Reviewerly/exHarmony huggingface dataset:
Pemanfaatan Teknologi Virtual Reality sebagai Media Pengenalan UMKM Jawa Barat Berbasis Aplikasi Artsteps

One option for introducing micro, small, and medium enterprises (UMKM) in West Java is through the use of virtual technology, which can potentially visualize different UMKM products interactively. Previously, the introduction of UMKM products relied solely on print media or social media for advertising. Therefore, Virtual Reality technology can be a solution to integrate information about UMKM in West Java. This research utilizes the MDLC (Multimedia Development Life Cycle) method to optimize the design and implementation process of Virtual Reality technology as an integrated information tool with 3D and other media. The end result of this virtual reality utilization research is a virtual tour simulation on "Introduction to West Java's UMKM," which allows users to interactively view the products that can be produced through the Artsteps application that has been integrated with the development of Virtual Reality technology.
Klasifikasi Mutu Fisik Tempe Menggunakan Metode Convolutional Neural Network (CNN)

T he quality of tempe h has until now been determined through direct physical observation. T he results of observations frequently show less consisten cy due to human visual limitations. Image processing is an alternative used to determining the quality of tempe h from the image aspect. Image processing has capabilities that are more sensitive, precise, and objective than human vision. C onvolutional N eural N etwork (CNN) is a deep learning model that is able to identify image objects in such a way as to determine the type of the object. In some cases, CNN algorithm is used to identify the condition of an object quality. This research aims to identify the quality of tempeh from the image aspect to ensure whether the tempeh can be classified as the tempeh having good condition or the one start ing to decompose. The image of tempeh is primary data obtained directly from one of the traditional markets in Medan. The number of images that were successfully obtained was 262. The result ed classification model went through seven phases: data preparation, preprocessing, data augmentation, dataset splitting, building a classification model with the CNN algorithm with the ReLU activation function, model testing, and evaluation. The results show that the model generated from 80% of the data ha s an accuracy of 98.71% and a loss rate of 0.0433%. In conclusion, t his study shows that the loss rate will stabili z e at this rate after 50 epochs.
Blockchain Privacy and Regulatory Compliance: Towards a Practical Equilibrium

We study Privacy Pools, a novel smart contract-based privacy-enhancing protocol. The protocol introduces a mechanism for users to reveal certain properties of their transaction without having to reveal the transaction itself. The core concept involves allowing users to publish a zero-knowledge proof, demonstrating that their funds (do not) originate from known (un-)lawful sources, without publicly revealing their entire transaction history. This is achieved by proving membership in custom association sets, which are designed to demonstrate compliance with regulatory frameworks or social consensus. We illustrate how this mechanism can create a separating equilibrium between compliant and non-compliant withdrawals. Our work describes the technical underpinnings, incentives and broader implications of this mechanism, highlighting how Privacy Pools-like protocols can create more private yet compliant blockchain transactions.
Healthy snack food campaign through educational games for elementary school student

Elementary school students like various snacks in the school environment, which frees children to choose the food and drinks available. They tend to consume foods containing causative spices, sweet foods, and beverages, and foods with additional ingredients, which will cause things that are not good for children's health. There are still many students who need help understanding about healthy snacks food. Education on healthy snacks in schools is not optimal because knowledge related to these snacks food still needs to be better socialized. This community service activity aims to increase knowledge about healthy snacks in elementary school students through educational games. The methods are socialization, focus group discussion, game simulation through snake and ladder educational games, healthy snack food puzzles, self-evaluation, and documentation. The results obtained from this activity were in the form of increasing the knowledge score of healthy snack food after education and educational games and commitment from partners to incorporate healthy snack food materials into PJOK subjects. This result shows that knowledge of healthy snacks can be improved through snake and ladder educational game activities and beneficial snack food puzzle games. It is recommended that the school continuously allocate time regularly for healthy snack food education for all students.
Infrastructure as Code for Cybersecurity Training

