Reviewerly/exHarmony huggingface dataset:
Improved Hybrid Sampling Strategy for Software Defect Prediction of Imbalanced Data Distribution

Software defect prediction using data mining techniques is one of the best practices for finding defective modules. The existing classification techniques can be used for efficient knowledge discovery on normal datasets. Most of the real-world data sources are biased towards any one of the classes. This type of data source is known as class imbalance or skewed data sources. The defect prediction rate for the class imbalance datasets reduces with the increases in the class imbalance nature. To handle such type of datasets, an approach with specific designing technique is required for improved performance. In this chapter, the authors propose an algorithm known as improved integrated sampling strategy (IISS) for improved performance using noisy removal strategy for software defect prediction. The experimental analysis conducted on skewed software defect prediction datasets provides the results that IISS algorithm have performed well when compared with C4.5, C4.5+Balance, RF, and RF+Balance algorithms with various class imbalance evaluation measures.
Incorporating Blockchain in QoS-Based Sematic Web Services Selection

Web service technology and cloud computing offer promising solutions for developing distributed business applications published and invoked over the internet. Web service discovery finds the most relevant web services satisfying the functional needs of users. Semantic description of web services offers more accurate matching results between user query and web services descriptions. User's quality of service (QoS)-based criteria select the best web service from a set of functionally similar web services. The risk of privacy leakage of users is possible as service providers collect user personal data without any agreement. It isn't easy to assess the trustworthiness of values of QoS attributes provided by different service providers. Blockchain is an emerging technique that can overcome privacy leakage and identifying web services with fake values of QoS attributes. This chapter gives an overview of semantic web services, discovery and QoS-based selection of semantic web services, and the use of blockchain technology in the selection of web services.
Perceptually enhanced shadows for OST AR

Shadows are important spatial cues for virtual objects in augmented reality (AR). The common assumption is that a shadow should be darker than its surroundings as it is the result of something obscuring the light source. However, darkening pixels is difficult in contemporary optical see-through (OST) head-mounted displays (HMDs) due to the additive display technologies that they employ. To address this issue, some previous methods create the illusion of darker shadow regions by brightening the surrounding pixels around a shadow, but the visibility of shadows created by such methods is limited in well-illuminated scenes and on surfaces with complex features. This paper presents a method that imbues cast shadows with specific hues to improve the perceptual contrast and, thus, the visibility of shadows in OST AR displays. The color of the cast shadow is computed as a complement of the perceptually weighted aggregate of the colors captured in the surrounding real scene. This approach is implemented on the Microsoft Hololens OST AR HMD, and coupled with a method for adjusting colors to compensate for the tint of the visor. The improvement in visibility is demonstrated with various sample scenes.
Deep Reinforcement Learning Scheduling of Container Cloud Workflow Considering Invalid Time-Consuming and Reliability

Cloud computing workflow scheduling problem in now hundreds of thousands, millions of sensors are distributed in the edge cloud, information is summarized to the proximal small data center, and then converged to the edge-cloud hybrid scene of data storage on several public cloud, with broad development prospects and application market. The systematic and quantitative reliability research of workflow scheduling problems based on cloud computing is one of the key factors to ensure the efficient and high-quality completion of workflow scheduling tasks. Therefore, this paper follows from three dimensions: cloud provider, task and service type, and data transmission, Combined with energy consumption factors including resource balance, GPU / CPU energy consumption model, energy consumption model with load change with voltage, By quantireliability parameters, simulated workflow scheduling and reasonably setting time window, a hybrid big data reliability optimization model are proposed for the optimization of workflow scheduling.
Macht: An Application Based on a Sentiment Analysis Model to the Identification of Messages in Spanish with Gender Violence Content on Twitter

During confinement, the number of anonymous complaints of gender violence in social networks has increased, affecting society. This work presents an alternative to detect messages with offensive content to women who have gone through a process of violence automatically. The applied methodology considers the construction of a public dataset with 1042 tweets in Spanish tagged by 48 volunteers. The model considers the fine-tuning process to 3 pre-trained BERT models (SpanBERT, BETO, multilingualBERT), with which 252 experiments were carried out to find the model with the best performance, obtaining an Area Under the Curve of 0.9349 and precision of 0.9043. The research contributes with a new public data labeled in Spanish in 5 age ranges where anyone from anywhere in the world will access the application and test the performance of the model.
A High-Accuracy Two-Stage Model for Automatic Speech Recognition

