Reviewerly/exHarmony huggingface dataset:
A New Approach in Cloud Environment to Improve Data Security Using Multiple Bits

Cloud computing is one of the prominent dynamic term, which provide different services for the user and also prevent the user from burdens of local storage issues. Security is one of the biggest issues and offers new challenges to researchers for protecting the data from unreliable services to provide the solution for this issues number of ways. This paper concentrates on the security with the help of steganography before storing into the cloud environment. In this proposed method, the data is hidden into the image and that image is stored into the cloud space. For this use, LSB steganography has been proposed by considering LSB and MSB pixels for hiding and retrieval of the data which is an improvised version of one bit LSB technique. The effectiveness of the proposed method has been estimated by computing mean square error (MSE) and peak signal to noise ratio (PSNR).
Asynchronously Parallel Decoding For Automatic Speech Recognition Services

We proposed a new automatic speech recognition (ASR) service architecture that is extendable to medium-scale ASR service and more flexible than the previous architecture. Improvement aims to substitute the distributed processing approach with an asynchronous parallel thread for decoding multiple voice streams. We replace our TCP-based communication protocol with a remote procedure call developed by Google (gRPC) that makes our ASR service become a developer-friendly, less overhead connection. Besides, the API gateway is employed to reinforce the ASR services by multiple servers so that we can increase our new ASR service to a larger scale. The experimental result shows that our new architecture performs faster than the previous architecture in terms of real-time factor.
SafeLib: a practical library for outsourcing stateful network functions securely

A recent trend is to outsource virtual network functions (VNFs) to a third-party service provider, such as a public cloud. Since the cloud is usually not trusted, redirecting enterprise traffic to such an entity introduces security concerns. In addition to protecting enterprise traffic, it is also desirable to protect VNF code, policies and states. Existing outsourcing solutions fall short in either supporting stateful VNFs, catering for all security requirements, or providing adequate performance.In this paper we present SafeLib, a trusted hardware based outsourcing solution built on Intel SGX. SafeLib provides i) support for stateful VNFs, ii) support for illegal SGX instructions by integrating Graphene-SGX, iii) protection of both packet headers and payload for enterprise user traffic, VNF policies and VNF code, and iv) integration of libVNF for streamlined VNF development. Our performance evaluation shows that SafeLib scales properly for multiple cores, and introduces a reasonable performance overhead. We also outline plans to further improve SafeLib to satisfy even more stringent functional, security and performance requirements.
Passphrase Authentication and Individual Physiological Differences

Computer passphrase authentication designed with usability consideration encourages memorability. This passphrase study suggests successful recollection using an assembly of meaningful word groups evidenced by individual physiological performance measurements. Participant data collected at the Hawaiʻi Interdisciplinary Neurobehavioral and Technology Lab (HINT) demonstrate physiological responses to passphrase decision making. The results from university students indicate psychophysiological influences predict passphrase characteristics. The repeated measures investigation of user-created and system-imposed passphrases contribute understanding toward user authentication selection that supports encoding and recalling a secret, unlike the current traditional password composed of alphanumeric and special characters. Passphrases constructed with usability considerations support security compliance requirements. This explanatory study employs pilot-tested passphrases designed to reveal predictive psychophysiological behavior. Overall results indicate user-created passphrases produce less cognitive load stressors on working memory than system-imposed passphrases. However, physiological measurements from heart rate, skin conductance, and the facial corrugator supercilii muscle signify a mixture of passphrase types that imply memorability. Study results suggest a platform for future passphrase design research of longer passwords that inform security access, improve memorability, and enhance usability evaluated by human-centered performance.
Implementation of Interdisciplinary Relations on the Example of Professional Training in “Business Informatics” in the Context of a Pandemic

The article considers the development of IT students' professional competencies and skills of modeling and reengineering of business processes on the example of training bachelors in the specialty 38.03.05 “Business Informatics” at NUST MISIS. The key problems of IT specialists training (especially, the issues of the development of professional competencies) and methods of their solution are considered. A sequence of modeling steps has been proposed, enabling one to perform a system and business analysis of an enterprise as an economic and management entity for the purpose of its business processes reengineering, up to the implementation of a prototype of the process under study with respect to its robotization as part of the graduation thesis. The article summarizes the experience of using the competence-activity approach, interdisciplinary communications and blended learning using an electronic educational environment and adaptive educational trajectories, including during the pandemic.
Improper priors and improper posteriors

