Reviewerly/exHarmony huggingface dataset:
The Research of Link Prediction in Knowledge Graph based on Distance Constraint

Large-scale knowledge graphs have a lot of hidden knowledge which has not been discovered, so the link prediction of the knowledge graph is an important topic. Translation models represented by TransE are the well-researched algorithms of link prediction. They project the entities and the relations in the knowledge graphs into some continuous vector spaces, and adjust the vector representations of the relations and the entities according to each piece of knowledge. However, in the case of a non-1-to-1 relationship, multiple entity vectors will compete for the same coordinate position in the space. Aiming at this problem, this paper proposes an improved method. By imposing a distance constraint on the competitive entities of a non-1-to-1 relationship, we can narrow the differences between them. Each entity will consider the other competitive entities while adapting itself to fit a triplet, so as to reach the status that each competitive entity is close to the coordinate point of the competition as a whole. Distance constraint can be applied to the existing translation models as a means of optimization. Experiments are conducted on the datasets: FB15K and WN18, and the experimental results show that the method we proposed is effective.
An Entity-Based Fine-Grained Geolocalization of User Generated Short Text

Recently, the fine-grained geolocalization of user-generated short text (UGST), which can benefit many location-based applications, has been attracting the attention of academica. The semantic information in UGST is seldom introduced in most existing work, which reduces the effectiveness of existing methods. To address this issue, we propose an entity-based fine-grained geolocalization of UGST, which consists of following steps. (1) We employ location-based social network to model the coupling between entities and locations, which can introduce much semantic information. (2) We extract entities from non-geotagged UGST, and discards this UGST if it has not location-related entities. Otherwise, (3) we utilize the built coupling model to rank the candidate locations for this UGST, and then select top $n$ locations as the result. The experiments demonstrate that our method shows marked improvement on $Accuracy\text{@}1km$ and average error distance compared to the state-of-the-art FRV, WMV and LW methods.
Lip Motion Recognition for Indonesian Vowel Phonemes Using 3D Convolutional Neural Networks

Lip motion recognition is a technique for interpreting visual data that focuses on the mouth area and aims to recognize lip movement. The development of lip motion recognition is expected to be used to develop communication tools with deaf people and to automate the speech-to-text process visually. In the Indonesian language, the existence of vowel phonemes is needed to produce sounds so that words and sentences in the Indonesian language can be formed. This paper proposes a model that can recognize Indonesian vowel phonemes (/a/, /i/, /u/, /e/, and /o/) in lip movements. We proposed a model that uses 3D Convolutional Neural Networks. The data in this paper were processed by resizing into 112×56 pixel resolution then, proceed to the data augmentation by reversing the data horizontally and add blur to the data. The results of the testing of the vowel phoneme recognition model on lip motion show the highest accuracy rate of 84%.
Low Cost and Integrable Healthcare Services Using VoIP for Remote Patient Monitoring

This paper presents the integration of VoIP (Voice over IP) in healthcare services through a specific proof-of concept telemedicine application. The proposed concept involves using certain procedures and resources for the acquisition and centralization of the medical data collected from monitored patients. With the solution presented in this article, the possibilities for remote monitoring of patients are extended through a custom entity that we called “smarthub”. By utilizing VoIP, new services and functions can be integrated into existing voice networks for more effective patient long distance communication and medical assistance. In order to achieve these purposes, an application was developed according to four major criteria: minimal cost, simple and intuitive interaction with the user, enhanced mobility and possibility to be integrated with other services and systems.
Creating Annotations for Web Ontology Language Ontology Generated from Relational Databases

Many approaches that have been proposed that allow users to create a Web Ontology Language (OWL) ontology from a relational database fail to include metadata that are inherent to the database tables. Without metadata, the resulting ontology lacks annotation properties. These properties are key when performing ontology alignment. This paper proposes a method to include relevant metadata through annotation properties to OWL ontologies, which furthers the ability to integrate and use data from multiple unique ontologies. The described method is applied to geospatial data collected from The National Map, a data source hosted by the U. S. Geological Survey. Following that method, an ontology was manually created that used the metadata from The National Map. Because a manual approach is prone to human error, an automated approach to storing and converting metadata into annotation properties is discussed.
Suitable encrypting algorithms in Parallel Processing for improved efficiency

Abstract Cryptographic algorithms play a vital role in providing security to the data and from malicious attacks. Now a day’s storing the data in the Cloud became essential for organizations to reduce the overhead of securing the data within the organization. “Stream ciphers” are more vulnerable to attacks when compared to “Block Ciphers”. In this article we used java platform to evaluate and compare all the block cipher techniques used for converting plain text to cipher text and we have identified the best block cipher technique which can be used in cloud.
eLearning System Evaluation: A Case Study Based on ARO Gapopin’s Lecturer’s Experiences

