Reviewerly/exHarmony huggingface dataset:
Video Content Analysis of Human Sports under Engineering Management Incorporating High-Level Semantic Recognition Models

In this paper, a high-level semantic recognition model is used to parse the video content of human sports under engineering management, and the stream shape of the previous layer is embedded in the convolutional operation of the next layer, so that each layer of the convolutional neural network can effectively maintain the stream structure of the previous layer, thus obtaining a video image feature representation that can reflect the image nearest neighbor relationship and association features. The method is applied to image classification, and the experimental results show that the method can extract image features more effectively, thus improving the accuracy of feature classification. Since fine-grained actions usually share a very high similarity in phenotypes and motion patterns, with only minor differences in local regions, inspired by the human visual system, this paper proposes integrating visual attention mechanisms into the fine-grained action feature extraction process to extract features for cues. Taking the problem as the guide, we formulate the athlete's tacit knowledge management strategy and select the distinctive freestyle aerial skills national team as the object of empirical analysis, compose a more scientific and organization-specific tacit knowledge management program, exert influence on the members in the implementation, and revise to form a tacit knowledge management implementation program with certain promotion value. Group behavior can be identified by analyzing the behavior of individuals and the interaction information between individuals. Individual interactions in a group can be represented by individual representations, and the relationship between individual behaviors can be analyzed by modeling the relationship between individual representations. The performance improvement of the method on mismatched datasets is comparable between the long-short time network based on temporal information and the language recognition method with high-level semantic embedding vectors, with the two methods improving about 12.6% and 23.0%, respectively, compared with the method using the original model and with the i-vector baseline system based on the support vector machine classification method with radial basis functions, with performance improvements about 10.10% and 10.88%, respectively.
Uncertainty-Aware Domain Adaptation for Action Recognition

Domain Adaptation (DA) has been a crucial topic for action recognition, as the test set and training set are not always subject to the identical distribution, which will lead to significant performance degradation. Existing researches focus on DA methods based on the entire videos, ignoring the different contributions of different samples and regions. In this paper, we propose an uncertainty-aware domain adaptation method for action recognition from a new perspective. The aleatoric uncertainty is firstly used in the classifier to improve the performance by alleviating the impact of noisy labels. Then the aleatoric uncertainty calculated with Bayesian Neural Network is embedded in the discriminator to help the network focus on the spatial areas and temporal clips with lower uncertainty during training. The spatial-temporal attention map is generated to enhance the features with the guidance of backward passing. Extensive experiments are conducted on both small-scale and large-scale datasets, and the results indicate that the proposed method achieves competitive performance with fewer computational workloads. The code is available at: https://github.com/ChickAndMushroom/Uncertainty-aware-DA-for-AR.
Smart Bubble Sort: A Novel and Dynamic Variant of Bubble Sort Algorithm

In the present era, a very huge volume of data is being stored in online and offline databases. Enterprise houses, research, medical as well as healthcare organizations, and academic institutions store data in databases and their subsequent retrievals are performed for further processing. Finding the required data from a given database within the minimum possible time is one of the key factors in achieving the best possible performance of any computer-based application. If the data is already sorted, finding or searching is comparatively faster. In real-life scenarios, the data collected from different sources may not be in sorted order. Sorting algorithms are required to arrange the data in some order in the least possible time. In this paper, I propose an intelligent approach towards designing a smart variant of the bubble sort algorithm. I call it Smart Bubble sort that exhibits dynamic footprint: The capability of adapting itself from the average-case to the best-case scenario. It is an in-place sorting algorithm and its best-case time complexity is Ω(n). It is linear and better than bubble sort, selection sort, and merge sort. In average-case and worst-case analyses, the complexity estimates are based on its static footprint analyses. Its complexity in worst-case is O(n<sup>2</sup>) and in average-case is Θ(n<sup>2</sup>). Smart Bubble sort is capable of adapting itself to the best-case scenario from the average-case scenario at any subsequent stages due to its dynamic and intelligent nature. The Smart Bubble sort outperforms bubble sort, selection sort, and merge sort in the best-case scenario whereas it outperforms bubble sort in the average-case scenario.
Handwritten Gurmukhi Digit Prediction using Convolutional Neural Network with Keras and Tensorflow

