Reviewerly/exHarmony huggingface dataset:
The Development Process and Characteristics of the 2022 Revised ‘Inquiry on Social Issues’ Curriculum

This study examines the development direction and characteristics of the 2015 revised curriculum 'Inquiry on Social Issues', the development direction and issues in the development process of the 2022 revised curriculum 'Inquiry on Social Issues'. The characteristics of 'Inquiry on Social Issues' were examined based on my experiences of participation in curriculum revision. The 2015 Revised Curriculum 'Inquiry on Social Issues' was newly established as a elective courses for career education in the field of social studies. It is designed to cultivate the ability to select a problem, identify the cause of the problem, and derive a solution. The 2022 revised curriculum, 'Inquiry on Social Issues' was also revised in consideration of the direction of revision pursued in the ‘chongron’ while maintaining the purpose of the 2015 revised curriculum. In addition, the level of difficulty was lowered by reflecting the review opinions raised during the revision process, and modifications were made to allow a more specific approach to social issues. In other words, while maintaining the existing subject goal of 'cultivating learner-initiated social problem-solving ability based on theoretical understanding of social problems', the number of areas and achievement standards was drastically reduced, and they were changed in consideration of the transition to a high school credit system and changes in the type of elective courses. In addition, the contents were more easily modified than the 2015 revised curriculum, the method of presenting social problems was adjusted, and as an elective courses for convergence, it was guided to approach social issues in an integrated manner. In accordance with the purpose of the curriculum revision, the tasks to make 'Inquiry on Social Issues' well used in the classroom was proposed by dividing it into textbook development and class execution.
The Effects of Monitoring-based Virtual Cooking Demonstration in Conjunction with the Promotion of Visual-Tactile Union on the Cooking Job Skills of KALGUKSU of Students with Visual Impairments and Multiple Disabilities

The purpose of this study is to investigate the effects of monitoring-based virtual cooking demonstrations linked to the promotion of visual-tactile union on the kalguksu cooking job skills of students with visual impairments and multiple disabilities. The independent variable consisted of a procedure in which a virtual cooking demonstration was applied in a special school cooking practice room based on monitoring through a job analysis table in conjunction with an intervention procedure combining visual promotion through video modeling data and tactile promotion through cooking tools and ingredients. The dependent variable is that participants cook kalguksu directly in the kitchen in a restaurant specializing in kalguksu, and 10 detailed sub-level techniques were constructed through task analysis. It was found that participants effectively acquired and generalized the performance of the positive response of the target behavior through the intervention program.. It was possible to suggest the possibility that the promotion of visual-tactile union and monitoring-based virtual cooking demonstration could contribute to the effects of performing cooking job skills of students with visual impairments and multiple disabilities in terms of cooperative intervention procedures and methods.
A Study on the Development of Educational Systems to Improve Job Competencies of Social Welfare Support Organizations: Focused on FGI

In this study, FGI was performed on members by job and position to develop an educational system to enhance the job competency of specialized institutions to support the social welfare system in Korea. Through the process of this study, job competencies for experts in the social welfare field were derived by areas of knowledge, skills, and attitudes, and an education system was constructed accordingly.&#x0D; First, as the derived job competency, professional knowledge for each job in the knowledge area, understanding social trends, social welfare-related legal knowledge, planning ability, communication ability, problem identification and solution ability in the skill area, and writing and publicity in other skills In the areas of , language, administrative ability, and attitude, there are interpersonal skills, leadership attitude, altruism, external relations ability, self-development ability, situation judgment and crisis coping ability, etc.&#x0D; Based on the derived competencies, the education system was composed of three tracks: leadership course, job competency and self-development, and statutory compulsory education.
Exploring the Research Trend and Concept of BYOD for the Use of BYOD as a Teaching Strategy for School Classes with Protracted COVID-19

