Reviewerly/exHarmony huggingface dataset:
REMOTE SURVEILLANCE: A MEANS OF INTELLIGENCE GATHERING FOR MINIMIZING SECURITY CHALLENGES IN NIGERIA

Nowadays, national security issues are increasing day by day in most countries. A multitude of measures to reduce the challenges have been presented and even implemented by many authors, but without exhaustive results. The use of computers and sophisticated IT tools by the terrorist group, increasing number of citizens, lack of social amenities and other factors have made some of them inadequate enough to control the problems in Nigeria. The purpose of this paper is to highlight national security challenges in Nigeria and how security oversight is operated. To achieve this, the authors analyze available secondary data, investigating national security modus operandi and presenting the general concept of surveillance. Related works were also investigated for discussion. Remote surveillance, wiretapping, geospatial intelligence and a consolidated national database are proposed to achieve digital intelligence collection for insecurity management.
Digital Well-being Through the Use of Technology – A Perspective

"No man is an island unto himself" - John Donne According to the World Health Organization, health is "a state of complete physical, mental and social well-being and not merely the absence of disease and infirmity." Our healthcare industry, public behaviors, and environment have grown exponentially with digital technologies in the era of the 4th industrial revolution. Due to rapid digitalization and easy availability of the internet, we are now online round the clock on our digital devices, leaving behind digital traces/information. These digital footprints serve as an increasingly fruitful data source for social scientists, including those interested in demographic research. The collection and use of digital data (quantitative and qualitative) also present numerous statistical and computational opportunities, further motivating the development of new research approaches to address health issues. In this paper, we have described the concept of digital well-being and proposed how we can use digital information for good health.
Practical Leakage-resilient Signcryption Scheme Suitable for Mobile Environments

Recently, in actual environments, an adversary may adopt leakage attacks (e.g., side-channel attacks) to retrieve some information of secret keys during the computations of cryptographic algorithms. Therefore, the research of leakage-resilient cryptography has obtained significant attention. In the past, numerous leakage-resilient signature, encryption and signcryption schemes have been proposed. However, these schemes still have some drawbacks. In the article, we propose a practical public-key-infrastructure-based leakage-resilient signcryption (PKI-LRSC) scheme which can resist continual leakage attacks. Performance analysis demonstrates that our scheme is suitable for running on PC and mobile device.
Wb-Proxshare: A Warrant-Based Proxy Re-Encryption Model for Secure Data Sharing in Iot Networks Via Blockchain

The Internet of Things (IoT) has become increasingly popular due to the enormous growth in the number of smart devices and the massive amount of data these devices generate. Data access and sharing has been one of the most valuable services of the IoT network. For this reason, the security and privacy of the data are of great essence to harnessing the full potential of the IoT network. Existing security measures for data access and sharing have proven to be insufficient. They usually need more credibility due to centralization and single-point-of-failure problems. In this paper, we propose WB-Proxshare, a data access and sharing model based on warrant and proxy re-encryption (PRE) with blockchain. We present a mechanism that further enhances collusion-resistance in data storage, access and sharing. Our model ensures tamper-proof, data privacy, provenance and auditing. We set a proxy server that issues warrant for data storage and sharing and re-encrypts data owners’ encrypted data to grant access to legitimate data users. Our security analysis and evaluation show that the proposed model has high efficiency and ensures strong data security, integrity, and confidentiality guarantee for data access and sharing in the IoT environment and is practicable.
BLOCKCHAIN IN TOURISM AND BC MODEL FOR EDUCATION OF THE STUDENTS IN TOURISM SECTOR

The Fourth Industrial Revolution brings us changes in digital technologies, using Artificial Intelligence (AI), Blockchain (BC), Machine learning, robotics, simulations, Internet of things (IoT), Nanotechnology, 3D printing etc. The aim of this paper is to present the possibilities of applying modern technologies in the education of students in tourism sector, with special emphasis on the application of blockchain technologies (BCT) as well as to bring the specifics of these technologies closer to the readers. In this paper is proposed a model of student education based on BCT. In addition, evaluation the students knowledge on application new technologies in this area is presented in the paper.
Side Information Driven Image Coding for Machines

With the continuous improvement of computer vision technology, more and more image information is consumed by machines rather than humans. Image coding for machines (ICM) is to compress image data such that they can be more efficiently sent to the receiver side for machines to conduct visual analysis. A typical deep learning-based ICM structure contains one codec network which compresses and transmits images through the Internet and one semantic analysis task network such as image classification and object recognition. In the codec part, the side information is the hyper-prior or hierarchical layers of hyper-priors for the compression of image latent representations. In this paper, we propose a Side Information Driven Image Coding (SIIC) framework based on deep learning. It only compresses and transmits the side information to the receiver for image classification tasks. We obtain a top-l accuracy of 70.38% on the ImageNet1K dataset with 0.046 bits per pixel.
Time series prediction with input noise based on the ESN and the EM and its industrial applications

