Reviewerly/exHarmony huggingface dataset:
STMKu Application Design for Financial Management Transparency in STM Organizations

The STM organization is a social organization that aims to establish friendship, help each other in joy or sorrow among Muslims. another goal is to maintain neighborhood harmony and social relations between villagers in general, especially those who are Muslim. in supporting the implementation of its activities the STM organization collects and manages funds originating from several sources including mandatory contributions from members, member registration fees and other donations. in managing these funds, all expenses or income are recorded manually. This makes it difficult for the treasurer to report. Under these conditions, the process of financial reporting to members is considered not transparent. This of course can raise suspicions which will cause the harmony of neighbors to be disrupted. Therefore, the STMKu website was created which can be a medium of information for members and the public about financial management information and activities carried out by STM. STM management is also assisted in recording and reporting activities and financial processes. This application is made based on the website using the waterfall system development method.
Smart Vacuum Clear Robot

Manual work is taken over the robot technology and many of the related robot appliances are being used extensively also. Here represents the technology that proposed the working of robot for Floor cleaning. Households of today are becoming smarter and more automated. Home automation delivers convenience and creates more time for people. Domestic robots are entering the homes and people’s daily lives, but it is yet a relatively new and immature market. However, a growth is predicted and the adoption of domestic robots is evolving. Several robotic vacuum cleaners are available on the market but only few ones implement wet cleaning of floors. The purpose of this project is to design and implement a Vacuum Robot for Autonomous dry and wet cleaning application using mop. Vacuum Cleaner Robot is designed to make cleaning process become easier rather than by using manual vacuum. The main objective of this project is to design and implement a vacuum robot prototype by using Arduino mini, Motor, Ultrasonic Sensor, and IR Sensor and to achieve the goal of this project. The whole circuitry is connected with 7.4V battery. Vacuum Robot will have several criteria that are user-friendly.
Exploiting Predictability of Random Vector Functional Link Networks in Forecasting Quality of Service (QoS) Parameters of IoT-Based Web Services Data

Background: QoS parameters are volatile in nature and possess high nonlinearity, thus making the IoT-based service and recommendation process challenging. Methods: An efficient and accurate forecasting model is lacking in this area and needs to be explored. Though an artificial neural network is a prominent option for capturing such nonlinearities, its efficiency is limited by the structural complexity and iterative learning method. The random vector functional link network (RVFLN) significantly reduces the time complexity by randomly assigning input weights and biases without further modification. Only output layer weights are calculated iteratively by gradient methods or non-iteratively by least square methods. It is an efficient algorithm with low time complexity and can handle complex domain problems without compromising accuracy. Motivated by these characteristics, this article develops an RVFLN-based model for forecasting QoS parameter sequences. Results: Two real-world IoT-enabled web service dataset series are used in developing and evaluating the effectiveness of RVFLN-based forecasts in terms of three performance metrics. Conclusion: Experimental results, comparative studies, and statistical tests are conducted to establish the superiority of the proposed approach over four other similar forecasting techniques.
Real time sentiment analysis of natural language using multimedia input

Semantics and Sentiments are parts of our daily speech and expressions that helps to convey the message in the tone intended. The accurate interpretation of emotions and actions is prudent as it expresses the true meaning of the message. This interpretation has been studied extensively in the past two decades, where professionals from various disciplines have pondered this question. Every action and expression-whether it's in a speech, in a video or through some written material-helps the recipient understand the intent behind the message. The primary motive in these studies has been to automate the analysis of these sentiments by teaching the computers to do so, using the audio, video and text-based data that has been collected so far. Machine Learning (ML) and Deep Learning (DL) is the discipline that can help us tackle such a problem which requires analysis and recognition of copious amounts of data. Classification based on these multi-media inputs has seen the application of several common and uncommon ML techniques such as Support Vector Machines (SVMs), Bayesian Networks (BNs), Decision Trees (DTs), Convolutional Neural Networks (CNNs) and K-Means Clustering. These techniques, to a certain level of accuracy, can classify a certain part of a message into a different emotion. Through this research, firstly, a comparison is represented between the previously conducted studies and secondly, a system is developed of our own that enables Real Time Sentiment Analysis and helps a user assess his/her day-to-day attitude and get appropriate recommendations for the same.
EcReID: Enhancing Correlations from Skeleton for Occluded Person Re-Identification

