Reviewerly/exHarmony huggingface dataset:
Using bandit algorithms for selecting feature reduction techniques in software defect prediction

Background: Selecting a suitable feature reduction technique. when building a defect prediction model, can be challenging. Different techniques can result in the selection of different independent variables which have an impact on the overall performance of the prediction model. To help in the selection, previous studies have assessed the impact of each feature reduction technique using different datasets. However, there are many reduction techniques, and therefore some of the well-known techniques have not been assessed by those studies. Aim: The goal of the study is to select a high-accuracy reduction technique from several candidates without preliminary assessments. Method: We utilized bandit algorithm (BA) to help with the selection of best features reduction technique for a list of candidates. To select the best feature reduction technique, BA evaluates the prediction accuracy of the candidates, comparing testing results of different modules with their prediction results. By substituting the reduction technique for the prediction method, BA can then be used to select the best reduction technique. In the experiment, we evaluated the performance of BA to select suitable reduction technique. We performed cross version defect prediction using 14 datasets. As feature reduction techniques, we used two assessed and two non-assessed techniques. Results: Using BA, the prediction accuracy was higher or equivalent than existing approaches on average, compared with techniques selected based on an assessment. Conclusions: BA can have larger impact on improving prediction models by helping not only on selecting suitable models, but also in selecting suitable feature reduction techniques.
V2C: Visual Voice Cloning

Existing Voice Cloning (VC) tasks aim to convert a para-graph text to a speech with desired voice specified by a ref-erence audio. This has significantly boosted the development of artificial speech applications. However, there also exist many scenarios that cannot be well reflected by these VC tasks, such as movie dubbing, which requires the speech to be with emotions consistent with the movie plots. To fill this gap, in this work we propose a new task named Vi-sual Voice Cloning (V2C), which seeks to convert a para-graph of text to a speech with both desired voice speci-fied by a reference audio and desired emotion specified by a reference video. To facilitate research in this field, we construct a dataset, V2C-Animation, and propose a strong baseline based on existing state-of-the-art (SoTA) VC techniques. Our dataset contains 10,217 animated movie clips covering a large variety of genres (e.g., Comedy, Fantasy) and emotions (e.g., happy, sad). We further design a set of evaluation metrics, named MCD-DTW-SL, which help eval-uate the similarity between ground-truth speeches and the synthesised ones. Extensive experimental results show that even SoTA VC methods cannot generate satisfying speeches for our V2C task. We hope the proposed new task together with the constructed dataset and evaluation metric will fa-cilitate the research in the field of voice cloning and broader vision-and-language community. Source code and dataset will be released in https://github.com/chenqi008/V2C.
MVDG: A Unified Multi-view Framework for Domain Generalization

To generalize the model trained in source domains to unseen target domains, domain generalization (DG) has recently attracted lots of attention. Since target domains can not be involved in training, overfitting source domains is inevitable. As a popular regularization technique, the meta-learning training scheme has shown its ability to resist overfitting. However, in the training stage, current meta-learning-based methods utilize only one task along a single optimization trajectory, which might produce a biased and noisy optimization direction. Beyond the training stage, overfitting could also cause unstable prediction in the test stage. In this paper, we propose a novel multi-view DG framework to effectively reduce the overfitting in both the training and test stage. Specifically, in the training stage, we develop a multi-view regularized meta-learning algorithm that employs multiple optimization trajectories to produce a suitable optimization direction for model updating. We also theoretically show that the generalization bound could be reduced by increasing the number of tasks in each trajectory. In the test stage, we utilize multiple augmented images to yield a multi-view prediction to alleviate unstable prediction, which significantly promotes model reliability. Extensive experiments on three benchmark datasets validate that our method can find a flat minimum to enhance generalization and outperform several state-of-the-art approaches. The code is available at https://github.com/koncle/MVRML .
Patients' Trust in Hospital Transport Robots: Evaluation of the Role of User Dispositions, Anxiety, and Robot Characteristics

For designing the interaction with robots in healthcare scenarios, understanding how trust develops in such situations characterized by vulnerability and uncertainty is important. The goal of this study was to investigate how technology-related user dispositions, anxiety, and robot characteristics influence trust. A second goal was to substantiate the association between hospital patients' trust and their intention to use a transport robot. In an online study, patients, who were currently treated in hospitals, were introduced to the concept of a transport robot with both written and video-based material. Participants evaluated the robot several times. Technology-related user dispositions were found to be essentially associated with trust and the intention to use. Furthermore, hospital patients' anxiety was negatively associated with the intention to use. This relationship was mediated by trust. Moreover, no effects of the manipulated robot characteristics were found. In conclusion, for a successful implementation of robots in hospital settings patients' individual prior learning history - e.g., in terms of existing robot attitudes - and anxiety levels should be considered during the introduction and implementation phase.
Detecting Darting Out Pedestrians With Occlusion Aware Sensor Fusion of Radar and Stereo Camera

