Reviewerly/exHarmony huggingface dataset:
Explainable AI for ICT: System and Software Architecture

Artificial Intelligence (AI) has become a revolution in the ICT domain due to the swift progress of analytical techniques and the availability of structured/unstructured data. With the indispensable role of AI in different applications, there are growing concerns over the lack of transparency and explainability. In addition, potential bias may affect the predictions of a model. This is where Explainable Artificial Intelligence (XAI) comes into the picture. XAI increases the trust placed in an AI system by researchers, medical practitioners, and others. Thus, it leads to widespread deployment of AI in healthcare, agriculture, online mart, and many more. The aim is to enlighten practitioners on the understandability and interpretability of EAI systems using a variety of techniques available which can be very advantageous in the ICT domain. In this chapter, we present two different techniques leveraging EAI where a user has to make the right choices based on his requirements. The software architecture of the first techniques is based on a medical diagnosis model where we need to be confident enough to treat a patient as instructed by a black-box model. Another approach presents an online Mart where a reliable pricing method can be developed by ML models that can read through historical sales data. The objective here is to match buyers and sellers, to weigh animals, and to oversee their sale. However, when AI models suggest or recommend a decision, that in itself does not reveal too much (i.e., it acts as a black box). Hence, a model capable of explaining the different factors that impact the price point is essential for the needs of a user.
REGULARITIES OF EDUCATIONAL MANAGEMENT OF MULTIPROFILE INSTITUTIONS OF EXTRACURRICULAR EDUCATION IN THE CONDITIONS OF AUTONOMY

In the last decade, one of the topics that has received considerable attention is the need for changes in educational management at all levels. Special attention is drawn to multidisciplinary institutions of out-of-school education, which are of great value to the individual, who acquires a variety of competencies, gets the opportunity to develop creativity, creativity. Recognition of the importance of basic laws, the possibility of summarizing on their basis of content and principles is the starting point for further transformation of extracurricular activities. In the educational management of such institutions, regularities should attract a lot of attention from various representatives of civil society, as children and young people who can study in such institutions receive additional opportunities to improve their quality of life. This study aims to consider the basic patterns on which it is possible to further build a new content and principles of the educational management of multidisciplinary out-of-school education institutions. We believe that the educational management in the era of posthumanism is based on values – knowledge, skills, competencies. This requires scientifically focused planning in order to improve the quality of extracurricular education, to anticipate changes in the process of managing creative teams, to take to a new level the position on the motivation of teachers and support their activities. Such knowledge will help to identify existing threats in time, turn them into additional opportunities and further use all possible resources to achieve high quality work of out-of-school teachers. The knowledge of the basic laws, the possibility of constructing new content and principles of educational management of multidisciplinary out-of-school educational institutions encourages administrative staff to perform work not only qualitatively, according to job responsibilities, but also creatively, taking into account innovations in management. The author highlights the main laws of educational management of multidisciplinary out-of-school education institutions, which are necessary for the development of a new system for management in autonomy: consistency of educational policy in the field of extracurricular activities in accordance with existing population demand; correlation of the requirements of modern educational management with those management practices that are available in institutions; optimal combination of traditions and innovations in the process of institution development; the dependence of the prosperity of multidisciplinary institutions of out-of-school education in the future on the development of management activities according to new modern requirements (adaptability, creativity, creativity, flexibility); coherence of work of different directions and conformity of their development to the needs of out-of-school education seekers.
Retention Prediction in the Gaming Industry: Fuzzy Machine Learning Approach

Traditional machine learning algorithms may not produce satisfactory results on high-dimensional and imbalanced datasets. Therefore, the popularity of the concept of ensemble learning has increased, especially in recent years. Standard machine learning algorithms try to learn a single hypothesis from the training dataset, while ensemble-learning algorithms create a set of hypotheses and try to combine them. In this way, they can produce better results than the common machine learning algorithms. The fuzzy logic concept is also used in machine learning problems, especially in clustering problems in recent years. The fuzzy logic approach, by its nature, is used to solve problems that do not have a definite result, such as real-life problems, brings this approach to the fore, especially in machine learning problems. In this paper, we survey the latest status of ensemble learning and fuzzy clustering methods. Also, we proposed a new approach that combines fuzzy clustering and ensemble learning. This approach is applied in a case study, and results are compared with existing ensemble learning algorithms in the methodology section.
The Value of Criminological Theories in Explaining Cybersecurity in South African Smart Cities

