Reviewerly/exHarmony huggingface dataset:
Ragam Pemanfaatan Buku Teks Sebagai Media Pembelajaran Bahasa Inggris Di Kota Bitung Sulawesi Utara

Various Utilization of Textbooks as Media for Learning English in Bitung City, North Sulawesi. This study aims to determine the criteria for determining textbooks as a media for learning English and the benefits, weaknesses, and advantages of textbooks. This research is qualitative and descriptive. The data collection techniques use in-depth interviews, observation, and documentation. The results of this study indicate that textbooks have a role as learning media in the teaching and learning process. 1) Criteria in selecting textbooks are: a) relevant to the curriculum, b) the material in textbooks is productive and receptive, c) The textbook material is based on the basic principles of learning foreign languages, d) Engaging content with media images in it, e) Have various types of evaluation that can measure learning achievement. 2) Using textbooks as media, learning resources, and learning evaluation tools. 3) Weaknesses and strengths of textbooks as a media for learning English a) unavailability of textbooks for each student, b) much material. Utilization Textbooks have advantages a) have a complete side, b) make it easier for teachers to deliver material, and visualization c) have varied questions
Towards Computing and Network Convergence: QOE-Oriented Service Anycast Based on SRV6

The new mobile Internet services represented by extended Reality (XR) have brought new challenges to computing and networks. A new generation of Information and Communication Technology (ICT) infrastructure with both computing and communication capabilities is being promoted. Traditional load balancing technologies cannot sense the connection between users and services. It is also difficult to support large-scale distributed computing service instances, so the service experience provided can be poor.In this paper a service anycast system based on a Segment Routing over IPv6 (SRv6) data plane is proposed, which can provide a Quality of Experience (QoE)-oriented load balancing capability for the network according to the obtained status information of network and computing service instances. Besides, an IP address identification mechanism is also proposed to help the control plane handle traffic engineering policies, and to support efficient service resource discovery, seamless mobility, and service continuity.
Weibull Analytics of Observed and Suspended Failure Data: A tutorial on a Weighted Linear Regression Approach

This paper describes Weibull data analytics in support of grid resilience as well as interpreting test and R&D results. The target was to develop a widely accessible spreadsheet IDA application that can be used on relatively light computing devices such as mobile phones and tablets. This became possible by the use of adjusted ranking with suspended data and development of high accuracy approximations of plotting positions and linear regression weights. A coherent methodology was developed for plotting, parameter estimation and various types of inferences in addition to data analytics. These are: quantifying the similarity of failure processes by their distributions, optimization of period based maintenance cycles, evaluation of expected time and confidence limits for a next failure. These are all integrated into IDA which is made available as freeware for common benefit.
A Study on the Current Status and Future Development Plan of Fisheries and Shipping High School Recognized by Fisheries and Shipping High School Teachers

Fisheries and shipping high schools are in a serious crisis due to problems such as a decrease in the school-age population, negative perceptions, and a decrease in quality jobs. In the era of the 4th Industrial Revolution, this study aims to overcome this crisis and seek a developmental future for fisheries and shipping high schools. For this study, focus group interviews with 21 teachers at 11 fisheries and shipping high schools were conducted. Based on the contents of the interview, open coding was conducted according to the theory of evidence, and a paradigm model for the crisis situation of fisheries and shipping high schools was developed through axial coding. In addition, through selective coding, future development plans for fisheries and shipping high schools were sought. As a result of this study, the following is presented. First, legal and institutional protection is needed to maintain fisheries and shipping high schools. Second, it is necessary to recruit teachers for quality education. Third, it is necessary to cultivate convergent talents in preparation for the era of the 4th industrial revolution.
A Study on the Effectiveness of Self-Directed Learning Ability According to Learning Community Participation and Learning Community Type

The purpose of this study was to confirm whether participation in the learning community has a positive effect on improving the self-directed learning ability of college students. To this end, we examined not only the effect of the learning community on the improvement of the self-directed learning ability of participating students, but also whether there are difference in the effects according to the type of learning community. For this purpose, 572 university students in the G region were selected as research subjects, and the collected data were analyzed using SPSS 20.0. The results are as follows. First, it was found that participation in the learning community had a positive effect on the self-directed learning ability of college students. Second, as a result of the analysis according to the type of learning community, it was found that both students who participated in the foundation program type and the advanced program type improved their self-directed learning ability. Through the results of the study, implications for improving students
Weakly Supervised Salient Object Detection by Hierarchically Enhanced Scribbles

