Reviewerly/exHarmony huggingface dataset:
Pembelajaran Maharah Al-Istima’ dengan Memanfaatkan Media Youtube: Problematika dan Solusi

In learning Maharah al-Istima, a lecturer at a college in Palangka Raya uses films from Youtube. Learning to listen to a foreign language cannot separated from problems, but students try to find solutions. This qualitative descriptive study aims to explore the use of films from Youtube in Maharah al-Istima's learning, listening problems and their solutions. The data were collected through observation, interview and documentation techniques, and the data were analyzed through the stages of data collection, data reduction, data presentation and conclusion drawing. The results showed that the lecturer used movies from Youtube offline and online by collecting, selecting, verifying credibility and evaluating the quality of the film. This activity trains students to listen to Arabic and increase their knowledge so that the results of listening of the students are good. The problems faced by students while learning to listen are the fast Arabic pronunciation, the lack of clarity in conversations and the difficulty in vocabulary. Students solve the first problem by playing back, focusing on listening or using dictionaries and other learning strategies. For the second problem, the students overcome them by focusing and understanding the context of the sentences, using a dictionary or other learning strategies. For the third problem, the solution is to train hearing, using a dictionary and reading frequently. The implication of this study is that multimedia communication can help improve the student's listening ability in both offline and online learning.
MGCN4REC: Multi-graph Convolutional Network for Next Basket Recommendation with Instant Interest

Sequential patterns involved in users’ historical behaviors have received extensive attention in recommendation system, which is important to represent item-level preferences. The existing works often combine the long-and short-term patterns to capture user’s preferences. But the short-term preferences modeled by the recent behavior patterns cannot clearly indicate the users’ instant interest. In this paper, we propose a sequential recommendation model MGCN4REC based on multi-graph to learn the representation of users and items and then model preferences and instant interests simultaneously. Firstly, this paper utilizes multi-graph convolutional network (MGCN) to learn users and items embeddings from multi-graph. Secondly, to aggregate preferences and instant interests, we use the attention mechanism to find the degrees of dependencies on these two features. Finally, this paper conducts experiments on real data sets of Amazon to evaluate the performance of MGCN4REC model, and the results show that our model outperforms the current state-of-the-art sequential recommendation methods over 15% on the metrics.
Fuzzy Logic Application in Multi-Period Performance Analysis of a SWTP

A preliminary study on artificial intelligence techniques applied to analyze a small wastewater treatment plant (SWTP), which is installed at FEG-UNESP, using fuzzy logic to take advantage of biogas produced through wastewater treatment for electric power generation, where different wastewater flows during small plant dairy work were considered, is the goal for this work. This study began with sub-systems identification. After that, this information was used to elaborate on the problem formulation, and they were organized as fuzzy rules representation for small WTP, with multi-period characteristics. After all, the system is simulated with MATLABs Fuzzy Logic Toolbox, using the Mamdani algorithm. Only wastewater flow and gasometer output valve were considered for the first simulation. Biogas volume in the gasometer was included in a second one. The first results pointed to efficiency in this technology use, which uses verbal language to represent information from staff and makes it so easy. Based on this, it is possible to conclude that a supervisory and control system for the small wastewater treatment plant (SWTP) is fully feasible.
Unsupervised Anomaly Detection of the First Person in Gait from an Egocentric Camera

Assistive technology is increasingly important as the senior population grows. The purpose of this study is to develop a means of preventing fatal injury by monitoring the movements of the elderly and sounding an alarm if an accident occurs. We present a method of detecting an anomaly in a first-person's gait from an egocentric video. Followed by the conventional anomaly detection methods, we train the model in an unsupervised manner. We use optical flow images to capture ego-motion information in the first person. To verify the effectiveness of our model, we introduced and conducted experiments with a novel first-person video anomaly detection dataset and showed that our model outperformed the baseline method.
An overview of portfolio optimization using fuzzy data envelopment analysis models

