Reviewerly/exHarmony huggingface dataset:
Mobile Application for Real-Time Food Plan Management for Alzheimer Patients through Design-Based Research

Alzheimer’s disease is a type of dementia that affects many individuals, mainly in an older age group. Over time, it leads to other diseases that affect their autonomy and independence. The quality of food ingestion is a way to mitigate the disease and preserve the patient’s well-being, which substantially impacts their health. Many existing applications for food plan management focus on the prescription of food plans but do not provide feedback to the nutritionist on the real amount of ingested calories. It makes these applications inadequate for these diseases, where monitoring and control are most important. This paper proposed the design and development of a mobile application to monitor and control the food plans of Alzheimer’s patients, focused on informal caregivers and respective patients. It allows both the realistic visualization of the food plans and users to adjust their consumption and register extra meals and water consumption. The interface design process comprises a two-level approach: the user centered design methodology that accounts for users’ needs and requirements and the user experience questionnaire to measure user satisfaction. The results show that the interface is intuitive, visually appealing, and easy to use, adjusted for users that require a particular level of understanding regarding specific subjects.
Towards Fault Tolerance of Reservoir Computing in Time Series Prediction

During the deployment of practical applications, reservoir computing (RC) is highly susceptible to radiation effects, temperature changes, and other factors. Normal reservoirs are difficult to vouch for. To solve this problem, this paper proposed a random adaptive fault tolerance mechanism for an echo state network, i.e., RAFT-ESN, to handle the crash or Byzantine faults of reservoir neurons. In our consideration, the faulty neurons were automatically detected and located based on the abnormalities of reservoir state output. The synapses connected to them were adaptively disconnected and withdrawn from the current computational task. On the widely used time series with different sources and features, the experimental results show that our proposal can achieve an effective performance recovery in the case of reservoir neuron faults, including prediction accuracy and short-term memory capacity (MC). Additionally, its utility was validated by statistical distributions.
Bolt Installation Defect Detection Based on a Multi-Sensor Method

With the development of industrial automation, articulated robots have gradually replaced labor in the field of bolt installation. Although the installation efficiency has been improved, installation defects may still occur. Bolt installation defects can considerably affect the mechanical properties of structures and even lead to safety accidents. Therefore, in order to ensure the success rate of bolt assembly, an efficient and timely detection method of incorrect or missing assembly is needed. At present, the automatic detection of bolt installation defects mainly depends on a single type of sensor, which is prone to mis-inspection. Visual sensors can identify the incorrect or missing installation of bolts, but it cannot detect torque defects. Torque sensors can only be judged according to the torque and angel information, but cannot accurately identify the incorrect or missing installation of bolts. To solve this problem, a detection method of bolt installation defects based on multiple sensors is proposed. The trained YOLO (You Only Look Once) v3 network is used to judge the images collected by the visual sensor, and the recognition rate of visual detection is up to 99.75%, and the average confidence of the output is 0.947. The detection speed is 48 FPS, which meets the real-time requirement. At the same time, torque and angle sensors are used to judge the torque defects and whether bolts have slipped. Combined with the multi-sensor judgment results, this method can effectively identify defects such as missing bolts and sliding teeth. Finally, this paper carried out experiments to identify bolt installation defects such as incorrect, missing torque defects, and bolt slips. At this time, the traditional detection method based on a single type of sensor cannot be effectively identified, and the detection method based on multiple sensors can be accurately identified.
The Vocabulary Building Audio-Visual Media: An Innovation in Vocabulary Expertise

The success of student learning cannot be separated from the appropriate media and learning strategies. With the support of appropriate resources and new technologies, adult learners may effectively learn languages on their own, anywhere. Lack of knowledge about new technologies lead students to the lack of vocabulary. The existence of new technology must also be supported by the ability of students to master the technology. In connecting with the abilities that students have, the teacher finds obstacles, especially for students with special needs, mentally disabled students. Audio-visual media in the form of Flash card, drawing book and animated learning videos are used in English subjects at SLB Ulaka Penca. This study was conducted to investigate whether or not Audio-visual could be effective media to enhance students’ vocabulary mastery. The findings in this study are in the form of Vocabulary Skills Innovation with the aim of knowing how effective the use of visual auditory strategies, known as the use of audio
The Future of Cryptocurrency and Its Effect on Organizational Structure, Culture, and Change

