Reviewerly/exHarmony huggingface dataset:
Design and Adoption of Bring Your Own Device (BYOD) in Smart Classroom

Ubiquitous computing provides the powerful support to information exchange. Mobile devices have been the mainstream Internet access tools. They are portable and used as both the private purposes and business purposes. Personal mobile devices have been used in workplaces, which is called Bring Your Own Devices (BYOD). BYOD is also used in education. In this paper, we discussed on the new teaching/learning mode based on BYOD and provided a system design to support using BYOD in primary school. We also shared our experiences in the construction and showed the effect.
An Approach on Predicting a Machine’s Effector Vibrations Based on Motor Vibrations Using a Regression Artificial Neural Network

Vibrations are mechanical movements which can offer valuable information about the state of a machine. Being able to simulate and predict this type of signal is of great importance in fault diagnosis and condition monitoring of a machine since vibrations are considered the most used and most efficient way to detect and diagnose a fault. Since simulating vibrations can be a difficult task given the non-linear equations that need to be solved, machine learning algorithms can offer a great solution to extract information from data and predict an outcome close to reality given an input signal. Obviously, the input signal is processed, normalized, and fetched to the neural network. In this paper, a regression neural network is used for predicting the vibrations of an effector of a one-dimensional system which moves in only one direction. The vibrations are measured in the direction of the effector’s movement while the vibrations of the motor are acquired by the motor’s support.
About SDGs and Kansai SDGs platform Initiatives

The 2030 Agenda for Sustainable Development is a plan of action for people, planet and prosperity. The 17 Sustainable Development Goals and 169 targets demonstrate the scale and ambition of this new universal Agenda. They seek to build on the MDGs and complete what they did not achieve.
IWSN: A novel method for modeling the interaction of web services

As the most notable implementation of service-oriented architectures, web services has brought some challenging research issues, one of which is the composition. Correct interaction among web services is the prerequisite of their successful composition. In this paper, we propose a novel approach to model dynamic interactive behavior of web services, aiming to effectively take advantage of the flexibility of service composition technology and guarantee the behavior compatibility of web services and further to facilitate the application of web service composition in healthcare field. An interaction model of web services based on Kahn process network (KPN) is presented in this paper, which is a data-flow and channel-based model supporting parallel and cooperative computation. Formal behavioral semantics of this model is defined by Pi-calculus process algebra and formal properties of the proposed model are discussed. An application case is demonstrated to illustrate how the proposed model can be applied to describe services behavior and their interaction. The reachability analysis of the application case is performed based on modal -calculus. How the supporting tool, i.e. CWB-NC, can be utilized to verify the properties we anticipate is presented.
Polynomial-Based Trajectory Planning for Affine Dynamical Systems under State and Input Constraints

In this note we deal with analytical time polynomial based motion planning considerations. A class of differentially flat affine dynamical systems that can be rendered as a set of second-order controlled subsystems is analyzed. It is shown that state and input box constraints can be readily accounted for by proper choice of time of motion or/and initial or final values of some of the state variables and their time derivatives.
Privacy in Financial Information Networks: Directions for the Development of Legal Privacy-Enhancing Financial Technologies

In light of the strategic role of financial information for law enforcement, the protection of privacy regarding financial data must be balanced with the advantages of automated mechanisms for the monitoring and recording of financial activities. The growing availability of financial data and the global dimension of financial networks, however, impose to carefully examine practices concerning the management of financial data, checking them against the core rules and principles of data protection. This paper steers this effort providing a framework for understanding privacy in the financial context; moreover, it overviews the relevant legal frameworks affecting the management of financial information and assesses concrete industry practices to expose some compelling privacy issues. The study suggests that further research is needed to establish (1) whether current practices in the financial industry determine a lack of legal protection with regard to users’ privacy; (2) whether it is desirable to develop technological solutions that allow a greater degree of anonymity for digital financial transactions; and, if so, (3) which is the most suitable governance/legal and institutional framework for anonymous digital transactions.
Research and application of distribution line information model and automatic mapping display technology based on SVG

In view of the existing distribution line information storage is not unified, the formation of the diagram is not clear, the protection configuration chaos and so on. This paper presents a standard information model based on the electric quantity of distribution lines. Based on this model, an automatic mapping and display method of distribution lines is proposed. On this basis, a set of auxiliary decision-making software for hierarchical protection configuration of distribution lines is developed based on scalable vector graphics (SVG) software mapping technology. The automatic optimization of distribution line protection configuration is realized. The effectiveness and practicability of the technology are verified by the software operation, manual calculation and field application.
Efficient Target Detection and Classification of SAR Images Using Z-Buffer Convolutional Neural Networks