A combination of projects, assets, programs, and other components put together in a set is called a portfolio. Arranging these components helps to facilitate the efficient management of the set and subsequently leads to achieving the strategic goals. Generally, the components of the portfolio are quantifiable and measurable which makes it possible for management to manage, prioritize, and measure different portfolios. In recent years, the portfolio in various sectors of economics, management, industry, and especially project management has been widely applied and numerous researches have been done based on mathematical models to choose the best portfolio. Among the various mathematical models, the application of data envelopment analysis models due to the unique features as well as the capability of ranking and evaluating performances has been taken by some researchers into account. In this regard, several articles have been written on selecting the best portfolio in various fields, including selecting the best stocks portfolio, selecting the best projects, portfolio of manufactured products, portfolio of patents, selecting the portfolio of assets and liabilities, etc. After presenting the Markowitz mean-variance model for portfolio optimization, these pieces of research have witnessed significant changes. Moreover, after the presentation of the fuzzy set theory by Professor Lotfizadeh, despite the ambiguities in the selection of multiple portfolios, a wide range of applications in portfolio optimization was created by combining mathematical models of portfolio optimization.
A Pragma-Semiotic Analysis of Emoticons in Social Media

Since the early days of the internet, researchers investigate in the peculiar use of language in social media. One distinctive feature is the use of emoticons as a way to express users’ emotions in digital settings. In spite of their popularity, researches on emoticons are still in its infancy. This research identifies the pragmatic and semiotic functions of emoticons in social media following qualitative and quantitative methods for analysing data. The corpus of this study are collected from Twitter and YouTube. The analysis of data follows the Three Levels Pragma-Semiotic Model. Quantitative analysis shows that the usage of emoticons with text appears to improve the expressivity and an overall sentiment scores of positive and negative opinions relatively more than the neutral opinions. It also shows that emoticons are preferably used with text or other emoticons. From the qualitative analysis, the current study has made visible the practices of using emoticons in social media.
Interrelation Of Labor Motivation and Adaptation Potential of Interior Ministery Members

The article is devoted to the problem of labour motivation and adaptation potential of law enforcement officials. It examines the concepts of «motivation», «labour motivation», «adaptation» and «adaptation potential». The article presents a study which purpose is to investigate the interrelationship of labor motivation and the adaptive potential of Interior Ministry members. To achieve this goal, the article uses methods designed to assess the personal adaptive abilities, taking into account socio-psychological and some psychophysiological characteristics that reflect generalized features of neuropsychic and social development, to measure the subjective feeling of personal effectiveness, to identify the motivation for success and the motivation for fear of failure, and also a questionnaire to identify types of labor motivations. The article carries out an empirical study of the interrelationship of labor motivation and the adaptive potential of members of the Ministry of Internal Affairs, as well as reveals positive correlation dependencies between the labor motivation and the adaptation potential of employees of the Internal Ministry members.
Application of Empirical Wavelet Transform in Digital Image Watermarking

Nowadays, rapid growth of the Internet and digital multimedia technologies make it possible to duplicate data without any loss of quality and at a very low cost. In this regard, manipulation of documents will easily be accomplished by applying digital art and without the copyright owner's permission. To deal with situation, more diverse security requirements are introduced every day. Watermarking is considered as one of the methods used for achieving this purpose. Watermarking is focused on inserting a subtle signal between the host media data somehow it does not change the original data, but they can be extracted if necessary. They are also used as a claim for ownership of the digital effect. Various methods have been presented for watermarking so far. These methods have been developed to overcome the weaknesses of previous methods. Empirical wavelet transform is taken into account as a new method for hiding and extracting military digital images with respect to alpha composition. As such, it is resistant to noise, low pass filter and compression. Analytical studies showed that this method is more efficient than other methods from quantitative and qualitative point of view.
Effectiveness of Self-Regulated Learning Intervention Program in Forethought Phase of a School Underachiever

When someone fails to achieve their potential, it is called underachievement. This phenomenon is predominantly observed for adolescent, particularly during the transition to middle school. This finding is observed because they are obligated to adapt to many internal and external changes. Several studies have confirmed that underachievement is caused by poor self-regulated learning skills. In this study, researchers adapted the Self-Regulation Empowerment Program (SREP) to help middle school student improve their self-regulation skills in learning. This program focuses on increasing the knowledge (cognitive domain) on self-regulated learning at the forethought phase by the means of SREP. This research adopts a single-case experimental design, particularly the A-B design. The analysis is performed by observing how well the participant performs in terms of success indicators, comparison between pretest and posttest scores, and qualitative analysis. Hence, the participant could achieve all success indicators in each session. This intervention program is effective in increasing knowledge on self-regulated learning at the forethought phase in empowerment and goal setting aspects. However, it is not effective in improving the time-management aspect. The procedures in this intervention can be adapted by parent and teachers to help the participant in improving their self-regulated learning ability.
A Few-Shot Modulation Recognition Method Based on Pseudo-Label Semi-Supervised Learning

