Reviewerly/exHarmony huggingface dataset:
A Multi-Round Zero Knowledge Proof Algorithm for Secure IoT and Blockchain Environments

Presented herein is a novel algorithm for multi-round, zero-knowledge proof (ZKP), devised specifically for authenticating factorisation proofs within a variety of cryptographic applications. This advanced algorithm, while maintaining computational complexity within acceptable bounds, offers a secure and proficient solution. The functionality of the algorithm is marked by multiple rounds of interaction between the Prover and Verifier. Initially, the Prover generates a random value and calculates a commitment. Subsequently, the Verifier issues a random challenge, eliciting a computed response from the Prover. To validate the proof, the Verifier verifies the equality of the commitment and the computed response. Efficaciousness of the proposed multi-round ZKP algorithm is demonstrated across diverse input sizes and parameters. Results indicate a success rate exceeding 90% on average, showcasing the robustness of the method. The recurring interaction between the Verifier and Prover enhances the Prover's authentication, thereby improving the algorithm’s reliability. Implementation of the algorithm, achievable through standard cryptographic tools and protocols, can fortify the security of multiple cryptographic applications. A significant application can be found in Digital Identity Management Systems (DIMS). Currently, these systems are vulnerable to a myriad of threats, including identity spoofing, data breaches, and internal security risks. The application of the ZKP algorithm can simultaneously augment security and withhold sensitive information, potentially transforming the DIMS security landscape. Future research may focus on improving the efficiency and scalability of the multi-round ZKP algorithm. There also remains a vast potential for exploring additional applications of this technique within various cryptographic domains.
Development of E-Modules Oriented by A Science, Technology, Engineering, Art, and Mathematics (STEAM) Approach to Improve High Level Thinking Ability

Standards for 21st century learning for educators and students relate to the application of technology learning. One of the ways teachers use to optimize digital learning activities is by creating teaching materials in the form of e-modules. The aim of this research is to develop an electronic module oriented towards the STEAM learning approach. This e-module is used to improve elementary school students' high-level thinking abilities. This research uses the Research and Development research method with the ADDIE development model. The subjects of this research were grade IV elementary school students in Pariaman Tengah, Pariaman City, namely SDN 17 Kampung Baru, SDN 04 Rawang, and SDN 02 Karan Aur. There are three instruments for this research, namely validation instruments in the form of validation sheets, practicality instruments and effectiveness instruments. The results of this research areThe average validation result for the three aspects of media, language and material is 85.65, which is included in the very valid category. The paired sample t-test results were obtainedsignificance value of Sig. 0.000 &lt;0.05so the data shows that there are differences significant difference between before using modules and afterwards, so it is stated that modules can increasing the high-level thinking abilities of high-class students
Measurement-Device-Independent Quantum Key Distribution with Practical Spontaneous Parametric Down-Conversion Sources

Measurement-device-independent (MDI) quantum key distribution (QKD) closes all the loopholes in the detection side, which greatly improves the practical security of QKD. Spontaneous parametric down-conversion (SPDC) sources play a significant role in MDI QKD by using the heralding strategies to achieve unique features, such as the passive-decoy method, which reduces side-channel information leakage from the source. However, photon pairs from realistic SPDC sources are often spectrally entangled. This lowers the Hong-Ou-Mandel (HOM) interference visibility, and thus increases the QKD error rate. Unfortunately, research efforts so far have not considered this effect quantitatively. This leads to an overestimation in the secure key rate; therefore their results cannot be applied to practical sources. In this article, we close this gap by decomposing the spectral entangled state into Schmidt eigenstates, then find their contributions to the secure key rate. In addition, our work allows an accurate optimization of pumping and filtering conditions to maximize the final key rate. We note that our model is general and can be applied to other quantum information processes involving HOM interference, such as quantum teleportation, entanglement swapping, and quantum computation.
A Combined Deep Q-Network and Graph Search for Three Dimensional Route Planning Problems for Multiple Mobile Robots

In recent years, automated multiple mobile robots are introduced for transporting loads and inspecting final products in factories to reduce the burden of human labor shortage. Mobile robots are required to develop automated systems that can make decisions as flexibly like human operators. Most conventional route planning problems for mobile robots have been utilizing either, optimization methods or learning methods. However, those conventional methods have a difficulty in applying it to the conflict-free route planning problems with a large number of states with three dimensional environment. We propose a method that combines deep reinforcement learning and graph search methods. In the proposed method, the routing is firstly determined by a graph search algorithm, and Deep Q-Network (DQN). A deep reinforcement learning method is used to avoid collisions. A route planning problem in a three dimensional environment is successfully solved by using DQN that can process multi dimensional states. The proposed method is also applied to the multiple drones route planning problem. The performance of the proposed method is compared with that of the optimization methods. As a result, it was found that a near optimal route planning was obtained in approximately 6% of the computation time required to find the optimal solution.
Prospects for the Development of Distance and Online Learning in the Context of Higher Education

