Reviewerly/exHarmony huggingface dataset:
PENGEMBANGAN MEDIA POWER POINT INTERAKTIF PADA PEMBELAJARAN MEMBACA PERMULAAN SISWA KELAS 1 SEKOLAH DASAR

Media is a tool that teachers use to convey material during the learning process. Therefore, media is very important to use during the learning process. The aim of this research is to produce digital-based media, namely interactive power points, which are suitable for use to help students who have difficulty reading and can attract interest in reading in grade 1 elementary school students. The type of research used is development research or Research and Development (R&amp;D). The method used is experimental. The research model used is the Addie model from Dick and Carey. The data collection techniques used were observation and interviews. The results of material expert validation and media expert validation stated that interactive PowerPoint media was very suitable for use and did not need revision. The value obtained from material expert validation was 84% and from media expert validation the value was 80%. Power point media was declared very suitable by getting the category very well used and implemented for reading learning at the beginning of grade 1 of elementary school. The students' response was very good, it can be seen that students felt enthusiastic when learning using interactive power point media. Therefore, this power point media can be used for initial reading learning and increasing reading interest in grade 1 elementary school students.
Model-View-Controller Design System of Motorcycle Damage Detection Using Forward Chaining Method

This study aims to design a motorbike damage detection system using the forward chaining method with a view controller model that can be run on a mobile-based system. Dealers and motorbike service providers receive and fulfil customer requests for motorbike service services. Mechanics who service vehicles still use conventional methods to check vehicle damage by scanning the paper (form). There is a list ofvehicle damage. This method takes quite a long time, and it is not sure that the problem will be resolved quickly. The research method used is forward chaining, and the model used is the Model View Controller, which separates data from the display by processing it. The result of this research is that with a motorbike damage detection system, mechanics from dealers and service areas do not have to carry out initial checks manually but instead use a system with a view controller model and initial check results. Detection can also be determined by applying the forward chaining method. Based on functional testing of the system using a black box, valid results were found; then, for logic testing using the forward chaining method, the results were free from logical errors.
Comparative Analysis of GA-PRM Algorithm Performance in Simulation and Real-World Robotics Applications

Abstract: This paper presents a comprehensive analysis of the performance of the Genetic Algorithm Probabilistic Roadmap (GA-PRM) algorithm in both simulated and real-world robotic environments. The GA-PRM algorithm is a promising approach for robot path planning, and understanding its behavior in different settings is crucial for its practical applications. In simulations, we explore the advantages of controlled and reproducible test conditions, allowing for extensive parameter tuning and algorithm improvement. Real-world testing is employed to validate the algorithm's performance in actual robotic environments, taking into account the inherent complexities and uncertainties present. In our comparative analysis, we found that the GA-PRM algorithm demonstrates significant improvements in real-world scenarios compared to simulations. Specifically, the algorithm produced shorter paths in real-world robot testing, with an average length of 21.428 cm, as opposed to 25.6235 units in simulations. Moreover, the computational efficiency of the algorithm was notably enhanced in the real-world environment, where it took only 0.375 seconds on average to plan paths, compared to 0.6881 seconds in simulations. The algorithm also exhibited higher path smoothness in the real world, with an average smoothness score of 0.432, compared to 0.3133 in simulations. These results underscore the algorithm's adaptability to real-world conditions and its potential for efficient navigation in practical healthcare and automation applications. Our research bridges the gap between simulation and reality, facilitating the development of more reliable and adaptable robotic systems. The insights gained from this comparative evaluation contribute to a deeper understanding of the GA-PRM algorithm's behavior and its potentials.
Custom ASIC Design for SHA-256 Using Open-Source Tools

The growth of digital communications has driven the development of numerous cryptographic methods for secure data transfer and storage. The SHA-256 algorithm is a cryptographic hash function widely used for validating data authenticity, identity, and integrity. The inherent SHA-256 computational overhead has motivated the search for more efficient hardware solutions, such as application-specific integrated circuits (ASICs). This work presents a custom ASIC hardware accelerator for the SHA-256 algorithm entirely created using open-source electronic design automation tools. The integrated circuit was synthesized using SkyWater SKY130 130 nm process technology through the OpenLANE automated workflow. The proposed final design is compatible with 32-bit microcontrollers, has a total area of 104,585 µm2, and operates at a maximum clock frequency of 97.9 MHz. Several optimization configurations were tested and analyzed during the synthesis phase to enhance the performance of the final design.
KnowledgeVIS: Interpreting Language Models by Comparing Fill-in-the-Blank Prompts

