Reviewerly/exHarmony huggingface dataset:
Design, Fabrication and Testing of Programmable LEGO VD Graaf Generators for Innovative STEM Education

The Technology Assisted Science, Engineering and Mathematics (TASEM) learning, with major focus on innovations in the use of technology to explain new and complicated concepts rather than on education research, goes far beyond the conventional demos of van de Graaf (VDG) generators to introduce programmable Lego-based VDG (PLVDG) for the first time.The PLVDG modules explain underlying concepts of micro-and nano-systems in a fun and fascinating way as evident from the level of interest seen in over 2000 learners at K-12, undergraduate and graduate levels during 2000-2010.The interest in PLVDG seems to be strongly related to the fact that the learners can design, build, program and explore PLVDG using different pulley & belt materials and a palm-size robot.The generated voltages are in the range of 5 -35 kV depending upon humidity and pulley speed.Sensors of positive and negative charges have also been developed using NMOS and PMOS switches embedded in LEGO-like bricks.Several new experiments are reported in this paper focusing on learning of a number of areas including materials, engineering (EE & ME), computers, and microsystems.
COVID-19: Putting the General Data Protection Regulation to the Test (Preprint)

<sec> <title>UNSTRUCTURED</title> The coronavirus disease (COVID-19) pandemic is very much a global health issue and requires collaborative, international health research efforts to address it. A valuable source of information for researchers is the large amount of digital health data that are continuously collected by electronic health record systems at health care organizations. The European Union’s General Data Protection Regulation (GDPR) will be the key legal framework with regard to using and sharing European digital health data for research purposes. However, concerns persist that the GDPR has made many organizations very risk-averse in terms of data sharing, even if the regulation permits such sharing. Health care organizations focusing on individual risk minimization threaten to undermine COVID-19 research efforts. In our opinion, there is an ethical obligation to use the research exemption clause of the GDPR during the COVID-19 pandemic to support global collaborative health research efforts. Solidarity is a European value, and here is a chance to exemplify it by using the GDPR regulatory framework in a way that does not hinder but actually fosters solidarity during the COVID-19 pandemic. </sec>
Fusion of Internal Similarity to Improve the Accuracy of Recommendation Algorithm

Collaborative filtering algorithms (CF) and mass diffusion (MD) algorithms have been successfully applied to recommender systems for years and can solve the problem of information overload. However, both algorithms suffer from data sparsity, and both tend to recommend popular products, which have poor diversity and are not suitable for real life. In this paper, we propose a user internal similarity-based recommendation algorithm (UISRC). UISRC first calculates the item-item similarity matrix and calculates the average similarity between items purchased by each user as the user’s internal similarity. The internal similarity of users is combined to modify the recommendation score to make score predictions and suggestions. Simulation experiments on RYM and Last.FM datasets, the results show that UISRC can obtain better recommendation accuracy and a variety of recommendations than traditional CF and MD algorithms.
Professional Productivity and Subjective Well-Being

Despite аn extensive research on subjective well-being conducted over the past decades, there remains uncertainty about whether happier workers are actually more productive. The aim of the study was to analyze the causal relationships between subjective well-being and job performance using employees of leading Russian universities as a case study. The article presents the analysis of subjective well-being of scientific-pedagogical employees in modern universities and indicators of their performance based on empirical research materials. The construct of subjective well-being assessment was based on the existing theoretical models by E. Diener, D. Kahneman, R. Inglehardt. The research program included the assessment of indicators of general perception of happiness, productivity, life satisfaction, job satisfaction indicators by questionnaire survey. For the validity of measurements, the research tools included proven methods and scales: Cantril's Self-Anchoring Scale, questions from studies of subjective well-being and quality of life in the European Social Survey (ESS), WCIOM surveys. In this study, statistical methods such as correlation analysis, Pearson's Chi-square test of agreement, and least squares regression analysis (LSR) were used to analyze the relationships between the level of subjective well-being and the level of scientific productivity. The results of the present study statistically proved a significant positive correlation between the level of subjective well-being and scientific productivity of higher education employees. These data are a signal to revise the views on faculty motivation towards the formation of effective social responsibility, which includes, among other things, the development and implementation of programmes to improve the well-being of their staff.
Channel-Supermodular Entropies: Order Theory and an Application to Query Anonymization

