Reviewerly/exHarmony huggingface dataset:
Hand Gesture Controlled Contactless Elevator

In the existing pandemic situation, it is not safe for people to touch all the objects/things present around them. In urban areas, majority of human population use elevators in their everyday lives and they are ought to touch the elevator buttons to move from one place to another. This increases the risk of COVID-19 spread. To overcome this challenge, a contactless system has been developed by incorporating a sensor to capture the hand gestures and control elevator functions. In this way, the suggested technique prevents the manual pressing of elevator buttons. In the proposed system, People wave their hands in front of the sensor to select the desired floor number and trigger the elevator to move from the current to the desired floor.
DIAGNOSIS WITH THE HACKATHON ECOSYSTEM OF THE UNIVERSITY OF THE READINESS OF STUDENTS FOR RESEARCH ACTIVITIES

BACKGROUND AND OBJECTIVES. The changes in society associated with the country's transition to an innovative way of development radically change the nature and goals of labor activity. Profession under such conditions is considered by a person as a source of material well-being and gaining the desired social status. Professional self-determination occurs too late or is realized as forced, so the lack of opportunity to realize one's professional preferences has a negative impact on the individual and on society as a whole. As a result, in recent decades there has been a significant increase in the requirements of modern employers to the professional training of graduates of educational institutions. A young specialist must improve in the profession, be ready to change professional activity, i.e. be socially and professionally mobile, scientifically established, which can be realized with the help of Hackathon-ecosystem.METHODS. Collection of information, survey of students and teachers were carried out using a systematic approach on the basis of the Hackathon ecosystem. Processing of the obtained information, definition of scientific statements, generation of conclusions and practical recommendations were carried out with the help of the software product "Methodology of studying students' adaptation to dual education in higher education".FINDINGS. It is proved that the research activity of students is a necessary condition for their professional self-determination, acts as part of a holistic, long-term, dynamic process of entry into the profession and as a result of the choice and design of their future professional activity. Students' involvement in research activity largely determines the effectiveness of their social and professional development.CONCLUSION. Research activity of university students is an effective means of forming knowledge, abilities and skills necessary for personal and professional self-determination. It not only stimulates and motivates personal and intellectual development, contributes to the continuation of education, proves the degree of its readiness and acts as one of the guarantors of the success of the process of professional self-determination in general.
A Novel Discriminative Virtual Label Regression Method for Unsupervised Feature Selection

Unsupervised Feature selection is an important dimensionality reduction technique to cope with high-dimensional data. It does not require prior label information, and has recently attracted much attention. However, it cannot fully utilize the discriminative information of samples, which may affect the feature selection performance. To tackle this problem, in this letter, we propose a novel discriminative virtual label regression method (DVLR) for unsupervised feature selection. In DVLR, we develop a virtual label regression function to guide the subspace learning based feature selection, which can select more discriminative features. Moreover, a linear discriminant analysis (LDA) term is used to make the model be more discriminative. To further make the model be more robust and select more representative features, we impose the ℓ2,1-norm on the regression and feature selection terms. Finally, extensive experiments are carried out on several public datasets, and the results demonstrate that our proposed DVLR achieves better performance than several state-of-the-art unsupervised feature selection methods.
Design and Implementation of Encoder/Decoder Quantum Circuits using Decimal to Binary Conversion Method and vice versa

Quantum Computing is an emerging technology. Quantum Computer will mainly focus on the principles of quantum theory. We design some major data processing circuits like encoder and decoder i.e., <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$16\times 4$</tex> encoder and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$4\times 16$</tex> decoder using decimal to binary conversion method and vice versa. Where an encoder is used to encode an incoming signal into an encoded value. A decoder is used in conjugation with the encoder to the decoder, the encoded signals, and recover the original signal. For all the given possible inputs we got probability=1.
In Search of Friction

Considering the key mediating role that smartphones play in everyday life, a postphenomenological analysis to better understand how we have power over these devices, how these artifacts empower and simultaneously can overpower us, seems highly relevant. This article will show that in order to engage in such a much-needed postphenomenological analysis, we will first have to address three fundamental, methodological challenges. The first challenge is brought forth by the personalized interface of smartphones, hindering postphenomenologists to unravel the so-called multistability of the device through variational analysis, which typically is an anchoring point in their analysis. The second challenge is that the networked ontology of smartphones disrupts the ideal-typical hermeneutic relationship end-users have with their smartphone. The third, closely related challenge, comes with the general focus of postphenomenology on the everyday life, first-person experience of users, which leaves many, significant stabilities hidden behind the smartphone’s interface.
Effect of Self-efficacy, Self-regulated Learning Ability, Learner Participation on On-line Class Satisfaction of Nursing Students