In today's digital society, the significance of distance and online learning in the context of higher education is escalating. Rapid technological advancements, Internet development, and the growing demand among students for educational flexibility and accessibility are generating fresh opportunities for higher education institutions. The paper's purpose is to identify effective methods and modeling processes to ascertain the prospects for the development of distance and online education within modern socio-economic conditions. The paper holds relevance as it scrutinizes current trends, while identifying the benefits, challenges, and opportunities that accompany this advancement. It delves into statistics concerning the growth in the number of students opting for distance learning, attributing this surge to the convenience, efficiency, and affordability offered by this mode of education. The paper also examines the favorable influence of this advancement on higher education and the global labor market. Moreover, the paper explores the advantages that distance and online learning furnish to higher education. It underscores the flexibility it affords students, enabling them to learn at their preferred time and location. It further underscores the potential for accessing a vast array of educational resources and experts from around the world, thereby broadening students' opportunities to attain a quality education. After a comprehensive contextualization of the research subject, the method of hierarchical indicator correlation analysis is employed. This method facilitates a systematic analysis and comparison of relationships among different indicators of student engagement, refusals, and success across universities A, B, C, D, E. The outcomes of the hierarchical indicator correlation analysis offer insights to better grasp the influence of various factors on the effectiveness of distance learning in each of the studied universities, ultimately culminating in informed recommendations for enhancing the quality of the educational process and fostering the growth of distance education in Ukraine. Research reveals that these learning formats possess substantial potential and can emerge as pivotal components within the modern education system. Nonetheless, their successful implementation and utilization necessitate meticulous attention to several pivotal aspects.
Improving Object Detectors by Exploiting Bounding Boxes for Augmentation Design

Recent advancements in developing pre-trained models using large-scale datasets have emphasized the importance of robust protocols to adapt them effectively to domain-specific data, especially when the available data is limited. To achieve data-efficient fine-tuning of pre-trained object detection models, data augmentations are crucial. However, selecting the appropriate augmentation policy for a given dataset is known to be challenging. In this study, we address an overlooked aspect of this problem - can bounding box annotations be utilized to develop more effective augmentation policies? Our approach InterAug reveals that, by leveraging the annotations, one can deduce the optimal context for each object in a scene, rather than manipulating the entire scene or just the pre-defined bounding boxes. Through rigorous empirical research involving various benchmarks and architectures, we showcase the effectiveness of InterAug in enhancing robustness, handling data scarcity, and maintaining resilience to diverse high background contexts. An important advantage of InterAug is its compatibility with any off-the-shelf policy, requiring no modifications to the model architecture, and it significantly outperforms existing protocols. We will release the codes upon acceptance.
Analisis Pembelajaran Daring Sebagai Upaya Menuju Paperless Policy Di Lingkungan Kampus FKIP, Universitas Sultan Ageng Tirtayasa

The outbreak of the Covid-19 pandemic has an impact on the transition of the learning system, namely from learning in the class to virtuallearning or commonly known online learning. During the process, online learning uses technology more than using paper. The use of technology has a positive impact on the environment in the form of a decrease in the amount of paper waste produced in the surrounding environment. From the reduced amount of waste, a policy called the paperless policy. Therefore, an analysis related to online learning during the Covid-19 pandemic was carried out as an effort towards a paperless policy.The purpose of this study was to determine the level of use of technology and use of paper among students during online learning during the Covid-19 pandemic. Qualitative descriptive research method by distributing questionnaires to students at the FKIP campus, Sultan AgengTirtayasa University. Sampling was done by purposive sampling.The results obtained from this study are 99% of respondents admitted that they use paper less when carrying out learning and use technology more. Thus, the FKIP campus of Sultan AgengTirtayasa University is declared ready to move towards a paperless policy.&#x0D; &#x0D; Keyword:Paperless Policy, Online Learning, Paper Usage, Covid-19 Pandemic.
Multimedia Security Through 1D Chaotic Systems

