Reviewerly/exHarmony huggingface dataset:
Corruption, ICT and Military Spending in Sub-Saharan Africa

It has been argued that large military spending is often a crucial constraint in Sub-Saharan Africa (SSA) countries, and high corruption has enabled these countries to keep such large military spending. One possible remedy is to promote information and communication technology (ICT), which may cause the anti-corruption policies to become more effective in increasing transparency for the public and reducing unnecessary and abusive military spending. This study discusses the nexus among corruption, military spending, and ICT with a panel data of 48 SSA countries from 2003 to 2015. Our analysis reveals that when ICT prevails, military spending is negatively associated with the control of corruption, so that anti-corruption policies with the usage of ICT could reduce the extravagant budget allocation to military spending. Policymakers should associate sound ICT policy with traditional anti-corruption factors to bring more transparency and less corrupt behaviors in the military department. The reduction in the misuse of military allocation enables the country to increase nonmilitary public spending to face sustainable development challenges for the sake of the population.
SafeTEE: Combining Safety and Security on ARM-based Microcontrollers

From industry automation to smart home, embedded devices are already ubiquitous, and the number of applications continues to grow rapidly. However, the plethora of embedded devices used in these systems leads to considerable hardware and maintenance costs. To reduce these costs, it is necessary to consolidate applications and functionalities that are currently implemented on individual embedded devices. Especially in mixed-criticality systems, consolidating applications on a single device is highly challenging and requires strong isolation to ensure the security and safety of each application. Existing isolation solutions, such as partitioning designs for ARM-based microcontrollers, do not meet these requirements. In this paper, we present SafeTEE, a novel approach to enable security- and safety-critical applications on a single embedded device. We leverage hardware mechanisms of commercially available ARM-based microcontrollers to strongly isolate applications on individual cores. This makes SafeTEE the first solution to provide strong isolation for multiple applications in terms of security as well as safety. We thoroughly evaluate our prototype of SafeTEE for the most recent ARM microcontrollers using a standard microcontroller benchmark suite.
Super-resolution algorithm using an improved generative adversarial network

SRGAN applies generative adversarial networks to super-resolution reconstruction, and the advent of this algorithm greatly facilitates image reconstruction based on deep learning. It introduces batch normalization for stable training, and although batch normalization can facilitate training, it destroys the original contrast information of the image and affects the quality of image reconstruction. For this problem, this paper proposes an super-resolution algorithm based on improved SRGAN, which removes the batch normalization in the residual block of the generative network to avoid its bad influence on the reconstructed image and improve the quality of the reconstructed image for better. In addition, the total variation is introduced in the loss function of the discriminative model to constrain the gradient change, which serves to stabilize the network training and accelerate the convergence. Experiments of the proposed super-resolution algorithm on a large number of public datasets show that this method achieves better results under various evaluation metrics compared with other similar algorithms.
OPTIC: A Practical Quantum Binary Classifier for Near-Term Quantum Computers

Quantum computers can theoretically speed up optimization workloads such as variational machine learning and classification workloads over classical computers. However, in practice, proposed variational algorithms have not been able to run on existing quantum computers for practical-scale problems owing to their error-prone hardware. We propose Optic, a framework to effectively execute quantum binary classification on real noisy intermediate-scale quantum (NISQ) computers.
Revocable and Scalable Access to Personal Health Records in a Cloud Computing Environment

The Personal Health Record (PHR) is one of the most significant models in the information exchange of the health reports, which is frequently extracted from some other third party storing the data, like as the cloud holder. Thus, for the privacy concern purpose of the patients and the users there emerged the promising technique to provide security for the data and its attributes shared among the PHR owners and the users, which is the Attribute Based Encryption (ABE). Yet, there existed issues in this technology for the security provision with adaptable access for the user and owner, scalability in key management, privacy exposure, and user revocation efficacy have been persisted towards the succession of the fine-grained and the cryptographical imposed access control over the data. In such case the paper is proposed upon a framework for a patient-centric control of PHR over the semi-trusted servers of the cloud data, where the encryption of the data can be accessed by multiple authority in a proper ciphertext policy applied over the attribute-based encryption termed as the MA-CP-ABE. Attaining the scalability and fine-grained, the ABE uses the encryption technique to each PHR documents and the data is secured from multiple domains of users and owners which is significantly divided by different terms of PHR in multiple security schemes for affording the key management difficulty in a reduced state. Additionally, a revocable and scalable mechanism is added in the ABE for the patient privacy and security of data sharing which is highly assured. The experimental results and the analytical reports are established which achieves the security, scalability and efficacy of the proposed work.
Forecasting the Traits of Cyber Criminals Based on Case Studies

