Reviewerly/exHarmony huggingface dataset:
Automated Features and Requirements Identification for Improving CPS Software Reuse using Topic Modeling

Software reuse is a common practice in software development due to its ability to reduce development costs, accelerate time to market, and mitigate the risks associated with building a new system from scratch. Cyber-physical systems (CPS) are no exception to this trend, with many existing reusable CPS software available in public repositories such as GitHub. However, identifying CPS software features and requirements from this legacy code is challenging, as it requires developers' domain knowledge to understand the system's functionality and configurations. Moreover, in many legacy software, the original design and documentation may be incomplete or unavailable, making it more challenging to extract these features and requirements manually. To address this challenge, we propose an automated approach for identifying CPS software features and requirements using topic modeling and code analysis techniques. We evaluate our approach compared to manual and rule-based approaches, and the results show that it outperforms these approaches in terms of precision, recall, and F1 score.
Citationwalk: Network representation learning with scientific documents

A network is a structure that can represent an organic relationship of observations. Network representation learning has the advantage of extracting latent features in a network. In recent years, various algorithms have been developed to embed network representation in a low-dimensional feature space. Conventional algorithms learn a sequence of vertices that are randomly searched from a network and convert a vertex into a vector. This study focused on the representation learning of document networks. Thus, this study proposes a scalable network representation learning algorithm that preserves the structure and content of the document network and reduces data sparsity. To do this, we used the shortest walks to extract a sequence of vertices that reflected semantic similarity from a network. The shortest walk is a method for finding several shortest paths that connect similar vertices. After this, the sequence of vertices was inputted into the language model, assuming that the sequence was a sentence. Scientific papers and patent datasets provided empirical evidence for the validity of our approach. The former, the bibliography network of scientific paper, consists of 17,716 nodes and 105,734 edges. The latter is a patent describing a technology for energy, representing a network of 24,894 nodes and 28,314 edges. The results showed that the proposed algorithm exhibited the lowest error of the tested methods in multi-class classification and link prediction tasks. The results of these tasks provided evidence that our algorithm is suitable for extracting feature representations of document networks.
Web-based mixed reality video fusion with remote rendering

Mixed Reality (MR) video fusion system fuses video imagery with 3D scenes. It makes the scene much more realistic and helps the users understand the video contents and temporalspatial correlation between them, thus reducing the user's cognitive load. Nowadays, MR video fusion has been used in various applications. However, video fusion systems require powerful client machines because video streaming delivery, stitching, and rendering are computation-intensive. Moreover, huge bandwidth usage is also another critical factor that affects the scalability of video fusion systems. The framework proposed in this paper overcomes this client limitation by utilizing remote rendering. Furthermore, the framework we built is based on browsers. Therefore, the user could try the MR video fusion system with a laptop or even pad, no extra plug-ins or application programs need to be installed. Several experiments on diverse metrics demonstrate the effectiveness of the proposed framework.
A Secure Energy Internet Scheme for IoV Based on Post-Quantum Blockchain

With the increasing use of distributed electric vehicles (EV), energy management in the Internet of vehicles (IoV) has attracted more attention, especially demand response (DR) management to achieve efficient energy management in IoV. Therefore, it is a tendency to introduce distributed energy such as renewable energy into the existing supply system. For optimizing the energy internet (EI) for IoV, in this paper, we introduce blockchain into energy internet and propose a secure EI scheme for IoV based on post-quantum blockchain, which provides the new information services and an incentive cooperation mechanism for the current energy IoV system. Firstly, based on the principles of constructing a short lattice basis and preimage sampling, a lattice signature scheme is proposed and used in blockchain for authentication, which provides anti-quantum security. Secondly, we design the EI based on the post-quantum blockchain model. Lastly, based on this model, we design a secure EI scheme for IoV based on post-quantum blockchain. Through our analysis and experiment, this new scheme can increase the efficiency of energy utilization and enrich EI’s application in IoV. In particular, we further illustrate and analyze its performance. It is shown that EI based on post-quantum blockchain is more secure and efficient in information communications and energy trading.
Energy Cost Minimization Using String Matching Algorithm in Geo-Distributed Data Centers

