Reviewerly/exHarmony huggingface dataset:
Cross-Domain Authentication Scheme Based on Blockchain and Consistent Hash Algorithm for System-Wide Information Management

System-wide information management (SWIM) is a complex distributed information transfer and sharing system for the next generation of Air Transportation System (ATS). In response to the growing volume of civil aviation air operations, users accessing different authentication domains in the SWIM system have problems with the validity, security, and privacy of SWIM-shared data. In order to solve these problems, this paper proposes a SWIM cross-domain authentication scheme based on a consistent hashing algorithm on consortium blockchain and designs a blockchain certificate format for SWIM cross-domain authentication. The scheme uses a consistent hash algorithm with virtual nodes in combination with a cluster of authentication centers in the SWIM consortium blockchain architecture to synchronize the user’s authentication mapping relationships between authentication domains. The virtual authentication nodes are mapped separately using different services provided by SWIM to guarantee the partitioning of the consistent hash ring on the consortium blockchain. According to the dynamic change of user’s authentication requests, the nodes of virtual service authentication can be added and deleted to realize the dynamic load balancing of cross-domain authentication of different services. Security analysis shows that this protocol can resist network attacks such as man-in-the-middle attacks, replay attacks, and Sybil attacks. Experiments show that this scheme can reduce the redundant authentication operations of identity information and solve the problems of traditional cross-domain authentication with single-point collapse, difficulty in expansion, and uneven load. At the same time, it has better security of information storage and can realize the cross-domain authentication requirements of SWIM users with low communication costs and system overhead.
DAAPS: A Deformable-Attention-Based Anchor-Free Person Search Model

Person Search is a task involving pedestrian detection and person re-identification, aiming to retrieve person images matching a given objective attribute from a large-scale image library. The Person Search models need to understand and capture the detailed features and context information of smaller objects in the image more accurately and comprehensively. The current popular Person Search models, whether end-to-end or two-step, are based on anchor boxes. However, due to the limitations of the anchor itself, the model inevitably has some disadvantages, such as unbalance of positive and negative samples and redundant calculation, which will affect the performance of models. To address the problem of fine-grained understanding of target pedestrians in complex scenes and small sizes, this paper proposes a Deformable-Attention-based Anchor-free Person Search model (DAAPS). Fully Convolutional One-Stage (FCOS), as a classic Anchor-free detector, is chosen as the model’s infrastructure. The DAAPS model is the first to combine the Anchor-free Person Search model with Deformable Attention Mechanism, applied to guide the model adaptively adjust the perceptual. The Deformable Attention Mechanism is used to help the model focus on the critical information and effectively improve the poor accuracy caused by the absence of anchor boxes. The experiment proves the adaptability of the Attention mechanism to the Anchor-free model. Besides, with an improved ResNeXt+ network frame, the DAAPS model selects the Triplet-based Online Instance Matching (TOIM) Loss function to achieve a more precise end-to-end Person Search task. Simulation experiments demonstrate that the proposed model has higher accuracy and better robustness than most Person Search models, reaching 95.0% of mean Average Precision (mAP) and 95.6% of Top-1 on the CUHK-SYSU dataset, 48.6% of mAP and 84.7% of Top-1 on the Person Re-identification in the Wild (PRW) dataset, respectively.
Penggunaan Metode Bernyanyi Dalam Meningkatkan Kemampuan Mengenal Angka di KB Budi Mulia Kecamatan Galing Tahun Pelajaran 2022-2023

The research was conducted because it saw the phenomenon of a lack of ability to recognize numbers in young children, therefore teachers used the singing method. The approach taken to answer the problems in this research is qualitative, with descriptive methods. Researchers chose observation, interviews and documentation methods. To arrive at a conclusion, the data analysis that researchers use after data collection consists of: data reduction, data display, verification and drawing conclusions. Meanwhile, to check the validity of the data, continuous observation, data triangulation and member checking are used. The results of the research can be concluded: firstly designing the implementation of singing method learning, planning to improve learning ability to recognize numbers, learning carried out leads to achieving the singing method learning objectives, the final target of learning design planning, secondly planning learning must involve all learning variables, organizing learning, delivering learning, learning management, the learning that is created is determining the optimal learning method to achieve the goals that have been set and how to evaluate the singing method in improving the ability to recognize numbers, summarizing the singing method material, improving the ability to recognize numbers.
Transfer Learning in Computer Vision: Technique and applications