Target detection and classification for military, geographical, and other scientific research areas are the demanding requirement. This paper aims to detect the targets effectively as well as enhances the quality of detected image before classification by deep learning techniques. A Z-buffer convolutional neural network (Z-BCNN) model consisting of two phases is more suitable for target detection, and classification of SAR image is proposed in this paper. In the first phase, the region of interest (ROI) is extracted from the background of the SAR image by means of cavity detection algorithm and elliptical Fourier descriptors are computed to describe the characteristics of target outline, whereas in the second phase, the noise present in the detected SAR image is reduced by using wavelet transform-based brute force thresholding algorithm with directional smoothing. Afterward the detection of hidden targets, classification of military vehicles from SAR images is done by using Z-buffer convolutional neural network. The proposed methodology gives significant results in terms of classification accuracy compared with the other existing algorithms.
Step-Factor Resampling Technique for Imbalanced Sequence Data Classification

Aberathne, Iroshan Walgampaya, Chamila Rathnayake, UdaraThe mobile ad fraud detection plays a vital role in research community nowadays since a large sum of money is being circulated in this industry. The fraudsters who generate illegal revenue by executing various types of fraudulent activities are difficult to be detected. Various researches are being conducted by academics and industry experts to find optimum solutions to tackle these fraudulent activities. One of the barriers they face is the class imbalance problem in the datasets. In an imbalance dataset, the frequency of the states of the target class varies significantly. A particular dataset which is suffered with class imbalance problem will mislead the final results of the predictive model. The researchers have proposed a number of methods to address this class imbalance problem. A novel technique which is composed with smoothing and resampling techniques has been proposed in this study to solve the class imbalance problem in the context of sequence data. The proposed technique is evaluated with a hidden Markov scoring model—HMSM. The model shows significant improvement in accuracy, recall, precision and F-score with this novel resampling technique and smoothing approach. Moreover, the model becomes more sensitive towards the specificity which is the key factor of any kind of fraud detection methods.
Securing a Remote Workforce

As more people join the cyber workforce, we need to be aware that we are exposed to other risks. Steps are given to help protect our online safety and security.
A Gray Box for Visualizing Instruction Sequence Based on Improved Suffix Tree

Gray box is a kind of device in which the working process of a program or system is locally recognized. Gray box testing, also known as gray box analysis, is a software debugging method based on the limited cognition of the internal details of the program. Testers may know how system components interact with each other, but they lack a detailed understanding of internal program functions and operation. So the construction of gray box is particularly important. The most original gray boxes are static debugger and dynamic debugger. And then reflexion model, which reduces the manual work greatly, is developed and applied. The latest gray boxes are focus on regarding instructions as a natural language using the mature mathematical model to mine their internal value. Adhering to the idea of latest researches, our paper improves the original suffix tree and use the improved suffix tree as a mathematical models to analyse and visualize the internal logic of instructions. Our gray box aims at solving three problems in practical application. In addition, we explain the complexity of instruction sequence and put forward a prediction formula for the building part. By experiment, we prove the time complexity of each part and the correctness of the prediction formula, and show the effect of visualizing part.
Bitcoin and Portfolio Diversification: Portfolio Optimization Approach

The fundamental objective of portfolio diversification is to construct a portfolio of uncorrelated or mildly correlated assets so as to maximize the risk-adjusted returns on a portfolio. Portfolio Optimization is one of the techniques used by investment professionals to explore the potential of different assets in maximizing the risk-adjusted returns of the portfolio by adjusting the weight of each asset using simulations or constrained scenarios. A significant amount of research has already been conducted in the area of portfolio diversification that helps investors in devising their investment strategies and policies. Cryptocurrencies in general, and Bitcoin in particular, have aroused significant interest among investment professionals, policymakers, and regulators alike. Although much research has primarily focused on the legal and technological aspects of Bitcoin, the examination of other financial, diversification, hedge, and safe-haven aspects of Bitcoin has not progressed as far. This study explores the potential of Bitcoin as an alternative asset, and its potential in portfolio diversification, by using the portfolio optimization approach. The study employs the portfolio optimization approach under multiple constraining scenarios to evaluate the effectiveness of Bitcoin in portfolio diversification. A Monte-Carlo Simulation approach is then employed to evaluate the outcomes under each scenario by randomizing the outcomes of portfolio optimization. This study suggests that Bitcoin, due to its exotic nature, unwavering appeal, and unknown set of drivers, could at best act as a diversifier rather than a hedge or a safe-haven.
Telling the story of solar energy meteorology into the satellite era by applying (co-citation) reference publication year spectroscopy