Industrial time series data usually have a high noise. In this paper, an echo state network (ESN) model with input noise is proposed to address the problem of predicting time series with noise. In the ESN, the introduction of the input noise makes it difficult to accurately estimate the non-linear states of the dynamical reservoir, therefore, in this study, the states are approximated by linearizing it through an extended Kalman filter (EKF). For the learning of the model parameters, the expectation maximization algorithm (EM) is used to iteratively update all the uncertain parameters to construct the prediction intervals, where the state estimation is performed using a forward back algorithm. To verify the effectiveness of the proposed method, two benchmark data sets and three real gas data sets from steel enterprises are used in this paper. Experimental results show that the prediction accuracy of the proposed method is better than that of the existing methods.
Boosting the performance of quantum annealers using machine learning

Abstract Noisy intermediate-scale quantum (NISQ) devices are spearheading the second quantum revolution. Of these, quantum annealers are the only ones currently offering real world, commercial applications on as many as 5000 qubits. The size of problems that can be solved by quantum annealers is limited mainly by errors caused by environmental noise and intrinsic imperfections of the processor. We address the issue of intrinsic imperfections with a novel error correction approach, based on machine learning methods. Our approach adjusts the input Hamiltonian to maximize the probability of finding the solution. In our experiments, the proposed error correction method improved the performance of annealing by up to three orders of magnitude and enabled the solving of a previously intractable, maximally complex problem.
An automatic alignment of the business process and business value models: a novel MDA method

With the massive development of end-users requirements, the model alignment has become an essential stage in software engineering, especially in the model driven architecture (MDA) approach, to absorb the end-user’s need. The purpose is to facilitate the alignment of new models from existing ones. Our contribution in this paper is to deal with the MDA higher abstraction lever by focusing on the automatic alignment of the business value with the business process models for the information system (IS). For our case, the data-flow diagram (DFD) illustrates the business process model, and the E&lt;sup&gt;3&lt;/sup&gt;value model illustrates the business value model. However, the ATLAS-transformation language (ATL) ensures automatic alignment. The main goal is to facilitate and accelerate IS implementation while enhancing its quality.
Effectiveness of Public Elementary School Operational Assistance Fund Management Policy

This study aims to analyze the effectiveness of school operational assistance management. The method in this research is descriptive qualitative. The type of data used in this study is qualitative data consisting of primary and secondary data. The research was conducted at the Ganggangpanjang State Elementary School in the 2019-2021 academic year. Sources of data obtained through library research are available online and offline, such as scientific journals, books, and news from trusted sources. In addition, data sources were also obtained from interviews with school principals, treasurers, and the 2019-2021 RKAS report archive. The study results indicate that managing the school's operational assistance funds has been effective. However, the facilities and infrastructure have not shown any effectiveness. In addition, the availability of student activity unit space is still integrated with the teacher's room; as a result, student health support has not been adequate. On the one hand, there is strict supervision carried out by the office of cultural education and the school committee so that the realization is by receiving school operational assistance funds.
Nearest Unitary and Toeplitz matrix techniques for adaptation of Deep Learning models in photonic FPGA

Photonic circuits pave the way to extremely quick computation and real-time inference in critical applications, such as imaging flow cytometry (IFC). Nevertheless, current photonic FPGA implementations display intrinsic limitations that restrict the complexity of Deep Learning (DL) models that could be sustained. One of these restrictions implies the weight matrices to be unitary. Thus, machine learning mechanisms to transform weight matrices to their nearest unitary one, are essential for the effective deployment of such demanding tasks. Furthermore, DL models that perform convolutions, require special handling so as to fit in the photonic system. In this work, several methods have been investigated for conversion of non-unitary matrices to unitary ones, as well as, linear algebra techniques for the transformation of Convolutional Neural Networks (CNNs) to Feed-Forward models, under the prism of discovery of the best candidate for the photonic FPGA in terms of accuracy and restrictions. Experimental results proved that post-training or iterative techniques to find the nearest unitary weight matrix can be applied for photonic chips with the minimum loss in accuracy, while CNNs adapted well in a photonic configuration employing a Toeplitz matrix implementation. The proposed approach envisions efficient tackling of DL models limitations for deployment in photonic FPGAs.
Quantification of Quantum Correlations in Two-Beam Gaussian States Using Photon-Number Measurements