Purpose: This study examined the effects of self-efficacy, self-regulated learning ability, and learner participation on the online satisfaction of nursing students. Methods: The participants of this study were 153 nursing students of a university in C province. The data were collected from December 24 to December 31, 2020. The data were analyzed using descriptive statistics, t-test, One-way ANOVA, Scheffe test, Pearson correlation coefficient, and multiple regression analysis in the SPSS 21.0/WIN program. Results: The factors affecting online class satisfaction were self-efficacy(β=.33, p<.001), self-regulated learning ability(β=.41, p<.001), and learner participation(β=.17, p<.05). These factors explain 50.8% of the variance in online class satisfaction. Conclusion: Universities need to develop on-campus programs that can enhance self-efficacy, self-regulated learning ability, and learner participation regularly to promote the online class satisfaction of nursing students.
A Comprehensive Analysis of Cyber Security Protection Approaches for Financial Firms: A Case of Al Rajhi Bank, Saudi Arabia

In the modern internet-connected society, technologies underpin almost every action in society. Although there have been positive effects of technologies in the organization, there have been forensic specialists indicating the issues and challenges with cyber security threats. The real-time conditions provide the capability of the organization in detecting, analyzing, and defending individuals against such threats. In this research project, the focus is on understanding the cyber security threats and the protection approaches to be utilized in safeguarding threats from financial institutions. With the Covid-19 pandemic, most of the financial firms, including Al Rajhi Bank, are utilizing technologies in their operations, and this has exposed them to cyber security threats. From the literature review conducted, the financial firms need to consider cyber security approaches including implementing triple DES, RSA, and blowfish algorithms in improving the security measures of the organizations.
Recognizing Hand Gesture in Still Infrared Images by CapsNet

In recent years, gesture recognition has been becoming a hot topic that attracts much attention from the computer vision community because of its great potential in many real-world applications. There is a need to design a robust hand-based gesture recognition algorithm to cope with hand gesture recognition tasks. Infrared image recognition has the characteristic of not being disturbed by illumination variation. As a promising alternative to Convolutional Neural Networks (CNN), Capsule Networks (CapsNet) can represent the orientations of features and capture the spatial relationships between features of an entity, which makes CapsNet possess higher generalization ability. In this paper, we propose IRHGR-CapsNet to investigate hand gesture recognition in still infrared images. To evaluate the testing accuracy, the convergence ability and the generalization ability of IRHGR-CapsNet comprehensively, we split the original dataset into three subsets according to different split proportions and get four different dataset split modes for experiments. We can achieve almost 100.00% testing accuracy on all the four dataset split modes. Meanwhile, we can also demonstrate that our proposed IRHGR-CapsNet has a strong convergence ability and generalization ability.
Discrete-Variable Quantum Key Distribution Based on Homodyne Detection

We propose and analyse a practical quantum key distribution protocol that combines the features of discrete and continuous-variable protocols. Our proposed protocol does not require the honest parties to share a phase reference.
A novel approach for identification and classification of verbs in Dogri language

Morphological analyser, POS data, Stemmer etc. are the basic tools required for any NLP tasks, which are not available for Dogri language which recently has been declared as an official language of J&K, UT. Because of the unavailability of the basic tools, it remains a very low resourced language. In this paper, we have presented a sub task for the development of morphological analyser for Dogri language. We have identified the morphological behaviour of the verbs and implemented the automatic process of the identification of the verbs in Dogri language using paradigm approach. The various forms of verb taken into consideration are specifically transitive, intransitive, non-finite, gerund and infinitives. The average accuracy attained in the process of identification of transitive, intransitive, non-finite, gerund and infinitive verbs is 80%, 83%, 76%, 93%, 88% respectively.
Representation Learning

With a machine learning approach and less focus on linguistic details, this gentle introduction to natural language processing develops fundamental mathematical and deep learning models for NLP under a unified framework. NLP problems are systematically organised by their machine learning nature, including classification, sequence labelling, and sequence-to-sequence problems. Topics covered include statistical machine learning and deep learning models, text classification and structured prediction models, generative and discriminative models, supervised and unsupervised learning with latent variables, neural networks, and transition-based methods. Rich connections are drawn between concepts throughout the book, equipping students with the tools needed to establish a deep understanding of NLP solutions, adapt existing models, and confidently develop innovative models of their own. Featuring a host of examples, intuition, and end of chapter exercises, plus sample code available as an online resource, this textbook is an invaluable tool for the upper undergraduate and graduate student.
Research on Follow-up Management of Blockchain Venture Capital Based on Cloud Computing