Early and accurate detection of crossing pedestrians is crucial in automated driving in order to perform timely emergency manoeuvres. However, this is a difficult task in urban scenarios where pedestrians are often occluded (not visible) behind objects, e.g., other parked vehicles. We propose an occlusion aware fusion of stereo camera and radar sensors to address scenarios with crossing pedestrians behind such parked vehicles. Our proposed method adapts both the expected rate and properties of detections in different areas according to the visibility of the sensors. In our experiments on a real-world dataset, we show that the proposed occlusion aware fusion of radar and stereo camera detects the crossing pedestrians on average 0.26 seconds earlier than using the camera alone, and 0.15 seconds earlier than fusing the sensors without occlusion information. Our dataset containing 501 relevant recordings of pedestrians behind vehicles will be publicly available on our website for non-commercial, scientific use.
Geochemical Indicators in Provenance Estimation

The geochemical and isotopic composition of sediments is a function of the host rock composition from where the sediment has been derived, climate and topography, weathering, mixing from variable sources, grain size and hydrodynamic processes, transportation, and post-depositional changes. Deconvoluting the sediment source is of prime significance for paleogeographic reconstructions, characterizing crust that is no longer exposed and understanding the evolution of the crust. The present chapter aims to review some geochemical and isotopic proxies frequently used for sediment source estimations. Nevertheless, the study has also provided glimpses of other methods wherein the applicability of statistical and geophysical approaches has been highlighted. The present study cautions on the implementation of the statistical approach to delineate the possible role of sorting or other processes which might have affected the geochemical composition. Further, the adoption of multiple approaches in sediment source determination is needed to have reliable provenance identification. The future of the robust identification of sediment provenance lies in the multidisciplinary approach that needs to be conceived and devised.
Aladdin: Joint Atlas Building and Diffeomorphic Registration Learning with Pairwise Alignment

Atlas building and image registration are important tasks for medical image analysis. Once one or multiple atlases from an image population have been constructed, commonly (1) images are warped into an atlas space to study intra-subject or inter-subject variations or (2) a possibly probabilistic atlas is warped into image space to assign anatomical labels. Atlas estimation and nonparametric transformations are computationally expensive as they usually require numerical optimization. Additionally, previous approaches for atlas building often define similarity measures between a fuzzy atlas and each individual image, which may cause alignment difficulties because a fuzzy atlas does not exhibit clear anatomical structures in contrast to the individual images. This work explores using a convolutional neural network (CNN) to jointly predict the atlas and a stationary velocity field (SVF) parameterization for diffeomorphic image registration with respect to the atlas. Our approach does not require affine pre-registrations and utilizes pairwise image alignment losses to increase registration accuracy. We evaluate our model on 3D knee magnetic resonance images (MRI) from the OAI-ZIB dataset. Our results show that the proposed framework achieves better performance than other state-of-the-art image registration algorithms, allows for end-to-end training, and for fast inference at test time. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> Source code: https://github.com/uncbiag/Aladdin.
RESAM: Requirements Elicitation and Specification for Deep-Learning Anomaly Models with Applications to UAV Flight Controllers

CyberPhysical systems (CPS) must be closely monitored to identify and potentially mitigate emergent problems that arise during their routine operations. However, the multivariate time-series data which they typically produce can be complex to understand and analyze. While formal product documentation often provides example data plots with diagnostic suggestions, the sheer diversity of attributes, critical thresholds, and data interactions can be overwhelming to non-experts who subsequently seek help from discussion forums to interpret their data logs. Deep learning models, such as Long Short-term memory (LSTM) networks can be used to automate these tasks and to provide clear explanations of diverse anomalies detected in real-time multivariate data-streams. In this paper we present RESAM, a requirements process that integrates knowledge from domain experts, discussion forums, and formal product documentation, to discover and specify requirements and design definitions in the form of time-series attributes that contribute to the construction of effective deep learning anomaly detectors. We present a case-study based on a flight control system for small Uncrewed Aerial Systems and demonstrate that its use guides the construction of effective anomaly detection models whilst also providing underlying support for explainability. RESAM is relevant to domains in which open or closed online forums provide discussion support for log analysis.
Virtual Reality In Education: Structural Design Of An Adaptable Virtual Reality System

