Reviewerly/exHarmony huggingface dataset:
Image Caption Method Combining Multi-angle with Multi-modality

Image caption generation technology has attracted the interest of researchers due to its wide application in practical applications. It involves two vital areas of artificial intelligence: image processing and natural language processing. The existing method is to predict the generation of the next word based on the image features and the words generated in the previous state. However, it ignores the important role of text information. In this paper, we propose an image caption generation method that combines multi-angle with multimodality. The model firstly uses the fusion features of the global and local images as input. Picture description of the first sentence is generated using the baseline encoding-decoding model. The image caption which is generated firstly is then input into the sentence encoding network to generate a semantic feature vector of the first sentence. Then, the local visual feature vector of the image and the semantic eigenvector of the first sentence which are two different modal features, are combined and input into the attention-based language generation model to generate the next sentence. This allows our model to generate multi-angle descriptions in a targeted manner.
Enhanced blockchain based key management scheme against key exposure attack

The data collected by IoT devices is of great value, which makes people urgently need a secure device key management strategy to protect their data. Existing works introduce the blockchain technology to transfer the responsibility of key management from the trusted center in the traditional key management strategy to the devices, thus eliminating the trust crisis caused by excessive dependence on third parties. However, the lightweight implementation of IoT devices limits the ability to resist side channel attacks, causing the private key to be exposed and subject to masquerading attacks. Accordingly, we strengthen the original blockchain based key management scheme to defend against key exposure attack. On the one hand, we introduce two hash functions to bind transactions in the blockchain to legitimate users. On the other hand, we design a secure key exchange protocol for identifying and exchanging access keys between legitimate users. Security analysis and performance show that the proposed scheme improves the robustness of the network with small storage and communication overhead increments.
Differences in language use

Search platforms can have more than one type of user, e.g., those who provide and those who consume content. As an example, in a job/talent search platform, content providers are: (1) job seekers who provide CVs, and (2) hirers who provide job advertisements; content consumers, on the other hand, are: (3) job seekers searching for specific jobs, and (4) hirers/recruiters searching for candidates to fill particular positions. As a result, there are four types of users, each with potentially different patterns of language use.
A Multi-labels Text Categorization Framework for Cerebral Lesion’s Identification

Road traffic accidents in Algeria are a major public health problem for children, since they are among the leading causes of death starting from the age of 1 year. Among these victims, traumatic brain injuries (TBIs) causing several sequelaes, which are the frequent causes of consultation in children. For a better support of these cases, we propose a Multi-Label Text Categorization (MLTC) framework to help health professionals for the best cerebral lesions’ identification found in TBIs that appear simultaneously. The aim through this article development is to evaluate a set of multi-label (ML) transformation approaches to detect cerebral lesions’ from medical reports collecting from the pediatric intensive care unit of Oran hospital -Algeria.
Integrated Mathematics and Computer Science Lessons in Primary School

The article substantiates the need for a scientific and practical orientation in organizing modern education, which meets the challenges of the time and the requirements for ensuring the competitiveness of our country, outlined in the “Forecast of the Scientific and Technological Development of the Russian Federation for the Period until 2030”. The possibilities of binary lessons in elementary school, integrating the content of instruction in mathematics and computer science, are revealed. Examples of educational tasks that are aimed at developing the skills of primary schoolchildren to put into practice the acquired theoretical knowledge are given.
Discovering Crime Trends and Patterns Using Three-Dimensional Visual Analytics

Crime analysts and law enforcement agencies can formulate effective strategies utilizing current crime trends and patterns. Modern computational technologies, particularly big data analysis and visualization can be applied to available crime data and other resources. Visual analytics, an analytical reasoning technique with interactive visual interfaces, is a powerful tool that serves to augment the trend and pattern recognition capabilities of experts. This paper introduces a three-dimensional (3D) visual analytics framework that interactively visualizes crime data and other relevant datasets on a highly accurate 3D model of the City of Vancouver, Canada. This 3D visualization advances crime analysis activities through a more accurate display of the area under study. The 3D visual analytics can improve identification of temporal and spatial criminal areas and provide strategic and effective plans for pro-active police deployment. The open data catalogue from the City of Vancouver, Canada and the Vancouver Police Department open source property crime data were used in this study.
Investigation about the practical teaching effect of financial transformation colleges and the research on the construction of evaluation model