Abstract The development of smart cities in South Africa has the potential to enrich quality of life, encourage economic growth and reduce the human ecological footprint. It can advance and elevate service delivery in urban areas by applying various information and communication technologies. However, despite the countless benefits available, smart cities are at risk for major cybersecurity breaches that can produce devastating consequences. Criminological theories provide a foundation for understanding and explaining the criminal activity. Advancements in computer technologies and increased use of electronic devices and the Internet have resulted in crimes committed in cyberspace. As such, criminology scholars have been forced to think differently about how crimes are committed in cyberspace and how theoretical perspectives can be advanced to explain these non-traditional crimes. This article contends that criminological theories can inform cybersecurity risks in smart cities. This paper is based on theoretical findings through a qualitative inquiry, and the data were analysed thematically. The authors illustrate the value of social learning theory, neutralization theory, code of the street theory, space transition theory, actor-network theory and integrated model theory in explaining cybersecurity risks in smart cities.
Usability Evaluation of Ataturk University Web Site with Morae Program and the Effect of Pandemic During Testing Process: An Application for Undergraduate Students

Ataturk University has renewed the university website within the scope of studies conducted to become a new generation university. In this context, the Usability Testing (UT) with Morae V.3.3.0 program was applied for the first time to the user group consisting of undergraduate students in order to determine the usability level of the Ataturk University New Website (AUNWS). According to UT results, the usability problems, usage needs, expectations from the website, and satisfaction levels of the undergraduate students were determined. As a result of the study, the problems that need to be solved primarily in the AUNWS are the complex design of the home page and the insufficiency of the search button. Moreover, the AUNWS and Ataturk University Old Website (AUOWS) was compared in terms of usability levels in the study, and the participants found the new website more usable. The tests conducted under COVID-19 pandemic conditions have caused the distance education students to concentrate on the tests more willingly and more carefully during the pandemic process. The test results were positively affected since students do all their processes through the university website due to distance education.
Light beam interacting with electron medium: exact solutions of the model and their possible applications to photon entanglement problem

We consider a model for describing a QED system consisting of a photon beam interacting with quantized charged spinless particles. We restrict ourselves by a photon beam that consists of photons with two different momenta moving in the same direction. Photons with each moment may have two possible linear polarizations. The exact solutions correspond to two independent subsystems, one of which corresponds to the electron medium and another one is described by vectors in the photon Hilbert subspace and is representing a set of some quasi-photons that do not interact with each other. In addition, we find exact solution of the model that correspond to the same system placed in a constant magnetic field. As an example, of possible applications, we use the solutions of the model for calculating entanglement of the photon beam by quantized electron medium and by a constant magnetic field. Thus, we calculate the entanglement measures (the information and the Schmidt ones) of the photon beam as functions of the applied magnetic field and parameters of the electron medium.
Automated Anomaly Detection in Real-Time Data Streams: An Application at Token Financial Technologies Company

Abnormalities are samples in data that do not fit the normal patterns. Various reasons such as malware, fraud, cyber-attack, terrorist activities, faults, system behavior changes, instruments, and human error might generate abnormalities. Anomaly detection is a technique that provides unexpected situations or patterns to be found in the data. These unexpected situations or patterns are called anomalies, outliers, and unexpected cases in the literature that do not fit the expected behavior of the data. Diverse research and applications have been carried out for anomaly detection, which is critical for the industries. To predict the anomaly, there are numerous learning methods as supervised, semi-supervised and unsupervised. In this scope, this paper proposes a novel concept as building an automated anomaly detector system for a business operation platform at Token Financial Technologies company, a leader in the payment systems industry in Turkey, by using the Isolation Forest algorithm developed in Python. Thanks to this system, as Token Financial Technologies company, abnormal data in the system might be detected in real-time. In this study, to integrate the business operation platform, we have first examined the data of deleting banking applications on the EFT-POS devices and detected the anomaly. The detection helped Token Financial Technologies to save more than 55% of the banking applications on the devices from deletion by contacting banks and customers instantly in the last quarter of 2020.
Optimized Code Generation for Deep Neural Networks

