Reviewerly/exHarmony huggingface dataset:
The Design of Academic Programs Using Rough Set Association Rule Mining

Program accreditation is important for determining whether or not a program or institution meets quality standards. It helps employers to evaluate the programs and qualifications of their graduates as well as to achieve its strategic goals and its continuous improvement plans. Preparing for accreditation requires extensive effort. One of the required documents is the program’s self-study report (SSR), which includes the PEO-SO map (which allocates the program’s educational objectives (PEOs) to student learning outcomes (SOs)). It influences program structure design, performance monitoring, assessment, and continuous improvement. Professionals in each academic engineering program have designed their PEO-SO maps in accordance with their experiences. The problem with the incorrect design of map design is that the SOs are either missing altogether or cannot be assigned to the correct PEOs. The objective of this work is to use a hybrid data mining approach to design the correct PEO-SO map. The proposed hybrid approach utilizes three different data mining techniques: classification to find the similarities between PEOs, crisp association rules to find the crisp rules for the PEO-SO map, and rough set association rules to find the coarse association rules for the PEO-SO map. The work collected 200 SSRs of accredited engineering programs by the ABET-EAC. The paper presents the different phases of the work, such as data collection and preprocessing, building of three data mining models (classification, crisp association rules, and rough set association rules), and analysis of the results and comparison with related work. The validation of the obtained results by different fifty specialists (from the academic engineering field) and their recommendations were also presented. The comparison with other related works proved the success of the proposed approach to discover the correct PEO-SO maps with higher performance.
Index Construction and Application of School-Enterprise Collaborative Education Platform Based on AHP Fuzzy Method in Double Creation Education Practice

At present, China’s education reform is developing rapidly, and many schools begin to study and implement school-enterprise cooperative education. There are also some conceptual deviations. In addition, the government’s weak implementation of the guarantee policy for the implementation of combination of school and enterprise education, coupled with the lack of relevant laws and regulations, rarely leads to the success and enthusiasm of combination of school and enterprise education. With the development of collaborative training companies, the participation rate needs to be improved, and the influence of school-enterprise colearning is not significant enough. Therefore, we should do more theoretical research on combination of school and enterprise education, so as to further improve the present situation of combination of school and enterprise education in China and promote the in-depth development of combination of school and enterprise education. At the same time, we should constantly improve relevant practices and systems, improve relevant laws and regulations, learn from the successful experience of cooperation between schools and enterprises training at home and abroad, and design a unique path of cooperation between schools and enterprises in combination with China’s reality. First of all, this paper deeply analyzes the synergy degree of combination of school and enterprise education. By defining the concepts of the combination of industry and teaching and the combination of colleges and enterprises, synergy degree, and cooperative development level, this paper makes an in-depth interpretation of the education and teaching of schools and enterprises. From the perspective of synergetic theory and interactive mechanism, school-enterprise cooperation needs to be strengthened. Secondly, the model is created through the analytic hierarchy process, in which the hierarchical model uses the 10/10-18/2 scaling method to form the classification matrix. Finally, this paper analyzes on the factors affecting the combination of school and enterprise education and puts forward some perfect countermeasures from three angles of government, school, and enterprise.
Research on Mathematical Method Image Classification of Convolutional Neural Network Based on Firework Algorithm Optimization

The exhibition of famous convolutional brain organizations (CNNs) for distinguishing objects progressively video takes care of is inspected in this exploration. AlexNet, GoogLeNet, and ResNet50 are the most well-known convolutional neural networks for object discovery and item classification arrangement from pictures. To survey the exhibition of various kinds, a variety of photo informative indexes are provided by CNNs. Standard benchmark datasets for estimating a convolutional neural organization’s exhibition include ImageNet, CIFAR10, CIFAR100, and MNIST picture informational indexes. The performance of the three well-known channels, Alexandra cash flow, search engine net, and recurrent neural networks, is investigated in this research. Because analyzing a cable network efficiency on a single dataset does not demonstrate all of its possibilities and limits, we mentioned two of the most prominent large datasets for research: significantly improve performance, FARCICAL, and CIFAR110. Clips are exploited as testing statistics rather than teaching statistics; it should have been mentioned. GoogLeNet and ResNet50, in comparison to AlexNet, are better at recognizing objects with greater precision. Furthermore, the performance of trained CNNs varies significantly across different object categories, and we will analyze the possible causes for this. The characterization rate is the goal work assessed by PSO in the main methodology; in the subsequent methodology, the fireworks produce various boundaries per layer, and the goal work is made out of the recognition rate related to the Akaike data model, which assists with finding the best organization per layer. As per the discoveries, the proposed strategy delivered positive results with a recognition pace of more prominent than close to 100%, exhibiting serious outcomes when contrasted with other cutting edge draws near.
Dance-Specific Action Recognition Method Based on Double-Stream CNN in Complex Environment