Abstract Studying the history of research fields by analyzing publication records and topical and/or keyword searches with reference publication year spectroscopy (RPYS) has been introduced as a powerful tool to identify the corresponding root publications. However, for some research fields (e.g., rather new and interdisciplinary fields) like solar energy meteorology, encompassing such research fields via a keyword- or topic-based search query is not feasible to get a reasonably exhaustive publication set. Therefore, we apply its variant RPYS-CO to all publications co-cited with two highly important marker papers, using the cited references explorer for inspecting the RPYS-CO results. We obtain two lists of seminal papers, which are able to adequately tell us the story of solar energy meteorology up to the 1990s, respectively in its subfield using satellite-based methods for solar irradiance estimation even to very recent years. Consequently, we recommend this method to gain valuable insights in (new) research fields.
Developing a security behavioural assessment approach for cyber rating UK MSBs

Micro and small businesses account for over 99% of all businesses in the United Kingdom (UK). Still, a growing perception gap is how these businesses perceive the relevance and value of cyber security and the potential impacts from this. Recent studies by the UK government have shown a significant increase in the average cost to these smaller businesses after suffering a disruptive attack. Yet, their engagement with recognised standards and best practices is still relatively low. This paper aims to evaluate current behavioural models in the context of information security and develop a new influence-led approach to help ensure smaller businesses are both cyber ready and secure behaviours can be promoted. For this purpose, new engagement channels are identified and methods are developed to drive inferential and influencing factors for effective behaviour change.
Implementation and Cryptanalysis of Lightweight Block Ciphers

Encryption has become an important need for each and every data transmission. Large amount of delicate data is transferred regularly through different computer networks such as e-banking, email applications and file exchange. Cryptanalysis is study of analyzing the hidden information in the system. The process of cryptanalysis could be done by various features such as power, sound, electromagnetic radiation etc. Lightweight cryptography plays an important role in the IoT devices. It includes various appliances, vehicles, smart sensors and RFID-tags (RFID). PRESENT is one such algorithm, designed for resource constrained devices. This requires less memory and consumes less power. The project propounds a model in which the cryptographic keys are analyzed by the trace of power.
Secure Data Transfer Through Whirlpool—A Miyaguchi-Preneel Mode

With increased usage of technology and internet in present scenario, there is huge need for data storage. According to internet world statistics the percentage of internet users are 100% of world’s population. Where data sharing is the major operation performed in the internet with this vast usage, Security for the data became a major concern. And there is a huge need to protect the information we store on the internet. One of the approach is implementing hashing. To define hashing, it is the process of converting an input of any length into a fixed sized string of text using a mathematical function. One main advantage of using hashing is that they are unique. This paper presents one way collision resistant compressed 512 bit cryptographic hash function that works on a miyaguchi preneel mode.
Poster: Programming Practices Among Interactive Audio Software Developers

New domain-specific languages for creating music and audio applications have typically been created in response to some technological challenge. Recent research has begun looking at how these languages impact our creative and aesthetic choices in music-making but we have little understanding on their effect on our wider programming practice. We present a survey that seeks to uncover what programming practices exist among interactive audio software developers and discover it is highly multi-practice, with developers adopting both exploratory programming and software engineering practice. A Q methodological study reveals that this multi-practice development is supported by different combinations of language features.
Standardization of cryptographic mechanisms in Russia

In 1989, the State Committee for Standards and Product Quality Management (Gosstandart) published the first Soviet national standard in the field of cryptography protection. Over the next 30 years, the standardization of cryptographic mechanisms in Russia has evolved significantly in the development of information technology. This article describes how Russian cryptography standards have changed over the years to match technology development and industry requirements.
Computer Network-Enhanced Blended Reading Strategies

This study examines the effects of computer network-enhanced blended reading strategies on transforming 86 Information Technology (IT) students' dependent reading learning attitudes into self-directed habits getting them to reach Bloom's Cognitive Taxonomy and Competency's C2 for academic reading comprehension at B grade level. The approach used was Action Research positioning the lecturers as the change agent and the students as the clients integrating qualitative and quantitative methods. Qualitative methods were initiated by WebQuest for Inquiry to provide the fittest reading strategies and materials for the method application and to describe how the method has changed students' academic reading learning habit and improved their comprehension using the lecturers' running records, classroom checklists, and post observation evaluations (POS). The qualitative results were quantitatively measured for triangulation by analyzing students' composite scores on tasks, quizzes and exams and by students' perspective analysis using Likert survey interpretation. The main finding is the computer network-learning environment has largely assisted lecturers to provide the most suitable cognitive skills for reading comprehension: skimming, scanning, inferring, identifying coherence, paraphrasing, QAR, summarizing, reviewing and vocabulary by context; to supply materials for syllabus design; and to create mesogogy environment for the students' best practice. The significance of the study is the computer network learning environment has shifted pure teacher-centered (instructive-pedagogy) learning into autonomous student-centered (constructive-andragogy) getting students to develop their self-efficacy and critical thinking
Research progress on single image rain removal technology

