Reviewerly/exHarmony huggingface dataset:
EQRC: A secure QR code-based E-coupon framework supporting online and offline transactions1

In recent years, with the rapid development and popularization of e-commerce, the applications of e-coupons have become a market trend. As a typical bar code technique, QR codes can be well adopted in e-coupon-based payment services. However, there are many security threats to QR codes, including the QR code tempering, forgery, privacy information leakage and so on. To address these security problems for real situations, in this paper, we introduce a novel fragment coding-based approach for QR codes using the idea of visual cryptography. Then, we propose a QR code scheme with high security by combining the fragment coding with the commitment technique. Finally, an enhanced QR code-based secure e-coupon transaction framework is presented, which has a triple-verification feature and supports both online and offline scenarios. The following properties are provided: high information confidentiality, difficult to tamper with and forge, and the ability to resist against collusion attacks. Furthermore, the performance evaluation of computing and communication overhead is given to show the efficiency of the proposed framework.
Analysis of selected methods of creating artificial intelligence on the example of a popular card game

The aim of the article was to analyze selected methods of creating artificial intelligence in a popular card game. Two experiments were conducted: with a human and with a computer. The following algorithms were analyzed: random, min-max, based on a neural network, statistical and statistical with the use of "cheating" technique. The examined parameters were as follows: efficiency, execution time, number of implementation code lines, implementation time and training duration. The indicator with the greatest impact on the selection of the most optimal method was efficiency. The research has shown no difference in efficiency for the neural network-based algorithm and the statistical algorithm. In other cases, the differences in this feature were significant. The use of the "cheating" technique has increased the efficiency.
A BSP Based Approach for NFAs Intersection

Large NFAs are automata that cannot fit in a single computer, or the computations would not fit within the computer RAM, or may take a long time. We describe and implement a BSP solution of such large NFAs intersection. Our method avoids producing unreachable states of the product automaton, contrary to previous solutions. These solutions, based on MapReduce for instance, process all the Cartesian product of inputs NFAs. A running example is provided with execution details. Finally, complexity analysis is given. This work will help in bringing out relevant programming artifacts for our long term goal that consists of a high level distributed graph language.
Semantics-Guided Clustering with Deep Progressive Learning for Semi-Supervised Person Re-identification

Person re-identification (re-ID) requires one to match images of the same person across camera views. As a more challenging task, semi-supervised re-ID tackles the problem that only a number of identities in training data are fully labeled, while the remaining are unlabeled. Assuming that such labeled and unlabeled training data share disjoint identity labels, we propose a novel framework of Semantics-Guided Clustering with Deep Progressive Learning (SGC-DPL) to jointly exploit the above data. By advancing the proposed Semantics-Guided Affinity Propagation (SG-AP), we are able to assign pseudo-labels to selected unlabeled data in a progressive fashion, under the semantics guidance from the labeled ones. As a result, our approach is able to augment the labeled training data in the semi-supervised setting. Our experiments on two large-scale person re-ID benchmarks demonstrate the superiority of our SGC-DPL over state-of-the-art methods across different degrees of supervision. In extension, the generalization ability of our SGC-DPL is also verified in other tasks like vehicle re-ID or image retrieval with the semi-supervised setting.
ASSESSING THE INSIGHTS ON EXPERIENTIAL LEARNING USING THE AUGMENTED REALITY EXPLORATION

In the tertiary educational setting, learning is often taken place in a classroom environment that is too formal.Thus, to break the norm and to make learning enjoyable, exploration of a new method of knowledge dissemination using Augmented Reality (AR) was experimented in the teaching and learning of steel construction in architectural education.This exploration is intended to initiate an active learning and enjoyable environment for students at the university level.Hence, this paper examines students' insights on their experiences of AR exploration, where they actively learn through playing, experimenting, socialising and collaborating with their peers.During the learning process, students produced virtual steel construction details models, and then visualised the models through AR application that shows an interface with real time and real space.Prior to the AR exploration, students went through a series of workshops where they collaboratively developed models of steel construction details, based on their completed research.Students' feedback was sought after they completed the projects, particularly on their impression towards the project.The collected data was in the format of verbatim, obtained by having open-ended questionnaires.The data was analysed using qualitative methods of content analysis using atlas.tisoftware.The generated frequency of coded factors was quantified to produce an empirical result.Based on the 77 collected responses, there are 109 quotations derived from the open-ended question on respondents' impression towards the AR Implementation.The analysed codes were grouped in three (3) principal codes, which are the Learning Content (6 quotations), Positive Feedback (86 quotations), and Negative Feedback (17 quotations).Most students positively gave feedback that the new method applied for learning is effective and exciting, and they suggested the same approach should be implemented to other courses in the architectural educational program.The AR application had also offered them to have very practical and economical techniques of active learning, with the merger of ICT and online conveniences.
Towards DO-178C certification of adaptive learning UAV agents designed with a cognitive architecture

