Reviewerly/exHarmony huggingface dataset:
Blockchain technology as a tool for online elections

The use of blockchain technology in relation to online elections is examined in this article. The article's introduction outlines the primary issues with the current electoral systems and suggests using blockchain technology as a fix. The blockchain technology is briefly explained in Section 1 along with its fundamental ideas and framework. The advantages of employing blockchain technology for online elections, including security, dependability, and transparency, are covered in Section 2. The limits and drawbacks of this technology are discussed in Section 3, including the difficulty of implementation, the absence of a uniform system, and the need for participant identification. The article's conclusion outlines and emphasizes the importance of conducting thorough research before employing blockchain technology to conduct online elections. For researchers, practitioners, and decision-makers interested in the use of blockchain technology in the subject of elections, this paper is a valuable resource. It provides an overview of the key elements of blockchain technology, as well as its potential and restrictions in the context of online elections, which helps to clarify the benefits and difficulties of its application in this field.
Performance and Cost Balancing in Vision Transformer-Based Image Captioning

Image captioning connects computer vision and natural language processing. Many deep learning models have been proposed for solving this problem, such as Transformer-based, CLIP-based and Diffusion-based models. However, the primary focus has been on increasing the accuracy of generating human-like descriptions for given images, leading to expensive SOTA models that cannot be implemented on computation-limited devices. For better performance, we propose a ViT-LSTM model for solving image captioning tasks by addressing the challenge of long-range dependencies. Our model consists of a ViT model pre-trained using the ImageNet 21k dataset, which captures global context, and an LSTM that generates captions reflecting both local and global visual cues. Additionally, we use convolutional layers to reduce feature map dimensionality while preserving spatial relationships and local patterns. This allows the model to understand local details and relationships between regions, achieving better performance with less computational cost.
Effectiveness of the Improvement Recommendations Model for Addressing the Syntactic Ambiguity in Malay Requirements Specifications

Malaysian government agencies have developed various citizen-service platforms and applications. However, unresolved Malay requirements specification (RS) ambiguities might disrupt the software development project's completion. Most prior research has focused on English RS but not Malay. Therefore, devising a model to recommend improvements for addressing the ambiguous Malay requirements specifications is challenging. This paper investigates the state-of-the-art approaches for establishing and validating the effectiveness of an improvement recommendations model for addressing syntactical ambiguity in Malay RS. We devised an improvement recommendations model using natural language processing (NLP), rule-based, part-of-speech (POS), subject-verb-object (SVO) patterns, and Malay boilerplate. Thirteen experts from various Malaysian public sector agencies validated the model's effectiveness based on improved Malay RS enhanced by disambiguating syntactic ambiguity terms. We surveyed the experts' opinions on the model's effectiveness and work experience through semi-structured interviews. This study revealed that 76.9% of the experts agreed and 23.1% strongly agreed that the improvement recommendations model effectively improved Malay RS by handling ambiguity. Experts with at least six years of experience in requirements engineering can comprehensively validate the improved Malay RS. The experts recognised the necessity for a model/ tool to aid requirements engineers in validating and enhancing the Malay RS by addressing ambiguity and recommending improved Malay RS structure based on Malay boilerplate syntax.
A Novel Data Hiding Scheme based on Block Features Enhanced AMBTC

In this paper, we first improve Chen et al.'s ternary representation-based Absolute Moment Block Truncation Coding (AMBTC), then we combine block features and AMBTC to design our novel block feature enhanced-AMBTC-based data hiding scheme called BFI-AMBTC-based DH scheme. In our scheme, AMBTC blocks are classified into three types: complex, smooth, and flat according to the distribution of pixels in a block. For each block type, we propose different data hiding strategies according to their unique features. Experiments confirm that our hybrid data hiding strategies work well. The image quality of stego images with our hybrid data hiding strategies not only has been significantly improved but also is much closer to that offered by the conventional AMBTC compared with other existing AMBTC-based data hiding schemes. In addition, the hiding capacity achieved by our BFI-AMBTC-based DH scheme is confirmed almost two times Ou and Sun's scheme.
FoodS and FoodIM: Food-Testing Item Recommendation Models for Two Different Users with Different Usage Abilities