Compared with the traditional multi-stage speech recognition model, the speech recognition model based on deep neural network has promising performance in accuracy and speed and can realize end-to-end speech conversion to text. However, its practical difficulties in training determine its performance limit. The method proposed in this paper is that combines the advantages of both by dividing the recognition task into two stages for processing. Both stages are carried out by a deep neural network model. First, the speech sequence is converted into a phoneme sequence, then the phoneme sequence is converted into a character sequence. The recognition process can be controlled more finely to achieve higher recognition accuracy with different loss functions and datasets. On the widely used open-source AiShell-1 Mandarin speech dataset, the acoustic model based on convolution in the first stage achieves a phonemic error rate of 1.90%, and the language model based on Bi-LSTM and self-attention in the second stage achieves a character accuracy of 99.4%. Finally, the speech recognition character error rate (CER) of the complete model with only 15M parameters is as low as 4.15%, achieving state-of-the-art accuracy.
Anomaly Detection in Biometric Authentication Dataset Using Recurrent Neural Networks

In the biometric authentication, the stored data is used for the verification of used identity. The unique biological traits commonly used for biological authentication are facial characteristics, fingerprints, and retinas. It also offers superior fraud detection and customer satisfaction, compared to all other traditional multi factor authentication. Deep learning algorithms plays a major role in anomaly detection and fraud identification in various real-time applications. RNNs have proven that they work well in analysing and detecting anomalies in time series data. RNNs have the unique ability for each cell to have its own memory of all the previous cells before it. This allows for RNNs to process sequential data in time steps which other machine learning models cannot do. RNNs can also be found sorting through your emails to sort out spam and phishing emails from friendly emails. This chapter reviews the methodologies, purposes, results, and the benefits of RNNs in anomaly detection in biometric authentication.
Mitigating Privacy Vulnerability Caused by Map Asymmetry

Geo-indistinguishability, an extension of differential privacy and thus, a mathematically rigorous privacy concept, has been researched extensively in recent years for protecting the location information in location-based services (LBSs). However the Laplace mechanism that satisfies GeoI outputs the location information equally in all directions considering the map is assumed to be symmetric. Therefore, when noise is added, the output can be directed to uninhabitable locations, such as the sea, To solve this problem, graph representation of the road network is used as a map and Geo-Graph-Indistinguishability (GeoI), which is differential privacy on the graph, and a graph-exponential mechanism (GEM), which satisfies GeoGI, were proposed. However, the problem that some vertices can be vulnerable in terms of privacy when measured using criteria based on adversary’s error remains unresolved. In this paper, we first calculate the degree of privacy protection using the adversary’s error when noise is added by GEM, and demonstrate that the privacy weakens at some vertices. We then propose two methods, the weight reduction method and the AE optimization method, to mitigate this problem. The experiments show these methods successfully mitigated the privacy vulnerability. Finally, we highlight the problems with these two methods and suggest possible solutions.
Computer Graphic Visualization as a Very Important Tool for Use in Illustrating Complex Data and Graphics

Statistical data such as Global Pandemic Informatics, Political Conflicted Hotspots or the Metrics of Economic Performance can be all very daunting and confusing when we are putting all this information into paragraphs with endless numbers. With the help of Computer Graphics a designer can gather all that information and compress it into visually pleasing Graphs and Charts to make the data easier to understand. When we think of 3D modeling normally what comes to mind are Video Games, CGI Animated movies, modeling and architecture. However, 3D modeling can help medical professionals better target cancerous tumors in the human body and also aid doctors and surgeons to better understand the human anatomy through highly rendered 3D graphics of the human body. Computer graphics and data visualization combined can generates more accurate information by using meshing in the 3D modeling which are the mathematical points that help create very accurate visual simulations.
Wmrenergycon: Matlab Simulink Model for Differential Drive Mobile Robot System that Advanced Energy Consumption Estimation

WMREnergyCon is a MATLAB SIMULINK model that enables a differential drive mobile robot to perform real-time movement and obtain energy consumption information. WMREnergyCon Model; It has path planning, path tracking, PID speed control, dynamic model that takes into account all internal and external parameters affecting the system, kinematic model and proposed advanced energy consumption estimation model that make up the mobile robot system. The performance of the proposed advanced energy estimation model was 98.56% in previous studies. At the same time, the WMREnergyCon model enables the control and analysis of physically mobile robots as well as simulation studies
An Isolated Filter Network for Scenic Text detection and Recognition on a synthetic dataset