In the COVID-19, classes at school were made possible by preparing for quarantine and minimizing the density. There has been little discussion on how to redesign and manage school classes, while various studies have been conducted to support the use of online classes. This study proposes BYOD(Bring Your Own Device) as one of the appropriate teaching strategies that teachers can utilize for school classes in response to the prolonged COVID-19. BYOD can be used as a teaching strategy that enables the minimization of contact between students in the classroom by using digital devices owned by individuals rather than face-to-face contact. In addition, the improvement of digital literacy of teachers and students, the expansion of available online educational contents, and the improvement of school network environment suggest the possibility of using BYOD in Korean education sites. However, research on the use of BYOD in Korean education sites and related research is limited. The purpose of this study is to develop the basic understanding on BYOD for the use of BYOD as an effective instructional strategy for teaching school classes in the context of COVID-19. To achieve research goal, this study collected and analyzed overseas research and related materials. As a result, this study derives BYOD research trends, definition, strengths, limitations, and strategies for use as the theoretical foundation of BYOD. The results of this study will be used as basic data not only to improve the understanding of BYOD by teachers and schools that want to use BYOD, but also to form a foundation for a plan to use BYOD.
College Students’ Class Satisfaction and Counseling before and after the COVID-19

This paper aimed at improving the qualities of untacted the untact online classes by examining the college students’ satisfaction with class teaching and counseling before and after the COVID-19 diffusion. The data were collected from the students at one of Korea’s largest National University in Jeonnam. The students’ class evaluation data, the professors’ counseling data, and the questionnaires about college life, class preference, and current concerns, etc. were used. In addition, the professional counselors’ phone counseling data and the students’ opinion about the untact online classes were analyzed. The findings were as follows. First, there was little difference in students’ class satisfaction before and after the COVID-19. Most students preferred the teaching style focused on lecture. Second, there was some decrease in college life adjustment and a lot in relationships with friends and professors. Third, satisfaction with counseling and guidance was down, in particular, with vocational guidance. Finally, the current concerns of student were academic and vocational tasks. Based on the results, supporting plans required under ongoing untact class and college life were suggested. Considerations for the faculty and staff of university in teaching and counseling were discussed.
A Case of NCS Flower Cultivation Course Operation through Integration of Online-based Class-Evaluation-Record

The purpose of this study is to present a class model using an online platform in a chaotic situation at school sites as non-face-to-face classes due to the infectious disease (COVID-19) for second-year students of specialized agricultural high schools. In this study, the online lesson design direction was set through the learners’ prior analysis of distance learning subjects from the opening of the online platform. Based on the instructional design direction such as one-way and two-way, we tried to share the method with the field teachers in the front line of the whole process from online student evaluation to writing the life record after the evaluation. Through this study, supplementary points and implications that can be applied to the same subject group and other subject groups were presented. In other words, in the important part of the practice of specialized high schools, the process from the distance learning method using the practice package and the fair performance evaluation using the e-study to the recording of the life record will be explained. It is expected that it will be used as an opportunity to share various information about non-face-to-face classes in specialized agricultural high schools based on the results of the school field research on the method for the unification of class-evaluation-record using the online platform.
Case Review of Professional Learning Communities supported by a Provincial Office of Education

Recently, regional educational offices have actively promoted several policies to support professional learning communities of teachers. The purpose of this study was to identify the teachers’ perceptions of participating in the professional learning communities supported by J provincial office of education in Korea. To this end, we analyzed 2,855 free-answer responses and 20 interview records. The results of the analysis are as follows. As a result of the review of the plan of the professional learning community, the scope of the concept that the professional learning community accepts in the school field is too wide and confusing. There is a weak motivation for voluntary participation in professional learning communities. The learning content of the professional learning community is not connected to the school site where as the professional learning community policy focused on short-term quantitative expansion.&#x0D; To put more, the participants in the professional learning communities provided opportunities for communication among members in school operations, classes, and student management. On the other side of their positive experiences, they were also suffered from some difficulties in supporting the members or the school during the activities due to lack of time. Furthermore, teachers, principals, and school boards revealed different perspectives on their expectations of professional learning community. At the end, the author put some suggestions related with the provision of ‘continuous policy’ and ‘guidelines for clear policy’.
Resilient labeled multi-Bernoulli fusion with peer-to-peer sensor network

This paper addresses fusion of labeled multi-Bernoulli (LMB) densities in a peer-to-peer sensor network subject to misbehaviors due to abnormal conditions of the signal propagation channel (i.e. low signal-noise-ratio) or to malicious attacks. In such situations unintentional or intentional modifications of the transmitted messages can occur which degrade the performance of standard fusion methods. In order to overcome the aforementioned difficulties, we propose a resilient approach to distributed fusion of LMB densities in which each node exploits the local density as trusted source of information to detect the modified/false data coming from neighboring nodes. The implementation issues of the proposed method as well as possible cyber-attack models in the context of multi-object filtering are also discussed. The performance of the proposed method is verified via simulation experiments concerning fusion under cyber-attacks.
TASTNet: An end-to-end deep fingerprinting net with two-dimensional attention mechanism and spatio-temporal weighted fusion for video content authentication