Recent growth in the popularity of large language models has led to their increased usage for summarizing, predicting, and generating text, making it vital to help researchers and engineers understand how and why they work. We present KnowledgeVis, a human-in-the-loop visual analytics system for interpreting language models using fill-in-the-blank sentences as prompts. By comparing predictions between sentences, KnowledgeVis reveals learned associations that intuitively connect what language models learn during training to natural language tasks downstream, helping users create and test multiple prompt variations, analyze predicted words using a novel semantic clustering technique, and discover insights using interactive visualizations. Collectively, these visualizations help users identify the likelihood and uniqueness of individual predictions, compare sets of predictions between prompts, and summarize patterns and relationships between predictions across all prompts. We demonstrate the capabilities of KnowledgeVis with feedback from six NLP experts as well as three different use cases: (1) probing biomedical knowledge in two domain-adapted models; and (2) evaluating harmful identity stereotypes and (3) discovering facts and relationships between three general-purpose models.
An Evolutionary Attack for Revealing Training Data of DNNs With Higher Feature Fidelity

Model inversion attacks aim to reveal information about sensitive training data of AI models, which may lead to serious privacy leakage. However, existing attack methods have limitations in reconstructing training data with higher feature fidelity. In this paper, we propose an evolutionary model inversion attack approach (EvoMI) and empirically demonstrate that combined with the systematic search in the multi-degree-of-freedom latent space of the generative model, the simple use of an evolutionary algorithm can effectively improve the attack performance. Concretely, at first, we search for latent vectors which can generate images close to the attack target in the latent space with low-degree of freedom. Generally, the low-freedom constraint will reduce the probability of getting a local optima compared to existing methods that directly search for latent vectors in the high-freedom space. Consequently, we introduce a mutation operation to expand the search domain, thus further reduce the possibility of obtaining a local optima. Finally, we treat the searched latent vectors as the initial values of the post-processing and relax the constraint to further optimize the latent vectors in a higher-freedom space. Our proposed method is conceptually simple and easy to implement, yet it achieves substantial improvements and outperforms the state-of-the-art methods significantly.
ASQ: An Ultra-Low Bit Rate ASR-Oriented Speech Quantization Method

For efficient transmission of speech signals, speech compression methodologies have attracted significant research attention for decades and are widely used in automatic speech recognition (ASR) services. However, most speech codecs are perception-oriented, leaving redundant information and introducing distortion, which harms ASR systems. Recently, the emergence of neural network-based models has significantly advanced the progress of ASR systems and speech coding, laying the foundation for building a speech compression method specially optimized for ASR systems. In this letter, we propose an ASR-oriented Speech Quantization (ASQ) method to reduce communication costs for speech recognition systems. In the proposed method, a speech quantization model first converts the speech into low bit rate tokens. Then the tokens are transmitted to the server and recognized by a quantized speech recognition model. The two models could be jointly trained in the end-to-end (E2E) style. To mitigate the performance degradation introduced by the quantization components, we design an entropy-guided 3-stage training method that encourages the model to fully utilize the token space and promote recognition accuracy. Experiment results on the LibriSpeech corpus show that compared to an existing non-quantized ASR model with a 256 kbps transmission bit rate, the proposed method can achieve a transmission bit rate of 0.6 kbps without any influence on word error rate (WER). It also significantly surpasses the 2-step pipeline that first performs speech codec and then recognizes with a several times lower bit rate.
Determination of C4.5 Algorithm Performance Based on Training Data and Testing Data in Determining Diseases in Rice Plants

Disease detection in rice plants at the Padang Lawas District Agriculture Service is carried out every year by collecting data from the Agriculture Service. Therefore, it is necessary to carry out classification to determine the best percentage of training data and test data in determining rice plant diseases and a comparison between testing data and training data for accurate accurate predictions. So data mining algorithms are needed that must be used to classify these diseases. Classification is carried out to group data into categories or groups so that objects from the same data are more similar to data objects from different groups. In this study, disease classification was processed using the C4.5 algorithm using disease data collection on rice plants from 2017 to 2020 which had a total of 248 data. The results of testing data and training data will be compared to see accuracy performance.
Cloud-based Fusion Rendering for AR Applications

To create digital twins, it is necessary to represent real objects or scenes in a virtual form. With the rapid development of Augmented Reality (AR) technology, its applications in various fields are becoming increasingly extensive. However, in traditional AR applications, the client has high performance requirements for device compute-intensive tasks, which cannot be met on low-performance devices. To solve this issue, in this paper, we propose and implement a novel digital twin method for AR technology, which is based on a cloud fusion platform. The approach includes deploying the Neural Radiance Field (NeRF) on a cloud server for high-quality model rendering and integrating simultaneous localization and mapping (SLAM) with AR visualization on the client side. This design effectively separates compute-intensive tasks from client-side rendering tasks, reducing the performance requirements of client devices and enhancing overall scalability and availability of the system. Practical application and tests show that the proposed AR system based on the cloud fusion platform performs well in terms of performance, stability, and scalability. This paper provides detailed discussions on the relevant technical methods and approaches, and showcases examples that serve as a foundation for the implementation of digital twins across diverse fields.
Dishpolish: Exploring the Recovery of Geometric Invariants in Deep Learning Models via Pose Estimation of Microwave Dish Antennae

