Reviewerly/exHarmony huggingface dataset:
Uncovering The Factors Influencing The Technological Adaptation of Twitter Usage Among Journalists in the Transforming Journalism Practice

The increased usage of digital technologies has radically impacted the journalism profession across the globe. This qualitative study focuses on the factors influencing the technological adaptation of Twitter usage among journalists in the transforming journalism practice. To gain insights on the central focus of this study, ten (10) local news journalists at the News and Current Affairs Media Prima Berhad (NCA MPB) with professional profiles on Twitter were interviewed. Data analysis were conducted simultaneously with data collection and the NVivo 10 software was used in the coding process. Thematic analysis was employed to analyse the data. Findings revealed THREE (3) significant themes that contributed to the technological adaptation of Twitter usage among journalists, as follows; (i) willingness to collaborate (ii) technology acceptance gap, and (iii) institutional readiness and encouragement, which plays a salient role in facilitating the adoption process as well as bridging the technological gap among the journalists. An interesting finding in this present study warrants that institutional readiness is necessary to understand journalist's propensity to embrace and use cutting-edge technologies, from being a conventional journalist to adapt self and engage in the digital realm. With the blossoming platforms due to online boom and social media, journalists must embrace technology as an enabler for their professional relevance and the news industry survival. In conclusion, the institutional readiness is therefore necessary to ascertain the journalist's propensity to embrace the cutting-edge technologies in performing their challenging role in the changing media industry today. Keywords: Technological adaptation, Twitter, journalism practice, qualitative, media industry.
A Realtime Stereo Vision System Design using Distinctive Similarity Measure

The stereo algorithm based on distinctive similarity measure shows better matching capability due to matching based on the distinctiveness of a point, however it may be difficult to achieve a realtime performance due to excessive computations. In this paper, we propose a realtime stereo vision system with a comparable matching rate. We separate rows and columns when computing the adaptive support-weights, so that we can reuse the intermediate data. Also, we increase the performance by utilizing 18 processing elements in parallel and efficiently schedule the input-output timing for the intermediate data. We minimize the critical path delay by applying the pipelining to division and the simple approximation to exponential functions. The proposed architecture has been designed with VerilogHDL and synthesized with DB Hitek 110nm standard cell library. It shows 565㎒ (56fps) of the maximum frequency with 74K gates and 2.4KB of memory while showing 2.11% of the error rate for the tsukuba image.
Explainability and Knowledge Representation in Robotics: The Green Button Challenge

As robots get closer to human environments, a fundamental task for the community is to design system behaviors that foster trust. In this context, we have posed the Green Button Challenge: every robot should have a green button that, when pressed, makes the robot explain what it is doing and why, in natural language. In this paper, we motivate why explainability is important in robotics, an why explicit knowledge representations are essential to achieving it. We highlight this with a concrete proof-of-concept implementation on our humanoid space assistant Rollin' Justin, which interprets its PDDL plans to explain what it is doing and why.
Information and Knowledge Management in the Scope of the Information Security Practices: The Human Factor within Organizations

Information security has always been an important requirement for the business.Such assets can be broken down into three main areas, namely people, process and technologies.The Internet, the proliferation of the web, networks and the ever-growing presence of innovation have triggered profound changes to procedures that are integral in individual and institutional routines.Such technological changes have led to an increase of competitiveness which decentralization and have contributed to the need for leadership, command, security, information and knowledge safety.The article presents the findings of an information security investigation, which examines the involvement with human aspects in the field of information security and knowledge management.The implication is that the "people" component is an important variable, even crucial, for the management of information security in organizations.It can be concluded that the human element is an important, perhaps crucial, parameter for the management of corporate information security.
WORD EMBEDDINGS IN ROMANIAN FOR THE RETAIL BANKING DOMAIN

Word embeddings are a considerable improvement in Natural Language Processing and Deep Learning research fields as a result of their effectiveness for representing textual data in form of fixed-length dense vectors.Currently, domain general pre-trained word embeddings such as Word2Vec and FastText are available in Romanian language.Retail banking domain is a context-sensitive field similar to financial or legal domains where people are using specific terms that are having particular meaning in that specific context.The main goal is to capture retail banking context in order to perform Sentiment Analysis tasks on retail banking reviews in Romanian language.Therefore, this research paper introduces a domainspecific word embeddings called RoBankingFastVec for representing textual data in Romanian language from retail banking field based on online resources such as Wikipedia, retail banking newspapers and books.A research framework is proposed in order to properly conduct the implementation of the retail banking domain-specific word embeddings.The word vectors are obtained by training a FastText model using Skip-gram architecture.By exploring embedding space, the word vectors are evaluated through different tasks related to word embeddings such as Clustering, Similarity, Analogy, Mismatching.
PROBLEMATIKA PENCAPAIAN KOMPETENSI KETERAMPILAN BERBICARA SISWADALAM PEMBELAJARAN BAHASA INDONESIA