Transfer learning is a computer vision technique that allows a pre-trained model to be used as a starting point for solving a different but related task. It leverages the knowledge and learned representations from one task to improve the performance on another task. In practice, it is relatively rare to have a dataset of sufficient size to train an entire Convolutional Neural Network from scratch. Instead, it is common to pretrain a ConvNet on a very large dataset.
SLHCat: Mapping Wikipedia Categories and Lists to DBpedia by Leveraging Semantic, Lexical, and Hierarchical Features

Wikipedia articles are hierarchically organized through categories and lists, providing one of the most comprehensive and universal taxonomy, but its open creation is causing redundancies and inconsistencies. Assigning DBPedia classes to Wikipedia categories and lists can alleviate the problem, realizing a large knowledge graph which is essential for categorizing digital contents through entity linking and typing. However, the existing approach of CaLiGraph is producing incomplete and non-fine grained mappings. In this paper, we tackle the problem as ontology alignment, where structural information of knowledge graphs and lexical and semantic features of ontology class names are utilized to discover confident mappings, which are in turn utilized for finetuing pretrained language models in a distant supervision fashion. Our method SLHCat consists of two main parts: 1) Automatically generating training data by leveraging knowledge graph structure, semantic similarities, and named entity typing. 2) Finetuning and prompt-tuning of the pre-trained language model BERT are carried out over the training data, to capture semantic and syntactic properties of class names. Our model SLHCat is evaluated over a benchmark dataset constructed by annotating 3000 fine-grained CaLiGraph-DBpedia mapping pairs. SLHCat is outperforming the baseline model by a large margin of 25% in accuracy, offering a practical solution for large-scale ontology mapping.
Development of the Polyglot Asian Medicine Knowledge Graph System

The Polyglot Asian Medicine system hosts a research database of Asian traditional and herbal medicines, represented as a knowledge graph and implemented in a Neo4j graph database system. The current coverage of the database is mainly traditional Chinese medicines with some Malay and Indonesian data, with plans to extend to other Southeast Asian communities. The knowledge graph currently links the medicine names in the original and English languages, to alternate names and scientific names, to plant/animal parts they are made from, to literary and historical sources they were mentioned in, to geographic areas they were associated with, and to external database records. A novel graph visualization interface supports user searching, browsing and visual analysis. This is an example of representing a digital humanities research dataset as a knowledge graph for reference and research purposes. The paper describes how the knowledge graph was derived based on a dataset comprising over 25 Microsoft Excel spreadsheets, and how the spreadsheet data were processed and mapped to the graph database using upload scripts in the Neo4j Cypher graph query language. The advantages of using a knowledge graph system to support user browsing and analysis using a graph visualization interface are illustrated. The paper describes issues encountered, solutions adopted, and lessons learned that can be applied to other digital humanities data.
Analisis Pengarsipan Digital Jaringan Dokumen Informasi Hukum (JDIH) di Kantor KPU Provinsi Bengkulu

The management of the Bengkulu Province KPU's Legal Documentation and Information Network (JDIH) began in February 2019 which was marked by the formation of a team based on the decision of the Bengkulu Province general election commission Number: 16/HK.03.1-Kpt/Prov/17/11/2019 regarding team composition Legal Documentation and Information Network (JDIH) at the General Election Commission of Bengkulu Province. The Legal Documentation and Information Network is a decision on the management of Legal Documentation and Information regarding technical guidelines for the management of Legal Documentation and Information within the General Election Commission (KPU, Regency/City). 2021 amounted to 191. The system used in the technical sub-section of election administration, participation, public relations, and human resources has been well programmed. On the Bengkulu Provincial KPU JDIH website page, there are KPU regulations, decisions which include (Provincial KPU Decisions and Regency/City Decisions). Data collection techniques in this journal were obtained from literature and were participatory in the activities of the Bengkulu Province KPU office. The results obtained from this activity can determine the comparison of the number of viewers and the number of PKPU that has been determined.
Learning Vector Quantization for Robusta and Arabica Coffee Classification

ANN or artificial neural network is a way to solve various kinds of problems to make decisions based on training. One of the methods of JSt which contains competitive and supervised learning. Where this layer will automatically learn the classification of the closest input distances and will be distributed to the same class. there are 2 types of coffee beans that are famous in the world, namely arabica and robusta, for some people or the layman it will be very difficult to distinguish these 2 types of coffee beans apart from the fact that the shape is almost the same the color looks almost the same but there are a number of differences in the two coffee beans which we can see from the shape of the seed. Robusta has a shape that tends to be round and smaller in size, and has a rougher texture. Arabica, on the other hand, is slightly flatter and longer in shape. The size is slightly bigger than Robusta but the texture of Arabica is smoother than Robusta. This is the basis of this study where the images of the two coffee beans will be extracted using the first-order texture feature extraction method based on MU parameters, standard deviation, skewness, energy, entropy, and smoothness. The method for collecting data was in the form of a quantitative method using images from each coffee bean, both Arabica and Robusta, with a total of 130 images. The comparison between training_data and test_data is 80:20. Through research conducted in the form of performance parameters with the best accuracy, including: Learning rate 0.01, max epoch or maximum iteration of 10 and 30%, the amount of training data used is 39 training images and 26 test images resulting in an accuracy presentation of 71% for the training process and error with a percentage of 96% for the test process.
A Brief Discussion on the Application of Task-based Language Teaching to Improve the Teaching Quality of English Communication Classes in Junior High Schools

