Reviewerly/exHarmony huggingface dataset:
Cluster-Aware Grid Layout

Grid visualizations are widely used in many applications to visually explain a set of data and their proximity relationships. However, existing layout methods face diffculties when dealing with the inherent cluster structures within the data. To address this issue, we propose a cluster-aware grid layout method that aims to better preserve cluster structures by simultaneously considering proximity, compactness, and convexity in the optimization process. Our method utilizes a hybrid optimization strategy that consists of two phases. The global phase aims to balance proximity and compactness within each cluster, while the local phase ensures the convexity of cluster shapes. We evaluate the proposed grid layout method through a series of quantitative experiments and two use cases, demonstrating its effectiveness in preserving cluster structures and facilitating analysis tasks.
Data Verifiable Personalized Access Control Electronic Healthcare Record Sharing Based on Blockchain in IoT Environment

Electronic Health Records (EHRs) based on the Internet of Things (IoT) can provide real-time health data for quick intelligent medical services and give convenience to many data-sharing scenarios. However, EHRs also face various security threats since they are highly private. To the best of our knowledge, no recognized data-sharing work can satisfy the stringent privacy requirements of EHRs. Motivated by this, we propose a blockchain-based personalized access control EHR sharing scheme with data verifiability, which can safeguard the interests of data owners and users simultaneously. Firstly, we take ciphertext-policy attribute-based encryption to achieve personalized access control for data owners. Secondly, we design an interactive zero-knowledge proof protocol between data owners and users, which can provide authenticity verification of EHR for users and prevent EHR away from forgery. In addition, smart contracts and the interplanetary file system are used to reduce the computation and storage costs of patients. Finally, the security analysis shows that the proposed scheme meets the pre-defined security goals. The performance analysis demonstrates that the proposed scheme is efficient, and can be applied to practical electronic medical record sharing scenarios.
Secure Semantic Communications: Challenges, Approaches, and Opportunities

Semantic communication has emerged as a promising technique that can integrate the meaning and context of information into a communication process. It goes beyond raw data transmission and enables a sender and a receiver to transmit and recover semantic messages through their respective knowledge bases. However, it is challenging to achieve secure semantic communication due to complicated security and privacy threats in semantic communication. In this article, we first introduce the background and architecture of semantic communication. Then, we sort out multiple potential security threats existing in each step of semantic communication. Following that, we summarize the requirements for secure semantic communication and provide possible defenses for each type of attacks. We also present a case study to illustrate the training process of a reliable semantic encoder/decoder. Finally, we discuss open issues and future directions in this research field.
Mind the Gap: Open Set Domain Adaptation via Mutual-to-Separate Framework

Unsupervised domain adaptation aims to leverage labeled data from a source domain to learn a classifier for an unlabeled target domain. Amongst its many variants, open set domain adaptation (OSDA) is perhaps the most challenging one, as it further assumes the presence of unknown classes in the target domain. In this paper, we study OSDA with a particular focus on enriching its ability to traverse across larger domain gaps, and we show that existing state-of-the-art methods suffer a considerable performance drop in the presence of larger domain gaps, especially on a new dataset (PACS) that we re-purposed for OSDA. Exploring this is pivotal for OSDA as with increasing domain shift, identifying unknown samples in the target domain becomes harder for the model, thus making negative transfer between source and target domains more challenging. Accordingly, we propose a Mutual-to-Separate (MTS) framework to address the larger domain gaps. Essentially we design two networks – (a) Sample Separation Network (SSN): which is trained to learn a hyperplane for separating unknown samples from known ones, and (b) Distribution Matching Network (DMN): which is trained to maximise domain confusion between source and target domains without unknown samples under the guidance of the SSN. The key insight lies in how we exploit the mutually beneficial information between these two networks. On closer observation, we see that SSN can reveal which samples in the target domain belong to the unknown class by instance weighting whereas, DMN pushes apart the samples that most likely belong to the unknown class in the target domain, which in turn reduces the difficulty of SSN in identifying unknown samples. It follows that (a) and (b) will mutually supervise each other and alternate until convergence, which can better align the source and target domains in the shared label space. Extensive experiments on five datasets (Office-31, Office-Home, PACS, VisDA, and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">mini</i> _DomainNet) demonstrate the efficiency of the proposed method. Detailed ablation experiments also validate the effectiveness of each component and the generality of the proposed framework. Codes are available at: https://github.com/PRIS-CV/Mutual-to-Separate.
GDPR-Compliant Personal Health Record Sharing Mechanism With Redactable Blockchain and Revocable IPFS

