Reviewerly/exHarmony huggingface dataset:
Petrichor: An Efficient Consensus Protocol Leveraging DAG and Sharding for Asynchronous BFT

As a core component of blockchain technology, the consensus mechanism provides the foundation for ensuring the trustworthiness and security of blockchain networks. However, existing consensus protocols suffer from low transaction throughput, high latency, and poor scalability. To address these challenges, we present Petrichor, an asynchronous Byzantine fault-tolerant consensus protocol based on Directed Acyclic Graph (DAG) and empowered with a sharding technique. Petrichor achieves significant performance improvements by dividing the blockchain network into multiple shards, allowing each shard to process transactions and generate blocks in parallel. We introduce the application of DAG within each shard to fully leverage its parallel propagation and multi-path confirmation properties. Within each shard, Petrichor employs a structured DAG, enabling transactions to propagate through multiple paths rather than relying on a single chain-like structure. This parallel propagation reduces transaction confirmation time and minimizes transaction latency. Furthermore, we propose a leader block election mechanism driven by random seeds, aiming to achieve fast consensus within each shard. With this mechanism, nodes within a shard can independently reach consensus without the need for direct communication, thereby facilitating efficient consensus within shards. We demonstrate the feasibility of Petrichor through theoretical analysis and validate its ability to meet the high throughput and low latency requirements of blockchain networks through extensive experiments.
Obric: Oblivious Keyword Search in Hyperledger Fabric Platform

Access pattern disclosure can reveal sensitive information related to search queries, posing a threat to user privacy. Malicious attackers may infer users’ search intentions, behavior, and interests from access patterns, which can be used for targeted attacks, phishing, or identity theft. Access pattern disclosure may also jeopardize the confidentiality of the searched data, as it may reveal sensitive information related to the queried data. Traditional searchable encryption allows for searching encrypted data directly, but suffers from privacy leaks due to access pattern disclosure and cannot guarantee data integrity. Additionally, traditional searchable encryption only supports single-keyword search and leads to significant computational overhead when searching for a large number of ciphertexts. To address these issues, we propose a two-stage protected ciphertext retrieval scheme called Obric that supports oblivious multi-keyword search in hyperledger fabric platform. In specific, we store encrypted data using the distributed point function (DPFs) on the Order node set and the ORAM encryption algorithm on the Peer nodes. Finally, we implement a secret information storage system so that users’ access pattern privacy is not compromised by malicious adversaries.
Three-dimensional shape measurement based on deep learning

In order to avoid the complicated phase with height mapping relationship calibration and phase unwrapping process of traditional 3D surface measurement methods, a three-dimensional surface measurement method based on deep learning was proposed. By sampling the rotating fringe pattern, data samples of different directions of the object to be measured are obtained as the training set. After the model is trained well, the mapping relationship between the deformed fringe pattern and the height of the measured object can be directly obtained to realize the 3D surface shape measurement of the object to be measured. The computer simulation and experiment show that the method is effective.
Asuhan Keperawatan Gerontik Pada Pasien Gout Arthritis Dengan Intervensi Pemberian Jus Nanas Untuk Menurunkan Kadar Asam Urat Di Panti Werdha Kasih Ayah Bunda Tangerang

Gout or gout arthritis is a disease associated with high levels of uric acid in the blood. There are two ways to reduce uric acid, pharmacological and non-pharmacological. Non-pharmacological can be done by giving pineapple juice Pineapple has a very high vitamin C content, pineapple also has a very good vitamin C content. This vitamin works by helping the system associated with the kidneys to excrete more uric acid. Besides that, it is also useful for keeping purines from being produced into uric acid. Purpose: The purpose of this paper is to provide an overview of nursing care for gout arthritis patients in reducing uric acid levels by administering pineapple juice. Method: Case study design using nursing care. This case study sample used 1 patient who was intervened for 7 days. Results: based on the implementation results on Mr. J by applying pineapple juice for 7 days, data on uric acid levels were obtained in Mr. J on the third day it decreased from 8.3 mg/dl to 8.0 mg/dl and uric acid levels on the 7th day were 7.3 mg/dl. So that the diagnosis of health maintenance is not effectively resolved and it can be concluded that there is a change, this shows the effect of giving pineapple juice in reducing uric acid levels.
Forcing the Network to Use Human Explanations in Its Inference Process