In recent years, constrained by the overall downturn of the global macro economy, the venture capital industry has encountered bottlenecks in raising funds and potential projects at the same time. In the past, “white horse” projects that have become popular based on the concept of “Internet +” have gradually decreased, and the hottest ones are now the “unicorn” project is difficult to control because of the accumulation of investment bubbles. Therefore, “hoarding goods for the winter” has become the main theme of the current venture capital industry. At the same time, venture capital companies have also begun to review the extensive investment concepts and management models of the past “throwing beans into soldiers”, and began to pay attention to the post-investment management of investment projects. This article studies the follow-up management of cloud computing-based blockchain venture capital.
Computing the Tandem Duplication Distance is NP-Hard

Related DatabasesWeb of Science You must be logged in with an active subscription to view this.Article DataHistorySubmitted: 29 July 2020Accepted: 08 September 2021Published online: 04 January 2022Keywordstandem duplication, text processing, formal languages, computational genomics, FPT algorithmsAMS Subject Headings68Q25, 68R10, 68W32Publication DataISSN (print): 0895-4801ISSN (online): 1095-7146Publisher: Society for Industrial and Applied MathematicsCODEN: sjdmec
Intelligent Early Warning Method Based on Drone Inspection

Commonly used UAV emergency inspection methods are executed by the instructions of the ground command center. The response rate depends on the stability of the communication network and the rapid response ability of the commander. The critical time window is fleeting, which is likely to cause unnecessary loss. Crisis rapid response capability has become the key to measuring system capabilities. In order to improve the system’s rapid response capability, a method of deploying decision-making agents on airborne computers and ground early warning systems is proposed. This early warning method uses key technologies such as multi-network integration, situation assessment, neural network architecture, deep learning, reinforcement learning, and intelligent cognitive reasoning to effectively ensure the effectiveness of crisis warning. The early warning method of the early warning system is as follows: the mission computer uniformly collects the flight control status parameters, the load status parameters and the load real-time data form a composite information flow. The task computer adopts the methods of protocol conversion, data classification, and danger recognition to the compound information flow to identify the crisis information and make a preliminary analysis and judgment of the crisis state. If it is determined that it is necessary to track the target in real time, the initial task assignment and parameter adjustment of the load are carried out, and the continuous tracking of the task target is carried out to realize the rapid response to the crisis on the edge side. At the same time, the composite data are downloaded to the command center. The command center performs the secondary crisis analysis and risk level determination and outputs the crisis plan deduced by the agent to realize the strategy assistance. The accuser refers to the plan strategy and issues instructions to the task computer, and the task computer receives it. Instruction and secondary adjustment and optimization of the load parameters. If there is a flight route adjustment instruction, the adjustment route will be sent to the flight controller, which greatly improves the flexibility and efficiency of handling the crisis in the UAV inspection process. By adopting this set of early warning methods, it can provide users with an updated, faster and more efficient way to realize the early warning requirements in drone inspections, which is a new breakthrough in the field of drone command methods.
Visual Analysis and Interactive Comparison for Heterogeneous Information Network Embedding Model

<p indent="0mm">To understand the differences between various heterogeneous information networks (HINs) embedding models, and to solve intricate and hidden problems in model evaluation, the comparative analysis method should include the following steps. First, the evaluation indicators and tasks are made consistent for all models. Then the parameters and structural features are extracted from the model training process and the complete and non-averaged evaluation results are retained for visualization. Based on the model parameters and characteristics data, we design and implement a visual analysis tool named HINCompare, which includes an overview of the distribution of basic evaluation indicators, a comparison view of recommended results, and a feature view of the local topology structure for model embedding. The tool allows developers to explore the common patterns of different feature aggregation methods of different models and the differences in their architectures. In addition, HINCompare shows user’s preferences for movie types and years with heat maps, which are combined with the contextual information of the recommended results for further analysis and evaluation. The system provides insights into black-box problems of models and increases interpretability by supplying information about the sources of the results. We conduct the preliminary evaluation study with Douban movie data to verify the effectiveness of the system.
Color and Texture Based Image Matching Algorithm

