Reviewerly/exHarmony huggingface dataset:
The use of electronic textbooks in the learning process: a statistical analysis

With the active introduction of information technology in the educational sphere, approaches to the textbook as the main means of presenting educational material have changed. Along with printed publications, electronic ones began to be actively used, which in their development have already gone from a simple text document to a complexly structured system that includes various ways of presenting educational material (text, audio, video, graphics). Their use today requires detailed study and reasonable implementation in professional training of specialists. The purpose of the article is to describe a pedagogical experiment on the effectiveness of using an electronic textbook in the training of computer science teachers. To achieve this goal, the methods of mathematical statistics for the quantitative and qualitative analysis of the results of the pedagogical experiment ($\chi^{2}$ Pearson, Student) were used. The corresponding results for the author's ET “Information Systems” by criteria X <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> Pearson, Student are given. Calculations for the positions are described: 1) availability (clarity) of the author's ET; 2) evaluation of the results of independent work based on the author's ET.
Information Flow Control with Decentralized Labeling Model in Information Security

Data security aims to prevent the use, modification, and spread of data by unauthorized people. In this study, our purpose was to provide data privacy and confidentiality with information flow control in distributed databases. In particular, a decentralized label model was developed that maintained confidentiality, including privacy, with data flow control. This model consists of an actor, an object, and a label. The owners of the objects are actors, and they need to share their data objects with others. Actors label the data objects and then send them out. A label contains the policy statements of data security issued by each of the owners. Each owner sets its own security and privacy policy independently of the other owners. The confidentiality of data in unsecured transport channels is ensured for all the actors in the system by means of labels while the data are in flow. Data objects are spread and shared securely among actors within unsecured environments. In addition, with the path compression, the long node chain that is formed while the data objects are passing between the source node and the destination is broken, so that the objects are retrieved fast, and the cost of access is reduced. This result was shown experimentally by modeling the distributed environment.
Weighted Meta-Path Embedding Learning for Heterogeneous Information Networks

A low-dimensional embedding can be easily applied in the downstream tasks for network mining and analysis. In the meantime, the popular models of random walk-based network embedding are viewed as the form of matrix factorization, whose computational cost is very expensive. Moreover, mapping different types of nodes into one metric space may result in incompatibility. To cope with the two challenges above, a weighted meta-path embedding framework (WMPE) is proposed in this paper. On one hand, a nearly-linear approximate embedding approach is leveraged to reduce the computational cost. On the other hand, the meta-path and its weight are learned to integrate the incompatible semantics in the form of weighted combination. Experiment results show that WMPE is effective and outperforms the state-of-the-art baselines on two real-world datasets.
The Borda Count as a Tool for Reducing the Influence of the Distance Function on kmeans

The aim of a clustering method is to create groups (clusters) of objects that are similar to each other. This similarity is usually measured by means of a distance, thus, the choice of distance function plays a crucial role in the clustering process. In this work, we propose a variant of the classical clustering method kmeans that combines the information given by different distances to group the objects. More precisely, the cluster to which an object is assigned is chosen by applying the Borda Count to the rankings of closest cluster centers induced by different distances. Experiments were carried out for 81 different datasets. For the vast majority of these datasets, the clusters obtained with the proposed method reduced the Total Distance Within Clusters (TDWC) in comparison with the clusters obtained with the classical kmeans.
Encryption and Decryption of Optical Images with Different Algorithms

While digital images are the most common expression of information on the internet, their security is of great concern, and image encryption technology is a key technology to ensure the security of images. Several kinds of image encryption and decryption methods were used to explore the encryption and decryption of optical images using MATLAB: randomly disrupting the row or column of each layer; randomly disrupting the pixel points; scaling the RGB values of pixel points; transposing, horizontal flipping and vertical flipping of RGB matrixes; and the one-dimensional and two-dimensional data reset of images. Different encryption algorithms have different image encryption effects and operating speeds. The research is to better assess this technology for beginners who are interested in image encryption and decryption technologies and to lay the foundation for these individuals to develop more advanced encryption and decryption technologies in the future. It is proved that these algorithms can be applied to image data protection.
Probabilistic Programming Languages for Modeling Autonomous Systems

