Reviewerly/exHarmony huggingface dataset:
Secure and Privacy-Preserving Voting System Using Zero-Knowledge Proofs

Electronic voting systems have the potential to improve the efficiency and accessibility of elections, but they also introduce unique challenges in terms of security, privacy, and voter anonymity. In this paper, we propose a secure and privacy-preserving voting system based on zero-knowledge proofs and homomorphic encryption. Our system ensures the integrity, confidentiality, and authenticity of votes while preserving the anonymity of voters. We present the system architecture, design, and implementation, along with a detailed analysis of the cryptographic techniques employed. The evaluation of our proposed system demonstrates its effectiveness, efficiency, and scalability, making it suitable for use in large-scale elections. This work contributes to the ongoing efforts to develop more secure, transparent, and accessible electronic voting systems for the future.
MANAJEMEN PEMBIYAAN PENDIDIKAN DI SEKOLAH DASAR

A school needs education funding in the form of education funds needed by the school. The factors that influence the education fund itself in a school are teachers, the percentage of students, and the use of education funding itself. The problems that occur in education financing are financial management in schools that are not well managed, besides that there are indications of misappropriation in finances committed by related persons, as well as a lack of school supplies, only coming from students. Funding for education in schools has not been maximized because direct cost and indirect cost sources of funds have not been used optimally. Apart from that, problems that often arise, namely in recording school financial administration and expenses to financial reporting. Current problems with the difficulty of life make the world of education only experienced by the wealthy. The study used in the writing of this article is to use the literature study method where the source comes from books, in Googlei Scholeir or in the form of previous articles and relevant articles related to the problem.
PENERIMAAN BERITA HOAKS MELALUI MEDIA SOSIAL SEBAGAI LITERASI INFORMASI DIKALANGAN REMAJA DI KOTA BANDA ACEH

Based on observations, almost the average student uses a cell phone so that teenagers make social media a trend among these teenagers. Most of them know social media through other peers who attend other schools. The purpose of this research is to encourage youth literacy in the city of Banda Aceh in responding to information on social media, using social media to view information, and teenagers in the city of Banda Aceh in understanding the spread of hoax information on social media. The theory used in this study is the theory of media literacy. This research method uses a qualitative descriptive approach. Research informants consisted of Community Leaders, Academics, Students. Data collection techniques were carried out through observation, interviews and documentation. Based on the research results, it is known that teenagers literacy in the city of Banda Aceh in responding to information on social media can be grouped into two responses. First, students who respond well to news information by observing and understanding and looking for the truth so that they are not mistaken in conveying greetings to other parties. Second, there are students who have unfavorable responses, because they do not pay close attention and understand well, so that they convey news that sometimes is not true to other parties. The understanding of most adolescents in Banda Aceh City regarding the dissemination of information on social media is good, marked by a critical attitude towards understanding it, such as seeking the truth of the same information in other media, so that they are not mistaken in understanding information on social media. However, some students are also not careful in understanding information messages on Social Media, because they are considered to have no benefits for their lives. The understanding of adolescents in Banda Aceh City in spreading hoax information on social media is always careful and not easily provoked by news circulating
The Advance of Dialogue System with Neural Network

With the rapid reform of artificial intelligence, the dialogue system as an important branch of artificial intelligence has also developed greatly in recent years. However, dialogue systems also face many challenges, and accurate and well-sampled training data in dialogue systems is often not readily available. How to use the relationship extraction model based on meta-learning from a small number of samples to improve the accuracy of the relationship prediction method on few-shot learning training data. In addition, this article also explores methods of data compression, summarizing previous compression methods in different ways and related work. At the same time, the application of data compression in the dialogue chat model is also studied. The comparison before and after data compression is carried out to make the dialogue chat model maintain the original answer quality when the data part is reduced, and how to use knowledge graph and reinforcement learning to improve the answer quality of the dialogue system after data compression.
Image Tampering Detection using Error Level Analysis and Concatenated Neural Networks

Abstract: Image tampering has become a leading issue in the digital age, which has given rise to serious implications in various fields such as journalism, forensics and photography. Detecting manipulated images with high accuracy is important to ensure the authenticity and credibility of visual content. In this research paper, we propose a robust and effective approach for image tampering detection utilizing a concatenated ResNet and XceptionNet model with Error Level Analysis which has achieved an accuracy of 98.58%.
Handwritten Digit Image Generation Using Improved Generative Neural Network

