Reviewerly/exHarmony huggingface dataset:
Self-Adaptation in Industry: A Survey

Computing systems form the backbone of many areas in our society, from manufacturing to traffic control, healthcare, and financial systems. When software plays a vital role in the design, construction, and operation, these systems are referred to as software-intensive systems. Self-adaptation equips a software-intensive system with a feedback loop that either automates tasks that otherwise need to be performed by human operators or deals with uncertain conditions. Such feedback loops have found their way to a variety of practical applications; typical examples are an elastic cloud to adapt computing resources and automated server management to respond quickly to business needs. To gain insight into the motivations for applying self-adaptation in practice, the problems solved using self-adaptation and how these problems are solved, and the difficulties and risks that industry faces in adopting self-adaptation, we performed a large-scale survey. We received 184 valid responses from practitioners spread over 21 countries. Based on the analysis of the survey data, we provide an empirically grounded overview the of state of the practice in the application of self-adaptation. From that, we derive insights for researchers to check their current research with industrial needs, and for practitioners to compare their current practice in applying self-adaptation. These insights also provide opportunities for applying self-adaptation in practice and pave the way for future industry-research collaborations.
Deep Convolutional Pooling Transformer for Deepfake Detection

Recently, Deepfake has drawn considerable public attention due to security and privacy concerns in social media digital forensics. As the wildly spreading Deepfake videos on the Internet become more realistic, traditional detection techniques have failed in distinguishing between real and fake. Most existing deep learning methods mainly focus on local features and relations within the face image using convolutional neural networks as a backbone. However, local features and relations are insufficient for model training to learn enough general information for Deepfake detection. Therefore, the existing Deepfake detection methods have reached a bottleneck to further improve the detection performance. To address this issue, we propose a deep convolutional Transformer to incorporate the decisive image features both locally and globally. Specifically, we apply convolutional pooling and re-attention to enrich the extracted features and enhance efficacy. Moreover, we employ the barely discussed image keyframes in model training for performance improvement and visualize the feature quantity gap between the key and normal image frames caused by video compression. We finally illustrate the transferability with extensive experiments on several Deepfake benchmark datasets. The proposed solution consistently outperforms several state-of-the-art baselines on both within- and cross-dataset experiments.
Secure and Fast Emergency Road Healthcare Service Based on Blockchain Technology for Smart Cities

Road accidents occur everywhere in the world and the numbers of people dead or injured increase from time to time. People hope that emergency vehicles and medical staff will arrive as soon as possible at the scene of the accident. The development of recent technologies such as the Internet of Things (IoT) allows us to find solutions to ensure rapid movement by road in emergencies. Integrating the healthcare sector and smart vehicles, IoT ensures this objective. This integration gives rise to two paradigms: the Internet of Vehicles (IoV) and the Internet of Medical Things (IoMT), where smart devices collect medical data from patients and transmit them to medical staff in real time. These data are extremely sensitive and must be managed securely. This paper proposes a system design that brings together the three concepts of Blockchain technology (BC), IoMT and IoV to address the problem mentioned above. The designed system is composed of three main parts: a list of hospitals, patient electronic medical record (EMR) and a network of connected ambulances. It allows the road user in the case of an accident to report their position to the nearby health services and ambulances.
Incremental unsupervised feature selection for dynamic incomplete multi-view data

Multi-view unsupervised feature selection has been proven to be efficient in reducing the dimensionality of multi-view unlabeled data with high dimensions. The previous methods assume that all views are complete. However, in real applications, the multi-view data are often incomplete, i.e., some views of instances are missing, which will result in the failure of these methods. Besides, while the data arrive in form of streams, these existing methods will suffer the issues of high storage cost and expensive computation time. To address these issues, we propose an Incremental Incomplete Multi-view Unsupervised Feature Selection method (I2MUFS) on incomplete multi-view streaming data. By jointly considering the consistent and complementary information across different views, I2MUFS embeds the unsupervised feature selection into an extended weighted non-negative matrix factorization model, which can learn a consensus clustering indicator matrix and fuse different latent feature matrices with adaptive view weights. Furthermore, we introduce the incremental learning mechanisms to develop an alternative iterative algorithm, where the feature selection matrix is incrementally updated, rather than recomputing on the entire updated data from scratch. A series of experiments are conducted to verify the effectiveness of the proposed method by comparing with several state-of-the-art methods. The experimental results demonstrate the effectiveness and efficiency of the proposed method in terms of the clustering metrics and the computational cost.
Intelligent Mining Road Object Detection Based on Multiscale Feature Fusion in Multi-UAV Networks