The use of IoT technology in collecting personal health records (PHR) within the eHealth environment is a growing trend. However, data integrity is a concern as cloud service providers (CSPs) often cannot guarantee it. Blockchain technology offers a solution to guarantee data integrity and traceability. However, the immutability of traditional blockchain conflicts with GDPR's requirements. To address scalability and privacy concerns, we have designed a comprehensive scheme that integrates the redactable blockchain with the existing revocable IPFS mechanism. Our scheme overcomes the disadvantage of residual downloading information in the traditional blockchain. Additionally, we have developed an enhanced proxy re-encryption scheme that simplifies access control for physicians without the need for complex group key management. Unlike traditional blockchains and P2P file sharing systems, our PHR platform allows for selective removal of records and files while maintaining auditable logs. Evaluation results demonstrate that our proposed scheme effectively enhances the exclusive revocation feature with acceptable overheads. To the best of our knowledge, this is the first work to provide the merit of fully complete record and file revocation on a blockchain-based system.
FRAUDS, EMBEZZLERS, THIEVES, AND OTHER BAD ACTORS: HOW CRIMINALS STEAL YOUR PROFITS AND PUT YOU OUT OF BUSINESS

Shoplifting, fraud, embezzlement, and now cybercrimes are only a few of the many types of crimes business owners lose sleep over.Business owners lose profits, sometimes significant profits, and small businesses are affected to any even greater extent.Small businesses often lack the resources to defend their business against various criminal activities and may not be able to recover from large losses.The U.S. Chamber of Commerce reports that as many as 30% of small business failures could be attributed to embezzlement or employee theft.And to make matters worse, thieves are becoming more sophisticated in the use of high-tech tools to steal.In this paper, the authors provide an overview of current criminal activity and offer several ways to address the situation.
Research on the Countermeasures for the Construction of High-level Practical Training Bases in Higher Vocational Colleges Based on the Integration of Industry and Education

Abstract To quickly establish high-level training bases in higher vocational colleges while integrating industry and education, this article first describes the concepts and layout of the space. Secondly, Using interest-related data, an assessment method of the indicators that demonstrate the efficiency of bringing together industry and schooling is developed, the weights of the indicators are determined, and the evaluation matrix is constructed for a comprehensive evaluation using ANP and gray fuzzy theory, respectively. Three colleges were used as examples to examine the effectiveness of integrating industry and education in creating advanced knowledge bases. The findings show that the greatest weighting of the indicators for how well industry and education are integrated is 0.3135, and the highest governance economic investment in building high-level training supports is 93.26, suggesting that building high-level training bases can enhance how well industry and education are integrated.
Blockchain technology in the formation mechanism and strategy of brand trust in liquor e-commerce

Abstract This paper firstly constructs a theoretical framework of a liquor e-commerce platform based on blockchain technology, analyzes the influence factors of brand trust, and discusses the influence mechanism of blockchain technology on liquor brand trust. Secondly, in the three-party trust degree calculation section, a blockchain-based method for calculating the three-party trust degree for an e-commerce platform is proposed. Finally, an evolutionary game model of the three parties involved in e-commerce transactions is constructed, and evolutionary stabilization strategies are explored before and after the introduction of blockchain. The results show that before the introduction of blockchain technology, the evolutionary stability point of the system was (0, 0, 1). After the introduction of blockchain technology, the trust power of merchants’ liquor brands is improved, and the evolutionary stability results of the three-party evolutionary game system eventually converge to (1, 1, 1). This study has some guiding significance for promoting the development of liquor e-commerce platforms and improving brand trust.
Title Pending 5759

