Reviewerly/exHarmony huggingface dataset:
Role of Blockchain Technology in Philanthrophy

Abstract: Our country has an excessive number of organisations, which deter individuals from donating since they are opaque and challenging to monitor. People frequently ponder whether giving to charitable organisations will affect their choice. Many non-profit organisations engage in unscrupulous business techniques to raise money. People's faith in charity has been shattered by these methods, which have also raised concerns about the reliability of charitable endeavours. Today's non-profit sector is highly concerned about transparency. By turning donations into digital assets and boosting trust in non-profit organisations, blockchain [1] and smart contracts [2] open up new opportunities for charitable giving. Now, donors can see the results of their donations, and charities can use crypto tokens [4] to encourage giving. In recent years, blockchain technology has become one of the most trending areas of technology. Specifically, distributed ledger technology has been used in the financial industry to create crypto currency and develop a new digital market sector. Despite this, blockchain with its immutability and transparency has a much wider range of potential applications. Blockchain technology has the ability to significantly alter the charity industry. Increased responsibility and confidence in charitable donations may result from blockchain's decentralised and transparent nature. The usage of blockchain in charity is examined in this research article along with its potential advantages, including greater accountability, increased efficiency, and increased transparency. The absence of regulations, technical complexity, and cost are some of the difficulties that must be overcome. The use of blockchain technology in philanthropy is significant and merits more research to ascertain how it may be most effectively incorporated into current systems to enhance the philanthropic sector.
Management Information System Implementation Doyan Drinks Using K-Means

Technological developments are currently very influential in business collaboration, to be precise in Doyan's drink, Doyan is a local beverage brand that has difficulty determining franchise partnerships to determine the type of package in the strategy of location, area, and facilities of prospective franchisees, by implementing a Management Information System on Doyan can determine the type of franchise based on the required criteria, this study uses the Waterfal method in designing management information systems and uses the K-Means algorithm in determining the type of franchise that suits the needs of the candidate. K-Means functions to cluster the franchise type criteria data that will produce the closest value so that the output can choose and determine the more appropriate type of franchise package, and the store can manage product sales, stock, and attendance at all franchise branches
Development of Service Mail Management Information System as a Supporting System for Calculating Recapitulation of Remuneration Performance Points at Universitas Negeri Makassar

This research is research and development (R&amp;D) which aims to produce an official letter management information system, a supporting system for calculating remuneration performance point recapitulation at Makassar State University. Application testing uses ISO/IEC 25010. This study uses the Waterfall development model consisting of five work steps, namely analyzing needs, conducting design, coding, testing and maintenance. The developed system is validated by two system experts, and two content or material experts. The data were collected using questionnaires, interviews and documentation as supporting data for the development of information systems. Data processing uses qualitative and quantitative descriptive analysis techniques. The test results prove that the official letter management information system that has been built meets ISO/IEC 25010 testing standards with the results of testing the functionality suitability aspect in the very decent category. Aspects of performance efficiency with good criteria, aspects of usability with very good category, security aspects obtained level 1 security is at low level criteria with sufficient information. Achievements in the reliability aspect have met Telcordia's testing standards. The maintainability aspect has met three Land testing standards. The compatibility aspect is very feasible and the portability aspect is in the very good category.
Twitter Social Media Conversion Topic Trending Analysis Using Latent Dirichlet Allocation Algorithm

In Indonesia, Twitter is one of the most widely used social media platforms. Because of the diverse and frequently shifting message patterns on this social media, it is extremely challenging and time-consuming to manually identify topics from a collection of messages. Topic modeling is one method for obtaining information from social media. The model and visualization of the results of modeling topics that are discussed on social media by the Makassar community are the goals of this study. The Latent Dirichlet Allocation (LDA) algorithm is used to model and display the results of this study. The modeling results indicate that the eighth topic is the most frequently used word in a conversation. In the meantime, the 7th and 6th topics emerged as the conversation's core based on the spread of the words with the highest term frequency. The study's findings led the researchers to the conclusion that in the Makassar community's social media discussions, capitalization and visualization using the LDA method produced the words with the highest trend and the topic with the highest term frequency.
Library Research: An Online Learning at High School as Learning Media

