Reviewerly/exHarmony huggingface dataset:
Tag-Based Attention Guided Bottom-Up Approach for Video Instance Segmentation

Video Instance Segmentation is a fundamental computer vision task that deals with segmenting and tracking object instances across a video sequence. Most existing methods typically accomplish this task by employing a multi-stage top-down approach that usually involves separate networks to detect and segment objects in each frame, followed by associating these detections in consecutive frames using a learned tracking head. In this work, however, we introduce a simple end-to-end trainable bottom-up approach to achieve instance mask predictions at the pixel-level granularity, instead of the typical region-proposals-based approach. Unlike contemporary frame-based models, our network pipeline processes an input video clip as a single 3D volume to incorporate temporal information. The central idea of our formulation is to solve the video instance segmentation task as a tag assignment problem, such that generating distinct tag values (here each tag could be any arbitrary value between 0 and 1) corresponding to different objects essentially separates individual object instances across the video sequence. To this end, we propose a novel spatio-temporal tagging loss that allows for sufficient separation of different objects as well as necessary identification of different instances of the same object. Furthermore, we present a tag-based attention module that improves instance tags, while concurrently learning instance propagation within a video. Evaluations demonstrate that our method provides competitive results on YouTube-VIS and DAVIS’19 datasets, and has minimum run-time compared to other state-of-the-art performance methods.
An Overview of Cybersecurity and Proposed Framework of Security &amp;amp; Protection

Cyber-security uses to manage a group of technologies used for protecting data, programs and networks in general from un-authorized access. The field of cybersecurity is vital because of growing usage of computers as well as smart devices. The internet technology has made the world smaller, while you can search and know what is happening in the world from your place. Furthermore, with the increasing of security level in the systems and networks, the hacking or harming level is increasing rapidly as well. Companies providing cloud computing are looking to be efficiently secured with modern encryption technologies to prevent any un-authorized access to their systems. In this research paper we have discussed cybersecurity and the need, possible threats with security algorithms and comparison among some of them. A proposed security framework, Recommendations with future work is given as well.
Enhanced Feature Alignment with Heterogeneous Relations for Collaborative Filter

User and item modeling are critical tasks in recom-mender systems, which emerge to leverage implicit or explicit user interactions. Heterogeneous relational learning is promising to depict the implicit and explicit interactions among users and items. Graph Neural Networks showed significant advantages on modeling heterogeneous user behaviors, and relations among users and items. It is promising to exploit its utility for rec-ommendation. This work aims at user and item representations according to user behaviors in recommendation and explicitly modeling heterogeneous relations of user-user, item-item, and user-item types based on user behaviors. We propose a unified heterogeneous graph model to incorporate these heterogeneous relations. Moreover, to fully exploit these heterogeneous relations, we realize an enhanced feature alignment to refine the represen-tations of users and items in the model. The proposed model was applied on Amazon Book, Gowalla, and Yelp 2018 datasets and compared with known models. Extensive experiments demon-strate our model has significant improvement on the Normalized Discounted Cumulative Gain metric relative to the state of the art collaborate filter methods, e.g., 262.7% for NGCF, and 74.0% for MGCCF in terms of NDCG@20.
An Attention Based CNN with Temporal Hierarchical Deployment for AVS3 Inter In-loop Filtering

Convolutional Neural Network (CNN) based in-loop filter in video coding has demonstrated its superiority in benefiting coding efficiency and enhancing visual quality. In this paper, we develop a lightweight CNN-based in-loop filter for AVS3 encoder. The proposed network consists of several residual blocks with two attention branches, namely Dual Attention Network (DAN). The added channel attention branch and spatial attention branch can take advantage of the correlation between channels and pixels, improving the quality of reconstructed frames. In addition, by analyzing the inter prediction reference structure, we propose a temporal hierarchical deployment strategy to incorporate DAN into AVS3 video encoder. Therefore reconstructed frames with different distortions and referenced levels can be enhanced according to their temporal layer. Experiments prove the effectiveness of our strategy and results show our method achieves up to 6.57% and on average 3.64% BD-rate reduction on Y component under Random Access configuration.
Construction and Empirical Analysis of College Students' Job Satisfaction Index Model Using Artificial Intelligence