Person re-identification is a challenging task due to the lack of person image information in occluded scenarios. The current methods for person re-identification only take into account global information, neglect local information, and are not responsive to changes in input. Additionally, these methods do not address the issue of inaccurate joint detection caused by occlusion. In this paper, we propose an occluded person re-identification method based on a graph model and deformable method, which is able to simultaneously focus on global and local information and can flexibly adapt to local information and changes in the input, efficiently resolving issues such as occluded or incorrect joint information. Our method consists of three modules: the mutual help denoising module, inter-node aggregation and update module, and graph matching module. The mutual help denoising module acquires global features and person skeleton node features using a CNN backbone network and a pose estimation model, respectively. It uses symmetric deformable graph attention to obtain the local and global features of the joint points in different views, correcting the information of incorrect nodes and extracting favorable human features. The inter-node aggregation and update module employs deformable graph convolution operations to enhance the relations between the nodes in the same view, resulting in higher-order information. The graph matching module uses graph matching methods based on the human topology to obtain a more accurate similarity calculation for masked images. Experimental results on the Occluded-Duck and Occluded-REID datasets show that our proposed method achieves Rank-1 accuracies of 64.8% and 84.5%, respectively, outperforming current mainstream methods such as HOReID. Our method also achieves good results on the MARKET-1501 and DukeMTMC-ReID datasets. These results demonstrate that our proposed method can extract person features well and effectively improve the accuracy of person re-identification tasks.
Exploring the Intersection of Lattice Attacks and Blockchain Technology: A Heuristic Approach Using TPM2.0 ECDSA to Ascertain and Approach the Boundary

Lattice attacks can compromise the security of encryption algorithms used in blockchain networks, allowing attackers to tamper with transaction records, steal private keys, and execute other forms of attacks. With symmetric encryption, both parties can encrypt and decrypt messages using the same key. Lattice attacks on digital signature algorithms (ECDSA) involve forming a basis and setting a target vector from signatures, then solving the closest vector problem (CVP) or shortest vector problem (SVP) in the generated lattice to obtain the private key. Prior research focused on obtaining leakage information from the signature’s random nonce to facilitate a CVP or SVP solution. This study establishes a clear boundary for a successful ECDSA attack and introduces a “double basis” lattice version that expands the boundary or reduces the necessary signatures by nearly half with the same lattice rank. To approach the boundary, a heuristic strategy is employed to shift the target vector in different directions with a feasible step size, using tests on the Trusted Platform Module (TPM) 2.0 ECDSA. The distance from the closest moved target vector to the boundary is reduced by a ratio of 424 to 179 to the minimal length of orthogonal vectors in the formed basis. Experimental results show that moving attempts in two directions with the original basis and 84 signatures take approximately 247.7 s on the experiment computer.
Italian Hospital Teachers&amp;rsquo; Perceptions of Technological and Methodological Innovations after the COVID-19 Pandemic

The COVID-19 pandemic has had a significant long-term impact on education worldwide. In many countries, schools and universities experienced a rapid switch to emergency remote teaching and learning (ERTL), which affected many education systems in the 2020–21 school year. This was true for the Italian educational context as a whole, including School in Hospital (SiHo) services. This study explored how the SiHo functioned in Italy during the 2020–2021 school year. The aim was to explore what, if any, changes the emergency brought about in educational practices and in the adoption of technologies in this specific context, with a particular focus on any differences between school levels. The study was conducted with 252 SiHo teachers using a questionnaire format. The results showed that after the forced adoption of distance modes during the spring 2020, face-to-face teaching returned to be the prevalent mode in the 2020–2021 school year, with some exceptions for upper-secondary school students (covered by ministerial provisions). The teaching approach that SiHO teachers prefer, both for face-to-face and distance lessons, remains frontal instruction, probably given the particular needs of their students. Younger students probably experienced the most significant changes due to the limits imposed on interpersonal contact in hospitals, which prevented group work and play, previously commonly adopted by kindergarten and primary school teachers. In terms of technology integration in educational practice, teachers stated that they had acquired greater competence in the use of a variety of technological resources.
Double-event-triggered cooperative maximum correntropy estimation over wireless sensor networks

This paper is concerned with the distributed state estimation problem for a class of multi-sensor network systems with bandwidth constraints, measurement loss, and shot noises. To tackle these issues, the double-event-triggered cooperative maximum correntropy estimation algorithm is proposed following the proposed double-event-triggered communication mechanism and maximum correntropy criterion. With such an algorithm, measurement errors between adjacent events in the first-event trigger are applied to determine whether the sensor node needs to transmit data, and then current innovation samples in the second-event trigger are to detect measurement anomalies. Compared to existing approaches, the proposed algorithm can significantly reduce communication costs and improve sensors’ service life, accuracy and robustness. A simulation case-study concerning moving target tracking with multiple unmanned aerial vehicles (multi-UAVs) is worked out to demonstrate the effectiveness of the considered algorithm.
Automating discussion structure re-organization for GitHub issues

