Reviewerly/exHarmony huggingface dataset:
SDN– based Dynamic Bandwidth Allocation for Multiple Video-on-Demand Players over HTTP

This nowadays, HTTP adaptive streaming (HAS) has been becoming the de-factor standard for video streaming over the multimedia network. However, HAS-alone cannot guarantee a seamless viewing experience, the adaptation of HAS in the management network faces a big challenge because of purely client-driven approaches which lead to unfair competition of available bandwidth resource when multiple bitrate-adaptive players share a same bottleneck network link. At that time, each HAS client tries independently to maximize its own bandwidth sharing, which leads to the competition of network resources among clients causing greatly reduced QoE (Quality of Experience) of end users. This competition will affect negatively several main metrics for each video player: fairness, efficiency, and stability. In this paper, we propose a scheme of bitrate adaptation for the HAS system combined with a Software Defined Networking (SDN)-based dynamic resource allocation, which aims to improve the quality of experience among competing clients. Our experiment results show that the proposed method significantly outperforms the conventional method on all the key QoE parameters under several scenarios.
Pengaruh Kecerdasan Emosi dan Motivasi Belajar terhadap Prestasi Belajar Siswa di SMP Negeri 1 Sengkang

Abstract : This study aims to determine the effect of emotional intelligence and learning motivation on student achievement in Junior High School 1 Sengkang. Quantitative research approach with the type of Ex Post Facto research. The population in this study were 297 students, the research sample was 149 students. The method used in data collection is the scale of psychology and documentation. Data analysis techniques used descriptive statistical analysis techniques and inferential statistical analysis. The results showed that the influence of emotional intelligence variables and learning motivation together on learning achievement was 3.5% with a significance value of p = 0.028 ≤α (0.05). This shows that there is a significant positive influence between emotional intelligence and learning motivation on student achievement in Junior High School 1 Sengkang. Abstract This study aims to determine the effect of emotional intelligence and learning motivation on student achievement in Junior High School 1 Sengkang. Quantitative research approach with the type of Ex Post Facto research. The population in this study were 297 students, the research sample was 149 students. The method used in data collection is the scale of psychology and documentation. Data analysis techniques used descriptive statistical analysis techniques and inferential statistical analysis. The results showed that the influence of emotional intelligence variables and learning motivation together on learning achievement was 3.5% with a significance value of p = 0.028 ≤α (0.05). This shows that there is a significant positive influence between emotional intelligence and learning motivation on student achievement in Junior High School 1 Sengkang.
Development and Efficiency Validation of Training Course on Machine Learning for Industrial Education

This article aims to develop and determine the efficiency of the training course on machine learning for industrial education. The developed training course consists of a trainer’s manual, a PowerPoint presentation, a simulation program using LabVIEW Program, achievement tests, and questionnaires for evaluation. The developed training course is implemented and evaluated with a sampling group of 15 selected trainees. The research results can be stated as follows; 1) the quality evaluation assessed by 5 experts obtains the average score of 4.23 (appropriate); 2) the developed training course is efficient in accordance with the Meguigans’ theory standard (equaled to 1.77). The trainees’ satisfaction towards the developed training package obtains the average score of 4.10 (satisfied). That leads to the conclusion that the developed training package is efficient according to Meguigans’ theory standard.
The Implementation of Kit Built Concept Mapping to Help Non-English Department Students Improve their Reading Skills

Reading skill is important especially for non-English departments students like those of Information Technology Department at State Polytechnic of Malang. However, based on the observation and the results of the tests, the students showed low performance and motivation in reading English written texts even though the texts were on IT related subjects. Therefore, the students were introduced to Kit-Built Concept Mapping to help them overcome the problems. This study is Classroom Action Research (CAR) to analyze the implementation of the method to help the students improve their reading skills as well as motivation to read. There were some procedures they had to follow and some tests to do in this study. This study was said to be successful and stopped when the students’ scores met the criteria of success previously determined, namely more than 50% of them had the average score more than 66 or B in accordance with the State Polytechnic of Malang Academic Handbook. After the first and second cycles, the average scores obtained showed an improvement both in the first and second cycles. Hence, after the two average scores were analyzed by the paired t-test it was found that there was no significant difference between them. Accordingly, it can be said that this method was not fully successful in improving the students' reading skills even though from the questionnaire and interview showed the students’ interests and motivation to read English written texts more than before.
A Study of Visual Attention Elements with Experiment Analysis Based on Composition