In complying with governmental advice through the ministry of education in combatting the rapid spreading of Covid-19 or Corona Virus, Akademi Refraksi Optisi dan Optometry Gapopin (ARO Gapopin) turns its traditional face to face learning to online. ARO Gapopin takes benefits from its E-Learning system. However, that system is newly built. It has never been used before as massively as now in the period of physical distancing. In other words, the quality and success of the system have never been evaluated yet. This research seeks to evaluate the quality and the success of that eLearning system from the view of lecturers. The result reveals that all faculties consider the eLearning system as technically qualified, although crash sometimes happens along the way. Faculty members are satisfied with the technical service available. Each faculty member is pleased with the overall system, including the performance of eLearning, experience in using eLearning, and the fulfillment of the needs. All faculty members confidently claim that the eLearning system is a useful and beneficial tool for their teaching. Lastly, faculty member regularly uses eLearning system for their education. Nonetheless, they do not depend on that system in performing teaching activities.
Inter-subject links within the content-methodical lines of Math school course

This paper considers some ways of inter-subject links realization within the content-methodical lines of Math school course. The course of Math at school is permeated with inter-subject links, which are the foundation of the subject and through which a succession is implemented in teaching of Mathematics. The implementation of inter-subject links in teaching Mathematics requires a special organization of educational material and the process of its study and, therefore, acts as a didactic principle. In this paper, inter-subject links are revealed at two levels: the level of content and the level of methods for solving problems. The inter-subject links at the substantial level are learning numerical sequences and linear equations and inequalities, while at the level of methods examples of transcendental equations. The implementation of inter-subject links at the content level takes place in conjunction with their implementation at the level of methods and has a wide practical application at Mathematics lessons. The conducted researches have shown that inter-subject links naturally extend on the subject area Mathematics and Computer Science according to the Federal state educational standards of the basic and secondary general education. The practical significance of the study is that its results can be used for teaching Mathematics at school.
CONTENT AND STRUCTURE OF PROFESSIONALLY SIGNIFICANT QUALITIES OF ECONOMICS STUDENTS

The subject of the study is the vocational training of Bachelors of Economics. The object of the study is the content and structure of professionally significant qualities of Bachelors of Economics. The aim of the work is to study the content and structure of professionally significant qualities of future of Bachelors of Economicsin the context of the requirements posed by modern labour market and the possibilities of professional training. The study appears relevant due to the need to find optimal ways of training professional economists who could meet the requirements of rapidly changing and digitalized world economies. The article clarifies the concept of professionally significant qualities of an economist. Based on the analysis of psychological and pedagogical literature, the expert survey results, and interviews of potential employers, the article gives the structure of professionally significant qualities of Bachelors of Economics. They are attitude-related qualities: standard (situational professional motivation, knowledge of professional ethics, stable civic stand) and supplementary (dominant professional motivation, professional values, strong civic identity and behaviour); individual and personal qualities and abilities: standard(emotional balance, discipline, objectiveness, integrity) and supplementary (mental flexibility, professional mobility, capability for self-education and self-development); special qualities and abilities: standard(analytical, synthetic and prognostic abilities) and supplementary (critical thinking, lean thinking, project thinking); social and personal qualities and abilities: standard(sociability, ability to resolve conflicts) and supplementary (client orientation, team building). The paper also presents the results of an ascertaining experiment, which confirmed the deficit of the supplementary professionally significant qualities of Bachelors of Economics.
Container Elasticity: Based on Response Time using Docker

Objective:: Cloud computing is an approach to provide the computing resources (machine) to end-users for running their application over the Internet. The computing resources consist of various things (e.g. RAM, Memory, CORE, etc.). These resources are allocated to an application without human intervention for managing the fluctuating workload. To manage the real-time fluctuating workload, cloud providers use VM based or Container-based virtualization to host the client services. Adding/removing resources dynamically as per the demand of application through cloud is known as elasticity. Cloud providers use the auto-scaling mechanism to implement elasticity. A machine that hosts an application can be either overloaded or under-loaded due to the real-time fluctuating workload. The cloud providers use an auto-scaling mechanism to automatically scale up or down the computing resources at the right moment for managing the real-time fluctuating workload. The failure of allocation/de-allocation of resources at right time leads to SLA violation, service unavailability, customers lost, more power consumption, minimum throughput and maximum response time. Hence, the allocation/de-allocation of resources at right moment becomes critical for successful completion of task in dynamic environment efficiently. Methods:: Resource provisioning for managing dynamic and fluctuating workload has been achieved through an algorithm (PID with dynamic HAProxy) which is based on decision-making approach that depends on response time of container using mechanism of control theory. Results:: The proposed work has improved performance of the system in terms of resource utilization and response time to manage the fluctuating workload. Conclusion:: The addition/removal of containers dynamically to manage fluctuating workload can be achieved more efficiently.
Prospects for Development of Artificial Intelligence in the Scientific Learning of the World

