focus on detecting posture, ergonomics, or distance from the screen.
Salient Object Detection based on Background Optimization

Abstract Aiming at the problems of low detection accuracy and blurred object edges in current salient object detection based on background algorithms, a new algorithm based on boundary prior to estimate background is proposed. Firstly, the super-pixel image segmentation algorithm (SLIC) is used to segment the image into super-pixels. According to the theory that the foreground and background are relative, the background is estimated by using the four-edges boundary prior of the image; At the same time, an optimal fusion algorithm is proposed to fuse the prior background and foreground with optimal weights to obtain the final saliency map. Quantitative and qualitative comparisons with 10 classic salient object detection algorithms, the results show that the proposed algorithm can not only detect salient objects of different sizes more accurately, but also effectively suppress background redundant information, preserving the object boundary. Therefore, the accuracy of our algorithm is better than 10 classical algorithms. The algorithm in this paper which serve as pre-processing for computer vision has great promise in the field of intelligent control of machine vision.
TF-TDA: A Novel Supervised Term Weighting Scheme for Sentiment Analysis

In text classification tasks, such as sentiment analysis (SA), feature representation and weighting schemes play a crucial role in classification performance. Traditional term weighting schemes depend on the term frequency within the entire document collection; therefore, they are called unsupervised term weighting (UTW) schemes. One of the most popular UTW schemes is term frequency–inverse document frequency (TF-IDF); however, this is not sufficient for SA tasks. Newer weighting schemes have been developed to take advantage of the membership of documents in their categories. These are called supervised term weighting (STW) schemes; however, most of them weigh the extracted features without considering the characteristics of some noisy features and data imbalances. Therefore, in this study, a novel STW approach was proposed, known as term frequency–term discrimination ability (TF-TDA). TF-TDA mainly presents the extracted features with different degrees of discrimination by categorizing them into several groups. Subsequently, each group is weighted based on its contribution. The proposed method was examined over four SA datasets using naive Bayes (NB) and support vector machine (SVM) models. The experimental results proved the superiority of TF-TDA over two baseline term weighting approaches, with improvements ranging from 0.52% to 3.99% in the F1 score. The statistical test results verified the significant improvement obtained by TF-TDA in most cases, where the p-value ranged from 0.0000597 to 0.0455.
Chinese Brand Identity Management Based on Never-Ending Learning and Knowledge Graphs

Brand identity (BI) refers to the individual characteristics of an enterprise or a certain brand in the market and in the mind of the public. It reflects the evaluation and recognition of the public on the brand and is the core of the market strategy. Successful BI management can bring great business value. Nowadays, the BI management methods based on Internet, big data, and AI are widely adopted. However, they are also confronted with problems, such as accuracy, effectiveness, and sustainability, especially for the Chinese BI. Our work applies the knowledge graph (KG) and never-ending learning (NEL) for exploring efficient Chinese BI management methods. We adapt the NEL framework for the sustainability. In order to improve the accuracy and effectiveness, we express the BI knowledge with KGs and propose two methods in the subsystem components of NEL: (1) the BI evaluation model based on KG and two-dimensional bag-of-words; (2) the Apriori based on KG. In the knowledge integrator of NEL, we propose the synonym KGs for suppressing the concept duplication and drift. The experimental results show that our method reached high consistency with the experts of BI management and the industry reports.
It’s All about Reward: Contrasting Joint Rewards and Individual Reward in Centralized Learning Decentralized Execution Algorithms

This paper addresses the issue of choosing an appropriate reward function in multi-agent reinforcement learning. The traditional approach of using joint rewards for team performance is questioned due to a lack of theoretical backing. The authors explore the impact of changing the reward function from joint to individual on learning centralized decentralized execution algorithms in a Level-Based Foraging environment. Empirical results reveal that individual rewards contain more variance, but may have less bias compared to joint rewards. The findings show that different algorithms are affected differently, with value factorization methods and PPO-based methods taking advantage of the increased variance to achieve better performance. This study sheds light on the importance of considering the choice of a reward function and its impact on multi-agent reinforcement learning systems.
Communication-efficient local SGD with age-based worker selection

A major bottleneck of distributed learning under parameter server (PS) framework is communication cost due to frequent bidirectional transmissions between the PS and workers. To address this issue, local stochastic gradient descent (SGD) and worker selection have been exploited by reducing the communication frequency and the number of participating workers at each round, respectively. However, partial participation can be detrimental to convergence rate, especially for heterogeneous local datasets. In this paper, to improve communication efficiency and speed up the training process, we develop a novel worker selection strategy named AgeSel. The key enabler of AgeSel is utilization of the ages of workers to balance their participation frequencies. The convergence of local SGD with the proposed age-based partial worker participation is rigorously established. Simulation results demonstrate that the proposed AgeSel strategy can significantly reduce the number of training rounds needed to achieve a targeted accuracy, as well as the communication cost. The influence of the algorithm hyper-parameter is also explored to manifest the benefit of age-based worker selection.
Contactless Interface Using Exhaled Breath and Thermal Imaging

