Reviewerly/exHarmony huggingface dataset:
Word Inhibitory Characteristics of Young and Older Adults using Probe Recognition Task

연구목적: 정상 노화 과정에서 발생하는 노년층의 인지 및 언어 능력 저하의 요인으로 불필요한 정보를 억제하는 기능이 관련되어 있음이 알려져 있다. 본 연구는 읽기 폭 과제를 변형한 탐사재인 과제를 통해 청년층과 노년층의 수행을 비교함으로써 노화에 따른 단어 억제 특성의 차이를 살펴보고자 하였다. 연구방법: 청년층에 해당하는’ 20~30대 성인 13명과 노년층에 해당하는 65세 이상 성인 13명이 연구에 참여하였다. 본 연구의 과제는 선행연구들의 읽기 폭 과제에 탐사재인 절차를 도입하여 구성한 것으로서, 주어지는 문장을 읽고 마지막 단어들을 기억하였다가 회상해야하는 통상적인 읽기 폭 과제의 절차에 더하여 각 문장 직후에 제시되는 단어가 문장 속에 있었는지 판단하는 탐사재인 수행을 요구한다. 이 때, 제시되는 단어는 억제가 요구되는 문장의 첫 단어, 저장 및 회상이 요구되는 끝 단어, 통제 조건인 새로운 단어의 세 조건으로 구성되었으며, 단어 유형에 따른 정반응률과 반응시간을 분석하였다. 연구결과: 두 집단은 공통적으로 모든 단어 유형에서 높은 정반응 점수를 획득하였고, 이를 통해 노년층에서도 단어의 억제와 활성화 기제가 적절하게 작용하고 있음을 확인할 수 있었다. 한편, 반응시간 측면에서 노년층이 청년층에 비해 유의하게 긴 반응시간을 보였으며, 두 집단 모두 저장이 요구되는 끝 단어 조건에서의 반응시간이 유의미하게 짧았다. 집단과 단어 유형의 상호작용은 나타나지 않았다. 결론: 본 연구 결과는 일반 노년층의 경우 정보를 억제하는 기능은 비교적 보존되어 있으나 노화의 진행에 따라 억제 처리의 속도나 효율성이 저하될 수 있음을 시사하였다. Purpose: The cognitive and linguistic decline in older adults that occurs during normal aging may be related to the ability to inhibit unnecessary information in communication. The purpose of this study is to compare the performance of young and older adults in word recognition tasks, and to examine the difference based on age. The probe recognition task, which is a modification of the reading span task, was used towards this end. Method: The participants of this study were 13 young adults in their 20s and 30s and 13 older adults over 65 years. The task of this study was to introduce the probe recognition procedure to the reading span tasks. A typical reading span task asks the participant to read a given sentence and recall the last word. In addition to this, the probe recognition procedure presents a word immediately after the sentence and asks the participant to judge whether it was in the sentence. The target word had to meet three conditions: the first word of the sentence required suppression, the last word required storage and recall, and the new word was a control condition. Further, the correct response score and reaction time according to word type were analyzed. Result: Both groups obtained high accuracy in all word types in common, revealing that the word suppression mechanism remained adequate even in older adults. While the reaction time of older adults was significantly longer, that of both groups was significantly shorter in the last word condition requiring storage. Conclusion: The results of this study suggest that the ability to inhibit information was relatively preserved in the general older adult population, but the speed or efficiency of inhibition processing may decrease as aging progresses.
Trends in Policy Supporting Distance Learning during the COVID-19 Pandemic in Other Countries: Focusing on Elementary Education