In complex mining environments, driverless mining trucks are required to cooperate with multiple intelligent systems. They must perform obstacle avoidance based on factors such as the site road width, obstacle type, vehicle body movement state, and ground concavity-convexity. Targeting the open-pit mining area, this paper proposes an intelligent mining road object detection (IMOD) model developed using a 5G-multi-UAV and a deep learning approach. The IMOD model employs data sensors to monitor surface data in real time within a multisystem collaborative 5G network. The model transmits data to various intelligent systems and edge devices in real time, and the unmanned mining card constructs the driving area on the fly. The IMOD model utilizes a convolutional neural network to identify obstacles in front of driverless mining trucks in real time, optimizing multisystem collaborative control and driverless mining truck scheduling based on obstacle data. Multiple systems cooperate to maneuver around obstacles, including avoiding static obstacles, such as standing and lying dummies, empty oil drums, and vehicles; continuously avoiding multiple obstacles; and avoiding dynamic obstacles such as walking people and moving vehicles. For this study, we independently collected and constructed an obstacle image dataset specific to the mining area, and experimental tests and analyses reveal that the IMOD model maintains a smooth route and stable vehicle movement attitude, ensuring the safety of driverless mining trucks as well as of personnel and equipment in the mining area. The ablation and robustness experiments demonstrate that the IMOD model outperforms the unmodified YOLOv5 model, with an average improvement of approximately 9.4% across multiple performance measures. Additionally, compared with other algorithms, this model shows significant performance improvements.
Degree of Acceptance of Virtual Reality by Health Sciences Students

Virtual Reality (VR) is an emerging technology with educational benefits demonstrated in numerous studies. Its integration into the curriculum implies the use of cognitive resources by students and the training of digital skills by teachers. The objective of this study is to determine the degree of acceptance of students with learning objects produced in VR and in 360°, as well as to analyze their evaluation and the established relationships. A sample of 136 medical students who completed questionnaires on the Technology Acceptance Model (TAM) and the quality of the training activity was used. The results show high levels of acceptance, both in VR and 360° objects. The students perceived the usefulness of the training activity as high, with significant correlations between the different dimensions. This study demonstrates the potential of VR as an educational technology and offers new perspectives for future research.
Bus Transport Process Network Synthesis

The current paper is about bus transport process network synthesis.Unlike previously discussed urban traffic modelling and solution methods, here, it is presented as a novel application of the p-graph methodology, while exploiting the peculiarities of the problem.The focus is on the synthesis step, where the set of potentially feasible solutions is determined, in other words, the maximal bus transport process structure is generated.The classical process network instances together with their properties are adapted to this new application field, i.e. to meet the special requirements of the bus transport.First, the meaning of the material type nodes and the operating unit type nodes are described in details.A new axiom is given to complete the set of p-graph's axioms.In addition, the utilization of the conventional maximal structure and solution structure generation algorithms, they are extended to gain advantage of the new axiom and to generate the potential solution structure in a more effective manner.Based on the solution structures a mathematical programming model is generated containing the constraints and the objective function of the bus transport problem.Thus, the generation of the bus launching list is prepared.The solution method presented for bus transport problems meets the high level expectations of decision-makers, i.e. the resulting system is complete, flexible and robust.
Leveraging Small-Scale Quantum Computers with Unitarily Downfolded Hamiltonians

A quantum unitary downfolding formalism that works on noisy and fault-tolerant hardware is proposed and tested on several challenging chemical problems, offering a viable approach for estimating molecular properties with chemical accuracy.
Deep learning techniques in liver tumour diagnosis using CT and MR imaging - A systematic review

Deep learning has become a thriving force in the computer aided diagnosis of liver cancer, as it solves extremely complicated challenges with high accuracy over time and facilitates medical experts in their diagnostic and treatment procedures. This paper presents a comprehensive systematic review on deep learning techniques applied for various applications pertaining to liver images, challenges faced by the clinicians in liver tumour diagnosis and how deep learning bridges the gap between clinical practice and technological solutions with an in-depth summary of 113 articles. Since, deep learning is an emerging revolutionary technology, recent state-of-the-art research implemented on liver images are reviewed with more focus on classification, segmentation and clinical applications in the management of liver diseases. Additionally, similar review articles in literature are reviewed and compared. The review is concluded by presenting the contemporary trends and unaddressed research issues in the field of liver tumour diagnosis, offering directions for future research in this field.
New Spark solutions for distributed frequent itemset and association rule mining algorithms