As Deep Neural Networks (DNNs) become more widely used in a variety of applications, the need for performance and portability on many different architectures, including CPUs, becomes increasingly important. Compiler-based methods offer opportunities for performance gains over statically-tuned libraries by exploiting data reuse and parallelism, efficient memory access, and vectorization for specific backends with the use of abstraction. The Batch Normalization (BN) operator can accelerate the training and increase the robustness of DNNs, making it a widely-used operator in many DNNs. LATTE is a domain-specific language for DNNs, and SWIRL is a compiler that can be used with LATTE. We extend the applicability of LATTE/SWIRL by incorporating the BN operator into the LATTE framework and by expanding the optimizations of SWIRL to this operator. The optimized BN operator in LATTE/SWIRL is compared to existing frameworks such as TensorFlow, TensorFlow with Intel MKL-DNN, TensorFlow with XLA, PyTorch with MKL-DNN and MXNet with MKL-DNN. The results show that a compiler-based approach for the BN operator can increase performance on CPU architectures.
Zero-Shot Hate to Non-Hate Text Conversion Using Lexical Constraints

Systems meant for tackling hate speech have been increasing in demand with the rapid growth of social media platforms. One way of controlling hate speech in texts is to transform the text into its non-hate version while preserving the rest of the contents. Without the use of parallel data, unsupervised back-translation-based text style transfer is a common method of tackling such problems. In this article, we propose a zero-shot style-transfer technique that does effective unsupervised hate to non-hate conversion without using any hate domain text for training. While decoding the outputs produced by the system, we define an additional step of introducing lexical constraints, for better preservation of contents. Detailed empirical evaluation shows that the zero-shot method outperforms classical unsupervised style-transfer methods while at the same time reducing the data required while training.
Expanding Opportunities for Array Privatization in Sparse Computations

Sparse computation, where sparse formats are used to compress nonzero values of big data, are commonly used in real world applications. However, the compiler-based data dependence analysis of sparse computations needed for automatic parallelization is difficult due to usage of indirect memory accesses through index arrays, e.g. col in val[col[j]], in these computations. One use of such data dependence analysis is to find opportunities for array privatization, which is an approach to increase available parallelism in a loop by providing each parallel thread its own copy of arrays where in each iteration the array reads are dominated by array writes in the same iteration. In this paper, we expand opportunities for compile-time array privatization in sparse computations by using newly formulated index array properties and a novel concept we call content-based privatization. Furthermore, we discuss existing opportunities to use our approach for detecting private arrays in existing library implementations of sparse computations.
Few-Shot Segmentation for Prohibited Items Inspection With Patch-Based Self-Supervised Learning and Prototype Reverse Validation

Prohibited items inspection using X-ray screening is essential for reducing the risk of crime and terrorist attacks. The difficulty in prohibited items inspection lies in accurately detecting prohibited items in complex X-ray images and limited access to X-ray images containing prohibited items. Few-shot segmentation aims at learning with limited examples and assigning a category label to each image pixel. However, current few-shot methods are mostly full-supervised and less robust to the prohibited items categories that did not appear during training process. In this paper, we propose a method for few-shot prohibited items segmentation tasks which utilize unlabeled data and better leverage the representation of input samples during model training process. Specifically, a patch-based self-supervised embedding network is firstly devised as the base learner to learn an abstract representation of the observation from unlabeled samples. Then we apply few-shot learning and generate abstract representation related to prohibited items from support sample within the embedding space, which is followed by obtaining the corresponding class-specific prototype representations via masked average pooling. The distance between each pixel of query sample and prototypes are calculated to predict the label of each pixel. Moreover, prototype reverse validation strategy (PRV) is proposed to further exploit the support representation to assist training. Extensive experiments show that our proposed method outperforms the state-of-the-art by delivering a higher accuracy on automated prohibited items inspection and requiring less labeled samples.
Mutual Channel Data Augmentation Network for Fine-Grained Visual Classification

Finding discriminative local regions corresponding to fine-grained features is the key to solving fine-grained image classification problems. Recently, fine-grained classification by weakly supervised data augmentation network (WS-DAN) has achieved excellent results, but its single cross-entropy loss (CE-Loss) makes the network focus on global discriminative regions and miss some local discriminative regions. To address this problem, a data augmentation network based on mutual channel loss (MC-Loss) is proposed. The mutual loss (MC-Loss) can force feature channels belonging to the same class to be more discriminative. Second, a counterfactual attention module (CAL) is introduced to encourage the network to learn more attention by counterfactual intervention. In addition, an attention module that combines spatial attention and channel attention is proposed to better focus on object regions in images. Comprehensive experiments on three public benchmarks show that the method is effective in achieving classification.
Multiview Deep Matrix Factorization for Shared Compact Representation