Recommendation systems should adopt different recommendation strategies for different users' usage abilities. For the question of what testing items are required for a food, we have designed two food-testing item recommendation models, called Food Similarity recommendation (FoodS) and Food Testing Item Matching recommendation (FoodIM). FoodS is suitable for unprofessional users who are not aware of testing items. FoodS processes different attributes by different techniques to calculate the similarity between foods, and directly recommends the testing items of the most similar food as the results. FoodIM is suitable for professional users who are aware of testing items. FoodIM calculates the degree of matching between food and testing items through the two-tower structure, and recommends the testing items that match the food. We use macBERT for embedding in FoodIM and named entity recognition (NER) to enhance the representation of food. To improve inference speed, we use GPT-3 for data augmentation and obtain embedding by contrastive learning instead of macBERT. Our experiments on the food-testing item dataset show that both of our recommendation models outperform state-of-the-art methods.
Blockchain in Action: Enhancing Transparency and Traceability in the Pharmaceutical Supply Chain - A Case Study from Vietnam

This study explores the critical role of digital advancements in supply chain management within the pharmaceutical industry, particularly focusing on the potential of blockchain technology to rectify the prevailing information access imbalance. The research establishes the link between the importance of increased transparency and traceability of pharmaceutical products and the prospective solutions that blockchain technology can offer. To substantiate this, we propose a novel application to facilitate end-to-end inclusion in the supply chain process. This application, developed to suit the specific requirements of the Vietnamese pharmaceutical industry, records pertinent product details in a centralized MS SQL database. A selection of these details is then transferred to a blockchain network, enhancing traceability and transparency by rendering the information immutable, verifiable, and widely accessible. It also underscores the promising horizon of integrating blockchain technology into supply chain management for the establishment of more efficient, resilient, and transparent systems in the era of digital transformation.
A Multi-label Classification Method for Positive and Unlabeled Dataset.

Positive and Unlabeled (PU) learning is a learning method which can be applied to various field such as recommendation and big data analysis. A direct method to solve PU learning is transform it into a weighted classification problem. However, previously proposed methods assume a strict condition. In this paper, we first investigate if the condition can be satisfied in real world situations and then propose a normalized weighted method which can relax the difficultly of PU learning.
LSA: A Novel State-Of-The-Art Sorting Algorithm for Efficient Arrangement of Large Data

Over the years, data generation from various sources (social media, business, medical, education, programming, images, videos, etc.) has increased exponentially due to technological development, application, and daily usage. Organizing these large amounts of data efficiently is not a trivial task. Therefore, an efficient sorting algorithm can be helpful in processing and arranging these large data. To address this issue, we propose a novel sorting algorithm called the Layered Sorting Algorithm (LSA) that organizes data using a layering approach. The LSA aims to improve sorting efficiency by exploiting the inherent structure and characteristics of the data. The algorithm divides the input data into layers, where the data of each layer has the same length (or digits). Single-length data is on layer 1, double-length data is on layer 2, and so on. Within each layer, a specific sorting technique is applied to efficiently arrange the elements. In this paper, we conducted experiments with more than one million random integer data, which have a maximum length of 10. The experimental results show that LSA obtained better results in terms of time complexity and comparisons compared to existing state-of-the-art sorting algorithms. The results demonstrate that LSA achieves the best case and average case when sorting data. Moreover, LSA reduces a significant number of comparisons than the original algorithms when sorting data as well as reduces time complexity. Furthermore, the proposed LSA can be used with existing sorting algorithms to achieve better performance for sorting large-scale data.
Structure Aware Representation Learning of Dual-View Knowledge Hypergraph

The knowledge hypergraph (KH) extends the knowledge graph (KG) and is a ubiquitous data structure representing hyper-relational facts in the real world. However, existing representation learning methods in KH mainly focus on the hyper-relational structure between multiple entities, ignoring the hierarchical structure describing type information. For example, Heath Ledger's hierarchical structure is actor/artist/person. To learn the hyper-relational and hierarchical structures, we propose a structure-aware representation learning model (SDKH) that jointly represents the instance view and ontology view of the KH. Considering the differences between the two views, we utilize different methods to learn the hierarchical structure of the ontology view and the hyper-relational structure of the instance view. Our SDKH includes three parts: instance view encoder, ontology view encoder, and cross-view encoder. Experimental results show that SDKH outperforms the baselines in link prediction and entity typing tasks.
A scenic spot boom evaluation algorithm based on electric power big data and its application

