Reviewerly/exHarmony huggingface dataset:
Gaze-Driven Links for Magazine Style Narrative Visualizations

Magazine Style Narrative Visualizations (MSNV) can be challenging due to the need to integrate textual and visual information. This problem has prompted researchers to design dynamic guidance meant to ease the mapping of the information provided in the two modalities. We contribute to this line of work by evaluating gaze-driven adaptive guidance that dynamically links relevant sentences in the text to the corresponding datapoints in the visualizations (see Fig. 1). We conducted a user study that involved participants reading a series of MSNVs extracted from real-world sources. Results show that the adaptive links significantly improve the comprehension of the MSNVs as compared to receiving no guidance. This improvement comes at no expense of user reading time, and is consistent regardless of the MSNV complexity.
Mobile Usage during COVID-19

The purpose of this article is to summarize and review the use of mobile devices during the COVID-19 pandemic, including issues related to security, data protection and privacy, as this new situation presents new opportunities but also new risks. As the goal for many countries and organizations is to return to normalas soon as possible in order to mitigate the economic downturn, uses for health, for example, may play an even more important role.
Semantic Metadata Information (SMI) Visualisation Technique Using the Integration of Ontology and UML Graph-Based Approach to Support Program Comprehension

Representing any ideas with pictures rather than words is intuitively more appealing because a visual presentation can be more readily understood than that of textual-based. Program visualisation is one of the techniques that can be used in teaching to help users in understanding how programs work. Program visualisation technique is a mental image or a visual representation of an object, scene, person or abstraction that is similar to visual perception. This technique is significant to users because the criteria of source code cannot be physically viewed. It is applicable in the process of writing programs as it helps users to understand their codes better. The purpose of program visualisation is to translate a program into a graphical view to show either the program code, data or control flow. Visualisation technique uses the capability of human visual system to enhance program comprehensibility. Thus, this study uses program visualisation technique to represent program domain in a graphical view to help novices in improving their comprehension. This research aims to support beginners or novice programmers who have been exposed to programming languages by providing effective visualisation technique.
Social Distancing Detection Using Open CV and Yolo Object Detector

Lately, social distancing has become a trending term, more because of the COVID-19 pandemic that has affected the entire world causing more than 1 million deaths. The world we lived in a few months prior is completely different from what it is now.The lack of any antidotes and the absence of immunity, capable of fighting off the virus has made humans more undefended. Hence, Social Distancing is the only best option for us to protect ourselves from diseases, not limited to COVID-19, that may be transmitted through human contact. Social distancing is a technique that may be used to reduce the rate of new cases during a pandemic outbreak. This publication is focusing on surveillance of public places and detecting whether the people are maintaining social distancing or not. It explains the development of technology through the use of AI-based procedures to detect whether the social distancing norm is followed or not, in any public video stream. The software embedded can distinguish between a person maintaining social distance (marked green) and a person who is not (marked red) and will also keep a count of incidents where social distancing was not followed.
PENGEMBANGAN APLIKASI “PONTIANAK HERITAGE” BERBASIS ANDROID SEBAGAI MEDIA PEMBELAJARAN SEJARAH

The purpose of developing the Pontianak Heritage application as an Android-based historical learning media that is able to display the location of cultural heritage, descriptions, and images and Knowing how the quality of applications developed is based on ISO / IEC 25010 software quality testing standards. R&amp;D). Research and development is a process or steps to develop a new product or improve existing products and can be accounted for. The results of this research that this research produces Pontianak Heritage application called Historoid as a history learning media for high school students who are able to display the location of cultural heritage in Pontianak, West Kalimantan, descriptions and images developed on the Android platform using Google Maps technology. The tool used in the application development process is Android Studio and the results of the Historoid application quality analysis results in functional suitability test results are very feasible because all application functions are running 100%.
Fast Training of Provably Robust Neural Networks by SingleProp

Recent works have developed several methods of defending neural networks against adversarial attacks with certified guarantees. However, these techniques can be computationally costly due to the use of certification during training. We develop a new regularizer that is both more efficient than existing certified defenses, requiring only one additional forward propagation through a network, and can be used to train networks with similar certified accuracy. Through experiments on MNIST and CIFAR-10 we demonstrate improvements in training speed and comparable certified accuracy compared to state-of-the-art certified defenses.
A Privacy Preserving and Efficient Multi Authority – CP-ABE Scheme for Secure Cloud Communication