Scene text detection and recognition is an intensive area of research in computer vision. It is a challenging task to detect texts due to their irregular shapes and varying sizes. According to studies, the datasets often utilized for the task of text detection and identification are composed of data in a structured format. When evaluated on curved data or applied to small-sized texts, these models fail to produce accurate results. To emphasize the challenges of reading curve text in real-life, we generated a dataset containing curve text along with variant sizes using Generative Adversarial Networks. The synthetic data generated is trained to detect curve datasets using atrous convolution and isolated filter networks along with variational autoencoder(s) based on CNN text recognition. The proposed text recognition model is tested on two standard datasets MNIST and ICDAR03 along with our synthetic dataset. The recognition is evaluated based on the accuracy and our proposed model yields 99.7 percentage of accuracy which is higher when compared to other state of art methods.
Evolution of scientific ideas on professional motivation in foreign and national research

We present a comparative analysis of the views of foreign and national scientists on professional motivation, describe two groups of theories of professional motivation (substantive and procedural), and identify the leading modern theoretical approaches to the study of professional motivation in Russia and abroad. In modern foreign studies, the leading ones in professional motivation are socio-cognitive and personality-oriented approaches, which allow a better understanding of motivation, considering individual differences; view of professional motivation as a set of recursive, time-related, mutually affective behavioral and cognitive processes and actions organized around the individual goals of the labor subject prevails. National research is based on fundamental methodological approaches (system, activity, structural-level, system genetic, metasystem, etc.), on the concept of the activity origin of the motivational sphere, according to which there are relationships of mutual influence of the content of motivation on activity and the specifics of activity on motivation. Despite the differences in methodological approaches to the study of the phenomenon of professional motivation, foreign and national researchers consider this phenomenon as a multidimensional dynamic, systemic, integral personality formation (psychological construct) that has a multi-level hierarchical structure that changes under the influence of external (social, economic, professional, etc.) and internal (mental, psychophysiological) factors.
Block-Wise Computation of Cyclic Redundancy Code Using Factored Toeplitz Matricesin Lieu of Look-Up Table

Cyclic Redundancy Check (CRC) or Cyclic Redundancy Code is a Cyclic Error Detection Code used to preserve the integrity of data in storage and transmission applications. CRC of a stream of message bits is usually calculated block-wise in parallel with the help of a Look-Up Table (LUT) in software or State Space transformation matrix in hardware. Presented here is a novel method and architecture of parallel computation of Cyclic Redundancy Codes without any Look-Up Table for an arbitrary generating polynomial which is programmable at runtime. The method reduces computational complexity and storage requirements by implicitly factorizing the transformation matrix needed to compute the remainder into two simpler Toeplitz matrices. The resulting hardware architecture is suitable for embedded applications.
Construction of Odd-Variable Strictly Almost Optimal Resilient Boolean Functions with Higher Resiliency Order via Modifying High-Meets-Low Technique

Construction of resilient Boolean functions in odd variables having strictly almost optimal (SAO) nonlinearity appears to be a rather difficult task in stream cipher and coding theory. In this paper, based on the modified High-Meets-Low technique, a general construction to obtain odd-variable SAO resilient Boolean functions without directly using PW functions or KY functions is presented. It is shown that the new class of functions possess higher resiliency order than the known functions while keeping higher SAO nonlinearity, and in addition the resiliency order increases rapidly with the variable number n.
Optimal Decision Threshold-Moving Strategy for Skewed Gaussian Naive Bayes Classifier

Abstract Gaussian Naive Bayes (GNB) is a popular supervised learning algorithm to address various classification issues. GNB has strong theoretical basis, however, its performance tends to be hurt by skewed data distribution. In this study, we present an optimal decision threshold-moving strategy for helping GNB to adapt imbalanced classification data. Specifically, a PSO-based optimal procedure is conducted to tune the posterior probabilities produced by GNB, further repairing the bias on classification boundary. The proposed GNB-ODTM algorithm presents excellent adaptation to skewed data distribution. Experimental results on eight class imbalance data sets also indicate the effectiveness and superiority of the proposed algorithm.
Integrating and Evaluating Quantum-safe TLS in Database Applications