This paper presents a theory of structure removal based on the operation of Bud. Bud is defined as a variant of External Merge. While External Merge always targets the root of a syntactic object, Bud targets a lower projection. The operation is constrained by minimality and is used to provide a novel account to analytic passive, causative constructions in Italian and infinitival constructions like raising predicates. It also offers an elegant way to account for structure pruning in cases of language disorder, root infinitives in child speech and preference for passive plus subject relativization over object relative clauses.
Reading Comprehension Problem and Solution in Online Classroom

The research focus on finding the problem and solution of reading comprehension in online learning class at ninth grade in 2022/2023 Academic Year. The research problems are focused on; 1. How do students learn reading comprehension in online learning class? 2. What kind of problems faced by students in studying reading comprehension in online learning class?, 3. What strategies can be used by students to solve the problems in online learning class?. To find the answer of the problem above descriptive qualitative research was conducted. The results of this research were in the form of words and sentences. It was found that some strategies were used by students as scan reading to save time and reduced eye strain due to gadget light, form a small group study with students who are near home to join reading class together and discuss about the material. if students found it was hard to read by smartphones or laptop some students print out the assignment given by teacher. Therefore, students could read easily, and underline the important information directly. Different from previous studies, this research, in addition to finding problems, also offers useful solutions for online reading learning.
A conventional and non-conventional analysis of SAR image despeckling technique

The importance of high-resolution Synthetic Aperture Radar (SAR) imaging is undeniable in applications for Earth monitoring since it offers useful data for analysis. Post-image processing methods including edge and object detection, segmentation, and speckle noise removal are important to performed to find the information of images. The key technique that makes images aesthetically pleasing and understandable is despeckling. The presented study assays the latest trends &amp; state of the art techniques for SAR image despeckling to gauge the performance &amp; agility of existing techniques by performing a contemporary combination of theoretical &amp; experimental analysis. This study analyses various techniques used in literature. The quantitative &amp; qualitative analysis is done using Peak Signal-to-Noise Ratio (PSNR) and Universal Image Quality Index (UIQI) indices to find the better approach
Network security in multiple threats: A critical study

Any organization nowadays needs a secure network. High-speed wireless networks and internet services are becoming increasingly unsafe as a result of the daily rise in security threats. Threats to information security come from within the company, in close to 80% of cases. With the latest trend of mobility (portable devices such as laptops and smartphones etc.), ubiquitous 3G connectivity and this trend increases but as more web-based applications are manufactured and available over the Internet, the instances of insider threats have been rising at an alarming rate. Current or former workers, contractors, or business partners who have permission to access the organization's network and servers are typically the source of insider threats. Confidential information is frequently stolen for monetary gain or wellful harm. Access to hacking tools via the Internet, USB drives, and wireless connectivity make break-ins simple. Here, Millions of dollars in losses in the event of IP theft, and customer / individual information theft are yet too considered. This article provides insight into Insider dangers, attackers, and their rationales and recommendations for organizational-level mitigation methods.
A comparison of feature extraction methods in image stitching

The usage of feature detectors for image stitching has become a popular research area in computer vision. Various feature extraction algorithms can be used in the process of image stitching process, but they perform varyingly when handling different images and no single algorithm could outperform all others. This paper focuses on the comparison of feature extraction algorithms used for panoramic image stitching. The research utilizes the SIFT, ORB, AKAZE, and BRISK to conduct feature points and match feature points on a group of image sets. The RANSAC algorithm is then used to filter out the outliers and calculate the homograph matrix. Completes the panoramic with image splicing and smoothing through the matrix transformation. Derived from the comparison of the matching and stitching results, the AKAZE detector is found to be the fastest feature point detection and extraction algorithm, while the SIFT detector will provide more feature points to make more accurate matches possible. These findings have implications for the development of efficient and effective computer vision technologies for various applications.
Homography transform enhance CNN prediction accuracy on image classification