The use of 1D chaotic maps in multimedia security applications is becoming increasingly attractive due to their advantages, including the high sensitivity to both initial and control parameters, rich dynamical behavior and ease of implementation for both software and hardware. This chapter presents a survey and analysis of recent multimedia encryption, watermarking, zero-watermarking and data hiding schemes involving the use of 1D chaotic maps. The present work also attempts to identify some drawbacks of existing 1D chaotic maps and multimedia security schemes involved the use of such maps, with suggestions for overcoming the identified limitations.
Evaluation on the Policy of Using the E-Learning System with Fully Online in the Pandemic-Covid-19 Period

The use of e-learning platforms during the Covid-19 pandemic and in the context of facing the era of the industrial revolution 4.0 is a must in almost all levels of education, especially in universities. Many efforts have been made and implemented in order to apply these demands, for example by developing applications that are expected to help organize the digital learning process. The method used in obtaining information is a quantitative method using an instrument, namely a questionnaire (distribution of questionnaires) through the Google form application. The subjects in this study were UNP students in the Fundamentals of Education course in the July-December 2020 semester. Based on the research results, information was obtained regarding the policy evaluation of the use of a full online (online) e-learning system that could be categorized as good and effective, but it needs to be improved in several parts, for example: effectiveness, content, interaction patterns, understanding of the material, network access, large/expensive data packets when using video conference collaborating with the platform e-learning. The element of effectiveness/efficiency of online learning as many as 23.5% of respondents considered it less effective / efficient, meaning that there were still gaps in the fully online policy implemented by universities in this case, namely UNP.
Education About English Competencies Needed by Nursing Students of Baiturrahim Vocational School Jambi

English is an international language that is very important in communicating, especially in the health sector. This implies that it is necessary to master English appropriately to respond to intensive interactions in every country, especially in the global era. Therefore, as the most widely spread international language, mastery of English is a must. In Indonesia, English is still seen as a luxury language. The reason why English is included as a subject in elementary schools, as a basis, is that English should be a necessity for students in their future, especially in the era of globalization. This Community Service (PkM) aims to educate the English language competencies needed by nursing students in learning English to prepare them to continue their health education and become nurses at an international level. The competencies needed by students and nurses will later be used for the future of students majoring in nursing at Baiturrahim Vocational School. This community service takes place at Baiturrahim Vocational School. This educational method uses the approach of providing materials and leaflets to students and teachers at Baiturrahim Jambi Vocational School which is located at Jalan H. Syamsoe Bachroen No. 32 RT. 04, Selamat District. Sipin Lake, Jambi City, Jambi Province. This series of service activities has started in September 2022 - March 2023. The results of this education include increasing students' knowledge about the ten main competencies that nursing students must master in order to function well in the nursing profession.
Neural Automated Short-Answer Grading Considering Examinee-Specific Features

Automated short-answer grading (ASAG) is the task of automatically assigning scores to examinees' textual responses to short-answer questions. Recently, various ASAG models based on deep neural networks (DNNs) have been proposed and some have achieved high accuracy. Conventional ASAG models are generally trained and used independently for each short-answer question, even when a given test contains multiple short-answer questions. However, because tests are tools for evaluating particular examinee traits, multiple questions on the same test are generally designed to measure similar latent traits in examinees. Latent examinee traits across multiple short-answer questions may thus function as effective auxiliary features for ASAG. We therefore propose a new DNN-based ASAG model with an examinee-aware architecture that extracts examinee-specific features, including latent traits, from answer texts across multiple questions.
A Study of the Resource Allocation Mechanism for Secure Video Transmission in 5G Networks

The 5G cellular networks are empowered for different possible smart bandwidth-thirsty applications. One potential strategy for improving 5G network throughput is frequency re-use through Device-to-Device (D2D) communication. With the fast improvement of video communication and multimedia technology, 5G network has gained extraordinary improvement and has been worked in many other fields. Media players are able to dynamically select the best bitrate based on connectivity performance allows for a Hypertext Transfer protocol (HTTP)-based on adaptive buffering technology. By dynamically optimizing the network, 5G aims to maximize resource utilization to achieve maximum capacity and Quality of Service (QoS). The primary focus of this survey is to review 25 research articles based on a variety of approaches related to resource allocation mechanisms through 5G for secure video transmission. The analysis is done based on the software utilized for the execution, performance metrics, and the publication year. Additionally, the research issues related to each approach are investigated along with recommendations for future work.
A Learner Behavioral Anslysis on the Effectiveness of Scaffoldings for Language Learning with Educational Robots and IoT-Based Tangible Objects

