Reviewerly/exHarmony huggingface dataset:
Anomaly Detection in Multichannel Data Using Sparse Representation in RADWT Frames

We introduce a new methodology for anomaly detection (AD) in multichannel fast oscillating signals based on nonparametric penalized regression. Assuming the signals share similar shapes and characteristics, the estimation procedures are based on the use of the Rational-Dilation Wavelet Transform (RADWT), equipped with a tunable Q-factor able to provide sparse representations of functions with different oscillations persistence. Under the standard hypothesis of Gaussian additive noise, we model the signals by the RADWT and the anomalies as additive in each signal. Then we perform AD imposing a double penalty on the multiple regression model we obtained, promoting group sparsity both on the regression coefficients and on the anomalies. The first constraint preserves a common structure on the underlying signal components; the second one aims to identify the presence/absence of anomalies. Numerical experiments show the performance of the proposed method in different synthetic scenarios as well as in a real case.
Machine Learning Classification Models for Banking Domain

The prime aim of machine learning is to develop techniques or methods which automatically detect patterns in the given data, and later to make use the discovered pattern to predict future or other results of interest. Machine learning practitioners around the globe are trying to model the huge variety of data of varying levels of complexity using their diverse tools and methods. The models thus built are representations of the observed data which will identify some the regularities or some interesting patterns. In this paper, models constructed are based on classification which is a function that maps or classifies a data instance into one of several predefined class labels. A separate testing set is used to test the classifying ability of the learned model or function which uses the features such as Term frequency- Inverse Document Frequency (TF-IDF). In recent years Artificial Intelligence has impacted the Banking industry to a great extent. For organizations working in the banking industry, it has become increasingly crucial to keep up with competition, and increase their standing as an innovative company. In this work, the machine learning classification models are generated for the Banking domain to improve the system by considering and involving the feedback provided by customers, by collecting the text data related to banking from the web. Web crawlers are used on target specific sites to collect raw data.
A Comparative Study of solving Traveling Salesman Problem with Genetic Algorithm, Ant Colony Algorithm, and Particle Swarm Optimization

To solve the problem of traveling salesman (TSP), some intelligent optimization algorithms such as genetic algorithm (GA), ant colony algorithm (ACO), and particle swarm optimization (PSO) algorithm were used. This paper attempts to discuss the optimal solutions and convergence speed change of the three algorithms in solving the problem. We conducted a simulation experiment, found that the PSO provided a better and more stable solution, ACO took the shortest running time and GA is less influenced by the problem scale.
Time-bin entanglement built in room-temperature quantum memory

The authors theoretically propose and experimentally demonstrate a scheme for generating time-bin entanglement with a quantum memory based on an ensemble of room-temperature atoms. The results are interesting for the manipulation of quantum information in quantum networks and may serve as an element in future quantum repeaters and quantum computers.
Personal and professional development of the individual in the civil service of the Russian Federation

The presence of professional highly qualified personnel in the civil service, together with modern information technologies, is possible thanks to the existence of a democratic state governed by the rule of law. The professional activity of civil servants acts as a subsystem within the civil service system. The study of the peculiarities of personal and professional development of the individual in the civil service will contribute to the disclosure of the psychological mechanisms of this process, the resolution of a number of pressing questions about the methods and possibilities of optimizing professional and personal development, which in turn will contribute to improving labour efficiency. To study the features of personal and professional development of the individual in the Russian Federation Civil Service. Survey among civil servants and comparative analysis of the results obtained in all study groups; statistical processing of the results. 120 respondents took part in the study: federal-state civil servants (40 people) and civil servants of other types (80 people). The main problems and features of personal and professional development of the individual in the civil service of the Russian Federation are identified and the main directions of optimization of the studied process are determined. The process of personal and professional development of civil servants, as will be shown in this study, is an overall result of the professional education and additional professional education, including retraining, advanced training, and internships. The main directions of optimization of the studied process are the following: improving scientific, methodological, and organizational support; increasing the motivation for professional activity and career advancement; improving the efficiency of additional professional education; developing a program for personal and professional self-development; introducing additional forms and technologies for improving professional skills of state civil servants.
Blockchain for scholarly journal evaluation: Potential and prospects

Key points Blockchain shows potential for supporting the multidimensional evaluation of scholarly journals. Blockchain‐based scholarly communications will generate new data, which may be used for evaluating aspects of journals that are currently not fully evaluated. Blockchain can help enrich journal evaluation by extending the evaluation content to the upstream of journal publishing and increase the economic dimensions of journal usage. Blockchain‐based scholarly journal evaluation would be more automated, more open, and more verifiable to the community.
Leveraging Blockchain and SDN for Efficient and Secure IoT Network