An organization's infrastructure rests upon the premise that cybersecurity professionals have specific knowledge in administrating and protecting it against outside threats. Without this expertise, sensitive information could be leaked to malicious actors and cause damage to critical systems. These attacks tend to become increasingly specialized, meaning cybersecurity professionals must ensure proficiency in specific areas. Naturally, recommendations include creating advanced practical training scenarios considering realistic situations to help trainees gain detailed knowledge. However, the caveats of high-cost infrastructure and difficulties in the deployment process of this kind of system, primarily due to the manual process of pre-configuring software needed for the training and relying on a set of static Virtual Machines, may take much work to circumvent. In order to facilitate this process, our work addresses the use of Infrastructure as Code (IaC) and DevOps to automate the deployment of cyber ranges. An approach closely related to virtualization and containerization as the code's underlying infrastructure helps lay down this burden. Notably, placing emphasis on using IaC tools like Ansible eases the process of configuration management and provisioning of a network. Therefore, we start by focusing on understanding what the State of the Art perspectives lack and showcasing the benefits of this new working outlook. Lastly, we explore several up-to-date vulnerabilities that are constantly messing with the lives of individuals and organizations, most related to Privilege Escalation, Remote Code Execution attacks, and Incident Forensics, allowing the improvement of skills concerning Red team and Blue team scenarios. The analysis of the attacks and exploitation of such vulnerabilities are carried out safely due to a sandbox environment. The expected results revolve around using IaC to deploy a set of purposely-designed cyber ranges with specific challenges. The main objective is to guarantee a complexity of scenarios similar to what we can observe in enterprise-level networks. Thus, this entails having a set of playbooks that can be run in a machine or laboratory, assuring the final state of the network is consistent. We expect this deployment strategy to be cost-effective, allowing the trainee to get deep insight into a wide range of situations. Nowadays, DevOps solutions work as a silver bullet against the issues derived from old-case-driven approaches for setting up scenarios. In short, one of the key takeaways of this work is contributing to better prepare specialists in ensuring that the principles of the National Institute of Standards and Technology (NIST) Cybersecurity Framework hold, namely: prevent, detect, mitigate, and recover.
INNOVATIVE TECHNOLOGIES IN THE FIELD OF DISTANCE EDUCATION

The article is devoted to the possibilities of expanding the potential of traditional distance education through Internet technologies, describes the grounds for their use and the advantages of using them, reveals the essential characteristics and specific features of distance learning. The author considers both positive and negative aspects of distance learning and focuses on certain issues of the criteria for evaluating the effectiveness of distance education. There are two types of factors for the use of distance learning in the education system: external and internal factors.
Towards Requirements Specification Collaboration Forum for Embedded Software Systems

Effective requirements specification for embedded software systems relies heavily on the collaboration between stakeholders to articulate accurate functional and non-functional requirements. At present, requirement engineers manually coordinate the sophisticated task of correct stakeholders selection for requirements and compilation of feedback in the embedded software domain. Because embedded software has an extensive number of stakeholders, the absence of an efficient collaboration platform causes a prolonged project completion time, elevated maintenance costs, or project failure. In this preliminary research paper, a stakeholder collaboration platform is proposed using an auto-encoder-based recommender system and SysML modeling language for embedded software systems. In the proposed framework, forums are the collaboration space for stakeholders to contribute to requirements analysis and specifications and are generated from the requirements diagram of the SysML modeling language. Owners of the requirements will be directly assigned to the forum from the SysML requirement profile information. To ensure adequate stakeholder engagement in the requirements specification process, the Collaborative Denoising Auto-Encoder (CDAE) recommender system is used for advanced auto-recommendations of requirement forums to stakeholders. This approach facilitates feedback collection and analysis to refine requirements specifications. The automatic forum creation and the advanced recommendation process of this framework will add no overhead to the requirements engineers or analysts; at the same time, a centralized collaboration platform for the stakeholders will save requirements analysis time and avoid future conflicts.
Performance and Scalability of DMN-Based LCNC Platforms

Low-code / no-code platforms (LCNCP) are on the rise, a trend that represents a step towards the decade-long goal to automate coding. The quality of an LCNCP can be measured by external attributes such as functionality, reliability, usability, flexibility, efficiency, and maintainability. When businesses decide on using an LCNCP, they place significant importance on efficiency, which can be evaluated based on factors like performance and scalability. This paper describes the performance and scalability aspects in the space of business decisions specified using the Decision Model And Notation (DMN) language, an industry standard for the specification of business decisions, and the execution engine jDMN. Furthermore, it outlines a range of efficient optimization techniques within the DMN domain.
Application of Internet of Things Optimization Engine Based on RETE Algorithm in English Interactive Teaching