Abstract Practical teaching is an effective approach to consolidating theoretical knowledge and deepening theoretical cognition, and also the important means to cultivate students to master scientific methods and improve their operational capacity. From the perspective of cultivating students’ occupational capacity, on the basis of reading lots of relevant references, the questionnaire is designed from several aspects, including the teaching methods, teaching mode, course evaluation, the enhancement of occupational capacity and the evaluation on the setting of practical teaching and designed to know about the feelings of students about practical teaching as well as to evaluate on relevant practical teaching conditions. Besides, it has also carried out factor analysis and regression analysis on investigation data respectively, and extracted four factors of occupational adaptability, occupational action capacity, practical teaching design and practical teaching assistance from the enhancement of occupational capacity and the setting of practical teaching. In combination with the questionnaire result and the four extracted factors, corresponding improvement thoughts are put forward based on the objective of building the practical teaching system focused on enhancing occupational capacity, establishing and perfecting practical evaluation system and teaching assurance system.
Multi-agent adaptive learning system based on incremental hybrid case-based reasoning (IHCBR)

The advent of Web 2.0 has contributed to the development of adaptive learning by expanding its possibilities for interaction such as personalization; It has also enabled it to involve a considerable variety of artificial intelligence technologies.
Methods for the formation and maintenance of professional motivation by the leader

The managerial activity of a leader from a psychological point of view is embodied in such a way as to select for his subordinate the conditions under which his stimulation will be carried out, namely, the conditions that enhance the motivational motives of work, increase interest, and a creative attitude to the matter. The article considers the role of the leader and his managerial activities in the formation and maintenance of professional motivation of subordinates. The basics of the formation and maintenance of professional motivation of employees are highlighted, as well as features that affect the formation of professional motivation are shown. The article identifies the most significant goals of professional motivation and gives recommendations to managers on their formation and maintenance.
PMD: An Optimal Transportation-Based User Distance for Recommender Systems

Collaborative filtering predicts a user's preferences by aggregating ratings from similar users and thus the user similarity (or distance) measure is key to good performance. Existing similarity measures either consider only the co-rated items for a pair of users (but co-rated items are rare in real-world sparse datasets), or try to utilize the non-co-rated items via some heuristics. We propose a novel user distance measure, called Preference Mover's Distance (PMD), based on the optimal transportation theory. PMD exploits all ratings made by each user and works even if users do not share co-rated items at all. In addition, PMD is a metric and has favorable properties such as triangle inequality and zero self-distance. Experimental results show that PMD achieves superior recommendation accuracy compared with the state-of-the-art similarity measures, especially on highly sparse datasets.
Individual Response to Stressors and Efficiency in Project Activities

This article investigates the way a graded approach can be implemented in the organization of the project-based learning process in accordance with the personality characteristics of subjects. This study is based on the results analysis of the key features of project-based learning as one of the methods for developing human resources. The assessment of subjects' individual response to stressors at different project stages is justified to be relevant in order to improve project efficiency in the framework of the learning process. The assessment of the individual response of subjects to stressors was carried out according to the activation dynamics of cerebral hemispheres. The research target was to determine features of the subjects' individual responses to the project activity stressors and to identify specifics of the individual responding at each of its stages. The study involved 56 final-year students of different majors. Aktivatsiometr ATs-9K, a device for comprehensive psychophysiological diagnosis designed by Yu. A. Tsagarelli, was used to monitor hemispheric activation. This hardware and software complex consists of a device diagnosing the activation of cerebral hemispheres based on the galvanic skin response and PC software for automatic data processing. The individual typological indicators of activation (13 measurements in a familiar environment) and activity-situational indicators of activation (seven single measurements at different stages of the project activity) were calculated. The stress response was diagnosed if the activity-situational activation indicators of the cerebral hemispheres exceeded the individual typological activation indices by more than 1.5 times. The results of the empirical study show different types of individual responses to stressors at different stages of the project activity. The analysis of the profiles of individual responses to stressors made it possible to differentiate subjects, and also identify groups of students with the maximum resources for each stage of activity.
Generation of Quantum Vector States Using the Exchange Interaction

In this paper, I study the invariant subspaces of quantum states under SWAP α gates arising from the exchange interaction and their use in quantum computation.I investigate the generation and characterization of invariant-subspace vector-states that arise from such gates.I also state a condition for the locus of states that are accessible using the SWAP α gates, given an initial input state.
Promoting Computer Graphics Research: The Tech Demos of SIGGRAPH