As a popular social code hosting platform, GitHub encourages developers to discuss and leave opinions on issues. However, the linear format of GitHub issue discussions makes popular discussions difficult for developers to organize and extract useful information effectively. In this paper, we propose an issue discussion re-organization approach, aiming at converting an issue discussion with the linear structure into a discussion tree with key information. First, we conduct a motivational study to investigate the current situation of issue discussions in GitHub. Further, to re-organize discussion structures, we employ a Transformer-based model with transfer learning to predict the response relationship between comments for re-building structures and utilize TF–IDF to extract key information from the content with different topics. The experimental results show that our approach outperforms other baselines, and achieves an average improvement of 14.54% on metrics in the task of predicting response relationships, as well as getting an average improvement of 27.19% in terms of metrics of the re-organizing task. To investigate our re-organized results from actual perspectives, we also conduct a human evaluation. The results show that our approach can predict the accurate response relationships for 80.74% of comments from actual perspectives and 63% of topics extracted by our approach are highly rated. Moreover, 90.00% of newcomers from the open-source community approve of re-organized discussion structures.
Abnormal identification of oil monitoring based on LSTM and SVDD

Oil monitoring has been considered as an essential tool in determining the operational state of an equipment. In other words, the data obtained from monitoring the oil represents the equipment's operational status. So, identifying the abnormal working status of the lubricating oil allows to understand its operating status in advance. Accordingly, the present study aims at proposing an anomaly identification model based on Long Short Term Memory (LSTM) and Support Vector Data Description (SVDD) for time series data of wear state collected by the oil online monitoring system. So, to predict the wear condition of the oil in the future, LSTM and grid search are utilized and a trend prediction model of oil monitoring metrics is constructed. Given that the fault is a gradual process, a multi-dimensional hypersphere classifier is generated by SVDD to train the healthy samples. The classifier is utilized to diagnose the lubricating oil condition data obtained by the LSTM prediction model as well as to realize the abnormal identification of the equipment's lubricating oil. The obtained experimental results corroborated the higher prediction accuracy and the lower prediction loss of the trend prediction model. Therefore, the future anomalies of the device can be argued to be reliably predicted in accordance with its actual performance using the LSTM-SVDD method. Furthermore, with regards to the actual operating conditions of the diesel engine, the alarm rate as well as the false alarm rate, the LSTM-SVDD method was demonstrated to be superior to the LSTM-PCA-SPE method. Hence, the novel model proposed in this study can be used to identify the status parameters of the lubricating oil abnormal wear. Moreover, the model can be argued to improve the accuracy of the abnormal identification as well as the online identification of the lubricating oil.
Solomia Krushelnytska and Ukrainian artistic emancipation in Galicia

Relevance of research. The problem of emancipation in the Ukrainian, particularly Galician musical environment of the end of the 19th and the first half of the 20th centuries is updated in the projection of the phenomenon of creativity of the outstanding opera singer Solomia Krushelnytska.&#x0D; The purpose of the article. Different approaches to the very phenomenon of emancipation in various European social and cultural traditions are analyzed, the socio-historical context of emancipation as a general trend of European civilization of the given period and its variant in the Ukrainian Galician society is presented.&#x0D; Methods. In the article comparative, systematic and historical research methods are used.&#x0D; The results and conclusions. It is indicated how the life style of S. Krushelnytska was formed in the context of a peculiar Galician version of the affirmation of the creative potential of Ukrainian women at the turn of the 19th and 20th centuries. A detailed study of the singer's epistolary, memoirs and scientific research on the issue of the emancipation of Ukrainian women artists made it possible to single out the main principles on which the "life project" of S. Krushelnytska as a national artist in the world opera continuum was formed. From the analysis of the views of the singer herself and the observations of her close circle and critics, it emerges, firstly, the integrity of the aesthetic, ethical and nationally oriented beliefs of her worldview, and secondly, the specificity of her "Apollonian" psychotype of creativity is determined, which allowed S. Krushelnitsky to overcome the limitations of a certain stage role and to grasp the most diverse repertoire of different national schools and eras, and thirdly, to naturally integrate the national soloist into concert programs with the widest panorama of European music, taking into account socio-historical processes of the past and present.
Seismic severity estimation using convolutional neural network for earthquake early warning