We introduce the concept of ForcedNet, a neural network that has been trained to generate a simplified version of human-like explanations in its hidden layers. The main difference with a regular network is that the ForcedNet has been educated such that its inner reasoning reproduces certain patterns that could be somewhat considered as human-understandable explanations. If designed appropriately, a ForcedNet can increase the model’s transparency and explainability. We also propose the use of support features, hidden variables that complement the explanations and contain additional information to achieve high performance while the explanation contains the most important features of the layer. We define the optimal value of support features and what analysis can be performed to select this parameter. We demonstrate a simple ForcedNet case for image reconstruction using as explanation the composite image of the saliency map that is intended to mimic the focus of the human eye. The primary objective of this work is to promote the use of intermediate explanations in neural networks and encourage deep learning development modules to integrate the possibility of creating networks like the proposed ForcedNets.
Secured Gray-Code-Based Steganographic Technique in Geometrical-Based Domain

The past decades witness a tremendous exchange in data and information in almost all domains. This leads to the need for applying data security to ensure information safety during communication. One of the main used technique is the steganography. Steganography is a technique that involves hiding information within objects, such as images, audio, video, and 3D objects, without leaving any noticeable alterations. We propose a 3D steganographic technique that increases security using Gray code sequence and a Least Significant Bit (LSB) technique. We construct the 64-binary representation for the 3D cover-object. A Gray code sequence is utilized to define the vertices that will be utilized for embedding the secret message. The binary representation of the secret message is to be hidden in the least significant bits of either the x- or the y- coordinates of the defined vertices. The least significant bits, are the last two bits in the binary representation of the vertex coordinates. Exploitation the binary representation, gives us the capability of increasing the data security, and the data capacity. The proposed steganographic technique has been evaluated through multiple evaluation metrics to assess its performance; including Mean Square Error Ratio (MSE), Peak Signal-to-Noise Ratio (PSNR), and Normalized Correlation (NC). Experimental results, show that the security level of the proposed technique is superior comparing to several existed techniques. Moreover, when testing against some common attacks such as the noise, the median, the Gaussian and the Laplacian filters; the results show the efficiency and the robustness of the proposed technique.
FEATURES OF STUDENTS 'CAREER ORIENTATIONS

The article deals with the theoretical approaches of the problem of students’ career development; factors that affect the development of a certain type of career guidance; features of the development of students’ career orientations in the process of professional training. The leading motives for choosing the profession of students are studied; criteria, levels of formation of career orientations of future specialists are determined. The structure of the motives for choosing a profession by students is focused on the content of professional activities, rather than on future career growth. It was studied that students who have internal motives, prone to career growth, clearly represent their professional path, strive for self-improvement, knowledge, and seek to help others, benefit people. Students, whose leading motives are external, rely on chance, career connections, have idealized ideas about future professional activities, focused on quick results, they are confident that their professional life will not be associated with the chosen specialty.&#x0D; The formation and development students’ career orientations of different specialties have similar trends, but there are some differences. This process is influenced by the motives for choosing a profession, the students’ attitude to future professional activities, personal qualities, the desire for self-improvement, self-realization, and success in the future professional activities. It was found that students are independent of other opinions in choosing a profession, but the structure of motives for choosing a profession is focused on current issues rather than on future career growth. Students take mostly passive position in life and professional sphere, most of them are not interested in professional self-realization. Applicants do not pay due attention to the improvement and development of professionally important knowledge, skills, abilities. Their career orientations are quite abstract, disordered, and contradictory; they are not incentives to work on themselves while studying at university.
Superlative Classification Model

Abstract: This project introduces an interactive platform designed to empower individuals with limited technical expertise to engage in machine learning tasks, specifically focusing on classifying diverse input data types like images, sounds, and sensory inputs. Leveraging transfer learning principles, the platform offers a user-friendly interface that simplifies the complexities of machine learning. Users can effortlessly upload, preprocess, and train machine learning models without extensive technical knowledge. The platform guides users through model selection, customization, and evaluation, providing insights into model performance through visualizations and performance metrics. Additionally, it enables the application of trained models to new data inputs, facilitating practical implementation without intricate deployment processes. With comprehensive educational resources and support, this platform aims to democratize machine learning, fostering innovation and problem-solving across various domains while eliminating technical barriers for users.
Pengembangan Sumber Daya Manusia Melalui Manajemen Sumber Daya Manusia Elektronik: Studi Literatur