In this paper, we propose a deep learning algorithm, called Dishpolish to exploit geometric invariances of microwave dish antennae to perform a dish mensuration task-the study of estimating a microwave antenna dish pointing direction from imagery. We synthesize a dish imagery dataset from 5 distinct dish models and evaluate it on the proposed Dishpolish deep-learning architecture. We find that the method is capable of recovering dish orientation to 5.36° and 1.03° azimuth and elevation error, respectively, when it has seen the dish model before, but performs less effectively on unseen dishes.
Designing Chatbot About Tourism Recommendations In North Sumatra

North Sumatra has a lot of tourism, but much tourism information is still unknown. Therefore, tourism information is needed. One of the technologies currently developing is chatbots. Chatbot is an interactive platform that combines artificial intelligence with human-machine communication to provide automated services to users. Chatbots can communicate in text form and use Natural Languages Processing (NLP) to understand and respond to user questions and requests. In this research, the chatbot was designed using a question matching scheme with existing patterns in the chatbot's knowledge. Chatbots use everyday language or natural language that humans use to communicate with each other. The aim is to make it easier for visitors to find the information they need, namely information about tourism in North Sumatra. The system modeling used for this website is using the Unified Model Language. The implementation of tourism chatbots in North Sumatra was successful because it was able to answer several questions given. Tourism routes are still being developed to be more specific.
Signcryption Techniques For Digital File Security Using the RSA Multi-Factor Algorithm and the ESIGN Algorithm

The modern digital era has made cryptography a tool capable of solving problems of confidentiality, authentication, integrity and non-repudiation. Encryption can solve the problem of message confidentiality and digital signatures can solve the problem of message authentication. The conventional method is to encrypt the message and then digitally sign it or vice versa. However, this method requires large computing and communication costs. Signcryption is a new way of encrypting digital signatures without requiring large computing and communication costs. This research uses the RSA Multi-Factor algorithm as encryption and the ESIGN algorithm as a digital signature. This research was carried out by comparing two Signcryption technique schemes, namely encrypt-then-sign and sign-then-encrypt to determine which scheme is better in terms of time performance. Example of comparison time results for testing algorithm calculations based on the bit length of 8 bit and 16 bit prime numbers. The sign-then-encrypt signcryption scheme has a time speed of 0.0059967041015625 seconds and 0.01286625862121582 seconds. Meanwhile, the encrypt-then-sign signcryption scheme has a time speed of 0.008978605270385742 seconds and 0.015513896942138672 seconds. This research also tested prime number bit lengths of 1024 bits and 2048 bits and plaintext character lengths of 100 and 1000. From the two signcryption schemes, it can be concluded that the best scheme in computing time is the sign-then-encrypt scheme.
Hybrid Cryptosystems MRC-4 Algorithm and RSA Dual Modulus to Secure Data

Cryptography is very important when we talk about the security of data. Cryptography is a science that studies techniques, both mathematical and intuitive, related to aspects of data security. Along with the development of cryptography, technological developments are also developing very rapidly. There is a lot of data theft and lack of efficiency in maintaining data security, in this case it is very important to maintain the security of data and information. This research is designed to provide a solution to this problem by using Hybrid cryptography. Hybrid in this research combines the MRC4 and RSA Dual Modulus algorithms. The MRC4 algorithm is a stream cipher algorithm whose system security depends on generating a stream of key bits (keystream), while the RSA Dual Modulus algorithm is an asymmetric algorithm variant of RSA whose security lies in the difficulty of factoring large numbers into prime factors but this algorithm takes a long time in the encryption process when compared to the MRC4 algorithm. In the research process, the MRC4 algorithm is used to encrypt the sender's message and the RSA Dual Modulus algorithm will encrypt the key from MRC4, then the results of the message and key encryption process will be sent to the message recipient. After the recipient gets the plaintext and key, the key is first decrypted using the RSA Dual Modulus algorithm to produce a key that can decrypt the message using the MRC4 algorithm.
Using Conversational AI to Service Organizational Agility and Flexibility: The Dynamic Capability and Option View