Employers' feedback on new college graduates' performance is a crucial piece of information that schools must consider to identify the relevance and responsiveness of their curriculum, programs, and services. Hence, in this paper, the artificial intelligence assisted satisfaction index model (AI-SIM) has been proposed to identify the college students' job satisfaction. Computer systems with advanced artificial intelligence can engage in reasoning, sensing, and responding in the most dynamic and complex environments. Artificial intelligence systems are being adopted rapidly by organizations to manage their workforce. This article aims to present the research results where contract the assessment made by graduates of the training at the University with necessary professional competences in the labour market. Job satisfaction toward colleague has the highest mean, meanwhile opportunities for promotion are the lowest. The implication of college student volunteer's systems and practices are discussed.
Mutual Information-Guided GA for Bayesian Network Structure Learning

Bayesian network structure learning (BNSL) from data is an NP-hard problem. Genetic algorithms are powerful for solving combinatorial optimization problems, but the lack of effective guidance results in slow convergence and low accuracy regarding BNSL. To address this problem, we propose a mutual information (MI) guided genetic algorithm (MIGA) for BNSL in this paper, which uses MI to effectively search BN structures. In the initialization phase of MIGA, the population is generated by adding additional constraints based on MI to reach a higher score without losing diversity. By employing normalized MI and defining the population support, the potential dominance in the population can be identified and then used to design a novel crossover operator in order to preserve the dominant genes with a higher probability. Moreover, with the guidance of MI for removing loops from the structures, infeasible solutions can be handled in a straightforward and practical way. The proposed MIGA is evaluated on eleven well-known benchmark datasets and compared with four GA-based methods and four other state-of-the-art BNSL algorithms. Experimental results show that MIGA outperforms the compared algorithms in convergence and learning accuracy.
Normalization in Task-Specific Applications

As previously stated, normalization methods can be wrapped as general modules, which have been extensively integrated into various DNNs to stabilize and accelerate training, probably leading to improved generalization. For example, BN is an essential module in the state-of-the-art network architectures for computer vision (CV) tasks [1–6], and LN is an essential module in natural language processing (NLP) tasks [7–9].
PERFECTIONISM IN THE STRUCTURE OF PROFESSIONAL MOBILITY OF STUDENTS – FUTURE RAILWAY WORKERS

Railway transport is a highly scientific segment of the transport industry. The high rate of technical, technological, informational, socio-economic and structural changes in the industry complicates the process of professionalization of young specialists, consisting of two main stages: professional adaptation and professional development. A specific feature of the personnel policy of the Joint Stock Company "Russian Railways" (JSCo "RZD") is the systematic horizontal and vertical rotation of personnel. This makes it possible to activate the internal activity of employees aimed at replenishing specialized knowledge and skills, but increases the level of influence of socio-psychological risks associated with joining a team, effective communication, maintaining labor discipline, making professional decisions in conditions of high personal responsibility. This situation necessitates the formation of professional mobility of future railway transport specialists as an integrative personality characteristic that allows them to successfully implement administrative and managerial functions. The totality of qualities that motivate development, improvement, self-realization and effort includes the phenomenon of perfectionism. The results of a pilot study of the initial level of development of characteristics in the structure of perfectionism and their correlation with the level of socio-psychological adaptation proved the need for the formation of indicators of positive (healthy perfectionism) in the process of professional training of students. The study involved 4th year students of the Samara State Railway University (96 people). The purposeful development of professional mobility of students-future specialists of railway transport will be facilitated by active forms and means of education (multimedia technologies, interactive tools, project activities, modeling and role distribution), stimulating motivation to achieve success, developing constructive communication skills, leadership qualities and abilities to stage and achieving goals.
A Proxy Re-encryption Scheme of Medical Data Sharing Based on Consortium Blockchain

In order to solve the problem of sharing medical data among different hospitals, this paper proposes a multi-party secure sharing scheme of medical information based on a consortium blockchain. Based on proxy re-encryption technology and user identity attributes, the scheme divides the permissions of data users in fine granularity, and solves the problems of data tracking and access control, uses cloud computing to store medical data, reduces the capital investment of hospital hardware equipment, reduces maintenance costs, and uses NTRU encryption technology to ensure the anonymity and anti-collusion security of medical data. Using Federation chains to store and record data operations ensures the safe sharing of data. The performance evaluation results show that the proposed re-encryption scheme for shared medical data agents based on a consortium blockchain has higher superiority in data protection, privacy security, rights allocation, etc. Compared with blockchain work in recent years, the result shows that the scheme has certain superiority. The purpose of this scheme is to provide new ideas for the safe sharing of medical data in the future.
Help from Meta-Path: Node and Meta-Path Contrastive Learning for Recommender Systems

