Reviewerly/exHarmony huggingface dataset:
Handwritten Phoenician Character Recognition and its Use to Improve Recognition of Handwritten Alphabets with Lack of Annotated Data

Unlike Latin, the recognition of Phoenician handwritten characters remains at the level of research and experimentation.In fact, such recognition can contribute to performing tasks such as automatic processing of Phoenician administrative records and scripts, the digitization and the safeguarding of the written Phoenician cultural heritage.As such, the availability of a reference database for Phoenician handwritten characters is crucial to carry out these tasks.To this matter, a database for Phoenician handwritten characters (PHCDB) is introduced for the first time in this paper.We also explore the challenges in the recognition of Phoenician handwritten characters by proposing a deep learning architecture trained on our database.Furthermore, we propose a transfer learning system based on Phoenician character shapes to improve the recognition performance of Tifinagh Handwritten character, and we thereby affirm the possibility of the Tifinagh alphabet being derived from the Phoenician alphabet.Finally, based on Phoenician characters, we introduce a fast, global and light-weight transfer learning system for the recognition of any alphabet which lacks annotated data.
Distributed Energy Systems Management Using the Case Based Reasoning

Regarding continued growth in the use of electrical energy, it is important to meet the final customer demand.In a smart grid context, there are several ways to do this.One of means is to add energy storages and infrastructures.We are so interested in this study to the modification of electrical infrastructures.By adapting organizations, managers and their teams must have the adequate behavior and decision making to modify existing materials or add new materials into the existing system.This paper shows a company team's behavior which operates in the field of energy supplying.The purpose of this paper is to show how teams' managers can use a past experience, and to evaluate the impact of structure modification according to decision making using the case based reasoning.The experiments concern more precisely the quantitative aspect of exchanged messages between managers and their teams.
Cash Flow Forecasting Model for Electricity Sale Based on Deep Recurrent Neural Network

Daily cash flow forecasting plays a very important role in enterprise development planning and strategic deployment. This paper makes use of a deep recurrent neural network model and applies it to the forecast of daily sales cash flow. This model adopts GRU unit structure. Through analyzing and mining historical payment flow data, the neural network model is used to automatically learn and extract the internal characteristics of information, and finally the daily cash flow prediction results are obtained. This method is the first successful application of artificial intelligence algorithm in the daily cash flow prediction of power grid. Experimental results show that the model is more accurate than ARIMA method.
Automated Machine Learning Tool: The First Stop for Data Science and Statistical Model Building

Machine learning techniques are designed to derive knowledge out of existing data.Increased computational power, use of natural language processing, image processing methods made easy creation of rich data.Good domain knowledge is required to build useful models.Uncertainty remains around choosing the right sample data, variables reduction and selection of statistical algorithm.A suitable statistical method coupled with explaining variables is critical for model building and analysis.There are multiple choices around each parameter.An automated system which could help the scientists to select an appropriate data set coupled with learning algorithm will be very useful.A freely available web-based platform, named automated machine learning tool (AMLT), is developed in this study.AMLT will automate the entire model building process.AMLT is equipped with all most commonly used variable selection methods, statistical methods both for supervised and unsupervised learning.AMLT can also do the clustering.AMLT uses statistical principles like R2 to rank the models and automatic test set validation.Tool is validated for connectivity and capability by reproducing two published works.
Students’ Perception of Digital Literacy Competence as Learning Sources

This study was carried out to define the students’ perception of digital literacy competencies as learning sources held by the students of English Education Study Program of STKIP Muhammadiyah Bangka Belitung as active users of internet pointed on the internet searching, hypertext navigation, content evaluation, and knowledge assembly by theory of Paul Gilster (1997). This research uses a descriptive qualitative method. Subjects in this research were 9 students determined using a purposive sampling technique, with the subject criteria is the students who are actively using the internet more than 4 hours a day to browse the internet as educational sources. The results of this research indicate that all of the respondents are not literate yet because they do not have all the indicators in digital literacy. But, only one of nine students answered all the indicators on digital literacy, the respondent has stated as well competencies in internet searching, hypertext navigation, content evaluation, and knowledge assembly. So, from the indicators of digital literacy competencies, it can be implied that only one of nine students who have a good perception of digital literacy as learning sources.
A Novel Key Scheduling Algorithm for Lightweight Cryptographic Applications