Generative adversarial network (GAN) is a new and effective neural network model. GANs are used in a lot of areas such as images and visual, speech and language, composing music, and creating 3D shapes. Although the GAN has a lot of applications, there are still some problems with it. Because not only the generator but also the discriminator needs to be trained, this model is easy to be unstable. And the GAN may not generate different images in human vision to make the generated images can safely pass the discriminator. Some methods will be shown to solve the problems of unstable and lack of diversity in this article by an example that generates handwritten digits. The methods are changing the optimizer from SGD to Adam or SGD with Adam, adding a Batchnorm, and combining GAN with CNN or RNN, each of the improvements will be contrasted with others by the chart of loss rate and the generated images. The research finds that each of the improvements has some advantages over the original GAN in the area of speed or stability. Especially, GAN with the optimizer Adam and DCGAN have the most stable consequence according to the image from the generator.
Algorithmic pricing

The rise of algorithmic pricing has transformed perfect price discrimination from a theoretical concept into a real possibility. Through self-learning pricing algorithms, a strategy can be developed that approximates consumers’ reservation prices with ever-improving accuracy. This paper analyzes algorithmic pricing from a law and economics perspective to identify the efficiency and equity effects that the practice could cause and determine to which extent it is regulated under the current legal framework. This paper finds that under competitive market conditions, algorithmic pricing could be welcomed from an efficiency perspective, but from an equity and ethical perspective serious concerns need to be raised. If these concerns are to be taken seriously, the legal framework provides only a partially functional approach to address algorithmic pricing. Additional appropriate remedies are, therefore, needed to protect consumers adequately and effectively against exploitation that reduces their welfare.
Comparative Study of Deep Learning Neural Networks for Image Classification

Image classification plays a crucial role in image recognition in present times. Researchers have developed numerous methods for achieving image classification through long-term research. Deep Learning, as one of the most important methodologies, is used to clarify the contents of an image. This paper provides an overview of four typical deep learning neural networks used for image classification, including conventional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), and deep neural networks (DNN). The architecture, function, and applications of each network are discussed and compared. CNN is commonly used for image recognition, while RNN is suitable for speech recognition, natural language processing, and time series forecasting. DNN is ideal for handling high-dimensional data, and GAN can generate new data samples. The StyleGAN is introduced as an application of GAN, which can produce high-quality images. Finally, the future work and challenges in image recognition are discussed.
Peningkatan Hasil Belajar Mail Merge Menggunakan Model Project Based Learning Pada Siswa Kelas X

The initial data mail merge learning skills students class X with very good criteria is only 14 from 34 people, so the outcomes average value reaches only 72.50. The purpose of study is to increase students' mail merge skills through the application of the Project Based Learning (PjBL) model. This research was conducted through classroom action consisting of two cycles, each cycle consisting of two meetings. The stages of each cycle are: planning, implementation, observation and reflection. Data on students' mail merge skills were taken using an instrument of learning skills observation sheets on indicators: (1) reading the steps for making mail merges, (2) designing mail merge letters, (3) making master letters, (4) creating data source tables, (5) combining the master letter and data source using the mailings menu in Microsoft Word. The results showed that the skills of students with very good criteria increased by 15 students from 14 students in cycle 1 becomes 29 students in cycle 2. Learning outcomes showed an increase of 13.62 from an average 73.26 in cycle 1 to 86.88 in cycle 2. Based on data, it can be concluded that the application of PjBL in learning mail merge can improve student learning outcomes.
Digital Globalization - A New Phase of Global Streams (Artificial Intelligence and Cyber Security Apps)

The widespread use of digital technology in international commerce, economics, and the circulation of financial capital has greatly contributed to defining the features of globalisation in the 20th century. By the year 2008, direct financial and economic turnover had drastically decreased, and a new sort of flow, known as "digital globalisation," was beginning to develop. Both of these trends may be attributed to the global financial and economic crisis of 2008. These two trends were both observable at the same time. These endeavours have been propelled forward by the use of data science, technologies such as AI systems, cyber security, virtual reality, social media, and the digital tax system, as well as other technological advancements. Direct financial and economic turnover. Because the process of developing digital globalisation did not happen overnight, it is essential to understand the historical development of that process within the context of the many different industrial revolutions. Robots, artificial intelligence, large amounts of data, and cloud computing are just few of the technologies that play an essential part in these processes. This argues that contemporary globalisation is the product of new digital flows and an unstoppably developing phenomenon that will connect nations around similar interests, economics, and politics; and that, despite the problems, digitalization will still be able to encompass all regions of the world. In other words, globalisation is the result of new digital flows. In conclusion, the evidence that has been shown in this article suggests that contemporary globalisation is the result of new digital flows and a phenomenon that is unstoppably spreading and will unify states around comparable interests, economics, and politics.
Assessing Word Frequency by Rating Method