Recently, contrastive learning alleviates data sparsity issues and improves the performance of the Graph Neural Network (GNN) recommender models by employing graph structure dropout augmentations. However, these models still face following limitations: (1) Information loss. Dropout may discard helpful information. (2) Insufficient utilization of path-level information. Meta-path is carried numerous high-order information, which has not been well considered in these models. To this end, in this paper, we propose a novel framework, Node and Meta-Path Contrastive Learning for Recommender Systems (NPCRS), which utilizes meta-path to capture path-level information for model learning. Specifically, our approach first generates a meta-path view on the user-item bipartite graph by leveraging meta-path instead of random dropout. Then, we learn the node representation on a user-item bipartite graph and meta-path view to capture both node and path-level information simultaneously. Further, a multi-positive sample mechanism is introduced to define positive and negative samples for contrastive learning. Finally, NPCRS utilizes contrastive learning to learn a more informative node representation. We evaluate the proposed model using three real-world datasets and our experimental results show that our model significantly outperforms the state-of-the-art approaches.
Mapping of the magnetic susceptibility of soils of the city of Chaykovskiy

The article presents the results of an assessment of the heterogeneity of the volumetric magnetic susceptibility of soils in the city of Chaikovsky, Perm Territory, based on the results of its spatial modeling. Determination of magnetic susceptibility makes it possible to estimate the concentration of technogenic magnetic particles in urban soils. Their sources in the atmosphere and soil cover of urbanized landscapes are emissions from vehicles, thermal power plants, and industrial enterprises. The non-stoichiometric technogenic magnetite-maghemite complex of minerals in urban soils has an abnormally high magnetic susceptibility and contains potentially dangerous chemical elements belonging to the heavy metals (HM) group in the composition of the crystal lattice of magnetic particles. Cartographic materials make it possible to assess the environmental risk for city residents, identify unfavorable areas and conduct a spatial analysis of the relationship between their location and pollution sources. The purpose of the research is spatial modeling of the magnetic susceptibility of soils in the city of Chaikovsky. The research area covers a section of the city with a total area of 22 km². Spatial modeling was carried out using geostatistical and deterministic methods based on 140 points. As a result of cross-validation, it was found that the most accurate interpolation method is “Ordinary Kriging” with the help of which the boundaries of the contaminated areas of the city of Chaikovsky are established. Within these boundaries, soil samples were taken, in which the content of nickel, copper, zinc, and lead was determined. According to the data obtained, areas with high magnetic susceptibility of soils have higher concentrations of heavy metals. To bring the research results to the administration and residents of the city, a Web GIS application has been developed, access to which is organized through the GitHub platform.
Cooperative Deep Unsupervised Anomaly Detection

In last years deep learning approaches to anomaly detection are becoming very popular. In most of the first methods the paradigm is to train neural networks initially designed for compression (Auto Encoders) or data generation (GANs) and to detect anomalies as a collateral result. Recently new architectures have been introduced in which the expressive power of deep neural networks is associated with objective functions specifically designed for anomaly detection. One of these methods is $$\textit{Deep-SVDD}$$ which, although created for One-Class classification, has been successfully applied to the (semi-)supervised anomaly detection setting. Technically, $$\textit{Deep-SVDD}$$ technique forces the deep latent representation of the input data to be enclosed into an hypersphere and labels as anomalies data farthest from its center. In this work we introduce $$\textit{Deep-UAD}$$ , a neural network approach for unsupervised anomaly detection where, iteratively, a network similar to that of $$\textit{Deep-SVDD}$$ is alternatively trained with an Auto Encoder and the two networks share some weights in order for each network to improve its training by exploiting the information coming from the other network. The experiments we conducted show that the performances obtained by the proposed method are better than the ones obtained both by deep learning methods and standard shallow algorithms.
Minimising the duration of a system-controlled virtual reality tour

The duration of a virtual reality tour can be a crucial factor in affecting user experience. Lengthy exposure to a virtual environment can cause severe health issues similar to motion sickness, and wearing the Head-Mounted Display for a long duration can make the user fatigued. Minimising the duration of VR exposure can always help in addressing these issues. In this work, we have proposed an approach to minimise the duration of a system-controlled virtual tour by optimising the path connecting all the sites of the virtual environment. To optimise the duration, we theoretically compute the optimal time and the path to cover all the places of a virtual environment by reducing the problem to the famous Vehicle Routing Problem (VRP). Following our approach, we have also created a VR tour for the largest river island in the world, Majuli (Assam, India).
Countermeasure of Polluting Health-Related Dataset for Data Mining