The elements in the image that can attract people's visual attention are visual attention elements. Composition is a kind of visual perception feature in the study of audio-visual fusion information processing. The balance of composition depends on the distribution of visual attention elements in the image. Aiming at the problems of poor pertinence of existing visual attention models, unsystematic classification, unscientific selection of visual attention elements and insufficient consideration of human visual perception, this paper proposes a method of visual attention elements screening based on composition. Firstly, a material library based on composition is constructed, and PAD and harmony are marked. Then, a subjective assessment experiment is designed to screen out visual attention elements based on human perception. Finally, the experimental data were analyzed, the elements of visual attention based on composition were screened out, classified and summarized.
Industrial Involvement in Information System Education: Lessons Learned from a Software Quality Course

As Information System (IS) development is closely related to industry and real-world applications, industrial involvement is a critical element in IS education. This paper studies one typical IS course - a Software Quality course, and reflects our experience with involving a mix of industrial experts in building a practical IS course that would increase students' competences in critical thinking about the consequences of the design and quality engineering decisions that they are making during software development. In the course design, the industrial experts are involved in lecturing, hands-on-exercise seminars and final students evaluation. We find that students are showing active course participation with our designed industrial involvement. Furthermore, we summarize lessons learned from the industry involvement, as well as the reflections on the value perceived by the industrial experts involved in the IS education.
A Cloud Computing Load Algorithm

For the issue of resource load prediction in cloud computing, a modified artificial bee colony algorithm and SVM are combined to construct a predictive model.First, by using reverse learning to initialize the population, differential evolution selects the individual population.The point strategy is used to construct the honey source selection route of the algorithm.The feedback mechanism reduces the shortcomings of the algorithm falling into the local optimum.Second, the parameters in the SVM prediction model are optimized and the best ones are found by using the improved bee colony algorithm.In the final simulation experiment, the proposed IABC algorithm has better prediction accuracy than the SVM, the LSSVM and other prediction algorithms, and so it has a certain promotional value.
Stochastic Gradient Methods with Block Diagonal Matrix Adaptation

Adaptive gradient approaches that automatically adjust the learning rate on a per-feature basis have been very popular for training deep networks. This rich class of algorithms includes Adagrad, RMSprop, Adam, and recent extensions. All these algorithms have adopted diagonal matrix adaptation, due to the prohibitive computational burden of manipulating full matrices in high-dimensions. In this paper, we show that block-diagonal matrix adaptation can be a practical and powerful solution that can effectively utilize structural characteristics of deep learning architectures, and significantly improve convergence and out-of-sample generalization. We present a general framework with block-diagonal matrix updates via coordinate grouping, which includes counterparts of the aforementioned algorithms, prove their convergence in non-convex optimization, highlighting benefits compared to diagonal versions. In addition, we propose an efficient spectrum-clipping scheme that benefits from superior generalization performance of Sgd. Extensive experiments reveal that block-diagonal approaches achieve state-of-the-art results on several deep learning tasks, and can outperform adaptive diagonal methods, vanilla Sgd, as well as a modified version of full-matrix adaptation proposed very recently.
Teacher Professional Development in Education 4.0: Awareness of Digital Literacy

The Revolution of Industry 4.0 (IR 4.0) brings a new stimulus to educational system in Indonesia and causes education transformation.Technology used in education field is a segment of education 4.0 that can train students to improve their learning achievement.The process of teaching learning to achieve this goal is started from the teachers' awareness of digital literacy.Lecturers as teachers or educator in higher education play a crucial role in their students' lives.Unfortunately, the motivation of lecturers to participate in teacher professional development program is still low.In fact, participating in the program support their professional development process.This study aims to explore how teachers' awareness of digital literacy support their work in conducting three principles of higher education teachers.This exploration was determined from literature review and analysis of policy documents.The finding shows that digital literacy and teachers' professional development in education 4.0 impact the teachers to have their professional jobs well done.Digital literacy can be implemented into numerous aspects.It is very practical and gives benefit for teachers as a professional lecturer to do Indonesia's three principles of higher education.Indonesian government must support developing good quality teachers since good education is the most crucial tool to solve various problems.Teachers as lifelong learners must maintain their professional development (PD) to face challenges in education 4.0.
Fall decision algorithm based on changeable velocity SVM threshold

In this paper, we focus on fall decision algorithm using a microcontroller module. We measure multiple sensor data and calculate signal vector magnitude (SVM) of acceleration, angular velocity, and angular variation to decide whether the falling accident occurs or not. We suggest a changeable velocity SVM threshold-based algorithm to implement an algorithm for detecting/deciding fall accidents.
A TS Bitstream Parsing System for ChinaDRM