The COVID-19 virus has affected every country on the globe; India is amongst the most with over 3.39 billion people who have been infected, and computer use has expanded since. As cybercrime (breaching, spoofing, DDOS assault, and phishing) is one of the most serious problems facing society today, it's crucial to understand what causes such attacks. Although many methods have been proposed to detect cybercrime, criminological theory of crime is one of them. But the most successful method for detecting these malicious activities is machine learning. This is because most of the cyberattacks have some common characteristics which can be identified by machine learning methods. In this context, an approach has been made in the chapter to review machine learning methods to understand the traits of cyber-criminals and crime committed on the dark web along with suitable methods to tackle them.
Automated Canteen Ordering System

Abstract: "Today, customers have little time to squander while waiting for a server to take their order at a cafeteria. A lot of clients come to the canteen during their lunch or recess, so they only have a short amount of time to eat before heading back to their workplaces or educational facilities. They can save time by ordering meals anytime they want without having to contact the waiter many times, thanks to this programme. With manual systems, you'll have to deal with a lot more files and manuals. It's a risky and time-consuming operation to save critical knowledge in files and manuals. The project includes a framework that shows how internet technology may be used gradually as abilities and confidence build as a progression from modifying materials to establishing an online environment. An e-menu card is available for end customers to utilize to read and pick food, and they may order food online by choosing the items they want. An E-menu card with your selections appears near the chef preparing your meal, so you can see what you've ordered right away. Android and iOS components coexist with a web application in this hybrid architecture. This system was built using PHP, CSS, JavaScript, MySQL, and HTML. Keywords: Canteen, Food, E-Commerce, WebApp, Self- Ordering
PlaceNet: Neural Spatial Representation Learning with Multimodal Attention

Spatial representation capable of learning a myriad of environmental features is a significant challenge for natural spatial understanding of mobile AI agents. Deep generative models have the potential of discovering rich representations of observed 3D scenes. However, previous approaches have been mainly evaluated on simple environments, or focused only on high-resolution rendering of small-scale scenes, hampering generalization of the representations to various spatial variability. To address this, we present PlaceNet, a neural representation that learns through random observations in a self-supervised manner, and represents observed scenes with triplet attention using visual, topographic, and semantic cues. We evaluate the proposed method on a large-scale multimodal scene dataset consisting of 120 million indoor scenes, and show that PlaceNet successfully generalizes to various environments with lower training loss, higher image quality and structural similarity of predicted scenes, compared to a competitive baseline model. Additionally, analyses of the representations demonstrate that PlaceNet activates more specialized and larger numbers of kernels in the spatial representation, capturing multimodal spatial properties in complex environments.
Analysis of the Serial Multiple Mediating Effect of Self-efficacy and Co-regulation in the Relationship between Growth Mindset and Team Creativity of College Freshmen in the Hackathon Program

In order to promote future competencies in the era of the 4th industrial age, universities need to provide challenging learning experiences and opportunities that enable students to take initiative. Universities also need to train their students in creative problem-solving skills. In this study, growth mindset, self-efficacy, and co-regulation were selected as predictors for team creativity in a hackathon program, which was considered to be a challenging learning context. In particular, concerning the relationship between growth mindset and team creativity, the serial multiple mediating effect of self-efficacy and co-regulation was tested. Data were collected from 806 students who participated in the freshman hackathon program in the first semester of 2021 at A University in Gyeonggi-do. The serial multiple mediation analysis was performed using PROCESS, and the main results were as follows: First, in the context of the hackathon program, growth mindset, self-efficacy, and co-regulation were found to be positive antecedent factors for improving team creativity. Second, the results showed that self-efficacy and co-regulation sequentially mediate the relationship between growth mindset and team creativity. Based on the research results, practical implications for developing and operating challenging educational programs were discussed, and the importance for providing training guidelines to students in order to promote a growth mindset among them, were suggested.
Augmented Reality EleReactor Organic Chemistry