이 연구는 코로나19 발생 이후 시행된 원격수업 지원 관련 해외 정책 및 제도를 분석함으로써 미래교육 혁신을 준비하기 위한 국내 정책 방안수립에 시사점을 도출할 목적으로 수행되었다. 이를 위해 미국, 캐나다, 독일, 핀란드, 일본을 중심으로 코로나19 대응 학교 정보화 측면과 교육 정보화 측면에서의 정책 및 제도를 살펴보고자 한다. 학교 정보화 측면의 세부 내용은 교육시설 및 환경, 교육 지원 플랫폼으로 구성되어 있으며, 교육 정보화 측면의 세부 내용은 교수·학습 방법, 교육과정으로 구성되어 있다. 분석한 결과 해외에서는 코로나19 발생 이전에는 교실 및 학교에서의 기기 활용에 초점을 두고 다소 수동적인 디지털 평등 지원이 이루어졌다면, 코로나19 발생 이후에는 시설 확충 및 무료지원 확대, 원격수업 관련 교수·학습 관련 법 제정, 구체화된 교육과정 도입과 같은 한층 실제적이고 구체적인 정책들이 함께 시행되어 디지털 평등이 실제화되고 있다는 것을 확인할 수 있었다. This study examined educational policies and systems in other countries related to distance education support implemented in the context of the COVID-19 pandemic to inform related domestic policy. For this purpose, we analyzed school and education informatization in the United States, Canada, Germany, Finland, and Japan. School informatization was defined in terms of educational facilities, environment, and educational support platforms. Educational informatization was defined in terms of teaching methods, learning methods, and curriculums. The results showed that, before the COVID-19 pandemic, some passive digital equality support was provided to promote the use of digital devices in classrooms. During the pandemic, digital equality was further promoted in various ways, such as expanding facilities and free support, enacting laws related to distance education, and developing related curriculums.
Astro MBSE: overview on requirement management approaches for astronomical instrumentation

Systems Engineering requires the involvement of different engineering disciplines: Software, Electronics, Mechanics (often nowadays together as Mechatronics), Optics etc. Systems Engineering of Astronomical Instrumentation is no exception to this. A critical point is the handling of the requirements, their tracing, flow down and the interaction with stakeholders (flow up) and subsystems (flow down) in order to have traceable and methodical evolution and management. In the Italian Astronomical Community, we are developing methodologies and tools to share the expertise in this field among the different projects. In this paper we will focus on the requirement management approach among different projects (ground and space based). We will analyses here different architectures and tools in order to provide to the end user a useful tool optimized for Astronomical instrumentation. The target and synthesis of this work will be a support framework for the Requirement management of the Italian Astronomical Community (INAF) projects.
Designing A Co-creative Communication Experience Utilizing Collaborative Drawing System

The purpose of this research is to enable users to experience co-creative communication with a distant partner using multiple drawing systems. To share information in real-time is worth generating new ideas and stimulating communication, however, COVID-19 has made face-to-face communication difficult. Thus, it is necessary to realize an environment where people can communicate even at a distance. From these backgrounds, we developed a co-creative drawing system inspired by “performance calligraphy.” The performance calligraphy is a collaborative drawing with someone attending on body action. With this system, multiple users can draw together even when users are in different locations. This paper presents our developed system and evaluates the usability of this system.
Interactive Demonstration of Dynamic Music Composition Based on Cohn's “Voice-Leading Zones” Theory

In digital games, music that dynamically changes in real time in response to interaction is becoming a widely known technique in the game industry and academic side. In this study, we use Cohn's Voice-Leading Zones to analyze chord progressions and dynamic music pieces from the music with special modulation methods and create an interactive demo using Wwise.
Secure Vehicular Ad Hoc Network Communication using BlockChain

The vehicular social networks supports diverse kinds of services such as traffic management, road safety, and sharing data. Among these, secure data transmission has turned to be a spotlight. Ciphertext- policy attribute- based encryption may be adopted for data sharing. In traditional schemes, access policy is stored and granted by the cloud, which lacks credibility. To end this, we present a Blockchain Based Multi-Domain Vehicular Authentication scheme, in a which privacy-preserving authentication method is proposed to guarantee the Security.
Predict Future Events Over Smart Environment Through Modified Apriori Algorithm