Abstract The large amount of data generated every day makes necessary the re-implementation of new methods capable of handle with massive data efficiently. This is the case of Association Rules, an unsupervised data mining tool capable of extracting information in the form of IF-THEN patterns. Although several methods have been proposed for the extraction of frequent itemsets (previous phase before mining association rules) in very large databases, the high computational cost and lack of memory remains a major problem to be solved when processing large data. Therefore, the aim of this paper is three fold: (1) to review existent algorithms for frequent itemset and association rule mining, (2)to develop new efficient frequent itemset Big Data algorithms using distributive computation, as well as a new association rule mining algorithm in Spark, and (3) to compare the proposed algorithms with the existent proposals varying the number of transactions and the number of items. To this purpose, we have used the Spark platform which has been demonstrated to outperform existing distributive algorithmic implementations.
SAM-VQA: Supervised Attention-Based Visual Question Answering Model for Post-Disaster Damage Assessment on Remote Sensing Imagery

Each natural disaster leaves a trail of destruction and damage that must be effectively managed to reduce its negative impact on human life. Any delay in making proper decisions at the post-disaster managerial level can increase human suffering and waste resources. Proper managerial decisions after any natural disaster rely on an appropriate assessment of damages using data-driven approaches, which are needed to be efficient, fast, and interactive. The goal of this study is to incorporate a deep interactive data-driven framework for proper damage assessment to speed up the response and recovery phases after a natural disaster. Hence, this paper focuses on introducing and implementing the Visual Question Answering (VQA) framework for post-disaster damage assessment based on drone imagery, namely Supervised Attention-Based VQA (SAM-VQA). In visual question answering, query-based answers from images regarding the situation in disaster-affected areas can provide valuable information for decision-making. Unlike other computer vision tasks, visual question answering is more interactive and allows one to get instant and effective scene information by asking questions in natural language from images. In this work, we present a VQA dataset and propose a novel supervised attention-based VQA framework (SAM-VQA) for post-disaster damage assessment on remote sensing images. Our model outperforms state-of-the-art attention-based VQA techniques, including Stacked Attention Networks (SAN) [1] and Multi-modal Factorized Bilinear (MFB) with Co-Attention [2]. Furthermore, our proposed model can derive appropriate visual attention based on questions to predict answers, making our approach trustworthy.
Analisis Efektivitas Penggunaan E-Modul Berbasis Aplikasi Flipbook Pada Pembelajaran Tematik Di SD

This study aimed to describe the effectiveness of using flipbook-based E-modules in thematic learning for fourth graders at SDN Girimoyo 02, Malang Regency. The study’s research method involves questionnaires and tests, students’ and teachers’ questionnaire responses, expert validation questionnaire results, and post-test and pre-test results. Based on the results of the study, the students’ questionnaire response was 92.85%, the teachers’ was 99%, the experts’ validation was 78%, the media validation questionnaire was 91%, the teacher questionnaire responses showed 99%, and the student questionnaire response was 93, 85. Based on these results, the E-module is categorized as valid and appropriate to use in learning activities, and the students’ cognitive result on the pre-test was 47.78, and the post-test was 83.34. Based on the results of the pre-test and post-test scores, it showed an increase, and the results of the analysis of the validation results and the responses of students and teachers showed that the E-module was effective by showing success in thematic learning, so that students understand the material well
Software Defect Prediction with Bayesian Approaches

Software defect prediction is an important area in software engineering because it helps developers identify and fix problems before they become costly and hard-to-fix bugs. Early detection of software defects helps save time and money in the software development process and ensures the quality of the final product. This research aims to evaluate three algorithms to build Bayesian Networks to classify whether a project is prone to defects. The choice is based on the fact that the most used approach in the literature is Naive Bayes, but no works use Bayesian Networks. Thus, K2, Hill Climbing, and TAN are used to construct Bayesian Networks. On the other hand, three public PROMISE data sets are used based on McCabe and Halstead complexity metrics. The results are compared with the most used approaches in the literature, such as Decision Tree and Random Forest. The results from different performance metrics applied to a cross-validation process show that the classification results are comparable to Decision Tree and Random Forest, with the advantage that Bayesian algorithms show less variability, which helps engineering software to have greater robustness in their predictions since the selection of training and test data do not give variable results, unlike Decision Tree and Random Forest.
Smart Attendance System based on improved Facial Recognition

