Reviewerly/exHarmony huggingface dataset:
Development and design of an FPGA-based encoder for NPN

This paper describes a cryptographic protection system hardware device designed to improve data encryption and decryption performance and preserve data integrity. The cryptosystem is implemented by hardware-software method, where the encryption and decryption of data are carried out in a stand-alone FPGA device based on non-positional polynomial number system (NPN). For data encryption the next block of text to be encrypted is divided into sub-blocks and represented as separate binary polynomials and binary polynomials-keys are assigned to them, as well as irreducible polynomials (modules). Then, split blocks are calculated in parallel and a ciphertext is formed. For this purpose, the special algorithm where calculation of NPN parameters and check on irreducibility of polynomials (modules) and the program of generation of direct and inverse keys are developed and application functional is developed that implements operations in the ring of polynomials with coefficients GF(2) using an object-oriented approach. We have developed polynomial multipliers modulo sequential and parallel action (matrix multiplier) on the basis of which data encryption and decryption are performed.
KGWE: A Knowledge-guided Word Embedding Fine-tuning Model

Existing word embeddings are trained based on word co-occurrence information, which shows to capture well the semantic information of words. However, only using word co-occurrence information is not enough for training word embeddings, the huge knowledge graph resources cannot be ignored for improving the performance of these word embeddings. This paper proposes a novel knowledge-guided word embedding fine-tuning model called KGWE, which aims to utilize the entity embedding from the knowledge graphs to guide the fine-tuning process of word embeddings. The main idea of KGWE is to use entity embeddings as the targets for fine-tuning the representations of words appeared in the entity description of a knowledge graph. We conduct experiments with two famous static word embeddings and five knowledge graph embedding models and evaluate the model with 14 word similarity benchmarks. Our results show that word embeddings fine-tuned through KGWE can outperform the baselined word embeddings.
Question Answering Systems for Covid-19

Abstract In the present scenario COVID-19 pandemic has ruined the entire world. This situation motivates the researchers to resolve the query raised by the people around the world in an efficient manner. However, less number of resources available in order to gain the information and knowledge about COVID-19 arises a need to evaluate the existing Question Answering (QA) systems on COVID-19. In this paper, we compare the various QA systems available in order to answer the questions raised by the people like doctors, medical researchers etc. related to corona virus. QA systems process the queries submitted in natural language to find the best relevant answer among all the candidate answers for the COVID-19 related questions. These systems utilize the text mining and information retrieval on COVID-19 literature. This paper describes the survey of QA systems-CovidQA, CAiRE (Center for Artificial Intelligence Research)-COVID system, CO-search semantic search engine, COVIDASK, RECORD (Research Engine for COVID Open Research Dataset) available for COVID-19. All these QA systems are also compared in terms of their significant parameters-like Precision at rank 1 (P@1), Recall at rank 3(R@3), Mean Reciprocal Rank(MRR), F1-Score, Exact Match(EM), Mean Average Precision, Score metric etc.; on which efficiency of these systems relies.
FEATURES OF TEACHING STUDENTS IN THE MULTISIM MODELING ENVIRONMENT

The report discusses the features of the Multisim software environment that can complicate the learning process.
Agile Approach on the Performance Prediction of ARM TrustZone-based Mandatory Access Control Security Enhancement

Mandatory Access Control (MAC) is one of the most important security mechanisms of Linux, but it may be threatened by vulnerabilities in kernel space. Currently, the ARM TrustZone-based MAC (TZ-MAC) security enhancement method has been proposed to protect the key security function of MAC with the hardware-based trusted execution environment. However, given that each call to the hook set of the TZ-MAC security modules will cause switching between the normal and secure worlds of ARM TrustZone, the specific design of the TZ-MAC framework will substantially affect system performance, and may even be considerably slow to prevent the security module from being applied. Therefore, when researchers design a security module, they need an agile method to predict the performance cost brought by the hook set realized, to assist the optimization of the design scheme and improve system performance. This study presents a performance prediction method of TZ-MAC based on the statistical analysis of the Linux Security Modules (LSM) hook calls. This method is universal for different TZ-MAC frameworks, security modules, and benchmarks. Moreover, the proposed method can predict only the performance based on the security module design of TZ-MAC, and the implementation of the module is not needed. An agile approach of performance prediction for TZ-MAC is conducted based on the performance prediction method. For two different TZ-MAC implementation ideas, we constructed prediction data sets based on the classic benchmarks, namely, LMbench and UnixBench, and verified the effectiveness of our method. The tests based on the security module SELinux show that over 50% of the overhead is caused by 3% of the LSM hook functions, which indicates the direction for future optimization.
Measurement of elastic properties of materials employing 3-D DIC in a Cornu’s experiment