Nowadays, machine learning is widely used in a variety of applications, but it still faces many security challenges. Among them, the security of dataset is particularly important, because the data set is the key factor in achieving high correctness for machine learning. Recently, it becomes more difficult for an attacker to directly modify or attack the machine learning models because these models are setup usually in a well-known and well-designed format. However, the attackers can easily manipulate the dataset in various ways. Therefore, we develop countermeasures of polluting o a health-related dataset for data mining, which is robust Data Washing, an algorithm based on denoising autoencoder. It effectively alleviates damages to datasets caused by poisoning attack. We implement several DNN models for different datasets. The proposed Our robust Data Washing algorithm efficiently recovers the poisoning dataset and detect several attacks with a high accuracy rate.
Hot-Start Transfer Learning Combined with Approximate Distillation for Mongolian-Chinese Neural Machine Translation

When parallel training data is scarce, it will affect neural machine translation. For low-resource neural machine translation (NMT), transfer learning is very important, and the use of pre-training model can also alleviate the shortage of data. However, the good performance of common cold-start transfer learning methods is limited to the cognate language realized by sharing its vocabulary. Moreover, when using the pre-training model, the combination of general fine tuning methods and NMT will lead to a serious problem of knowledge forgetting. Both methods have some defects, so this paper optimizes the above two problems, and applies a new training framework suitable for low correlation language to Mongolian-Chinese neural machine translation. Our framework includes two technologies: a) word alignment method under hot-start, which alleviates the problem of word mismatch between the transferred subject and object in transfer learning. b) approximate distillation,not only retains the pre-trained knowledge, but also solves the forgetting problem, so that the encoder of NMT has stronger language representation ability. The results show that BLEU is increased by 3.2, which is better than ordinary transfer learning and multilingual translation system.
The Implementation Of Hand Gestures On The Fountain Burst Pattern

The technology has now been implemented to display variations of the fountain burst. The fountain burst is made dynamic to be an attraction for people who see it. In this research, the PAJ7620 sensor was used to capture hand gestures that were inputted by the user, then processed by the microcontroller, then displayed a burst of fountains according to the pattern. There are four hand gestures, to the right, to the left, upwards, and downwards. Four fountain burst patterns follow the hand gestures input by the user. The testing of this research showed that the system could capture hand gestures and display a fountain burst pattern with a 100% success rate.
RELNet-An Efficient and Lightweight Classification Network

In the present era of information explosion, the ability of the computer to replace artificial recognition images are widely used in our life, such as face recognition, intelligent robot, information filtering and so on. Driven by the rapid development of deep learning technology and led by Convolutional Neural Network (CNN), these applications are maturer and maturer. In this paper, we designed a network for image classification on the CIFARtO, CIFARtOO and MNIST which is based on the convolutional neural network. we independently set the hyper-parameters and design the model structure to generate a convolutional neural network model with a small number of convolutional layers. Based on understanding the ResNet network structure, we got our network named RELNet. This model has certain advantages over other models with a similar number of convolutional layers, and it is an improved model that can achieve certain improvements in various evaluation indicators. In addition, this model has a faster convergence speed and fewer training parameters, which is considerable progress compared with other similar models.
Enriching Pre-Trained Language Model with Multi-Task Learning and Context for Medical Concept Normalization

Herein, we focus on the problem of automatically medical concept normalization in social media posts. Specifically, the task is to map medical mentions within social media texts to the suitable concepts in a reference knowledge base. We propose a new medical concept normalization model using multi-task learning. The model uses BioBERT to encode mentions and their contexts, and classifies their concept IDs and types of mention. We evaluate our approach on two datasets and achieve new state-of-the-art performance.
Information and communication technologies in the implementation of physical education in the university

Human-computer interaction is a dynamically developing area that being introduced into various spheres of life. Innovative processes, in particular in education, guide the academic teaching staff on new development, improvement of their knowledge, skills, mastery of new types of activities, which is an urgent area of higher education. The article substantiates the need for teachers of the university to develop skills in information and communication technologies, including search skills, information exchange, communication, creating conditions for systematically improving the quality of education, expanding opportunities for self-education. The authors considered the directions of information and communication technologies application in the field of physical education on the example of implementation in the online format of the additional educational program "Functional fitness training". Groups of software tools have been allocated to perform specific tasks of distance learning. The use of empirical methods made it possible to analyze the state of the research problem and conclude that in the era of informatization, mastering of the competencies of information education by the teachers of physical education of a higher educational institution, contributes to improving the quality of education, training specialists who are fluent in mobile and Internet technologies in building their physical development path.