Learning language skills in schools does not only emphasize theory, but students are required to be able to use language as its function. Language skills in listening, writing, reading, and speaking are a component that cannot be separated from language activities. The ability to speak must also be able to analyze and ensure that what is said can be received appropriately by the listener. In fact, there are still various obstacles in speaking, both internal and external factors. Evaluation of learning to speak is still guided by speaking theory, not emphasizing speaking practice.
METAMORPHOSIS VISUALIZATION WITH AUGMENTED REALITY USING MARKER-BASED TRACKING

Metamorphosis is a cycle of biological animal growth. Learning Metamorphosis is a part of learning for students in schools specifically in the area of biology subjects. Unfortunately, the observing activities take time, and finding an animal specimen is limited to study the metamorphosis cycle. This research proposes an innovative solution to overcome these problems which is the implementation of augmented reality technology. The animal metamorphosis cycle process is visualized into 4-dimensional objects to improve interaction for the student on learning metamorphosis during learning sessions. The Marker-Based Tracking method is used as an approach where the location of the tracking pattern on the marker has been determined in advance as the place where the augmented reality object appears. The results of this study indicate that using a marker-based tracking method can improve students' understanding of metamorphosis.
Public Key Infrastructure

It is not possible to discuss authentication without at least touching the concept of public key infrastructure or PKI. This is because today’s complex ecommerce or any Internet transactions require rigorous and thorough security mechanisms. Public key cryptography is one such mechanism that can support the required security measures and solve security problems.
An improved dynamic cooperative random drift particle swarm optimization algorithm based on search history decision

A novel dynamic cooperative random drift particle swarm optimization algorithm based on entire search history decision (CRDPSO) is reported. At each iteration, the positions and the fitness values of the evaluated solutions in the algorithm are stored by a binary space partitioning tree structure archive, which leads to a fast fitness function approximation. The mutation is adaptive and parameter-less because of the fitness function approximation enhancing the mutation strategy. The dynamic cooperation between the particles by using the context vector increases the population diversity helps to improve the search ability of the swarm and cooperatively searches for the global optimum. The performance of CRDPSO is tested on standard benchmark problems including multimodal and unimodal functions. The empirical results show that CRDPSO outperforms other well-known stochastic optimization methods.
Regulating the Use of Genetic Information in the Life Insurance Industry

Sara Golru explores the Australian life insurance industry’s treatment of genetic information in the underwriting process. The Australian life insurance industry has struggled to keep up with scientific and international legal advancements, resulting in an increase in the potential for genetic discrimination. Therefore, Sara Golru provides a critical review of the current international legal frameworks governing the use of genetic information in life insurance, highlights contemporary policy concerns and, recognising the need for extensive reform, provides insightful recommendations.
RETRACTED: Detecting Faults within a Cloud Using Machine Learning Techniques

Abstract In distributed computing, clients can get to cloud administrations using the web. In present days, in superior registering and cloud frameworks, disappointment is an inexorably significant issue. Alleviating the impact of misfortune and making stable conjectures with excellent lead time stays an overwhelming exploration issue as huge scope frameworks keep on creating in scale and multifaceted nature. Because of the advancing unpredictability of elite figuring frameworks, specific current adaptation to internal failure procedures, for Example, successive registration and replication are not adequate. It includes the significance of having a productive and useful way to deal with disappointment the executives set up to relieve the impacts of disappointment inside the framework. With the approach of AI methods, the capacity to gain from past data to anticipate future personal conduct standards makes it conceivable to foresee potential framework disappointment all the more precisely. Along these lines, in this paper, by applying a few calculations to improve the exactness of disappointment forecast, we investigate the prescient capacities of AI. We have set up an expectation of disappointment. The fundamental analysis that we have Random backwoods (RF), SVM, Classification and Regression Trees (CART) considered). Exploratory discoveries show that comparative with different calculations, the typical expectation precision of our paradigm utilising SVM while foreseeing breakdown is 92% exact and productive. This discovering implies that all likely future device and application disappointments can be viably imagined by our cycle inside.
Pengembangan Sistem Penggajian Pada CV. City Collection