Intelligent Character Recognition is a term which is specifically used for the recognition of handwritten character or digit. It is a prominent research area of computer vision field of machine learning or deep learning which trained the machine to analyze the pattern of handwritten character image and identify it. Recognition of handwritten character is a hard process because single person can handwrite the same text in number of ways by making a little variation in holding the pen. Handwriting has no specific font style or size. It differs person to person or more specifically it differs how one is holding the pen. Deep Leaning has brought the breakthrough performance in this research area with its dedicated models like Convolutional Neural Network, Recurrent Neural Network etc. In this paper, we have trained model with Convolutional Neural Network with different number of layers and filters over 10,559 handwritten gurmukhi digit images and validate over 1320 images. Consequently we could achieve the maximum accuracy of 99.24%.
Classification Models for Medical Data with Interpretative Rules

The raging of COVID-19 has been going on for a long time. Thus, it is essential to find a more accurate classification model for recognizing positive cases. In this paper, we use a variety of classification models to recognize the positive cases of SARS. We conduct evaluation with two types of SARS datasets, numerical and categorical types. For the sake of more clear interpretability, we also generate explanatory rules for the models. Our prediction models and rule generation models both get effective results on these two kinds of datasets. All explanatory rules achieve an accuracy of more than 70%, which indicates that the classification model can have strong inherent explanatory ability. We also make a brief analysis of the characteristics of different rule generation models. We hope to provide new possibilities for the interpretability of the classification models.
Software Developer's Engagement Recognition Based on In-Ide Activity Sensing

Work engagement is a specific psychological state of workers being highly vigorous and energetically involved in work. In many fields, engaged employees often achieve higher performance and produce better service than others. Due to its strong correlations with productivity and job-related well-being, understanding employees’ state of engagement during work is critical. In this paper, we study the recognition of software developers’ work engagement states based on their in-IDE activities by tackling practical problems and challenges of data collection and model training. We propose an approach to non-intrusively track activities performed by developers in the IDE and subsequently recognize their work engagement states based on the activities with a theory-guided machine learning model. The model is trained in a semi-supervised learning way to address the practical problem of sparse training labels. We develop a prototype system and organize a field study over a month with 29 software developers in a large-sized IT company in China to collect a real-world dataset. The results show that our approach is effective by achieving a recognition accuracy of 86.8% compared to random forest. We also conduct a simulation experiment to show the robustness of our approach with a reducing number of training labels.
A Novel Adaptable Framework for Covert Communications in Anonymized Protocols

As digital trust has declined, services purporting to provide privacy and anonymity have become increasingly popular in today&#x0027;s online environment. While there are several examples of these types of applications, blockchain-based services like Bitcoin and Ethereum have emerged as a potential answer to some of these privacy concerns. Unfortunately, many of the same features that facilitate that privacy and anonymity can also be leveraged by nefarious actors to transmit and store information covertly. These features can also be used by government and military organizations for communications purposes. In this paper, we present a generic information hiding model incorporating anonymity that builds on existing classical steganographic models like the Prisoners&#x0027; Problem. We then analyze our model with regards to blockchain protocols and present a novel blockchain-based address embedding scheme. Finally, we implement our scheme using the Ethereum platform.
Improving Shallow Neural Networks via Local and Global Normalization

Convolutional neural networks (CNNs) have achieved great success in computer vision. In general, CNNs can achieve superior performance depend on the deep network structure. However, when the network layers are fewer, the ability of CNNs is hugely degraded. Moreover, deep neural networks often with great memory and calculations burdens, which are not suitable for practical applications. In this paper, we propose Local Feature Normalization (LFN) to enhance the local competition of features, which can effectively improve the shallow CNNs. LFN can highlight the expressive local regions while repressing the unobvious local areas. We further compose LFN with Batch Normalization (BN) to construct an LFBN by two ways of concatenating and adding. LFN and BN are excel at handle local features and global features, respectively. Therefore LFBN can significantly improve the shallow CNNs. We also construct a shallow LFBN-Net by stacking the LFBN and conduct extensive experiments to validate it. LFBN-Net has achieved superior ability with fewer layers on various benchmark datasets. And we also insert the LFBN to exiting CNNs. These CNNs with the LFBN all achieve considerable performance improvement.
From Laws and Decrees to a Legal Dictionary

The mass of information in the legal field, which is constantly increasing, has generated a capital need to organize and structure the content of the available documents, and thus transform them into an intelligent guide, capable of providing complete and immediate answers to queries in natural language. Therefore, the Question Answering System (QAS), which is an application of the Automatic Language Processing domain (NLP), responds perfectly to this need by offering different mechanisms to provide adequate and precise answers to questions expressed in natural language. The general context of our work is the construction of an ontology-based legal question-answering system, allowing users to ask questions about desired information using natural language without having to browse through documents.In this article, we will mainly focus on the construction of a legal dictionary from textual laws and decrees, for the natural language automatic processing platform NooJ. The legal dictionary that we propose to build from laws and decrees, will bring together the terminological material that will serve as a linguistic resource for the automatic processing of users’ questions in natural language, and in particular during the information extraction step which is necessary for the formulation of SPARQL queries equivalent to users’ questions.
Learning Based Superpixel Merging Model for Image Segmentation