Data centers are being distributed worldwide by cloud service providers (CSPs) to save energy costs through efficient workload allocation strategies. Many CSPs are challenged by the significant rise in user demands due to their extensive energy consumption during workload processing. Numerous research studies have examined distinct operating cost mitigation techniques for geo-distributed data centers (DCs). However, operating cost savings during workload processing, which also considers string-matching techniques in geo-distributed DCs, remains unexplored. In this research, we propose a novel string matching-based geographical load balancing (SMGLB) technique to mitigate the operating cost of the geo-distributed DC. The primary goal of this study is to use a string-matching algorithm (i.e., Boyer Moore) to compare the contents of incoming workloads to those of documents that have already been processed in a data center. A successful match prevents the global load balancer from sending the user’s request to a data center for processing and displaying the results of the previously processed workload to the user to save energy. On the contrary, if no match can be discovered, the global load balancer will allocate the incoming workload to a specific DC for processing considering variable energy prices, the number of active servers, on-site green energy, and traces of incoming workload. The results of numerical evaluations show that the SMGLB can minimize the operating expenses of the geo-distributed data centers more than the existing workload distribution techniques.
Weakly Supervised Abstractive Summarization with Enhancing Factual Consistency for Chinese Complaint Reports

A large variety of complaint reports reflect subjective information expressed by citizens. A key challenge of text summarization for complaint reports is to ensure the factual consistency of generated summary. Therefore, in this paper, a simple and weakly supervised framework considering factual consistency is proposed to generate a summary of city-based complaint reports without pre-labeled sentences/words. Furthermore, it considers the importance of entity in complaint reports to ensure factual consistency of summary. Experimental results on the customer review datasets (Yelp and Amazon) and complaint report dataset (complaint reports of Shenyang in China) show that the proposed framework outperforms state-of-the-art approaches in ROUGE scores and human evaluation. It unveils the effectiveness of our approach to helping in dealing with complaint reports.
Data and Ensemble Machine Learning Fusion Based Intelligent Software Defect Prediction System

The software engineering field has long focused on creating high-quality software despite limited resources. Detecting defects before the testing stage of software development can enable quality assurance engineers to concentrate on problematic modules rather than all the modules. This approach can enhance the quality of the final product while lowering development costs. Identifying defective modules early on can allow for early corrections and ensure the timely delivery of a high-quality product that satisfies customers and instills greater confidence in the development team. This process is known as software defect prediction, and it can improve end-product quality while reducing the cost of testing and maintenance. This study proposes a software defect prediction system that utilizes data fusion, feature selection, and ensemble machine learning fusion techniques. A novel filter-based metric selection technique is proposed in the framework to select the optimum features. A three-step nested approach is presented for predicting defective modules to achieve high accuracy. In the first step, three supervised machine learning techniques, including Decision Tree, Support Vector Machines, and Naïve Bayes, are used to detect faulty modules. The second step involves integrating the predictive accuracy of these classification techniques through three ensemble machine-learning methods: Bagging, Voting, and Stacking. Finally, in the third step, a fuzzy logic technique is employed to integrate the predictive accuracy of the ensemble machine learning techniques. The experiments are performed on a fused software defect dataset to ensure that the developed fused ensemble model can perform effectively on diverse datasets. Five NASA datasets are integrated to create the fused dataset: MW1, PC1, PC3, PC4, and CM1. According to the results, the proposed system exhibited superior performance to other advanced techniques for predicting software defects, achieving a remarkable accuracy rate of 92.08%.
Fine-Grained Features for Image Captioning

Image captioning involves two different major modalities (image and sentence) that convert a given image into a language that adheres to visual semantics. Almost all methods first extract image features to reduce the difficulty of visual semantic embedding and then use the caption model to generate fluent sentences. The Convolutional Neural Network (CNN) is often used to extract image features in image captioning, and the use of object detection networks to extract region features has achieved great success. However, the region features retrieved by this method are object-level and do not pay attention to fine-grained details because of the detection model’s limitation. We offer an approach to address this issue that more properly generates captions by fusing fine-grained features and region features. First, we extract fine-grained features using a panoramic segmentation algorithm. Second, we suggest two fusion methods and contrast their fusion outcomes. An X-linear Attention Network (X-LAN) serves as the foundation for both fusion methods. According to experimental findings on the COCO dataset, the two-branch fusion approach is superior. It is important to note that on the COCO Karpathy test split, CIDEr is increased up to 134.3% in comparison to the baseline, highlighting the potency and viability of our method.
PCATNet: Position-Class Awareness Transformer for Image Captioning