Technology has begun to applied in the field of education, not only in universities but also in high school. The existence of technology for education encourages the development of learning media, especially online learning. The purpose of this research was to examine implementation of online learning at high school level. The method used is literature review research, which is the research results come from several sources. The data obtained comes from books, research, and articles related to online learning media. Based on the research’s results, it was concluded that the use of online learning media in high school can be applied appropriately and well if it meets certain criteria. The use of online learning as learning media can also directly improved student’s learning outcomes. This statement obtained from research articles related to the topic.
A CORRELATIONAL STUDY OF LANGUAGE LEARNING STRATEGIES AND ENGLISH PROFICIENCY OF UNIVERSITY STUDENTS AT EFL CONTEXT

Mastering English requires the students to have their own strategy. The use of appropriate language learning strategies may help the students to be proficient in English. This research investigates the language learning strategies used by EFL students and their relationship with the Students’ English Proficiency. This study adopted Ex-post facto quantitative research design in which the data were collected from the result of students Language Learning Inventory and also the data of their latest English Proficiency Score. 38 students majoring in English participated in this research. The result of this research shows that metacognitive strategies were the most frequent strategy, followed by cognitive strategy, compensation strategy, memory strategy, affective strategy, and social strategies. Additionally, there is a significant correlation between language learning strategy use and students’ English proficiency.
The Effectiveness of Accounting Information Systems on Vehicle Sales Growth

To get big profits, companies must have a good management system, namely how the business is managed professionally, one of which is having a sales accounting information system. This system helps companies record all transactions, thereby generating information that helps companies monitor business progress. This research was conducted on the basis of qualitative methods with descriptive analysis techniques. This research was conducted at PT. Istana Kebon Jeruk by analyzing data flow diagrams and internal control questionnaire techniques. The results show that the accounting system provides an actual picture of all business processes, thereby reducing various risks of loss. In addition, the accounting system provides information about sales and revenue trends, thereby helping operations managers make informed decisions.
Contemporary multimedia applications in the cultural communication discourse of the museum

AR/VR applications are gaining prominence in exhibition communication. In this field research project, we developed an assessment model to identify major AV/VR application types and their functions. We then used this model to describe 32 contemporary multimedia exhibition applications in 12 countries. During our visits to the exhibitions, we assumed the perspective of the non-specialist visitor to better identify communicative effects of AR/VR applications and compare them with traditional guides developed for similar exhibitions. Our results show that these innovative sources of information may significantly contribute to visitor enjoyment as well as knowledge gain and retention
Older yet Sharp: No General Age-Related Decline in Focusing Attention

Attention is a multifaceted mechanism operating on space, features, and memory. Previous studies reported both decline and preservation of attention in aging. Yet, it is unclear if healthy aging differentially affects attentional selection in these domains. To address these inconsistencies, we evaluated the ability to focus attention in a battery of 11 tasks in a large sample of younger and older adults (n = 172/174). We addressed if (1) aging effects are consistent across different attention tasks, and (2) if there is domain-specific or domain-general age-related decline in focused attention. Both younger and older adults benefited from focusing attention on space, features, and memory representations. Within the same domain, some tasks showed a decline, whereas others showed improvement with aging; and, on average, attentional benefits were similar across age groups. These results are inconsistent with the claim that aging is associated with either domain-specific or domain-general deficits in focused attention.
A Study on English Learning Motivation of Sports Major Students in Vocational Colleges of Henan Province

After becoming the universal language of the world, English has effectively promoted the development of global integration and become an essential tool for international communication, and sports international exchange activities have increased greatly. Therefore, for PE Students in independent Colleges, to master English well is not only a requirement of times, but also a necessity to improve their professional competence.This study believes that the cultivation of their professional English learning is highly necessary, exploring their interest and study their English learning motivation is very significant. English teachers are supposed to stimulating PE students’ interest in English learning, exploring their learning enthusiasm based on students’ characteristics and help students to promote learning initiative and achieve the purpose of improving students’ English interest and performance.
Model analysis of smart supply chain finance of platform-based enterprises under government supervision