With the progress and development of society and the continuous advancement of curriculum standard reform, society has higher requirements for students’ English skills. The junior high school stage is the best time to cultivate students’ communication skills, however, junior high school English classes are currently still based on traditional teaching as the main teaching method, which only focuses on cultivating students’ reading and writing skills, thus neglecting the training of listening and speaking skills. Despite its evident benefits, the implementation of the task-based language teaching (TBLT) method in junior high school English communication classes is not without challenges. The current state of these classes reveals a significant gap in the integration of communicative tasks into the teaching process. This gap stems from a variety of factors, including the lack of appropriate resources, insufficient teacher training, and the deeply ingrained reliance on traditional teaching methods. This study will briefly explain the definition and characteristics of the task-based language teaching method, analyze the current situation of junior high school English communication classes, the difficulties of using TBLT in junior high school English classrooms, and the application methods of task-based teaching methods. The teaching method makes up for the shortcomings of English communication classes in junior high schools and improves the quality of teaching.
Effectiveness of Mobile-Assisted Language Learning in Enhancing the English Proficiency

In response to the significant impact of the widespread use of digital devices and mobile technologies on language teaching and learning in this time of Internet information technology, this study aims to investigate the effectiveness of Mobile-Assisted Language Learning (MALL) in enhancing the English proficiency of students, while exploring the potential advantages of mobile devices for assisted learning in the English learning environment in China and the potential for mobile applications to assist English learning to foster learner autonomy. Anchored with the design thinking approach, the researchers used the empirical analysis methodology in developing an efficient mobile-assisted language learning model. Usability testing was conducted using a case study of two mobile applications, WeLearn and Flipped English, in Heilongjiang University of Finance and Economics to measure the extent of usability and acceptability of MALL on English language acquisition among college students identified through surveys, interviews, and quantitative assessments. Mobile technology is a perfect tool for every student that enhances their experience and increases their joy while improving their English language skills. It adds new value and brings new opportunities for both English learners and the language education industry. Indeed, MALL is English learners’ new ally.
RANCANG BANGUN SISTEM INFORMASI TABLE SERVICE BERBASIS MOBILE DENGAN MENGGUNAKAN METODE AGILE

When evoria all transactions via the internet, the newly growing MSMEs are no exception utilizing this information technology to make transactions and assist in their operational processes. Kedai Jahe 22 is no exception, which is one of the UMKM in the city of Bekasi which quickly applies for orders when the customer is already at the table (table service). This shop previously faced problems recording customer orders manually which caused delays and misunderstandings. To overcome this problem, an Android-based Point of Sale (POS) system with table service was implemented with the help of Agile methods. Test results show that this system has good performance, enabling Kedai Jahe 22 to improve operational efficiency and service to customers. The use of smartphone technology in the culinary business has helped increase customer satisfaction and profits for the shop.
PEMANFAATAN APLIKASI PRIMASI DI YPPSB DALAM PENYELENGGARAAN LAYANAN PENDIDIKAN BERBASIS DIGITAL

The developments in the globalization era have made technological developments rapidly develop in all lines of human life. The technological developments are not only limited to the development of fission tools or hardware therapy but also in the form of software. The development of application technology in the world of education for orderly administration and the digitalization of educational administration is growing rapidly. One of them is YPPSB, which is developing an application for administrative services for teachers and employees called the PRIMA application. In 2022, YPPSB, through the business manager and support, published an application called PRIMA. With this application, YPPSB provides easy administrative services, especially for teachers and employees who are under the auspices of YPPSB. Based on this, researchers are interested in knowing how to use the administrative services of teachers and employees at YPPSB through the PRIMA application at YPPSB. The research was conducted using qualitative methods by collecting data from results, interviews, observation, and documentation. Furthermore, the data was analyzed using interactive analysis, namely by condensing data, presenting data, and ending with conclusions. The results of the study can convey that the use of the PRIMASI application service at YPPSB has been going well with the convenience of teachers and employees in reporting administration at school. Based on the results of this analysis, it can be concluded that administrative services through the PRIMA application are registration and data collection of teachers and employees, validating teacher data, payroll and payment of overtime allowances for teachers and employees, submission of proposals, and data collection and repair of assets. Everything automatically uses the PRIMA application held by the operator at YPPSB, although I still found various obstacles.
Digital Design and Data Visualization for Society Health and Wellbeing

