Reviewerly/exHarmony huggingface dataset:
E-commerce Product Analysis Using Sentimental Analysis

International Journal of Computer Sciences and Engineering (A UGC Approved and indexed with DOI, ICI and Approved, DPI Digital Library) is one of the leading and growing open access, peer-reviewed, monthly, and scientific research journal for scientists, engineers, research scholars, and academicians, which gains a foothold in Asia and opens to the world, aims to publish original, theoretical and practical advances in Computer Science,Information Technology, Engineering (Software, Mechanical, Civil, Electronics & Electrical), and all interdisciplinary streams of Computing Sciences. It intends to disseminate original, scientific, theoretical or applied research in the field of Computer Sciences and allied fields. It provides a platform for publishing results and research with a strong empirical component. It aims to bridge the significant gap between research and practice by promoting the publication of original, novel, industry-relevant research.
Overview of UP-Fall Detection Project

In this paper, we present an overview of our project of UP-Fall Detection. The aim of this project is to configure a simple, comfortable and fast fall detection and human activity recognition system. This system must be accurate in fall detection and easy to implement and adopt. We briefly describe the achievements and advances obtained in two years.
MPEG column: 128th MPEG meeting in Geneva, Switzerland

The 128th MPEG meeting concluded on October 11, 2019 in Geneva, Switzerland with the following topics: •Low Complexity Enhancement Video Coding (LCEVC) Promoted to Committee Draft •2nd Edition of Omnidirectional Media Format (OMAF) has reached the first milestone •Genomic Information Representation --- Part 4 Reference Software and Part 5 Conformance Promoted to Draft International Standard The corresponding press release of the 128th MPEG meeting can be found here: https://mpeg.chiariglione.org/meetings/128. In this report we will focus on video coding aspects (i.e., LCEVC) and immersive media applications (i.e., OMAF). At the end, we will provide an update related to adaptive streaming (i.e., DASH and CMAF).
Privacy as a Service: Anonymisation of NetFlow Traces

Effective data anonymisation is the key to unleashing the full potential of big data analytics while preserving privacy. An organization needs to be able to share and consolidate the data it collects across its departments and in its network of collaborating organizations. Some of the data collected and the cross-references made in its aggregation is private. Effective data anonymisation attempts to maintain the confidentiality and privacy of the data while maintaining its utility for the purpose of analytics. Preventing re-identification is also of particular importance. The main purpose of this paper is to provide a definition of an original data anonymisation paradigm in order to render the re-identification of related users impossible. Here, we consider the case of a NetFlow Log. The solution includes a privacy risk analysis process to classify the data based on its privacy level. We use a dynamic K-anonymity paradigm while taking into consideration the privacy risk assessment output. Finally, we empirically evaluate the performance and data partition of the proposed solution.
Experimental verification of a coherence factorization law for quantum states

As a quantum resource, quantum coherence plays an important role in modern physics. Many coherence measures and their relations with entanglement have been proposed, and the dynamics of entanglement has been experimentally studied. However, the knowledge of general results for coherence dynamics in open systems is limited. Here we propose a coherence factorization law that describes the evolution of coherence passing through any noisy channels characterized by genuinely incoherent operations. We use photons to implement the quantum operations and experimentally verify the law for qubits and qutrits. Our work is a step toward understanding of the evolution of coherence when the system interacts with the environment, and will boost the study of more general laws of coherence.
Temporal Planning with Incomplete Knowledge and Perceptual Information

In real-world applications, the ability to reason about incomplete knowledge, sensing, temporal notions, and numeric constraints is vital.While several AI planners are capable of dealing with some of these requirements, they are mostly limited to problems with specific types of constraints.This paper presents a new planning approach that combines contingent plan construction within a temporal planning framework, offering solutions that consider numeric constraints and incomplete knowledge.We propose a small extension to the Planning Domain Definition Language (PDDL) to model (i) incomplete, (ii) knowledge sensing actions that operate over unknown propositions, and (iii) possible outcomes from non-deterministic sensing effects.We also introduce a new set of planning domains to evaluate our solver, which has shown good performance on a variety of problems.
CNN structure optimization using differential evolution with individual dependent mechanism

While Convolutional Neural Network (CNN) is known to be a prominent method, there is still a need for appropriate structures to achieve high accuracy. Structure optimization is usually handcrafted, and there is a raising need for an automated optimization method. In this research, we introduce an auto-encoder to create new structures and apply Differential Evolution with an Individual-Dependent Mechanism (IDE) to a simple CNN Structure optimization problem. Experiments using the proposed framework have been conducted on the Cifar10 dataset. Experimental results showed that the proposed method is fit as a structure optimizer.
FROM THE ANALYSIS OF A CASE STUDY TO THE CREATION OF STORYTELLING AS AN ACTIVE LEARNING METHODOLOGY AND ITS COMPARISON IN RELATION TO ACADEMIC PERFORMANCE AND SATISFACTION