Existing image captioning models usually build the relation between visual information and words to generate captions, which lack spatial information and object classes. To address the issue, we propose a novel Position-Class Awareness Transformer (PCAT) network which can serve as a bridge between the visual features and captions by embedding spatial information and awareness of object classes. In our proposal, we construct our PCAT network by proposing a novel Grid Mapping Position Encoding (GMPE) method and refining the encoder-decoder framework. First, GMPE includes mapping the regions of objects to grids, calculating the relative distance among objects and quantization. Meanwhile, we also improve the Self-attention to adapt the GMPE. Then, we propose a Classes Semantic Quantization strategy to extract semantic information from the object classes, which is employed to facilitate embedding features and refining the encoder-decoder framework. To capture the interaction between multi-modal features, we propose Object Classes Awareness (OCA) to refine the encoder and decoder, namely OCA<sub>E</sub> and OCA<sub>D</sub>, respectively. Finally, we apply GMPE, OCA<sub>E</sub> and OCA<sub>D</sub> to form various combinations and to complete the entire PCAT. We utilize the MSCOCO dataset to evaluate the performance of our method. The results demonstrate that PCAT outperforms the other competitive methods.
MFF-Net: Multimodal Feature Fusion Network for 3D Object Detection

In complex traffic environment scenarios, it is very important for autonomous vehicles to accurately perceive the dynamic information of other vehicles around the vehicle in advance. The accuracy of 3D object detection will be affected by problems such as illumination changes, object occlusion, and object detection distance. To this purpose, we face these challenges by proposing a multimodal feature fusion network for 3D object detection (MFF-Net). In this research, this paper first uses the spatial transformation projection algorithm to map the image features into the feature space, so that the image features are in the same spatial dimension when fused with the point cloud features. Then, feature channel weighting is performed using an adaptive expression augmentation fusion network to enhance important network features, suppress useless features, and increase the directionality of the network to features. Finally, this paper increases the probability of false detection and missed detection in the non-maximum suppression algorithm by increasing the one-dimensional threshold. So far, this paper has constructed a complete 3D target detection network based on multimodal feature fusion. The experimental results show that the proposed achieves an average accuracy of 82.60% on the Karlsruhe Institute of Technology and Toyota Technological Institute (KITTI) dataset, outperforming previous state-of-the-art multimodal fusion networks. In Easy, Moderate, and hard evaluation indicators, the accuracy rate of this paper reaches 90.96%, 81.46%, and 75.39%. This shows that the MFF-Net network has good performance in 3D object detection.
Automated Video-Based Face Detection Using Harris Hawks Optimization with Deep Learning

Face recognition technology automatically identifies an individual from image or video sources. The detection process can be done by attaining facial characteristics from the image of a subject face. Recent developments in deep learning (DL) and computer vision (CV) techniques enable the design of automated face recognition and tracking methods. This study presents a novel Harris Hawks Optimization with deep learning-empowered automated face detection and tracking (HHODL-AFDT) method. The proposed HHODL-AFDT model involves a Faster region based convolution neural network (RCNN)-based face detection model and HHO-based hyperparameter optimization process. The presented optimal Faster RCNN model precisely recognizes the face and is passed into the face-tracking model using a regression network (REGN). The face tracking using the REGN model uses the features from neighboring frames and foresees the location of the target face in succeeding frames. The application of the HHO algorithm for optimal hyperparameter selection shows the novelty of the work. The experimental validation of the presented HHODL-AFDT algorithm is conducted using two datasets and the experiment outcomes highlighted the superior performance of the HHODL-AFDT model over current methodologies with maximum accuracy of 90.60% and 88.08% under PICS and VTB datasets, respectively.
A Sentence Retrieval Generation Network Guided Video Captioning