Technology for dance-specific motion recognition is widely used in many industries, but Chinese research in this area is still in its early stages. Recognizing specific dance movements is the key to learning about and comprehending human actions and behaviors. The fault-tolerant feature of standardized sign language recognition is extended under the condition of small sample sizes, but the recognition accuracy remains a challenge. This issue needs to be resolved by fusing the essential details of particular dance movements. A dual-stream convolution neural network is suggested in this paper to investigate the recognition of particular dance movements. In this paper, a dual-stream convolution neural network is used to study the recognition of particular dance movements. The time spent by this algorithm gradually increases as the number of people in the image does, but only slightly. The algorithms proposed by Bergonzoni (2017) and Liu et al. (2021) both experience linear increases in running time as the population grows. In contrast, the running time of the algorithm in this study essentially increases negligibly. It has become a problem deserving in-depth study. Double-stream convolution neural network improves the practical value and technical complexity of dance motion automatic generation technology in art and cultural heritage protection, dance teaching, dance video retrieval, and dance arrangement.
Graph Visualization of Cyber Threat Intelligence Data for Analysis of Cyber Attacks

Threat intelligence enables us to make faster, more informed, data-backed security decisions and change their behavior from reactive to proactive in the fight against threat actors. Cyber threat intelligence sources include open-source intelligence, social media intelligence, human intelligence, technical intelligence, device log files, forensically acquired data or intelligence from the internet traffic, and data derived for the deep and dark web. In this study, graph visualization is discussed for the intelligible and accurate analysis of complex cyber threat intelligence data, including network attacks. The processes of collecting, cleaning, organizing, and visualizing cyber intelligence data in different formats and contents on a single platform are given step by step. Dynamic graphs play an active role in these systems, where the attack locations and targets from different points are constantly variable. Therefore, research on dynamic graph solutions and visualization in the visual analysis of cyberattacks is presented.
Infrared-to-Optical Image Translation for Keypoint-Based Image Registration

Multi-modal image registration is a critical step in many remote sensing and visual navigation applications. While image registration techniques developed for single modality images do not perform well for multi-modal registration, techniques developed for multi-modal image registration are not suitable for real-time applications. In this study, we aim to achieve real-time registration of infrared and optical (visible range) images. Specifically, we investigate the use of image-to-image translation deep network to convert infrared images to optical images, which is then applied for keypoint-based image registration.
Occlusion-Aware Self-Supervised Stereo Matching with Confidence Guided Raw Disparity Fusion

Commercially available stereo cameras used in robots and other intelligent systems to obtain depth information typically rely on traditional stereo matching algorithms. Although their raw (predicted) disparity maps contain incorrect estimates, these algorithms can still provide useful prior information towards more accurate prediction. We propose a pipeline to incorporate this prior information to produce more accurate disparity maps. The proposed pipeline includes a confidence generation component to identify raw disparity inaccuracies as well as a self-supervised deep neural network (DNN) to predict disparity and compute the corresponding occlusion masks. The proposed DNN consists of a feature extraction module, a confidence guided raw disparity fusion module to generate an initial disparity map, and a hierarchical occlusion-aware disparity refinement module to compute the final estimates. Experimental results on public datasets verify that the proposed pipeline has competitive accuracy with real-time processing rate. We also test the pipeline with images captured by commercial stereo cameras to show its effectiveness in improving their raw disparity estimates.
TemporalNet: Real-time 2D-3D Video Object Detection

Designing a video detection network based on state-of-the-art single-image object detectors may seem like an obvious choice. However, video object detection has extra challenges due to the lower quality of individual frames in a video, and hence the need to include temporal information for high-quality detection results. We design a novel interleaved architecture combining a 2D convolutional network and a 3D temporal network. To explore inter-frame information, we propose feature aggregation based on a temporal network. Our TemporalNet utilizes Appearance-preserving 3D convolution (AP3D) for extracting aligned features in the temporal dimension. Our temporal network functions at multiple scales for better performance, which allows communication between 2D and 3D blocks at each scale and also across scales. Our TemporalNet is a plug-and-play block that can be added to a multi-scale single-image detection network without any adjustments in the network architecture. When TemporalNet is applied to Yolov3 it is real-time with a running time of 35ms/frame on a low-end GPU. Our real-time approach achieves 77.1 &#x0025; mAP (mean Average Precision) on ImageNet VID 2017 dataset with TemporalNet-4, where TemporalNet-16 achieves 80.9 &#x0025; mAP which is a competitive result.
A Simple Method to Boost Human Pose Estimation Accuracy by Correcting the Joint Regressor for the Human3.6m Dataset

