Reviewerly/exHarmony huggingface dataset:
Highlights-based Bitrate Adaptation Scheme for Mobile Video Streaming Service

Bitrate adaptation for video streaming is the de facto solution to optimize viewing experiences of users under the time-varying network conditions. However, most of the existing bitrate adaptation schemes do not consider user-related information, such as video highlights, in determining a bitrate of video chunk to be requested next. The user is more sensitive to the viewing quality in the video highlights than other parts of a video. To maximize Quality of Experience (QoE) for users, the bitrate adaptation needs to consider the video highlights. In this paper, we propose a highlights-based bitrate adaptation scheme for mobile video streaming service. The proposed scheme extracts the video highlights using deep learning-based method and utilizes it in the QoE optimization to determine the next video bitrate. Through the performance evaluation, we confirm that the proposed scheme can provide different video bitrates according to the video highlights.
Synthesis and Analysis Non-recursive Rejection Filters Transient Mode

The non-recursive rejection filter (RF), which is improved with the purpose of transient acceleration at arriving of the passive interference edge caused by disturbing reflections from fixed or slow-moving objects, is synthesized by the state-variables method.The structural diagram is offered of the tunable RF in the transient with the purpose of improvement of signal extraction effectiveness from the moving targets on the background of the passive interference edge.The comparative analysis is performed of RF effectiveness for fixed and tunable structure in the transient according to the criterion of the normalized interference suppression coefficient and the improvement coefficient of the signal-to-interference ratio.The essential increase of the signal extraction effectiveness from the moving objects on the background on the interference edge for the wide class of the spectral-correlation characteristics at RF structure modification.
On a representation of partially-distinguishable populations

A representation of heterogeneous stochastic populations that are composed of sub-populations with different levels of distinguishability is introduced together with an analysis of its properties. It is demonstrated that any instance of this representation where individuals are independent can be related to a point process on the set of probability measures on the individual state space. The introduction of the proposed representation is fully constructive which ensures the meaningfulness of the approach.
Optical scrambling system for document authentication by image decryption

The continuous development of new authentication techniques is necessary to prevent illegal data access from unauthorized users or counterfeiting. In this paper, we present a simple optical method to decode encrypted images using a multi-scopic optical system. The method uses a scrambled lens-let array which provides a high level of security. In a simple configuration, the encrypted image is scrolled in front of the multi-scopic imaging system. Due to a particular arrangement of lens-lets, the image will only be correctly imaged at a unique position with respect to the imager. Experimental demonstrations are presented and potential applications discussed.
Panorama of Researches Related to the Application of Virtual Reality in the Health Area in SVR

The use of Virtual Reality for the development of applications that can help in the treatment of different pathologies and the training of health professionals has been increasing gradually in the last decade. In this context, the purpose of this paper is to present a systematic review of the articles describing virtual reality applications for the health area, published in the last five editions of the Symposium on Virtual and Augmented Reality (SVR). Seven research questions were defined to identify the current panorama and possible trends. The results of the research present quantitative data regarding the number of published works, universities and states involved, types of treatments and training performed, as well as presenting data regarding the technologies used and the incidence of games.
Can Automated Impact Analysis Techniques Help Predict Decaying Modules?

A decaying module refers to a module whose quality is getting worse and is likely to become smelly in the future. The concept has been proposed to mitigate the problem that developers cannot track the progression of code smells and prevent them from occurring. To support developers in proactive refactoring process to prevent code smells, a prediction approach has been proposed to detect modules that are likely to become decaying modules in the next milestone. Our prior study has shown that modules that developers will modify as an estimation of developers' context can be used to improve the performance of the prediction model significantly. Nevertheless, it requires the developer who has perfect knowledge of locations of changes to manually specify such information to the system. To this end, in this study, we explore the use of automated impact analysis techniques to estimate the developers' context. Such techniques will enable developers to improve the performance of the decaying module prediction model without the need of perfect knowledge or manual input to the system. Furthermore, we conduct a study on the relationship between the accuracy of an impact analysis technique and its effect on improving decaying module prediction, as well as the future direction that should be explored.
Live Demonstration: Low Power Embedded System for Event-Driven Hand Gesture Recognition