Currently, the video captioning models based on an encoder-decoder mainly rely on a single video input source. The contents of video captioning are limited since few studies employed external corpus information to guide the generation of video captioning, which is not conducive to the accurate description and understanding of video content. To address this issue, a novel video captioning method guided by a sentence retrieval generation network (ED-SRG) is proposed in this paper. First, a ResNeXt network model, an efficient convolutional network for online video understanding (ECO) model, and a long short-term memory (LSTM) network model are integrated to construct an encoder-decoder, which is utilized to extract the 2D features, 3D features, and object features of video data respectively. These features are decoded to generate textual sentences that conform to video content for sentence retrieval. Then, a sentence-transformer network model is employed to retrieve different sentences in an external corpus that are semantically similar to the above textual sentences. The candidate sentences are screened out through similarity measurement. Finally, a novel GPT-2 network model is constructed based on GPT-2 network structure. The model introduces a designed random selector to randomly select predicted words with a high probability in the corpus, which is used to guide and generate textual sentences that are more in line with human natural language expressions. The proposed method in this paper is compared with several existing works by experiments. The results show that the indicators BLEU-4, CIDEr, ROUGE_L, and METEOR are improved by 3.1%, 1.3%, 0.3%, and 1.5% on a public dataset MSVD and 1.3%, 0.5%, 0.2%, 1.9% on a public dataset MSR-VTT respectively. It can be seen that the proposed method in this paper can generate video captioning with richer semantics than several state-of-the-art approaches.
A Progressive Approach to Generic Object Detection: A Two-Stage Framework for Image Recognition

Object detection in images has been identified as a critical area of research in computer vision image processing. Research has developed several novel methods for determining an object's location and category from an image. However, there is still room for improvement in terms of detection efficiency. This study aims to develop a technique for detecting objects in images. To enhance overall detection performance, we considered object detection a two-fold problem, including localization and classification. The proposed method generates class-independent, high-quality, and precise proposals using an agglomerative clustering technique. We then combine these proposals with the relevant input image to train our network on convolutional features. Next, a network refinement module decreases the quantity of generated proposals to produce fewer high-quality candidate proposals. Finally, revised candidate proposals are sent into the network's detection process to determine the object type. The algorithm's performance is evaluated using publicly available the PASCAL Visual Object Classes Challenge 2007 (VOC2007), VOC2012, and Microsoft Common Objects in Context (MS-COCO) datasets. Using only 100 proposals per image at intersection over union (IoU) = 0.5 and 0.7), the proposed method attains Detection Recall (DR) rates of (93.17% and 79.35%) and (69.4% and 58.35%), and Mean Average Best Overlap (MABO) values of (79.25% and 62.65%), for the VOC2007 and MS-COCO datasets, respectively. Besides, it achieves a Mean Average Precision (mAP) of (84.7% and 81.5%) on both VOC datasets. The experiment findings reveal that our method exceeds previous approaches in terms of overall detection performance, proving its effectiveness.
A Model for Helmet-Wearing Detection of Non-Motor Drivers Based on YOLOv5s

Detecting non-motor drivers’ helmets has significant implications for traffic control. Currently, most helmet detection methods are susceptible to the complex background and need more accuracy and better robustness of small object detection, which are unsuitable for practical application scenarios. Therefore, this paper proposes a new helmet-wearing detection algorithm based on the You Only Look Once version 5 (YOLOv5). First, the Dilated convolution In Coordinate Attention (DICA) layer is added to the backbone network. DICA combines the coordinated attention mechanism with atrous convolution to replace the original convolution layer, which can increase the perceptual field of the network to get more contextual information. Also, it can reduce the network’s learning of unnecessary features in the background and get attention to small objects. Second, the Rebuild Bidirectional Feature Pyramid Network (Re-BiFPN) is used as a feature extraction network. Re-BiFPN uses cross-scale feature fusion to combine the semantic information features at the high level with the spatial information features at the bottom level, which facilitates the model to learn object features at different scales. Verified on the proposed “Helmet Wearing dataset for Non-motor Drivers (HWND),” the results show that the proposed model is superior to the current detection algorithms, with the mean average precision (mAP) of 94.3% under complex background.