The image removal of rain is of great significance to solve the problem of limited computer vision system under rainy weather conditions. Most of the current research work is focused on video rain. Unlike video rain removal, a single image has no prior time domain information, making rain removal more difficult. This article first classifies the existing single image rain removal methods, then introduces the typical single image rain removal methods, and summarizes the improvements, advantages, limitations and use scenarios of each method. Finally, the possible development direction of single image rain removal technology is prospected.
Mathematical Reconciliation of Medical Privacy Policies

Healthcare data are arguably the most private of personal data. This very private information in the wrong hands can lead to identity theft, prescription fraud, insurance fraud, and an array of other crimes. Electronic-health systems such as My Health Record in Australia holds great promise in sharing medical data and improving healthcare quality. But, a key privacy issue in these systems is the misuse of healthcare data by “authorities.” The recent General Data Protection Regulation (GDPR) introduced in the EU aims to reduce personal-data misuse. But, there are no tools currently available to accurately reconcile a domestic E-health policy against the GDPR to identify discrepancies. Reconciling privacy policies is also non-trivial, because policies are often written in free text, making them subject to human interpretation. In this article, we propose a tool that allows the description of E-health privacy policies, represents them using formal constructs making the policies precise and explicit. Using this formal framework, our tool can automatically reconcile a domestic E-health policy against the GDPR to identify violations and omissions. We use our prototype to illustrate several critical flaws in Australia’s My Health Record policy, including a non-compliance with GDPR that allows healthcare providers to access medical records by default.
Building a perfomance protocol of a low cost seismograph

&amp;lt;p&amp;gt;The low cost seismograph under study consists of low cost open source hardware and software microprocessor boards (Arduino Uno R3&amp;amp;#160; and Raspberry Pi 3 B+ ), customized low noise design signal amplifiers, low power dissipation sophisticated power supply, two (2) kinds of earth ground shaking sensors a) Ceramic Accelerometer with cutoff frequency fc=0.15Hz and b) moving coil geophone with cutoff frequency fc=4.5Hz. The signals from the two sensors are amplified independently, while an active second order low-pass anti-alias filter and an 8th order active low-pass anti-alias filter have been used. Finally, a low-cost microprocessor board is responsible for digitizing the analog data from the amplified signal of the sensors with a frequency sampling rate of 345Hz. The aim of the present work is to design and test a systemic protocol in order to evaluate the performance of the proposed low cost seismograph for monitoring local to regional seismicity and micro seismicity. The proposed low-cost system was installed in an area of high seismic activity (Lefkada Island &amp;amp;#8211; Village Evgiros) and the recordings are transmitted to the database continuously from the day of its installation up today. Thus have create an amount of data for more than 280 days and all of those data have been stored to our database. Collocated with a high resolution 24 bits digitizer equipped with a broad band seismometer give us the opportunity to compare the recordings. To this end, a testing list of 15 local events has been created with different epicenters and magnitudes. For each event the recording signals have been analyzed in terms of a) power spectrum analysis, b) estimation of first arrival times of both P and S waves, c) signal amplitudes and d) earthquake duration. The choice of those specific measures was done in order to evaluate the performance of the low-cost seismograph in terms of certain seismic parameters such as magnitude, epicenter and source properties. Initial results in terms of the proposed protocol are also presented showing an adequate performance of the propose low cost seismograph.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;&amp;amp;#160;&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;&amp;lt;strong&amp;gt;Keywords:&amp;lt;br&amp;gt;&amp;lt;/strong&amp;gt;low-cost instruments, Ionian islands, performance protocol&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;&amp;lt;strong&amp;gt;Acknowledgements&amp;lt;br&amp;gt;&amp;lt;/strong&amp;gt;&amp;amp;#171;Telemachus &amp;amp;#8211; Innovative Seismic Risk Management Operational System of the Ionian Islands&amp;amp;#187; which is part of the Operational Program &amp;amp;#171;Ionian Islands 2014-2020&amp;amp;#187; and is co-financed by the European Regional Development Fund (ERDF) (National Strategic Reference Framework - NSRF 2014-20).&amp;lt;/p&amp;gt;