Reviewerly/exHarmony huggingface dataset:
3D Hand Pose Estimation with a Single Infrared Camera via Domain Transfer Learning

Previous methods successfully estimated 3D hand poses from unblurred depth images with slow and smooth hand motions. However, the performance drops when the depth images are contaminated by motion blur due to fast hand motion. In this paper, we exploit an infrared (IR) image input, which is weakly blurred under fast hand motion. The proposed method is based on domain transfer learning from depth to infrared images. Note we do not have IR images with hand skeletons, thus proposing self-supervision rather than direct supervision using the skeleton labels. We train a Hand Image Generator (HIG) and two Hand Pose Estimators (HPEs) on paired depth and infrared images via self-supervision using a consistency loss, guided by an existing HPE trained on paired depth and hand skeleton entries. The IR-based HPE is then refined on the weakly blurred infrared images. The qualitative and quantitative experiments demonstrate that the proposed method accurately estimates 3D hand poses under motion blur by fast hand motion, while existing depth-based methods fail. Our solution therefore supports fast 3D manipulation of virtual objects for augmented reality applications. Our model and dataset are publicly available for future research. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>
Vision-Based Distance Measurement in Advanced Driving Assistance Systems

As the forward-looking depth information plays a considerable role in advanced driving assistance systems, in this paper, we first propose a method of depth map estimation based on semi-supervised learning, which uses the left and right views of binocular vision and sparse depth values as inputs to train a deep learning network with an encoding–decoding structure. Compared with unsupervised networks without sparse depth labels, the proposed semi-supervised network improves the estimation accuracy of depth maps. Secondly, this paper combines the estimated depth map with the results of instance segmentation to measure the distance between the subject vehicle and the target vehicle or pedestrian. Specifically, for measuring the distance between the subject vehicle and a pedestrian, this paper proposes a depth histogram-based method that calculates the average depth values of all pixels whose depth values are in the peak range of the depth histogram of this pedestrian. To measure the distance between the subject vehicle and the target vehicle, this paper proposes a method that first fits a 3-D plane based on the locations of target points in the camera body coordinate using RANSAC (RANdom SAmple Consensus), it then projects all the pixels of the target to this plane, and finally uses the minimum depth value of these projected points to calculate the distance to the target vehicle. The results of the quantitative and qualitative comparisons on the KITTI dataset show that the proposed method can effectively estimate depth maps. The experimental results in real road scenarios and the KITTI dataset confirm the accuracy of the proposed distance measurement methods.
Optimal Deep Belief Network with Opposition based Pity Beetle Algorithm for Lung Cancer Classification: A DBNOPBA Approach

This research proposes a successful method of extracting Gray-Level Co-occurrence Matrix (GLCM) picture handling models to classify low-and high-metastatic cancer organisms with five prevalent cancer cell line pairs, coupled with the scanning laser picture projection technique and the typical textural function, i.e. contrast, correlation, power, temperature and homogeneity. The most significant level of disease for highly metastatic cancer cells are the degree of disturbance, contrast as well as entropy refers to the energy and homogeneity. A texture classification scheme to quantify the emphysema in Computed Tomography (CT) pictures is performed. Local binary models (LBP) are used to characterize areas of concern as texture characteristics and intensity histograms. A wavelet filter is used to acquire the informative matrix of each picture and decrease the dimensionality of the function space in the suggested method. A four-layer profound creed network is also used to obtain characteristics of elevated stage. Local Tangent Space Alignment (LTSA) is then used to compress the multi-domain defect characteristics into low dimensional vectors as a dimension reduction method. An unmonitored deep-belief network (DBN) is intended for the second phase to learn the unmarked characteristics. The strategy suggested uses Opposition Based Teaching (OBL), Position Clamping (PC) and the Cauchy Mutation (CM) to improve the fundamental PBA efficiency. This research presents a fresh meta-heuristic algorithm called Opposition-Based Pity Beetle Algorithm (OPBA), which assesses effectiveness against state-of-the-art algorithms. OBL speeds up the convergence of the technique as both PC and CM assist OPBA with escaping local optima. The suggested algorithm was motivated by the behaviour of the beetle, which had been named six-toothed spruce bark beetle to aggregate nests and meals. This beetle can be found and harvested from weakened trees ' bark in a forest, while its populace can also infest healthy and robust trees when it exceeds the specified threshold. The methodology has been evaluated on CT imagery from the Lung Image Database Consortium and Image Resources Initiative (LIDC-IDRI), with a maximum sensitivity of 96.86%, precision of 97.24%, and an accuracy of 97.92%.
Revisiting the Impact of Concept Drift on Just-in-Time Quality Assurance