One of the most important assets to support a work program carried out at CV convection. City Collection is a payroll system. In the current process, it is certain that a company will produce a lot of work that requires quite a lot of employees to produce it. System development is one of the problem solvers that is reliable enough to develop a problem system in payroll transactions. In making the development of the payroll system using the Visual Basic 6.0 programming language as a database for data storage and using observation and interview methods using the Prorotype model. The application of the development of a payroll system that is supported by a desktop-based application which makes it an application that can make it easier for admins and financial officers in payroll, so that the level of effectiveness and efficiency of the employee's payroll system at convection City Collection has been reduced from before.
Factors Contributing to Students’ Difficulties in Academic Writing Class: Students’ Perceptions

Writing is one of the most difficult skills in learning English as a Second Language (ESL) or English Foreign Language (EFL). Many language learners encountered difficulties from the writing process. At the tertiary level, students are required to produce academic essays, which are difficult and may frustrate students. The purpose of this study was to analyze factors contributing to students’ difficulties in an Academic Writing course based on the students’ perceptions. This study used a qualitative method. The instruments used were open-ended questionnaires and interview protocol. In this study, all the participants’ answers were coded and analyzed descriptively. The participants were 22 students who were taking Academic Writing class for repeaters in Semester I, 2018/2019 academic year, in an English Language Education Program, in a university in Central Java, Indonesia. Questionnaire data were coded and classified by the researcher. In conducting the interview, data were audio-recorded and transcribed. Those data were analyzed to answer the research questions in this study. Based on the findings, there were two factors that caused students’ difficulties in writing academically. The two factors were internal and external. Internal factors consisted of self-motivation, self-confidence, lack of knowledge and feeling of under pressure. External factors consisted of the teacher’s teaching style, classroom atmosphere, materials, and writing aspects. This research hopefully can help teachers and instructors in developing appropriate teaching techniques in Academic Writing and minimize students' difficulties in Academic Writing.
MADM System for the Development of Adaptable Mobile Applications for People with Intellectual Disabilities

Many people with intellectual disabilities (PID) can experience problems in different aspects of their everyday life. The capabilities of modern mobile applications offer promising opportunities to help PID to cope with their everyday life problems and to feel more confident in everyday activities. Mobile application development is one of the most fast-moving industries and to support the mobile applications development process special software tools are designed. However, the development of mobile applications for PID has its own specifics due to capabilities and limitations of this category of users. The article presents a mobile application development management (MAMD) system, that takes into account these specifics and covers both the stage of the development of mobile applications for PID and the stage of their usage. The system supports modular development of mobile applications based on reusing software components. The system allows to conFigure the interface of mobile applications and adapt it to the users with different capabilities and limitations. The system is universal in terms of platform selection, so the developers of mobile applications for PID can always chose the most suitable platform for different categories of PID.
A Hybrid Approach for Improved Image Similarity Using Semantic Segmentation

Content Based Image Retrieval (CBIR) is the task of finding the images from the datasets that consider similar to the input query based on its visual characteristics. Several methods from the state of the art based on visual methods (Bag of visual words, VLAD, ...) or recent deep leaning methods try to solve the CBIR problem. In particular, Deep learning is a new field and used for several vision applications including CBIR. But, even with the increase of the performance of deep learning algorithms, this problem is still a challenge in computer vision. To tackle this problem, we present in this paper an efficient CBIR framework based on incorporation between deep learning based semantic segmentation and visual features. We show experimentally that the incorporate leads to the increase of accuracy of our CBIR framework. We study the performance of the proposed approach on four different datasets (Wang, MSRC V1, MSRC V2, Linnaeus).
BAAS: Bayesian Tracking and Fusion Assisted Object Annotation of Radar Sensor Data for Artificial Intelligence Application

This paper introduces BAAS, a new Extended Object Tracking (EOT) and fusion-based label annotation framework for radar detections in autonomous driving. Our framework utilizes Bayesian-based tracking, smoothing and eventually fusion methods to provide veritable and precise object trajectories along with shape estimation to provide annotation labels on the detection level under various supervision levels. Simultaneously, the framework provides evaluation of tracking performance and label annotation. If manually labeled data is available, each processing module can be analyzed independently or combined with other modules to enable closed-loop continuous improvements. The framework performance is evaluated in a challenging urban real-world scenario in terms of tracking performance and the label annotation errors. We demonstrate the functionality of the proposed approach for varying dynamic objects and class types.
СПЕКТРАЛЬНЫЙ АНАЛИЗ РЕЛЬЕФА ДЛЯ РЕШЕНИЯ ПРОГНОЗНО-ПОИСКОВЫХ ЗАДАЧ НА ПРИМЕРЕ РИФТОВОЙ ЗОНЫ СРЕДИННО-АТЛАНТИЧЕСКОГО ХРЕБТА