Abstract What is a good prior? Actual prior knowledge should be used, but for complex models this is often not easily available. The knowledge can be in the form of symmetry assumptions, and then the choice will typically be an improper prior. Also more generally, it is quite common to choose improper priors. Motivated by this we consider a theoretical framework for statistics that includes both improper priors and improper posteriors. Knowledge is then represented by a possibly unbounded measure with interpretation as explained by Rényi in 1955. The main mathematical result here is a constructive proof of existence of a transformation from prior to posterior knowledge. The posterior always exists and is uniquely defined by the prior, the observed data, and the statistical model. The transformation is, as it should be, an extension of conventional Bayesian inference as defined by the axioms of Kolmogorov. It is an extension since the novel construction is valid also when replacing the axioms of Kolmogorov by the axioms of Rényi for a conditional probability space. A concrete case based on Markov Chain Monte Carlo simulations and data for different species of tropical butterflies illustrate that an improper posterior may appear naturally and is useful. The theory is also exemplified by more elementary examples.
Effect of JPEG compression on Sensor-based Image Forensics

For the forensic task of camera identification and manipulation localization, the most promising and widely used algorithms are the ones based on Photo-Response Non-Uniformity (PRNU) or the camera sensor noise. However, the performance of camera identification and manipulation localization is strongly affected by JPEG compression. Typically, for images with lower quality factors, the accuracy of camera identification as well as manipulation localization reduces considerably. In this article, we present the results of a large scale demonstration on how JPEG compression has an influence on the performance of camera identification and manipulation localization. Our results on three popular datasets indicate that camera identification works even for a quality factor as low as thirty. Also we found that false positives increase significantly for PRNU-based manipulation localization.
Organizational and technical peculiarities of presenting for identification

The research object is social relations connected with the legal regulation, organization and the tactics of identification. The authors consider the types of identification mentioned in the criminal procedure law, and those used during criminal investigation. Having compared their volume and contents, the authors note that the number of types of identification used in the work of law-enforcement bodies is larger than that of those formalized in the Criminal procedure Law of the Russian Federation. This situation doesn&amp;rsquo;t violate the law, since such types of identification as the identification of animals, territories, etc. are the components (subtypes) of the types of identification formalized in the law. The authors study the correlation between the organization and tactics of presenting for identification and attempt to systematize the investigator&amp;rsquo;s actions during the preparation and organization of various types of presenting for identification. The scientific novelty of the research consists in the formation of tactical techniques of presenting for identification through the lens of registration and correlation of subjects of the forensic tactics (the most rational ways of organizing an investigation&amp;nbsp; - the problem scope) and the organization of criminal investigation (the creation of a structure of the investigative activities, the conditions for its effective implementation, and the investigation management - the organizational and administrative scope). This approach helps to create a detailed list of tactical recommendations for the organization, preparation and realization of various types of presenting for identification with account for the specificity of the object of identification, and their classification in accordance with the stages of this investigation.&amp;nbsp; &amp;nbsp;
Relation Extraction Method Based on Two-Way Attentions of Syntactic Knowledge

Relation extraction is in order to obtain the relationship between two entities in one sentence. It is the upstream task for natural language processing tasks. The deep learning method eliminates the need of manually feature engineer. However, existing methods often ignore the importance of syntactic features. We propose a neural network model named TWARL with a two-way attention mechanism to improve relation extraction task by learning from auto-analyzed syntactic knowledge. We experiment with three types of knowledge, namely, part-of-speech tagging, syntactic constituent, and dependency relation in our experiments. The experimental results of the benchmark datasets illustrate the effectiveness of our model, where performance is achieved about the F1 value of 85.2% on SemEval-2010 Task 8 dataset.
Quality in Use -Case Study for Evaluation-

As system and software products are widely used in our life, use of them is influence on not only their direct uses but also organizations and society. However, it is responsibility for society of the manufacturer to be controllable these influences as much as possible. From this, quality is regarded influence on stakeholders by using. The purpose of quality in use is that manufacturers and managers are able to enforce to “use” for improvement of quality by measuring and evaluating. “Quality in use” is defined as “degree to the system satisfied the stakeholder needs related to use when operator uses the system and software” and stakeholders are classified into four groups, they are “operator”, “customer”, “responsible organization” and “society”. Analyzing the relationship among them, quality characteristics as quality in use model are extracted. They are “beneficialness”, “freedom from risk” and “acceptability”. And subcharacteristics for each quality characteristics are also extracted. In the future, we continue to collect any examples and improve the accuracy of the model.
Society Education Media About Islamic Law Through Web-based Application