The contribution deals with the theme of the design of wearable and active devices, capable of communicating with the end user, mediators of the health condition and perceived and effective well-being of individuals. The state of the art on the subject highlights how the use of wearable devices in the field of e-health, capable of monitoring the biometric data of individuals, has grown. Nonetheless, these devices are used for limited periods of time, becoming – in fact – a sort of fad, or in any case a passing phenomenon. One therefore wonders whether it is possible to intervene on the design of this type of artefact, in such a way as to trigger a relational link between the object and the user, in order to increase the latter’s awareness of his/her health condition, with an impact relevant, with respect to the widespread quality of life, especially in cases in which one is confronted with more fragile sections of the population, in the presence of some pathological conditions, and for which monitoring of this type is therefore relevant. The approach used will make use of the analysis of the scientific literature and the state of the art of reference, useful for understanding the interest and relevance of the contribution, especially after a hybridization between knowledge and between different disciplinary areas of the design culture, namely that of digital design, communication design and data visualization, used to outline an innovative approach described in the contribution.
Quantum Algorithms for Shapley Value Calculation

In the classical context, the cooperative game theory concept of the Shapley value has been adapted for post hoc explanations of Machine Learning (ML) models. However, this approach does not easily translate to eXplainable Quantum ML (XQML). Finding Shapley values can be highly computationally complex. We propose quantum algorithms which can extract Shapley values within some confidence interval. Our results perform in polynomial time. We demonstrate the validity of each approach under specific examples of cooperative voting games.
A Max K-Cut Implementation for QAOA in the Measurement Based Quantum Computing Formalism

The MaxCut is a combinatorial problem which belongs to the NP-hard class. The algorithm to solve it can be mapped into a quantum system, where the optimal configuration matches the ground state of a target Hamiltonian. It is possible to implement the adiabatic theorem into the circuit model via the QAOA algorithm, by arbitrarily choosing from both the gate-based and the measurement-based quantum computing (MBQC) settings. Recently, the MaxCut problem has been generalized to the Max K-Cut version also in the MBQC frame, where K stands for the number of cuts to implement. Here, the QAOA algorithm for a Max K-Cut is solved for a graph with arbitrary number of vertices V and number of cuts K scaling as a power of 2, along with the circuit depth p. The problem is solved on a emulator based on classical HPC resources. In order to test the performances of a Max K-cut implemented in the MBQC frame, we report how the average runtime and the approximation ratio scale by increasing V, K and p, comparing the results from two different topologies of graphs, 2-regular graphs with null weights on the diagonals versus the the fully-connected graphs. Eventually, the approximation ratio, evaluated as a parameter of success, is shown to yield optimal performances.
Towards an Enhanced and Lightweight Face Authentication System

Face recognition is one of the most well-adopted ways to verify someone’s identity, which however might be spoofed by presenting for example a fake image or video. Therefore, it is essential to include additional face liveness detection for a safer application. Among the existing solutions, it is common to plug in a separate model for face liveness detection. Therefore, the current authentication platform will provide two models in order to provide a safe authentication. However, in many practical scenarios, the platform (e.g., IoT devices) has limited resources in terms of computation power and storage, and this may prevent the two-models from being deployed successfully. Observed that both recognition and liveness detection work on the same face image, we believe it is possible to integrate two functions into a unified model, which will reduce the computational workload and storage requirements. To achieve this, we explore two works with different model designs, research focuses, and potential solutions. In the first work, we try to enhance a usual face recognition model with additional task capability without any additional storage cost. Concretely, we first analyze the two task’s relationship, and by a mathematics formulation, we insert the observed dual-task relationship to a novel deep model with distance-ranking feature. The training of the model focuses on the feature-learning and it does not directly use the task ground truth labels, which makes the model has a good generalization capability on new data. We have conducted experiments on a benchmark dataset and the results show that our average performance has a minimal 15% improvement compared to the baselines. In the second work, we adopt the classic multi-task learning model to combine the two tasks. Rather than using a deep multi-task model, we compress the original deep model to a lightweight version. Additionally, in order to compensate the performance degradation due to compression, a multi-teacher assisted knowledge distillation is applied where a good balance between accuracy and model size is achieved.