Due to the increasing demand for domestic digital audio and video services, broadcast TV intelligent terminals are rapidly iterating, and global content producers are turning to researching 4K HDR technology. China needs to realize the importance of digital rights, which is more conducive to the development of ultra-high-definition digital services in China. Therefore, this paper will introduce the architecture of China digital rights management (ChinaDRM) and its application in TS encapsulation format. In the meantime, we proposed an analytical method about the TS bitstream which had been encrypted. In the TS bitstream, it also carried the ChinaDRM descriptor and Content Encryption Information (CEI). Finally, we designed a bitstream parsing system to parse the data of CEI and ChinaDRM descriptor.
Using MAQAO to Analyse and Optimise an Application

One of the major issues in the performance analysis of HPC codes is the difficulty to fully and accurately characterize the behaviour of an application. In particular, it is essential to precisely pinpoint bottlenecks and their true causes. Additionally, providing an estimation of the possible gain obtained after fixing a particular bottleneck would surely allow for a more thorough choice of the optimizations to apply or avoid. In this paper, we present MAQAO, a performance analysis framework aiming at providing a comprehensive human-friendly view of performance issues and also guide the user’s optimization efforts on the most promising performance bottlenecks.
An Optimal Transfer of Knowledge in Reinforcement Learning through Greedy Approach

Transfer Learning has been used in Reinforcement Learning methods by integrating information from preceding task. Although, these algorithms generally have to be assigned either a complete prototype of the task or direct mapping of information from input task to output task. The agent may not have the knowledge of such complex information. However, it is able to examine the resemblance between two tasks which are similar in taking action or approach. In this paper efforts has been made to solve mountain car problem (a famous problem domain in the area of Reinforcement Learning) using the SARSA ( $\boldsymbol{\lambda}$ ) [10] algorithm and MASTER [3] method by using greedy approach. The greedy approach has been used in applying parameters for transferring the knowledge within 2-dimensional mountain car to 3-dimensional mountain car. In the existing techniques the best results are obtained with use of parameters like epsilon decay factor, eligibility traces with greater decay factor and positive weight initialization. In the article, greedy approach has been applied with these parameters to get optimal results.
Adaptive intelligence system for a predictive process for the Industry 4.0 in Tobacco factory

Abstract Speaking of the biggest innovations for the manufacturing industry of the day, we are talking about intelligent production systems with “self-aware”, “self-contemplative and” self-sustaining “capabilities. Building such an intelligent system that is adapted and predictable provides the aforementioned capabilities in production, processes and machines. The intelligent system is able to combine various technologies and techniques for mixing statistical data, data, and artificial intelligence methods. Cigarette production is selected because it is highly serial. In such production, the use of expert systems in quality management in this area is not sufficiently developed, and with direct management, it generates great savings. Mistakes and errors are inversely proportional to productivity. This paper deals with the application of an intelligent system that uses the key principle of lean production. We need to build an adaptive system for predictive error and reduce the machine’s failure time in the cigarette industry.
The utilization of Sentinel 1-A dual polarization data for 20 - 22 February 2017 flood inundation mapping In Jakarta, Indonesia

Flood mapping is important to do as part of emergency response efforts in the event of a disaster. Usually, there are limitations in flood mapping with a direct approach and ineffective mapping efforts, such as: lack of time, cost, and number of personnel in the field. Utilization of remote sensing data can be used as an alternative to overcome this problem with an indirect approach. One of the uses of remote sensing data can be used to provide objective information and can be used to monitor, detect and map floods spatially-temporally. In this paper we will discuss of flood inundation mapping with several color composite combinations from dual-polarization Sentinel 1-A data using three classification methods. The data used is Sentinel 1-A dual-polarization data, in vertical-vertical receive (VV) and vertical-horizontal receive (VH) transmission mode. These datasets were acquired pre- (08 December 2016) and post- (22 February 2017) the flood even of 20 - 22 February 2017. This method is the detection of flooding of surface changes, the classification process for flood inundation mapping (maximum likelihood, minimum distance, and Mahalanobis distance), and then calculating an accuracy assessment. Six kinds of color combinations polarization combination Red Green Blue from the three classification methods. Flood detection of surface changes from a combination of six types of color composites gives very diverse results. The six types are VH_pre-flood - VH_post-flood - VH_pre-flood / VH_post-flood. VH_preflood - VV_post-flood - VH_pre-flood / VV_post-flood. VV_pre-flood - VV_post-flood - VV_pre-flood / VV_postflood. VV_pre-flood - G: VH_post-flood - VV_pre-flood / VH_post-flood. VV_pre-flood - VH_post-flood - VV_postflood. VH_pre-flood - VH_post-flood - VV_post-flood. From the calculation of accuracy for each classification method, the combination of VV_pre-flood - VH_post-flood - VV_post-flood using the minimum distance classification has the highest level of accuracy compared to other combinations. Therefore, to make a flood inundation map using dualpolarization dual-sentinel 1-A data, this composite is the best.
Design of the Operator Tracing Robot for Material Handling