Abstract Measurement of elastic properties, especially the Poisson’s ratio, utilizing non-contact techniques in a tensile experiment is very challenging. This is primarily due to the poor spatial resolution and the large strain noise inherent to these techniques. The difficulty level increases many folds when Poisson’s ratio of less elongating, stiffer, and/or brittle materials, like ceramics and ablatives, is measured. This paper reports a newer approach that employs 3-D digital image correlation (3-D DIC) in a Cornu’s experiment to enable accurate measurement of elastic properties in a single test. The deflection field obtained from 3-D DIC in the form of anticlastic surfaces during Cornu’s experiment is utilized for determining Poisson’s ratio. In the same experiment, the elastic modulus is estimated using the center point deflection method. The proposed methods are validated with standard materials and extended to newly developed materials. Cornu’s method with 3-D DIC can provide the elastic properties with ease and has many advantages over other conventional techniques.
CloudJS: novel cloud-based design framework for text-file encryption

Purpose Cloud computing is becoming increasingly popular as it facilitates convenient, ubiquitous, on-demand network access to a shared pool of configurable computing resources and applications that can be quickly retrieved and released. Despite its numerous merits, it faces setbacks in data security and privacy. Data encryption is one of the most popular solutions for data security in the cloud. Various encryption algorithms have been implemented to address security concerns. These algorithms have been reviewed along with the Jumbling Salting algorithm and its applications. The framework for using Jumbling Salting to encrypt text files in the cloud environment (CloudJS) has been thoroughly studied and improvised. The purpose of this paper is to implement the CloudJS algorithm, to discuss its performance and compare the obtained results with existing cloud encryption schemes. Design/methodology/approach The paper uses six research questions to analyze the performance of CloudJS algorithm in the cloud environment. The research questions are about measuring encryption time and throughput, decryption time and throughput, the ratio of cipher to the plain text of CloudJS algorithm with respect to other Cloud algorithms like AES and DES. For this purpose, the algorithm has been implemented using dockers-containers in the Linux environment. Findings It was found that CloudJS performs well in terms of encryption time, decryption time and throughput. It is marginally better than AES and undoubtedly better than DES in these parameters. The performance of the algorithm is not affected by a number of CPU cores, RAM size and Line size of text files. It performs decently well in all scenarios and all resultant values fall in the desired range. Research limitations/implications CloudJS can be tested with cloud simulation platforms (CloudSim) and cloud service providers (AWS, Google Cloud). It can also be tested with other file types. In the future, CloudJS algorithm can also be implemented in images and other files. Originality/value To the best of the knowledge, this is the first attempt to implement and analysis of a custom encryption algorithm (CloudJS) in the cloud environment using dockers-containers.
Predicting Video Bitrate from Encrypted Streaming Traffic in SDN-based 5G Networks with ML

Video streaming applications currently dominate mobile network traffic. This predominance motivates network operators to optimize the network aiming at the quality of experience (QoE) of video streaming users. However, due to the widely adopted end-to-end traffic encryption, several key video-QoE indicators (KQI) that are useful to infer QoE are not readily available to the network operator. This work proposes a method to predict the video bitrate KQI from encrypted video streaming traffic. We compare the following machine learning (ML) algorithms for this task: Random Forest, Multilayer Perceptron, and Long Short-Term Memory networks. Since the only information available to the network operator are key performance indicators (KPIs), such as throughput, we use only information obtained from KPIs in our evaluations. We implement an open-source emulation setup for networking experiments to generate the data to train ML algorithms and a feature engineering procedure to obtain statistical features from the raw KPI data. Furthermore, we evaluate the proposed method in two ML learning cases: offline and incremental, and discuss issues regarding the generalization capability of the ML algorithms.
Pattern and Poet Recognition of Arabic Poems Using BiLSTM Networks

Poetry is the oldest form of Arabic literature and continues to play a prominent status in the Arabs' lives. We think that emerging deep learning techniques could provide needed solutions to compose, analyze, process, and enjoy this fine art. We investigate here two related problems: recognizing the poetry pattern of a poetry sample and using machine learning classification techniques to recognize the poet of a poetry verse. Using a dataset of 93,276 diacritized samples and 319 poetry patterns, we demonstrate that deep bidirectional long short-term memory (BiLSTM) network supplemented by a transfer learning solution provides excellent poetry pattern recognition of 99.5%. We also show that the proposed deep BiLSTM network can recognize the poet with 81.2% accuracy when trained on a 100,000-verse dataset of 10 famous Arabic poets. We suggest that the proposed solutions are efficient and need extensions to recognize poetry patterns of undiacritized verses and larger number of poets.
Text-based Recommendation Systems for Software Developers: A Systematic Literature Review