Interaction is a process in which two or more people exchange thoughts and feelings with each other, transmit information and influence both sides. Class interaction refers to the interaction between teachers, students and students in the special environment of classroom. IoT is a network that connects any goods with the Internet through various information sensing devices according to the agreed protocol, and carries out information exchange and communication, so as to realize intelligent identification, positioning, tracking, monitoring and management. Applying the conceptual model and thinking mode of the Internet of Things (IoT) to English Language Teaching (ELT) can also get good results, which leads to the change of ELT mode. This paper analyzes the shortcomings of the current IoT technology application process, and expounds the application of IoT optimization engine based on RETE algorithm in university ELT process. The simulation results show that the accuracy of IoT optimization engine based on RETE algorithm is higher than that of traditional algorithm. With the increase of the number of experiments, the accuracy of RETE algorithm is stable at about 95%, and the real-time wavelength tends to be stable. Therefore, applying the IoT optimization engine based on RETE algorithm to English interactive teaching can make IoT technology play a more active role in ELT in universities and achieve better implementation effect.
Posture Parameters for Personality-Enhanced Virtual Audiences

This paper presents the development and preliminary evaluation of a personality enhancer behaviour model for virtual audiences to increase their realism and individualism. We conducted a systematic literature review and identified sixteen posture parameters to modify seated animations dynamically, calling them personality enhancers. We grouped them into four main categories: body, gaze, face and gesture behaviour modifiers. We implemented a unique animation modifier system on top of a game engine to apply these personality enhancers on existing pre-recorded generic seated animations. The first results with sixty participants in an online video survey show that the model can successfully simulate individuals with low and high levels of extroversion as well as with low and high levels of emotional stability.
Використання дистанційного навчання у формуванні іншомовної компетентності у студентів під час дії воєнного стану в Україні

Ukraine is on the way to rebuilding its educational system, which requires new approaches to reorganizing the learning process. The training of highly qualified specialists has become more complicated as the outflow of students from Ukraine has accelerated due to the outbreak of hostilities. That is why it is important to create appropriate conditions to qualitatively prepare future specialists for their professional activities through distance learning (DL). The aim was to study the possibility of forming students' foreign language communicative competence (FLCC) with the help of DL. Given the conditions for studying in our country, the most effective way to develop FLCC is hybrid DL, which combines synchronous and asynchronous modes. Thanks to this approach, students can work through part of the curriculum in real time with a teacher using various online platforms, and the rest of the material is available in a format that is accessible for self-study. When developing FLCC while studying at a university, it is important to combine foreign language skills with the content of students' professional activities, which will help achieve the key goal of learning – the formation of their own professional competence. One of the most important tasks for any teacher is to create a favorable atmosphere in the classroom, establish trusting relationships with students and provide them with psychological support. An important challenge is the psychological aspect when using DL to form FLCC, as it is more difficult for a teacher to convey the material on the topic of the class to students without having full visual contact and personal communication with them, as it happens in a regular classroom during a face-to-face class. That is why, in order to form the student's FLCC during the DL, the teacher should create a motivational space for better learning of the material by students. Conclusions. DL has a positive effect on the process of forming students' FLCC, provided that an effective communication environment with the teacher during DL is created, which allows to stimulate students' motivation and ensures uninterrupted learning management.
MODERN FOREIGN LANGUAGE LESSON IN THE CONTEXT OF EDUCATION DIGITAL TRANSFORMATION

Among the goals of digital transformation, there is a transformation of the model from a traditional one into a model of continuing education, and under these circumstances, school becomes the stage where, among others, the basic skill of a modern person is formed — the ability to learn. Teaching to learn is one of the most important ideas of current federal state educational standards. The authors propose to consider a modern foreign language lesson in the unity of aspects-components: communicative-interactive, logistical, procedural, instrumental. Each of the conditional components, representing an independent whole, is determined by and determines itself the normal functioning of content and activity, internal and external entities, as regards the lesson as a form of organization of the educational process at school. It is suggested that the complete replacement of teaching tools and means with digital analogues is impractical, due to the need to take into account certain patterns of mastering linguistic norms, a foreign language communication, as well as compliance with the health care rules for children and adolescents – students of educational organizations implementing general education programs in the country. The purpose of the study is to reveal the prospects of digitalization within the framework of the modification of components-aspects of a foreign language lesson, as well as to analyze the basic principles and conditions required for the transition to one of the levels of digital transformation of education as proposed by modern researchers. The authors come to conclusion about the need of a multi-level approach to change, to digitalization of each of the components-aspects of the lesson, about the potential choice of the desired depth and intensity of digital transformations in each case.
BiCOD: A Camouflaged Object Detection Method Directed by Cognitive Attention