Many human pose estimation methods estimate Skinned Multi-Person Linear (SMPL) models and regress the human joints from these SMPL estimates. In this work, we show that the most widely used SMPL-to-joint linear layer (joint regressor) is inaccurate, which may mislead pose evaluation results. To achieve a more accurate joint regressor, we propose a method to create pseudo-ground-truth SMPL poses, which can then be used to train an improved regressor. Specifically, we optimize SMPL estimates coming from a state-of-the-art method so that its projection matches the silhouettes of humans in the scene, as well as the ground-truth 2D joint locations. While the quality of this pseudo-ground-truth is chal-lenging to assess due to the lack of actual ground-truth SMPL, with the Human 3.6m dataset, we qualitatively show that our joint locations are more accurate and that our regressor leads to improved pose estimations results on the test set without any need for retraining. We release our code and joint regressor at https://github.com/ubc-vision/joint-regressor-refinement
Aspect-based Sentiment Analysis for Improving Online Learning Program Based on Student Feedback

This paper presents an empiric results of aspectbased sentiment analysis in education to extract and classify opinions, sentiments, evaluations, attitudes, and emotions from newly graduates of an online learning program. As part of continuous education monitoring system, the sentiment analysis process produces valuable input to leverage service quality of online learning program. In this study, the aspect-based sentiment analysis is implemented to analyze a set of feedbacks from 162 newly graduate from Binus Online Program majoring in Accounting, Management, Information System, and Computer Science. The important qualitative results of this study are confirmation that the main benefits of online learning from student perspective are mainly: the knowledge they gained from the program, learning guidance, reliable student team to work on thesis, quality of education support system, and learning happiness.
A Blockchain-based Vaccination Model for COVID-19 and Other Infectious Diseases

Several authorities require individuals to be vaccinated with WHO-approved vaccines to allow cross-border travel or access to public areas. Recently vaccination certificates have become crucial in curbing the spread of the corona virus. Therefore, it is crucial to ensure the integrity and authenticity of vaccination records. A secure mechanism is required to maintain the immunization records and issue an immutable vaccination certificate to individuals that may be used as a passport for uninhibited movement. We propose a blockchain-based solution that issues verifiable and trustworthy vaccination certificates for not just COVID-19 vaccines but also other diseases. Our proposed model reduces the time required to retrieve vaccination status. The solution is scalable due to its simplicity as it does not require multiple participants on the network for its operation. Moreover, the system can be easily integrated with existing enterprise applications for its functionality.
3DVQA: Visual Question Answering for 3D Environments

Visual Question Answering (VQA) is a widely studied problem in computer vision and natural language processing. However, current approaches to VQA have been investigated primarily in the 2D image domain. We study VQA in the 3D domain, with our input being point clouds of real-world 3D scenes, instead of 2D images. We believe that this 3D data modality provide richer spatial relation information that is of interest in the VQA task. In this paper, we introduce the 3DVQA-ScanNet dataset, the first VQA dataset in 3D, and we investigate the performance of a spectrum of baseline approaches on the 3D VQA task.
Thermal-Based Vehicle Detection System using Deep Transfer Learning under Extreme Weather Conditions

The future of autonomous driving is fast approaching. As driving assistance and safety features increase, basic driving tasks will be automated, eliminating the requirement for human judgement and interaction. In today's world, features such as adaptive cruise control and lane departure warnings have become common. Using predictive ride technology, the car senses weather conditions and adjusts its ride settings to keep passengers comfortable on bumpy or uneven roads and to reduce travel sickness. This paper presents a thermal based vehicle detection system for autonomous car testing under extreme weather conditions. We utilize transfer learning by using three pre-trained convolutional neural networks and trained them on our data. The three networks were (GoogleNet, ResNet-50, SqueezeNet). The pre-trained networks showed different performance in terms of validation accuracy and frequency of alteration. The ResNet-50 network has the best performance, which shows a validation accuracy of 100%.
A Comprehensive Analysis of Trusted Execution Environments

The Trusted Execution Environment (TEE) is designed to improve the security of computing environments. TEE is the computational isolation of the application's execution environment from the rest of the computing system. Therefore, applications will run safely in an isolated environment. Existing research definitions and explanations for TEE added confusion to the research community because they are ambiguous and inconsistent. It is hard to differentiate TEE from other related Trusted Environments. In this paper, we classify TEE according to industrial and academic research and experiments. A comparison of TEE features will be introduced in addition to further TEE exploration and research directions.
MRI Condition Monitoring with Explainable AI and Feature Selection