We present a robotic development framework called ROSPPL, which can accomplish many of the essential probabilistic tasks that comprise modern autonomous systems and is based on a general purpose probabilistic programming language (PPL). Benefiting from ROS integration, a short PPL program in our framework is capable of controlling a robotic system, estimating its current state online, as well as automatically calibrating parameters and detecting errors, simply through probabilistic model and policy specification. The advantage of our approach lies in its generality which makes it useful for quickly designing and prototyping of new robots. By directly modeling the interconnection of random variables, decoupled from the inference engine, our design benefits from robustness, re-usability, upgradability, and ease of specification. In this paper, we use a SDV as an example of a complex autonomous system, to show how different sub-components of such system could be implemented using a probabilistic programming language, in a way that the system is capable of reasoning about itself. Our set of use-cases include localization, mapping, fault detection, calibration, and planning.
Example Phrase Adaptation Method for Customized, Example-Based Dialog System Using User Data and Distributed Word Representations

We have developed an adaptation method which allows the customization of example-based dialog systems for individual users by applying “plus” and “minus” operations to the distributed representations obtained using the word2vec method. After retrieving user-related profile information from the Web, named entity extraction is applied to the retrieval results. Words with a high term frequency-inverse document frequency (TF-IDF) score are then adopted as user related words. Next, we calculate the similarity between the distrubuted representations of selected user-related words and nouns in the existing example phrases, using word2vec embedding. We then generate phrases adapted to the user by substituting user-related words for highly similar words in the original example phrases. Word2vec also has a special property which allows the arithmetic operations “plus” and “minus” to be applied to distributed word representations. By applying these operations to words used in the original phrases, we are able to determine which user-related words can be used to replace the original words. The user-related words are then substituted to create customized example phrases. We evaluated the naturalness of the generated phrases and found that the system could generate natural phrases.
A color correction method in uniform color space

In the process of restoring the image of space optical camera, it is necessary to use the standard 24 color card to correct the image. In order to improve the accuracy of the mapping function in the process of color correction and prevent the occurrence of overfitting at the same time, a neural network correction method with comprehensive objective function is proposed. Using the idea of regularization as a reference, a constraint term with smooth performance is added to the objective function of the neural network to improve the generalization ability of the network map to data. The experimental results on the dataset of the real color card of the space camera sho-ore the correction.
Mobile Agent Based-Approach for Enhancing Energy Efficient Cloud Data Centre Network

Cloud computing has become the bedrock of new, emerging smart technology. It has numerous benefits that encourage small, medium and large enterprises to use other technology through the internet. Despite the benefits of cloud technology, the challenge of the high power consumption rate it incurs as its leverages its promised attributes remains. This paper used mobile agent technology to solve the complexity problem in the data centre system, which happens to be the backbone of cloud technology. The mobile agent was embedded to both servers and switches to regulate their activities and then shut down underutilised components. Mobile agent (Java agent) is the first of its kind used on a cloud network. Our proposed approach saved a significant amount of energy and also improved the entire system performance during runtime.
Optimization of Fuzzy Systems Through Metaheuristics in Control Systems

Although there are modern computational methods that we can use to solve minimum or maximum problems in different areas, it is good to use classical or semi-there are various types of membership functions. This depends on the problem to be treated. The most used are triangular, trapezoidal, Gaussian among others. For this particular case within this writing we will use the triangular and Gaussian ones. As it is already known through optimization we can come up with the best solution for a particular problem. Since this does not work for all cases in general. For this reason, we will be using in this work Differential Evolution Algorithm (DEA) to optimize a fuzzy system, for the application of this in control as a inverted pendulum system. Similarly, the proposed methodology for optimizing the system is analyzed and explained.
Semi-Supervised Incremental Three-Way Decision Using Convolutional Neural Network