This article aims to review and seek to inform about the concept of human resource development through electronic human resource management. The research method used is qualitative and literature studies or library research. Based on the results of the discussion above, it can be concluded that the recruitment process carried out electronically/online ( e-recruitment ) can help organizations attract more diverse applicants. The choice to move to the e-recruitment model was driven by several organizational objectives, including the need for the following: (a) Increase recruitment efficiency and reduce costs, (b) Increase the quality and quantity of applicants, e-recruiting which enables organizations to easily reach applicants in all corners of the country and even around the world, (c ) Build, communicate and expand brand identity, as means of business promotion that can attract applicants to join the organization, (d) Increase objectivity and standardize recruiting practices, (e) Increase applicant convenience, potential applicants will take advantage of web-based support for the hiring cycle to learn about and find out about the company’s activities, culture and opportunities online. &#x0D;
Secure data access using blockchain technology through IoT cloud and fabric environment

Abstract In the current landscape, staying abreast of the latest technological advancements is a formidable challenge, especially given the deluge of data inundating the internet. The realization has dawned that effectively managing the surge in emerging data necessitates the integration of multiple technologies. In pursuit of this objective, the Internet of Things (IoT), renowned for its sensor‐based data capture capabilities, is frequently coupled with blockchain technology to ensure secure data storage and access. This amalgamation, in turn, leverages the cloud environment when data volume surpasses a machine's processing capacity, thus mitigating infrastructure and maintenance costs. This study endeavors to optimize data storage within the blocks of the blockchain (BCT) by storing an index that points to the actual data. This innovative approach not only conserves storage space but also enhances operational efficiency. Furthermore, it simplifies the task of identifying malicious or faulty nodes deployed at different locations for data capture within the prescribed time frame. To exemplify this implementation, a case study is presented, focusing on securing user votes through the creation of contracts. The results showcased underscore the preference for a permissioned blockchain, such as Fabric, over a permissionless one, like Ethereum, particularly in the context of security considerations. The findings reveal that as the number of operations (in this case, votes cast) increases, Ethereum's performance deteriorates, while Fabric exhibits exceptional robustness. Additionally, the study analyzes sensor data simulated via IoT nodes before and after the application of security algorithms to underscore the significance of the proposed Secure Cloud‐Based Blockchain (SCB2) model. The analysis encompasses various facets, including the creation, validation, and computation times of transactions and blocks within a node, and positions the model favorably in comparison to existing literature.
Code Switching Analysis as Found in Bunga Citra Lestari Youtube Channel

The objectives of this study are to determine the various types and functions of code switching. Poplack's (1980) theory defines three forms of code switching in evaluating the data: tag switching, inter-sentential switching, and intra-sentential switching. Moreover, the theory from Appel and Muysken (1987) distinguishes the six functions of code switching are: referential function, directive function, expressive function, phatic function, poetic function, and metalinguistic function. The data source was taken from Bunga Citra Lestari YouTube’s channel entitled Love Story #1 – BCL Sebelum dan Sesudah Ashraf was published on February 12th, 2021. The method that was used to collect the data is the qualitative method. This research found 79 data of code switching. In analyzing the data, intra-sentential switching is commonly used by the participants in the video which found 50 (63,3%) data. Meanwhile, the metalinguistic function is dominantly used which found 32 (40,50%) data for the functions of code switching utterances by the participants. The result of this analysis because both of the speakers, Daniel Mananta and Bunga Citra Lestari are a bilingual person and they often switch the language into other languages because their society also has an acceptable use of English terms.
Plannig for Drainage Development in Jakarta's Urban Settlements with The Concept of a Healthy City

The need for land use in Jakarta can be a factor causing the reduced function of infiltration and water flow, resulting in changes and problems in the drainage system. It is suspected that downstream areas such as Jakarta are unable to accommodate excess water, because there may be problems with the channels or drainage system. For this reason, it is necessary to plan a drainage system (drainage network) so that it can function optimally in accordance with Minimum Service Standards (SPM) to accommodate water flow discharge. which is normal, especially during the rainy season and there are high tides so that you don't experience inundation or flooding. This research aims to plan the construction of drainage that is optimal according to standards in Jakarta in order to raise public awareness of the importance of keeping the environment clean, not throwing garbage in the drainage canals and always keeping the drainage canals clean so that they can drain wastewater from settlements, rainwater and puddles smoothly.
Visual image design of the internet of things based on AI intelligence

