Reviewerly/exHarmony huggingface dataset:
The Best Selection of PIP Scholarship: AHP-TOPSIS Vs Fuzzy AHP-TOPSIS

Abstract The Smart Indonesia Program (PIP) is a program created by the government to address education problems in Indonesia. Selection of students who are entitled to PIP assistance is the main problem in this study, so this study aims to provide a comparison and evaluation of the selection of students who are entitled to PIP assistance. Previous research has been evaluated using the AHP-TOPSIS method and in this study comparing the method between AHP-TOPSIS vs Fuzzy AHP-TOPSIS, the purpose of this study is to obtain the effectiveness of both methods. The criteria in this study are the impact of natural disasters, physical disabilities, the convict’s family, PKH / KPS / KKS holders, destitute, orphans, parents’ income and conflict areas. In this study, the results show that the two methods have the same ranking results, even though they have different weight values. This is because the importance value of each criterion is close together so that the results of the ranking of the two methods are the same. From these results, it can be concluded that the two methods are feasible to be used in the PIP scholarship selection, where the ranking of the choices is in alternatives 2 3 and 4.
Low power consumption fall detection using three features

Abstract The increasing of longevity rates contributed to elderly and in line with the increased of medical needs especially on health care and monitoring systems area. To facilitate continuous monitoring and to address the need of healthcare which are non-invasive, affordable, easy-to-use, and non-invasive healthcare solutions are becoming increasingly important. Smartphone is a perfect device to detect human fall because it has various sensors (accelerometer, gyroscope, GPS, and many more), already been accepted by most of the people, and can reduce the electronic waste by giving a smartphone a second chance to become a health care and monitoring system. The research proposed a low power consumption human fall detection by using three features: Signal Vector Magnitude (with modification), Alim, and Tilt Angle Change as a solution to overcome two problems (health care and environment) by using the device that already been existed to reduce the electronic waste. Our proposed solution was able to reach 0.97 of accuracy result and work on the smartphone (low power device) for green computing.
Making 3D Virtual Cities VR Ready: A Performance Study

It is important when making 3D virtual cities for virtual reality (VR) that they perform well as a “break in presence” (BIP) might occur when the FPS becomes too low. The key contributions of this research paper are a performance analysis that checks whether two separate virtual cities are “VR ready” on a recommended specification system to qualify for the Oculus Store (i.e., applications must meet a consistent 90 frames per second (FPS)) and several guidelines that are important when rendering large virtual cities for VR. Both virtual cities (Endless City Driver versions) without pedestrians and parked vehicles (but with foliage in the case of the next generation) are VR ready. Reducing the city tile size by half resulted in a much better performance in general.
Dealing With the Risks of Information Overload

While guests go on farm holidays in the hope of receiving authentic and memorable experiences, information overload is a factor that is increasingly detracting from such experiences. For instance, the steady stream of emails, text messages, and news received through social media channels tends to distract from the main experience. To cope with such overload, script theory suggests that guests are increasingly ‘satisficing' their information intake and subconsciously tuning-out much incoming stimuli from their surroundings. This subconscious satisficing makes it more difficult for farm holiday providers to win the guest's full attention in order to create memorable and authentic experiences. Therefore, this chapter synthesizes theories and empirical research from several disciplines with a view to gaining a better understanding of how this modern information overload serves as a formidable threat to farm holiday providers, who typically operate from small family farms. This synthesis is then employed as the basis for a number of practical and research recommendations.
Filters that Fight Back Revisited: Conceptualization and Future Agenda

Online scams, unsolicited advertisements, messages containing malicious files and other forms of spam continue to be a nuisance in today’s internet, wasting users’ time and causing financial damage to companies and organizations. There have been many proposals on how spam should be stopped, from various kinds of spam filters to legislative measures. One of the more extreme suggestions is fighting back by bombarding spammers’ servers with a deluge of HTTP requests. In the current study, we revisit this idea “filters that fight back” originally proposed by Graham in 2003, and investigate why the approach has received little attention recently. We also showcase an example solution that automatically sends false information back to spammers by filling forms on their websites or replying to mail addresses they have provided. We offer a conceptualization and future agenda of filters that fight back, and discuss the ethical and technical challenges related to this solution.
What Linguistic Information Does Reading Comprehension Require?