The article presents the results of applying spectral analysis (the method of two-dimensional Fourier transformation) for the study of the deep-water relief of the Mid-Atlantic Ridge (MAR). Due to the difficult research conditions in the World Ocean, a significant part of the work, primarily at the exploratory stage, is carried out using remote sensing techniques, including using a variety of prognostic maps based on morphometric characteristics of the bottom surface. The new method appying spectral analysis of the deep-sea relief to predict the localization of the sulphides using the Student’s t-test, was developed by authors. Using the bathymetric data of ten ore fields in the studied region, we identified the criteria for the spectral deformation components of the bottom surface as prospecting signs for sulphide deposits. As a result of the research, a number of perspective local areas connected to geologic structures, where hydrothermal ore formation is possible, were identified. A prognostic map has been created for prospecting deep-sea polymetallic sulphides in the southern part of the Russian exploration area.
Diversity amplification and data generation of Chinese Sign Language based on Generative Adversarial Network

There are many factors affecting the effectiveness and accuracy of Sign Language Recognition (SLR) based on wearable device combing sEMG and IMU signals. Among them, the diversity of sign language signals caused by various factors will greatly affect the effect of SLR in the process of sign language acquisition and the use of SLR system. In the algorithm design stage, such diversity and difference should be taken into account, so that the designed algorithm can be more robust to these factors. Therefore, it is necessary to design some schemes for data amplification and data generation to solve the problem of sign language data diversity. In this paper, a random core extraction method is proposed for fast data amplification according to the characteristics of time migration without deformation. And considering that a lot of manpower and time are often consumed in the actual data acquisition process, the data volume is not sufficient. We proposed a method of sign language data generation based on Generative Adversarial Networks (GAN). This data amplification method can not only solve the problem of temporal diversity of sign language data set, but also effectively prevent model overfitting by increasing the sample numbers of sign language data set. In the experimental part, the effectiveness of the method is proved by comparing and analyzing the corresponding experimental results in the process of data generation and amplification.
Student’s Perception, Attitude and Advantage Towards the Use Of Whatsapp Mobile Learning Outside Classroom: An Indonesian Student’s Experience

Today, many institutions of higher education worldwide frequently utilize online instruction through trendy WhatsApp as representing an instant messaging application for Smartphone. The present study is aimed at introducing a mobile gadget that become useful in practices of teaching learning. The objectives of the study are to examine the student’s perception on omnipresent aspect of WhatsApp mobile learning, to examine the students’ attitude on WhatsApp mobile learning and to examine educational advantages of WhatsApp mobile learning. The participants of this study were 33 students who were all in seventh semester in English Education Study Program of Teacher Training and Education Faculty, University of Madura and pursuing Teaching Practice Program, academic session 2018-2019, in some selected government schools located in Pamekasan area when this study was conducted. The study employs mixed method, viz, by using quantitative and qualitative method since the study describes student’s experience regarding WhatsApp mobile learning. In this research, questionnaire and interviews were employed to collect data. The results reveal that omnipresent WhatsApp mobile learning is not only appealing but also useful for their education. The students’ positive attitude of WhatsApp mobile learning is emphasized in the study. Some have a preference to employ traditional learning inside classroom to WhatsApp mobile learning. However some feel disposed to keep employing WhatsApp for future teaching learning. Furthermore, WhatsApp mobile learning is not quite compatible with the have family - students hence they like better traditional teaching learning in classroom because the learning make their family life troublesome.
Effective Figurative Meanings Interpretation For Online Translation Packages

The modern theory holds that linguistic software is not yet completely able to decode figurative meanings like metaphors and idioms. The suggested approach to the way the words and their meanings function in the lexicon can help to solve the language problem of artificial intelligence systems, which process information like human brains. We claim that figurative meanings of polysemous words are based on lexical invariants. Their revealing will help to decode contextual idiomatic figurative meanings, improve online translation packages and make a valuable contribution to on-line lexicography. The pressing problems presented in this article in connection with the proposed invariant theory aim at proving whether lexical invariants really make complicated semantics of metaphors more transparent. The article presents an invariant empirical analysis of a polysemous word “key”. We claim that there are numerous interconnected semantic networks of polysemous words in the lexicon. They function as multi-level configurations of meanings, which are cemented by dominant invariant meanings. Invariant meanings are eventually formed as a result of multiple use of the word metaphorical meanings, which allow native speakers to effectively navigate in the surrounding language environment. The invariant lexical components form clusters of integral and differential types that, when combined in different configurations, form a word context meaning.