With the rapid development of tourism, the prosperity degree of scenic spots has become an important research topic. The main purpose of this study is to put forward a scenic spot climate evaluation algorithm based on power big data, aiming to better understand the climate index of scenic spots. First of all, a large number of scenic spot power data were collected, including electricity consumption, electricity time and other information. Then, the GMM algorithm is used to group the scenic spots, dividing the scenic spots into different categories, with each category representing different prosperity degrees. Then, the Knee point algorithm is used to determine a threshold value, which helps us to identify the boom state of the scenic spot. Finally, the algorithm is experimentally verified, and the actual scenic spot data are used to evaluate the accuracy and efficiency of the algorithm. The results show that the boom evaluation algorithm based on power big data can effectively identify the boom state of scenic spots. This algorithm can not only be used for the daily operation and management of scenic spots, but also can be used as a decision support tool to help the government and relevant departments to better formulate tourism policies and resource allocation strategies.
Joint Trajectory Planning and Simulation of a Six Axis Welding Robot

In order to ensure the smoothness and work efficiency of the joint space trajectory curve of the welding robot during movement, reduce the impact force and movement time of the welding robot during welding operations, the trajectory of the six axis spot welding robot was optimized. Firstly, establish a physical model of the welding robot and analyze the motion characteristics of the six axis robot. On this basis, a theoretical model is constructed using the quintic polynomial interpolation algorithm. Analyze whether there are sudden changes in angular velocity and angular acceleration during the welding process of the robot. Propose a time regulation strategy to reduce joint movement time. Write a program model based on MATLAB software and conduct trajectory simulation experiments on the joints of the welding robot. The results show that there is no sudden change in angular velocity and angular acceleration, and they are all zero when reaching each point, greatly reducing the impact force when the robot reaches the welding point. And through reasonable time adjustment, the joint movement time is reduced and the welding efficiency is improved.
Planning and Optimization of Indoor Climbing Routes

Indoor rock climbing, as a challenging and fun sport, has become more and more popular among sports enthusiasts in recent years, and in order to successfully complete the climb, it is necessary to select suitable routes and evaluate the difficulty of the routes. This study focuses on rock climbing route planning, evaluation and optimization. Canny edge detection is used to preprocess the image to obtain the coordinates of the rock point locations, K-means clustering is used to classify the rock points based on the rock point location-based model, DFS algorithm is used for data analysis, and multiple linear regression modelling is used to optimize the climbing routes using the A* algorithm by taking into account a number of key variables, including the weather, the temperature, and the density of the rock points, and to determine the best climbing route profile..
Campus Card Logistic Regression Algorithm Application

With the progress of computer technology and communication technology, the national economy develops rapidly, and the information level of all walks of life is getting higher and higher. All the time, all produce huge data, and in-depth mining and analysis of these data will help promote the development of related industries. In this paper, the data mining technology is applied to the data analysis of campus card. Through the analysis of relevant records, the students with excellent academic performance and poor families are found out for the reference of relevant departments, and the necessary proof materials are combined to make more poor students who study hard get financial assistance. In this paper, logistic regression algorithm is used for modeling, and Bayes algorithm is introduced for comparison experiment. The model is applied to the test data set, and the label attribute value (whether poor students or not) is successfully predicted.
Convolutional Neural Network-Based Research on Software Engineering Defect Prediction

Defect prediction plays a crucial role in software engineering by identifying potential issues before they manifest as costly problems. In this research, we focus on enhancing defect prediction techniques using Convolutional Neural Networks (CNNs). CNNs have demonstrated significant success in various domains, primarily image analysis, due to their ability to capture complex patterns and relationships within data. We propose a novel approach that leverages the power of CNNs to automatically learn and extract features from software engineering datasets, enabling improved defect prediction accuracy. Our experimental results showcase the effectiveness of the CNN-based technique in comparison to traditional methods. The proposed CNN model exhibits promising potential to advance defect prediction capabilities and contribute to the overall quality and reliability of software systems. This research opens up new avenues for applying deep learning techniques to software engineering challenges and paves the way for further exploration in this interdisciplinary field.
Accessing marginalized musics through adaptable, culturally sustaining music technology modules

Black music technology has innovated across genres such as dub, hip hop, techno and Afrobeats, redefining music as we know it. Music research subfields (performance, composition, musicology, technology) have historically excluded black musics but music education has begun to include them. Critical Sonic Practice Lab created a culturally-sustaining music technology toolkit and modules in Accra, Ghana. We exploit music technology’s constant evolution, engaging critical sonic practice’s intersectional approaches to the continuum of improvisation and composition, and music theory. Adjusting teaching resources for students with less finances and internet access can give access to STEAM (science, technology, arts and mathematics) and music creation. We recenter Black, Latinx and Indigenous musics and participatory music practices to expand music creation. Therefore, this research design offers a series of decolonizing music technology and creation prompts to adapt with local music practitioners as teaching-artists for community-specific teaching and learning.
The VAT Treatment of Cryptocurrencies