Visual object detection has emerged as a critical technology for Unmanned Arial Vehicle (UAV) use due to advances in computer vision. New developments in fields like communication technology and the UAV needs to be able to act autonomously by gathering data and then making choices. These tendencies have brought us to cutting-edge levels of health care, transportation, energy, monitoring, and security for visual image detection and manufacturing endeavors. These include coordination in communication via IoT, sustainability of IoT network, and optimization challenges in path planning. Because of their limited battery life, these gadgets are limited in their range of communication. UAVs can be seen as terminal devices connected to a large network where a swarm of other UAVs is coordinating their motions, directing one another, and maintaining watch over locations outside its visual range. One of the essential components of UAV-based applications is the ability to recognize objects of interest in aerial photographs taken by UAVs. While aerial photos might be useful, object detection is challenging. As a result, capturing aerial photographs with UAVs is a unique challenge since the size of things in these images might vary greatly. The study proposal included specific information regarding the Detection of Visual Images by UAVs (DVI-UAV) using the IoT and Artificial Intelligence (AI). Included in the study of AI is the concept of DSYolov3. The DSYolov3 model was presented to deal with these problems in the UAV industry. By fusing the channel-wise feature across multiple scales using a spatial pyramid pooling approach, the proposed study creates a novel module, Multi-scale Fusion of Channel Attention (MFCAM), for scale-variant object identification tasks. The method's effectiveness and efficiency have been thoroughly tested and evaluated experimentally. The suggested method would allow us to outperform most current detectors and guarantee that the models will be useable on UAVs. There will be a 95 % success rate in terms of visual image detection, a 94 % success rate in terms of computation cost, a 97 % success rate in terms of accuracy, and a 95 % success rate in terms of effectiveness.
Advancing Italian Biomedical Information Extraction with Transformers-based Models: Methodological Insights and Multicenter Practical Application

The introduction of computerized medical records in hospitals has reduced burdensome activities like manual writing and information fetching. However, the data contained in medical records are still far underutilized, primarily because extracting data from unstructured textual medical records takes time and effort. Information Extraction, a subfield of Natural Language Processing, can help clinical practitioners overcome this limitation by using automated text-mining pipelines. In this work, we created the first Italian neuropsychiatric Named Entity Recognition dataset, PsyNIT, and used it to develop a Transformers-based model. Moreover, we collected and leveraged three external independent datasets to implement an effective multicenter model, with overall F1-score 84.77%, Precision 83.16%, Recall 86.44%. The lessons learned are: (i) the crucial role of a consistent annotation process and (ii) a fine-tuning strategy that combines classical methods with a “low-resource” approach. This allowed us to establish methodological guidelines that pave the way for Natural Language Processing studies in less-resourced languages.
A Systematic Review on Automatic Speech Recognition for Odia Language

Automatic speech recognition (ASR) systems need to be trained on large data sets of audio and its transcript of a particular language to produce more accurate results. In our daily life, the ASR system in our smartphones like Siri is very helpful for people who try to communicate in English; however, the same system is very less helpful to those who speak the “low resource” languages. From the previous analysis, it is found that more than 10% of searches are made by voice for the English language and also the majority of them are by Smartphones. Keeping all these aspects in mind, a successful ASR system will be beneficial for all to communicate, and also the life of the language would be prolonged. Else in due course of time, people will face a lot of problems communicating; as a result, widely spoken language (English) may replace Odia, and the Odia language will be abolished. No such work done on building an ASR system for Odia speech. In our paper, we plan to prepare an Odia speech corpus and also plan to use machine learning and deep learning techniques and various online toolkits, resources, and language models helpful to prepare an Odia ASR. Till now no work done in Odia ASR, so working in Odia ASR will be of great help to other researchers as well as society.
Learning Dense UV Completion for 3D Human Mesh Recovery

Human mesh reconstruction from a single image is a challenging task due to the occlusion caused by self, objects, or other humans. Existing methods either fail to separate human features accurately or lack proper supervision for feature completion. In this paper, we propose Dense Inpainting Human Mesh Recovery (DIMR), a two-stage method that leverages dense correspondence maps to handle occlusion. Our method utilizes a dense correspondence map to separate visible human features and completes human features on a structured UV space with an attention-based feature completion module. We also design a feature inpainting training procedure that guides the network to learn from unoccluded features. We evaluate our method on several datasets and demonstrate its superior performance under heavily occluded scenarios compared to other methods. Extensive experiments show that our method obviously outperforms prior methods on heavily occluded images and achieves comparable results on the standard benchmarks. Moreover, our method is comparable with previous methods on no heavily occluded images.