This chapter studies the shifting function and aesthetics of computer graphics tech demos at the ACM SIGGRAPH annual computer graphics conference, with a particular emphasis on the role media industries began to play at the conference during the 1980s. It finds that the promotional and explanatory function of the tech demo shifted during this period toward demonstrating media industries viability. As a consequence of this shift, the tech demos of SIGGRAPH transformed aesthetically, conforming more to existing film and television conventions.
A Comparison Between SWIFT and Blockchain from a Cyber Resiliency Perspective

Payments critical infrastructure is subject to rapid technological change. Increasingly sophisticated threats must be addressed to ensure the banking and financial system security and integrity. Several high-profile cyber-incidents have recently shaken the global financial community and stimulated renewed efforts to reinforce and bolster its security framework. Two different cross border payments management approaches have emerged over the years: the SWIFT financial messaging standard and the innovative peer-to-peer transaction model based on the blockchain technology. Debates about which one will prevail as the best practice are currently a very popular topic. Security, and more specifically resiliency to evolving cyber threats, will likely be the main point of concern. Both the SWIFT and the blockchain models present potential exposure to such vulnerabilities. Ultimately, the discussion boils down to an assessment of whether a decentralized, distributed system like the blockchain better meets the integrity requirements of a modern payments infrastructure and is more suitable to mitigate the root cause of cyber incidents, which is human error.
Compression with Authenticated Encryption for Enhanced Security on Data Centric Products

Todays era is data-centric and so are the products. The approach of Data-Centric companies is to ensure that an end to end solution is provided to cloud customers to handle data transfers efficiently and at the same time ensure data security. Unbreakable security, effective storage and transmission, quicker processing and overall improved efficiency of an algorithm makes it ideal for the enormous data handling in present day. This study talks about a novel approach which embeds compression, authentication and encryption for secure data flow in cloud computing. The emphasis being on faster encryption decryption cycles, enhanced data security and optimized storage utilization. Network Accelerators which support Intels Quick Assist Technology in the data centric products include encryption/decryption and compression engines but most of the industry use cases do not use cryptographic and compression services together on the incoming / outgoing data. This case study uses both compression and cryptographic engines in tandem to process the data. The implementation uses the existing hardware capabilities with modification to software stack. In this flow outgoing data packet would be first compressed and then encrypted and any incoming data packet would be first decrypted and then uncompressed. For data with repeated and random patterns, we could see significant improvement in reduction of the data size which allows efficient transfer of data with enhanced security from point A to B.
SSHTDNS: A Secure, Scalable and High-Throughput Domain Name System via Blockchain Technique

Domain name system is an important infrastructure of the Internet, which is responsible for mapping domain name to IP address. However, it is facing serious security threats due to its distributed structure with a center. Blockchain has received extensive attention due to its decentralized, secure and trusted characteristics since the existence of Bitcoin. In this paper, we propose a new domain name system based on blockchain techniques called SSHTDNS. The form of consortium chain is adopted in SSHTDNS to guarantee the management of Top-Level Domain names, and linkable ring signature technique is used for identity anonymity and voting fairness of these consortium nodes. Multi-chain structure is designed with sharding protocol to enhance the scalability and throughput of the system. In addition, SSHTDNS allows nodes to use index databases outside the chain to speed up domain name resolution and verification. A combination of these techniques enables a secure, scalable and high-throughput domain name system.
Picking Groups Instead of Samples: A Close Look at Static Pool-Based Meta-Active Learning

Active Learning techniques are used to tackle learning problems where obtaining training labels is costly. In this work we use Meta-Active Learning to learn to select a subset of samples from a pool of unsupervised input for further annotation. This scenario is called Static Pool-based Meta-Active Learning. We propose to extend existing approaches by performing the selection in a manner that, unlike previous works, can handle the selection of each sample based on the whole selected subset.
Kinematic Modeling of Distal Radioulnar Joint for Human Forearm Rotation

This paper presents the kinematic modeling of the human forearm rotation constructed with a spatial four-bar linkage. Especially, a circumduction of the distal ulna is modeled for a minimal displacement of the position of the hand during the forearm rotation from the supination to the pronation. To establish its model, four joint types of the four-bar linkage are, firstly, assigned with the reasonable grounds, and then the spatial linkage having the URUU (Universal-Revolute-Universal-Universal) joint type is proposed. Kinematic analysis is conducted to show the behavior of the distal radio-ulna as well as to evaluate the angular displacements of all the joints. From the simulation result, it is, finally, revealed that the URUU spatial linkage can be substituted for the URUR (Universal-Revolute-Universal-Revolute) spatial linkage by a kinematic constraint.
A Density Discriminant Index for Cluster Validation