Quantum computing promises to build computers capable of solving complex problems much faster than today’s computers. This will benefit to many real-world applications such AI, machine learning, optimization problems, complex financial modelling, etc. Quantum computing will also have an impact on cryptology by allowing to break many public-key encryptions in use today. As a response to this threat the cryptography community is investigating Post-Quantum Cryptography (PQC). PQC provides encryption schemes that run on conventional computers and are believed to be secure against attacks from both classical and quantum computers. Such schemes are therefore called quantum-safe or quantum-resistant. In this work, we experiment with quantum-safe TLS to secure the communication for PostgreSQL which is an open source client-server database management system. In a client-server database, the database resides on a server, and client applications can access the database by sending requests to the server. These requests usually include requesting or modifying data. To establish a quantum-safe client-server communication, we first integrate quantum-safe TLS in PostgreSQL source code and evaluate connections between client and server. Our evaluation goal consists of measuring the overhead of TLS connections, especially when using quantum-safe algorithms. We experiment with different certificate chain lengths (from 0 to 4). Since NIST recommends applying hybrid schemes (combining both quantum-weak, i.e., current public-key crypto, and quantum-safe schemes) during the transition to quantum-safe algorithms, we also evaluate hybrid algorithms but first focus on lattice-based schemes (KYBER for key exchange and DILITHIUM for authentication) as they are believed to be the only TLS-ready alternative. Finally, we consider three security levels (128, 192, and 256), and evaluate both the running time and the bandwidth. Our results confirm that lattice-based schemes are promising, but come with significantly higher communication overhead.
Deep Person Re-identification with the Combination of Physical Biometric Information and Appearance Features

Abstract In this paper, we propose a novel Person Re-identification model that combines physical biometric information and traditional appearance features. After manually obtaining a target human ROI from human detection results, the skeleton points of target person will be automatically extracted by OpenPose algorithm. Combining the skeleton points with the biometric information (height, shoulder width.) calculated by the vision-based geometric estimation, the further physical biometric information (stride length, swinging arm.) of target person could be estimated. In order to improve the person re-identification performance, an improved triplet loss function has been applied in the framework of [1] where both the human appearance feature and the calculated human biometric information are utilized by a full connection layer (FCL). Through the experiments carried out on public datasets and the real school surveillance video, the effectiveness and efficiency of proposed algorithm have been confirmed.
Relevance of the use of synesthetic methodologies in the field of aesthetic education

The article discusses the prospects for the use of synesthetic methodologies in the field of aesthetic education. It is indicated that the use of pedagogical techniques based on synesthesia promotes self-actualization of the personality, the development of creative imagination, memory, imaginative and associative thinking and the expansion of the scope of creative activity. The experience of conducting international scientific conferences on the study of synesthesia and the use of synesthetic methodologies in pedagogical and psychological practice are summarized.
EAC-GAN: Semi-supervised Image Enhancement Technology to Improve CNN Classification Performance

Deep neural networks require a large amount of data for supervised training and learning, but it is often difficult to obtain a large amount of label data in practical applications. Since semi-supervised learning can reduce the dependence of deep networks on label data, the generative confrontation network based on semi-supervised learning can improve the classification effect. Although researchers have made progress in semi-supervised learning, small-scale, fully-supervised tasks have not been solved well, because even unlabeled data cannot be obtained in large quantities in such tasks. Therefore, we propose a new GAN model EAC-GAN based on the auxiliary classifier generative adversarial network (ACGAN), which uses genetic algorithms and semi-supervised algorithms to improve classification under fully supervised conditions. Our method utilizes ACGAN to generate artificial data for supplementing supervised classification. More specifically, we attached an external classifier to the original ACGAN generator, so it was named EAC-GAN instead of sharing an architecture with the discriminator. Our experiments show that the performance of EAC-GAN is far superior to standard methods based on data augmentation and regularization, and it is effective on a small amount of real data sets.
An Improved TextRank-Based Method for Chinese Text Summarization

Text summarization extraction is a natural language processing technology that can quickly extract key information to improve the efficiency of daily transaction processing. The traditional TextRank algorithm is limited by Chinese word segmentation, which results in the low accuracy of Chinese text summarization, and leads to loss important information. This paper proposes an Improved TextRank-based method for Chinese text summarization, which makes full use of the 1-g model analysis in N-Gram to obtain the candidate word vector, and combines the 2-g model and hidden Markov model to achieve part-of-speech tagging and optimized word segmentation. Finally use the improved TextRank model on the optimized word vector to realize the Chinese text summarization extraction. The experimental results show that when the number of summary sentences is no more than 8, the accuracy of Chinese text summary extraction by Improved TextRank-based method is significantly better than traditional TF-IDF and TextRank methods.