Monitoring multisensor MRI devices using anomaly detection for multivariate time series consists a challenging task. In this use case, we investigate and provide explanations for abnormalities detected in sensors during failed scans and propose a framework for MRI sensor condition monitoring using XAI and feature selection. Sensor properties are preprocessed (normalized, aggregated and resampled) and used to generate statistical features that are fed to machine learning models for Anomaly Detection (AD), namely Isolation Forest. A feature selection step using an XGBoost classifier is applied before the AD models to improve performance by removing unrelated sensors properties. Explanations for the total output of the models and selected instances are provided using SHAP (SHapley Additive exPlanations). Results are presented using plots with anomaly scores against scan statuses and SHAP explanations for selected anomalous instances along with the top highly contributing sensor properties. Our models were able to successfully detect all failed scans with unknown causes as anomalies, achieving a total accuracy up to 95%. Models that successfully detect anomalies during such failed scans, could provide a tool for a comprehensive condition monitoring of the device using Explainable Artificial Intelligence (XAI) for the domain experts.
Expert System Integrated with Medical Record for Infectious Diseases using Certainty Factor

Humans with weak immune systems are very susceptible to infectious diseases. Infectious diseases can cause the risk of premature death if not handled properly. This research integrates an expert system with a health care system to diagnose and treat infectious diseases. The integration system aims to optimize the database of medical records of patients. The result of the integration system is that physicians can use the medical record data in the health care system as initial instructions for examinations, and expert systems can use the medical record data to acquire new knowledge. Tests on the expert system were carried out using the Certainty Factor (CF) method on 35 medical record data. The test results obtained an accuracy value of 80%, which indicates that the expert system can diagnose the disease quite well.
Classifying the Swallow Nest Quality Using Support Vector Machine Based on Computer Vision

Swallow Nest is a valuable export commodity, particularly in Indonesia. It is produced when a swallow's saliva hardens and is frequently encountered in high-rise buildings. Swallow nests can be utilized to treat various ailments in the medical sector. The price of a swallow nest varies according to its quality, which is commonly classified into three grades: quality 1 (Q1), quality 2 (Q2), and quality 3 (Q3). Q1 is of the highest quality, while Q3 is of the lowest. Each grade has a different physical appearance. Currently, many people lack knowledge regarding the grade of a swallow nest. Therefore, a method is needed to automatically classify the quality of swallow nests based on computer vision. The proposed method consists of several main processes, including image acquisition, ROI detection, pre-processing, segmentation, feature extraction, and classification. The feature extraction was applied based on shapes, followed by the Support Vector Machine (SVM) implementation in the classification process. This process was performed with cross-validation using the k-fold values of 5. The performance evaluation was done using three parameters: precision, recall, and accuracy, by achieving the value of 90.6%, 89.3%, and 89.3%, respectively.
Data-Driven Modelling For Tsunami Forecasting Using Computational Intelligence

Numerous tsunami disasters have happened in Indonesia as located across Ring of Fire, and it brings casualties to both the economy and welfare of the people in the event of their occurrence. Frequent tectonic earthquakes in the oceanic areas may lead to tsunami disasters that can cause significant damages to the infrastructures and people. Therefore, the development and implementation of a significantly improved early warning system's performance is essential. This paper presents the research on finding an appropriate machine learning algorithm for provisioning fast and accurate tsunami forecasts using spatiotemporal data of tsunami event in Aceh occurred on December 26th, 2004. A mixture of two modelling paradigms: physically based and data-driven modelling was explored and developed by utilizing 3D numerical models with essential measurement data. The outputs of numerical computations are in the form of time series datasets with various time windows and forecast horizons. Three machine learning algorithms namely fully connected neural network (FCNN), convolutional neural networks (CNN), and recurrent neural network (CNN) with long short-term memory (LSTM) were employed and compared to achieve accurate tsunami wave forecasts, evaluated according to specific set of evaluation metrics. The model forecast comparison with window size of 15 minutes and forecast horizon of 1 minute indicate that FCNN model outperform the CNN and RNN with LSTM models, with RMSE of 0.299. This modelling results show that the proposed modelling framework has been able to support in provisioning towards fast and accurate tsunami early warning system.
Requirements and Prototypical Implementation of a Study Planning Assistant in CS Programs

Planning and organizing their studies by selecting modules and the order in which to attend them is an important challenge for students. Bachelor students, in particular, usually perceive this as an obstacle [1]. However, universities rarely provide tools to support students in this decision-making process. Using the method of qualitative interviews, this paper elicits requirements for a digital tool that supports the process of study planning for computer science students at the University of Bamberg, Germany. The resulting requirements are used as a foundation for a prototypical implementation. Based on this prototype, the feasibility of the collected requirements is analyzed and potential further developments are discussed.