Most conventional segmentation methods are superpixel-based. Recently, the convolutional network (CNN) has been adopted in image segmentation. However, most existing CNN-based segmentation algorithms are pixel-wise. Due to the irregular shape and the non-fixed size of superpixels, it is hard to apply superpixels into the CNN architecture directly. In this work, several ideas are proposed to solve this problem. Instead of applying the whole image as the input directly, we apply a square patch that contains only two superpixels as the input of the CNN. Also, instead of generating the segmentation result directly, the output of the CNN is whether the two superpixels should be merged. The proposed algorithm integrates the merits of conventional superpixel-based methods, feature-based methods, and CNN-based methods. Simulations show that the proposed algorithm can achieve very high accurate segmentation results and outperform state-of-the-art methods in all metrics.
Social Media and Library Services

The chapter discussed the integration of social media to library and information services rendered in libraries. The authors paid more attention to how this is possible in Nigeria reviewing what other authors have done in related areas and the current reality in our libraries. The authors gave a background information of what social media, library, and library services are. The chapter is broken down into the following sub-headings for easier discussion and understanding: overview and types of social media that libraries can adopt, services rendered by libraries social media can be integrated into, benefits of social media to library services, challenges of social media use in Nigerian libraries, and the future of social media in a world of changing technologies.
Architecture of an On-Time Data Transfer Framework in Cooperation with Scheduler System

Technological advancement in networking and IoT have given researchers new methods or techniques to perform numerical analysis and simulation with the latest data observed on IoT sensors and other measurement devices. In general, large-scale simulations necessitate high-performance computing (HPC) systems. Such HPC systems are operated in a shared manner among researchers. Therefore, it becomes inherently difficult for researchers to use the latest observation data generated on remote data sources for their simulations. To enable researchers to utilize fresh data on a remote data source for computation, we propose an on-time data transfer framework that enables the execution of jobs with fresh data generated on a remote site data on a shared HPC system by extending the SLURM scheduler. The proposed framework consists of two functions: Job pinning and On-time data transfer. With the job pinning function, the proposed framework prevents the scheduling algorithm from rearranging the scheduled start time of jobs. The on-time data transfer function is in charge of data transfer from a remote site to the data transfer node. It attempts to complete the data transfer at just the time of the pinned start time of jobs. The evaluation in this paper indicates that the proposed framework can keep data freshness high and minimize the job waiting time for data transfer.
INFORMATION AND COMMUNICATION TECHNOLOGIES AS A “NEW LEARNING” APPROACH IN HIGHER EDUCATION

In the modern world, qualitative transformations are constantly taking place. Technology is a part of the effective educational system. The relevance of this topic is caused by the global introduction of computer technologies in all spheres of activity, the formation of new communications and a highly automated information environment have become not only the beginning of the transformation of the traditional education system, but also the first step towards the formation of an information society. The main existing segments of the education system are educational programs and curricula, teacher qualifications, education management and support infrastructures in the field of information technologies (IT). According to UNESCO, information and communication technology (ICT) can complement, enrich and transform education for the better [1]. These values are also significant for society, since the rapid development of technologies in all areas of science, culture, production involves the use of the creative potential of educated people not only in the field of management, but also for servicing technologies. Therefore, the “new learning” method is considered as one of the priority directions of informatization of society. Integration of ICT is being integrated into educational system and we can call it as a “new learning” approach.
Diaper Wetness Detection Based on Sensor and Using IOT

Wearing a wet diaper for an extended amount of time will cause dermatitis. To avoid the dermatitis, diapers should be modified in a very timely manner. In this paper, diaper wetness detection system is introduced that sends a notification to parent's smart devices whenever the elimination event happens. The IOT based system detects the wetness with the help of DHT11 sensor which senses the increase in temperature and humidness on outside of the diaper. It is a little size and reusable wetness detection system that is hooked up outwardly to the baby diaper.
Listen to Social Media Users: Mining Chinese Public Perception of Autonomous Vehicles after Crashes