FROM THE ANALYSIS OF A CASE STUDY TO THE CREATION OF STORYTELLING AS AN ACTIVE LEARNING METHODOLOGY AND ITS COMPARISON IN RELATION TO ACADEMIC PERFORMANCE AND SATISFACTION
EDUCATION AND THE 2030 AGENDA: IMPACT ON INFORMATION AND COMMUNICATIONS TECHNOLOGIES

Appears in: EDULEARN22 Proceedings Publication year: 2022Pages: 1258-1266ISBN: 978-84-09-42484-9ISSN: 2340-1117doi: 10.21125/edulearn.2022.0331Conference name: 14th International Conference on Education and New Learning TechnologiesDates: 4-6 July, 2022Location: Palma, Spain
CREATION OF AN APP PROTOPTYPE FOR THE RŪTA’S CHOCOLATE MUSEUM

1 ISCAP / P.PORTO (PORTUGAL)2 Šiauliai State College (LITHUANIA)3 CEOS.PP / ISCAP / P.PORTO (PORTUGAL)
ENHANCING AVIATION ENGLISH COURSE THROUGH SIMULATION-BASED INSTRUCTION

Appears in: EDULEARN22 Proceedings Publication year: 2022Page: 10456 (abstract only)ISBN: 978-84-09-42484-9ISSN: 2340-1117doi: 10.21125/edulearn.2022.2540Conference name: 14th International Conference on Education and New Learning TechnologiesDates: 4-6 July, 2022Location: Palma, Spain
Research on edge data Processing security technology in Industrial Internet

The rapid development of "Internet" has brought people into the era of "interconnection of all things", changed people's living habits, and also brought changes to the production mode of enterprises. In the security protection architecture of industrial Internet, how to ensure the secure storage of data from the edge layer to the cloud platform, and how to solve the security problem of edge data has always been the focus of the industry. This paper mainly analyzes the main security threats faced by the current industrial Internet from the perspective of security vulnerabilities and attack technologies. Aiming at how to improve the data security in the security architecture of industrial Internet, this paper proposes to use hybrid encryption to encrypt and decrypt the edge data; The principle and performance of DES algorithm, 3DES algorithm and AES algorithm are compared; In particular, the principle and implementation process of SHA-1 hash algorithm are studied; Finally, the improved elliptic curve digital signature algorithm is used to effectively improve the efficiency of digital signature and the security of private key, which further shows that the data security protection system is feasible at the edge.
Effects of Word Frequency on Incidental Vocabulary Learning under Tasks with the Same Involvement Load

Most researches of the involvement load hypothesis have investigated the effect of incidental vocabulary learning with different tasks involvement load, and there is a relative lack of studies that analyze incidental vocabulary learning from the perspectives of word frequency.To investigate the effect of incidental vocabulary learning of junior high school students, the study controlled the involvement index and conducted an empirical study on word frequency.The results of the immediate test and the delayed test showed that word frequency (twice, four times, and six times) had a certain effect on incidental vocabulary learning under equal involvement load, and the word frequency rates were proportional to the effect of incidental vocabulary learning.However, in the delayed test, the data analysis showed that there was no significant difference between the scores obtained by subjects exposed to different word frequencies.Therefore, in vocabulary teaching and learning, we should pay attention to the role of word frequency in incidental vocabulary learning on the one hand, and consider the influence of time factor on vocabulary learning on the other.
An Optimal Path Planning Issue Parameters with Potential Feasible Area Using Evolutions Algorithm

The traditional artificial potential field method’s parameter values are usually determined empirically would cause unsmooth planned paths, low calculation efficiency, unreachable goal spots. This study suggests optimization parameters in the artificial potential field in finding the shortest route for mobile robots using the differential evolution algorithm (EA). Three parameters of the positive proportional gain coefficient of the gravitational field, the repulsive field gain coefficient, and the influence distance of obstacles to find the shortest path are considered to modify the infeasible ways of search space in optimizing the algorithm in locating the fastest route. A collision-free path model is constructed by using known global environment information for the mobile robot, and the EA algorithm is used to plan the robot’s best path. In the experiment section, the obtained results from the suggestion scheme are compared with the other techniques in the literature. The results show that the suggested approach can produce faster convergence and higher satisfaction quality than the other.
Using Possibilistic Networks to Compute Learning Course Indicators