In the cloud computing environment, Multi authority Ciphertext Policy-Attribute Based Encryption (CP-ABE) schemes are used as a key escrow free solution to securely and efficiently share data over cloud. However, the length of ciphertext in existing Multi Authority-CP-ABE schemes increases with the number of attributes in the access policy. Moreover, these schemes do not protect against dishonest attribute authorities. In this paper, a constant length ciphertext Multi Authority-CP-ABE scheme is proposed that reduces the communication overhead over the network. The scheme also prevents dishonest authority from compromising the system. Apart from this, for enhanced privacy of receivers, the access policy is communicated in hidden form. Thus, the presented scheme provides an efficient corrupt resistant, key escrow free Multi Authority-CP-ABE scheme by generating constant length ciphertext and hidden access structure. Results demonstrate the enhanced security and reduced cost of encryption and decryption by 8% and 48% respectively as compared to other existing works.
Identification of Linear Power System Models Using Probing Signals

This paper compares the accuracy of two methods to identify a linear representation of a power system: the traditional Eigensystem Realization Algorithm (ERA) and the Loewner Interpolation Method (LIM).ERA is based on time domain data obtained using exponential chirp probing signals and LIM system identification method is based on frequency domain data obtained using sinusoidal probing signals.The ERA and LIM methods are evaluated with the noise produced by the nonlinear characteristics of the system, these characteristics are caused by increasing the amplitude of the applied probing signal.The test systems used are: the two-area Kundur system and a reduced order representation of the Northeastern portion of the North American Eastern Interconnection.The results show that the LIM method provides a more accurate identification than the ERA method.
Everything on Tower (EoT) – Enabling Broadband Access in Rural Areas Through License-Exempt Spectrum – Making the Business of Rural Broadband Happen

This paper mainly aims to throw the light on telecom technologies of today and tomorrow. It briefly discusses, how technologies are now starting to be available to connect every village and also it details challenges faced by the technologies in rural areas. With these in mind, a total innovative business model is required to deliver Internet services to rural areas. In order to meet this need, a Danish technology start-up called BLUETOWN took the initiative to significantly enhance the quality of life of rural people in India in various villages of Eastern India by setting up a network of public Wi-Fi hotspots in various villages of India. The challenges from a technology point of view were many. The systems that provided the solution needed to be cost-effective, low maintenance and affordable to the rural masses [1].
Artificial intelligence-based cloud data center fault detection method

The cloud data center has become the infrastructure for the digital transformation and upgrading of electric power, storing and managing a large amount of key data. This paper designs a deep learning framework based on the gated loop unit to automate the diagnosis of equipment failures in the power cloud data center computer room, and combines timing information to predict the future state based on the past equipment operating state information. It is verified through the data set that the GRU-based deep learning framework proposed in this paper can detect cloud data center failures more accurately than common models such as LSTM, SVM and KNN.
A Survey of Personalized Recommendation Based on Machine Learning Algorithms

Personalized recommendation is a key technology to effectively solve the overload of online information and eliminate information islands. It is widely known as an important way to improve the quality of information services. However, cold start, data sparseness, algorithm performance, recommendation accuracy and surprise are still the key issues that restrict users' personalized recommendations. Firstly, we review the development trend of personalized information recommendation algorithms in the past 15 years. And then we propose a new classification method for users' personalized recommendation based on machine learning algorithms with cold start, data sparseness, and the performance of the algorithm as the main goals. On this basis, we summarize and compare the ideas, practices and conclusions of related machine learning algorithms. Finally, we further summarize the main advantages and disadvantages of the 10 kinds of personalized recommendation algorithms from the perspective of classification proposed, and look forward to the development directions, difficulties, focus and methods of personalized recommendation algorithms.
Learning Non-Taxonomic Relations of Ontologies

Ontologies, as semantic knowledge representation, have a crucial role in various information systems. The main pitfall of manually building ontologies is effort and time-consuming. Ontology learning is a key solution. Learning Non-Taxonomic Relationships of Ontologies (LNTRO) is the process of automatic/semi-automatic extraction of all possible relationships between concepts in a specific domain, except the hierarchal relations. Most of the research works focused on the extraction of concepts and taxonomic relations in the ontology learning process. This article presents the results of a systematic review of the state-of-the-art approaches for LNTRO. Sixteen approaches have been described and qualitatively analyzed. The solutions they provide are discussed along with their respective positive and negative aspects. The goal is to provide researchers in this area a comprehensive understanding of the drawbacks of the existing work, thereby encouraging further improvement of the research work in this area. Furthermore, this article proposes a set of recommendations for future research.
Deep Coherent Exploration for Continuous Control