This demonstration presents a low power embedded system to classify hand movements. The surface ElectroMyo-Graphic (sEMG) signals acquired from the forearm are preprocessed using the Average Threshold Crossing (ATC) event-driven technique, which heavily reduces hardware complexity and power consumption. The quasi-digital output is sent to an ultra low power microcontroller, which implements a fully-connected Neural Network (NN). A small Arduino-based tank is used to demonstrate the real-time behavior of the system and to show the correctness of the predicted gestures <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> .
FSM-Based Cyber Security Status Analysis Method

Cyber-attacks occur frequently within the Internet, and the forms of occurrence are ever-changing. In simple terms, attacks are divided into single-step attacks and APT attacks. For the two types of attack, this paper is based on the corresponding attack analysis model: diamond model and kill-chain model, respectively analyze single-step attacks and APT attacks. First, the commonality of each type of attack is obtained by analyzing the above models, and then, according to the commonality, list the characteristics of each stage of the known attack on the network. Therefore, the cyber-security status knowledge base is obtained. This knowledge base is part of the cyber-security knowledge graph. Thence, the first and most important thing that must be done is to build cyber-security knowledge graph. By emulating the attacks and collecting data on the simulation platform, the collected data is subjected to pre-processing such as fusion and de-redundancy, the security status features of different hosts at each moment are obtained. Match these network security status features with the cyber-security knowledge graph, correlate the security status of different hosts, and obtain the security status of the entire network. At the same time, utilize the trigger mechanism of the finite-state machine, and the fault tolerance mechanism is added in the matching process to ensure the accuracy of the analysis results and reduce false positives and false positives.
TENDENCIES IN SOFTWARE ENGINEERING EDUCATION

Appears in: ICERI2019 Proceedings Publication year: 2019Pages: 3601-3609ISBN: 978-84-09-14755-7ISSN: 2340-1095doi: 10.21125/iceri.2019.0927Conference name: 12th annual International Conference of Education, Research and InnovationDates: 11-13 November, 2019Location: Seville, Spain
BARRIERS OF SECONDARY SCHOOL TEACHERS IN THE USE OF ICT FOR TEACHING

Appears in: ICERI2019 Proceedings Publication year: 2019Pages: 2426-2431ISBN: 978-84-09-14755-7ISSN: 2340-1095doi: 10.21125/iceri.2019.0644Conference name: 12th annual International Conference of Education, Research and InnovationDates: 11-13 November, 2019Location: Seville, Spain
THE SKILLS OF THE FUTURE WORK LIFE – RECOGNIZING AND ASSESSING SOFT SKILLS IN HIGHER EDUCATION

Appears in: ICERI2019 Proceedings Publication year: 2019Pages: 7020-7028ISBN: 978-84-09-14755-7ISSN: 2340-1095doi: 10.21125/iceri.2019.1667Conference name: 12th annual International Conference of Education, Research and InnovationDates: 11-13 November, 2019Location: Seville, Spain
On the Learning Activities and Outcomes of an Information Security Course

This paper presents an early information security course for IT students. We followed teaching methods and good practices presented in relevant literature to design our course outline. As a result, we arranged a semester long course which covers information security topics, offensive and defensive training activities, and overall security mindset. Finally, the initial findings from running the course are discussed.
Implementation of Finite State Automata in an Amusement Park Automatic Ticket Selling Machine