The performance of salient object detection (SOD) has been significantly advanced by using deep convolutional networks. However, it largely depends on the high cost of pixel-level annotations. To reduce human effort while improving the prediction accuracy, we propose a novel two-phase learning framework. The weakly supervised information in terms of scribbles is provided as initial labels. Then, as the first phase, high-quality pseudo-labels are generated by mapping scribbles onto object/object-part contours. These contour maps are predicted by the hierarchical contour detection algorithm, providing superior accuracy and smoothness. In the second phase, a deep neural network is alternately trained and predicted. The pseudo-labels are refined in an iterated process, where a conditional random field (CRF) model and a filter module are designed to promote the performance. Extensive experiments on five benchmarks show that our framework can achieve comparable results with the state-of-the-art fully and weakly supervised methods.
Quantifying emotions through quantum computations

Quantum computations are extensively used to solve challenging problems and emotion detection is one such field that has lot of potential to be explored. Classical studies can identify emotions but fail to observe their intensities at such minute level. The proposed research primarily intends to quantify the intensities related with happy and sad emotions on a quantum computer examining subjects within Amsterdam Dynamic Facial Expression Set (ADFES) dataset. Leveraging quantum ability to compute quantum state fidelity established closeness of the encoded states. We used quantum variational classifier technique for this experiment performed on the quantum simulator available on the IBM Quantum Experience platform.
Video Recommendation Method Based on Deep Learning of Group Evaluation Behavior Sequences

Massive video resources satisfy the interests of users on online video platforms but have led to the problem of the “explosion” of video resources. Meanwhile, some problems will also occur such as the sparse data, difficulty in extracting deep features and dynamic changes in user interests in video recommendation. Aiming at the problems, a video recommendation method is proposed based on the deep learning of group evaluation behavior. Using the Word2Vec word vector model, a video is mapped into a high-dimensional feature vector in an evaluation behavior sequence, a video feature vector library is generated, and a feature vector model of the video sequence is established. The convolutional neural networks (CNN), residual networks, and attention mechanisms are integrated to learn the deep connections between video feature vectors and to predict the candidate video sets. The candidate set is expanded by cosine similarity, and a dynamic interest model is established to filter and sort it. Experiments on the Movie-1M dataset show that this method can effectively improve the accuracy and recall rate of video recommendation, which verifies the feasibility and effectiveness of the method.
Demo: A Multi-Perspective Video Streaming System with Privacy Preservation in Trauma Room

More and more hospitals are now deploying mul-tiple cameras in trauma room for a multi-perspective remote observation. but video surveillance system can cause privacy breach by showing and storing sensitive information of patients and staff. We use OpenPose which is a state-of-the-art human body skeletons estimation framework to extract 18 human key skeleton points. For privacy preservation, we can apply the image obfuscation techniques to human heads, we also can use human skeleton to replace the human body in the truth background. we proposed a head detection method based on the 5 key points of each head output from OpenPose. We applied the st-gcn algorithm to recognize human actions, we propose a interactive algorithm for multiple cameras to recognize and trace the same person in different cameras, Based on multi-view action recognition for the same person, we can take action recognition accuracy to a high level. Our experiment results prove that our proposed technique has a high performance in privacy protection applications. Now we focus on the interactive algorithm for multiple cameras.
Poster: EdgeShell - A language for composing edge applications

The edge computing ecosystem is young and diverse - there is a lack of a programming standard such as POSIX in operating systems and ECMAScript in the web. Developers today need to combine a variety of libraries and services to build a distributed edge application. As a result, the application becomes tightly coupled with the implementation choice made, such as the protocol chosen to exchange data or the storage server chosen to store data. This makes applications less reusable. We propose a domain-specific language called EdgeShell that can be used to compose edge applications in a manner similar to writing UNIX pipelines.
Evaluating Deep Neural Network-based Speaker Verification Systems on Sinhala and Tamil Datasets