Identification, and subsequent quantification of quantum correlations, is critical for understanding, controlling, and engineering quantum devices and processes. We derive and implement a general method to quantify various forms of quantum correlations using solely the experimental intensity moments up to the fourth order. This is possible as these moments allow for an exact determination of the global and marginal impurities of two-beam Gaussian fields. This leads to the determination of steering, tight lower and upper bounds for the negativity, and the Kullback-Leibler divergence used as a quantifier of state nonseparability. The principal squeezing variances are determined as well using the intensity moments. The approach is demonstrated on the experimental twin beams with increasing intensity and the squeezed super-Gaussian beams composed of photon pairs. Our method is readily applicable to multibeam Gaussian fields to characterize their quantum correlations.
Cognitive approach to the formation of a robotic system in education

The article deals with the features of modeling systems for managing educational processes using robot assistants based on a cognitive approach. The main objective of the study is the use of hybrid human-machine intelligence in the education system. The authors propose a new version of the use of fuzzy cognitive maps and the capabilities of the corresponding software systems to solve a number of practical problems using examples of automated learning. Within the framework of the cognitive approach, which allows to take into account the various linguistic features of people, it is possible to use appropriate fuzzy cognitive maps that allow you to determine cause-and-effect networks that reflect the subjective view of researchers about the system in the form of a set of semantic categories called factors or concepts. The study shows that taking into account the NON-factors that take place in the educational process, as well as the possibilities of their application for the formation of individual learning trajectories in the context of the use of human-machine intelligence, it is possible to obtain fundamentally new opportunities for training personnel. The authors propose a fuzzy cognitive model of strategic management with the participation of a robotic system, identify the main concepts for its construction and analysis. The parameters of the cognitive map are identified using the principles of intelligent generation of the best alternatives based on the modified method of pair comparisons. The study is aimed at employees of educational institutions responsible for the digitalization of education and teachers.
Towards advanced robotic manipulation

Robotic manipulation and control has increased in importance in recent years. However, state of the art techniques still have limitations when required to operate in real world applications. This paper explores Hindsight Experience Replay both in simulated and real environments, highlighting its weaknesses and proposing reinforcement-learning based alternatives based on reward and goal shaping. Additionally, several research questions are identified along with potential research directions that could be explored to tackle those questions.
Remotely Controlled Entanglement Generation

We consider a system of multiple qubits without any quantum control. We show that one can mediate entanglement between different subsystems in a controlled way by adding a (locally) controlled auxiliary system of the same size that couples via an always-on, distant dependent interaction to the system qubits. Solely by changing the internal state of the control system, one can selectively couple it to selected qubits, and ultimately generate different kinds of entanglement within the system. This provides an alternative way for quantum control and quantum gates that does not rely on the ability to switch interactions on and off at will, and can serve as a locally controlled quantum switch where all entanglement patterns can be created. We demonstrate that such an approach also offers an increased error tolerance w.r.t. position fluctuations.
Light Auditor

Despite many conveniences of using IoT devices, they have suffered from various attacks due to their weak security. Besides well-known botnet attacks, IoT devices are vulnerable to recent covert-channel attacks. However, no study to date has considered these IoT covert-channel attacks. Among these attacks, researchers have demonstrated exfiltrating users' private data by exploiting the smart bulb's capability of infrared emission.
PENGEMBANGAN E-BOOK PADA MATA KULIAH SOSIOLOGI PENDIDIKAN PROGRAM STUDI TEKNOLOGI PENDIDIKAN FAKULTAS ILMU PENDIDIKAN UNIVERSITAS NEGERI MAKASSAR

In the Sociology of Education Course, the problems that occur for students require a combination of learning resources into one such as an E-Book. So the researchers developed the E-Book for Educational Sociology Courses. This study aims to: 1) describe the level of need for the E-Book, 2) describe the design of the development of the E-Book, 3) describe the level of validity and practicality of the E-Book. This research used the R&amp;D approach and was developed using the ADDIE model. Data collection used a needs identification questionnaire, a content/material expert validation questionnaire, a design and media expert questionnaire, a group trial questionnaire, an individual trial questionnaire, and a questionnaire for the responses of the lecturers of the Education Sociology Course. The results of the research show that the identification of the level of student needs is at a percentage of 87.05% of the required qualifications. Furthermore, the validation results from content/material experts were at a percentage of 92.8%, very valid qualifications, the validation of design experts was at a percentage of 96.66%, very valid qualifications, in group trials, the percentage results were 98.33%, very practical qualifications. individual trials are in the percentage of 100% very practical qualifications and the responses of the lecturers who teach the course are in the percentage of 100% very practical qualifications. The conclusion of the research results is that the level of needs analysis for the Sociology of Education E-Book is in the required qualifications and this E-Book is very valid and very practical.&#x0D;
Answer-Set Programming for Lexicographical Makespan Optimisation in Parallel Machine Scheduling