In order to solve the problem of traditional SURF (Speed Up Robust Feature) based only on image gray information features while ignoring image color and texture information, a SURF feature matching algorithm based on color and texture is proposed. First, extract the image feature points through the Hesssian matrix, describe the feature points through the SURF algorithm, and add the normalized RG color space feature point neighborhood difference information to the feature descriptor to form an improved SURF feature vector; then The two-way matching strategy is used to match the feature points, and then the random sampling consensus algorithm is used to eliminate the mismatched points of the rough matching point set, and reduce the mismatch rate. The experimental results show that the algorithm has a higher performance than the traditional algorithm under the perspective change. Matching accuracy and higher robustness.
Ascertaining Abnormal Regions in Mammogram Images using Gravitational Search Local Map View Technique

Segmentation of mammogram images has gained importance in many medical treatments and diagnostic processes. Mammogram image segmentation aims at correctly separating different tissues, organs, or pathologies in volumetric image data. Most of the existing algorithms for image segmentation have a "scattered" cluster problem (disconnected clusters) happened in many clustering techniques (agglomerative, k-means, Dbscan) . Above algorithms not taken into account of both quality value and connectivity of points and region varying shapes. Two methods are proposed in this paper. The first technique is LMV (Local Map View) and the second technique is GSLMV (Gravitational Search Local Map View). LMV concentrates on determination of local quality for each point in all instances of the region in the comparative similarity view by applying the initial cluster technique. This view allows the user to choose instances for detailed analysis and filter the outlier instances from the input, next specific feature selection process identifies regions with systematic characteristics across the images. In this research work, pixels in groups with high intensity are assumed to be abnormal regions in cancer and non-cancer images. Fuzzy clustering is used to cluster the pixels. The optimal threshold from GS are initialized as cluster centres. This increases the speed of GSLMV algorithm. Performances of GSA, LMV and GSLMV methods are measured using False Rejection Rate, pixel count, Peak signal to noise ratio and runtime metrics. GSLMV showed better results based on pixel count, PSNR and runtime.
Perbandingan Metode CBR dan Dempster-Shafer pada Sistem Pakar Terintegrasi Layanan Kesehatan

Infectious disease is a very dangerous disease with a high mortality rate. Delays in handling the spread of an infectious disease can be minimized using an expert system. This study uses an expert system as a disease consulting service that is integrated with the health care system. Integration with the health care system is used for the knowledge acquisition process. The knowledge base on the expert system uses patient medical record data obtained through the health care system. The expert system can diagnose infectious diseases of sore throat (Pharyngitis), diphtheria, dengue fever, Typhoid fever, tuberculosis, and leprosy. The knowledge acquisition process produces 43 symptoms. These symptoms are used to diagnose new cases using Case-Based Reasoning (CBR) and Dempster-Shafer methods. In the CBR method, the similarity measurement process is determined by comparing the K-Nearest Neighbor, Minkowski Distance, and 3W-Jaccard similarity measurement methods. The expert system obtains accuracy values ​​for the CBR K-Nearest Neighbor, CBR Minkowski Distance, and CBR 3W-Jaccard methods at a threshold of 70%, respectively 65.71%, 80%, and 85.71%. The average length of retrieve time required for each similarity method is 0.083s, 0.107s, and 6.325s, respectively. While the diagnosis of disease with Dempster-Shafer gets an accuracy value of 88.57%.&#x0D;
Cloud-Based Data Analytics: Applications, Security Issues, and Challenges

Big data is rising quickly nowadays and it is very difficult to maintain the data by having individual system architecture. So in the present scenario, it is approachable to go for a cloud service to maintain data easily by hiring the required configurations. But there we observe many security issues and challenges. In this chapter, we tried to observe the need of cloud to maintain big data and simultaneously represented different security measures need to focus on handling data. Also, there are different challenges observed on storing data, processing huge data in the cloud using its techniques, transportation of data in a multi-cloud. As the Big data is growing exponentially, the cloud can be the best service if we use it with proper authentication and security features.
Applying Machine Learning Models for Detecting and Predicting Militant Terrorists Behaviour in Twitter

In today’s digital world, counter-terrorism is considered as one of the highest priorities in defense departments worldwide. Organizations are investing in the development of new tools that harness advanced information technology to detect and counter terrorism through in-depth analysis of online data, especially online social networks (OSNs). A militant terrorist groups are exploiting these networks in the aim to promote their organizations and recruit more naive people inside their dangerous communities, the most used social network by these groups is Twitter. However, the existing approaches are not very efficient or they do not study the behaviors of these malicious users and they only rely on textual data provided by the users. In this paper, we propose a novel computational model using various machine learning and recommender systems techniques for detecting and predicting the influence of terrorists’ behaviors on social networks from their text-posted and image-posted content, as well as building social graphs of terrorist networks that are helpful for any further social network analysis.