Adaptive and Learning Agents (ALAs) bring computational intelligence to their Cyber Physical host systems to adapt to novel situations encountered in their complex operational environment. They do so by learning from their experience to improve their performance. RTCA DO-178C specifies a stringent certification process for airborne software which represents several challenges when applied to an ALA in regards of functional completeness, functional correctness, testability and adaptability. This research claims that it is possible to certify an Adaptive Learning Unmanned Aerial Vehicle (UAV) Agent designed as per a Cognitive Architecture with current DO-178C certification process when leveraging a qualified tool (DO-330), Model-Based Development and Verification (DO-331) and Formal Methods (DO-333). The research consists in developing, as a case study, an ALA embedded in a UAV aimed at neutralizing rogue UAVs in the vicinity of civil airports and test it in the field. This article is the plan to complete, by end 2022, a dissertation currently in its confirmation phase.
IoT–Blockchain Integration-Based Applications Challenges and Opportunities

In the era of the revolutionized world with a huge enhancement in the technology, various interconnecting devices are able to communicate in order to automate the today tasks for us. These IoT nodes require security, reliability, robustness and efficient management. This abundant information that shares in the network requires security measures. Blockchain technology has revolutionary impact on the world by transforming the way for sharing the information and provides a distributed environment with no centralized authority support in various industries. This paper emphasizes on the challenges faced in the integration of IoT and blockchain, surveys the various applications to analyse the potential of blockchain in the upgradation of IoT and addresses the development of some privacy preservation techniques for IoT system operating over blockchain infrastructure. This paper reflects the comparative analysis of various smart contract mechanisms used for the IoT platforms like Iota, Iotex and Iotchain. In this, we proposed the methodologies of integration used for the integration of both the technologies for the performance enhancement with respect to various parameters.
Research on Internal Resource Quality Evaluation and Cascade Utilization Strategy of virtual power plants

Abstract With the continuous promotion of power market reform, the trading mode of power market is constantly enriched. In this context, the virtual power plant technology breaks the traditional “source grid load storage” boundary, and realizes the multi-energy complementary and multi-source interaction of the power system, but the resource utilization strategy for the virtual power plant is not enough. Therefore, starting from the composition of virtual power plant, this paper constructs the internal resource quality evaluation system of virtual power plant. Based on the division of resource quality, the cascade utilization strategy of virtual power plant resources is provided. The practical results show that the quality evaluation of internal resources of virtual power plant is helpful to classify the internal resources of virtual power plant, help them realize the cascade utilization of resources, and maximize the economic benefits of virtual power plants.
Preliminary Investigation of Potential Steganographic Container Localization

In this paper we present the findings discovered when conducting a perceptual experiment with the goal of analyzing Russian native speaker text juncture partition. This experiment is one of the first forays into applying experimental methods to discovering optimal methods of steganography in general and the first to do so on the basis of Russian written texts. The participants were to read through a semantically cohesive phonetically neutral and balanced Russian text that was modified to exclude any spaces, capital letters, paragraph indicators and punctuation symbols. The participants were further asked to divide the resulting string of symbols into paragraphs, phrases, syntagmas and words. The experiment goal was to discover statistically notable junctures with the prospect of analyzing and comparing their discovery rates in all four mentioned juncture paradigms. The results allow to determine prospective optimal minimal text modifications for the use of steganographic trigger-containers.
Web Conference Internet Traffic Analysis during Study-from-Home Period: Case in Sampoerna University

Web conference feature embedded in Learning Management System (LMS) has been implemented in Sampoerna University (SU) to support mandatory Teaching-and-Learning from home activities during early stage of Covid-19 spreading period in Jakarta. The use of technologies and Internet connection to support these academic activities became very essential. The objec-tive of this research is to analyze internet speed and quota con-sumptions to meet the web conference requirements. Lecturers and students need sufficient Internet quota, stable Internet connection with proper speed, either using wired or wireless connection, in prepaid or postpaid subscriptions for smooth online learning. This research was an exploratory and quantitative research with surveys using non-probability with identified voluntary response sampling. The results showed that web conferences using BigBlueButton for audio-call, text-based chat, and rarely updated presenter shared-screen consumed only 3.11% of average students’ Internet connection speed, while the quota consumptions for 3 Credit Points Course session were 129.15 MB (Megabyte) per session, and for 4 Credit Points Course session were 140.2 MB per session. It was concluded that students are supposed to experience no or less delay during web conferences, and still have plenty of Internet bandwidth that can be utilized to support Study-from-Home activities.
Discovering the same job ads expressed with the different sentences by using hybrid clustering algorithms