The performance of software defect prediction(SDP) models is known to be dependent on the datasets used for training the models. Evolving data in a dynamic software development environment such as significant refactoring and organizational changes introduces new concept to the prediction model, thus making improved classification performance difficult. In this study, we investigate and assess the existence and impact of concept drift on SDP performances. We empirically asses the prediction performance of five models by conducting cross-version experiments using fifty-five releases of five open-source projects. Prediction performance fluctuated as the training datasets changed over time. Our results indicate that the quality and the reliability of defect prediction models fluctuate over time and that this instability should be considered by software quality teams when using historical datasets. The performance of a static predictor constructed with data from historical versions may degrade over time due to the challenges posed by concept drift.
MPT‐embedding: An unsupervised representation learning of code for software defect prediction

Abstract Software project defect prediction can help developers allocate debugging resources. Existing software defect prediction models are usually based on machine learning methods, especially deep learning. Deep learning‐based methods tend to build end‐to‐end models that directly use source code‐based abstract syntax trees (ASTs) as input. They do not pay enough attention to the front‐end data representation. In this paper, we propose a new framework to represent source code called multiperspective tree embedding (MPT‐embedding), which is an unsupervised representation learning method. MPT‐embedding parses the nodes of ASTs from multiple perspectives and encodes the structural information of a tree into a vector sequence. Experiments on both cross‐project defect prediction (CPDP) and within‐project defect prediction (WPDP) show that, on average, MPT‐embedding provides improvements over the state‐of‐the‐art method.
AI and Data Use: Surveillance Technology and Community Disquiet in the Age of COVID-19

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Task assignment based on PSO algorithm based on Logistic function inertia weight adaptive adjustment

this paper, the improved particle swarm optimization(PSO)algorithm was used to solve the task assignment problem of multiple unmanned aerial vehicles (UAV), and was compared with the standard PSO algorithm under different conditions. Firstly, the mathematical model of task assignment of multiple UAV is established. Secondly, the inertia weight factor introduced in the particle speed update phase of the particle swarm optimization algorithm was improved. The improved particle swarm optimization algorithm was used to solve the task allocation model, and the simulation analysis and comparison were made. the results show that the improved algorithm has faster calculation speed and global optimization capability.
A Rigorous Cognitive Theory for Autonomous Decision Making

Autonomous decision-making is a central challenge to theories and technologies of AI and computational intelligence. It could not be implemented because the inexhaustive, indeterministic, and uncertain bottlenecks in traditional machine inference theories. This paper presents a formal study on mathematical models of decision and decision-making. Generic mathematical models of formal decisions are created for qualitative, quantitative, and serial decision-making. Cognitive algorithms are developed to extend traditional intuitive, empirical, or heuristic decisions to machine-learnt inferences for decision making. Experiments have demonstrated the power of cognitive decision-making algorithms and technologies for autonomous systems and computational intelligence.
Redesigning User Interface Based On User Experience Using Goal-Directed Design Method

The use of a product is one of the success criteria of information technology (IT) product development, including a web-based application. It can be seen from the usability of the product, how users use the application. Two important issues of product usability are related to the user interface (UI) and user experience (UX) issues. This research was conducted to investigate the UX of a web-based application and then redesign its UI based on the Goal-Directed Design (GDD) method. The reason for using this method is to understand the goals, needs, and characteristics of users and to produce user interface design recommendations that are following the needs and objectives of the website which is the case study. The six phases of the GDD method carried out are preliminary research employing the cognitive walkthrough, system usability scale (SUS), and structured interview techniques, modeling, requirements, frameworks, refinement, and support. The results show that overall values of the effectiveness, efficiency, and satisfaction attributes in the post-redesign evaluation were increased around 30% from the initial evaluation in the preliminary study stage.
Interpretable Test Case Recommendation based on Knowledge Graph

Reproducing bugs and identifying causes is essential for the debugging of complex software systems. However, existing test case selection and recommendation technique diagnose bugs but failed to provide information to understand the cause. In this paper, we present an interpretable test case recommendation technique by building up knowledge graphs based on massive test cases, bug reports, code changes, and documents stored in software repositories. Specifically, it identifies correlations between new issue reports and historical information based on the knowledge graph and thus present test cases and corresponding documents to support the bug diagnosis. We conduct an empirical study on autonomous driving systems to show our technique is capable of identifying the proper test case. Further, we validate the effectiveness of recommended interpretation. The study shows that the recommended interpretation can help testers to comprehend bug reports and diagnose bugs efficiently.
A robotic vision system for inspection of soiling at CSP plants