AbstractIn a smart environment, mining of patterns of occurrences of the past events leads to the prediction of the appropriate event to be occurred as next event. As such, this work is to identify the challenges appearing in smart environment research with respect to finding the pattern of occurrences, followed by a proposed solution. The proposed solution is designed and implemented over the existing Apriori data mining algorithm with certain required modifications to incorporate the time information of occurrences of events. During the implementation of the said modified algorithm, appropriate datasets related to the events of daily activities of the inhabitants of the given environments have been provided; and it is found that the implementation has given the appropriate results with respect to the requirements of the work. Moreover, one more modification over the Apriori algorithm is made for considering all possible subsets of a candidate item set; otherwise impact of the introduction of the time duration information is not up to the level as desired. From the experiment through implementation, it is claimed that the proposed mechanism can choose the next event from the list of predicted events more accurately after inclusion of time duration information between consecutive events.KeywordsApriori algorithmSmart environmentInternet of ThingsAssociation rulesFrequent item set
Block Chain Based Electronic Vaccination Record Storing System

In this paper, a technique has been proposed in order to maintain the immutability of the vaccination records and providing provisional access to one&#x0027;s record and validate the certificate which is generated when one&#x0027;s records are entered on the blockchain network via smart contract which will be later stored on the IPFS where the key roles of the system is to maintain the integrity of the data by keeping it safe from malicious invaders who try to tamper with the records. And once the certificates are generated, they can be further validated so forgery of records can be prevented. Every certificate will consist of a uniquely generated hash value key which will be further used to validate the legitimacy i.e., the authenticity of the certificate through a portal. The purpose of this project is to come up with a safe and secure environment. It is based on smart contracts built over a blockchain network stored on IPFS. The paper will further give detailed explanation of all the components that will be used also the data flow as well and design and development of the project.
Drama Critiques’ Database

Drama Critiques’ dataset was produced as part of Mylène Maignant’s PhD project at the Ecole Normale Supérieure (Ulm) between 2018 and 2022. Her research aims to explore the reception of contemporary London theatre (2010 to 2020) by analysing a large corpus based on the reviews written by two distinct groups of theatre critics: the journalistic one on the one hand, and the digital one on the other hand (bloggers). By relying partly on the data made available by <em>Theatre Record</em> (<a href="https://www.theatrerecord.com/" target="_blank">https://www.theatrerecord.com/</a>) and by automatically extracting the content of 28 blogs, we built a corpus constituting more than 40,000 theatre reviews. Only 36,000 are open access, as some of the bloggers have not given their authorisation yet. The purpose of this data collection consisted of exploring the similarities and differences between these two literary communities. We were interested in better understanding the cultural discourse both journalists and bloggers construct. Given the amount of data, we relied on digital technologies to investigate this field. Using various digital techniques, such as computational linguistics, sentiment analysis, and Geographic Information Systems, we conducted a number of different analyses to map this cultural phenomenon. If some publications have already tackled the literary theme of English digital theatre criticism, none of them have examined it from a computational perspective. Drama Critiques’ dataset is then the first corpus which not only offers so many contemporary reviews based on journalists and bloggers’ publications, but which also proposes a study of its content (<a href="https://doi.org/10.5281/zenodo.6799656" target="_blank">https://doi.org/10.5281/zenodo.6799656</a>). Repository location: <a href="https://doi.org/10.5281/zenodo.6799656" target="_blank">10.5281/zenodo.6799656</a>.
New algorithm for determining 3D coordinates of the eye cornea center in a non -contact eye-tracking system

In a number of non-contact systems for tracking the direction of gaze, based on video data generated by a video camera remote from the client's head, the relative position of the camera and the client's eyes is determined by the glares created on the cornea of the eyes by point sources of IR radiation rigidly connected to the camera. We propose in this paper a new algorithm for estimating the three-dimensional coordinates of the cornea center from images of glares generated by a pair of point radiation sources. We show that when the distance between the eye and the camera is much greater than the radius of the cornea, the problem comes down to solving one nonlinear equation, which significantly reduces computational costs. The results of a numerical experiment confirm the preservation of the estimates accuracy.
Towards Bundle Adjustment for Satellite Imaging via Quantum Machine Learning