Introduction: Amusement Park is a place that provides various attractions for entertainmentpurpose. People can enjoy games, rides such as roller coaster rides, merry-go-round, etc.Over the time, technology has grown. Many things that are usually done manually by humansare now being replaced by computers. With an automated ticket selling machine, the processof buying the ticket of an amusement park becomes easier for the user. Automata theory is atheoretical branch that has not been widely known to many yet plays essential role in the fieldof computer science. The main concept of automata theory itself is how to make machinesworks automatically. Therefore, this study aims to show the implementation of the concept ofAutomata theory in an amusement park automatic ticket selling machine.Methods: Themethod used to develop the application is formal methods known as Finite State Automata.We also used Sequential Linear method to design the application. Formal method is amathematical modelling that links the production, development, and verification of softwareand hardware. In this application, the concept of Finite State Automata was applied torecognize and then capture the pattern on the process of ticket selling machine.Results: Theresult of this study shows that Finite State Automata can become one of the alternatives todesign an automatic ticket selling machine for an amusement park by reading each inputgiven by the user and then converts it to the language know by the Finite StateAutomata.Discussion: In this application, the payment method is by inserting a certainamount of cash into the machine and the machine will proceed to the next process. However,for future development another payment method can be added. For example, instead ofinserting cash, user can choose to pay using debit as well as credit card.
DESIGN AND IMPLEMENTATION OF A DISTANT LEARNING ROBOTICS TOOL TO STUDY SPATIAL ABILITY OF STUDENTS

Appears in: ICERI2019 Proceedings Publication year: 2019Pages: 3224-3230ISBN: 978-84-09-14755-7ISSN: 2340-1095doi: 10.21125/iceri.2019.0819Conference name: 12th annual International Conference of Education, Research and InnovationDates: 11-13 November, 2019Location: Seville, Spain
Feature Matching and Assessment of Similarity rate on geometrically Distorted Side Scan Sonar Images

This paper presents the geometric transformation on different shades of Side Scan Sonar (SSS) images and application of different feature extraction and matching algorithms for submerged (debris or salvage) object contained images in the underwater environment. These SSS images can be classified into semi shades (Sh1), high shades (sh2), low shades (Sh3) and no shades (Sh4) by depending on the shadows in the images. During SSS acquisition, geometric distortion can occur due to the variations in the trajectory, speed, or orientation of the tow-fish. So, We have applied the geometric transformation such as rotation, translation and scaling transformation on each shade of the SSS images and feature extraction is done using Scale Invariant Feature Transform (SIFT), Speeded Up Robust Feature (SURF) and Oriented FAST and rotated BRIEF (ORB) and then matching algorithms Fast Library for Approximate Nearest Neighbour Search (FLANN) and RANdom Sample Consensus (RANSAC) is used. The evaluation of feature matcher is done Qualitatively and Quantitatively by using the matching accuracy between the images. Finally, we had concluded that which algorithm will work better for different transformations and shades of SSS images.
PrivateEye: Scalable and Privacy-Preserving Compromise Detection in the Cloud

Today, it is difficult for operators to detect compromised VMs in their data centers (DCs). Despite their benefits, the compromise detection systems operators offer are mostly unused. Operators are faced with a dilemma: allow VMs to remain unprotected, or mandate all customers use the compromise detection systems they provide. Neither is appealing: unprotected VMs can be used to attack other VMs. Many customers would view a mandate to use these detection systems as unacceptable due to privacy and performance concerns. Data from a production cloud show their compromise detection systems protect less than 5% of VMs. PrivateEye is a scalable and privacy-preserving solution. It uses sanitized summaries of network traffic patterns obtained from the vSwitch, rather than installing binaries in customer VMs, introspection at the hypervisor, or packet captures. The challenge it addresses is protecting all VMs at DC-scale while preserving customer privacy using low-signal data. We developed PrivateEye to meet the needs of production DCs, and our data collection agent is deployed across all DCs of a large cloud. Evaluation on VMs of both internal and customer VM’s shows it has an area under the ROC curve — the curve showing the model’s true positive rate vs its false-positive rate — of 0.96.
Quantum Key Distribution over Hybrid Fiber-Wireless System for Mobile Networks

A novel method of quantum key distribution (QKD) based on hybrid fiber-wireless system is proposed in this paper. Quantum key from a center station (i.e., Alice) is transmitted through an optical fiber to a base station and then wirelessly forwarded to mobile nodes (i.e., Bobs). QKD protocol is implemented by using subcarrier intensity modulation, where binary phase shift keying (BPSK) signaling is used for encoding and direct detection with a dual-threshold receiver is employed for decoding. We derive the mathematical expressions for security analysis of the proposed system in terms of quantum bit error rate and ergodic secret-key rate taking into account the channel loss and receiver noise. The feasibility of the proposed QKD system is validated via the numerical results.
THE INFLUENCE OF FORMATIVE ASSESSMENT ON THE DEVELOPMENT OF STUDENTS´ SCIENTIFIC SKILLS IN NATURAL SCIENCE TEACHING AT LOWER SECONDARY SCHOOLS