Augmented reality is extremely imperative in envisioning real models or thoughts. It can be used to create erudition applications that can encourage the teaching and learning process. Augmented reality technology is easy and can be understood by the students. The innovation of computer equipment empowers 3D modelling to perform effectively and comparatively easy manner. Chemical reactions were in chemistry usually uninteresting by students for learning. Through augmented reality, an application will help the students to visualize and understand the 3D prototypical of each chemical component and can get the simulation of integration of two or more elements of it and other types Furthermore, several chemical reactions can be combined based on different markers or tracker detected from the camera. The elements from the periodic table we are using H, N, C, Cl, O has been tested with this application. Similarly, different combining elements like HCl, H2O, CO2, HCN, and NH3 etc. have been tested by augmented reality applications. The results of the present study indicated the learning benefits of this application as tested on different students.
Scalable Wireless Anomaly Detection with Generative-LSTMs on RF Post-Detection Metadata

Signal anomaly detection is commonly used to detect rogue or unexpected signals. It has many applications in interference mitigation, wireless security, optimized spectrum allocation, and radio coordination. Our work proposes a new method for anomaly detection on signal detection metadata using generative adversarial network output processed by a long short term memory recurrent neural network. We provide a performance analysis and comparison to baseline methods, and demonstrate that through the usage of metadata for analytics, we can provide robust detection, while also minimizing computation and bandwidth, and generalizing to numerous effects which differs from many prior works that focus on A.D. based signal processing on the raw RF sample data.
“Yomi” (readings) in bibliographic data for materials in Japanese

In bibliographic and authority data of materials in Japanese, it is necessary to represent kanji (Chinese characters) and its reading as a pair. Readings, called “yomi” in Japanese, are represented in katakana or the alphabet (romanization form). The readings in Japanese language have many variations depending on the context, so Japanese bibliographic information needs to provide both kanji and its reading as a pair to avoid misunderstandings.The JAPAN/MARC is a machine readable version of the Japanese National Bibliography and provides sets of kanji, katakana-yomi and romaji-yomi (romanization form) of bibliographic and authority data. This paper introduces how readings of bibliographic and authority data in Japanese have been expressed and handled mainly in JAPAN/MARC, compared with international standards.
Model-based Gaze Estimation with Transparent Markers on Large Screens

Several technical issues that affect eye-tracking have arisen concomitantly with the steadily increasing sizes of personal displays recently. One such issue is the loss of the illumination reflection of the near-infrared light-emitting diodes used as reference points around the edge of the display. Another issue is that reference identification is required for practical usage. Therefore, this paper proposes gaze estimation with transparent markers for large display environments to solve these problems. The transparent markers can be distributed on the screen, and a unique ID is assigned to each marker using linear polarization angles. The reference is detected using a polarization camera through the reflection on the cornea. The results of experiments conducted using a 50-inch display indicate that the proposed method can estimate the point-of-gaze to within 2.1 degrees of error. We confirmed that on-screen markers in sizable displays could be effectively used as references instead of illumination sources.
Natural language why-question in Business Intelligence applications: model and recommendation approach

Business Intelligence is the key technology for users to effectively extract valuable information from oceans of data for decision-making. Data warehouses and on-line analytical processing systems have therefore been developed to contribute effectively to the decision-making process. To extract information that is useful to decision-making, decision-makers express their needs in natural language. Such requirements may be formulated in natural language interfaces in free syntax, avoiding unfamiliar language (SQL, MDX). Natural Language queries can stand for WH-questions (“What, Who, Where, Why, etc.”) or a set of Keywords. In this paper, we emphasize on the “Why-Question”. This type of question provides answers that help in the diagnosis analysis of the data Warehouse. To deal with a Why-Question, we propose a model that mainly captures the components that reflect the multidimensional aspect of the Data Warehouse. When a decision-maker formulates his Why-Question in natural language, he uses his own terms. This decisional need can be not precise because the decision-maker is not always aware of the Data Warehouse’s lexicon as well as the Why-Question’s model. Consequently, the decision-maker must reformulate his initial question. Otherwise, the Why-Question’s answering process will not be triggered. This situation is not obvious for a decision-maker, especially when the reformulation of the question becomes iterative. To handle these issues, we lean towards a Why-Question’s recommendation approach based on both the Data Warehouse’s content and the decision maker’s requirement. This proposal aims to recommend to the decision-maker a set of natural language Why-Questions instead of rephrasing his initial question. To guide the recommendation process, we rely on a grammar that formalizes the Why-Question’s model. To validate our approach, a tool called “WQ-Recommender” is developed. An experimental study is presented to evaluate the relevance of the proposal.
Federated Learning Based on OPTICS Clustering Optimization