This work introduces channel-supermodular entropies, a subset of quasi-concave entropies. Channel-supermodularity is a property shared by some of the most commonly used entropies in the literature, including Arimoto-Rényi conditional entropies (which include Shannon and min-entropy as special cases), k-tries entropies, and guessing entropy. Based on channel-supermodularity, new preorders for channels that strictly include degradedness and inclusion (or Shannon ordering) are defined, and these preorders are shown to provide a sufficient condition for the more-capable and capacity ordering, not only for Shannon entropy but also regarding analogous concepts for other entropy measures. The theory developed is then applied in the context of query anonymization. We introduce a greedy algorithm based on channel-supermodularity for query anonymization and prove its optimality, in terms of information leakage, for all symmetric channel-supermodular entropies.
The Impact of E-marketing on Consumer Purchase Decision in Jordan

This study explored the effect of e-marketing on consumer purchase decision in Jordan. The population includes all the accessories E-marketing companies that are located in Jordan. The sample consist of (100) individuals who work as mangers. The researcher used a questionnaire that sheds a light on e-marketing. The targeted variables are: (website, social media). It was found that the severity of the impact of E-marketing on consumer purchase decision in Jordan is high. It was found that website and social media have a high impact on consumer purchase decision in Jordan. It was found that there is a significant difference between the managers’ attitudes which can be attributed to the company age regarding for the favor of the companies which has been established since more than 10 years. The researcher recommends assisting local companies in developing their electronic marketing methods. That shall foster the development of commercial companies, especially small and medium companies.
Efficient Translation, Rotation, and Scale Invariants of Discrete Tchebichef Moments

Translation rotation and scale invariants of Tchebichef moments are commonly used descriptors in image analysis. Existing invariant algorithms either indirectly compute from geometric moments or directly using Tchebichef moments. The former approach is relatively simple, but inefficient, especially when the system consists only of Tchebichef moments. Likewise, the latter approach is complicated, mainly because of the method used to formulate the invariant algorithm. Hence, in this paper, we introduce a new set of translation, rotation and scale Tchebichef moment invariants (TRSI) using moment normalization, which is much computationally efficient and accurate. This is achieved by formulating the recurrence relationship of the descriptors and successfully resolve uniqueness issues of principal axis normalization. Experimental studies show that the proposed method is computationally much faster and possesses higher discriminative power in classification when compared with present invariant algorithms. The main contribution of this paper is a novel fast computational algorithm that simplifies translation, rotation and scale invariant algorithms of Tchebichef moments and a novel normalization scheme that preserve invariants’ orthogonality from the moment functions. The technique can be deployed to derive more complex invariants like affine invariants for other orthogonal moments like Krawtchouk, Hahn, Racah moments etc.
Digital Contact Tracing in Combating COVID-19 Pandemic in Malaysia, New Zealand and China (Preprint)

<sec> <title>BACKGROUND</title> Digital contact tracing is a method of tracing contact relying on tracking systems using artificial intelligence (AI) as an epidemic prevention solution for the COVID-19 pandemic. Normally it relies on mobile devices using Bluetooth, Quick Response (QR) code, or Global Positioning System (GPS) technologies </sec> <sec> <title>OBJECTIVE</title> This review compares the digital contact tracing (DCT) systems that are used in Malaysia, New Zealand and China. </sec> <sec> <title>METHODS</title> Data sources were from existing electronic articles, newspapers, press releases, government documents, reports, and web pages that are available online </sec> <sec> <title>RESULTS</title> There are five mobile applications (app) and one Bluetooth device in used by these countries; Malaysia uses MySejahtera app and MyTrace app, New Zealand uses NZ CovidTrace and Bluetooth COVIDCard, China uses a Health Code app that is available in WeChat and Alipay. However, Malaysia has suspended the MyTrace app, while New Zealand is awaiting the government’s decision on more general adoption of the COVIDCard. All apps are available via download through Apple store, Google Play and App Gallery, except for the NZ COVID tracer is not supported by App Gallery that operates Huawei devices. All 3 countries utilises the QR code as the main DCT with additional extra features. However, the number of users could not be determined as certain official platform such as the App Store does not provide such data. But, based on other available databases; MySejahtera has recorded around 74.9% users in Malaysia as at December 2020, NZ CovidTrace logged around 55.1% as at 19th April 2021, and Alipay tracked around 64.3% as at early 2021. </sec> <sec> <title>CONCLUSIONS</title> Bluetooth component and framework that is added to the NZ COVID Trace could apply to both MySejahtera and Health Code to improve their existing DCT system to assist in identification of close proximity. Conversely, dissimilarities in social, economic, political and cultural aspect could differentiate the outcome of several public health interventions. </sec>
Analysis of Information Security Using ISO 27001 and Triangular Fuzzy Number Weighting