Abstract Purpose: The aim of this SLR is to look at recommendation systems which receive textual information as an input. By analysing them it is possible to understand how the textual information is preprocessed and which algorithms are then used to generate recommendations. Methods: With the Search Query I frst identifed 487 papers, from which 65 were removed as duplicates. After the IC and EC application, 28 articles remained as relevant. Results: From these articles’ analysis, it was found that the most commonly used pre-processing techniques are tokenization, TF-IDF, and stopwords removal. I also determined that all algorithms for suggestions generation in such systems can be divided into 4 categories: classifcation, ranking, clustering, and heuristic-based algorithms. In the last step I found that the most frequent output of such systems are API, code, and workers suggestions. Conclusion: With this work, I looked at which pre-processing techniques are used in the text-based recommender systems for software developers and which are the most common. I have also looked at the classifcation of algorithms for such recommendation systems. Finally, I considered what kind of objects are recommended by these text-based recommendation systems.
Quality Assessment for National Libraries

National libraries as well as any other progressive institutions have to assess the results of their performance. Depending on the type, functions, structure, areas of activities, organizational and financial methods, any national library can test wide sets of performance indicators consistent with its mission. Those indicators may differ from the indicators being tested by other national libraries. Quite often national libraries can also use additional indicators if financial/sponsoring organizations or target user groups etc. require this. The paper considers the methods and procedures for measuring quantitative indicators of national libraries’ performance according to the new international standard ISO 21248:2019 “Information and documentation: Quality assessment for national libraries”, which is partially based upon the ISO 11620:2008 standard “Information and documentation. Library performance indicators”. The author presents specific examples for calculating indicators using formulas and detailed description of steps for calculation of indicators depending on the library mission and goals. Along with qualitative indicators, the new standard recommends to use qualitative indicators to assess the impact of national libraries not only on their users, but also on society as a whole. In recent years, libraries have developed and probated methods to justify their importance. One of those methods is user surveying by means of special questionnaires made on the basis of ISO 16439:2014 standard “Information and documentation. Methods and procedures for assessing the impact of libraries” and circulated by libraries among their users to educe and differentiate interests of different user groups to provide them with subsequent target service.
Survey on Agri-Food Supply Chain Using Blockchain

Blockchain is a new digital technology that allows dispersed untrusted parties to conduct financial dealings without the use of middlemen like banks. This paper looks at the influence of blockchain technologies on the food supply, shows current projects and efforts, explores the general implications, problems, and possibilities, all while keeping a critical eye on the maturation of such projects. Our observations show that blockchain is a viable technology for a translucent food supply-chain along with numerous ongoing proposals in a variety of food materials as well as food-related concerns, but there are still numerous obstacles and difficulties that prevent it from gaining broader adoption between systems and farmers. Technical factors, education, legislation, and regulatory structures are all part of these difficulties. This research paper conducts a survey to examine both the methodologies and uses of blockchain technologies in food supply sectors and agriculture. The technical components, such as cryptographic techniques, structural data, and consensual processes, are initially thoroughly discussed. Next, to show the usage of blockchain technology, current blockchain applications in agriculture are classified and analyzed. In fact, examples of how professionals leverage prominent platforms and smarter contracts to create agricultural applications are presented. Finally, this research work explores the major obstacles that many future agricultural systems face, along with the attempts and viable solutions that have been made to address these issues.
Data Mining based Inferences about Software Parameters

Up to now, several criteria (software parameters) have been determined in order to measure and evaluate software development projects: Productivity, engagement, attention to quality, code base knowledge and management, adherence to coding guidelines and techniques, learning and skills, personal responsibility and etc. However, there isn’t any universally accepted criteria or a methodology to measure and evaluate software development projects. In this context, for preparing the background of the study, several researches have been studied about “Software Development Projects”, “Software Development Process” and “Software Development Measurement and Evaluation”. Also, with this literature study, the common criteria set about measurement and evaluation of software development projects has been created, generated and presented. In addition, some information has been got and taken from 105 software experts (software analyzers, software developers and managers) with 55 different software companies so as to evaluate the use of the common criteria in real work life, and to identify criteria which are not seen in researches before, but used in real work life. Accordingly, a measurement and evaluation criteria set (software parameters) about the software development projects has been created based on the data mining algorithm – “Association Rule Mining Apriori Algorithm” – with its 12 inferences. This set has also consisted of 10 software parameters with 6 dual relationships. With the light of these data, the designed and developed software parameters have had high validation with more than 75 percent accuracy rate. As a natural result of this, the study will have had a positive effect on software engineering by shedding light on its working domain – software development.
An empirical evaluation on the applicability of the DALiuGE execution framework