The COVID-19 pandemic has made it more difficult and expensive for medium-sized enterprises (SMEs) to finance. In this context, relying on the network platform, smart supply chain finance effectively solves financing problems for small and SMEs. However, in the development of smart supply chain finance, there are still some problems such as unstable willingness of SMEs to participate in financing, difficulty in determining the optimal development mode of platform-based core enterprises and lack of appropriate regulatory measures. Based on whether the network platform can use its own capitals for lending, this study introduces two smart supply chain financial models (the dominant and cooperation models of platform-based core enterprises) to solve the above problems. In this study, we construct two evolutionary game models: the tripartite model, including government, platform-based core enterprises, and SMEs, and the quadrilateral model, including government, financial institutions, platform-based core enterprises, and SMEs. This study presents the evolution and stability strategies of each participant under different modes. In addition, we discuss the willingness of platforms to choose different modes and corresponding government supervision measures. This study offers several important conclusions. (1) Core enterprises that do not have the conditions to build a highly intelligent platform choose the cooperation model; otherwise, they will preferentially choose the dominant mode. (2) Under the dominant mode, the stable development of smart supply chain finance must rely on strict government supervision. (3) By adjusting the scope of tax rates and subsidies, the government can control the trend of mutual transformation of the two modes, so that the dominant mode and the cooperative mode can develop in a balanced way in the market.
Conditional Variational AutoEncoder based on Stochastic Attacks

Over the recent years, the cryptanalysis community leveraged the potential of research on Deep Learning to enhance attacks. In particular, several studies have recently highlighted the benefits of Deep Learning based Side-Channel Attacks (DLSCA) to target real-world cryptographic implementations. While this new research area on applied cryptography provides impressive result to recover a secret key even when countermeasures are implemented (e.g. desynchronization, masking schemes), the lack of theoretical results make the construction of appropriate and powerful models a notoriously hard problem. This can be problematic during an evaluation process where a security bound is required. In this work, we propose the first solution that bridges DL and SCA in order to get this security bound. Based on theoretical results, we develop the first Machine Learning generative model, called Conditional Variational AutoEncoder based on Stochastic Attacks (cVAE-SA), designed from the well-known Stochastic Attacks, that have been introduced by Schindler et al. in 2005. This model reduces the black-box property of DL and eases the architecture design for every real-world crypto-system as we define theoretical complexity bounds which only depend on the dimension of the (reduced) trace and the targeting variable over F2n . We validate our theoretical proposition through simulations and public datasets on a wide range of use cases, including multi-task learning, curse of dimensionality and masking scheme.
On Protecting SPHINCS+ Against Fault Attacks

SPHINCS+ is a hash-based digital signature scheme that was selected by NIST in their post-quantum cryptography standardization process. The establishment of a universal forgery on the seminal scheme SPHINCS was shown to be feasible in practice by injecting a fault when the signing device constructs any non-top subtree. Ever since the attack has been made public, little effort was spent to protect the SPHINCS family against attacks by faults. This paper works in this direction in the context of SPHINCS+ and analyzes the current algorithms that aim to prevent fault-based forgeries.First, the paper adapts the original attack to SPHINCS+ reinforced with randomized signing and extends the applicability of the attack to any combination of faulty and valid signatures. Considering the adaptation, the paper then presents a thorough analysis of the attack. In particular, the analysis shows that, with high probability, the security guarantees of SPHINCS+ significantly drop when a single random bit flip occurs anywhere in the signing procedure and that the resulting faulty signature cannot be detected with the verification procedure. The paper shows both in theory and experimentally that the countermeasures based on caching the intermediate W-OTS+s offer a marginally greater protection against unintentional faults, and that such countermeasures are circumvented with a tolerable number of queries in an active attack. Based on these results, the paper recommends real-world deployments of SPHINCS+ to implement redundancy checks.
Self-attention in Vision Transformers Performs Perceptual Grouping, Not Attention

Recently, a considerable number of studies in computer vision involves deep neural architectures called vision transformers. Visual processing in these models incorporates computational models that are claimed to implement attention mechanisms. Despite an increasing body of work that attempts to understand the role of attention mechanisms in vision transformers, their effect is largely unknown. Here, we asked if the attention mechanisms in vision transformers exhibit similar effects as those known in human visual attention. To answer this question, we revisited the attention formulation in these models and found that despite the name, computationally, these models perform a special class of relaxation labeling with similarity grouping effects. Additionally, whereas modern experimental findings reveal that human visual attention involves both feed-forward and feedback mechanisms, the purely feed-forward architecture of vision transformers suggests that attention in these models will not have the same effects as those known in humans. To quantify these observations, we evaluated grouping performance in a family of vision transformers. Our results suggest that self-attention modules group figures in the stimuli based on similarity in visual features such as color. Also, in a singleton detection experiment as an instance of saliency detection, we studied if these models exhibit similar effects as those of feed-forward visual salience mechanisms utilized in human visual attention. We found that generally, the transformer-based attention modules assign more salience either to distractors or the ground. Together, our study suggests that the attention mechanisms in vision transformers perform similarity grouping and not attention.
QAOA with $N\cdot p\geq 200$