The mass adoption of autonomous vehicles (AVs) can bring safety, environmental and social benefits. However, AVs crashes are currently viewed as significant events, often attracting public and media attention. Understanding the changes in public perception of AVs is imperative, which is likely to affect the future evolution of AVs significantly. Respondents have few opportunities to express their views freely in survey-based studies relying on structured questionnaires (e.g., Likert scale). We collected 21789 comments about the NIO crash in August 2021 and 20322 comments about the Tesla brake crash in April 2021 from Sina Weibo and Tik Tok in China. We applied advanced text mining technology (Latent Dirichlet Allocation topic model, text network analysis, and sentiment analysis) to understand changes in public perception of AVs before and after the crashes. The results of topic models show that the public has expressed more blame and concern for AVs after the crash. We also conclude that the public has misunderstandings about the current development of AVs. Furthermore, it is found that the public's awareness of AVs is closely connected with publicity from automobile companies and media through text network analysis. The sentiment analysis results show that the public's negative sentiment towards AVs is higher than positive, and women are more negative than men after the crash. Moreover, the public's attitudes towards Tesla are even more pessimistic. This research provides new ideas and methods for timely understanding of public perception after AVs crashes. Findings contribute to putting forward a wide range of policy recommendations. For instance, the government should strengthen the supervision of automobile companies and the media's supervision and guide the public to understand and use AVs correctly.
Large-Scale Contact Tracing, Hotspot Detection, and Safe Route Recommendation

Recently, the COVID-19 pandemic created a worldwide emergency as it is estimated that such a large number of infections are due to humanto-human transmission of the COVID-19. As a necessity, there is a need to track users who came in contact with users having travel history, asymptomatic and not yet symptomatic, but they can be in the future. To solve this problem, the present work proposes a solution for contact tracing based on assisted GPS and cloud computing technologies. An application is developed to collect each user's assisted GPS coordinates once all the users install this application. This application periodically sends assisted GPS data (coordinates) to the cloud. To determine which devices are within the permissible limit of 5 m (tunable parameter), we perform clustering over assisted GPS coordinates and track the clusters for about t mins (tunable parameter) to allow the measure of spread. We assume that it takes around 3–5 mins to get the virus from an infected object. For clustering, the proposed M-way like tree data structure stores the assisted GPS coordinates in degree, minute, and second (DMS) format. Thus, every user is mapped to a leaf node of the tree. The crux of the solution lies at the leaf node. We split the "seconds" part of the assisted GPS location into m equal parts (a tunable parameter), which amount to d meter in latitude/longitude. Hence, two users who are within d meter range will map to the same leaf node. Thus, by mapping assisted GPS locations every t mins (usually t = 2:5 mins), we can find out how many users came in contact with a particular user for at least t mins. Our work's salient feature is that it runs in linear time O(n) for n users in the static case, i.e., when users are not moving. We also propose a variant of our solution to handle the dynamic case, that is, when users are moving. Besides, the proposed solution offers potential hotspot detection and safe-route recommendation as an additional feature, and proof-of-concept is presented through experiments on simulated data of 2/4/6/8/10M users.
Fast Approximate All Pairwise CoSimRanks via Random Projection

Given a graph G with n nodes, and two nodes \(u,v\in G\), the CoSimRank value s(u, v) quantifies the similarity between u and v based on graph topology. Compared to SimRank, CoSimRank is shown to be more accurate and effective in many real-world applications including synonym expansion, lexicon extraction, and entity relatedness in knowledge graphs. The computation of all pairwise CoSimRanks in G is highly expensive and challenging. Existing solutions all focus on devising approximate algorithms for the computation of all pairwise CoSimRanks. To attain a desired absolute accuracy guarantee \(\epsilon \), the state-of-the-art approximate algorithm for computing all pairwise CoSimRanks requires \(O(n^3\log _2(\ln (\frac{1}{\epsilon })))\) time, which is prohibitively expensive even \(\epsilon \) is large. In this paper, we propose \(\mathtt {RPCS}\), a fast randomized algorithm for computing all pairwise CoSimRank values. The basic idea of \(\mathtt {RPCS}\) is to approximate the \(n\times n\) matrix multiplications in CoSimRank computation via random projection. Theoretically, \(\mathtt {RPCS}\) runs in \(O(\frac{n^2\ln (n)}{\epsilon ^2}\ln (\frac{1}{\epsilon }))\) time and meanwhile ensures an absolute error of at most \(\epsilon \) in each CoSimRank value in G with a high probability. Extensive experiments using six real graphs demonstrate that \(\mathtt {RPCS}\) is more than up to orders of magnitude faster than the state of the art. In particular, on a million-edge Twitter graph, \(\mathtt {RPCS}\) answers the \(\epsilon \)-approximate (\(\epsilon =0.1\)) all pairwise CoSimRank query within 4 h, using a single commodity server, while existing solutions fail to terminate within a day.