Reviewerly/exHarmony huggingface dataset:
Charity Management Using Blockchain Technology

Beneficiaries have no confidence in the way donated money is spent. Humanitarian organizations are at a similar risk to the forms of fraud that plague businesses, such as money laundering. Recently, blockchain technology has been implemented in various fields. The use of blockchain technology allows you to perform the process of giving and receiving money openly. It is necessary to build a single donation tracking platform that will track all information about gifts, transactions, and sponsors. The purpose of this work is to explain building blockchain-based infrastructure tracking donations. Based on blockchain technology, the system provides a clear accounting of donor performance, foundations of giving, and recipients. The donation channel should be available from the charity platform, which allows community users and donors to follow and monitor where, when, and to whom donations are made. This work summarizes the activities carried out in the design and implementation of the web-based blockchain management system.
Data Breach in the Healthcare System

Computers, mobile devices, tablets, and other electronic devices are essential in our daily lives so the data plays a major role in it. Data is a valuable thing that might be stolen or leaked. Our ability to secure emerging technology is outpacing their development. Data storage is a critical component in any industry. Data security must be ensured across all industries, which is crucial. Data breaches, with an emphasis on the healthcare industry, are still on the rise as a result of inefficient data storage techniques like paperwork. Digital data storage is therefore becoming more secure. To store the data digitally, the proper software is needed. EHR (electronic health records) is the relevant program; information about patients, doctors, and medical histories is kept in the EHR. Blockchain technology might offer a way to safeguard this software, in particular if you desire more data security. The secure sharing of electronic data with patients, other doctors, and healthcare providers is made possible by blockchain-powered EHR systems.
Modern synergetic neural network for imbalanced small data classification

Abstract Deep learning’s performance on the imbalanced small data is substantially degraded by overfitting. Recurrent neural networks retain better performance in such tasks by constructing dynamical systems for robustness. Synergetic neural network (SNN), a synergetic-based recurrent neural network, has superiorities in eliminating recall errors and pseudo memories, but is subject to frequent association errors. Since the cause remains unclear, most subsequent studies use genetic algorithms to adjust parameters for better accuracy, which occupies the parameter optimization space and hinders task-oriented tuning. To solve the problem and promote SNN’s application capability, we propose the modern synergetic neural network (MSNN) model. MSNN solves the association error by correcting the state initialization method in the working process, liberating the parameter optimization space. In addition, MSNN optimizes the attention parameter of the network with the error backpropagation algorithm and the gradient bypass technique to allow the network to be trained jointly with other network layers. The self-learning of the attention parameter empowers the adaptation to the imbalanced sample size, further improving the classification performance. In 75 classification tasks of small UC Irvine Machine Learning Datasets, the average rank of the MSNN achieves the best result compared to 187 neural and non-neural network machine learning methods.
Research on transformer fire alarm threshold based on multi-sensor data analysis

Based on literature, on-site investigation and accident case analysis, this paper selects multi-sensor for transformer fire alarm, builds the transformer fire test platform, and carries out the transformer fire test and interference test to obtain the training and test data of neural network data fusion. This paper determines the fire alarm threshold of multi-sensor signal. It provides a benchmark for comparing the alarm results of multi-sensor data simulation fusion with those of single sensor. This paper studies the preprocessing multi-sensor data, compiles the corresponding algorithm, and obtains the multi-sensor preprocessed data after dimension reduction and noise reduction. It provides data support for multi-sensor data fusion.
Information System for Internship Applications in the Yogyakarta City Communication, Informatics, and Encoding Office

The Office of Communication, Informatics and Encoding (DISKOMINFOSAN) is a Yogyakarta City government agency tasked with carrying out government affairs in the field of Communication, Informatics and Encoding to meet the needs of society in the current digital era. Needs regarding registration and internship activities or field work practices for Yogyakarta City students, DISKOMINFOSAN is needed to provide better service. Based on the data obtained from interviews with the Sub Coordinator of the Information System Planning and Implementation Substance Group explained that the apprentice registration system and apprentice activities that are currently running are not computerized so that the data cannot be managed optimally. Research is also carried out by direct observation as an active participant in internship activities so that it can analyze deficiencies and constraints on the running system. In the early stages, students must submit documents for the requirements to register for an apprenticeship directly to the agency so that it will take time. Next, students fill in the required data on the Google Form. If an internship has been accepted, students must fill in the progress and final report on the Google Form. This activity is considered inefficient for students because they have to access many Google Form links during registration and internship activities. The software development method used in this study is the Waterfall method, the framework used for application development uses the CodeIgniter framework while the software testing used is the black-box method. With the design of this website application system, it can be a solution to problems that occur in the running system for the registration process and internship activities at the Yogyakarta City Informatics and Encryption Communication Service.
Improved Attention Mechanism and Adversarial Training for Respiratory Infectious Disease Text Named Entity Recognition

