Reviewerly/exHarmony huggingface dataset:
A Bibliometric Analysis of Fault Prediction System using Machine Learning Techniques

Fault prediction in software is an important aspect to be considered in software development because it ensures reliability and the quality of a software product. A high-quality software product consists of a few numbers of faults and failures. Software fault prediction (SFP) is crucial for the software quality assurance process as it examines the vulnerability of software products towards failures. Fault detection is a significant aspect of cost estimation in the initial stage, and hence, a fault predictor model is required to lower the expenses used during the development and maintenance phase. SFP is applied to identify the faulty modules of the software in order to complement the development as well as the testing process. Software metric based fault prediction reflects several aspects of the software. Several Machine Learning (ML) techniques have been implemented to eliminate faulty and unnecessary data from faulty modules. This chapter gives a brief introduction to SFP and includes a bibliometric analysis. The objective of the bibliometric analysis is to analyze research trends of ML techniques that are used for predicting software faults. This chapter uses the VOSviewer software and Biblioshiny tool to visually analyze 1623 papers fetched from the Scopus database for the past twenty years. It explores the distribution of publications over the years, top-rated publishers, contributing authors, funding agencies, cited papers and citations per paper. The collaboration of countries and cooccurrence analysis as well as over the year’s trend of author keywords are also explored. This chapter can be beneficial for young researchers to locate attractive and relevant research insights within SFP.
Sub-Planck phase-space structure and sensitivity for SU(1,1) compass states

We investigate the sub-Planck-scale structures associated with the SU(1,1) group by establishing that the Planck scale on the hyperbolic plane can be considered as the inverse of the Bargmann index $k$. Our discussion involves SU(1,1) versions of Wigner functions, and the quantum-interference effect is easily visualized through plots of these Wigner functions. Specifically, the superpositions of four Perelomov SU(1,1) coherent states (compass state) yield nearly isotropic sub-Planck structures in phase space scaling as $1/k$ compared with $1/\sqrt{k}$ scaling for individual SU(1,1) coherent states and anisotropic quadratically improved scaling for superpositions of two SU(1,1) coherent states (cat state). We show that displacement sensitivity exhibits the same quadratic improvement to scaling.
A survey of detection-based video multi-object tracking

Multiple Object Tracking (MOT) has emerged as a hot issue in the field of computer vision recently. MOT has academic and commercial potential in urban public security, military, self-driving and other fields. Although researchers have put forward different approaches to solve the problem, it is still challenging due to factors such as appearance mutation and severe object occlusion. This study makes a comprehensive research on the problem of detection-based video multi-object tracking. The main contributions of this review are as follows: (1) The important aspects of MOT system are studied, including the basic principles, main framework, technical difficulties, classification and evaluation; (2) This work review the classification and latest progress in object detection due to its close relevance to the field of MOT; (3) This work introduce a taxonomy based on the MOT problem on this basis, the original and improved versions of each classical tracking algorithm are discussed, analyzed and compared; (4) This work summarize the results of 40 algorithms on popular datasets to provide quantitative comparison and analysis of experimental results. Finally, the possible research directions for the future are put forward, by analyzing the commonness of several methods with excellent performance.
Question Answer System: A State-of-Art Representation of Quantitative and Qualitative Analysis

Question Answer System (QAS) automatically answers the question asked in natural language. Due to the varying dimensions and approaches that are available, QAS has a very diverse solution space, and a proper bibliometric study is required to paint the entire domain space. This work presents a bibliometric and literature analysis of QAS. Scopus and Web of Science are two well-known research databases used for the study. A systematic analytical study comprising performance analysis and science mapping is performed. Recent research trends, seminal work, and influential authors are identified in performance analysis using statistical tools on research constituents. On the other hand, science mapping is performed using network analysis on a citation and co-citation network graph. Through this analysis, the domain’s conceptual evolution and intellectual structure are shown. We have divided the literature into four important architecture types and have provided the literature analysis of Knowledge Base (KB)-based and GNN-based approaches for QAS.
Use of artificial intelligence to enhance e-government services