Multiview learning aims to learn beneficial patterns from heterogeneous data sources and has captured growing attention in recent years. Most of the previous research studies focused on searching for an effective feature embedding of downstream tasks using diverse optimization algorithms, however, very limited work has been conducted to explore the connection between multiview learning and deep neural networks of structure sharing hidden layers. In this article, we propose a multiview deep matrix factorization model to learn a shared compact representation from multiview data. First, the proposed model constructs a multiview auto-encoder architecture with one shared encoder and multiple decoders, where each view corresponds to a factorization and the shared encoder leads to a common hidden layer. Accordingly, matrix factorizations from multiview data share the last hidden layer for a high-level semantic representation. Second, the nonnegativity constraint of the learned representation is transformed to the projection operation, which can be easily achieved by activating weights of the shared encoder network. Third, this network is trained with a joint loss of the reconstruction error and the compactness loss. By employing the clustering layer, the proposed method serves as an end-to-end multiview clustering method. Finally, comprehensive experiments on nine real-world datasets demonstrate the superiority of the proposed method against state-of-the-art multiview clustering methods.
Smart Fall Detection System: Kinect Depth and Cnn Integrated Approach

This study proposes a smart fall detection system integrating Kinect depth and convolutional neural network (CNN) approaches. The system uses raw data from Kinect depth images to locate the ground, identifies the foreground pixels with a background subtraction algorithm, and tracks the foreground for analysis. This paper compares the proposed system with four neural network types for evaluation and implementation including the support vector machine (SVM), backpropagation neural network (MLP), radial basal function neural network (RBF) and deep learning neural network (DNN). The findings demonstrate an efficient and reliable system with accuracy rate approximately at 95% with KAPPA at 0.96.
Privacy and Securization of Patient Data in a Neural Blockchain: The Case of the Ehealth Phisicos Platform

Blockchain technology has promising features regarding decentralization, persistence, anonymity, and auditability that could greatly improve health data mangement process. This paper focuses on presenting a novel approach to integrate blockchain and cloud computing services to promote patient-centric control of healthcare data sharing. Particularly, we will present PhisicOS, a Cloud medical platform that we are developing to manage health centres and their patients’ Electronic Health Records (EHR). Our proposal improves the privacy and security of health data through the use of a private permissioned blockchain. The use of smart contracts allows patients to know how their data is used, by whom, with which objectives and when.
Using Distributed Ledger Technologies to Manage a Collection of Business Process Models

This research letter considers the approach to managing a collection of business process models using the ideas of Blockchain and Directed Acyclic Graph structures. It is expected to make shared web resources more secure and transparent using this approach when managing cross-enterprise collections of business process models.
Opportunities To Develop Students Soft Skills In Russian Language Course

In the modern labor market there is a demand for comprehensively developed specialists who are not only professional, but also have various soft skills. The paper outlines the problem of forming soft skills for foreign students in the course of Russian language teaching. The technology of forming soft skills through the analysis of the scenario of Russian speech communication in the course of Russian as a foreign language is proposed.
Feature Subset Selection for Big Data Via Parallel Chaotic Binary Differential Evolution Island Model Under Apache Spark

Feature subset selection (FSS) using a wrapper approach is essentially a combinatorial optimization problem having two objective functions viz., (i) the cardinality of the selected feature subset, and (ii) the area under the ROC curve (AUC) of a classifier built on this subset. They should be minimized and maximized respectively. In this paper, we propose a novel multiplicative single objective function involving cardinality and AUC for FSS in high-dimensional and large-scale datasets. Since the randomness involved in the Binary Differential Evolution (BDE) may yield less diverse solutions and get the algorithm entrapped in local minima, we embed Logistic and Tent Chaotic maps into the BDE, called Chaotic Binary Differential Evolution (CBDE). We (i) develop an adaptive version of CBDE based on SHADE (called CSHADE) and (ii) propose a scalable island (IS) based parallelization approach, where the data is divided into multiple partitions/islands thereby enabling the solutions evolve individually and get combined eventually in a migration strategy/policy. The results empirically show that the proposed parallel Chaotic BDE (P-CBDE-IS) and its corresponding adaptive version can find better quality feature subsets than the Parallel BDE (P-BDE-IS). Overall, P-CBDE-IS performed best in most of the cases. Logistic Regression (LR) is used as a classifier owing to its simplicity and power. The speedup attained by the proposed parallel approach signifies its importance.