The majority of Indonesia's population are Muslims who cannot separate the rules of the Islamic religion in the practice of daily life.Therefore, it is important for Muslims to know the rules in Islamic law.This study aims to build a websitebased application that can be a media of education for the society that is practical, effective, and efficient, which can be accessed anywhere and anytime, which related to Islamic law.This study uses Scrum Software Development Methodology which is one of the Agile methods.For software analysis and design modeling, this study use object oriented approach with Unified Modeling Language.The application that built has 37 product backlogs with 4 main function, among others function for discussion forum, personal chat with expert, video, and e-book.This study succeeded in producing a prototype and its implementation in the form of a web-based application with all the functions that have been tested with the black-box testing approach.The result of black-box testing shows that 37 product backlogs or functional requirements of the application already corrected.This study contributes to providing information, communication and consultation media for public education related to Islamic law
A Panel to Confront the Differences in Intersectional HCI

Computer science history better known in society is predominantly male, white, and heteronormative. It is also part of our culture, discourse, and society. We base our research on the third-wave of Human-Computer Interaction Studies. We bring a feminist technoscience perspective to investigate the disparities in gender, race, ethnicity, and class. From our systematic mapping, we learn that third-wave HCI studies are organized in three strands: Feminist HCI, Gender HCI, and Intersectional HCI. In the Intersectional HCI perspective, we identified that the proposals generally use just one category (for example: gender), and forget that intersectionality is a crossing categories. From this contradiction, we identified the gaps in 11 publications and produce a panel, that discusses the re-signification of categories. Our main objective is to create the panel to criticize the differences when HCI researches appropriate intersectional theory without considering black women's struggles and resistance. The study results are: a) structural barriers in low women participation; b) gender, race and ethnicity and class disparities is in computer science area but also in society and culture; c) intersection of race, class, and gender in third-wave HCI is controversially appropriate; d) power relations in macro axes reveal intentions of the majority in computer science and also in power spaces in society.
Recent Developments of Automated Machine Learning and Search Techniques

The recent successes of artificial intelligence, in particular machine learning, for solving real-world problems have motivated the advances towards automated design of algorithms and systems with less human involvement. In machine learning and meta-heuristic search algorithms, different lines of relevant research are now emerging, with findings feeding into each other. This book presents a selection of some recent advances across automated machine learning (AutoML) and automated algorithm design (AutoAD), where the effectiveness and efficiency of techniques and algorithms has been enhanced with the support of new taxonomies, models, theories, as well as frameworks and benchmarks. The emerging new lines of exciting research directions in AutoML and AutoAD present new challenges across multiple research communities in machine learning, evolutionary computation and optimisation research.
Network Intrusion Detection Based on Subspace Clustering and BP Neural Network

This paper proposes a novel network intrusion detection algorithm based on the combination of Subspace Clustering (SSC) and BP neural network. Firstly, we perform a subspace clustering algorithm on the network data set to obtain different subspaces. Secondly, BP neural network intrusion detection is carried out on the data in different subspaces, and calculate the prediction error value. By comparing with the pre-set accuracy, the threshold is constantly updated to improve the ability to identify network attacks. By comparing with K-means, DBSCAN, SSC-EA and k-KNN intrusion detection model, the SSC-BP neural network model can detect the most attacked networks with the lowest false detection rate.
A Causal Model Analysis Approach to Literature Reviewing: A Tutorial on the Application of Graph Theory

Current literature search approaches rely on searching publications with keywords, reading related papers, and manually synthesizing knowledge. This approach is inefficient and limits the pace of scientific progress. To address such issues, we develop a causal model-based approach for literature reviewing. The method focuses on coding the core knowledge in publications (i.e., causal or process models) as directed graphs following a well-defined regimen and combining the directed graphs into a labeled property graph database. We provide guidance on coding publications as graphs using the Cypher programming language. An application is available to facilitate scholars to code publications as a graph database. We discuss the contributions of our method in terms of query-based knowledge extraction and assisting knowledge synthesis using various network analysis metrics.
Research on Human Body Detection and Trajectory Tracking Algorithm Based on Multi-sensor Fusion

At present, most mobile robots regard the human body as a moving obstacle in the process of autonomous movement in a scene with pedestrians, and avoid the human body when planning a local path. This situation is not convenient for pedestrians, and it will also cause problems for robot navigation. In many cases, the path of the robot and the pedestrian conflicts, causing the robot to fall into a freezing (stand-by-place) posture until the pedestrian disappears from the local map of the robot. In order to solve this problem, the solution proposed in this paper adopts multi-sensor data fusion technology, uses laser scanners and depth cameras to detect pedestrians, and establishes a pedestrian motion model to realize human body recognition and motion prediction based on multisensor fusion.
On Sketch-Based Selections From Scatterplots Using KDE, Compared to Mahalanobis and CNN Brushing