One of the major challenges faced by researchers in linguistics and psycholinguistics in India is the lack of corpora of linguistic information such as word frequency, letter frequency, phonological and phonetic data on sounds, and morphological data on words, etc, in Indian languages. When they do not get the stimulus material readily available in any corpus, researchers resort to preparing a stimulus set that meets the criteria for their specific study. This paper presents an attempt of preparing a list of words for an ongoing psycholinguistic study on lexical access in Kannada by a method of expert rating on selected words' frequency. A total of 60 Kannada words were selected as stimuli, and seven native Kannada experts rated them on frequency on a six-point scale. Descriptive statistics and Pearson's correlation were used for data analysis. Results revealed that the selected Kannada words have high-frequency ratings with good inter-rater reliability.
Szegedy Walk Unitaries for Quantum Maps

Szegedy developed a generic method for quantizing classical algorithms based on random walks [Proceedings of FOCS, 2004, pp. 32-41]. A major contribution of his work was the construction of a walk unitary for any reversible random walk. Such unitary posses two crucial properties: its eigenvector with eigenphase $0$ is a quantum sample of the limiting distribution of the random walk and its eigenphase gap is quadratically larger than the spectral gap of the random walk. It was an open question if it is possible to generalize Szegedy's quantization method for stochastic maps to quantum maps. We answer this in the affirmative by presenting an explicit construction of a Szegedy walk unitary for detailed balanced Lindbladians -- generators of quantum Markov semigroups -- and detailed balanced quantum channels. We prove that our Szegedy walk unitary has a purification of the fixed point of the Lindbladian as eigenvector with eigenphase $0$ and that its eigenphase gap is quadratically larger than the spectral gap of the Lindbladian. To construct the walk unitary we leverage a canonical form for detailed balanced Lindbladians showing that they are structurally related to Davies generators. We also explain how the quantization method for Lindbladians can be applied to quantum channels. We give an efficient quantum algorithm for quantizing Davies generators that describe many important open-system dynamics, for instance, the relaxation of a quantum system coupled to a bath. Our algorithm extends known techniques for simulating quantum systems on a quantum computer.
Local Unitary Equivalence of Quantum States Based on the Tensor Decompositions of Unitary Matrices

Since two quantum states that are local unitary (LU) equivalent have the same amount of entanglement, it is meaningful to find a practical method to determine the LU equivalence of given quantum states. In this paper, we present a valid process to find the unitary tensor product decomposition for an arbitrary unitary matrix. Then, by using this process, the conditions for determining the local unitary equivalence of quantum states are obtained. A numerical verification is carried out, which shows the practicability of our protocol. We also present a property of LU invariants by using the universality of quantum gates which can be used to construct the complete set of LU invariants.
Evaluation of Online Learners’ Learning Performance Based on Fuzzy Borda Method

Since the traditional evaluation system of learning focuses only on the summative evaluation of learners’ test scores, ignoring the process evaluation of learners in learning process, the evaluation system should not only consider the content of the online learning process but also that of face-to-face classroom teaching process to evaluate learners’ learning performance in multiple dimensions and levels. This approach evaluates online learning more reasonably, operably and feasibly. To overcome limitation of using more than one method in the evaluation of learning performance, the learning performance of online learners was first evaluated using the principal component analysis method, entropy method, comprehensive indicator method, and TOPSIS method. Based on these four evaluation results, a combination evaluation model of online learners’ learning performance was constructed. The Fuzzy Borda method was used to evaluate nine administrative classes of accounting majors in three colleges and universities in Hainan. The results show that simply using a single evaluation method to evaluate the learning performance of online learners has defects, and the evaluation results are too biased. By using the Fuzzy Borda method, multiple evaluation methods can be combined, allowing a single evaluation method to realize complementary advantages and obtain more comprehensive and credible evaluation results. Using the Fuzzy Borda method to systematically evaluate the learning performance of online learners improves the scientificity of evaluation results and provides a new idea and method for the evaluation of online learning performance. The results of this study have significant reference value for using scientific evaluation methods and objective data to evaluate online learning performance, rank online learners scientifically, and summarize teaching experience to improve online learning performance.
Grade 9 teachers’ experience of digital technologies in the classroom