Machine comprehension is one of the primary goals in Artificial Intelligence (AI) and Natural Language Processing (NLP). Accessing the difficulty level of machine reading comprehension (MRC) questions is important for building accurate MRC systems. In order to tackle this problem, we propose a novel idea to access the difficulty level of MRC questions, according to the amount of linguistic information required to answer them. Specifically, we systematically analyze and compare the performance for each BERT layer representation per question type on MRC datasets, and highlighted the characteristics of the datasets according to linguistic information of different layers. Our extensive analysis suggests that the superficial categories (or question types) of MRC questions do not directly reflect their difficulty levels and that it is possible to analyze the MRC questions’ difficulty levels according to the amount of linguistic information required.
Design and Implementation of Port Video Terminals Security Access Authentication System Using Blockchain Technology

In the face of the increasing scale of port video surveillance systems, edge computing is gradually used to improve the real-time processing efficiency of surveillance systems and reduce the load on the cloud with the advantages of distributed computing. In order to improve the security factor of terminal access in the edge computing environment, this paper proposes a decentralized terminal security access authentication system based on blockchain, which realizes the identity registration, revocation and mutual authentication of two interfaces between terminal devices and edge computing through the smart contract technology of blockchain, and establishes a trusted channel for data transmission between terminals and edge nodes. Through the functional test of the system and the security analysis of the system on the Hyperledger Fabric platform, it shows that the system has more security attributes and higher security.
Development of Mubadarah System- An Intelligent System for proposals at a University

this paper gives a complete description for the designing, and the development of a system which will be responsible and useful for proposing various ideas at University level. The main target for designing such a system is supposed to be the interactivity between the higher officials and the common people to propose ideas. A transparent system between that decision-makers and the stakeholder for the University can be managed with the help of such a system. The proposals can be submitted with the help of the system, online and can be put to vote by other stakeholders. The decision can be made with the help of positive vote for the idea proposed. Mubadarah (meaning "Idea" in Arabic) can be collected from various stakeholders of the system and can reach the decision-makers of the University. The system can be helpful enough for various other purposes.
Information extraction from digital social trace data with applications to social media and scholarly communication data

Shubhanshu is a Machine Learning Researcher at Twitter working on the Content Understanding Research team. He finished his Ph.D. at the iSchool, University of Illinois at Urbana-Champaign, where he worked as a research assistant with Dr. Jana Diesner and Dr. Vetle Torvik on projects funded by NIH, NSF, KISTI, and Army Research Lab. Shubhanshu's research work is at the intersection of machine learning, information extraction, social network analysis, and visualizations. His work won then Best Student Paper Award at ASIS&amp;T SIGMET workshop. Shubhanshu has published multiple open source projects as well as open datasets related to his research. More information about Shubhanshu can be found at: https://shubhanshu.com. In his thesis, Shubhanshu worked on unifying the concept of information extraction from social media data and scholarly data under the framework of Digital Social Trace Data (DSTD). This framework allow us to answer questions like conceptual novelty, expertise in scientific communities as well as influential users in social media corpus. He further proposed improved information extraction techniques for social media text by using active, semi-supervised, and multi-task machine learning along with the inclusion of meta-data. Additionally, his work resulted in the development of a visualization framework for DSTD along with multiple open source tools and datasets. For more details visit https://shubhanshu.com/phd_thesis/.
Multi label image retrieval based on depth hash

Abstract In recent years, hash is a popular method of image retrieval. Convolutional neural network is used to generate image features and hash codes, so as to achieve fast and effective image retrieval. In order to solve the problem of poor image retrieval effect caused by complex background of Multi-target Image, this paper proposes a hash generation method based on the combination of target region recommendation network and convolutional neural network. The RPN network is selected as the target region recommendation algorithm. After the image passes through the RPN network, multiple target regions and the four-dimensional coordinates of each region will be generated. According to the four-dimensional coordinates, the main target region will be filtered and extracted. Then, the feature of the largest target area is extracted by the convolutional neural network (GoogleNet), and the corresponding hash code is generated, which is finally retrieved in the database. Experiments are carried out on voc2012 data set and self collected data set to verify the algorithm. When the number of test images is 1000, the experimental results show that the total correct rate of the retrieval results of this method is 95.5%, which is about 5 percentage points higher than the existing methods.
On Building a Knowledge Graph of Open Courses