The business process of an organization can’t be done properly without appropriate information management, in which information is an important asset that needs to be protected with the utmost care and concern. Information security is a way to protect information from large scale threats, thus to ensure the sustainability of the organization's operational, to reduce business risks and to increase business opportunity and return of investment. This research is conducted to measure the accountability of ISO 27001 in assisting the organization to document the information security policy. ISO/IEC 27001:2005 is a standard of information security that is widely used, openly accepted and implemented, and suitable for providing rules related to implementation and evaluation of the information security system. The assessment from ISO controls and objectives will be converted into a triangular fuzzy number to help in the analysis purpose. The fuzzy number is used to simplify the measurement. The result shows that the organization is not yet complying with the standard procedures of the Information Security Management System so it is needed to document the security policy based on the ISO 27001 framework standard.
Effective Learning of a GMRF Mixture Model

Learning a Gaussian Mixture Model (GMM) is hard when the number of parameters is too large given the amount of available data. As a remedy, we propose restricting the GMM to a Gaussian Markov Random Field Mixture Model (GMRF-MM), as well as a new method for estimating the latter&#x2019;s sparse precision (<i>i.e</i>., inverse covariance) matrices. When the sparsity pattern of each matrix is known, we propose an efficient optimization method for the Maximum Likelihood Estimate (MLE) of that matrix. When it is unknown, we utilize the popular Graphical Least Absolute Shrinkage and Selection Operator (GLASSO) to estimate that pattern. However, we show that even for a single Gaussian, when GLASSO is tuned to successfully estimate the sparsity pattern, it does so at the price of a substantial bias of the values of the nonzero entries of the matrix, and we show that this problem only worsens in a mixture setting. To overcome this, we discard the nonzero values estimated by GLASSO, keep only its pattern estimate and use it within the proposed MLE method. This yields an effective two-step procedure that removes the bias. We show that our &#x201C;debiasing&#x201D; approach outperforms GLASSO in both the single-GMRF and the GMRF-MM cases. We also show that when learning priors for image patches, our method outperforms GLASSO even if we merely use an educated guess about the sparsity pattern, and that our GMRF-MM outperforms the baseline GMM on real and synthetic high-dimensional datasets.
Booze-free booze

Low-alcohol tipples are booming in popularity and tastiness, but are they really good for us, asks Graham Lawton
Open Source Experiments in Quantum Photonics: an Affordable Approach

Advances in quantum technologies have made canonical experiments, such as the Hanbury-Brown-Twiss interferometer, common in state-of-the-art optics labs. Here we demonstrate a path towards an open-source low-cost single-photon HBT-interferometer, targeted at the photonic maker-community.
Can Deep Learning Solve Parametric Mathematical Programming? An Application to 0–1 Linear Programming Through Image Representation

Deep learning has been widely applied in many fields. Efficient optimization algorithms contribute a lot to the enhancement of deep learning. However, reverse studies on how deep learning can solve optimization problems, especially mathematical programming, are relatively scarce. In this work, we aim to initiate a discussion on using deep learning to solve parametric mathematical programming, which can be converted to find a mapping from parameter space to solution space. Given that deep convolutional neural networks (DCNNs) have been successfully applied in the field of computer vision, converting a mathematical programming problem into an image representation may provide the solution. This work takes the 0–1 knapsack problem (KP) as an example to build an original image representation method. After modifying the image details, the proposed method is extended to the general 0–1 linear programming (LP) problem, and a deep learning architecture is designed to solve the transferred computer vision problem. The efficiency of the proposed DCNNs method is validated on a large number of problems, and results show that this method can solve the 0–1 LP accurately and efficiently without iteration. The solution speed is 20 times faster than that of traditional optimization solvers.
Multi Robot Environment Exploration Using Swarm

This chapter describes the exploration of unknown environment using multiple robots. We designed this system in order to overcome the various disadvantages of exploration and mapping robots. When it comes to the mapping of large unknown environment, single robot would be inefficient and inaccurate. It leads to more exploration time and inaccurate mapping of the environment. There are multiple algorithms which already exist and are used but seeing their inefficiency we have worked on RRT that is random-exploration random tree. Multiple robots will be deployed in an unknown environment and based on which the robots will explore environment, to make robot communicate with each other we have implemented swarm algorithm so that robot communicate with each other in coordinated way. The Swarm algorithm will help robots explore the environment in a coordinated way because of which the two robots will never explore the same area repeatedly and hence will same time and will lead to more accurate mapping of the environment. To implement this solution, we used ROS as the middle ware, with the help of the ROS we can encounter and handle many real time parameters which will make our system more accurate and efficient. GAZEBO simulator is used in order to test and implement the system. This multi robot system will be very useful in the future smart city ecosystems.
FPGA-based Deep Learning Accelerator for RF Applications