This paper describes efforts to develop a method of determining soiling levels on heliostat mirrors from images taken from aerial drones. Lessons learned from a flight mission relate to drone positioning errors due to heat and GPS drift, and the challenges of handling lots of data. A method to determine waypoints for missions is suggested, to gather data in a random manner with a known confidence level. Methods of interpreting the images taken are described, including a processing pipeline for identification and segmentation of individual mirror facets. Finally, the paper focusses on efforts to develop and validate a method of determining reflectance from visual images, including development of a sky fitting model, examination of the impact of image resolution, and presentation of the estimated reflectance compared to reference results taken with a hand-held reflectometer. The initial results show promise, currently with accuracy within around 2-3% for soiling cases most typical in a commercial heliostat field, however further improvements is required to achieve the target accuracy of <1%.
Automated Perpendicular Parking System With Approximated Clothoid-Based Local Path Planning

This letter proposes an automated perpendicular parking system using approximated clothoid-based local path planning and a virtual towing method. Clothoid path planning provides smooth maneuvering, but we cannot obtain a closed-form solution. Further, it costs computational overhead. To cope with these problems, we present a novel vehicle control algorithm with approximated clothoid-based local path planning. We show that a parking control system could be presented as a closed-loop system by integrating path planning and control. The closed-loop system is represented as a discrete linear time-varying system and its stability is proved using the Lyapunov stability theorem. In the proposed system, we also see that a numerical singularity problem may incur, which could bring undesirable steering. To avoid numerical singularity, we also propose a virtual towing method. From numerical simulations, we show that the vehicle converges smoothly to the parking spot.
Roman Urdu Multi-Class Offensive Text Detection using Hybrid Features and SVM

Hate content has become a significant issue worldwide due to the increase in social networking sites. Detection of hate content from a language other than English is challenging. We propose a new technique that automatically detects the Roman Urdu comments from YouTube videos into five classes. These classes, including, Religious Hate, Violence Promotion, Extremist (Racist), Threat/Fear, and Neutral. We have generated dataset by scrapping Roman Urdu comments from YouTube videos and labeled by the language experts. We have considered N-grams and TF-IDF values for feature extraction followed by SVM classification. Some classes have relatively less instances, and we employed SMOTE for class-balancing. The developed model offers a high classification performance of 77.45% using the 10-Fold cross-validation technique. The proposed approach offers superior classification results as compared to others.
Diverse Keyphrase Generation with Neural Unlikelihood Training

In this paper, we study sequence-to-sequence (S2S) keyphrase generation models from the perspective of diversity.Recent advances in neural natural language generation have made possible remarkable progress on the task of keyphrase generation, demonstrated through improvements on quality metrics such as F 1 -score.However, the importance of diversity in keyphrase generation has been largely ignored.We first analyze the extent of information redundancy present in the outputs generated by a baseline model trained using maximum likelihood estimation (MLE).Our findings show that repetition of keyphrases is a major issue with MLE training.To alleviate this issue, we adopt neural unlikelihood (UL) objective for training the S2S model.Our version of UL training operates at (1) the target token level to discourage the generation of repeating tokens; (2) the copy token level to avoid copying repetitive tokens from the source text.Further, to encourage better model planning during the decoding process, we incorporate K-step ahead token prediction objective that computes both MLE and UL losses on future tokens as well.Through extensive experiments on datasets from three different domains we demonstrate that the proposed approach attains considerably large diversity gains, while maintaining competitive output quality.
A Non-Negative Matrix Factorization Framework for Privacy-Preserving and Federated Learning

The uncontrolled growth in domains such as a surveillance system, health care, and finance produce a large amount of data and contain potentially sensitive data that can become public if they are not appropriately sanitized. Motivated by this issue, we introduce a privacy filter (PF), a novel non-negative matrix factorization (NMF) framework aiming to preserve the privacy of data before publishing based on a alternating least squares (ALS) approach. More specifically, this framework enables data holders to choose the data dimension that protects user privacy without being aware of the privacy leakage. We also consider the problem of privately learning a PF across multiple sensitive datasets, leading to a federated learning algorithm that guarantees the protection of private data and high accuracy classification for non-private information. Finally, the experiments conduct and illustrate the superior performance of the proposed algorithms under the premise of protecting usersa private data.
Adversarial Analysis for Source Camera Identification