This paper aims to develop a novel cost-sensitive face recognition framework, which can gain the desirable recognition results with the least total cost. By combining two recently rising techniques: deep convolutional neural networks (CNNs) and sequential three-way decision (3WD) method, our framework can automatically label new samples and incorporates the delayed decision into decision-making process. We first explore the semi-supervised face recognition method in the case of the scarcity of labeled training data. By learning the class estimation and the deep convolution feature extraction of the unlabeled data jointly, the CNN trained by both labeled training data and unlabeled data is generated. Then, rather than getting a lower recognition error rate, we focus on seeking the minimum cost of misclassification at each decision step. For this purpose, we introduce the method of sequential 3WD in our cost-sensitive face recognition framework, which take each iteration of semi-supervised learning as a decision-making step. When there are insufficient labeled samples, a delayed decision will be adopted to reduce the decision cost. Finally, the test cost is also considered in the decision-making process, and the sum of misclassification cost and test cost is taken as the total cost. Using the total cost as the objective function, optimizing the performance indicators, training to get the classifier with the smallest total cost. In short, the model strives to get an optimal decision step, so that the reliable identification result can be obtained with only a small number of labeled data. The work value of this paper is to prove the effectiveness of our method in two face datasets.
Two-Stage Search on Web Pages: Effects of Information Organisation and Task Complexity

In this paper we describe the results of an experimental study of a complex web search. Participants completed a two-stage search task: they were asked to find plane tickets with given parameters. The influence of two factors was considered: (1) web page design and (2) task complexity. We analysed the speed of the search, parameters of eye movements and mouse pointer movements during the search task. The results demonstrated the influence of both factors. Higher task complexity (linked to the higher working memory load) was associated with an increase in the average search time, and also with an increase in the average fixation duration and blink frequency. The organisation of information on web pages determined the strategy used to accomplish the search task. The one-page design resulted in an insignificant increase in search time, and in the use of perceptual search strategies. The two-page design resulted in the use of faster, but more resource demanding cognitive strategies.
INFLUENCE FUNCTIONS FOR DIMENSION REDUCTION METHODS

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Toward EU-GDPR Compliant Blockchains with Intentional Forking

With the introduction of the European Union’s General Data Protection Regulation service, providers have to make various adjustments to their business processes and software. Under the GDPR users can, at any time, request that their data is deleted when they no longer use a service or have their data rectified when the service provider stores inaccurate or false data. However, blockchains or distributed ledgers all have in common that they are “append only”. Also every modification to historic data can be immediately detected through the now invalidated hash values stored in every block. In this chapter, we propose a transaction model based upon our previously devised dispute mechanism to cope with the GDPR requirements of deletion and rectification. By introducing the longest-chain-rule into a BFT-PoS Blockchain, we can achieve a validator approved intentional fork to modify previous data.
IoT Using Raspberry PI and Apache Cassandra on PV Solar System

Internet of Things (IoT) and Big Data are one of important and promising technologies that have attracted more attention today in industry. In this work is created a custom electronic circuit which can be used to get sensor's data, using Raspberry Pi IoT device on PV solar panel. Python programming language is used to write and run program in Raspberry Pi, and establish a connection to database for storing data. The main aim of this paper is to model an Apache Cassandra database for processing and storing massive amounts of data that come from sensors. As a case study is used a PV polycrystalline solar module of 250 W.
Safety Analytics for AI Systems

Growing AI technologies are a threat to safety and security in systems due to its obscurity and uncertainty. This study introduces a prevailing Deep Learning model, Convolutional Neural Network (CNN) and it’s deep weaknesses through a simple case study of the CNN model based on Keras for handwriting recognition. It reveals that CNN algorithms don’t adapt well to changes. Adding new cases to the training data may improve accuracy, but not to the same level as before. Synthetic training data may improve the accuracy superficially because of the similarity of data distributions between generated data and original data. Prevailing ML models such as Generative Adversarial Networks (GAN) have their limitations such as similarity-addiction and modality collapse. They could be toxic to safety engineering without domain expertise.The study proposed four test strategies: 1) AI systems should be tested by the third parties, not the developers; 2) test datasets should be categorically different from training datasets; the test data should not be a part of the training data; the test data should be collected from independent sources to increase the “diversity” of data modality; 3) avoid fake data, or simulated data; and 4) don’t collect the data that are conveniently available, but actively collect disastrous event data, unexpected, or the worst scenarios that may destroy the model. The study also introduces a multidimensional checklist for AI safety analysis, including sensors, data and environments, default and recovery mode, system architectures, and human-system interaction.
A Research on Testing Strategies of OSS Used by Equipment Software