Fast and accurate brushing is crucial in visual data exploration and sketch-based solutions are successful methods. In this article, we detail a solution, based on kernel density estimation, which computes a data subset selection in a scatterplot from a simple click-and-drag interaction. We explain how this technique relates to two alternative approaches, i.e., Mahalanobis brushing and CNN brushing. To study this relation, we conducted two user studies and present both a quantitative three-fold comparison as well as additional details about the prevalence of all possible cases in that each technique succeeds/fails. With this, we also provide a comparison between empirical modeling and implicit modeling by DL in terms of accuracy, efficiency, generality, and interpretability.
CNN and Computational Configuration Mathematical Chromosomal Defect Analysis in Medical Images

Abstract Chromosomal defect analysis plays an important role in current medical care and diagnosis as one of the principal methods in cytogenetics via the processing of a medical picture. There are two main elements of human karyotype analysis: first, chromosomes are separated by digital images of the chromosome metaphase under the microscope. Chromatids are then closely analyzed, compared, organized and categorized. The segmentation and classification operation is tedious, where conventional geometric or mathematical approaches have only limited impact due to low precision, according to this technique. In most cases however, the workflow is still highly supervised and errors are still required by humans. This paper provides an optimised workflow to isolate and automatically identify chromosomes by a combination of many CNN and mathematical optimizations called mCNN GO. Mask R-CNN is investigated to separate the chromosome from chromosome metaphase images and train mCNN GO to identify the sub-images. We apply a new functional approach to synthesize images on the labelled data in order to enhance the efficiency of the segmentation network. Moreover, to ensure accuracy of the results, we create computational algorithms to straighten the genomes before registration. Experimental findings indicate that our methods for automated karyotype analysis are greatly superior to state-of-the-art.
COMPONENTS OF THE TRAINING PROGRAM FOR EDUCATORS WORKING ON THE DEVELOPMENT OF THE MATHEMATICALLY GIFTED STUDENTS POTENTIAL

The article is devoted to the formation and improvement of competencies of teachers and psychologists of secondary schools to identify and develop mathematically gifted students. It has been identified the components of the training program of basic competencies that psychologists and subject teachers must have to recognize and develop mathematical talent. The results of an empirical study of an educational project are online training for educators to deepen their theoretical knowledge of mathematical talent and the development of practical skills of organizing the educational process for students with a high level of ability in the field of exact sciences. It was found that training in the development of competencies is an effective way to improve the skills of teachers to understand the essence of talent, the peculiarities of its detection in students, prevention of loss of potential, development of individual educational trajectories, use of new learning technologies and ways to develop personal skills.
Applications of the ESPNet architecture in medical imaging

Medical imaging is a fundamental part of clinical care that creates informative, noninvasive, and visual representations of the structure and function of the interior of the body. With advancements in technology and the availability of massive amounts of imaging data, data-driven methods, such as machine learning and data mining, have become popular in medical imaging analysis. In particular, deep learning-based methods, such as convolutional neural networks, now have the requisite volume of data and computational power to be considered practical clinical tools. We describe the architecture of the ESPNet network and provide experimental results for the task of semantic segmentation on two different types of medical images: (1) tissue-level segmentation of breast biopsy whole slide images and (2) 3D tumor segmentation in brain magnetic resonance images. Our results show that the ESPNet architecture is efficient and learns meaningful representations for different types of medical images, which allows ESPNet to perform well on these images.
Improving Relation Extraction via Joint Coding Using BiLSTM and DCNN

Neural network methods based on distant supervision has been widely used in studies concerning relation extraction, however, a traditional convolutional neural network can not effectively extract the dependency relationship and structured information between words in sentences. In order to solve this problem, we propose a novel approach to improve relation extraction results. Specifically, we propose to first apply a neural network-based model to encode sentences, feature vectors obtained are then fed into a one-dimensional dilated convolutional neural network to extract the relation. Finally, sentence-level attention mechanism is used to reduce the noise caused by the mislabeling problem of distant supervision. Our approach has been evaluated on real world datasets NYT10 and compared with a wide range of baselines. Experimental results show that: (1) our approach can improve the performance of neural network relation extraction based on distant supervision; (2) the proposed approach achieves outstanding results on the datasets.