It is increasingly acknowledged that e-governance is crucial for implementing government services. The ultimate goal of e-governance is to enable the government to deliver services to its citizens and decision-makers efficiently and cost-effectively. E-Government services have the potential to benefit both governments and citizens by increasing access to services while decreasing the cost of providing them. Over a period of time, there has been a significant advancement in technology. Governments are facing many challenges in rendering e-Government services to their citizens. To ensure that the benefits reach the last person in society, the government must promote e-Government services. Therefore, governments must apprehend the main barriers to achieving comprehensive government transformation while developing and delivering e-government services using artificial intelligence (AI) and the Internet of Things (IoT) to improve the efficiency and effectiveness of the benefits. For this, a reference model is required which is independent of technical platforms and organizational structure, which comprehends the framework and guidelines to deploy e-government services, analyze the services, and ease use for the citizens. The Internet is the backbone of any e-Governance services. Worldwide, efforts are being made to make e-Government services available to their citizens to reduce costs and provide efficient and effective systems. E-Governance offers new means of communication between the government and its citizens. If not correctly integrated, E-Government services may lead to various threats and will tend to fail. The breach in data privacy and security are some of the threats associated with it. Artificial intelligence (AI) techniques can provide more sophisticated solutions to these threats by detecting anomalies and securing the data to prevent threats. The Internet of things (IoT) can offer an unprecedented improvement and can significantly help in walks of our lives, and so is with the e-Government services. It can help in the surveillance and monitoring of the areas' critical security and safety, controlling traffic management, environmental pollution, fire detection, e-Health, e-Agriculture, e-Bill payment, etc. The proposed paper will use Artificial Intelligence (AI) driven Internet of Things (IoT) techniques to analyze and enhance e-government services to all stakeholders. Also, such AI techniques will support mitigating the associated risk of cyber-attacks. Factors affecting the adoption of e-government services, design, development, and implementation initiatives based on the proposed reference framework will undoubtedly benefit all stakeholders.
Visual Attention-Based Self-Supervised Absolute Depth Estimation Using Geometric Priors in Autonomous Driving

Although existing monocular depth estimation methods have made great progress, predicting an accurate absolute depth map from a single image is still challenging due to the limited modeling capacity of networks and the scale ambiguity issue. In this paper, we introduce a fully Visual Attention-based Depth (VADepth) network, where spatial attention and channel attention are applied to all stages. By continuously extracting the dependencies of features along the spatial and channel dimensions over a long distance, VADepth network can effectively preserve important details and suppress interfering features to better perceive the scene structure for more accurate depth estimates. In addition, we utilize geometric priors to form scale constraints for scale-aware model training. Specifically, we construct a novel scale-aware loss using the distance between the camera and a plane fitted by the ground points corresponding to the pixels of the rectangular area in the bottom middle of the image. Experimental results on the KITTI dataset show that this architecture achieves the state-of-the-art performance and our method can directly output absolute depth without post-processing. Moreover, our experiments on the SeasonDepth dataset also demonstrate the robustness of our model to multiple unseen environments.
Path Planning for UAV Based on Improved PRM

In this paper, an improved probabilistic roadmap (IPRM) algorithm is proposed to solve the energy consumption problem of multi-unmanned aerial vehicle (UAV) path planning with an angle. Firstly, in order to simulate the real terrain environment, a mathematical model was established; secondly, an energy consumption model was established; then, the sampling space of the probabilistic roadmap (PRM) algorithm was optimized to make the obtained path more explicit and improve the utilization rate in space and time; then, the sampling third-order B-spline curve method was used to curve the rotation angle to make the path smoother and the distance shorter. Finally, the results of the improved genetic algorithm (IGA), PRM algorithm and IPRM algorithm were compared through a simulation. The data analysis shows that the IGA has significant advantages over other algorithms in some aspects, and can be well applied to the path planning of UAVs.
Twitter sentiment analysis: An Arabic text mining approach based on COVID-19

The 21st century has seen a lot of innovations, among which included the advancement of social media platforms. These platforms brought about interactions between people and changed how news is transmitted, with people now able to voice their opinion as opposed to before where only the reporters were speaking. Social media has become the most influential source of speech freedom and emotions on their platforms. Anyone can express emotions using social media platforms like Facebook, Twitter, Instagram, and YouTube. The raw data is increasing daily for every culture and field of life, so there is a need to process this raw data to get meaningful information. If any nation or country wants to know their people's needs, there should be mined data showing the actual meaning of the people's emotions. The COVID-19 pandemic came with many problems going beyond the virus itself, as there was mass hysteria and the spread of wrong information on social media. This problem put the whole world into turmoil and research was done to find a way to mitigate the spread of incorrect news. In this research study, we have proposed a model of detecting genuine news related to the COVID-19 pandemic in Arabic Text using sentiment-based data from Twitter for Gulf countries. The proposed sentiment analysis model uses Machine Learning and SMOTE for imbalanced dataset handling. The result showed the people in Gulf countries had a negative sentiment during COVID-19 pandemic. This work was done so government authorities can easily learn directly from people all across the world about the spread of COVID-19 and take appropriate actions in efforts to control it.
Dispatch optimization of a concentrating solar power system under uncertain solar irradiance and energy prices