Artificial intelligence has been changing our lives with the evolution of the times. Although human wisdom brings convenience, the use of conversational artificial intelligence by organizations will expand the scope of their operations. This will give the organization sufficient resources to reduce operating costs and time. On the other hand, conversational artificial intelligence is one of the important dynamic capabilities of organizations and will also allow organizations to respond quickly to market needs. This study examines usage, resilience, and agility in conversational AI. Based on the experience of 132 organizational information personnel, this study used questionnaires, surveys, and statistical analysis. The results show that conversational AI use is related to organizational agility. This dynamic capability will be related to organizational resilience. In conversational artificial intelligence, dynamic capabilities theory can be applied. High organizational resilience will allow organizations to respond quickly to needs when using conversational artificial intelligence. In practical applications, when organizations use conversational AI, they need to consider the breadth of resources generated and use it as an option to make contributions.
Image Processing of Cultural Relics Fragments Splicing Through Hybrid Folded Mesh Simplification Algorithm

Most of the cultural relics were broken and damaged when they were excavated. However, manual splicing has many problems, such as heavy workload, long periods, and secondary damage to cultural relics. Therefore, using the computer-aided system for 3D model virtual fragment splicing and cultural relics restoration can provide an excellent matching algorithm and fast and accurate splicing technology for restoring damaged cultural relics. The existing matching algorithms based on fragment features have some problems, such as low matching accuracy, slow convergence speed, too strict constraints, easy to cause local best matching, spending a lot of time on useless matching, and poor matching effect of clouds with low overlap ratio. The study presents the automatic matching algorithm of cultural relics fragments based on feature region division. The experiment on the fragments of G10-52 Terracotta Warrior in the Museum of the Terra Cotta Warriors, it shows that the algorithm can not only greatly reduce the size of the matching data, but also effectively improve the overlapping range of the feature areas. It is a fast and accurate algorithm for matching cultural relics pieces, especially for the pieces of the low overlap fracture surface, with good matching results.
Blockchain-Based Reviewer Scores for Reliable Peer Review Processes

Traditional peer review systems have many demerits such as lack of transparency, authenticity of the reviewer, and unnecessary delays. This paper proposes a novel solution to engage reviewers proactively with the help of incentives. The solution broadly aims to build trust among the stakeholders participating in the research community. Blockchain technology is used for the transparency and immutability features that it provides. The blockchain-based solution requires additional efforts from the authors as well as reviewers. However, integrity is appreciated by the system in the form of rewarding the reviewers. A web application prototype has been created to give the readers a flavor of the solution being proposed.
Digital Reading: A Complex Endeavor

“Educational Prospects” seeks to be a channel that helps education specialists and policymakers in the Arab World stay updated on international research in the field of education. This issue focuses specifically on the topic of digital reading skills. Research has shown that digital reading is distinct from print reading in a variety of ways and that reading digital texts requires different comprehension strategies. Because print comprehension strategies do not necessarily transfer to digital contexts, teaching students reading skills in a digital age is complex.
DEVELOPING A MEASUREMENT SCALE FOR PERCEIVED SERVICE QUALITY IN HIGHER EDUCATION: AN EMPIRICAL STUDY

Education plays a pivotal role in the peace and economic growth of the nation. Consequently, it is essential to provide every student with a high-quality education. A person’s life is significantly impacted by the level of education. As a result, students are highly concerned about the quality of education especially when selecting a higher educational institute. The purpose of the study is to develop a measurement scale to evaluate the service quality provided by higher education. The study applied a positivism research philosophy by adopting a deductive research approach. The quantitative research method was applied, and data were collected through a questionnaire from 225 respondents who are pursuing a Higher National Diploma in Management (HNDM) and Business Administration (HNDBA) courses in SLIATE, ATI - Dehiwala. A stratified random sampling technique was adopted to select the respondents from each course. The researcher applied the SERVQUAL model using its five dimensions tangibility, reliability, responsiveness, assurance, and empathy to evaluate the level of service quality in higher education. Cronbach Alpha was used to measure the reliability and the value of 0.816 indicates that the questionnaire used to gather data on the service quality construct is highly reliable. Based on the result, composite reliability (CR) values for all constructs are above 0.70 and the AVE values are within 0.523 and 0.935 which confirmed the internal consistency in scale items. The validity of the measurements was performed through convergent and discriminant validity using the Fornell and Larcker (1981) test. The finding of the test shows that the average variance extracted (AVE) value of all the constructs is higher than 0.5 and the AVE square root in the diagonal was greater than the squared correlation with other constructs in off-diagonal which confirmed convergent and discriminant validity. Initially, 20 items were developed using five dimensions of the SERVQUAL model. After conducting the test, 20 items were reduced to 14 items, and 6 items that didn’t meet the necessary criteria were removed. Finally, a new scale was developed to measure the service quality with 5 dimensions responsiveness, reliability, tangible, assurance, and empathy for higher education.