Text mining studies on job ads have become widespread in recent years to determine the qualifications required for each position. It can be said that the researches made for Turkish are limited while a large resource pool is encountered for the English language. Kariyer.Net is the biggest company for the job ads in Turkey and 99% of the ads are Turkish. Therefore, there is a necessity to develop novel Natural Language Processing (NLP) models in Turkish for analysis of this big database. In this study, the job ads of Kariyer.Net have been analyzed, and by using a hybrid clustering algorithm, the hidden associations in this dataset as the big data have been discovered. Firstly, all ads in the form of HTML codes have been transformed into regular sentences by the means of extracting HTML codes to inner texts. Then, these inner texts containing the core ads have been converted into the sub ads by traditional methods. After these NLP steps, hybrid clustering algorithms have been used and the same ads expressed with the different sentences could be managed to be detected. For the analysis, 57 positions about Information Technology sectors with 6,897 ad texts have been focused on. As a result, it can be claimed that the clusters obtained contain useful outcomes and the model proposed can be used to discover common and unique ads for each position.
Fuzzy K-Means Clustering in Measuring Course Outcome Attainment System for Higher Educational Institutes

Higher education is meant to produce knowledge in society. With this objective, the apex bodies of higher education frame policies to achieve it. One such policy is introducing outcome-based education (OBE) system in education institutes offers higher studies. As outcome-based assessments are integral part of it, the mechanism to measure the outcome’s attainment becomes crucial. In this technological era, the need for a system that automatically measures course outcome’s (CO) attainment is obvious. But challenges like missing data and one-to-many mapping between question and COs exist. The purpose of this paper is to model a system that can be used by academic bodies to measure CO attainment automatically given the abovementioned constraints.
Comparative Study of Cryptocurrency Algorithms: Coronavirus Towards Bitcoin’s Expansion

The widespread presence of Corona virus (COVID-19) is causing organizations and individuals major economics downsizing.The way this virus is transmitted from one individual to another is the real cause of the problem.For that, researchers in different fields started seriously looking for touch-less and contact-less exchange.Particularly in the finance world, cash transactions and key pad based transactions are becoming obsolete because they are some of the major causes of the spread of this virus (and other viruses and bacteria).Cryptocurrency could be one of the solutions to the above mentioned situation.This novel money is based on Blockchain technology, which is based on cryptography algorithms for the safety and the security of the transactions.This paper exhibits a comparative study of the asymmetric cryptography algorithms.This helps the user to best choose the most secure, safe and reliable method to encrypt/decrypt the transactions created in the Blockchain.
Empirical Evaluation of Mimic Software Project Data Sets for Software Effort Estimation

To conduct empirical research on industry software development, it is necessary to obtain data of real software projects from industry. However, only few such industry data sets are publicly available; and unfortunately, most of them are very old. In addition, most of today's software companies cannot make their data open, because software development involves many stakeholders, and thus, its data confidentiality must be strongly preserved. To that end, this study proposes a method for artificially generating a “mimic” software project data set, whose characteristics (such as average, standard deviation and correlation coefficients) are very similar to a given confidential data set. Instead of using the original (confidential) data set, researchers are expected to use the mimic data set to produce similar results as the original data set. The proposed method uses the Box-Muller transform for generating normally distributed random numbers; and exponential transformation and number reordering for data mimicry. To evaluate the efficacy of the proposed method, effort estimation is considered as potential application domain for employing mimic data. Estimation models are built from 8 reference data sets and their concerning mimic data. Our experiments confirmed that models built from mimic data sets show similar effort estimation performance as the models built from original data sets, which indicate the capability of the proposed method in generating representative samples.
Targeted design for adaptive clinical trials via semiparametric model

Abstract Precision medicine approach that assigns treatment according to an individual’s personal (including molecular) profile is revolutionizing health care. Existing statistical methods for clinical trial design typically assume a known model to estimate characteristics of treatment outcomes, which may yield biased results if the true model deviates far from the assumed one. This article aims to achieve model robustness in a phase II multi-stage adaptive clinical trial design. We propose and study a semiparametric regression mixture model in which the mixing proportions are specified according to the subjects’ profiles, and each sub-group distribution is only assumed to be unimodal for robustness. The regression parameters and the error density functions are estimated by semiparametric maximum likelihood and isotonic regression estimators. The asymptotic properties of the estimates are studied. Simulation studies are conducted to evaluate the performance of the method after a real data analysis.
Specification of initial Kalman recursions of symmetric nonlinear state-space model