SUMMARY In this study, magnitude estimation in earthquake early warning (EEW) systems is seen as a classification problem: the single-channel waveform, starting from the P-wave onset and lasting 4 s, is given in the input, and earthquake severity (medium and large earthquakes: local magnitude (ML) ≥ 5; small earthquakes: ML &amp;lt; 5) is the classification result. The convolutional neural network (CNN) is proposed to estimate the severity of the earthquake, which is composed of several blocks that can extract the latent representation of the input from different receptive fields automatically. We train and test the proposed CNN model using two data sets. One is recorded by the China Earthquake Networks Center (CENC), and the other is the Stanford Earthquake Dataset (STEAD). Accordingly, the proposed CNN model achieves a test accuracy of 97.90 per cent. The proposed CNN model is applied to estimate two real-world earthquake swarms in China (the Changning earthquake and the Tangshan earthquake swarms) and the INSTANCE data set, and demonstrated the promising performance of generalization. In addition, the proposed CNN model has been connected to the CENC for further testing using real-world real-time seismic data.
Human Facial Expression Recognition based on Attention Mechanism

In the current study of facial expression recognition, CNNs have been extensively used and proven to be useful models. In this work, we analyze and improve VGG-16 network by embedding SENet between the convolutional layers and three fully connected layers of the original network to form the SE-VGG-16 network. The proposed network architecture is able to extract additional spatial information regarding interdependencies among feature channels, which is complementary to facial expression recognition. The experiment is performed on dataset FER2013 and improve the accuracy with top-1 error 33.5.
A novel information extraction model integrating multi-granularity global information for medical dialogue

Electronic medical records (EMRs) are one of the ways to help doctors effectively manage and analyze patient medical records. These EMRs can not only help doctors save a lot of time in analyzing medical records, but also reduce the hospital's demand for doctors and reduce the hospital's expenditure cost. Therefore, we propose a novel information extraction model integrating multi-granularity global information to efficiently extract information about a patient's physical condition in a doctor-patient dialogue. Experimental results show that our model achieves better results compared to the baseline model, indicating the effectiveness of the model.
Mobile Application Design For Learning Digital Engineering Based On Figma And Android Studio

The purpose of this research is to create and implement effective learning media to facilitate lecturers and students in conducting learning at the Electrical Engineering Study Program, Faculty of Engineering, University of Muhammadiyah Sumatera Barat, namely designing Teknik Digital App learning applications by designing using use case diagrams, making the system in accordance with the design that has been made and implement the system that has been created and perform testing on the system. In collecting data, researchers used the method of observation, interviews and literature study. Meanwhile, in developing the system, researchers use the type of Research and Development (R&D) research referring to the waterfall model. Applications made using Android Studio, Figma and Adobe Illustrator CS3. The application that has been made is tested using the black box testing method. This research produces a Teknik Digital App learning application that helps parties in carrying out the learning process during lectures and also helps students to make it easier to learn basic digital engineering materials anywhere and anytime.
Accountability, Competence, and Accounting Information Systems on the Performance of Private Schools

Quality human resources in the implementation of education, of course, will make it easier for schools to achieve the goals set, one of which is to improve school performance. While the specific objectives are to identify Accountability, Competence, Accounting Information Systems, and mformulateModel of Private School Performance Improvement in Medan City. This research is a basic research (basic research). The first activity, is to identify the Private School Performance Improvement Model in Medan City. The second activity is compiling a Model for Improving the Performance of Private Schools in the City of Medan. To obtain certain data such as to find out the problems faced, the method of focus group discussion (focus group discussion) is used. The population in this study are those who play a role in improving school performance involving the school, namely the principal and school treasurer, who are in Private Upper Middle School in Medan City. Data collection techniques were carried out by means of documentation studies, interviews and questionnaires. The data analysis method used in this study is the SPSS method.
TEN-YEAR REVIEW ON THE EFFECT AND PROSPECT OF POSTGRADUATE EDUCATION REFORM IN ONE CHINESE UNIVERSITY

The connotative development of postgraduate education has contributed to the cultivation of high-level innovative professionals and has promoted the high-quality development of higher education. During the past decade,Chinese University has achieved remarkable results in the practice of the connotative development of postgraduate education by constructing the work system of ideology and politics for postgraduates upgrading the educational structure and the cultivation mode,emphasizing the supervisor's responsibility for moral education and guaranteeing the support system. In the future, to promote the postgraduate education and cultivate professionals responsible for national rejuvenation 's imperative to innovate the education model and strengthen the foundation of its own with the characteristics of the Chinese university.