Clustering analysis is widely applied in several domains of study. Using a suitable number of clusters is one of the most important factors to influence the performance of clustering. Several algorithms of cluster validation have been developed to find such a number. In this paper, we proposed a method for cluster validation adapted from the Discrimination Evaluation via Optic Diffraction Analysis (DEODA) algorithm to derive an appropriate number of clusters. In particular, our method uses DEODA to perform within- and between-cluster discrimination analysis in order to find the suitable number of clusters. We evaluate our method by comparing similarity score against the existing cluster validation algorithm i.e., the Silhouette index. The results show that the similarity scores derived from our method are higher than results yielded from the Silhouette index.
Understanding complex predictive models with ghost variables

Abstract Framed in the literature on Interpretable Machine Learning, we propose a new procedure to assign a measure of relevance to each explanatory variable in a complex predictive model. We assume that we have a training set to fit the model and a test set to check its out-of-sample performance. We propose to measure the individual relevance of each variable by comparing the predictions of the model in the test set with those obtained when the variable of interest is substituted (in the test set) by its ghost variable, defined as the prediction of this variable by using the rest of explanatory variables. In linear models it is shown that, on the one hand, the proposed measure gives similar results to leave-one-covariate-out ( loco , with a lowest computational cost) and outperforms random permutations, and on the other hand, it is strongly related to the usual F -statistic measuring the significance of a variable. In nonlinear predictive models (as neural networks or random forests) the proposed measure shows the relevance of the variables in an efficient way, as shown by a simulation study comparing ghost variables with other alternative methods (including loco and random permutations, and also knockoff variables and estimated conditional distributions). Finally, we study the joint relevance of the variables by defining the relevance matrix as the covariance matrix of the vectors of effects on predictions when using every ghost variable. Our proposal is illustrated with simulated examples and the analysis of a large real data set.
Experimental Comparison of Video Streaming Platforms and Devices with Objective Quality of Experience Metrics Towards Reliable Multimedia Applications in Education

Abstract This paper presents the result of experimental comparison of the video quality of streamed videos using streaming devices and streaming platforms. The objective metrics considered for the comparison of these media are the Peak Signal-to-Noise Ratio (PSNR), Structural Similarity (SSIM), Visual Information Fidelity (VIF), Information Fidelity Criterion (IFC), and Visual Signal-to-Noise Ratio (VSNR). To determine the better platform for effective multimedia applications especially for educational purposes, videos were obtained and streamed via Data video, YouTube and Facebook. In the first two scenarios, Data video at two different settings were engaged at the encoding end and PotPlayer was used for decoding and recording of the transmitted videos. In the third and fourth scenarios, Videos were streamed via YouTube and Facebook. The aforementioned objective metrics were implemented in MATLAB R2017b for the experimental analyses and our results showed that YouTube produced streamed videos of the best quality with PNSR of 37.98, SIMM of 0.98 and VSNR of 33.12.
Blockchain in Healthcare

Blockchain is a decentralized, digital ledger that keeps a permanent, unalterable record of transactions between users. One of the greatest advantages of Blockchain is that it is much more secure than other data storage platforms, and thus particularly relevant for healthcare. The chapter serves to outline areas and opportunities for deploying this technology into key healthcare contexts to support effective and efficient operations as well as heightened transparency and trust around activities between and within stakeholders.
CS-ACELP Speech Coding Simulink Modeling, Verification, and Optimized DSP Implementation on DSK 6713

The emerging of real-time telecommunication systems over low bandwidth channels enforces constraints on the transmitted data rate and it also needs an optimum speech quality at received destination, so many standards of hybrid speech coding techniques are being developed. Conjugate Structure Algebraic Code Excited Linear Prediction (CS-ACELP) hybrid speech coding was being utilized and optimized in the current work, Firstly, a CS-ACELP frame analysis with MATLAB SIMULINK model was implemented and its corresponding DSP based C++ code was generated too. Then additional optimizations were done, then, a CS-ACELP encoder/decoder with International telecommunication union (ITU) MATLAB CS-ACELP code was developed, and then MEX tool was used to generate C++ files that could compile and run it into desktop PCs. This desktop application is helpful in chat applications that require low bandwidth over the Internet and low connection speed. The performance of both DSP and Desktop implementations are evaluated using different quality evaluation tests. These tests are segmented signal-to-noise ratio (seg-SNR), log-likelihood ratio (LLR), mean opinion score (MOS) and praat software, The results showed that the optimizations done in the MATLAB Simulink work decreased the processing time by 8.564 us and reduced the used memory by 8% with acceptable speech quality.