A new class of nonlinear Time Series model referred to as Symmetric Nonlinear State-Space Model (SNSSM) was successfully developed using Kalman filter methodology. Some vital properties of the SNSSM such as optimal Kalman gain and optimal filter state covariance were derived. We finally initialized the filter which enabled us obtained the initial Kalman recursions under stationarity and nonstationarity assumptions. Under the former, the mean and variance were obtained unconditionally using Kronecker products and vec operator. But under the later, the mean and variance/covariance of the system were conditionally obtained using a well-known marginal and conditional property of multivariate normal distribution. It is expected that the former will be better than the later if the system is stationary, otherwise the later will be better.
Beyond Language: Learning Commonsense from Images for Reasoning

This paper proposes a novel approach to learn commonsense from images, instead of limited raw texts or costly constructed knowledge bases, for the commonsense reasoning problem in NLP.Our motivation comes from the fact that an image is worth a thousand words, where richer scene information could be leveraged to help distill the commonsense knowledge, which is often hidden in languages.Our approach, namely Loire, consists of two stages.In the first stage, a bi-modal sequence-tosequence approach is utilized to conduct the scene layout generation task, based on a text representation model ViBERT.In this way, the required visual scene knowledge, such as spatial relations, will be encoded in ViBERT by the supervised learning process with some bimodal data like COCO.Then ViBERT is concatenated with a pre-trained language model to perform the downstream commonsense reasoning tasks.Experimental results on two commonsense reasoning problems, i.e. commonsense question answering and pronoun resolution, demonstrate that Loire outperforms traditional language-based methods.We also give some case studies to show what knowledge is learned from images and explain how the generated scene layout helps the commonsense reasoning process.
Experimentation in the Teaching of Galvanic Cells Using the Jigsaw Method

The difficulty usually presented by high school students with the electrochemical theme based the development of experimental kits to aid in the teaching of galvanic cells, cells and batteries, and series and/or parallel associations.Three experimental electrochemistry kits, made from low cost and easy access materials, were used to explore series and parallel associations of commercial cells and galvanic cells, and that served to teach concepts of electrochemistry, through cooperative learning using the jigsaw method.The students were divided into six groups of specialists (GEs); each of these groups contained four or five students.Six experimental kits were distributed, related to three subtopics of electrochemistry: (1) Understanding the operation of commercial cells, (2) Understanding the functioning of galvanic cells, (3) Checking the occurrence of oxidoreducation reactions.Each subtopic was worked by two GEs.After the work, the GEs separated as pieces of a puzzle, and students formed six base groups (GBs), each containing at least one expert from each subtopic.A diagnosis was made of how the students evaluated the proposed didactic activity (in relation to the traditional teaching methodology) and verified if the didactic proposal helped to help students learn from the content worked.The didactic proposal, in addition to motivating, helped students learn, providing good results, which consolidate the performance of experimental activities, associated with the jigsaw method, in the teaching of galvanic cells.A dificuldade normalmente apresentada pelos estudantes do Ensino Médio com o tema Eletroquímica fundamentou a utilização de kits experimentais para auxiliar no ensino de células galvânicas, pilhas e baterias, e associações em série e/ou em paralelo.Os três kits experimentais de Eletroquímica, confeccionados a partir de materiais de baixo custo e fácil acesso, exploraram associações em série e em paralelo de pilhas comerciais e células galvânicas, e serviram para ensinar conceitos de Eletroquímica, por meio da aprendizagem cooperativa utilizando o método jigsaw.Os alunos foram divididos em seis grupos de especialistas (GEs); cada um desses grupos continha quatro ou cinco alunos.Foram distribuídos seis kits experimentais, relativos a três subtópicos de Eletroquímica: (1) Entendendo o funcionamento das pilhas comerciais, (2) Entendendo o funcionamento das células galvânicas, (3) Verificando a ocorrência de reações de oxidorredução.Cada subtópico foi trabalhado por dois GEs.
Development of Mobile Learning for Simulation and Digital Communications

Simulation and digital communication subjects at SMKN 1 Singkarak do not have support other than text books and modules. This study aimed to develop a practical and effective learning platform that can support students in learning. This study used a 4D development model, and the trial subjects in this study were 31 students of class X in the 2020/2021 academic year. The instruments used were test and questionnaire instruments. The research method used was pre-experimental. The data obtained were analyzed using quantitative descriptive statistical analysis methods. From the research results, it was found that classical completeness reached 90% and the average score in terms of the gain score of students before and after using mobile learning was 0.49. The conclusion of this research is that the results of assessments from both teachers and students show that mobile learning in simulation and digital communication subjects can be categorized as very practical and effective