Given is a set of images, where all images show views of the same area at different points in time and from different viewpoints. The task is the alignment of all images such that relevant information, e.g., poses, changes, and terrain, can be extracted from the fused image. In this work, we focus on quantum methods for keypoint extraction and feature matching, due to the demanding computational complexity of these sub-tasks. To this end, k-medoids clustering, kernel density clustering, nearest neighbor search, and kernel methods are investigated and it is explained how these methods can be re-formulated for quantum annealers and gate-based quantum computers. Experimental results obtained on digital quantum emulation hardware, quantum annealers, and quantum gate computers show that classical systems still deliver superior results. However, the proposed methods are ready for the current and upcoming generations of quantum computing devices which have the potential to outperform classical systems in the near future.
Investigation of the effectiveness of the stochastic modulation method for steganographic embedding in thermal video data

The method of steganographic embedding of information into thermal camera data is investigated. After evaluating the features of the noise component of thermal video data, it is proposed to use the stochastic modulation method, which consists in replacing the camera noise with noise having similar parameters and containing embedded information. To evaluate the embedding effectiveness, a number of parameters are examined, such as the volume of embedded information, compliance of the embedded noise with the original one, and resistance to steganalysis.
Error probability and computational complexity of classifying objects in a space of multilevel representations

In a space of the tree-structured object representations, we study a classification fidelity in terms of an error probability depending on a processed information amount. Using an information-theoretic model, a lower bound to the average error probability as a function of the average mutual information between the objects and their class-label decisions is given. For any collection of the discriminant functions defined at the successive representation levels, an algorithm of a guided search for the decision on a submitted object is proposed. Also, a redundancy of the average error probability relative to the lower bound is defined. Given datasets of face and signature images, the evaluations of the above characteristics show a possibility of a trade-off between the average error probability and a computational complexity of the decision algorithm.
Securing Privacy and Integrity of Patient's Data in Healthcare 4.0 by Countering Attack using Blockchain

Healthcare 4.0 has become an important asset as it deals with human life and uses the most advanced technologies to carry out its daily work. Healthcare 4.0 consists of Artificial Intelligence (AI), Internet of Things (IoT) and other recent and advanced technologies. This makes Healthcare 4.0 more complex in heterogeneous in implementation. Moreover, the use of more personal data in healthcare 4.0 lures adversaries to perform various attacks to gain financials using patients’ personal data. Various cyber-attacks are possible on healthcare 4.0 because of its heterogeneous nature. The vulnerabilities in any system become more when devices used in it are from different manufactures. The healthcare 4.0 is combination of such devices. It is important to prevent, protect and identify external or internal attacks on healthcare 4.0. The blockchain, being a decentralised concept, could be used efficiently in many technologies that need data protection and attack traceability. This paper deeply analyses various parameters of healthcare 4.0 that makes it vulnerable. The paper also presents the challenges faced by blockchain. Moreover, this paper also provides blockchain as a state-of-art solution to various cyber critical issues in healthcare 4.0.
Optimalisasi Akurasi Baca Meter dengan implementasi Peta RBM (Rute Baca Meter) Berbasis App Mobile di ULP Belanti

The PLN Company already has an application called ACMT or more precisely the Centralized Meter Record Application. This application also has the advantage that customers can access their meter data online. However, this application is not fully effective because not all customers have Android or if they already have Android but do not have an internet package because of the difficulty of finding input during this pandemic. Therefore, the appsheet describes the solution to this problem, namely the appsheet application can only be accessed by PLN officers who are carrying out their duties and the data is always updated. The use of this appsheet can make it easier for customers and can also help officers make their job easier
Design of a sequential pattern mining algorithm based on effective items prefixspan