A new type of interface using a conduction hot spot reflecting the user's intention is presented. Conventional methods using fingertips to generate conduction hot points cannot be applied to those who have difficulty using their hands or cold hands. In order to overcome this problem, an exhaling interaction using a hollow rod is proposed and extensively analyzed in this paper. A preliminary study on exhaling interaction demonstrated the possibility of the method. This paper is an attempt to develop and extend the concept and provide the necessary information for properly implementing the interaction method. We have repeatedly performed conduction hot-point-generation experiments on various materials that can replace walls or screens to make wide use of the proposed interfaces. Furthermore, a lot of experiments have been conducted in different seasons, considering that the surface temperature of objects also changes depending on the season. Based on the results of an extensive amount of experiments, we provide key observations on important factors such as material, season, and user condition, which should be considered for realizing contactless exhaling interfaces.
Comparative analysis of web applications implemented in: PHP and Python

This article presents a comparative analysis of two web applications implemented in PHP and Python. Test applications were created and equipped with the same functionality used in tests consisting in measuring the server response times to INSERT, SELECT, UPDATE and DELETE requests - handling database operations. The purpose of the research was to compare both languages in terms of selected criteria. Their performance, source code volume and popularity were compared.
Personalized, context-aware, and adaptable persuasive approach for encouraging physical activity among older adults

Seclusion and sedentary lifestyle are the main causes of many psychological and physical health problems. They may be among the top 10 causes of death and disability in the world. The pandemic crisis context of COVID has deepened these problems, especially for older adults who have been isolated, deprived of their relatives and of doing physical activities. In this paper, we introduce an adaptive, personalized, and context-aware persuasive platform to stimulate physical activities of older adults without deception or coercion. Our persuasion approach is customizable, in the sense that every older adult has its personal profile. It is also adaptive because it can use a persuasion loop to change the persuasion strategy when the older adult does not adhere to the proposed persuasion strategy. Furthermore, our persuasion approach is context-aware as it takes account of contextual location and weather information in the provision of the persuasion strategy. To validate our approach, we implemented “ActiveSenior”. Then, we carried out a large-scale challenge for one month to approve the results of our persuasive approach. The evaluation of the acceptance of our ActiveSenior system was encouraging as most of the interviewed participants were satisfied. In addition, the obtained results showed a marked improvement in the physical activity of older adults, quantified by the number of steps taken per day.
Abnormal Behavior Recognition Model of Power Grid Based on Multi-Scale Feature Fusion

Abstract Artificial intelligence technology is applied to power grid transmission and operation status identification to improve the intelligence level of power inspection. However, the existing grid anomalous behavior recognition model has the problem of unsatisfactory accuracy for small target detection. To address this problem, this paper proposes an abnormal behavior recognition model of power grid based on multi-scale feature fusion. First, the texture feature extraction network with a multi-scale mechanism is constructed to obtain object features. Secondly, the graph relation network between target and background is constructed. Finally, the grid abnormal behavior recognition model based on multi-scale feature fusion is constructed. The model proposed in this article is compared with the existing object recognition model in the simulation of the different datasets. In terms of the evaluation accuracy index, the proposed model is 93.27, which is improved by 2.18%.
The Application of Artificial Intelligence and Big Data Technology in Basketball Sports Training

INTRODUCTION: Basketball involves a wide variety of complex human motions. Thus, recognizing them with Precision is essential for both training and competition. The subjective perceptions and experiences of the trainers are heavily relied upon while training players. Big data and Artificial Intelligence (AI) technology may be utilized to track athlete training. Sensing their motions may also help instructors make choices that dramatically improve athletic ability. OBJECTIVES: This research paper developed an Action Recognition technique for teaching basketball players using Big Data, and CapsNet called ARBIGNet METHODS: The technique uses a network that is trained using large amounts of data from basketball games called a Whale Optimized Artificial Neural Network (WO-ANN) which is collected using capsules. In order to determine the spatiotemporal information aspects of basketball sports training from videos, this study first employs the Convolution Random Forest (ConvRF) unit. The second accomplishment of this study is creating the Attention Random Forest (AttRF) unit, which combines the RF with the attention mechanism. The study used big data analytics for fast data transmissions. The unit scans each site randomly, focusing more on the region where the activity occurs. The network architecture is then created by enhancing the standard encoder-decoder paradigm. Then, using the Enhanced Darknet network model, the spatiotemporal data in the video is encoded. The AttRF structure is replaced by the standard RF at the decoding step. The ARBIGNet architecture is created by combining these components. RESULTS: The efficiency of the suggested strategy implemented on action recognition in basketball sports training has been tested via experiments, which have yielded 95.5% mAP and 98.8% accuracy.
Evidential value of country location evidence obtained from IP address geolocation