E-learning systems generate more and more data that can be used to improve pedagogy. They can also provide a better understanding of a student’s learning style. As a result, it is possible to propose a differentiated pedagogy which takes into account learners’ needs. The aim of this research is to build course indicators by using expert knowledge in order to provide a synthesis of information about students. As knowledge is often imprecise and uncertain, we used possibility theory to represent knowledge through a possibilistic network. Firstly, we used a message passing algorithm to compute learning course indicators, then we proposed several improvements. Indeed, the use of uncertain gates allows us to generate automatically Conditional Possibility Tables (CPT) instead of eliciting all parameters. Next, we compiled the junction tree of the possibilistic network in order to improve computation time. We compared our compiling approach with message passing inference. A decision support system is generated automatically at the end of the computations. The indicators are presented in a decision support system in which color codes illustrate certainty.
Visual Cue Effects on a Classification Accuracy Estimation Task in Immersive Scatterplots

Immersive visualization in virtual reality (VR) allows us to exploit visual cues for perception in 3D space, yet few existing studies have measured the effects of visual cues. Across a desktop monitor and a head-mounted display (HMD), we assessed scatterplot designs which vary their use of visual cues—motion, shading, perspective (graphical projection), and dimensionality—on two sets of data. We conducted a user study with a summary task in which 32 participants estimated the classification accuracy of an artificial neural network from the scatterplots. With Bayesian multilevel modeling, we capture the intricate visual effects and find that no cue alone explains all the variance in estimation error. Visual motion cues generally reduce participants’ estimation error; besides this motion, using other cues may increase participants’ estimation error. Using an HMD, adding visual motion cues, providing a third data dimension, or showing a more complicated dataset leads to longer response times. We speculate that most visual cues may not strongly affect perception in immersive analytics unless they change people's mental model about data. In summary, by studying participants as they interpret the output from a complicated machine learning model, we advance our understanding of how to use the visual cues in immersive analytics.
ILASR: Privacy-Preserving Incremental Learning for Automatic Speech Recognition at Production Scale

Incremental learning is one paradigm to enable model building and updating at scale with streaming data. For end-to-end automatic speech recognition (ASR) tasks, the absence of human annotated labels along with the need for privacy preserving policies for model building makes it a daunting challenge. Motivated by these challenges, in this paper we use a cloud based framework for production systems to demonstrate insights from privacy preserving incremental learning for automatic speech recognition (ILASR). By privacy preserving, we mean, usage of ephemeral data which are not human annotated. This system is a step forward for production levelASR models for incremental/continual learning that offers near real-time test-bed for experimentation in the cloud for end-to-end ASR, while adhering to privacy-preserving policies. We show that the proposed system can improve the production models significantly(3%) over a new time period of six months even in the absence of human annotated labels with varying levels of weak supervision and large batch sizes in incremental learning. This improvement is 20% over test sets with new words and phrases in the new time period. We demonstrate the effectiveness of model building in a privacy-preserving incremental fashion for ASR while further exploring the utility of having an effective teacher model and use of large batch sizes.
ARTIFICIAL INTELLIGENCE AND EDUCATION: MYTH AND FACTS

Appears in: EDULEARN22 Proceedings Publication year: 2022Pages: 996-1001ISBN: 978-84-09-42484-9ISSN: 2340-1117doi: 10.21125/edulearn.2022.0278Conference name: 14th International Conference on Education and New Learning TechnologiesDates: 4-6 July, 2022Location: Palma, Spain
A Study on the Selection of Neologism for Professionals and Learning in Advanced Korean: Focusing on the “social” Theme Field

The purpose of this study is to select and suggest neologism for teaching and learning for advanced Korean learners. For this purpose, 1,040 Neologisms in the topic area of “society” were extracted from the Newly-coined word sourcebook and analyzed how continuously they have been used for 10 years from 2009 to 2019 using the news big data analysis system BIGKINDS. Based on this analysis criteria of this study, 60 neologism were finally selected for teaching and learning. Furthermore, we analyzed the social factors that influenced these retention and the use cases. At the time, the group was divided into four categories: “neologism showing an increasing phase”, “neologism showing a decreasing phase”, “neologism showing high usage at a certain period” and “neologism that is used steadily”. This analysis is significant in that it extracted the neologism needed at the Korean language education field from the Newlycoined words sourcebook published by the prestigious National Institute of Korean Studies and analyzed examples sentence and social cause to supporting this. The Neologism proposed in this study can be used as basic materials in the field of education in that they are analyzed by finding and analyzing the background of the neologism and the possibility of future use.&#x0D;
An Exploratory Research for Diagnosis and Improvement Plan of Supervisor’s Gabjil Issues in the Organization: Using Text Mining and Semantic Network Analysis