The Square Kilometre Array (SKA) project is an international cooperation project to build the largest radio telescope worldwide. Data processing is one of the biggest challenges of building the SKA telescope. As a distributed execution framework, the Data Activated Liu Graph Engine (DALiuGE) was proposed to be one of the candidates for addressing the massive data of the SKA. DALiuGE has many distinctive features, but its actual ability to handle scientific data is still not clear. In this paper, we perform an objective evaluation of the usability of DALiuGE for the execution performance, developer workload, and implementation difficulty of porting the SAGECal to DALiuGE. The evaluation results show that the DALiuGE enables fast integration of astronomical software, but there are significant differences in the efficiency of different parallel granularities. Even with the deep optimization of the program, there is still a gap between the current DALiuGE and the traditional MPI in execution performance. Therefore, we come to a preliminary conclusion that the DALiuGE has no performance advantage in batch processing of massive data. At the same time, it may be more suitable for application scenarios with more customized computational tasks, such as SKA science regional centers.
Postural evaluation based on body movement and mapping sensors

According to the World Health Organization (WHO), about 85 percent of the world’s population suffers from back pain, the main cause being poor posture. Persistent lower back pain is found to be persistent in both the populations. The proposed solution is composed of flexible sensors that capture all the moves made by the spine, being able to identify postural deviations and help in postural reeducation. Using the Microsoft Kinect V2 sensor and photogrammetry, the body parts are identified, and their asymmetry is calculated. In this way, we support specialists in the diagnosis and treatment of postural changes. To validate the proposed approaches, students and physiotherapists performed various tests and compared the results obtained through traditional methods, showing that both the methods are showing promising outcomes to diagnose and treat diseases due to poor posture. Twelve exams were performed, to analyze the system’s repeatability and evaluate the asymmetry of the limbs selected. As a result of the nine students evaluated, all the variations were between 0 and 1 centimeter, so it turned out to be efficient in evaluating the student’s posture.
An integrated study of Landsat ETM and Cartosat DEM data in identification of banded iron formations (BIFs) associated with sulphide mineralization

This paper deals with the identification of BIFs and associated sulphide mineralisation. An integrated approach, including the use of Landsat ETM-plus and Cartosat DEM data, GIS analysis, and geological data, is adopted for this purpose in the Nagavi area of Gadag Schist Belt (GSB), India. This integrated approach has enabled in identifying BIFs and structures. Band-7 of the ETM-plus sensor of Landsat-7 is used to identify BIFs and Band-5 for lineaments and shear zones. As a result of this study, the presence of gold mineralisation in sheared zones is noticed. BIFs are the economically prominent litho-units in the GSB hosting high-grade iron ore deposits along with sulphide mineralised shear zones. The strata bound ore is hosted primarily by BIF, consisting of chlorite, alternating chert and magnetite, sulphides and carbonate bands of a millimetre to centimetre scale.
Context and Orientation Aware Path Tracking

Autonomous vehicles on city roads and especially in pedestrian environments require agility to navigate narrow passages and turn in tight spaces, leading to the need for a real-time, robust and adaptable controller. In this paper, we present orientation and context aware controllers for autonomous vehicles that can closely track the reference path wit alh respect to the current state of the vehicle, environmental properties, and the desired target orientation at the desired target location. Our proposed controllers are derived from the widely used pure pursuit controller. We validate our proposed controllers with respect to the baseline pure pursuit controller in simulation and on a full-size autonomous vehicle in a pedestrian environment. Our experimental results suggest significant improvements in adaptability and tracking performance compared to the pure pursuit controller.
An Effective Technology for Secured Data Auditing for Cloud Computing using Fuzzy Biometric Method

The utilization of "cloud storage services (CSS)", empowering people to store their data in cloud and avoid from maintenance cost and local data storage. Various data integrity auditing (DIA) frameworks are carried out to ensure the quality of data stored in cloud. Mostly, if not all, of current plans, a client requires to utilize his private key (PK) to generate information authenticators for knowing the DIA. Subsequently, the client needs to have hardware token to store his PK and retain a secret phrase to actuate this PK. In this hardware token is misplaced or password is forgotten, the greater part of existing DIA plans would be not able to work. To overcome this challenge, this research work suggests another DIA without "private key storage (PKS)"plan. This research work utilizes biometric information as client's fuzzy private key (FPK) to evade utilizing hardware token. In the meantime, the plan might in any case viably complete the DIA. This research work uses a direct sketch with coding and mistake correction procedures to affirm client identity. Also, this research work plan another mark conspire that helps block less. Verifiability, yet in addition is viable with linear sketch Keywords– Data integrity auditing (DIA), Cloud Computing, Block less Verifiability, fuzzy biometric data, secure cloud storage (SCS), key exposure resilience (KER), Third Party Auditor (TPA), cloud audit server (CAS), cloud storage server (CSS), Provable Data Possession (PDP)