Since the advent of the Internet of Things (IoT), we are witnessing a massive growth in the development and usage of IoT enabled devices. These devices are of great importance in our day-to-day life. Many real-world applications, like smart cities, smart homes, smart wearables, connected cars, smart health, etc., are using IoT as the underlying technology. IoT devices generate a massive data for further analysis and decision-making. However, IoT devices and generated data are vulnerable to various security issues, broadly categorized as software attacks, network attacks, encryption attacks, and physical device attacks. Therefore, it is important to guard data and the IoT devices against these security, efficiency, and robustness issues. To address these IoT networks’ challenges, we can utilize two emerging technologies—Blockchain and SDN as a sustainable solution. Blockchain is a distributed and decentralized collection of validated, verified, and permanent transaction records across a large peer-to-peer (P2P) network. These transaction records are considered as blocks and other peers verify the blocks before adding it to a previous chain. Further, it applies various public/private key-based cryptographic schemes. Therefore, it is almost impossible for an attacker to change the data once it is added to a blockchain. Further, its distributed and decentralized nature avoids a centralized attack or failure. This security feature of blockchain can quickly be adopted for securing data over an IoT network. Additionally, Software-Defined Network (SDN) also offers a broad set of services to enable security in the IoT network. As SDN decouples the data plane and the control plane, it offers logically centralized control management of underlying network devices. Using this centralized control and management, we can quickly identify a security attack as early as it occurs and instruct the underlying network devices to block it by specifying dynamic flows. In this chapter, we initially aim to provide an insight into the possible security attacks in an IoT network. We also present a brief overview of blockchain and SDN technologies. Further, we discuss different blockchain and SDN-based frameworks available for creating a secure IoT network.
Sign Language Recognition Using Convolutional Neural Network

Abstract Sign language is a lingua among the speech and hearing-impaired community. It is hard for most people who are not familiar with sign language to communicate without an interpreter. Sign language recognition appertains to track and recognize the meaningful emotion of human-made with head, arms, hands, fingers, etc. The technique that has been implemented here, transcribes the gestures from sign language to a spoken language which is easily understood by the listening. The gestures that have been translated include alphabets, words from static images. This becomes more important for the people who completely rely on gestural sign language for communication tries to communicate with a person who does not understand the sign language. Most of the systems that are under use face a recognition problem with the skin tone, by introducing a filter it will identify the symbols irrespective of the skin tone. The aim is to represent features that will be learned by a system known as convolutional neural networks (CNN), which contains four types of layers: convolution layers, pooling/subsampling layers, nonlinear layers, and fully connected layers.
Pronunciation correction of students in music classroom based on computer voice simulation

In order to improve the pronunciation effect of students in music classroom, based on computer speech simulation technology, this paper combines speech recognition technology and speech feature extraction technology to summarize the various acoustic parameters of speech and the perception of the information by the human ear, and establishes the physical model and digital model of the speech signal. Through the analysis of voice signals, this paper selects several acoustic parameters that can reflect individual characteristics, and studies the methods of their extraction and adjustment to construct a pronunciation correction system for music students based on computer voice simulation. Finally, this paper designs experiments to verify the performance of the system constructed in this paper. The research results show that the system constructed in this paper has certain practical effects and enriches the expressive power of machine speech. The process of synthesizing speech is simple, and the practical effect is good, which meets the current music teaching needs.
Application of Intelligent Terminal Mobile Learning Platform in English Teaching

Abstract Under the Internet+ operating model. The emergence of new teaching models has broken the traditional teaching methods, and a series of online teaching methods such as the Internet have provided children with newer teaching models. This not only facilitates the way of learning, but also brings more convenience to English teaching, and further promotes the method of English learning. Compared with traditional learning methods, it is more convenient to use the smart terminal mobile learning platform through the Internet. Teaching and other methods provide students with a better platform. Under the influence of smart terminals, such a learning platform can not only provide a diversified learning atmosphere, but also provide students with a better way of thinking, breaking conventions, and pioneering Innovation, and further enhance students’ interest in learning. And this kind of learning platform is more commonly used in English teaching [1].
Method of text mining based on fuzzy logic and neural network in hotel management