Gabjil, which is regarded as a chronic disease in Korean society, occurs in various classes and places. In particular, supervisor’s Gabjil takes advantage of their superior position in the workplace to provide inappropriate work instructions and verbal abuse to subordinates, causing side effects that undermine an excellent workplace culture. This study aims to use major social media to collect atypical big data that is highly related to supervisor’s Gapjil within the organization and to analyze it to explore the overall perception and issues of supervisor’s Gapjil. In addition, based on the results derived from the analysis, we would like to suggest an improvement plan for supervisor’s Gapjil in the future. A total of 7,500 keywords were collected through online services provided by Naver, Daum, and Google, and the final 60 keywords were selected, which were highly related to supervisor’s Gapjil. The period of collecting keywords related to supervisor’s Gapjil was limited to three years, from April 1, 2019, to March 31, 2022. As a result, first, keywords that are highly related to supervisor’s Gapjil, such as employees, companies, bosses, work, words, reports, verbal abuse, damage, suicide, etc., were collected. Second, through CONCOR analysis, two major groups, five Surrounding groups, and seven groups were formed.
Main Trends in the Organization of the Postgraduate Research and Education Process in the Context of Digitalization of Higher Education

The relevance of implementing digital learning environment is that digital technologies influence the level of development. Therefore, the state and society need specialists capable of operating the most sophisticated equipment, devices and robots to remain globally competitive. In the context of the global transformation of education, it is necessary to nurture professionals capable of rapidly adapting to the ever-improving modern educational processes and technologies. Digitalization serves as a tool for innovative development of higher education, changing the demand for professions and competencies of university graduates. A specific feature of the digital transformation of education should be the creation and mass dissemination of new models of educational organizations based on new high-performance teaching practices and methods, which are successfully implemented in the digital educational environment.
A STUDY ON STUDENT AGENCY OF COLLEGE STUDENTS WHO PARTICIPATED IN NON-FACE-TO-FACE CLASSES

"The purpose of this study is to look at the student as agency who leads the class, and to examine the experience of mutual communication as agency. For this purpose, 4 students who took the social education and major compulsory classes at University A in South Korea were selected as research participants. In order to increase the coherence of the research purpose, in addition to the interview data, the class impressions and midterm and final test assignments written by the students were supplemented. The research results according to this study are as follows. First, the non-face-to-face discussion class allowed students to experience both fear and unfamiliarity from the non-face-to-face environment at the same time. In particular, the non-face-to-face environment made people hesitate about how to communicate. Second, group discussion improved communication between theory and reality by analyzing textbooks based on class theory and sharing the analysis results among students. Third, field trip facilitated the connection between online and offline, allowing them to look back on their own world of life and build confidence as a future teacher. The following are the suggestions accordingly. First, it should promote interaction with students and teachers. Second, the class should provide various connections outside the class. Third, strong motivation for class is required. This study does not suggest a universal law of student agency in that it only targeted students from the College of Education of A University in Korea. However, it is meaningful in that by exploring the process of developing student agency, it gave hints on what direction to take in the future."
Online Black-Box Confidence Estimation of Deep Neural Networks

Autonomous driving (AD) and advanced driver assistance systems (ADAS) increasingly utilize deep neural networks (DNNs) for improved perception or planning. Nevertheless, DNNs are quite brittle when the data distribution during inference deviates from the data distribution during training. This represents a challenge when deploying in partly unknown environments like in the case of ADAS. At the same time, the standard confidence of DNNs remains high even if the classification reliability decreases. This is problematic since following motion control algorithms consider the apparently confident prediction as reliable even though it might be considerably wrong. To reduce this problem real-time capable confidence estimation is required that better aligns with the actual reliability of the DNN classification. Additionally, the need exists for black-box confidence estimation to enable the homogeneous inclusion of externally developed components to an entire system. In this work we explore this use case and introduce the neighborhood confidence (NHC) which estimates the confidence of an arbitrary DNN for classification. The metric can be used for black-box systems since only the top-1 class output is required and does not need access to the gradients, the training dataset or a hold-out validation dataset. Evaluation on different data distributions, including small in-domain distribution shifts, out-of-domain data or adversarial attacks, shows that the NHC performs better or on par with a comparable method for online white-box confidence estimation in low data regimes which is required for real-time capable AD/ADAS.
The Need for a Comprehensive Ontology for Smart Buildings

Digitization and automation in the building sector are constantly increasing. Developments around the Internet of Things (IoT) also affect this industry. However, due to the lack of common standards, the development of building automation has been complicated, expensive, and slowly progressing. This paper presents an approach to increase the utilization of sensor data. The approach will help develop smart buildings where energy efficiency is increased and where better interaction with surrounding infrastructure is facilitated. The paper’s focus is on ontologies that make communication more intelligent, data labelling more efficient and utilization of collected and real-time data more expedient relative to the building's performance, area utilization and user experience. The idea is not to replace existing ontologies, but to create an overarching structure that connects essential concepts representing different perspectives of a building and interactions with surrounding infrastructure. We call this structure a hyper-ontology. The presented in the paper approach reflects ongoing work in the project DataCat.