In the algorithm of sequential pattern mining, experts and scholars spent a lot of time and energy to put forward many typical methods, such as: SPADE, GSP (Generalized Sequential Pattern mining algorithm), PrefixSpan algorithm, etc. In this paper, the above algorithm will be analyzed and studied. Through experiments, PrefixSpan is verified to be faster than the other two algorithms under the same conditions. Therefore, PrefixSpan will be used as the research algorithm in this paper. Based on the research on the characteristics of the PrefixSpan algorithm, aiming at the problem of too many projection databases in the process of operation, we optimize the PrefixSpan algorithm, introduce effective item number, put forward EIPrefixSpan algorithm, and optimize the PrefixSpan algorithm in space/time.
Semantic Alignment for Multi-Item Compression

Coding algorithms usually compress independently the images of a collection, in particular when the correlation between them only resides at the semantic level, i.e., information related to the high-level image content. In this work, we propose a coding solution able to exploit this semantic redundancy to decrease the storage cost of data collections. First we introduce the multi-item compression framework. Then we derive a loss term to shape the latent space of a variational auto-encoder so that the latent vectors of semantically identical images can be aligned. Finally, we experimentally demonstrate that this alignment leads to a more compact representation of the data collection.
Swarm intelligence-based neural network applied in predicting breast cancer

This paper investigates the effectiveness of a swarm intelligence algorithm namely the time-varying binary particle swarm algorithm in finding an optimal subset of the breast cancer dataset’s features. After the feature selection phase, an artificial neural network was used for predicting the presence of malignant lesions in female breasts. Empirical results show that our approach achieved significant performance in the breast cancer prediction task: Accuracy of 0.9807 (±0.01), Precision of 0.9766 (±0.02), Recall of 0.9728 (±0.02), and F1-score of 0.9742 (±0.01).
Analysis Study of Research Trends Related to Middle School Students’ Careers Using Big Data

The purpose of this study is to analyze career-related research trends for middle school students by using text mining and keyword network analysis, which are two of the big data research methods. And it has significance in obtaining implications for future career-related research for middle school students. For this purpose, 451 journal papers related to
Evaluation of Different Feedback Conditions on Worker’s Performance in an Augmented Reality-based Support System for Carbon Fiber Reinforced Plastic Manufacturing

Carbon fiber reinforced polymers (CFRP) are materials with excellent mechanical properties. However, slight variations in the manufacturing process lead to substantially decreasing mechanical stability and additional production costs for assuring the product’s quality. For large lot sizes a high process quality can be achieved through automation. However, for small lot sizes manual processes are required, which introduce variances in process quality. To support workers in the manual production however, there is little support to guide them and to reduce the variance in product quality. Augmented Reality (AR) applications are successfully used to offer guidance in other manufacturing processes. Yet, no empirical studies of the applicability of AR to support CFRP processing have been carried out. Therefore we present an AR based prototypic worker support system for CFRP manufacturing. Empirically, the impact of different feedback modalities (none, auditory, visual, auditory+visual) as well as individual factors on effectivity, efficiency and user satisfaction was investigated. Key findings show that combined feedback worked best for accuracy, however no feedback was fastest. Regarding user preferences, auditory followed by auditory+visual feedback was rated best. Taking user preferences and performance into account will provide useful guidelines for the development of a support system.
Visual Analytics Research at Universidade Federal do Ceará

In this paper, we describe the research in visual analytics that is a partnership between two research groups at the Federal University of Ceara, CRAB – Computer Graphics, Virtual Reality, Animation, and Visualization Group and Insight Data Science Lab. This fruitful collaboration joined CRAB’s expertise in Visualization with Insight Lab’s expertise in Data Science to bring about valuable research and development projects in the public and private sector in many areas, including crime analysis, mobility data visualization, education, and games. We briefly describe our main projects in these areas and their social, technological, and scientific contributions in the following sections.