Abstract We deal with a challenging scheduling problem on parallel machines with sequence-dependent setup times and release dates from a real-world application of semiconductor work-shop production. There, jobs can only be processed by dedicated machines, thus few machines can determine the makespan almost regardless of how jobs are scheduled on the remaining ones. This causes problems when machines fail and jobs need to be rescheduled. Instead of optimising only the makespan, we put the individual machine spans in non-ascending order and lexicographically minimise the resulting tuples. This achieves that all machines complete as early as possible and increases the robustness of the schedule. We study the application of answer-set programming (ASP) to solve this problem. While ASP eases modelling, the combination of timing constraints and the considered objective function challenges current solving technology. The former issue is addressed by using an extension of ASP by difference logic. For the latter, we devise different algorithms that use multi-shot solving. To tackle industrial-sized instances, we study different approximations and heuristics. Our experimental results show that ASP is indeed a promising knowledge representation and reasoning (KRR) paradigm for this problem and is competitive with state-of-the-art constraint programming (CP) and Mixed-Integer Programming (MIP) solvers.
Organizational Productivity: A Critical Analysis of the Impact of Employee Motivation

Purpose: The main objective of this research was to examine the importance of training and development in the workplace. Methodology: Several dimensions of employee performance were analyzed, including productivity, job satisfaction, employee satisfaction, employee commitment, and decision-making. An adopted five Likert scale questionnaire was adopted for the online data collection from 100 respondents from the telecommunication industry. Convenience sampling was used for sampling and the PLS-SEM was the main technique for data analysis using smart PLS software. Findings: The results suggest that organizational performance and employee performance in the telecommunication sector in Pakistan increase if there is a significant relationship between employees and decision-making. Similarly, employees with a high level of job satisfaction and affective commitment will ultimately have a higher potential for productivity and career satisfaction. Conclusions: The study concluded that employee performance improves as teamwork increases. Teamwork within the company is very valuable; it directly affects the performance of employees. When an employee gets enough teamwork possibilities, his performance will automatically develop.
How to Deduce the Other 91 Valid Aristotelian Modal Syllogisms from the Syllogism IAI-3

This paper firstly formalizes Aristotelian modal syllogisms by taking advantage of the trisection structure of (modal) categorical propositions. And then making full use of the truth value definition of (modal) categorical propositions, the transformable relations between an Aristotelian quantifier and its three negative quantifiers, the reasoning rules of classical propositional logic, and the symmetry of the two Aristotelian quantifiers (i.e. some and no), this paper shows that at least 91 valid Aristotelian modal syllogisms can be deduced from IAI-3 on the basis of possible world semantics and set theory. The reason why these valid Aristotelian modal syllogisms can be reduced is that any Aristotelian quantifier can be defined by the other three Aristotelian quantifiers, and the necessary modality ( ) and possible modality ( ) can also be defined mutually. This research method is universal. This innovative study not only provides a unified mathematical research paradigm for the study of generalized modal syllogistic and other kinds of syllogistic, but also makes contributions to knowledge representation and knowledge reasoning in computer science.
The Impact of Multicore CPUs on Eco-Friendly Query Processors in Big Data Warehouses

Given the large and growing volume of big data and frequent use of complex analytical queries, understanding energy efficiency of query processing has become a critical research issue, as highlighted by database systems papers in the last few years. Common software solutions mainly consider IO cost models to estimate energy consumption when executing queries. On the other hand, current hardware solutions benefit from advances in the development of green components and their associated tuning techniques, especially dynamic voltage and frequency scaling (DVFS), which can balance the performance and power consumption of multicore CPUs. Unfortunately, to the best of our knowledge, there is an absence of solutions mixing both (hardware and software). Heeding this gap, we propose a novel predictive model to measure and predict energy consumption of analytical queries when using multi-core processors and different frequency configurations. We first experimentally illustrate the surprising impact of CPU frequency and the number of processor cores on execution time, power and energy consumption. Second, we introduce an extended predictive model that enriches a well-known machine learning cost model with our new angle, the frequency scaling in multi-core environment. Specifically, by using Support Vector Regression and Random Forest Regression, we compute the energy coefficients of an accurate regression model for energy prediction. Experiments with benchmark data sets TPC-H and TPC-DS evaluate our proposed framework in terms of energy consumption reduction, showing promising results.