The use of digital technologies in the classroom has escalated, especially during the COVID-19 pandemic. Although technology and teacher development centres are available for teachers to use, they are often not optimally utilised. This study used explorative qualitative case study methodologies to understand Grade 9 teachers’ use of digital technologies as they integrate it into their lessons and classrooms. Seven teachers were purposely selected to be sources of data collection. Framed by the Technological Pedagogical and Content Knowledge (TPACK) framework, data was generated using semi-structured interviews to explore the Grade 9 teachers’ experiences and views on the integration of digital technology post the outbreak of the COVID-19 pandemic. To analyse the data, a thematic analysis was conducted. Although the findings show limited digital technology integration, some teachers are found to be using videos, projectors, and laptop computers in their classrooms. Inadequate infrastructure and lack of support contribute and impact teachers’ experience of integrating digital technology in the Grade 9 classroom.
Challenges of Extended Reality Technology in Higher Education: A Review

Extended reality is proposed as a means to deliver qualitative material to students in higher education institutions. The usage of this technology has increased due to Covid-19 lock down. This paper aims to categorize the challenges encountered by literature that deployed extended reality (XR) in higher education during and after the lockdown. However, the number of available articles addressing these challenges is limited, with a total number of 40 articles obtained from dimensions’ dataset. In this study, bibliometric analysis is used to highlight the number of publications, citations, and authors in this field. Thematic analysis with inductive reasoning is applied to extract the primary challenge themes mentioned in 23 articles. After reviewing the eligible articles; we categorized the challenges into three main themes: Teacher, Student, and Technology.
Overcoming Integration Thresholds for Augmented Reality

The advent of augmented reality (AR) is reshaping the way people experience physical and virtual environments, from observation to immersion. Growing interest in adopting AR provides opportunities for immersive learning, upskilling, and renewal. However, uncertainties exist in how to maneuver a transition toward making use of this technology through systematic integration. Due to the turmoil caused by the global pandemic health crisis, implementation of AR now faces urgency in minimizing adoption thresholds and establishing a more systematic escalation approach. This paper investigates the characteristics of such learning approaches and examines the integration of AR with customized progression. Two solution suppliers were investigated to uncover the integration process of AR, which, to the best of our knowledge, is scarcely explored in existing research. This study reveals that a balanced escalation of user-centric learning activities, i.e., an onboarding process, harmonizes anticipated cognition levels for a designated AR application tool.
Efficient volumetric mapping of multi-scale environments using wavelet-based compression

Volumetric maps are widely used in robotics due to their desirable properties in applications such as path planning, exploration, and manipulation.Constant advances in mapping technologies are needed to keep up with the improvements in sensor technology, generating increasingly vast amounts of precise measurements.Handling this data in a computationally and memory-efficient manner is paramount to representing the environment at the desired scales and resolutions.In this work, we express the desirable properties of a volumetric mapping framework through the lens of multi-resolution analysis.This shows that wavelets are a natural foundation for hierarchical and multi-resolution volumetric mapping.Based on this insight we design an efficient mapping system that uses wavelet decomposition.The efficiency of the system enables the use of uncertainty-aware sensor models, improving the quality of the maps.Experiments on both synthetic and real-world data provide mapping accuracy and runtime performance comparisons with state-of-the-art methods on both RGB-D and 3D LiDAR data.The framework is open-sourced to allow the robotics community at large to explore this approach.
Demonstrating Large Language Models on Robots

block_names = detect_objects("blocks") bowl_names = detect_objects("bowls") for bowl_name in bowl_names: if is_empty(bowl_name): empty_bowl = bowl_name break objs_to_stack = [empty_bowl] + block_names stack_objects(objs_to_stack) def is_empty(name):...
The Mediating Effect of Knowledge Sharing Between Organizational Climate and Individual Innovative Behavior in UAE Organizations

In the age of digitalization, globalization, and rapid changes, an innovative workforce is essential for success.A literature study indicates extensive research on the mediating role of knowledge sharing (KS) between organizational climate (OC) and innovative work behavior.This study examines the mediating role of knowledge sharing between organizational climate and innovative work behavior in United Arab Emirates (UAE) public institutions.The researcher will adopt a cross-sectional approach because measurement of sample behavior is done at a single point in time.The study collected a large amount of data to address knowledge sharing in the organizational climate and individual innovative behavior (IIB) in the UAE.Multiple regression analysis is adopted to analyze the data and presents the findings.