In order to solve the problem of insufficient labeled samples in modulation recognition, this paper proposes a few-shot modulation recognition algorithm based on pseudo-label semi-supervised learning (pseudo-label algorithm). First of all, high quality artificial feature, excellent classifier and data-labeling method are used to build efficient pseudo label system, and then the pseudo label system is combined with signal classification method based on the deep learning to realize the modulation classification under the condition of a small number of labeled samples and a large number of unlabeled samples. The simulation results show that the pseudo-label algorithm can improve the model recognition performance by 5%-10% when the six kinds of digital signals are classified and identified and its SNR is greater than 5 dB. At the same time, the algorithm has a simple network design and is of great application value.
Remote Data Auditing in Multi-tenancy Cloud Storage by Using File Attribute Test Technique

Abstract Most popular computation technique in recent days is cloud computing, where we utilizing the virtual computing resources like processing data, and storage. Virtual computing environment is leads the customer lose control on his/her data, to answer this problem cloud computing has auditing techniques by cloud service provider (CSP), Third party auditor (TPA) by remote auditing data (RDA) methods. Cloud storage architectures are single tenant or multi tenant, where we compare the architectures, multi-tenancy cloud storages which contain the serious data privacy problem. To provide an efficient privacy solution for multi-tenancy cloud storage we apply RDA methods belongs to RDA taxonomy. In this paper we propose a technique to provide privacy on customer data on multi-tenant cloud storage with file attribute test.
Impact of Organization Learning Capability on Performance Innovation: Mediating role of Information Technology

Modern business innovation has close contact with the adoption of information technology and learning abilities in business procedures. Therefore, this study analysed the impact of organizational learning capability on the innovation performance of the firm. This study also observes the mediating role of information technology adoption between the relationship of organizational learning capability and innovation performance. Data were collected from employees of banks via questionnaire method and furthered analysed by using SmartPLS software. The results explained that organizational learning capability has a positive and significant impact on innovation performance directly. Moreover, the said variables also positively and significantly affect innovation performance when mediated by the firm's information technology adoption.
NEW PARADIGM IN ACCOUNTING INFORMATION SYSTEMS – THE ROLE OF THE LATEST INFORMATION TECHNOLOGY TRENDS

The purpose of this paper is to identify the impact and benefits of the latest information technologies on Accounting Information Systems (AIS). Taking into account the numerous papers related to new technologies and their application in the accounting profession within Industry 4.0, and conducted survey about perception of practitioners in Republic of North Macedonia, this paper summarizes the characteristics and key benefits of some of the new technologies for the functioning of AIS in the digital age. First of all, the evolution of AIS is elaborated, based on theoretical and empirical analysis of the accounting process from the appearance of the first AIS up to nowadays’ services and techniques available for supporting the accounting function. The first technology to be elaborated is Big Data and its potential to change the business landscape, especially in the field of automating operation processes, customer engagements, and predictive decision-making process. Secondly, the Blockchain Technology as an example of Distributed Ledger Technology (DLT), which adoption brings new possibilities in eliminating or redefining the role of entities external to the company. Cloud Computing i.e. Cloud Accounting is the third technology which is elaborated in this paper through the services it offers on the cloud, especially the way AIS process, store and backup the sensitive and confidential data. Last but not least, Artificial Intelligence (AI), a technology that could change the professional services, the need, and opportunities that are provided for a solution to the current accounting issues. In summary, taking into account the relevant literature and the perception of the respondents-practitioners, increased use of these technologies is necessary because their application reduces costs; increases transparency and confidence in information; flexibility, i.e. no time and space restrictions on their use, etc., which is especially useful in the current state of Pandemic, caused by the virus COVID-19.
Investigating Deep Learning for Predicting Multi-linguistic Interactions with a Chatterbot

Deep Learning (DL) becomes a mainstream technique for Artificial Intelligence (AI) machine learning because of its success in performing many tasks, such as image recognition, speech interpretation, language prediction and translation. We are investigating the underlying principles of DL Neural Networks (NN) to design optimal DL NN for predicting human multi-linguistic conversations with a chatterbot. This research attempts to tackle the well-known open problem of finding optimal NN designs for data of various characteristics. We are in particular focusing on Recurrent Neural Networks (RNN) models with time progression, which takes into consideration the results from the previous steps plus the current input to predict the next step, i.e. it `remembers' what it has previously learnt. Through the experiments of tuning an RNN to achieve an optimal performance in terms of accuracy and training time, we found that characteristics such as word counts and layers of neurons could affect the training performance. We applied the tuned optimal model to a game implementation, inspired by IBM Watson, where users can guess the words to be generated by a computer, called “Beat AI” to have human predict the machine prediction.
Single Image Deblurring Based on Auxiliary Sobel Loss Function