Speaker verification, a biometric identifier, determines whether an input speech belongs to the claimed identity. The existing models for speaker verification have reported performances mainly in English, and no study has experimented with Sinhala and Tamil datasets. This study proposes a semi-automated pipeline to curate datasets for Sinhala and Tamil from videos on YouTube filmed under noisy and unconstrained conditions which represent real-world scenarios. Both Sinhala and Tamil datasets include utterances for 140 persons of interest (POIs) with more than 300 utterances per POI under one or more genres: interviews, speeches, and vlogs. Moreover, this study investigates how domain mismatch affects a speaker verification model trained in English and applied to Sinhala and Tamil. Two deep neural network models trained in English show significant performance drops on Sinhala and Tamil datasets compared to an English dataset as expected due to domain mismatch, however, it is observed that AM-softmax performed better than vanilla softmax. In the future, robust speaker verification models with domain adaptation techniques will be built to improve performance on Sinhala and Tamil datasets.
Enhanced Network Solution for Flexible Working Environment

Modern networks include data, phone, and video applications that are essential to an organization's operations. They are more than just a collection of switches and routers. An intelligent network offers dependable and secure connectivity for workers, clients, and data. Critical business and communications applications can be delivered using the platform that it provides for campus, branch, data centre, and wide-area networks. To achieve efficiency and continuity features in the solution, the routing and switching method provide the best solution for required demands. The suggested approach focuses on the improvement that can be made to switching and routing solutions for a flexible working environment and how the First Hop Redundancy protocol can be implemented to increase the efficiency of the solution.
Learn to Learn on Chip: Hardware-aware Meta-learning for Quantized Few-shot Learning at the Edge

Recent years have seen a growing trend of deploying deep neural network-based applications on edge devices. Many of these applications, such as biometric identification, activity tracking, user preference learning, etc., require fine-tuning of the trained networks for user personalization. One way to prepare these models to handle new, unseen tasks, is to pre-train them on a distribution of known tasks. This observation has led to increasing research into meta-learning based few-shot learning techniques. However, basic meta-learning approaches do not account for the limited memory and computational resources during on-chip training. We propose a modified meta-learning algorithm that enables quantized fine-tuning to optimally condition the models for on-chip few shot learning. The modification involves the inclusion of target hardware constraints upfront in the meta-learning process. Block floating point datatypes with low precision mantissa bits are utilized in the forward and backward passes, to allow hardware-friendly adaptation. Experiments show that our algorithm provides better initializations than conventional algorithms, more suitable for efficient quantized fine-tuning. This allows the few-shot learner to achieve better convergence, in terms of accuracy and speed. Extensive experiments are also performed to analyze the impact of initialization on quantized fine-tuning and further corroborate the benefits of our method.
Multi Agent Based Intelligent Personal Research Assistant

Since the very early days, developments of mankind have been energized by research. Despite people having developed many research methods, including Scientific Method, Milestone Approach, and IMRD, research conduct remains a challenge. We have examined various research methods and discovered reading and writing as the foundation of all research methods regardless of a novice or an experienced researcher. This was practised even before formally acknowledging what we called research methods today. We developed an Intelligent Personal Research Assistant, InPRA, which guides a researcher in systematically reading past research papers and, more importantly, developing research proposals, progress reports, thesis, and papers while doing the research, without waiting for the last moment. While InPRA is based on the reading-writing model of research, within InPRA, a researcher can access research management tools, scientific writing tools, specific research methods and other resources. InPRA has been developed as Multi Agent System to enable interaction among the steps in the research process beyond sequential processing. Research students and experienced researchers like supervisors have evaluated the guidance of InPRA to generate material for a project proposal, which is the first document in a research process. According to the evaluation results, the multi-agent-based approach in InPRA could outperform traditional text-based search approaches. Moreover, based on survey results, the system is very useful for researchers as it generates more relevant results.
The Magical Art of Technical Presentations [Perspectives]

At the time of publication, all of the links in this article were operational. However, since we do not host the videos, we have no control over whether or not they will continue to be active. In many cases, similar or related videos can be found by typing the performer’s name in an appropriate search engine. For convenience, all of the referenced links can also be accessed at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">www.fourhandedillusions.com/matp</uri> . On occasion, new and related links might also be posted on that site.
Security Analysis of Docker Containers for ARM Architecture