With the rapid economic development, people cannot do without hotels in daily life and travel. The number of hotels is increasing rapidly, and the competitiveness between hotels is gradually increasing. A reasonable and sound hotel management system has become the key to the survival and development of an enterprise. The operation and operation of a hotel generates a large amount of information and data every day. Data text mining is an important method in current information processing technology. The purpose of this article is to explore the specific application effects of text mining methods based on fuzzy logic and neural networks in hotel management. Through the design of a hotel management system based on fuzzy logic and neural network, text mining database design is carried out according to hotel management needs, and the application effect of the hotel management system is evaluated. The evaluation indicators include customer satisfaction, operating costs, management efficiency, and hotel income. The results of the study show that the hotel management system based on fuzzy logic and neural network text mining can increase customer satisfaction by 37.4%, the hotel’s comprehensive management efficiency by 28.6%, and the hotel’s revenue level by 23.5%. At the same time, through the text mining of the key information generated in the hotel operation and management, the weak links in the management system can be strengthened and the hotel operation cost can be saved. Therefore, it is feasible to apply the text mining method based on fuzzy logic and neural network to hotel management.
Semantic Approach To Live Data Mining Using Clustering Technique

In recent times, Social Networks became ubiquitous in our daily life. These networks can be used to obtain high-volume data related to any given event or topic. Twitter, one of the famous networks, became the wealthy supply of discussed topics. The users on Twitter express their point of views or opinions by tweets concerning different topics in a variety of fields. The data generated on Twitter can be extracted via Sentiment Analysis. In this proposed system, an artificial intelligence concept (NLP) is applied that understands the sentiment of extracted data and polarizes them into positive, negative, or neutral. These polarized data are then grouped based on their similarities and differences through a grouping algorithm. The data obtained is then represented graphically in form of a bar-chart. The information obtained from this text, an analysis based on emotions is used to create daily trends related to the event which is used to identify emerging patterns of this data or the current user sentiments related to the given event or topic.
Functional Clustering Based on Weighted Partitioning around Medoid Algorithm with Estimation of Number of Clusters

More and more data are recorded continuously in an interval or discretely only at finite time points nowadays due to the frequency of data collection. These data are functional data which belong to big data. This paper introduces functional clustering in functional data analysis. We propose a weighted partitioning around medoid algorithm with estimation of number of clusters for functional clustering, and use an example to prove its effectiveness.
Domain Transformer: Predicting Samples of Unseen, Future Domains

The data distribution commonly evolves over time leading to problems such as concept drift that often decrease classifier performance. Current techniques are not adequate for this problem because they either require detailed knowledge of the transformation or are not suited for anticipating unseen domains but can only adapt to domains, where data samples are available. We seek to predict unseen data (and their labels) allowing us to tackle challenges s a non-constant data distribution in a proactive manner rather than detecting and reacting to already existing changes that might already have led to errors. To this end, we learn a domain transformer in an unsupervised manner that allows generating data of unseen domains. Our approach first matches independently learned latent representations of two given domains obtained from an auto-encoder using a Cycle-GAN. In turn, a transformation of the original samples can be learned that can be applied iteratively to extrapolate to unseen domains. Our evaluation of CNNs on image data confirms the usefulness of the approach. It also achieves very good results on the well-known problem of unsupervised domain adaption, where only labels but no samples have to be predicted. Code is available at https://github.com/JohnTailor/DoTra.
Quick Response Code Validation and Phishing Detection Tool

A Quick Response (QR) Code is a type of barcode that can be read by the digital devices and which stores the information in a square-shaped. The QR Code readers can extract data from the patterns which are presented in the QR Code matrix. A QR Code can be acting as an attack vector that can harm indirectly. In such case a QR Code can carry malicious or phishing URLs and redirect users to a site which is well conceived by the attacker and pretends to be an authorized one. Once the QR Code is decoded the commands are triggered and executed, causing damage to information, operating system and other possible sequence the attacker expects to gain. In this paper, a new model for QR Code authentication and phishing detection has been presented. The proposed model will be able to detect the phishing and malicious URLs in the process of the QR Code validation as well as to prevent the user from validating it. The development of this application will help to prevent users from being tricked by the harmful QR Codes.
VR: a new challenge in digital teaching of optics and photonics