Named entity recognition(NER) of biomedical texts is a meaningful task. It extracts medical entities from unstructured text data. With the development of deep learning, the combination of attention mechanism and deep learning has become the research trend of NER. However, calculating attention is quite expensive, especially for long sequences. And noise data will also have a negative impact on the robustness of NER model. This paper proposes a NER method AIANM based on adversarial training and improved attention to solve these problems. The improved attention mechanism approximates the attention matrix, which reduces the overall self-attention complexity from $$O(n^2)$$ to O(n) in both time and space. And the attention mechanism greatly improves the recognition accuracy of the model by extracting information of interest. In addition, the applied adversarial training makes it keep steady performance even in face of micro disturbance. In this case, the correctness, robustness, and generalization ability are all improved on this new model. Experiments on respiratory infectious disease datasets show that, this method is featured with significantly improved accuracy, recall rate, and F1 score compared to previous entity recognition methods.
Coordinated Network of Sensors Over 5G for High-Resolution Protection of City Assets During Earthquakes

This paper introduces a novel concept in earthquake early warning for the smart city (EEW-SC) environment based on a finely spaced network of seismic sensors with a wireless backbone 5G network. The sensors are spaced 50–100 m apart to predict the damage from one structure to another depending on the resonances of the structure and the shape of the seismic waves striking the structure a few seconds before an earthquake arrives. The capability of performing predictive real-time performance-based damage assessment (PBDA) is unique and not possible with existing sensor technologies. Depending on the expected severity of the damage, automated actions, such as disconnecting electricity and gas, can be triggered to protect the asset and prevent fires and explosions.
Using a self-attention architecture to automate valence categorization of French teenagers’ free descriptions of their family relationships: a proof of concept

Background: This paper proposes a proof of concept of using natural language processing (NLP) techniques to categorize valence of family relationships described in free texts written by French teenagers. The proposed study traces the evolution of techniques for word embedding. Methods: After decomposing different texts in our possession into short texts composed of sentences and manual labeling, we tested different word embedding scenarios to train a multi-label classification model where a text can take several labels: labels describing the family link between the teenager and the person mentioned in the text and labels describing the teenager’s relationship with them (positive/negative/neutral valence). The natural baseline for word vector representation of our texts is to build a Term Frequency-Inverse-Document-Frequency (TF-IDF) and train classical classifiers (Elasticnet logistic regression, gradient boosting, random forest, support vector classifier) after selecting a model by cross validation in each class of machine learning models. We then studied the strengths of word-vectors embeddings by an advanced language representation technique via the Bidirectional Encoder Representations from Transformers (BERT) CamemBERT transformer model, and, again, used them with classical classifiers to compare their respective performances. The last scenario consisted in augmenting the CamemBERT with output dense layers (perceptron) representing a classifier adapted to the multi-label classification and fine-tuning the CamemBERT original layers. Results: The optimal fine-tuning depth that achieved a bias-variance trade-off was obtained by a cross-validation procedure. The results of the comparison of the three scenarios on a test dataset showed a clear improvement of the classification performances of the scenario with fine-tuning beyond the baseline and of a simple vectorization using CamemBERT without fine-tuning. Conclusions: Despite the moderate size of the dataset and the input texts, fine-tuning to an optimal depth remains the best solution to build a classifier.
Poster Session: Eye-tracking while learning greebles.

There has been a lot of attention on so-called ‘adversarial images’ that fool machine learning models (MLM). Manipulations to an image or including an unexpected object in a scene can severely disrupt object labeling and parsing by state-of-the-art MLM such as convolutional neural networks (CNN). In contrast, human observers may not even notice these changes, highlighting a significant gap between the robustness of human vision and CNNs. One well-studied class of novel objects are ‘Greebles,’ introduced by Gauthier and Tarr (1997; Gauthier et al., 1998). A large number of ‘families’, ‘genders’ and individuals can be created by systematically varying the arrangements and shapes of Greeble components. We report on eye-tracking (SMI iRed 250) participants as they are taught to recognize Greebles following the procedure described by Gauthier, Tarr and colleagues. Greeble expertise is assessed using a naming task and a verification task. Once an individual became a Greeble ‘expert’ we assessed their tolerance for rotations of the Greebles around the y-axis. The pattern of eye movements when learning and being tested on the Greebles are compared to the output of CNNs trained to recognize Greebles.
PELATIHAN PEMBUATAN DAN PENGADAAN BIOPORI SEDERHANA SEBAGAI SOLUSI DRAINASE DI DESA JAYALAKSANA