The integration of thermal energy storage into a concentrating solar power system allows for mitigating some of the risk associated with uncertain solar irradiance and uncertain energy prices. We solve a 48 h dispatch optimization model with continually updated conditional point forecasts of both direct normal irradiance (DNI) and electricity prices with a rolling-horizon scheme at hourly resolution over the course of a year. Joint, conditional forecasts for DNI and prices are formed using an autoregressive moving-average time series model with exogenous weather predictors. We guide dispatch using a mixed-integer programming model, but in order to evaluate performance we use the System Advisor Model (SAM) of the National Renewable Energy Laboratory. SAM is a techno-economic simulation model that accounts for plant thermodynamics with higher fidelity. Our conditional DNI forecasts improve annual revenue by 4%–12% over using historical forecasts based on data from previous years. Conditional price forecasts improve annual revenue by 6%–19% in the real-time market over analogous historical forecasts. Updating these forecasts every six hours, rather than every 24 h, further improves annual revenue by 5%–6%. We also investigate a method that values terminal inventory in our dispatch optimization model, again when used in a rolling-horizon scheme.
Weakly Supervised Video Salient Object Detection via Point Supervision

Fully supervised video salient object detection models have achieved excellent performance, yet obtaining pixel-by-pixel annotated datasets is laborious. Several works attempt to use scribble annotations to mitigate this problem, but point supervision as a more labor-saving annotation method (even the most labor-saving method among manual annotation methods for dense prediction), has not been explored. In this paper, we propose a strong baseline model based on point supervision. To infer saliency maps with temporal information, we mine inter-frame complementary information from short-term and long-term perspectives, respectively. Specifically, we propose a hybrid token attention module, which mixes optical flow and image information from orthogonal directions, adaptively highlighting critical optical flow information (channel dimension) and critical token information (spatial dimension). To exploit long-term cues, we develop the Long-term Cross-Frame Attention module (LCFA), which assists the current frame in inferring salient objects based on multi-frame tokens. Furthermore, we label two point-supervised datasets, P-DAVIS and P-DAVSOD, by relabeling the DAVIS and the DAVSOD dataset. Experiments on the six benchmark datasets illustrate our method outperforms the previous state-of-the-art weakly supervised methods and even is comparable with some fully supervised approaches. Our source code and datasets are available at: https://github.com/shuyonggao/PVSOD.
Adaptive Structural Similarity Preserving for Unsupervised Cross Modal Hashing

Cross-modal hashing is an important approach for multimodal data management and application. Existing unsupervised cross-modal hashing algorithms mainly rely on data features in pre-trained models to mine their similarity relationships. However, their optimization objectives are based on the static metric between the original uni-modal features, without further exploring data correlations during the training. In addition, most of them mainly focus on association mining and alignment among pairwise instances in continuous space but ignore the latent structural correlations contained in the semantic hashing space. In this paper, we propose an unsupervised hash learning framework ASSPH to solve the above problems. Firstly, we propose an adaptive learning scheme, with limited data and training batches, to enrich semantic correlations of unlabeled instances during the training process and meanwhile to ensure a smooth convergence of the training process. Secondly, we present an asymmetric structural semantic representation learning scheme. We introduce structural semantic metrics based on graph adjacency relations and meanwhile align the inter- and intra-modal semantics in the hash space with an asymmetric binary optimization process. Finally, we conduct extensive experiments to validate the enhancements of our work in comparison with existing works.
Rethinking Optical Flow Methods for Micro-Expression Spotting

Micro-expressions (MEs) spotting is popular in some fields, for example, criminal investigation and business communication. But it is still a challenging task to spot the onset and offset of MEs accurately in long videos. This paper refines every step of the workflow before feature extraction, which can reduce error propagation. The workflow takes the advantage of high-quality alignment method, more accurate landmark detector, and also more robust optical flow estimation. Besides, Bayesian optimization hybrid with Nash equilibrium is constructed to search for the optimal parameters. It uses two players to optimize two types of parameters, one player is used to control the ME peak spotting, and another for optical flow field extraction. The algorithm can reduce the search space for each player with better generalization. Finally, our spotting method is evaluated on MEGC2022 spotting task, and achieves F1-score 0.3564 on CAS(ME)3-UNSEEN and F1-score 0.3265 on SAMM-UNSEEN.
Invariant Representation Learning for Multimedia Recommendation