Convolutional Neural Network (CNN) image classification is a well-established algorithm that has been implemented in many fields. Benefit from the digitalization process and the exponential increase in the base of smart devices, this algorithm can be applied to even more traditional or casual contexts in the future driven by the trend of Internet of Things. Thus, the needs for optimizing image classification in specific domains may have turned out to be ongoing valuable research direction. This paper focuses on providing optimization under one example context which is using traffic signs as the experimental target. For the randomly selected traffic sign samples in the experiment, the accuracy obtained from the samples treated by the homography transform compared to control group passed all three statistical tests: Two Sample t-test, McNemar's test, and Fisher's exact test. Therefore, research direction has achieved small-scale validation and presents optimism for large-sample experiments and further research in optimization using the introduced strategy in the paper.
Research on the applications of natural language processing

In recent years, there has been a rapid advancement in natural language processing (NLP), leading to notable improvements in areas like sentiment analysis, machine translation, and text recognition. However, belonging to the same field under AI, the research materials and academic topics of NLP are not so adequate. Therefore, this paper introduces and shows the reader a general introduction to NLP for the current research environment, so that the reader can clearly understand the history of NLP, its uses, and the current and future research directions. It also analyzes the current mainstream applications of NLP, including the most basic Rule Based method and the more popular deep learning, development of pre-trained language models such as GPT. Besides, this article also analyzes the current mainstream application scenarios of NLP and its related use of technology, so that readers can have a clearer picture of the research direction of NLP. At the end, this paper also summarizes the general development of NLP, research methods, and application scenarios to provides an outlook on the future development of NLP with respect to these points.
A research on applications of neural network-based cryptography

With the rapid development of neural networks, they have been integrated into more and more fields, and cryptography is no exception. Research on the combination of neural networks and cryptography is developing rapidly, and numerous scholars have made significant progress in these fields. Neural network-based cryptography is a complex system that deserves an in-depth study. This paper will first introduce the foundation types of neural networks, some of the other types of neural network , and cryptographic techniques. Immediately after, this paper will introduce the scheme of Generative Adversarial Neural Networks (GANs) generating cryptography and the scheme of combining GANs and Convolutional Neural Networks (CNNs) generating cryptography and analyze their differences and advantage and disadvantages. This paper will then introduce the application of neural networks in blockchain, key exchange technology, and the combination of neural networks and Advanced Encryption Standard (AES). As well as at the end of this paper will show some views of combining neural networks and cryptography in the future.
Implementasi Teori Belajar Behavioristik Pada Pembelajaran Matematika Kelas V SDN 13 Tilamuta

This study was conducted due to elementary school students' lack of understanding of mathematics. The aim is to assess the implementation of behavioristic learning theory in mathematics learning in class V. This study uses qualitative descriptive methods with observation, interviews, and documentation. The results showed that the implementation of behavioristic learning theory improved students' understanding in mathematics learning.&#x0D;
Deep Learning based Real Time Radio Signal Modulation Classification and Visualization

Radio Modulation Classification is implemented by using the Deep Learning Techniques. The raw radio signals where as inputs and can automatically learn radio features and classification accuracy. The LSTM (Long short-term memory) based classifiers and CNN (Convolutional Neural Network) based classifiers were proposed in this paper. In the proposed work, two CNN based classifiers are implemented such as the LeNet classifier and the ResNet classifier. For visualizing the radio modulation, a class activation vector (w) is used. Finally in the proposed work, it is performed the classification by using the Deep learning models like CNN and LSTM based modulation classifiers. These deep learning models extract the important radio features that are used for classification. Here, the bench mark dataset RadioML2016.10a is used. This is an open dataset which contains the modulated signal I and Q values fewer than ten modulation categories. After evolution of proposed model with bench mark dataset, it is applied with real time data collected through the SDR Dongle receiver. From the obtained real time signal, the modulation categories have been classified and visualized the radio features extracted from the radio modulation classifiers.
How far has the transition of classrooms come at the turn of the times?