To help online learners to efficiently get open courses they want to learn, we build a large knowledge graph of open courses. Our knowledge graph contains structured information of a large number of open courses, and also provides important links among concepts and courses. Based on the extracted prerequisite relations in the knowledge graph, learning paths of concepts and courses can be recommended to learners. More specifically, we define an ontology for open courses and extract structured information of courses guided by the ontology. Concept linking, prerequisite extraction, and course linking are performed, which enables the knowledge graph to provide helpful assistance to learners.
Design of Wearable Fall Detection Alarm Insole

In order to improve the convenience and health of the elderly, this paper designs a new wearable fall detection alarm insole. The overall scheme and detailed module design of the system are described in detail. The experimental results show that the designed insole has good warning performance.
The Convergence of EDI, Blockchain, and Big Data in Health Care

Electronic Data Interchange (EDI) has been a trusted form of communication standard for numerous industries since the 1970's. EDI is used for the exchange of data between two or more entities using an agreed upon EDI standard. With the plethora of data being generated across industries and research, the reliance on technologies such as EDI is more significant than ever. For an industry dealing with sensitive information such as health care, the parameters of security and reliability are of the utmost importance. A traditional EDI protocol provides a structured mechanism for data sharing between a sender and receiver creating pockets of disconnected data across the health care network. With the advent of blockchain there is now a more complete way to connect all these pockets of data. Blockchain is a distributed ledger that can be accessed by any entity, who has been granted access, to the blockchain. Every partner on the blockchain can see the most current data between all partners removing the typical delays and mishandling of data. By coupling together an industry standard such as EDI with blockchain creates a system that can allow for continued use of a trusted system but revolutionize it with ease of access for everyone involved. Creating an end-to-end data mechanism will not only generate more data, but more complete data since it will include data from every entity in the network on a distributed ledger. While blockchain in health care is not a new idea what this paper is proposing is the next step in this trend by looking at how big data analytics can be used along with the improved data transmission system. With the more complete data that would be generated from an EDI and blockchain system big data will be able to make better predictions, and even open the door to some completely new ways of analyzing data. The objective of this paper is to outline the current technologies and explore some possibilities for how integrating these technologies could benefit any industry, but more specifically health care.
Code Complexity Analyser and Visualiser for Novice Programmer

Code complexity can have a significant influence on software quality. With studies showing program developed by novice programmers can influence software complexity due to lack of experience, practice, and understanding of the concept of programming. This paper investigates the utilisation of machine learning techniques to analyse code complexity levels. Using a public collection of JavaScript codes, we developed a machine learning model to identify the relationship between code characteristics and complexity level. We selected six methods and performed k-fold cross-validation. It was observed that Classification and Regression Trees (CART) and K-Nearest Neighbours (KNN) yielded the best prediction results. Finally, we also implemented a visualisation tool to present the code analysis results providing a means to gain insights on JavaScript codes through their characteristics and complexity level.
An Aided Vehicle Diagnosis Method Based on the Fault Description

To promote the efficiency of fault diagnosis and improve user experience, we proposed an aided vehicle fault diagnosis method based on user’s fault description. According to the description of faults phenomenon, we combined both natural language processing technologies and vehicle structure knowledge to diagnose to locate the fault. First, we collected descriptions of failure from users and made a standardized processing of vehicle fault to build the vehicle fault database and summarized a vehicle proper noun dictionary. Second, according to the design of vehicle, we create a vehicle structure tree and combined both distribution statistics and topic semantic which to build a semantic vector model of vehicle fault description. Finally, we use cosine distance to evaluate the semantic similarity of fault description, based on the result, we can conclude the reason of fault and location. The experiment shows that the precision of fault diagnosis has reached 86.7% and the precision of fault locating has also reached 81.8%. The vehicle diagnosis method will help providers of vehicle maintenance to build a self-service fault inquiry system based on the database from internet. It can collect user’s maintenance requirements ahead, optimizing the service progress and improving the service efficiency.
Scholar Knowledge Graph and Its Application in Scholar Encyclopedia