Camouflaged object detection (COD) is a typical application of deep-coupled unmanned platform combat support which aims to detect objects that are highly similar to the background in terms of structure, details, and texture while improving the efficiency and accuracy of detecting camouflaged objects. Existing COD methods are built upon extraction and segmentation of image features and lack of theoretical interpretability. In this paper, the task of COD was revisited and analyzed. From the perspective of cognition, the cognitive laws of camouflaged objects were assessed through eye movement experiments to form an entire cognitive process, which serves as a guide for designing COD methods. Feature extraction, position attention, and channel attention modules were utilized as the basic framework. The residual-in-residual module was introduced to improve the accuracy of feature learning and transmission. Then a bidirectional attention module was added to guide the feedforward and feedback of attention features. And a closed loop was formed to achieve efficient feature transmission and use. As a result, the performance of our method BiCOD was promoted. In addition, a COD dataset containing both natural and artificial camouflage objects was compiled to evaluate the generalization ability of the camouflaged object recognition algorithm. The experiment results showed that BiCOD achieved an advanced level in quantitative results and visual comparisons in general, and the effectiveness and accuracy of the method in different environments were verified.
dAPRE:Efficient and Reliable Attribute-Based Proxy Re-Encryption Using DAG for Data Sharing in IoT

The explosive growth of the Internet of Things (IoT) data and its analytical applications can bring convenience to better intelligent life, efficient work and other aspects. However, the secure sharing of IoT data is the biggest obstacle to this convenience. Numerous schemes have been developed to address the security challenges associated with IoT data sharing recently, but the existing schemes still face the challenge of how to achieve effective fine-grained access control and low overhead in IoT data security sharing. To tackle this challenge, we propose an efficient and reliable attribute-based proxy re-encryption scheme using Directed Acyclic Graph (DAG), namely dAPRE. In the phase of encryption, re-encryption and delegator decryption, dAPRE remains constant computation overhead with the increasing number of attributes. At the same time, dAPRE’s attribute-based verifiable ciphertext access mechanism and DAG-based key recovery algorithm realize ciphertext integrity check and ensure key security. Furthermore, dAPRE has been evaluated through both theoretical analysis and practical experiments, demonstrating its efficiency and practicality.
Dual Feature Fusion Tracking with Combined Cross-correlation and Transformer

Siamese networks have found applications in various fields, notably object tracking, due to their remarkable speed and accuracy. Siamese tracking networks rely on cross-correlation to obtain the similarity score between the target template and the search region. However, since cross-correlation is a local matching operation, it cannot effectively capture the global context information. While the Transformer for feature fusion can better capture long-range dependencies and obtain more semantic information, more localized edge information is needed to distinguish the target from the background. Cross-correlation fusion and Transformer fusion have their advantages. They can complement each other, so we combine them and propose a dual feature fusion tracker (SiamCT) to obtain the local correlations and global dependencies between the target and the search region. Specifically, we construct two parallel feature fusion paths based on cross-correlation and Transformer. Among them, for cross-correlation fusion, we adopt the more efficient two-dimension pixel-wise cross-correlation (TDPC), which performs correlation operations from both spatial and channel dimensions, and the interaction of multidimensional information helps to realize more accurate feature fusion. Subsequently, the fused features are augmented by coordinate attention (CA) for orientation-dependent positional information. For Transformer fusion, we introduce cos-based linear attention(ClA) to improve Transformer’s ability to acquire global context information. Our SiamCT outperforms existing leading methods in GOT-10k, LaSOT, TrackingNet, and OTB100 benchmarks based on extensive experiments.
Implementing the quantum fanout operation with simple pairwise interactions

It has been shown that, for even $n$, evolving $n$ qubits according to a Hamiltonian that is the sum of pairwise interactions between the particles, can be used to exactly implement an $(n+1)$-qubit fanout gate using a particular constant-depth circuit~[\href{https://arXiv.org/abs/quant-ph/0309163}{arXiv:quant-ph/0309163}]. However, the coupling coefficients in the Hamiltonian considered in that paper are assumed to be all equal. In this paper, we generalize these results and show that for all $n$, including odd $n$, one can exactly implement an $(n+1)$-qubit parity gate and hence, equivalently in constant depth an $(n+1)$-qubit fanout gate, using a similar Hamiltonian but with unequal couplings, and we give an exact characterization of the constraints that the couplings must satisfy in order for them to be adequate to implement fanout via the same circuit.}{In particular, we show the following:Letting $J_{ij}$ be the coupling strength between the $\ordth{i}$ and $\ordth{j}$ qubits, the set of couplings $\{J_{ij}\}$ is adequate to implement fanout via the circuit above if and only if there exists $J&gt;0$ such that 1. each $J_{ij}$ is an odd integer multiple of $J$, and 2. for each $i$, there are an even number of $j\ne i$ such that $J_{ij}/J \equiv 3\pmod{4}$.