Knowledge of the previous location of an Internet device is valuable information in forensics. The previous device location can be obtained via the IP address that the device used to access Internet services, such as email, banking, and online shopping. However, the problem with the device location using its IP address is the unknown evidential value, which is used to admit the evidence in the case. This work introduces a method to process free and constantly updated data to assess the evidential value of the IP country location. The evidential value is assessed for several countries by analyzing historical data over 8 years. Tampering with the location evidence is discussed, as well as its detection. The source code to replicate the results and to apply the updated data to future evidence is available.
Analysis of the usability and accessibility of public transport online timetables in selected cities in Poland

The purpose of the study was to verify various aspects related to the accessibility and usability of the timetable websites of two companies providing public transportation services. The issues addressed mainly concerned the graphic form of accessibility tools, the way of accessing these tools, their location in the site, the use of alternative names and descriptions. The objects of the research were two existing websites of two people transportation companies. In addition, two prototype sites were made for the purposes of the research. An experiment was prepared and conducted with 13 participants. A research method using the eye tracking technique was applied. The results were obtained in the form of heat maps, scan paths, task completion times, task performance ratings and the number of fixations on the object of interest. After analyzing the collected results, the following conclusions were drawn: the use of similar graphic symbols often confuses users; indirect access to accessibility tools generally makes it difficult for users to use them; the ill-conceived design of some page elements creates problems for content viewers; experimenting with the placement of some tools causes difficulty in finding them; a great deal of confusion on websites is introduced by unusual names, links, menu options; a large amount of content and the lack of prominence of what is most important cause problems in finding the right information; the lack of captioning of graphics often causes users to have to think longer and check if what they want to use is the right option; placing an item/information at the bottom of the page generally extends the time to reach it.
A Researching users' knowledge in the Field of Instant Messengers Security

Nowadays, many people contact other people through various types of social networks and instant messaging. However, these platforms are often not a secure way to exchange information among people. In recent times, most people have started to pay attention to the level of security offered by communicators and the technologies used in them, which results in the emergence of communicators focused primarily on safe communication. However, the question arises: "Are user actions not negatively affecting security?" Do users consciously choose instant messaging? Do they use modern methods to protect accounts? The conducted study confirmed the relationship between the user's actions and the security of com-munication. Additionally, the aspects most influencing the choice of communicator were found.
A Multisensor Fusion Scheme and Its Engineering Application

Abstract Sensor fusion is a key technology in ADAS (FCW, PCW, AEB, ACC, etc.) In this paper, the target-level fusion method is used to realize the recognition of key targets through the algorithms such as estimation of the driving area, screening for effective targets, targets matching, target fusion and target tracking. Simulation test and real vehicle test show that the algorithms can identify key targets steadily and screen out radar jamming targets such as guardrails and viaducts.
Using the Linked List Algorithm to Create a Learning Environment for Online Students

College and university students have requirements to pass prior income degrees on Thursdays. Their time is equally split between art and responsibilities to their own family. It is crucial that students have the option of taking online courses and reading on their own schedule. At the same time, many public colleges are unable to accommodate all of the college students who want to wait for their classes on campus. This problem is described as one of the methods for learning the Data Structure path. It demonstrates how to use RAM Memory by putting data in it and showing how to use it.. Things also offers a video that explains it in a more understandable way. The number of students enrolling in educational institutions has increased significantly over the coming years, and the institutions providing superior training have indicated a strong preference to provide outstanding online MBA programs. It is unquestionably a website with a video demonstrating one of the intricate methods for quickly storing information in the computer's hard drive. Additionally, this website has a dedicated education page that sets it apart from other educational websites by making it easier to teach students how to solve any issues they might encounter while taking a physical lesson. The website offers advice on how to delete the listing from memory and utilize the pop returned method, pop the front approach, posh returned approach, and posh front approach.
Attention and Pixel Matching in RGB-T Object Tracking

Visual object tracking using visible light images and thermal infrared images, named RGB-T tracking, has recently attracted increasing attention in the tracking community. Deep neural network-based methods becoming the most popular RGB-T trackers, still have to balance the robustness and the speed of calculation. A novel tracker with Siamese architecture is proposed to obtain the accurate object location and meet the real-time requirements. Firstly, a multi-modal weight penalty module is designed to assign different weights to the RGB and thermal infrared features. Secondly, a new pixel matching module is proposed to calculate the similarity between each pixel on the search and the template features, which can avoid bringing excessive background information versus the regular cross-correlation operation. Finally, an improved anchor-free bounding box prediction network is put forward to further reduce the interference of the background information. The experimental results on the standard RGB-T tracking benchmark datasets show that the proposed method achieves better precision and success rate with a speed of over 34 frames per second which satisfies the real-time tracking.