THE INFLUENCE OF FORMATIVE ASSESSMENT ON THE DEVELOPMENT OF STUDENTS´ SCIENTIFIC SKILLS IN NATURAL SCIENCE TEACHING AT LOWER SECONDARY SCHOOLS
Embedding of QR code image for arbitrary image by CNN

In this paper, the authors aim at embedding and restoring arbitrary information in an arbitrary image using CNN. In order to achieve this objective, we propose a model composed of two CNNs of different roles of an Encoder CNN that embeds information on an image and a Decoder CNN that restores information embedded in the image. In the proposed method, the QR code is used as the medium of information to be embedded. Thanks to the error correction nature of QR code, embedded information is expected to be restored without errors.
Development of Coding Education to Enhance 4Cs Competency

4차 산업혁명으로 인해 현재 사회는 전례 없는 혁신과 마주하고 있으며, 그에 따른 미래 인재상도 함께 변화하고 있다. 이러한 미래 인재에 대한 핵심가치 역량으로서 4Cs(Critical thinking, Communication, Collaboration, Creativity)가 주목을 받으며, 전문대학에서의 교육도 기존의 NCS 기반의 기술 중심의 교육에서 4Cs역량을 함양하는 교육으로 발 빠르게 대응해야 할 필요성이 요구되고 있다. 그럼에도 불구하고 전문대학에서 학생의 역량 함양을 위한 교육과정이나 수업에 대한 사례 연구는 매우 부족한 것이 사실이다. 이에 이 연구에서는 코딩 교육을 교양 교과목에 적용 후, 학습자의 4Cs의 역량을 측정하여 그 효과성을 분석하고자 하였다. 특히 컴퓨터 관련 비전공자도 흥미를 가지고 학습할 수 있도록 언플러그드, 스크래치, 드론을 단계적으로 활용하였다. 수업 적용 단계에서는 서울 소재 S 보건대 재학생을 대상으로 총 15주에 걸친 코딩 교육을 교양 교과에 편성하여 실시하였다. 또한 4Cs 역량이 실제로 증진되었는지를 확인하기 위해 선행연구를 통해 검증된 4Cs 진단도구를 수정·보완하여, 수업 사전-사후에 적용하였다. 그 결과 수업에 참여한 학습자의 4Cs 역량이 전 항목에서 유의미하게 증가함을 양적, 질적 평가를 통해 확인하였다. 이 연구가 향후 전문대학 교양 교육에서 4Cs 역량을 함양할 수 있는 교육과정 및 수업 개발 사례 연구 확산에 있어 기여하기를 바라며, 그 결과로써 전문대학 전반에서 현시대가 요구하는 미래사회 인재를 양성될 수 있는 교육적 토대가 마련되기를 기대한다.As a result of the 4th Industrial Revolution, the society we live in is facing unprecedented innovation while future talent is changing. Critical thinking, Communication, Collaboration, and Creativity (4Cs) attract attention as core values for the future of a talented person. Recently, education of junior colleges has been rapidly shifting, from using the NCS curriculum toward competency-based education. Nevertheless, there are few case studies on instruction for improving competencies in junior college. Therefore, this study tried to analyze the effectiveness of 4Cs by measuring learners’ capabilities after applying coding education to a liberal curriculum. In particular, unplugged coding, scratch coding, and coding for drones were introduced gradually so that even students who are not studying IT could gain an interest in learning it. In the class, a total of 15 weeks of coding education was provided for students of S Junior College located in Seoul. In addition, to confirm whether the 4Cs competency was actually improved or not, the diagnostic tool for each competency was developed by previous studies. As a result, it was confirmed that the 4Cs competence of the learners in the class increased significantly in each area. This research is intended to contribute to the future development of various special classes for liberal education of junior colleges.
Structure of Deep Learning Inference Engines for Embedded Systems