Software testing is an important method to guarantee the quality of equipment software. With the rapid development of open source technology, more and more kinds of equipment software are using open source software(OSS). However, the relevant test standards and test specifications for guiding the testing of OSS used by equipment software are lacking at present. This article combines the features of OSS and the testing process of equipment software, proposes for the first time the testing strategies covering the main process of software testing for the testing of OSS used by equipment, which is of great significance in improving the quality of equipment software. This article mainly researches the tasks in test preparation and test implementation stages, and proposes the entry conditions for software testing and the confirmation principles for the testing scope of OSS in the test preparation stage; in the test implementation stage, according to the relevant requirements of equipment software testing, this article puts forward the test specifications applicable to OSS from the perspective of document review, static analysis, code review, and configuration item/system testing processes, and analyzes the potential risks in the implementation process and the feasible degradation strategies.
DAMCREM: Dynamic Allocation Method of Computation REsource to Macro-Tasks for Fully Homomorphic Encryption Applications

Smart computing aims to improve the quality of life by utilizing Internet-of-Things devices and cloud computing. Typically, this computing handles private and/or personal information so concealing such sensitive information is a challenge. Adopting fully homomorphic encryption (FHE) is one approach for handling such sensitive information safely; that is, we can calculate the encrypted data without decryption. However, the time and space complexity of the FHE operation is high. Thus, its computation takes a long time. In this study, we aim to shorten FHE execution time by adopting our new scheduling algorithm, which divides a task into several macro-tasks and then assigns a set of threads. We assume a cloud computing system that is equipped with a many-core CPU. Thus, we propose the dynamic allocation method of computation resource to macro-tasks (DAMCREM), which dynamically allocates a certain number of threads (selected from pre-defined candidates) to each macro-task of every given job. In the evaluation, we compared DAMCREM to naive methods that allocate a pre-defined number of threads to each macro-task. The result shows that the average latency and maximum latency of job execution is less than those of naive methods, even when the average interval of job arrival is short.
Bulk infrastructure management for facilities management

Facilities management utilises several data sources to make informed decisions and these sources are often overlooked during risk assessments for cyber threats. These data sources are coming from several critical systems which are responsible for keeping the daily operations of the facility running. Having a cyber attack on these systems, or even just manipulating the information in the data sources can have catastrophic impacts for the facility. Keeping the services running will ensure research efforts continue and minimise financial losses in the process. These systems include electrical bulk infrastructure, water management, biosafety labs, and physical security systems. This paper provides an overview of the system and is used to identify potential cyber security risks. A system is proposed to mitigate the risks while highlighting the need for cyber security training for all staff members. This includes making staff aware of social engineering attacks that may be used to gain access to the sensitive information that can be used to penetrate and disrupt system operations.
A Comprehensive Dialect Conversion Approach from Chittagonian to Standard Bangla

We present a comprehensive conversion system to convert the Chittagonian dialect to standard Bangla language. It is a text to text conversion system based on word-to-word mapping adopting a bilingual dictionary, rule-based morphological transformation on suffixes, and a supportive word suggestion module. The system tokenizes the regional input text and processes the tokens through word-to-word mapping and morphological transformation using suffix transformation rules if word-to-word mapping fails. We are also introducing an aiding tool that generates suggested words for the dialectal input. The system achieved an accuracy of 94.75% for producing standard Bangla translation from Chittagonian words. It must be noted that there is no published work on the Chittagonian dialect conversion from a computational point of view. We are the first ones to have built such a system for Chittagonian dialect to standard Bangla conversion.
Efficient procedure to generate generalized Gaussian noise using linear spline tools

In this paper, we propose a simple method to generate generalized Gaussian noises using the inverse transform of cumulative distribution. This inverse is expressible by means of the inverse incomplete Gamma function. Since the implementation of Newton's method is rather simple, for approximating inverse incomplete Gamma function, we propose a better and new initial value exploiting the close relationship between the incomplete Gamma function and its piecewise linear interpolant. The numerical results highlight that the proposed method simulates well the univariate and bivariate generalized Gaussian noises.