Recent studies highlight the vulnerability of convolutional neural networks (CNNs) to adversarial attacks, which also calls into question the reliability of forensic methods. Existing adversarial attacks generate one-to-one noise, which means these methods have not learned the fingerprint information. Therefore, we introduce two powerful attacks, fingerprint copy-move attack, and joint feature-based auto-learning attack. To validate the performance of attack methods, we move a step ahead and introduce the higher possible defense mechanism relation mismatch. which expands the characterization differences of classifiers in the same classification network. Extensive experiments show that relation mismatch is superior in recognizing adversarial examples and prove that the proposed fingerprint-based attacks are more powerful. Both proposed attacks show excellent attack transferability to unknown samples. The Pytorch® implementations of these methods can download from an open-source GitHub project <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/Dlut-lab-zmn/Source-attack</uri> .
Towards multi-scale deep features learning with correlation metric for person re-identification

Previous person re-identification (Re-ID) methods usually focus on extracted features to against the appearance variations of pedestrians under different circumstances. The problem caused by the scale variations did not attract much attention and is not well addressed either. In this work, we propose a novel Multi-scale Deep Feature Learning with correlation metric (MDFLCM) model to handle the scale problem in Re-ID. Specifically, multi-scale high-level features are extracted by a specially designed end-to-end multi-scale deep convolutional network (MS-DCN) at various resolution levels. By adding an extra correlation layer in our MDFLCM model, we can achieve the accuracy of image patch matching up to pixel-wise level. Different from other methods extracting multi-scale features through multiple networks, we extract multi-scale features via a single network with one input image. Extensive comparative evaluations with state-of-the-art methods on four public datasets: CUHK01, CUHK03, Market 1501 and DukeMTMC-reID, demonstrate the effectiveness of the proposed MDFLCM model on Re-ID.
LDA Categorization of Security Bug Reports in Chromium Projects

Security bug reports (SBR) depict potential security vulnerabilities in software systems. Bug tracking systems (BTS) usually contain huge numbers of bug reports including security-related ones. Malicious attackers could exploit these SBRs. Henceforth, it is very critical to pinpoint SBRs swiftly and correctly. In this work, we studied the security bug reports of the Chromium project. We looked into three main aspects of these bug reports, namely: frequencies of reporting them, how quickly they get fixed and is LDA effective in grouping these reports to known vulnerabilities types. We report our findings in these aspects.
Augmented Reality Development for Supporting Cultural Education Role in SMB II Museum During Covid-19 Pandemic

The Covid-19 pandemic has changed normal life including the learning process.Computer-assisted learning is a method applied when it is not possible to do it face-to-face, one of which is cultural learning at the SMB II Museum.The museum was closed during the Covid-19 pandemic.This study developed the AR application to support the education role of the SMB II museum.Through the AR application, users can view the museum's virtual collection and learn about the culture related to the collection without having to come to the museum.The museum collection is presented virtually reconstructed in the Vuforia, Unity, and Blender 3D development environment that blends real and virtual environments.The testing results show that the AR application has a good performance with the results of the SUS user survey showing a score> 70 and effectively supports the education role of the museum as measured by an improvement knowledge about Palembang culture after using the AR application of the SMB II museum.It is also found that there was different usability on a user with age>30.Millennial generation having no difficulty in using the AR for their experience in using mobile apps and devices.
Real-Time Safe Stop Trajectory Planning via Multidimensional Hybrid A*-Algorithm

A reliable autonomous driving system should be capable of performing safe stop when proceeding the normal driving becomes impossible. It is essential for safe stop planning to be able to provide a trajectory that leads the vehicle to a specific stopped goal in real-time. With this as a main challenge and distinction, the safe stop planning should still be able to avoid collision with static and dynamic obstacles like normal motion planning. To guarantee a meaningful solution and be real-time capable, we propose to utilize path-velocity decomposition, which provides a non-globally optimal solution but reduces the computational burden. Firstly, by sampling piecewise quintic polynomials that connect a series of sampled points from the current location to the goal, a set of path candidates in Frenét frame are generated. The path that meets kinematic constraints, maximizes comfort, and avoids static obstacles is selected. Afterward, we generate the length-time (ST) graph by projecting the dynamic obstacles on our driving corridor along the chosen path in space and time domain. The velocity planning is performed on the ST-graph with an extended multidimensional Hybrid A-Star $(\mathrm{A}^{*})$ Algorithm. Finally, our approach is evaluated in several simulation scenarios and also in CoInCar-Simulation framework, which shows a real-time capability and promising driving behaviors.