Some of the constraints that cryptocurrencies now face, such as the fact that one's digital wealth might be lost in the event of a computer breakdown, may in the future be solved by technical advancements. Organizational structure, power distribution, and decision-making capacity are susceptible to disruption as a result of technological development. Consequently, culture will become fragile if executives do not take the essential steps to go ahead. Given that disruption will alter the present world, the time has come to comprehend blockchain and its consequences for corporate culture. This chapter discusses the future of cryptocurrencies and their impact on corporate culture and structure.
An Improved Partitioning Method via Disassociation towards Environmental Sustainability

The amount of data created by individuals increases daily. These data may be gathered from various sources, such as social networks, e-commerce websites and healthcare systems, and they are frequently made available to third-party research and commercial organisations to facilitate a wide range of data studies. The protection of sensitive and confidential information included within the datasets to be published must be addressed, even though publishing data can assist organisations in improving their service offerings and developing new solutions that would not otherwise be available. The research community has invested great effort over the past two decades to comprehend how individuals’ privacy may be preserved when their data need to be published. Disassociation is a common approach for anonymising transactional data against re-identification attacks in privacy-preserving data publishing. To address this issue, we proposed three new strategies for horizontal partitioning: suppression, adding and remaining list. Each strategy identifies a different approach for handling small clusters with fewer than k transactions. We used three real datasets for transactional data in our experiments, and our findings showed that our proposed strategies could decrease the percentage of information loss of disassociated transactional data by almost 35%, comparing it with the previous original disassociation algorithm. As a result, the utility of published data will be improved.
Fraudulent Banking Transaction Classification Using Deep Learning Algorithm.

Financial fraud is a significant problem in the banking industry, and detecting fraudulent transactions is a critical task for banks to protect their customers and maintain trust in the financial system. Traditional rule-based approaches for detecting fraud often rely on pre-defined thresholds and heuristics, which can be circumvented by sophisticated fraudsters. As a result, machine learning techniques have gained increasing attention in recent years for their ability to automatically learn from data and adapt to changing fraud patterns .In this project, we propose a novel approach for classifying fraudulent banking transactions using a deep learning algorithm. Our approach leverages the power of deep neural networks to automatically extract meaningful features from transaction data, and then use these features to accurately classify transactions as either fraudulent or legitimate
Tracing IP Address and Details of Unidentified Participants in Webinars

During pandemic the mode of sharing information with the multiple participants in physical mode is reduced, to overcome this the online mode of sharing information and discussions through webinars has been increased globally. In case of any misbehaviors from unidentified participants to stop the webinars, we need to identify the participant who is misusing the opportunity. So, we came with the idea of IP tracking. In this project we provide the option to monitor the unidentified participants and we can access the details of the participant such as IP Address. Through IP Address we can identify the participant and necessary action can be taken from the concerned authorities
Detection of the Bolt Loosening Angle through Semantic Key Point Extraction Detection by Using an Hourglass Network

Damage in bolts, which are used as connecting fasteners in steel structures, affects structural safety. Sophisticated machine vision methods have been formulated for the detection of loose bolts, but their accuracy remains an area for improvement. In this paper, a method based on a stacked hourglass network is proposed for automatically extracting the key points of a bolt and for obtaining the bolt loosening angle by comparing the rotations of the key points before and after the bolt is damaged. A data set containing 100 images of key bolt loosening points was collected, and rotation was performed as data augmentation to yield 1800 images. Moreover, a method was designed for automatically annotating the augmented image data set. In this study, 70%, 10%, and 20% of the annotated image data set were used for training, validation, and testing, respectively. Subsequently, a neural network model based on a stacked hourglass network was established to train the annotated image data set. The detection results were evaluated in terms of normalized errors (NEs), percentage of correct key points (PCK), detection speed, and training time. In testing, the proposed method accurately and efficiently identified the bolt loosening angle, with a PCK value as high as 99.3%. The accuracy of the proposed method was also highly robust to different shooting distances, viewing angles, and illumination levels.
A Quantitative Analysis of Existing and Expected Status of the Ethics and Morality in High School Curriculum in Punjab