Advancement in the area of wireless communication has increased the demands for lightweight security schemes as the sensor nodes and the devices used in these networks are resource constrained devices.One such area where the lightweight cryptographic system can be applied is the medical Internet of things.Since most of the devices in medical IOT is resource constrained it poses a challenge to traditional cryptographic algorithms which tend to lower the network performance as it requires high computational complexities, power consumption, and delays and hence the need for lightweight security in these devices.To prevent any unauthorized access or interruption it is crucial to ensure that data secrecy is maintained throughout the medical treatment.The most crucial thing in securing these resource constrained devices is the key used in encryption process since the entire security of the data is dependent on the key used.Even if the attacker has little knowledge of the key used in the encryption process, the entire security of the system can be lost.In this paper a lightweight key scheduling algorithm is proposed which can be used to generate the keys to be applied for encryption process in IOT devices used in medical care to improve the security of data transmitted in the healthcare environment.
THE DEVELOPMENT OF A BACHELOR’S SOFT SKILLS THROUGH PROJECT ACTIVITIES IN A FOREIGN LANGUAGE

The present period of higher education modernization is characterized by the need to improve the bachelor’s professional training aimed at the comprehensive development of the student’s personality, which, along with hard skills, should have a wide range of soft skills. On the basis of the content analysis of the concept ‘soft skills’ the authors define them as universal socio-personal and general cultural skills and qualities that contribute to successful professional activities and effective interpersonal interaction. The relevance of the study is due to the fact that, despite an active discussion in the scientific literature on the development of soft skills among bachelor students, the level of these skills’ acquisition is quite low and requires a systematic and focused work to improve them.The article presents an appeal to the topic of soft skills through the practice of project activities in a foreign language. The research is aimed at the theoretical substantiation of the psychological and pedagogical effectiveness of project activities for developing soft skills and the generalization of the results of experimental training using the project method in foreign language teaching of non-linguistic students. According to the authors, project work contributes to the obtaining of a complex of non-specialized, career-important supraprofessional skills that are responsible for successful participation in the work process, high productivity and are not related to a specific subject area. Among the most relevant soft skills the authors define communicative, interpersonal and cognitive skills, emotional intelligence, management skills.
Sentiment Analysis of Twitter Social Media to Online Transportation in Indonesia Using Naïve Bayes Classifier

The application of information technology in transportation services is a positive development felt by the community with the emergence of various online transportation services. On the other hand, nowadays society cannot escape the use of social media in communicating and interacting with other parties. This research was conducted to determine the relationship between the use of social media and the use of online transportation services. The study was conducted using the Naïve Bayes Classifier method to analyze Twitter social media sentiment towards online transportation in Indonesia. The study was conducted by processing 1009 data, consisting of 900 training data and 109 test data. From the results of testing of 109 training data obtained 11% had a positive value, 14% negative value, and the rest, which is 75% neutral value. While the test accuracy test for 109 data resulted in an accuracy of 84%. The results of the study show that most of the use of Twitter social media in Indonesia does not affect the user's decision to utilize online transportation services.
Optimized Authentication Model for Online Transaction Payments

The rapid growth of the internet across the globe has gained attention in the world of business.The internet has become the major driver for business growth in the world; due to several security lapses online; it is necessary to implement measures and standards on the internet to protect transactions online.These security lapses have led to the development of various online payment protocols to ensure the safety of online transactions such as Secure Electronic Transaction (SET), internet Keyed Payment (iKP) and The Secure Socket Layer (SSL).There are several methods of paying for online transactions; these include direct payment with bank accounts and the use of electronic card.The payments are subjected to the verification system, which ensures no one uses someone's card to transact business online.Each card has a security feature known as the Card Verification Value (CVV) number, which is used as authentication for online business.The key feature of the card which validates the card owner as the user is the CVV number, which is found at the back of the card.The problem is that when the card gets lost or falls into the hands of another person, it is likely the person might use the card for a fraudulent activity online.This is because all the information required for e-payment is on the card.In this paper, we propose an optimized conceptual model which ensures the removal of the CVV number from the all-electronic card, the paper also recommended a framework that deployed Unstructured Supplementary Services Data (USSD) technology in the online transaction and payment process.In a real-world implementation, the proposed optimized model shall enhance e-commerce payments, card user participation, reduce threats, improves the security of conducting online business and then offer the card user the opportunity to deny or accepts payment.
FINITE INTEGRAL FORMULA INVOLVING ALEPH&amp;ndash;FUNCTION AND GENERALIZED MITTAG&amp;ndash;LEFFLER FUNCTION