Nowadays, the fourth industrial revolution has achieved significant advancement in high technology, in which artificial intelligence has had vigorous development. In practice, facial recognition is one most essential tasks in the field of computer vision with various potential applications from security and attendance system to intelligent services. In this paper, we propose an efficient deep learning approach to facial recognition. The paper utilizes the architecture of improved FaceNet model based on MobileNetV2 backbone with SSD subsection. The improved architecture uses depth-wise separable convolution to reduce the model size and computational volume and achieve high accuracy and processing speed. To solve the problem of identifying a person entering and exiting an area and integrating on advanced mobile devices limits to (such as limited memory and on-device storage) highly mobile resources. Especially, our approach yields better results in practical application with more than 95% accuracy on a small dataset of the original face images. Obtained frame rate (25 FPS) is very favorable compared to the field of facial recognition using neural network. Besides, the deep learning based on solution could be applicable in many low-capacity hardware or optimize system’s resource. Finally, the smart automated attendance systems is successfully designed basing on the improved efficient facial recognition.
A multi-modal deep neural network for multi-class liver cancer diagnosis

Liver disease is a potentially asymptomatic clinical entity that may progress to patient death. This study proposes a multi-modal deep neural network for multi-class malignant liver diagnosis. In parallel with the portal venous computed tomography (CT) scans, pathology data is utilized to prognosticate primary liver cancer variants and metastasis. The processed CT scans are fed to the deep dilated convolution neural network to explore salient features. The residual connections are further added to address vanishing gradient problems. Correspondingly, five pathological features are learned using a wide and deep network that gives a benefit of memorization with generalization. The down-scaled hierarchical features from CT scan and pathology data are concatenated to pass through fully connected layers for classification between liver cancer variants. In addition, the transfer learning of pre-trained deep dilated convolution layers assists in handling insufficient and imbalanced dataset issues. The fine-tuned network can predict three-class liver cancer variants with an average accuracy of 96.06% and an Area Under Curve (AUC) of 0.832. To the best of our knowledge, this is the first study to classify liver cancer variants by integrating pathology and image data, hence following the medical perspective of malignant liver diagnosis. The comparative analysis on the benchmark dataset shows that the proposed multi-modal neural network outperformed most of the liver diagnostic studies and is comparable to others.
Application of video image processing in sports action recognition based on particle swarm optimization algorithm

The existing sports training methods are mainly aimed at the sports field environment. The traditional sports training is only based on the coaches' visual inspection and combined with their own experience to put forward suggestions, which is relatively inefficient, thus limiting the rise of athletes' sports training level to a certain extent. Based on this background, combining traditional physical education teaching methods with video image processing technology, especially using particle swarm optimization algorithm, can promote the application of human motion recognition technology in physical training. This paper mainly investigates the optimization process of particle swarm optimization algorithm and discusses the development of particle swarm optimization algorithm; Secondly, through video decoding, image noise removal, video enhancement and other methods, complete video image processing and establish the structure of the manikin to achieve the collection of key points of the target, and then collect relevant data with experimental methods The results show that the motion recognition system proposed in this paper can effectively detect the changes of athletes' sampling point path, and can be compared with standard movements, which has a good auxiliary role. With the application of video image processing technology in sports training becoming more and more common, athletes can analyze their training videos in a more intuitive way and find out shortcomings, so as to improve the training effect. This paper studies particle swarm optimization algorithm and applies it to the field of video image processing, which promotes the development of sports action recognition technology based on video processing.
Substructure Aware Graph Neural Networks

Despite the great achievements of Graph Neural Networks (GNNs) in graph learning, conventional GNNs struggle to break through the upper limit of the expressiveness of first-order Weisfeiler-Leman graph isomorphism test algorithm (1-WL) due to the consistency of the propagation paradigm of GNNs with the 1-WL.Based on the fact that it is easier to distinguish the original graph through subgraphs, we propose a novel framework neural network framework called Substructure Aware Graph Neural Networks (SAGNN) to address these issues. We first propose a Cut subgraph which can be obtained from the original graph by continuously and selectively removing edges. Then we extend the random walk encoding paradigm to the return probability of the rooted node on the subgraph to capture the structural information and use it as a node feature to improve the expressiveness of GNNs. We theoretically prove that our framework is more powerful than 1-WL, and is superior in structure perception. Our extensive experiments demonstrate the effectiveness of our framework, achieving state-of-the-art performance on a variety of well-proven graph tasks, and GNNs equipped with our framework perform flawlessly even in 3-WL failed graphs. Specifically, our framework achieves a maximum performance improvement of 83% compared to the base models and 32% compared to the previous state-of-the-art methods.