Containers provide a better performance, faster deployment than virtual machines and provide near-native performance, with isolation and security drawbacks. Although the security of containers for the Intel architecture has been investigated in more detail, there is limited work on the security of containers for the ARM architecture. In this paper, we address this gap in research and focus on the security of containers designed for the ARM architecture, which is heavily used in IoT devices. Edge computing offers many advantages, including reduced latency and resource requirements at the cloud because data can be processed at the edge before it is sent to the cloud. Using containers at the edge nodes of IoT-Edge-Cloud systems can enhance such advantages at the cost of increasing security vulnerabilities in such systems. Therefore, it is essential to investigate the security of containers designed for the ARM architecture. Accordingly, we obtained official ARM images from DockerHub and used various security tools to scan these ARM images. We found that 72% of all the vulnerabilities show varying severity levels and each tool seems to work best for particular base images. We investigated how each tool detects sub-packages and achieves a different hit ratio while none of them alone can detect at least 80% of all the vulnerabilities. In addition, we also investigated how the Docker images and their vulnerability landscape change over a period of six months by running the scanning tools twice. Finally, we also conducted a dynamic analysis of some of the images on the Raspberry Pi and study their effects. We believe this paper will facilitate the use of ARM containers at the ARM-based edge nodes by addressing security issues.
Quantum Data Reduction with Application to Video Classification

We investigate a quantum data reduction technique with application to video classification. A hybrid quantum-classical step performs data reduction on the video dataset generating “representative” distributions for each video class. These distributions are used by a quantum classification algorithm to firstly reduce the size of the videos and then classify the reduced videos to one of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$k$</tex> classes. We verify the method using sign videos and demonstrate that the reduced videos contain enough information to successfully classify them using a quantum classification process. The proposed data reduction method showcases a way to alleviate the “data loading” problem of quantum computers for the problem of video classification. Data loading is a huge bottleneck, as there are no known efficient techniques to perform that task without sacrificing many of the benefits of quantum computing.
Implementation of Evacuation Routes with Augmented Reality in a Programmed Evacuation

An AR second level-based application for general institutional evacuations was created with Unity 2020 in conjunction with Vuforia. Its function consisted of markers that serve as physical representations that mobile phones can identify to guide the user. The markers were designed with different patterns to be unique and thus be correctly recognized by the application. The objective of the implementation was to analyze the integration of RA in a scheduled general evacuation, namely, the comparison of evacuation times without application use and with application use. Based on the above, the evacuation was planned to define time, personnel, location of the markers, and time collection. Once the evacuation was completed, the statistical analysis was carried out using two sample T-tests, Dunnett's comparison, multiple regression analysis, and others. The result was that in an evacuation with many people, there was no statistical difference in the times between an evacuation with the use of the application and without the use of the application. Some of the aspects observed during the implementation that affected the evacuation time werethe processing capacity of each user's cell phone, the lighting, the trigger position, and the user's walking speed.
A Correlational Investigation of Personal Medical Data Safety and Blockchains

Any emerging technology needs a catalyst to help its adoption into mainstream utilization. The blockchain has been around for over 10 years now and although it has been widely adopted in the crypto asset world, its real-world implementation in the healthcare domain has been relatively slow. Considering the advantages possessed inherently by blockchains, it is perplexing as to why it has not found widespread acceptance. With most of the proposed implementations focusing on health records and patient centered access control, it was necessary to gain status of public perception of firstly, their health records, secondly, their outlook on the security aspects of their health data and finally understand to what extent have blockchains and its existence has infiltrated into the general populous. The Box’s M test significance value, p-value= 0.613, helps establish a correlation between an individual knowledge of blockchains and their ease in use of electronic devices. The ANOVA test was used to reject any correlation between an individual’s age and their perception of healthcare data safety and accessibility. The results of this analysis would help provide understanding on the feasibility of healthcare blockchain implementations.
Detection of Compressed DeepFake Video Drawbacks and Technical Developments

The rapid advancement in artificial intelligence (AI) has revolutionized the creation of synthesized multimedia and given rise to DeepFake, a highly realistic fake video or image depicting a person doing or saying something he/ she has never done or said in reality. Attackers use DeepFake to tarnish individuals’ reputations and disseminate fake news, which in turn undermines societies’ stability and security. In response to this cyber security threat, many DeepFake detection methods have been proposed, which show outstanding performance in detecting high-quality DeepFake videos. However, their performance decreases when detecting compressed fake videos. This article investigates the problem of compressed DeepFake video detection. Firstly, popular detection methodologies are reviewed focusing on their abilities to distinguish between real and fake compressed video footage. Then, we attempt to identify and discuss the weaknesses of the methods by examining factors that contribute to decreasing detection efficiency. At the end of this article, we present new generation DeepFake detector techniques that reportedly exhibit improved performance and robustness against video compression. We hope that the contribution from this work inspires innovation for more reliable solutions to combat potential security threats posed by DeepFake videos.