저자 존카우치(John Couch)는 애플의 교육 담당 부사장으로 실리콘밸리의 급변하는 산업과 기술을 생생하게 목격하고 현장 내 리더들의 교육관을 이용하며 디지털 시대에 우리가 정말 주목해야 하는 본질은 무엇인가에 대한 화두를던진다. ‘디지털 네이티브’라 불리우는 오늘날의 세대들에게 우리는 여전히 암기와 지식을 가르치려고 하는 교육현장에서 탈피하고 있지 못함에 아쉬워하며 더불어필자는 ‘무엇을 배우느냐?’가 아니라 ‘왜 배워야 하느냐?’와 같은 동기부여가장기적으로 최고의 성과를 낼 수 있다고 덧붙였다. 기술이 발달할수록 교실 내 교사의 역할도 더욱 뚜렷해지는 이유가 기존의 ‘정보 전달자’가 아닌 학생들의 생각과 시각을 확장케 하는 조력자의 역할로 변모해야 하기 때문임을 강조하며 우리 사회가 제대로 된 미래 교육을 꿈꾼다면 과거의 교실을 서둘러 잊기를 바란다.The author, John Couch, is Apple's Vice President of Education. He vividly witnessed Silicon Valley's rapidly changing industries and technologies. He uses the educational perspectives of leaders in the field and explains what we really need to pay attention to in the digital age. It raises the topic of what is essence. For today's generation, who are called 'Digital natives', it is regrettable that we are still unable to break away from the educational field that tries to teach memorization and knowledge. In addition, the author asks 'Why should you learn?' rather than 'What are you learning?' He added that the ‘Why should you learn?’ motivation can lead to the best performance in the long run. As technology develops, the teacher's role in the classroom becomes more distinct, emphasizing that the role of the teacher must be transformed from the existing 'information transmitter' to a facilitator who expands students' thoughts and perspectives, I hope you quickly forget the our classroom of the past for our society must provide proper future education.
Alternative education research for cultural transformation

이 연구는 기독자들이 세상의 빛과 소금(마 5:13-16)으로서의 정체성을 갖고문화를 변혁시킬 선교지로 인식하기 위한 대안적 교육 방안을 제시하기 위해진행되었다. 이 연구는 기독교학자들, 목회자들, 심지어는 신학생들을 비롯한기독자들의 반문화적 태도로부터 출발한다. 연구과정으로는 기독교 윤리학자리차드 니버의 『그리스도와 문화』에 나타난 문화에 대한 기독자의 5가지 유형을 제시하였고, 5가지 유형 중 문화를 변화시키는 그리스도(Christ Transforming Culture)의 모델이 문화 변혁에 있어서 가장 적절한 모델임을제시한다. 이를 통해, 문화 변혁에 대한 신학적 근거를 마련하였다. 이어서, 기독교적 민주 시민 양성이론을 바탕으로 문화 변혁에 대한 대안적 교육의 근거를 마련하였다. 연구 결과로는 첫째, 문화 창조를 위한 교사 개발을 진행한다.둘째, 문화적 공간을 창출한다. 셋째, 지역 사회에서 개최되는 문화행사에 적극 후원한다.This research was conducted to propose alternative educational methods for Christians to perceive themselves as a missionary force to transform culture, embodying the identity of the world's light and salt (Matthew 5:13-16). The study originates from the countercultural attitudes of Christians, including Christian scholars, pastors, and even theology students. In the research process, five types of Christian attitudes towards culture, as presented in Richard Niebuhr's "Christ and Culture," were examined, with the conclusion that the model of "Christ Transforming Culture" is the most suitable for cultural transformation. Through this, theological foundations for cultural transformation were established. Furthermore, based on Christian civic education theory, the study provided a basis for alternative education on cultural transformation. The research results include: Firstly, the development of teachers for cultural creation. Secondly, the creation of cultural spaces. Thirdly, active support for cultural events held within the local community.