In policy search methods for reinforcement learning (RL), exploration is often performed by injecting noise either in action space at each step independently or in parameter space over each full trajectory. In prior work, it has been shown that with linear policies, a more balanced trade-off between these two exploration strategies is beneficial. However, that method did not scale to policies using deep neural networks. In this paper, we introduce Deep Coherent Exploration, a general and scalable exploration framework for deep RL algorithms on continuous control, that generalizes step-based and trajectory-based exploration. This framework models the last layer parameters of the policy network as latent variables and uses a recursive inference step within the policy update to handle these latent variables in a scalable manner. We find that Deep Coherent Exploration improves the speed and stability of learning of A2C, PPO, and SAC on several continuous control tasks.
APLIKASI PEMETAAN LOKASI WISATA AIR TERJUN DI PROVINSI BANTEN BERBASIS SISTEM INFORMASI GEOGRAFIS

The purpose of this research is to create a mapping application for waterfall tourism locations in Banten Province, using the Geographical Information System (GIS) approach. The research methodology used in this study includes data collection taken with literature studies, interviews, and observations then analysis, system design, programming, testing, and implementation. System design is by using Unified Modeling Language (UML), MySQL database with PhpMyAdmin as a tool for managing MySQL databases, creating program code using Notepad ++, with programming language PHP version 5.6 23, javascipt, database integration with Google Maps API to display map, as well as the browser to check the display generated by the program code through the local server, the results of the application development will be tested for the black box system. The results of this research are the formation of a waterfall tourism mapping application system in Banten Province, which includes information on waterfall tourist locations, facilities and infrastructure around the tour, waterfall features, general information about the waterfall, facilities on the waterfall, information on the route to the waterfall tourist location.
KORELASI KETERAMPILAN MENYIMAK TEKS BERITA DENGAN KETERAMPILAN MENULIS TEKS BERITA

ABSTRACT The purpose of this research there are three. First, describing news text writing skills. Second, describe the skills of listening to news texts. Third, describe the correlation of listening to news text skills with news text writing skills. This type of research is quantitative with descriptive method. The design of this research is correlational. The population in this research were all eighth grade students of class VIII SMP Negeri 34 of Padang who were enrolled in the 2018/2019 school year, namely 223 student. There are two variable in this research, namely the dependent variable (Y) the ability to write news text and the independent variable (X) the skill of listening to news text. The data in this study were scores on the skills of writing news texts and skills score listening to news texts. The instrument used is a test, which is an objective test for listening to news text skills and performance tests for news text writing skills. The results of this research are three. First, news text writing skills of class VIII SMP Negeri 34 of Padang are in good qualification (80,75). Second, listening to the news text skills of clas VIII SMP Negeri 34 of Padang are in good qualification (77,57). Third, listening to news text skill correlates with news text writing skills. Based on this , it can be conclude that to improve writing skills, listening skills must be improved. Kata kunci : Korelasi, Menyimak, Menulis, dan Teks Berita
TermFrame: A Systematic Approach to Karst Terminology

We describe a systematic and data-driven approach to karst terminology where knowledge from different textual sources is structured into a comprehensive multilingual knowledge representation. The approach is based on a domain model which is constructed in line with the frame-based approach to terminology and the analytical geomorphological method of describing karst phenomena. The domain model serves as a basis for annotating definitions and aggregating the information obtained from different definitions into a knowledge network. We provide examples of visual knowledge representations and demonstrate the advantages of a systematic and interdisciplinary approach to domain knowledge.
The Improvement of Learners’ Proficiency in English Second Language Through Remedial Work

This study focused on the remedial program on learners' proficiency in English second language, which was conducted in two township schools in King William's Town District.This explored the remedial program that could be used to help learners become proficient readers who could be able to read for meaning and comprehend.A case study research design was used where two township schools were sampled.A qualitative approach located in the interpretive paradigm was used to carry out the study.The purposive and the sampling procedure were used to solicit information from principals, educators, and learners.The data collected through interviews and observations.Its focus was to establish a remedial program to improve learners' proficiency in English second language.Thus, the findings revealed that a remedial program goes a long way in improving learners' proficiency in English Second Language." Cheng and Slavin" [11] found out that the remedial programmes showed positive results had in common and the use of extensive professional development, coaching, and cooperative learning.In this view, the English second language learners could benefit more from proficient personnel in the language.The study recommends that there should be provision for trained remedial educators in all schools to attend to the individual needs of learners appropriately.Besides, the study should explore the models, methods, and theories on reading, the strategies that could be used to boost or alleviate the learner's proficiency in English second language.
A Framework for Optimizing GCN Inference on FPGA