In the process of comprehending the prospects for the artificial intelligence development, the authors come to a conclusion that in the scientific learning of the world the problematic issues of the artificial intelligence are connected with problematic issues of recognizing the artificial intelligence systems and ordinary human thinking The article performs an analysis of the concepts of «intelligence» and «artificial intelligence», in the process of which the intelligence is viewed through a systematic approach in its broad sense. The purpose of the article is to present a number of conclusions about the levels of development of scientific studies of the problems under investigation, is there any reason to argue that attempts to implement the epistemological characteristics of thinking in modern artificial intelligence systems have not only been undertaken but also successful, and whether is it possible to talk about full transfer of the intellectual functions to the technical systems, endowing them with epistemological tools (in the context of the discussion about strong and weak versions of the artificial intelligence). The authors study the concept of «phenomenology of intelligence», the perception of intelligence in various historical eras by famous philosophers and scientists of other branches of knowledge; they identify the artificial intelligence as a special branch of science, analyze the existing problems in this field. In writing the article, they use the system approach, the theoretical analysis of and generalization of the scientific information, the historical, predicted, critical and dialectical methods of investigation.
Deep Learning Defense Method Against Adversarial Attacks

Recent research has revealed that the output of Deep Neural Networks (DNN) can be easily altered by adding relatively small perturbations to the input pixels. These pixels have to be filtered out for defending DNN that will cause lots of computations. To reduce the computation, in this paper, the pixels that their slight change will cause the neural network to have a wrong result for prediction are found out and filtered out for defense goal. Experiment results show that our defense method has achieved about 85% defense success rate with filtering out 50 mutation pixels.
Raw-Guided Enhancing Reprocess of Low-Light Image via Deep Exposure Adjustment

Enhancement of images captured in low-light conditions remains to be a challenging problem even with the advanced machine learning techniques. The challenges include the ambiguity of the ground truth for a low-light image and the loss of information during the RAW image processing. To tackle the problems, in this paper, we take a novel view to regard low-light image enhancement as an exposure time adjustment problem and propose a corresponding explicit and mathematical definition. Based on that, we construct a RAW-Guiding exposure time adjustment Network (RGNET), which overcomes RGB images’ nonlinearity and RAW images’ inaccessibility. That is, RGNET is only trained with RGB images and corresponding RAW images, which helps project nonlinear RGB images into a linear domain, simultaneously without using RAW images in the testing phase. Furthermore, our network consists of three individual sub-modules for unprocessing, reconstruction and processing, respectively. To the best of our knowledge, the proposed sub-net for unprocessing is the first learning-based unprocessing method. After the joint training of three parts, each pre-trained seperately with the RAW image guidance, experimental results demonstrate that RGNET outperforms state-of-the-art low-light image enhancement methods.
Improving English Phoneme Pronunciation with Automatic Speech Recognition Using Voice Chatbot

Computer Aided Pronunciation Training (CAPT) is gaining attention in recent years especially for Second Language (L2) and Foreign Language (FL) learners. Nevertheless, operating such learning tools usually requires some training which may hinder the users’ interest. To address these issues, we propose a Phonics Learning Voice Chatbot (PLVC) using Automatic Speech Recognition (ASR) and Triplet Neural Networks (TNNs). TNNs have excellent capabilities in discerning different entities that are similar unless examined in fine grain scales. This distinctive feature enables them to be utilized in classifying and assessing phonemes. The employment of chatbot in our teaching process not only relieves users from using keyboard, mouse, and menus, the teaching storyline can also be adjusted automatically and imperceptibly based on the user replies during teaching. Finally, PLVC has one unique feature that it can be used to assess the performance of the users in phonic learning. We performed an experiment with 10 users and compared the ratings given by PLVC and experienced phoneme assessors and found that the correlation coefficient is 0.71, showing the assessment done by PLVC is reliable.
Network-aware task selection to reduce multi-application makespan in cloud