The aim of this paper is to establish general definite integrals involving product of the Aleph function and the generalized Mittag-Leffler function with general arguments.This integral yields a number of known results as special cases.For the sake of illustration, several corollaries are also presented as special case of our main results.
What Do You Want from Us? Evaluating Student Interest in Technology-Based Services in Academic Libraries

Following two previous studies in 2010 and 2013, this study intends to identify current trends of Sam Houston State University students’ technology and communication needs and preferences regarding library services and resources. Drawing comparisons to two previous local studies and national data sets, such as those from Pew Internet Research and the EDUCAUSE Center for Analysis and Research (ECAR), the findings revealed, among other things, a decline in tablet usage and interest, continued need for library computers, and a complex relationship between social media usage and engagement with the library. The results of this survey will inform and guide the library and the profession to most efficiently and effectively offer desired library services and engage students on appropriate social media platforms.
Parallel Peer Group Filter for Impulse Denoising in Digital Images on GPU

A new two-steps impulsive noise parallel Peer Group filter for color images using Compute Unified Device Architecture (CUDA) on a graphic card is proposed. It consists of two steps: impulsive noise detection, which uses a Fuzzy Metric as a distance criterion and a filtering step. For the needed ordering algorithm we are using the Marginal Median Filter with forgetful selection sort. Comparisons with other color filters for Graphics Processing Unit (GPU) architectures are presented, demonstrating that our proposal presents better performance in color preservation and noise suppression.
Comparison of Control Methods Based on Imitation Learning for Autonomous Driving

Recently, some learning-based methods such as reinforcement learning and imitation learning have been used to address the control problem for autonomous driving. Note that reinforcement learning has strong reliance on the simulation environment and requires a handcraft design of the reward function. Considering different factors in autonomous driving, a general evaluation method is still being explored. The purpose of imitation learning is to learn the control policy through human demonstrations. It is meaningful to compare the control performances of current main imitation learning methods based on the provided dataset. In this paper, we compare three typical imitation learning algorithms: Behavior cloning, Dataset Aggregation (DAgger) and Information maximizing Generative Adversarial Imitation Learning (InfoGAIL) in the The Open Racing Car Simulator (TORCS) and Car Learning to Act (CARLA) simulators, respectively. The performance of algorithms is evaluated on lane-keeping task in racing and urban environment. The experiment results show DAgger performs best in simple lane keeping problem, and InfoGAIL has the unique advantage of distinguishing different driving styles from expert demonstrations.
The Extent to which Individuals in Saudi Arabia are Subjected to Cyber-Attacks and Countermeasures

In light of the rapid development of technology and the increase in the number of users of the Internet via computers and smart devices, cybercrimes impact on enterprises, organizations, governments and individuals has been significant.Researches and reports on the impact of cybercrime and methods of prevention and protection are been introduced regularly.However, the majority focuses on the impact on organizations and governments.This paper aims to use a survey methodology in order to highlights the impact of cybercrimes on the individuals in Saudi Arabia and measure the awareness of cybersecurity among individuals.In addition, this research aims to investigate the common cybercrimes which target the individuals in Saudi Arabia and the countermeasure taken by them.
Constructing Object Groups Corresponding to Concepts for Recovery of a Summarized Sequence Diagram

Comprehending the behavior of an object-oriented system solely from its source code is troublesome owing to its dynamism. To aid comprehension, visualizing program behavior through reverse-engineered sequence diagrams from execution traces is a promising approach. However, because of the massiveness of traces, recovered diagrams tend to become very large causing scalability issues. To address these issues, we propose an object grouping technique that horizontally summarizes a reverse-engineered sequence diagram. Our technique constructs object groups based on Pree's meta patterns in which each group corresponds to a concept in the domain of a subject system. By visualizing interactions only among important groups, we generate a summarized sequence diagram depicting a behavioral overview of the system. Our experiment showed that our technique outperformed the state-of-the-art trace summarization technique in terms of reducing the horizontal size of reverse-engineered sequence diagrams. Regarding the quality of object grouping, our technique achieved an F-score of 0.670 and a Recall of 0.793 on average under the condition of #lifelines (i.e., the horizontal size of a sequence diagram) < 30, whereas those of the state-of-the-art technique were 0.421 and 0.670, respectively. The runtime overhead imposed by our technique was 129.2% on average, which is relatively smaller than other figures found in the reference literature.
Data Privacy Quantification and De-identification Model Based on Information Theory