The integration of educational robots and IoT-based tangible objects (R&T system) holds great potential for enhancing the learning outcome and providing scaffoldings for language learners. This study was focused on examining the learners' behaviors during language learning activities, to analyze the effectiveness of the scaffoldings provided by the R&T system. The aim was to assess the task completion and responsiveness to learners' learning states. A method called Lag sequential analysis was employed to identify significant sequential behavioral patterns. The results showed that, in addition to confirming the effectiveness of the scaffoldings, the sequential pattern analysis was useful in capturing the dynamics of interaction among learners, robots, teachers, and peers in real-life learning scenarios.
DSKCA-UNet: Dynamic selective kernel channel attention for medical image segmentation

U-Net has attained immense popularity owing to its performance in medical image segmentation. However, it cannot be modeled explicitly over remote dependencies. By contrast, the transformer can effectively capture remote dependencies by leveraging the self-attention (SA) of the encoder. Although SA, an important characteristic of the transformer, can find correlations between them based on the original data, secondary computational complexity might retard the processing rate of high-dimensional data (such as medical images). Furthermore, SA is limited because the correlation between samples is overlooked; thus, there is considerable scope for improvement. To this end, based on Swin-UNet, we introduce a dynamic selective attention mechanism for the convolution kernels. The weight of each convolution kernel is calculated to fuse the results dynamically. This attention mechanism permits each neuron to adaptively modify its receptive field size in response to multiscale input information. A local cross-channel interaction strategy without dimensionality reduction was introduced, which effectively eliminated the influence of downscaling on learning channel attention. Through suitable cross-channel interactions, model complexity can be significantly reduced while maintaining its performance. Subsequently, the global interaction between the encoder features is used to extract more fine-grained features. Simultaneously, the mixed loss function of the weighted cross-entropy loss and Dice loss is used to alleviate category imbalances and achieve better results when the sample number is unbalanced. We evaluated our proposed method on abdominal multiorgan segmentation and cardiac segmentation datasets, achieving Dice similarity coefficient and 95% Hausdorff distance metrics of 80.30 and 14.55%, respectively, on the Synapse dataset and Dice similarity coefficient metrics of 90.80 on the ACDC dataset. The experimental results show that our proposed method has good generalization ability and robustness, and it is a powerful tool for medical image segmentation.
Diagnosis of Heart Disease Using Optimized Naïve Bayes Algorithm with Particle Swarm Optimization and Gain Ratio

Abstract. &#x0D; Purpose: This study aims to apply feature selection particle swarm optimization (PSO) and gain ratio to the naïve Bayes algorithm and gauging the level of accuracy before and after applying PSO feature selection and gain ratio to the naïve Bayes algorithm in the diagnosis of heart disease.Methods/Study design/approach: Data collection is done by using taking the Cleveland dataset obtained from the UCI machine learning repository. The data used in this study were 303 samples. The data is processed using the preprocessing stage. The naïve Bayes algorithm is used for a classifier, while PSO and gain ratio for feature selection.Result/Findings: The results of the study revealed that the classification accuracy of the naïve Bayes algorithm without the application of feature selection in the Cleveland dataset is 86.88%, while the results of the classification accuracy of the naïve Bayes algorithm after applying PSO and gain ratio in the Cleveland dataset is 93.44%. Application of PSO and gain ratio as feature selection algorithms can improve classification accuracy by 6.56%.Novelty/Originality/Value: This study combines the PSO feature selection and gain ratio on the naïve Bayes algorithm using the Cleveland dataset. The research model that was carried out was enriched by carrying out the preprocessing stages, namely data cleaning, changing the number of class labels, data normalization, and data discretization. This study shows that using a combination of the PSO feature selection algorithm and the gain ratio gives better accuracy to the naïve Bayes algorithm in diagnosing heart disease.
Proof of Work and Secure Element in Electronic Voting

Electronic voting consistently fails to supplant conventional paper ballot due to a plethora of security shortcomings. Not only are traditional voting methods mediocre in terms of transparency, audit, and costs, but they also encompass a principal-agent problem, where acting governments have real capability for tampering. Here I propose Proof of Work by user devices to fortify integrity of votes as cast and seed-isolated time-based one-time passwords to force observable polling stations. Coupled with end-to-end verifiability, the measures proposed are intended to improve on the issues mentioned. A state would only issue single-use authorizations to vote, while an untrusted publisher would collect and publish two unrelatable lists; votes and voters. Eventually, anyone could tabulate results. Content distribution networks are shown to be instrumental security providers. Weaknesses of proposed architecture are also discussed.