In this paper, a robust fingerprinting scheme for video content authentication with two-dimensional attention mechanism and spatio-temporal weighted fusion called TASTNet is proposed, which can automatically extracts key spatio-temporal features from the input video and maps them to the corresponding fingerprint. Detailedly, the two-dimensional attention mechanism is applied to resist different kinds of digital manipulations for robustness enhancement. To incorporate perceptual characteristics, a spatio-temporal weighted fusion method based on LTSM is presented to integrate frame-level features into video-level features while retaining the temporal order. In the process of fusion, key frames are allocated with larger weights according to inter-frame correlation. With these two steps, we can obtain representative video features that contain principal perception information. In addition, the proposed scheme utilizes deep metric learning for training, and we design multiple constraints to make the generated fingerprint more compact and discriminable. Extensive experiments demonstrate that our scheme can achieve superior performances with respect to robustness and discrimination compared with some state-of-the-art schemes.
Improving sparse graph attention for feature matching by informative keypoints exploration

Feature matching refers to building correct point correspondences from two-view images. Typical methods create coarse matches merely based on the similarity of local descriptors, then additionally use geometrical constraints or coherence to get a clean match set. However, this indirect paradigm would be strictly limited by the pre-matching results. This can be alleviated by a GNN-based matching network that constructs correct correspondences directly from feature positions and descriptions. Attentional pooling is utilized to reduce the high computational complexity and memory usage by sparsing the graph attention. In this paper, we propose an enhanced sparse GNN, namely KeyGNN, for fast feature matching, by designing Guided Attentional Pooling to emphasize the informative cues in GNN layers. Specifically, we propose Informative Keypoints Exploration Module and Guided Sparse Module to respectively extract information-rich points and use them to guide the attentional pooling for better information preservation. This process is constrained by the proposed Structural Importance Ranking Loss and Descriptor Distribution Loss, that ensure the structural information can be correctly extracted and the generated feature vectors be well distributed in the deep space. Unlike existing techniques, our method can focus and preserve more on the main information in the sparse GNN stage, thus avoiding information loss and enhancing the accuracy. Extensive experiments on large and public datasets reveal that our method can obtain better performance than the state-of-the-arts in the tasks of camera pose estimation, fundamental matrix estimation, and visual localization. The runtime and memory test shows that our network takes low computational and memory usage, which is friendly to real-time vision tasks.
Application of Secure Two-Party Computation in a Privacy-Preserving Android App

Data privacy has become increasingly important in recent years, with the rise of cyber threats and the unauthorized sharing of sensitive information. Our work within the E-Corridor project has focused on developing a secure framework for sharing information in multimodal transport systems, while ensuring data privacy is maintained. Our implementation of a two-party computation schema using Yao’s garbled circuits in an Android mobile setting has enabled us to create an application that allows users to find points of interest, e.g., restaurants or hotels, near specific areas without sharing any personal information. The application matches user requests using the secure two-party without disclosing any of the user’s preferences with external actors. We design a threat model based on LINDDUN to show the reliability of our project. It highlights also the potential of using secure computing techniques to enable information sharing while maintaining privacy. Our work demonstrates the importance of prioritizing data privacy in our increasingly interconnected world and the potential of secure two-party computing techniques in achieving this goal. Besides, this framework is flexible and can be extended to various domains where data privacy is of utmost importance.
A DASH server-side delay-based representation switching solution to improve the quality of experience for low-latency live video streaming

This work addresses the integration of real-time transmission systems, including IP cameras and production systems (like OBS or vMix), that use protocols such as RTSP (Real Time Streaming Protocol) or SRT (Secure Reliable Transport), with content distribution technology based on LL-DASH (Low Latency DASH -Dynamic Adaptive Streaming over HTTP-), taking advantage of the fact that DASH offers significant well-known advantages for content distribution over the Internet and via CDNs (Content Delivery Networks). Considering the limitations of the LL-DASH standard regarding the adaptation to network conditions, this paper proposes a new solution called Server-Side Representation Switching (SSRS). SSRS uses an approach based on the server measuring the delay in the requests made by clients, whose variation may be due to a decrease in bandwidth, as occurs in Wi-Fi networks with a high number of clients. To evaluate the effectiveness of the proposed solution, a testbed has been developed that allows the performance evaluation of both the LL-DASH system and the solution based on server-side decision-making. In addition, the developed solution has been compared with known algorithms (L2A and LoL+) integrated into the Dash.js player. The results show that the Server-Side Representation Switching solution offers a good trade-off between the transmitted quality and the final delay measured at the client, compared to the other algorithms evaluated. Moreover, it holds the advantage of being straightforward to implement and does not require any modifications to the players used. • Networks • Network performance evaluation • Network performance analysis
Joint Message Passing: Solving Heterogeneous Graphs Challenges in Few-Shot Object Detection