Since their creation in 2009, crypto-assets have evolved from niche products into assets held and used much more widely. These assets pose challenges for policymakers and tax administrations, because, as pointed out by the OECD, they can be transferred and held without the participation of traditional financial intermediaries and without central administrators being aware of the transactions carried out or the location of crypto-assets holdings.&#x0D; On the indirect taxation side, the VAT Committee discussed the issues relating to the VAT treatment of crypto-assets and, in particular, of cryptocurrencies, on several occasions. The discussion on the most recent of the working papers on this subject, No. 1037 on the VAT treatment of crypto-assets, resulted in the adoption of the Guidelines which aim at harmonising tax administrations’ practice regarding the VAT implications of the different transactions linked to crypto-assets.&#x0D; The article highlights the main challenges posed by cryptocurrencies in terms of VAT while focusing on the main supplies with the use of cryptocurrencies and their qualification for the VAT purposes. Those transactions range from the creation, verification, validation, and supply of cryptocurrencies through their modification, storage, transfer, to exchange. The article explains in this context the position of the VAT Committee reflected in the Guidelines.
Design And Build an Android-Based Educational Gaming App Using Construct 3 Software at PT. EDUCA SISFOMEDIA INDONESIA

When it has entered the era of technology that is increasingly developing, one of which is the rampant development of gadgets, especially android-based, which is more owned by users and there are various applications, one of which is games that make children's concentration distracted so that children are more fun to spend their time on gadgets because the display and multimedia offered are not only that games can be played from an early age to an age causing inhibiting reading attention impact on the shrinking development of education at an early age [1]. To be able to keep pace with the growth that is certainly about to increase until the combination of technology with education that creates educational games can be an important role in the context of learning as a form of innovation. The purpose of this research is to improve a guidance game by using an efficient and instant construct 3 application This research uses the necessary research methodology. The subject of this study is PT. Educa Sisfomedia Indonesia as a game development partner who can help how to improve educational games. The educational aspect of this game has an educational module such as material introduction so that it can again increase interest in reading.
Cyclic codes over F2 × (F2 + vF2) and binary quantum codes

In the present study, we define cyclic codes over the commutative principal ideal ring F2 ? (F2 + vF2) with v2 = v and obtain some results on cyclic codes over F2 ? (F2 + vF2). We also investigate the dual of a cyclic code over F2 ? (F2 + vF2) depending on two inner products. We determine a generator polynomial of cyclic codes and give the calculation of the number of cyclic codes over F2 ? (F2 + vF2). Furthermore, we show that the Gray images of a cyclic code over F2 ? (F2 + vF2) of length n are binary quasi-cyclic codes of length 3n and of index 3. We find numerous binary codes as Gray images of cyclic codes over F2 ? (F2 + vF2) and tabulate the optimal ones. Moreover, we show that it is possible to obtain binary quantum error-correcting codes (QECCs) from cyclic codes over F2 ? (F2 + vF2).
Application analysis of collaborative filtering algorithm in course system construction and recommendation system under new business background

Filtering out required information from a large amount of complex data has always been a difficult point in the construction of curriculum systems. This article starts from the perspective of cultivating cross-functional high-quality accounting talents under the new business background, draws on the idea of course learning, divides course stages and training routes, and finally uses a user-based collaborative filtering algorithm to establish a set of student course learning recommendation system algorithm models. , which provides certain practical reference for in-depth study of the "new business + general education" model.
Improving Up-Close Remote Sensing of Occluded Areas in Vineyards through Customized Multiple-Unmanned-Aerial-Vehicle Path Planning

This study presents a novel approach to address challenges regarding data acquisition for object detection and tracking purposes by enhancing UAV path planning specifically designed for fruit detection in woody crops trained on vertical trellises, considering the biophysical environment of the field. The proposed method implements the Ant Colony Optimization (ACO) algorithm and enables single and multiple UAVs to fly synchronously while ensuring a safe distance between platforms. The results highlight that ACO is able to generate optimal and safe routes, considering the vegetation and covering the whole agricultural area. Moreover, it shows potential to solve partial leaf occlusion for fruit identification.