This paper presents an effective auxiliary loss function which uses the Sobel operator to improve the performance of image deblurring methods based on deep learning. Conventional deep learning-based image deblurring methods exploit mean square error (MSE) loss function that simply measures the intensity difference in pixel-wise manner. Although recovering the lost high-frequency component is the main purpose of image deblurring, MSE loss function often fails to train the network in recovering the high-frequency components. To alleviate this issue and further improve the performance of conventional methods, we propose an auxiliary Sobel loss function which guides the network to focus on recovering the high-frequency components of resultant images. The experiment results show that the networks trained by the Sobel loss function and the conventional MSE loss function outperform the existing methods in both quantitative and qualitative evaluations.
Dragos Gheorghiu, Livia Stefan, Immersing into the Past: an Augmented Reality Method to Link Tangible and Intangible Heritage

The current IT and digital technologies such as Mobile Augmented Reality (MAR) enable the overlap of digital and real world information in relation with a topic, in an engaging and efficient manner, and therefore can be used to store intangible heritage and to study it in the context as well. The current paper refers to such an augmentation of cultural information, performed at the Kallatis site, whose ruins, at present mostly covered by the modern town, do not offer sufficient information on the complexity of the Greek civilization. The implementation of a MAR application consisted in defining several points of interest of the important local archaeologic discoveries, which can trigger, for the visitors using our application, an augmentation of the historical site with images and videos. With the current research work, the authors propose and demonstrate that a mobile MAR application can constitute a modern method for providing visitors with an immersive and holistic experience for understanding the local material and intangible heritage.
Latest Image Recommendation Method for Automatic Base Image Update in Dockerfile

In recent years, an application deployment method using Docker container has attracted attention by researchers. Docker containers are fast and lightweight, can improve the portability and reproducibility of applications, and are thus often used with CI/CD and DevOps to accelerate the release cycle. However, if a Docker image is not updated, problems such as security risks or a lack of the latest features may occur. Therefore, in this paper, we propose a method for automatically updating the base image to the latest version when the image is considered to be the old version. Our method extracts the information of the base image from the Dockerfile described by the user, and infers the version of the base image that is considered to be certainly used. By applying our method, the user can regularly update the base image. Based on the evaluation result, we confirmed that our method recommends an approximately correct version to the users.
STEM Classroom; Creating a Phyton Application for an EV3 Brick Robotic System Used to Transport 3D Printed Boxes

Robotics is certainly one of the key tools within the STEM based education of the future.The acquisition of practical knowledge and skills in the field of technical disciplines will certainly be more comprehensive through teaching based on the STEM curriculum.This paper presents a student project within the course "Programming of Robotic Systems" at the Faculty of Science and Education at the University of Mostar.LEGO® MINDSTORMS® EV3 robotics training set was used to create a simple robotic system for transporting of small 3D printed boxes.In order to unlock the full potential and all the possibilities of the Lego Mindstorms set and the created robotic system, EV3 MicroPython firmware was used.A 3D printer "Ultimaker 2+" was used to produce suitable small boxes that are transported within a robotic system.The project proved to be very successful.The robotic transport system had all the functionalities that were planned.Through working on this project, students have greatly improved their practical skills and abilities related to programming, design and production, creativity, working with advanced technical tools, teamwork, etc.
Key Distribution Scheme Based on Uneven PSK Modulation Using Phase Noise

We proposed a key distribution scheme based on uneven PSK modulation by using phase noise. Simulation results show that with the proposed scheme, the original key consistency rate can reach above 99%.
On the Futility of Contact Tracing

Throughout the COVID-19 epidemic, public health authorities have promoted contact tracing as a key tool to combat the spread of the SARS-CoV-2 virus. Jay Bhattacharya and Mikko Packalen argue that contact tracing does not deserve the central place it has received in the tool kit public health authorities use to control the virus, and that, in some cases, it may make an outbreak worse.
Child Tracking and Monitoring

Child safety is an important issue across the world as crimes against children are rapidly rising. Tracking and monitoring the child can help assure the safety of children and any necessary action can be taken if there is cause for worry. This paper proposes a system for monitoring and tracking children, which includes tracking the child’s movement to and from school using a wearable device and a companion application on the parents’ smartphone. This system gives us the location using a geofence and basic health information of the child.