For the last several years, various types of deep learning applications have been introduced. Most deep learning related research and development have been done on servers or PCs with GPUs. Recently there have been a number of moves to apply those applications to the industrial sector. When deep learning techniques are applied to actual targets, we can face some spatial and environmental constraints unlike the laboratory environment.In this paper, we describe requirements when deep learning applications run for embedded systems. We introduce our ongoing project on developing a deep learning framework for embedded systems, especially automotive vehicles. Generally, deep learning application development process can be divided to two steps: training a data model with a big data set and executing the data model with actual data. In our framework, we focus on the execution step. We try to design an inference engine to satisfy the operational requirements for embedded systems. We describe our design direction and the structure. We also show preliminary evaluation result.
Distributed Representations for Arithmetic Word Problems

We consider the task of learning distributed representations for arithmetic word problems. We outline the characteristics of the domain of arithmetic word problems that make generic text embedding methods inadequate, necessitating a specialized representation learning method to facilitate the task of retrieval across a wide range of use cases within online learning platforms. Our contribution is two-fold; first, we propose several 'operators' that distil knowledge of the domain of arithmetic word problems and schemas into word problem transformations. Second, we propose a novel neural architecture that combines LSTMs with graph convolutional networks to leverage word problems and their operator-transformed versions to learn distributed representations for word problems. While our target is to ensure that the distributed representations are schema-aligned, we do not make use of schema labels in the learning process, thus yielding an unsupervised representation learning method. Through an evaluation on retrieval over a publicly available corpus of word problems, we illustrate that our framework is able to consistently improve upon contemporary generic text embeddings in terms of schema-alignment.
Designing Disaster Recovery Plan of Data System for University

Abstract According to the QS World University Rankings, Andalas University is a 3-star university and is in ranked 12th of the best universities in Indonesia based on the Indonesian Ministry of Research and Higher Education criteria. The university is located in three different areas which are Padang, Payakumbuh, and Dhamasraya. Currently, the university‘s Information and Communication Technology Development Unit which is responsible in managing the information and communication technology has no formal documentation of response procedures in an event of an emergency. Being used by some 26,702 students and staffs, the system is potentially corrupted by a few possible disasters like viruses, hackers, and server downs. Therefore it is crucial for the organization to plan a documented security action to protect essential archives and application software in anticipating the catastrophes. This study developed a Disaster Recovery Plan (DRP), as a document to act on accidents which may occur in occur in information system failures. It is a plan for rapid recovery from the disaster or the emergency response so that the organization does not perceive the impact given by the information system failure.
Graph-based Deep Learning in Natural Language Processing.

This tutorial aims to introduce recent advances in graph-based deep learning techniques such as Graph Convolutional Networks (GCNs) for Natural Language Processing (NLP). It provides a brief introduction to deep learning methods on non-Euclidean domains such as graphs and justifies their relevance in NLP. It then covers recent advances in applying graph-based deep learning methods for various NLP tasks, such as semantic role labeling, machine translation, relationship extraction, and many more.
Dummy Trajectory Generation Scheme Based on Deep Learning

Nowadays, the traditional dummy trajectory generation algorithm used to protect users’ trajectory privacy usually uses statistical methods to build trajectory model. Because the human mobility model is a complex equation, it is difficult to use mathematical methods to model, so the established trajectory model can not consider the human mobility model which restricts the formation of trajectory. Therefore, traditional dummy trajectory generation algorithms can not defend against data mining attacks based on in-depth learning. In this paper, LSTM (Long Short-Term Memory) is used to design the real and dummy trajectory discriminator. Experiments show that data mining based on deep learning can eliminate more than 95% of the algorithm generated trajectories, and the error rate of real trajectory is less than 10%. We restrict the traditional dummy trajectory generation algorithm to human mobility model, and design a dummy trajectory generation strategy so that the generated trajectory can defend against multiple recognition attacks.