The study aimed to analyze the current and expected level of the ethics and morality in high school education. A Quantitative Survey Research Design was applied by the study. The required data needed to be specific and relevant, therefore, the stratified random sampling technique was used to collect data from a population of all stakeholders of education in the Punjab: teachers, parents, students, administrators, policy makers and curriculum developers. The data was collected from 500 participants who used Thurstone scale to show their priorities by scaling the 77 items of the 7 questions regarding their importance and these collected priorities were further analyzed by using Borda Count Method “to explore the degree of intensity of the prioritized items”. According to the result of the study, the current status of the concepts of ethics and morality taught at the secondary level is not up to the required standards and lack planning. Therefore, to develop moral attitude, the religious and spiritual values need to be inculcated through moral stories and disciplinary rules using lecture, group discussion and story-telling methods to ensure overall personality development with social adjustability. It is recommended by the researchers to revise the existing curriculum to make it practical and operational for the optimum effectiveness.
Math Word Problem Solver using Machine Learning

This paper presents a deep neural solver to automatically solve math word problems. In contrast to previous statistical learning approaches, we directly translate math word problems to equation templates using a recurrent neural network (RNN) model, without sophisticated feature engineering. We further design a hybrid model that combines the RNN model and a similarity-based retrieval model to achieve additional performance improvement. Experiments conducted on a large dataset show that the RNN model and the hybrid model significantly outperform state of-the-art statistical learning methods for math word problem solving.
Position-Wise Gated Res2Net-Based Convolutional Network with Selective Fusing for Sentiment Analysis

Sentiment analysis (SA) is an important task in natural language processing in which convolutional neural networks (CNNs) have been successfully applied. However, most existing CNNs can only extract predefined, fixed-scale sentiment features and cannot synthesize flexible, multi-scale sentiment features. Moreover, these models' convolutional and pooling layers gradually lose local detailed information. In this study, a new CNN model based on residual network technology and attention mechanisms is proposed. This model exploits more abundant multi-scale sentiment features and addresses the loss of locally detailed information to enhance the accuracy of sentiment classification. It is primarily composed of a position-wise gated Res2Net (PG-Res2Net) module and a selective fusing module. The PG-Res2Net module can adaptively learn multi-scale sentiment features over a large range using multi-way convolution, residual-like connections, and position-wise gates. The selective fusing module is developed to fully reuse and selectively fuse these features for prediction. The proposed model was evaluated using five baseline datasets. The experimental results demonstrate that the proposed model surpassed the other models in performance. In the best case, the model outperforms the other models by up to 1.2%. Ablation studies and visualizations further revealed the model's ability to extract and fuse multi-scale sentiment features.
Interest-Aware Message-Passing Layer-Refined Graph Convolutional Network for Recommendation

Graph convolutional networks (GCNs) show great potential in recommendation applications, as they have excellent performance in propagation node information propagation and capturing high-order connectivity in user-item interaction graphs. However, in the current recommendation model based on GCN, incoming information from neighbors is aggregated during information propagation, and some of this information may be noisy due to negative information. Additionally, the over-smoothing problem occurs when the model layers are stacked too high. During the embedding learning of users in the graph convolution operation, an important factor is that high-order neighbor users with no interest are involved, leading to similar embedding for users with no interest. These issues can degrade the recommendation performance. To address these problems, this paper proposes a method called IMPLayerGCN. In this method, high-order graph convolution is performed within subgraphs, which are composed of users with similar interests and their interaction items. The higher-order graph convolution is carried out in the subgraph and the layer representation in the process of GCN re-information propagation and node update is refined. The convolution process uses a symmetrical matrix. This approach avoids the spread of negative information from higher-order neighbors to embedded learning.
Prospects of Blockchain Technology in Finance Sustainability

Blockchain technology is a multidisciplinary approach that is growing fast in the economy. The attributes possessed by blockchain are evident in many sectors in the economy such as banking, financial services, innovation, creativity, etc. Blockchain has brought out a revolutionary change in the economy as it provides transparency, cost efficiency, accuracy. Application of blockchain technologies in finance will help in attaining sustainability. The chapter provides comprehensive review of past studies on the role of blockchain technologies in financial services and in enhancing financial performances. The study will provide better insights to the managers of the financial institutions regarding the pros and cons of blockchain technology.
Knowledge-Based Features for Speech Analysis and Classification: Pronunciation Diagnoses