De-identification enables to protect privacy of data from different attacks. At present, the specific de-identification standards and privacy quantification methods are given in many models, such as K-anonymity model and differential privacy model. But, the K-anonymity model does not provide an effective method to prove its degree of privacy protection, and when the model parameters change, the degree of privacy protection cannot be quantified. And due to rigorous calculation method of the differential privacy model, which quantifies the degree of privacy protection based on a mathematical basis, it is difficult to be used widely in organizations and institutions. So, this paper proposes a de-identification model, which includes quantification solution of identifying the sensitivity of the personal information, de-identification approach for adaptively matching different standard de-identification methods to personal information with different sensitivities and de-identification effect detection function. The objective is to provide an automatic, efficient and widely used de-identification model, which is built by quantitatively analyzing degree of privacy protection based on conditional entropy. Finally, performance analysis results show that the model makes the trade-off between privacy and secure of data and the widespread use of data.
Robust Visual Tracking Based on Fusional Multi-Correlation-Filters with a High-Confidence Judgement Mechanism

Visual object trackers based on correlation filters have recently demonstrated substantial robustness to challenging conditions with variations in illumination and motion blur. Nonetheless, the models depend strongly on the spatial layout and are highly sensitive to deformation, scale, and occlusion. As presented and discussed in this paper, the colour attributes are combined due to their complementary characteristics to handle variations in shape well. In addition, a novel approach for robust scale estimation is proposed for mitigatinge the problems caused by fast motion and scale variations. Moreover, feedback from high-confidence tracking results was also utilized to prevent model corruption. The evaluation results for our tracker demonstrate that it performed outstandingly in terms of both precision and accuracy with enhancements of approximately 25% and 49%, respectively, in authoritative benchmarks compared to those for other popular correlation- filter-based trackers. Finally, the proposed tracker has demonstrated strong robustness, which has enabled online object tracking under various scenarios at a real-time frame rate of approximately 65 frames per second (FPS).
Informal IT and Routine Activity Theory -A Theoretical Review

This study seeks to explore the risks of insider threat due to frequent informal technology (IT) usage in organizations. The use of informal IT tools and resources at the workplace becomes a routine phenomenon which has diminished the boundaries between work and personal life domains. Also, it has increased the complexity of employees' IT usage and created indistinctness in work ethics. It is a fact that the biggest threat organizations face today is the threat posed by their own employees. I propose that utilizing routine activity theory (RAT) from criminology to information system literature perspective is critical to understand the likelihood of insider threat as a consequence of informal IT usage. RAT assumes that a crime can arise from changes in the situation or environmental settings. Prior research reveals several positive outcomes of informal IT such as; employees' productivity, job satisfaction, efficiency, collaboration, knowledge sharing, and lowering of operational costs for organizations. Bdes positive outcomes, prior research has also indicated the likelihood of security risks through informal IT usage. Guided by RAT, this research seeks to present a conceptual framework that explains that informal IT usage may jeopardize information security and may consider the activity as an insider threat. This research will, therefore. assist in developing more precise empirical and theoretical knowledge related towards informal-IT usage research in particular and overall to the information systems security research.
PENGEMBANGAN MEDIA PEMBELAJARAN BERBASIS LECTORA INSPIRE YANG DIINTEGRASIKAN DENGAN CAMTASIA STUDIO PADA MATA PELAJARAN GEOGRAFI MATERI SISTEM INFORMASI GEOGRAFI

This research aims to develop Lectora Inspire based learning media on Geographic Information System material. Lectora Inspire is an application used to create learning media associated with Geographic Information System material which is integrated with Camtasia Studio. It applies a development research method with ADDIE model as an approach in compiling the learning media, Moreover, the validation are conducted by material expert, media expert, and subject expert. This learning media is tried out limited to high school student of grade XII trough questionnaire. The collected data is research finding regarding the quality of learning media and suggestions for product revisions. The validation result from material expert reveals that the value of media validation provided by validator I or material/content experts is 83.75% with valid category, validator II or product design expert is 88% with very valid category, while validator III or subject expert or teacher of geography subject is 98% with very valid category. Thus, the average of students respond towards the learning media is 88.8%.