Knowledge graphs (KGs) are developing rapidly and has a wide range of applications in various fields. But there have been relatively few Chinese encyclopedias with the scholar knowledge graph so far. Therefore, we rely on the academic social network-SCHOLAT to complete the construction of scholar KG. The purpose of this scholar KG is to solve the problems that the information of scholars is not updated in time and scattered in the organizations’ websites. This paper introduces in detail the key technologies for constructing scholar KG, such as knowledge extraction, organization and management of scholar KG. At the same time, we take advantages of KG in semantic expression into account. We apply the scholar KG to the scholar encyclopedia system. Based on the scholar KG, we completed the scholar recommendation on the Scholar Encyclopedia. On the other hand, the scholar KG will also provide the basis for more intelligent applications of SCHOLAT in the future.
Problems of the Shaping Universal and professional competencies among students of Domestic Universities in the implementation of the federal state educational standard of higher education 3++ and ways to solve them

Today, Russia is actively working to introduce the Federal State Educational Standard of Higher Education 3++ (FSES HO 3++) into the educational process. At the same time, it is assumed that these higher education standards will contribute to improving the quality of training students of domestic universities for professional activities in domestic organizations. However, as practice shows, the continuous improvement of FSES HO, according to the Bachelor-Master system, does not solve the key problems associated with the training of professionals demanded by domestic organizations, who should be trained by the universities. This paper shows the problems associated with the formation of professional competencies among students of domestic universities in the implementation of FSES HO 3++, as well as possible ways to solve them and to improve the quality of training students as future professionals for domestic organizations. At the same time, all proposals are based on the practical experience of the authors and scientific analysis of existing problems, as well as feedback from employers justifying their requirements for graduates of domestic universities from the standpoint of the needs of organizations in the young professional staff they need, which allows them to successfully solve the issues of timely provision of the required level of competitiveness.
Analyzing the distributed training of deep-learning models via data locality

In the last few years, deep-learning models are becoming crucial for numerous scientific and industrial applications. Due to the growth and complexity of deep neural networks, researchers have been investigating techniques to train those networks more efficiently. Many efforts have been made to optimize deep-learning models by parallelizing or distributing their training computation across multiple devices. Current state-of-the-art techniques, such as Horovod, have shown to maximize the performance of both the training computation and the inter-node communication of models for different deep-learning frameworks. However, some applications cannot take advantage of the above techniques due to an I/O bottleneck caused by the input data, thus limiting the scalability of the trainings. In this paper, we study an approach based on data locality - that has not been fully studied yet - for those neural networks that cannot benefit from scaling their computation due to a significant bottleneck in the data I/O.
Adversarial Vulnerability of Active Transfer Learning

Two widely used techniques for training supervised machine learning models on small datasets are Active Learning and Transfer Learning. The former helps to optimally use a limited budget to label new data. The latter uses large pre-trained models as feature extractors and enables the design of complex, non-linear models even on tiny datasets. Combining these two approaches is an effective, state-of-the-art method when dealing with small datasets.In this paper, we share an intriguing observation: Namely, that the combination of these techniques is particularly susceptible to a new kind of data poisoning attack: By adding small adversarial noise on the input, it is possible to create a collision in the output space of the transfer learner. As a result, Active Learning algorithms no longer select the optimal instances, but almost exclusively the ones injected by the attacker. This allows an attacker to manipulate the active learner to select and include arbitrary images into the data set, even against an overwhelming majority of unpoisoned samples. We show that a model trained on such a poisoned dataset has a significantly deteriorated performance, dropping from 86% to 34% test accuracy. We evaluate this attack on both audio and image datasets and support our findings empirically. To the best of our knowledge, this weakness has not been described before in literature.
Context Ontology for Smart Healthcare Systems

This paper proposes an improved Context Ontology for Smart Healthcare Systems. The main contribution of this work is the simplification, sufficiently expressiveness, and extendability of the smart healthcare context representation, in which only three contextual classes are required—compared to several classes in the related context ontologies. This is achieved by adapting the feature-oriented domain analysis (FODA) techniques of software product line (SPL) for domain analysis, and subsequently, the lightweight unified process for ontology building (UPON Lite) is used for ontology development. To validate the applicability of the proposed context ontology, sustAGE smart healthcare case study is used. It is found that the proposed context ontology can be used to sense, reason, and infer context information in various users, environments, and smart healthcare services. The ontology is useful for healthcare service designers and developers who require simple and consolidated ontology for complex context representation. This paper will benefit the smart healthcare service developers, service requesters as well as other researchers in the ontology-based context modeling domain.