Accurate pronunciation of speech sounds is essential in communication. As children learn their native language, they refine the movements necessary for intelligible speech. While there is variability in the order of acquisition of speech sounds, there are some sounds that are more complex and are later developing. The rhotic /r/ is a later-developing sound in English, and some children require intervention to achieve accurate production. Additionally, individuals learning English as a second language may have difficulty learning accurate /r/ production, especially if their native language does not have an /r/, or the /r/ they produce is at a different place of articulation. The goal of this research is to provide a novel approach on how a knowledge-based intelligence program can provide immediate feedback on the accuracy of productions. In the proposed approach, the audio signals will first be detected, after which features of audio signals will be extracted, and finally, knowledge-based intelligent classification will be performed. Based on the obtained knowledge and application scenarios, novel features are proposed and used to classify various speaker scenarios.
Research on New Virtualization Security Protection Management System Based on Cloud Platform

As an emerging product under the condition of informatization, the utilization of cloud platform in many industries has brought fundamental changes to the production and business model in related fields. The cloud platform provides rich and diverse utilization services to terminals through multi-dimensional integration of different IT resources. With the in-depth utilization of cloud platform, the security problems it faces are becoming more and more prominent. The traditional network security protection means have been difficult to effectively adapt to and deal with the security threats under the new situation of cloud platform utilization. As a prominent part of building cloud platform, the construction level of virtualization security protection system will have an intuitive impact on the security of cloud platform. At present, the virtualization security protection management system under cloud platform is facing direct threats from virtual machine deployment, virtual machine communication and virtual machine migration. Based on this, this paper studies the virtualization security protection management system of cloud platform from the perspective of virtualization security tech, so as to ameliorate the stability, reliability and security of cloud platform.
The Correlation Between Gender and Narrative Story In Student Listening Skill

The purpose of this study was to determine the correlation between gender and narrative stories with students' listening skills. The research will be held in the second semester of grade eight students of SMPN 3 TANGSEL. The method of this study is a quantitative method. The results are there are 68% of female students who have an interest and ability in narrative stories through listening skills while only 32% of boys. There is a significant positive and significant relationship between genre and narrative story on the listening skills of 9th graders (1) SMPN 3 Tangerang Selatan City with r count greater than r table (0.773 &gt; 0.148) and a significance value of 0.000 which means less than 0.05 (0.000 &lt; 0.05). Thus, the genre and narrative story variables together have an important role in improving students' ability to write suggestive narratives. So based on the results of the research, gender has a significant correlation with students' listening skills in narrative stories.
Age Estimation Based on Front Shot Using Deep Learning

In recent years, biometrics has attracted the utmost interest and established itself as the most trustworthy choice for recognition due to the availability of realistic technology resulting from an extensive study in this field. Nevertheless, attempts are still underway to produce a more user-friendly system that satisfies the standards of security systems, provides more accurate results, protects assets, and secures privacy. The human age estimate is a crucial and challenging undertaking. Different biomarkers and techniques have been studied for biological age estimation, each having pros and cons. Age estimation based on face images is vital in a variety of scenarios, such as security and security systems, border security. Estimation accuracy is a significant measure for evaluating algorithms and their effectiveness in predicting absolute ages through the use of deep learning, which created a fundamental revolution in the subject of face systems. The technique was assessed using a data set that is regarded as the basis in this field of the face estimation system, namely (IMDB). The cornerstone of the job depends on the front half of the face, that is, the essential features of the eyes, cheeks, nose, lips, and forehead. CNN has a system result age accuracy of up to 98%.
MSTGAN: A Multi-Slot Conditional Generative Adversarial Network Based on Swin Transformer for Channel Estimation

Accurate channel estimation is the key to enhance the transmission performance of communication systems. In this letter, we propose a multi-slot conditional generative adversarial network (cGAN) based on Swin Transformer called MSTGAN for channel estimation in a single-input single-output (SISO) scenario. Specifically, the proposed MSTGAN could learn the temporal correlation feature from continuous channel slots by the 3D convolution and extract the deep feature well by the Swin Transformer to improve the accuracy of channel estimation. The model is trained with data augmentation to reduce the computational cost for offline training. The simulation results demonstrate that the proposed method outperforms the linear minimum mean square error (LMMSE) method and other deep learning methods. Furthermore, extension schemes of the MSTGAN to the multiple-input multiple-output (MIMO) case are also provided.