One new metric that plays a vital role in evaluating the cloud service is the multi-application makespan. There are usually multiple applications without a deadline in the cloud, while the makespan of each application should be minimized, such as parallel applications for training neural networks. However, the previous scheduling rule, called moving the computation tasks of applications closer to data, fails to reduce multi-application makespan. Because it is unknown which application the task belongs to, the number of optimized tasks (close-to-data tasks) is unbalanced among multiple applications, which causes a big gap between the makespan of multiple applications. To address this issue, we propose a cooperative scheduler that considers the application-task relationship, whose goal is reducing the multi-application makespan. Firstly, we propose a cost sharing game model to guide the balance of optimized task selection between multiple applications, in which the cost is referred to as the makespan. Specifically, we develop a network-aware method that can accurately estimate the makespan with a given task selection strategy. In the end, we evaluate the cooperative scheduler in the actual Hadoop cluster with diverse network environments. Experimental results demonstrate that the gap between multi-application makespan decreases. Moreover, compared to the baseline schedulers, the longest makespan decreases by 61.5%, and the network traffic is saved by more than 50%.
The presentational dimension of Geert Wilders’s populist argumentative style

Abstract This study responds to van Eemeren’s ( 2019 , 2021 ) call for research on the prototypical argumentative styles used in particular domains or communicative activity types by particular individuals or groups. It explores the argumentative style of Dutch politician Geert Wilders in presenting populist arguments, i.e., arguments claiming that if many people hold a certain standpoint, this standpoint should be accepted. A corpus study of 27 texts taken from the website of Wilders’s political party reveals four characteristics of this presentation that deviate significantly from the general descriptions of this type of argument given in the textbooks: (1) absence of indicators, (2) implicit standpoint, (3) wide range of verbs to indicate what “the people” think or believe, (4) use of a construction indicating that the speaker is acting as a mouthpiece (“on behalf of the people, I say”).
Attribute-Based Access Control for Vehicular Edge Cloud Computing

Edge computing provides a new platform to support real-time connected vehicle applications. One of the challenges is to ensure communication security among vehicles, roadside devices, and edge servers, while addressing group communication nature and fast changing group membership. This paper presents the design and evaluation of a secure communication scheme for vehicular edge computing applications based on decentralized attribute-based encryption (ABE), which provides flexible data encryption and access control according to attribute-based policies, without requirement for knowing the recipient identities and establishment of a secure communication channel between the sender and recipient. Key-policy ABE and ciphertext-policy ABE are employed to collect sensed data for process and disseminate processed results to the involved entities, respectively. The communication protocols are designed and an experimental prototype considering an edge cloud-assisted collision warning application is implemented for proof of the concept. The evaluation results show that the proposed ABE-based scheme is efficient and feasible.
Embedded Feature Construction in Fuzzy Decision Tree Induction for High Energy Physics Classification

Fuzzy decision trees have been successfully applied in numerous domains. The popularity of these models comes notably from their interpretability, namely the ability of humans to understand them. However, on the contrary to neural networks, the induction of such models does not include a generation of their own feature space. In this work, the embedding of feature construction in fuzzy decision tree induction algorithms is studied, so that they can create new input features, without affecting the overall interpretability of the model. This method is successfully applied to a classification problem in high-energy physics to study the benefits of having constructed features in fuzzy decision tree on the classification scores, allowing them to have their own interpretable representation of the data.
Virtual Convention Center: A Socially Emotional Online/VR Conference Platform

A cognitive model, producing believable socially emotional behavior, is used to control the Virtual Actor behavior in an online scientific conference paradigm. For this purpose, a videogame-like platform is developed, in which Virtual Actors are embedded as poster presenters, receptionists, etc. The expectation is that the combination of somatic factors, moral appraisals and rational values in one model has the potential to make behavior of a virtual actor more believable, humanlike and socially acceptable. Implications concern future intelligent cobots and virtual assistants, particularly, in online conferencing and distance learning platforms and in intelligent tutoring systems.
A New Hidden Markov Model Approach for Pheromone Level Exponent Adaptation in Ant Colony System

We propose in this paper a Hidden Markov Model (HMM) approach to avoid premature convergence of ants in the Ant Colony System (ACS) algorithm. Indeed, the proposed approach was modelled as a classifier method to control the convergence through the dynamic adaptation of the \( \alpha \) parameter that weighs the relative influence of the pheromone. The implementation was tested on several Travelling Salesman Problem (TSP) instances with different number of cities. The proposed approach was compared with the standard ACS and the existing fuzzy logic in the literature. The experimental results illustrate that the proposed method shows better performance.