The accumulation of silt and grass in the neighborhood around residents' homes and around the drainage channels is one of the factors that hinders water from flowing into the river, causing overflow at several points. Knowledge on how to make and manage biopori is still not widespread, especially among rural communities. Training on the manufacture and procurement of simple biopores as a drainage solution is expected to make a positive contribution to the community's quality of life, the environment, and overall sustainability. This service applies a combination of methods in the form of science and technology diffusion and training to the people of Dea Jayalaksana, Bekasi Regency. The impact of the success of this program is improving local drainage thereby reducing the risk of flooding, soil erosion and environmental pollution in Jayalaksana Village.
Investigating Students’ Difficulties in Mastering English Listening Skills

This research aimed to find out the difficulties of students in mastering English listening skills at one of non-goverment MTs Bengkulu Tengah. This his study employed descriptive qualitative research method. The research subjects in this study are students at VII A Class of MTs Panca Mukti Bengkulu Tengah as the primary data source with the total subjects are 35 Students. The instruments used to collect the data in this research were observation, interview and documentation. The Semi Structured Interview consists of 12 questions. The researcher observed the class, after that, the researcher choosed fifteen students who had most difficulties in listening to be interviewed. The results of this study showed that the difficulties that the students faced in listening were related into four categories namely students’ difficulties related to the listening material, students’ difficulties related to the speaker, students’ difficulties related to the listener, students’ difficulties related to the physical environment. Besides, the factors which cause students’ difficulties in listening were lack of vocabulary, having hearing problems, unfocussed, the speaker spoke too fast.
Motivasi Belajar dalam Mengikuti Pembelajaran Daring Siswa Kelas X SMA

&lt;p&gt;This study aims to analyze the level of learning motivation of class X, differences in learning motivation between male and female and differences in motivation learning between students majoring in science and social studies, difference in scores between before online learning and after online learning at SMAN 2 Surakarta. This study uses a quantitative research approach with survey research methods. The sample in this study was 190 students of class X at SMAN 2 Surakarta with non- probability sampling technique. Data collection techniques using a questionnaire. The data analysis in this study used descriptive analysis, independent sample t-test and paired sample t-test. The results of this study indicate that (1) the students of class X at SMAN 2 Surakarta have moderate learning motivation, (2) there is no significant difference between the learning motivation of male students and female students, (3) no significant difference was found between the learning motivation of students majoring in science and students majoring in social studies, (4) no significant difference was found between student learning outcomes before online learning and after online learning.&lt;/p&gt;
The performance Analysis of Mutual Fund Investment using Weight Product Method

A mutual fund is an investment vehicle that combines money from multiple investors to buy a diverse portfolio of securities, like stocks, bonds, and other assets. It is managed by a professional fund manager who makes investment decisions on behalf of the investors. Mutual funds offer individuals the opportunity to access a variety of assets and benefit from pooled investments. They can be obtained through brokers, financial advisors, or directly from the mutual fund company. Various types of mutual funds exist, each with its own investment objective and risk level. Examples include equity funds, bond funds, money market funds, and balanced funds. Before investing in mutual funds, individuals should consider their investment goals, risk tolerance, and time frame. Researching a fund's performance, fees, and expenses is also important for making an informed decision. Mutual funds provide advantages such as diversification, professional management, and liquidity. However, potential disadvantages include high fees, minimum investment requirements, and redemption fees that may deter smaller investors.
Detecting the Pre-impact of Falls in the Elderly, Along with the Use of an Airbag Belt for Protection Against Femoral Neck Fractures

Abstract Falls are a significant health risk for older adults, and fall-related injuries are a leading cause of morbidity and mortality in this population. Elderlies are particularly vulnerable to falls due to age-related declines in mobility, balance, and muscle strength, as well as chronic medical conditions with use of certain medications. These injuries can range from minor bruises and scrapes to more severe like fractures, head trauma, or internal bleeding. To prevent falls in older adults, some solutions propose to ensure a safe living environment, others to maintain physical activity, and others to manage chronic medical conditions. This article presents the implementation and test of a system preventing hip fractures resulting from falls using a fall detection and prediction system designed to protect and alert individuals during falls.
ICDT: Maintaining Interaction Consistency for Deformable Transformer with Multi-scale Features in HOI Detection