Few-Shot Object Detection involves detecting novel objects with a limited number of samples in data-scarce scenarios. Learning a universal representation (prototype) directly from rare categories is impractical, so the key to this task is establishing a robust relationship between rare (novel) and abundant (base) categories. However, current graph-based methods have not effectively addressed this issue. This is because the three entities involved in the problem - the base prototype, the novel prototype, and the region proposals (usually from RPN) - exhibit significant differences, forming a heterogeneous graph. The lack of feature influence between these different types of entities hinders the novel class prototypes and proposals from benefiting from the base class. To meet this challenge, we proposes a joint message passing framework. First, we construct a graph network to achieve bidirectional propagation between prototype and query image proposals, leading to mutual enhancement of their semantic representations. Then, We design a joint propagation strategy for this heterogeneous graph, which includes introducing category priors, building adaptive weights, and removing label self-loops. It compensates for the lack of feature influence by incorporating label influence, strengthens edges in the graph, and enhances propagation reliability. Our approach significantly improves the baseline while matching the state-of-the-art methods on the PASCAL VOC and MS COCO datasets.
Exploring NFT Validation through Digital Watermarking

Blockchain technology has brought notable advancements to diverse industries. The introduction of non-fungible tokens (NFTs) has particularly led to a lucrative market for unique digital asset ownership verification, including digital artworks. However, this trend has also given rise to concerns such as fraud, stolen works, authenticity, and copyright issues. Illicit traders exploit the market by trading unauthorized copies of digital objects as NFTs. In this study, we propose the use of digital watermarking as a means to establish the authenticity of NFTs and enhance the marketplace's credibility.
Game Theoretic Modelling of a Ransom and Extortion Attack on Ethereum Validators

Consensus algorithms facilitate agreement on and resolution of blockchain functions, such as smart contracts and transactions. Ethereum uses a Proof-of-Stake (PoS) consensus mechanism, which depends on financial incentives to ensure that validators perform certain duties and do not act maliciously. Should a validator attempt to defraud the system, legitimate validators will identify this and then staked cryptocurrency is ‘burned’ through a process of slashing.
Semantic Queries with Transformer for Referring Image Segmentation

Referring image segmentation aims to segment the target region from an image according to the query language description. One of the main challenges behind this fundamental task is to find a qualitative query representation to index the referred object or stuff. In this study, we introduced a query-based framework with Transformer architecture for referring image segmentation, dubbed SQFormer. It treats the sentence and word embeddings as components of two types of semantic queries: (i) mask queries conditioned on the sentence embeddings and (ii) word queries induced from text inputs, to directly attends to the most relevant areas in the image. The semantic queries are input-specific to diverse language expressions while maintaining the prior knowledge of intrinsic image patterns. Concretely, word queries enable flexible and adaptive interactions between vision-language modalities. Mask queries are obligated to generate a set of prototype masks. Then in Prototype Mask Balance (PMB) module, the prototype masks are weighted sum according to the holistic understanding of language expression to get the final mask prediction. Besides, to better fuse linguistic and visual features, we propose a language-aware feature pyramid network (LA-FPN) to enhance the cross-modal alignment. Extensive experiments show our method surpasses the previous state-of-the-art approaches on RefCOCO, RefCOCO+, and G-Ref datasets.
Creating a Decryption Proof Verifier for the Estonian Internet Voting System

This paper describes the efforts made for and lessons learnt from creating a decryption proof verifier for the Estonian IVXV Internet voting system. Our main conclusion is that cryptographic protocols aiming at providing transparency through verifiability should also take into account a non-functional requirement of low implementation complexity. We identify several steps of the verification protocol that could be made easier to implement without sacrificing security. A side-product of our effort is a fully functional IVXV decryption proof verifier written in Go that we used during the latest Estonian parliamentary elections of March 2023.