Graph convolutional networks (GCNs) have revolutionized many big data applications. However, accelerating GCN inference is still challenging due to (1) massive external memory traffic and irregular memory access, (2) workload imbalance because of the skewed degree distribution, and (3) intra-stage load imbalance between feature aggregation and feature transformation steps. To address the above challenges, we propose a framework to optimize GCN inference on FPGA. First, we propose a novel Partition-Centric Feature Aggregation (PCFA) scheme to increase the data locality and reduce the number of random memory accesses in feature aggregation step. Second, we propose a novel hardware architecture to enable pipelined execution of the two heterogeneous computation steps. Then, a low-overhead task scheduling strategy is proposed to achieve stall-free execution of the two computation steps. Third, we provide a complete GCN acceleration framework on FPGA, and define key parameters for users to fine-tune the throughput. The model-specific operators can be customized to support a wide-range of GCN models. Using our framework, we design accelerators on a state-of-the-art FPGA. We evaluate our work using widely used datasets and. Experimental results show the accelerators produced by our framework achieve significant speedup compared with state-of-the-art implementations on CPU (≈100x), GPU (≈30x), and FPGA (4.5-32x).
Lossless Medical Image Compression Using Hybrid Block-Based Algorithm for Telemedicine Application

Image compression is a serious problem in the medical field for the storage and transmission of medical data. When medical images are transmitted, it consumes transmission bandwidth, storage capacity, and power consumption due to its large amount of information, and hence a hybrid block-based technique with Hadamard transform and Huffman encoder compression technique is proposed for transmitting medical data via networks. Initially, the integer wavelet transform (IWT) decodes the input image, and transformation is performed in LL sub-bands by lossless Hadamard transform (LHT) and DC prediction (DCP) to discard the correlation inside and in adjacent blocks, respectively. Encoding can be done with or without transformation by LHT. Finally, to compress the resultant coefficients, IWT-LHT-Huffman encoder/IWT-LHT-Arithmetic encoder is used. Experimental result reveals the comparison of IWT-LHT-Huffman, IWT-LHT-Arithmetic compression, and JPEG lossless compression models. From the obtained results, the IWT-LHT-Arithmetic compression algorithm generates superior results with less computational complexity when compared with the IWT-LHT-Huffman and JPEG lossless compression schemes.
Interactive Smart Writing Technology (ISWT)

With the latest inventions, human life standards have been changed dramatically. Intensive work can be seen to make environment human-friendly. By keeping this view, this paper presents the future of advanced classroom/ meeting rooms, training areas, etc. by bringing down the count of paper used for the purpose of making lecture notes. Our proposed system (Interactive smart writing technology (ISWT)) which we claim as an assistance to paperless documentation, is a pen/ marker sleeve that works on the mechanism of taking motion reading through the sensor that can work on any flat writing surface and whatever is written from it, will get digitalized on the application in real-time, making it easier to take notes and much more. You do not need to focus on writing, rather you can focus on the learning and understanding of the topics being discussed. ISWT can also work as a liaison between industry and academia. The novelty of ISWT includes surface independence, low cost, portable, repairable, real-time and very economical for personal usage.
A Novel Approach in Determining the Reasons of Student Attrition based on Enhanced Genetic Algorithm with Cross-Average Crossover Operator

Prediction helps organizations in their decision-making activities for the improvement of the different functions, services, and even their income. The C4.5 algorithm is the most commonly used prediction algorithm having difficulty in choosing attributes that lead to low prediction accuracy. Hence, integration of k-means segmentation for feature selection has been done showing an improvement of prediction accuracy. However, the accuracy result is still not good enough for a prediction model. Thus, there is a need to develop a new strategy to increase the accuracy to an acceptable level. In the present study, Genetic Algorithm (GA) is enhanced and integrated into the prediction model. A new crossover mechanism called Cross-Average Crossover (CAX) is introduced to address the problem of the original GA that can lead to not so accurate prediction results. The student's reasons for attrition in an educational institution was used as the dataset to determine the accuracy of the newly developed model having GA with CAX as against to a model having unenhanced GA. Results showed that an enhanced GA outperformed the unenhanced GA in terms of variable optimization. Further, a prediction model having an improved GA yields a more accurate prediction results as against to a model having unenhanced GA.