In order to improve the safety and efficiency of using material handling equipment for operators, we implemented a two-wheeled robot to assess the autonomous movement technology of the material handling equipment such as forklift, Automated Guided Vehicle(AGV) and crane. The autonomous control system requires a system that identifies the current location and direction of a robot and an operator. We implement the location of each component using the tag recognition technology which utilizes a camera. The operator can send the autonomous movement command to the robot through the Human-Machine-Interface (HMI) device, and the location of the operator and robot can be confirmed through the map information provided by the HMI device. The system collects and uses the location and direction values of the robot and the operator in real time and traces the operator when he/she moves.
Facial Expression recognition using higher order moments on facial patches

The discriminative features between different facial expressions are mostly constrained to some regions of a face when an expression occurs on a face. Extracting these discriminating features is one of the most important aspect of a facial expression recognition (FER) system. Though significant amount of work has been done in identifying optimum features for expression recognition, it is still a challenging part of FER systems. Identifying the most significant regions and selectively applying higher order moments on these patches of the face image is found to give good feature vectors that have high discriminative ability between the classes (expressions). Point distribution model (PDM) has been used in the proposed method for locating the facial landmarks required to extract facial patches. Higher order Zernike moment (ZM) invariants were evaluated for these facial patches which form the features to be used for classification. ZMs are rotation invariant orthogonal moments having a high degree of image representation capability, hence they can satisfactorily extract significant amounts of both local and global information contents from the facial patch. The final feature vector consists of higher order Zernike moment invariants from all facial patches. Finally, support vector machines (SVM) classifiers are trained which are used to predict the facial expressions. Experiments on the extended CK+ dataset and JAFFE dataset show the effectiveness of the proposed FER system.
The environment of distant support in the process of training future Natural Sciences teachers to organize pupils’ safe life activities

A social role of Natural Sciences teachers has been becoming of particular importance in the context of dynamic changes occurring in the modern educational space. Therefore, the professional training of students, in particular, to organize safe life activities of high school students should be grounded on the combining of deep mastering of the basics of theoretical professional knowledge and skills in applying this knowledge in their future educational activities. Thus, there arises a problem of forming the readiness of future Natural Sciences teachers to organize high school students’ safe life activities. The aim of the study is to substantiate theoretically the pedagogical condition for training future teachers of Natural Sciences to organize high school students’ safe life – to create an environment of remote (distant) support for students in the process of training them to organize of high school students’ safe life activities. It has been found out that the environment of remote support of students in the process of training them to organize of high school students’ safe life activities should be considered as an integrated environment of information and educational resources, software, telecommunication means, rules for their administration and use which provide opportunities for interaction during their learning and self-guided work. It has been proved that creating a remote support environment for students in the process of training them to organize of high school students’ safe life activities (distance course + virtual class), using the services GoogleSites and Google Classroom, will improve the efficiency and quality of the training aimed at future Natural Sciences teachers to organize high school students’ safe life activities, expand the educational and methodological basis, implement an individual approach to learning, provide an interactive control over the fulfilment of tasks alongside with the use of the individual and group methods within the online environment, increase the motivation to learn. Keywords: future teachers, Natural Sciences, safe life activities, pupils, distant education, remote / distant support environment.
Epistemic Integrity Constraints for Ontology-Based Data Management

Ontology-based data management (OBDM) is a powerful knowledge-oriented paradigm for managing data spread over multiple heterogeneous sources. In OBDM, the data sources of an information system are handled through the reconciled view provided by an ontology, i.e., the conceptualization of the underlying domain of interest expressed in some formal language. In any information systems where the basic knowledge resides in data sources, it is of paramount importance to specify the acceptable states of such information. Usually, this is done via integrity constraints, i.e., requirements that the data must satisfy formally expressed in some specific language. However, while the semantics of integrity constraints are clear in the context of databases, the presence of inferred information, typical of OBDM systems, considerably complicates the matter. In this paper, we establish a novel framework for integrity constraints in the OBDM scenarios, based on the notion of knowledge state of the information system. For integrity constraints in this framework, we define a language based on epistemic logic, and study decidability and complexity of both checking satisfaction and performing different forms of static analysis on them.