One of the central goals of the DARPA Optimization with Noisy Intermediate-Scale Quantum (ONISQ) program is to implement a hybrid quantum/classical optimization algorithm with high $N\cdot p$, where $N$ is the number of qubits and $p$ is the number of alternating applications of parameterized quantum operators in the protocol. In this note, we demonstrate the execution of the Quantum Approximate Optimization Algorithm (QAOA) applied to the MaxCut problem on non-planar 3-regular graphs with $N\cdot p$ of up to $320$ on the Quantinuum H1-1 and H2 trapped-ion quantum processors. To the best of our knowledge, this is the highest $N\cdot p$ demonstrated on hardware to date. Our demonstration highlights the rapid progress of quantum hardware.
Classification of Maturity Levels in Areca Fruit Based on HSV Image Using the KNN Method

Areca nut (Areca catechu) is a kind of palm plant that grows in Asia and Africa, the eastern part of the Pacific and in Indonesia itself, areca nut can also be found on the islands of Java, Sumatra and Kalimantan. At the stage of classifying the maturity of the betel nut so far, it is still using the manual method which at that stage has subjective weaknesses. Based on these problems, researchers will create a system that is able to classify the level of maturity of areca nut using HSV feature extraction with assistance at the classification stage using the KNN method. In this study, 842 datasets were used which were divided into 3 types of classes, namely ripe, unripe and old fruit. The dataset was divided into 683 training data and 159 test data. In the next stage, the data is tested using the K-Nearest Neighbor method by calculating the closest distance using k = 1. From the results of the calculation of the closest distance k1 produces an accuracy rate of 87.42%. Kata kunci— Matlab, Areca Ripeness, KNN, HSV.
Expert System For Career Early Determination Based On Howard Gardner's Multiple Intelligence

The problem that exists is how to design a tool to help students recognize their potential and abilities so that they can recognize the right potential in higher education based on their potential and abilities, with the influence of technological changes that have penetrated into life and even the world of education, then the purpose of this research is to develop software in the form of an expert system as a technology that can be used by students to be able to recognize their potential. The development of this expert system uses the Software Development Life Cycle (SDLC) method, the design of an expert system to determine the potential in higher education based on Multiple Intelligence. This expert system is designed using the Unified Modeling Language (UML).
Web-Based Batik Clothing Information System on Cv. Desta Bogor

CV.Desta is one store that is located in the Bogor area. On the CV.Desta authors conducted an analysis of sales system that is running thr current. Sales are only made in the store are still not getting the maximum result. Income and lact of promotion does not necessarily make CV.Desta still not known to the public. Moreover want development and expansion of sales and increased sales not only in the store. To overcome such problems, the authors intend to try to handle the sales process by designing an information system is a web-based sales of goods or more often in the know as e-commerce using the programming language php and mysql
Result-pattern-hiding Conjunctive Searchable Symmetric Encryption with Forward and Backward Privacy

Dynamic searchable symmetric encryption (DSSE) enables the data owner to outsource its database (document sets) to an untrusted server and make searches and updates securely and efficiently. Conjunctive DSSE can process conjunctive queries that return the documents containing multiple keywords. However, a conjunctive search could leak the keyword pair result pattern (KPRP), where attackers can learn which documents contain any two keywords involved in the query. File-injection attack shows that KPRP can be utilized to recover searched keywords. To protect data effectively, DSSE should also achieve forward privacy, i.e., hides the link between updates to previous searches, and backward privacy, i.e., prevents deleted entries being accessed by subsequent searches. Otherwise, the attacker could recover updated/searched keywords and records. However, no conjunctive DSSE scheme in the literature can hide KPRP in sub-linear search efficiency while guaranteeing forward and backward privacy. In this work, we propose the first sub-linear KPRP-hiding conjunctive DSSE scheme (named HDXT) with both forward and backward privacy guarantees. To achieve these three security properties, we introduce a new cryptographic primitive: Attribute-updatable Hidden Map Encryption (AUHME). AUHME enables HDXT to efficiently and securely perform conjunctive queries and update the database in an oblivious way. In comparison with previous work that has weaker security guarantees, HDXT shows comparable, and in some cases, even better performance.