The paper describes the implementation of practical laboratory settings in a virtual environment. With the entry of VR glasses into the mass market, there is a chance to establish educational and training applications for displaying some teaching materials and practical works. Therefore our project focuses on the realization of virtual experiments and environments, which gives users a deep insight into selected subfields of Optics and Photonics. Our goal is not to substitute the hand on experiments rather to extend them. By means of VR glasses, the user is offered the possibility to view the experiment from several angles and to make changes through interactive control functions. During the VR application, additional context-related information is displayed. By using object recognition, the specific graphics and texts for the respective object are loaded and supplemented at the appropriate place. Thus, complex facts are supported in an informative way. The prototype is developed using the Unity Engine and can thus be exported to different platforms and end devices. Another major advantage of virtual simulations to the real situation is the high degree of controllability as well as the easy repeatability. With slight modifications, entire experiments can be reused. Our research aims to acquire new knowledge in the field of e-learning in association with VR technology. Here we try to answer a core question of the compatibility of the individual media components.
AUCseg: An Automatically Unsupervised Clustering Toolbox for 3D-Segmentation of High-Grade Gliomas in Multi-Parametric MR Images

The segmentation of high-grade gliomas (HGG) using magnetic resonance imaging (MRI) data is clinically meaningful in neurosurgical practice, but a challenging task. Currently, most segmentation methods are supervised learning with labeled training sets. Although these methods work well in most cases, they typically require time-consuming manual labeling and pre-trained models. In this work, we propose an automatically unsupervised segmentation toolbox based on the clustering algorithm and morphological processing, named AUCseg. With our toolbox, the whole tumor was first extracted by clustering on T2-FLAIR images. Then, based on the mask acquired with whole tumor segmentation, the enhancing tumor was segmented on the post-contrast T1-weighted images (T1-CE) using clustering methods. Finally, the necrotic regions were segmented by morphological processing or clustering on T2-weighted images. Compared with K-means, Mini-batch K-means, and Fuzzy C Means (FCM), the Gaussian Mixture Model (GMM) clustering performs the best in our toolbox. We did a multi-sided evaluation of our toolbox in the BraTS2018 dataset and demonstrated that the whole tumor, tumor core, and enhancing tumor can be automatically segmented using default hyper-parameters with Dice score 0.8209, 0.7087, and 0.7254, respectively. The computing time of our toolbox for each case is around 22 seconds, which is at least 3 times faster than other state-of-the-art unsupervised methods. In addition, our toolbox has an option to perform semi-automatic segmentation via manually setup hyper-parameters, which could improve the segmentation performance. Our toolbox, AUCseg, is publicly available on Github. ( https://github.com/Haifengtao/AUCseg ).
Reconfigurable Blockchains for Dynamic Cluster-based Applications

The introduction of Bitcoin cryptocurrency has inspired businesses and researchers to investigate into the technical aspects of blockchain and DLT systems. However, the blockchain technologies today still have distinct limitations on scalability and flexibly in terms of large-size and dynamic reconfigurability. Sharding appears to be a promising solution to scale out the blockchain system horizontally by dividing the entire network into multiple shards or clusters. However, flexibility and reconfigurability of these clusters need further research and investigations. In this paper, we propose two efficient mechanisms to enable flexible dynamic re-clustering of the blockchain network including blockchain cluster merging and splitting operations. Such mechanisms offer a solution to specific application scenarios such as microgrids and other edge-based applications where clusters of autonomous systems potentially require structure reconfigurations. The proposed mechanisms offer three-stage procedures to merge and split multiple clusters. Based on our simulation experiments, we show that the proposed merging and splitting operations based on proof of work (PoW) consensus algorithm can be optimized to reduce the merging time considerably (in the magnitude of 1/22000 based on 100 blocks) which effectively reduces overall merging and splitting completion time, interruption time and required computation power.
A technique for parallel query optimization using MapReduce framework and a semantic-based clustering method

Query optimization is the process of identifying the best Query Execution Plan (QEP). The query optimizer produces a close to optimal QEP for the given queries based on the minimum resource usage. The problem is that for a given query, there are plenty of different equivalent execution plans, each with a corresponding execution cost. To produce an effective query plan thus requires examining a large number of alternative plans. Access plan recommendation is an alternative technique to database query optimization, which reuses the previously-generated QEPs to execute new queries. In this technique, the query optimizer uses clustering methods to identify groups of similar queries. However, clustering such large datasets is challenging for traditional clustering algorithms due to huge processing time. Numerous cloud-based platforms have been introduced that offer low-cost solutions for the processing of distributed queries such as Hadoop, Hive, Pig, etc. This paper has applied and tested a model for clustering variant sizes of large query datasets parallelly using MapReduce. The results demonstrate the effectiveness of the parallel implementation of query workloads clustering to achieve good scalability.