A key obstacle within the design of cognitive radios has always been the spectrum sensing component that implements the function automatic modulation classification (AMC). With the transition to software-defined radios (SDRs) followed by the introduction of field-programmable gate arrays (FPGAs) and deep learning (DL), it becomes possible to surmount this obstacle. However, the design of DL models is still detached from synthesized FPGA designs in current implementation frameworks. Consequently, the design process is a tedious and lengthy one. In this paper, a novel implementation framework is presented for implementing deep learning inference models within signal processing chains on FPGAs. The framework focuses on optimization for radio-frequency (RF) transceiver applications, aiming for high-throughput, low latency and a small FPGA resource footprint enabling the scaling to larger DL models. Demonstration of the implementation framework for automatic modulation classification (AMC) results in an operational throughput of 585k classifications per second.
A Novel Shoulder Surfing Resistant Password Authentication Scheme

Usability and security challenges with standard text passwords lead researchers and professionals to consider more complex authentication strategies. This paper analyzes the traditional text-based password scheme and their challenges and provides the solution over their drawbacks. This paper discusses different kinds of attacks by which authentication schemes can be compromised. The objective of this paper is to propose a new user authentication scheme to reduce the risk of identity theft. This text-based password scheme prevents shoulder surfing and is easy to use. The scheme can be used to create more secure passwords that are memorable.
Stable High Utility Itemset Mining

High Utility Itemset Mining (HUIM) aims at finding all sets of items that have high importance in a database, as measured by a utility function. Although HUIM has many applications, a key limitation is that the discovered patterns often have an unstable utility over time. For example, while a set of products may yield a high utility (profit) over a year, that utility may fluctuate from weeks to weeks. To discover patterns that have a stable utility and hence that are more suitable for decision-making, this paper redefines HUIM as the task of discovering Stable High Utility Itemsets (StableHUI). An efficient tree-based and pattern-growth algorithm named Stable-Growth is proposed to extract all the StableHUI. Several experiments on two real-world datasets and two synthetic datasets show that Stable-Growth is up to 60% faster than a baseline and that it can filter out numerous unstable HUI.
Real-Time Face Mask Detection and Analysis System

Due to COVID-19 situation, we need to wear face masks in public places. Reports say that wearing face mask at public places and at workspace reduces the transmission of virus as the SARS-CoV-2 spreads through atmosphere among people, at gathering in any environment. In this paper, a real-time face mask detection system is presented which will detect mask presence on the face using TensorFlow. We are using MobileNetV2 model to provide a greater accuracy in determining the mask presence. Accuracy obtained is 99%. Older systems do not provide a proper working system. A face mask detector has been designed with computer vision using Python, OpenCV, Keras, and TensorFlow. Video surveillance input can be given directly, and our primary purpose is to identify to check people are wearing masks on daily basis or not wearing masks and prepare a weekly and monthly report based on this observation and display the data on an interactive web application. System provides option to see the historical records, thereby reducing transmission.
Psychological Tools Affecting Increasing Motivation to Learn Two Foreign Languages

The problem of the formation and development of motivation to learn occupies one of the central places in educational institutions. Its relevance is due to the priority areas of development and modernization of education. In the article, the authors analyzed the motivation for learning a foreign language, the factors that affect it. Analysis of the literature revealed two main factors that can increase students' motivation to learn a second foreign language. The authors examined the maximum effectiveness of increasing student motivation to learn a second foreign language as a synergy of three components: the teacher's personality, which can create a safe motivating environment, focusing on other people's culture and the connection between two foreign languages. The authors proposed psychological and methodological tools to increase the motivation of students to learn two foreign languages. A distinctive feature is the synergy of 3 components (personality of the teacher, safe motivating environment and connection between two foreign languages). This toolkit was tested in 2 stages during 2019-2021 and showed its effectiveness.
A Privacy-Preserving Medical Data Traceability System Based on Attribute-Based Encryption on Blockchain

Abstract With the continuous development of distributed technology, blockchain has been widely applied to solve practical problems in various fields. Its advantages such as decentralization and non-tamperability perfectly meet the needs of permanent query of traceability scene information, and can solve the trust problem of multi-party participation in various links. At the same time, as data security has received more and more attention. The privacy protection and permission control of “on-chain” data need to be resolved. Medical data records are very valuable and sensitive data. Reliable storage and credible traceability are of great significance to the health of patients and the long-term development of medical undertakings. To issue the above problems, here, we propose a secure medical data traceability system that combines data desensitization and attribute-based encryption algorithms to achieve the traceability capabilities and ensure the reliable storage of medical data records on blockchain. Data desensitization provides a certain degree of security for sensitive data involving personal information in medical data. The introduction of attribute encryption has solved the problem of unauthorized access to data. Finally, we analyzed the security of the solution and Verify the efficiency of the program.