Multimedia recommendation forms a personalized ranking task with multimedia content representations which are mostly extracted via generic encoders. However, the generic representations introduce spurious correlations --- the meaningless correlation from the recommendation perspective. For example, suppose a user bought two dresses on the same model, this co-occurrence would produce a correlation between the model and purchases, but the correlation is spurious from the view of fashion recommendation. Existing work alleviates this issue by customizing preference-aware representations, requiring high-cost analysis and design.
Geometry Aligned Variational Transformer for Image-conditioned Layout Generation

Layout generation is a novel task in computer vision, which combines the challenges in both object localization and aesthetic appraisal, widely used in advertisements, posters and slides design. An accurate and pleasant layout should consider both the intra-domain relationship within layout elements and the inter-domain relationship between layout elements and image. However, most previous methods simply focus on image-content-agnostic layout generation, without leveraging the complex visual information from the image. To this end, we explore a novel paradigm entitled image-conditioned layout generation, which aims to add text overlays to an image in a semantically coherent manner. Specifically, we propose an Image-Conditioned Variational Transformer (ICVT) that autoregressively generates various layouts in an image. First, self-attention mechanism is adopted to model the contextual relationship within layout elements, while cross-attention mechanism is used to fuse the visual information of conditional images. Subsequently, we take them as building blocks of conditional variational autoencoder (CVAE), which demonstrates appealing diversity. Second, in order to alleviate the gap between layout elements domain and visual domain, we design a Geometry Alignment module, in which the geometric information of the image is aligned with the layout representation. In addition, we construct a large-scale advertisement poster layout designing dataset with delicate layout and saliency map annotations. Experimental results show that our model can adaptively generate layouts in the non-intrusive area of the image, resulting in a harmonious layout design.
Augmented Dual-Contrastive Aggregation Learning for Unsupervised Visible-Infrared Person Re-Identification

Visible infrared person re-identification (VI-ReID) aims at searching out the corresponding infrared (visible) images from a gallery set captured by other spectrum cameras. Recent works mainly focus on supervised VI-ReID methods that require plenty of cross-modality (visible-infrared) identity labels which are more expensive than the annotations in single-modality person ReID. For the unsupervised learning visible infrared re-identification (USL-VI-ReID), the large cross-modality discrepancies lead to difficulties in generating reliable cross-modality labels and learning modality-invariant features without any annotations. To address this problem, we propose a novel Augmented Dual-Contrastive Aggregation (ADCA) learning framework. Specifically, a dual-path contrastive learning framework with two modality-specific memories is proposed to learn the intra-modality person representation. To associate positive cross-modality identities, we design a cross-modality memory aggregation module with count priority to select highly associated positive samples, and aggregate their corresponding memory features at the cluster level, ensuring that the optimization is explicitly concentrated on the modality-irrelevant perspective. Extensive experiments demonstrate that our proposed ADCA significantly outperforms existing unsupervised methods under various settings, and even surpasses some supervised counterparts, facilitating VI-ReID to real-world deployment. Code is available at https://github.com/yangbincv/ADCA.
Hierarchical Scene Normality-Binding Modeling for Anomaly Detection in Surveillance Videos

Anomaly detection in surveillance videos is an important topic in the multimedia community, which requires efficient scene context extraction and the capture of temporal information as a basis for decision. From the perspective of hierarchical modeling, we parse the surveillance scene from global to local and propose a Hierarchical Scene Normality-Binding Modeling framework (HSNBM) to handle anomaly detection. For the static background hierarchy, we design a Region Clustering-driven Multi-task Memory Autoencoder (RCM-MemAE), which can simultaneously perform region segmentation and scene reconstruction. The normal prototypes of each local region are stored, and the frame reconstruction error is subsequently amplified by global memory augmentation. For the dynamic foreground object hierarchy, we employ a Scene-Object Binding Frame Prediction module (SOB-FP) to bind all foreground objects in the frame with the prototypes stored in the background hierarchy according their positions, thus fully exploit the normality relationship between foreground and background. The bound features are then fed into the decoder to predict the future movement of the objects. With the binding mechanism between foreground and background, HSNBM effectively integrates the "reconstruction" and "prediction" tasks and builds a semantic bridge between the two hierarchies. Finally, HSNBM fuses the anomaly scores of the two hierarchies to make a comprehensive decision. Extensive empirical studies on three standard video anomaly detection datasets demonstrate the effectiveness of the proposed HSNBM framework.