Localization and interaction recognition are two main tasks in HOI (Human-Object Interaction) detection, which require high-level image and scene understanding. Existing HOI detection methods typically rely on single architecture to solve these tasks. However, such a strategy would frequently ignore the intrinsic links between two tasks. Meanwhile, the previous methods lack scale information, leading to a poor understanding of the visual scene. To overcome these drawbacks, we propose a method called ICDT(Interaction Consistency Deformable Transformer). ICDT performs a pre-trained hierarchical backbone with a deformable transformer encoder to extract contextual multi-scale features for accurate feature representations. Moreover, ICDT utilizes a cascaded deformable decoder architecture with learnable anchors to consistently obtain interaction predictions in sophisticated surroundings. Experimental results show the effectiveness of our method on the HICO-DET and V-COCO benchmarks. The codes are available at https://github.com/bingnanG/ICDT .
Fast Generalizable Novel View Synthesis with Uncertainty-Aware Sampling

Recent generalizable NeRF methods synthesize novel view images without optimizing per-scene via constructing radiation fields from 2D features. However, most of the existing methods are slow in the rendering process due to querying millions of 3D points to the NeRF model. In this paper, we propose a photorealistic novel view synthesis method with generalizable and efficient rendering. Specifically, given a set of multi-view images, we utilize a multi-scale scene geometry predictor consisting of MVS and NeRF to infer key points from coarse to fine. In addition, to obtain more accurate key point positions and features, we design an uncertainty-guided sampling strategy based on depth prediction and uncertainty perception. With the key points and scene geometry features, we propose a rendering network to synthesize full-resolution images. This process is fully differentiable, allowing us to train the network with only RGB images. Compared with state-of-the-art baselines, the experimental results show that our model is more efficient and has higher rendering quality on various synthetic and real datasets. With the multi-scale scene geometry predictor and uncertainty-aware sampling strategy, our approach infers geometry information efficiently and improves the rendering speed significantly.
An Optimized and Privacy-Preserving System Architecture for Effective Voice Authentication over Wireless Network

The speaker authentication systems assist in determining the identity of speaker in audio through distinctive voice characteristics. Accurate speaker authentication over wireless network is becoming more challenging due to phishing assaults over the network. There have been constructed multiple kinds of speech authentication models to employ in multiple applications where voice authentication is a primary focus for user identity verification. However, explored voice authentication models have some limitations related to accuracy and phishing assaults in real-time over wireless network. In research, optimized and privacy-preserving system architecture for effective speaker authentication over a wireless network has been proposed to accurately identify the speaker voice in real-time and prevent phishing assaults over network in more accurate manner. The proposed system achieved very good performance metrics measured accuracy, precision, and recall and the F1 score of the proposed model were98.91%, 96.43%, 95.37%, and 97.99%, respectively. The measured training losses on the epoch 0, 10, 20, 30, 40, 50, 60, 70, 80, 90, and 100 were 2.4, 2.1, 1.8, 1.5, 1.2, 0.9, 0.6, 0.3, 0.3, 0.3, and 0.2, respectively. Also, the measured testing losses on the epoch of 0, 10, 20, 30, 40, 50, 60, 70, 80, 90, and 100 were 2.2, 2, 1.5, 1.4, 1.1, 0.8, 0.8, 0.7, 0.4, 0.1 and 0.1, respectively. Voice authentication over wireless networks is serious issue due to various phishing attacks and inaccuracy in voice identification. Therefore, this requires huge attention for further research in this field to develop less computationally complex speech authentication systems.
Exploring the Path of Inclusive Development in Higher Education Teaching Centers to Support Special Education

The Center on Research for Learning and Teaching in University of Michigan has developed a robust operational mechanism and distinctive training programs over half a century, making it an exemplary model for educational centers in the United States. In China, the establishment of university teaching development centers and the initiation of special education are relatively recent endeavors. Exploring a path for inclusive development in higher education teaching centers to support special education, creating an inclusive educational environment, establishing a comprehensive service system for special education within university teaching centers, and promoting the cultivation of advanced and practical educators for inclusive education, all represent innovative approaches towards achieving the ultimate goal of high-quality education. Building upon the insights from the educational consulting services at the University of Michigan, this paper endeavors to investigate the construction of a path that ensures the development of special education within university teaching centers in China.