Federated learning (FL) has emerged for solving the problem of data fragmentation and isolation in machine learning based on privacy protection. Each client node uploads the trained model parameter information to the central server based on the local training data, and the central server aggregates the parameter information to achieve the purpose of common training. In the real environment, the distribution of data among nodes is often inconsistent. By analyzing the influence of independent identically distributed data (non-IID) on the accuracy of FL, it is shown that the accuracy of the model obtained by the traditional FL method is low. Therefore, we proposed the diversified sampling strategies to simulate the non-IID data situation and came up with the OPTICS (ordering points to identify the clustering structure)-based clustering optimization federated learning method (OCFL), which solves the problem that the learning accuracy is reduced when the data of different nodes are non-IID in FL. Experiments indicate that OCFL greatly improves the model accuracy and training speed compared with the traditional FL algorithm.
Estimation of Photovoltaic Power Generation by Using Deep Learning-based Method

It is important to predict the power output of distributed energy resources (DERs) like solar photovoltaic (PV) so as to prevent the power variation impact to power systems. In this paper, the techniques of using weather graphs have been introduced for the estimation of PV power generation. First, traditional Heliosat method is introduced. Secondly, a cloud-type method based on several cloud groups classified by different cloud top altitudes and rainfall intensities is presented and integrates with look-up-table mechanism to determine the PV power generation. Finally, this paper further proposed a deep learning-based method for overcoming the limitations of using above-mentioned methods. In proposed method, not only BILSTM neuron network but also a time mark technique are considered. To validate the performance of proposed method, Experiments based on the PV power generation data collected from a real PV site are included. Analysis results show nRMSE of cloud-type method is 16.83%, which is not better than Heliosat method of nRMSE 6.61%. On the contrary, the nRMSE of 4.67% is obtained from proposed deep learning method that presents the excellent performance among all methods.
Contract-Based Quality-of-Service Assurance in Dynamic Distributed Systems

To offer an infrastructure for autonomous systems offloading parts of their functionality, dynamic distributed systems must be able to satisfy non-functional quality-of-service (QoS) requirements. However, providing hard QoS guarantees without complex global verification that are satisfied even under uncertain conditions is very challenging. In this work, we propose a contract-based QoS assurance for centralized, hierarchical systems, which requires local verification only and has the potential to cope with dynamic changes and uncertainties.
Collaborative Collection Building: Greater than the Sum of Its Parts

As institutions shape collections to meet current and future needs, effective management of high density storage facilities and collaborative collection building will be vital to the success of academic and research libraries. The Oak Street Library, the University of Illinois’s high density storage facility, holds the largest single concentration of library materials on campus with a collection of more than 4 million items. These holdings include transfers from the main book stacks and other departmental libraries on campus, blanket orders and library gifts that are added directly to the Oak Street Library collection, and material held in trust for consortial partnerships. The collection represents collaborative collection building locally between subject specialists, library divisions, and central services, as well as cooperative agreements with consortiums such as the Big Ten Academic Alliance (BTAA) and the Consortium of Academic Research Libraries in Illinois (CARLI). This article describes building a large collection through unconventional means, demonstrating the benefits of effective collaborative collection building and management of shared and other non-traditionally acquired collections.
“Implementation of Testing Accreditation Analytical Framework for Higher Education System.”

Generating document is a frustrating experience in any reputed institution. The institutions should maintain significant and direct documents which can be used as evidence for quality. The Manual documentation process is extremely tedious and time-consuming. The paper presents the working of a developed application which will give a chance to minimize the institute work and makes the Accreditation process easier by conducting an indirect survey using grade points. The points are gathered from the head of the department based on the exit survey, placements, additional programs, parents’ feedback and course end surveys. The purpose of the developed application is to Test the Quality of the Technical Education Programme assessed by the Accredation Portal where a certificate of accreditation is awarded for a specific Programme. Historically technical institutes should conduct exercises for self-assessing the quality of their programmers on Naac criteria.