The modern education environment is changing rapidly, as is the technology that is being utilized for educational purposes. Emerging advanced technologies such as artificial intelligence (AI), machine learning, cloud computing, and virtual reality (VR) are reshaping education systems and creating new ways to digitalize and modernize education for a new generation of learners. In this paper, a structural design of an adaptable virtual reality system for education is proposed for distance learning and educational support of higher education courses, aiming to increase the degree of interactivity and informational feedback between students and the virtual environment, as well as provide multiple means of interconnectivity, student learning progress analysis, and communication between multiple users to work simultaneously.
A Lightweight YOLOv4-EDAM Model for Accurate and Real-time Detection of Foreign Objects Suspended on Power Lines

Foreign objects suspended on transmission and distribution lines like nests, kites, balloons, and trash, etc. may lead discharge faults and greatly affect the safety of power grid. How to perform accurate and timely detection of these foreign objects is an urgent problem. This paper proposes a lightweight YOLOv4 model with embedded dual attention mechanism (YOLOv4-EDAM) to detect foreign objects from visible images, using MobileNetV2 embedded with the squeeze and excitation networks (SENet) to replace the CSPDarkNet53 feature extraction network, the depthwise separable convolutions (DSC) to replace the standard convolutions in SPP and PANet module, and embedding the convolutional block attention module (CBAM) into SPP and PANet module to improve the detection accuracy. Firstly, an image dataset was constructed using inspection images and public datasets, and expanded by Poisson blending and some data augmentation methods. The denoising convolutional neural network (DnCNN) was applied for image preprocessing. Next, the lightweight YOLOv4-EDAM model was trained combining Mosaic data enhancement, cosine annealing and label smoothing skills. Several detection cases were carried out and the experimental results show that the proposed model has a high accuracy with the mean average precision (mAP) of 96.71%, and a fast detection speed with the frames per second (FPS) of 45, whose overall performance is better than other object detection models. This study offers a reference for power line inspection and provides a possible way to deploy edge computing devices on unmanned aerial vehicles.
Programmable Distributed Point Functions

A distributed point function (DPF) is a cryptographic primitive that enables compressed additive sharing of a secret unit vector across two or more parties. Despite growing ubiquity within applications and notable research efforts, the best 2-party DPF construction to date remains the tree-based construction from (Boyle et al., CCS'16), with no significantly new approaches since. We present a new framework for 2-party DPF construction, which applies in the setting of feasible (polynomial-size) domains. This captures in particular all DPF applications in which the keys are expanded to the full domain. Our approach is motivated by a strengthened notion we put forth, of programmable DPF (PDPF): in which a short, input-independent "offline" key can be reused for sharing many point functions. Our approach offers multiple new efficiency features and applications:
RI-Fusion: 3D Object Detection Using Enhanced Point Features With Range-Image Fusion for Autonomous Driving

The 3D object detection is becoming indispensable for environmental perception in autonomous driving. Light detection and ranging (LiDAR) point clouds often fail to distinguish objects with similar structures and are quite sparse for distant or small objects, thereby introducing false and missed detections. To address these issues, LiDAR is often fused with cameras due to the rich textural information provided by images. However, current fusion methods suffer the inefficient data representation and inaccurate alignment of heterogeneous features, leading to poor precision and low efficiency. To this end, we propose a plug-and-play module termed range-image fusion (RI-Fusion) to achieve an effective fusion of LiDAR and camera data, designed to be easily accessible by existing mainstream LiDAR-based algorithms. In this process, we design an image and point cloud alignment method by converting a point cloud into a compact range-view representation through a spherical coordinate transformation. The range image is then integrated with a corresponding camera image utilizing an attention mechanism. The original range image is then concatenated with fusion features to retain point cloud information, and the results are projected onto a spatial point cloud. Finally, the feature-enhanced point cloud can be input into a LiDAR-based 3D object detector. The results of validation experiments involving the KITTI 3D object detection benchmark showed that our proposed fusion method significantly enhanced multiple mainstream LiDAR-based 3D object detectors, PointPillars, SECOND, and Part <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\text{A}{^{2}}$ </tex-math></inline-formula> , improving the 3D mAP (mean Average Precision) by 3.61%, 2.98%, and 1.27%, respectively, particularly for small objects such as pedestrians and cyclists.
Towards Developing a Multi-Modal Video Recommendation System

With the surge of digitized entertainment systems in recent years, the element of personalised experience for users gives a competitive edge to entertainment businesses. Hence, the importance of recommendation systems, in particular for video or movie recommendation, is evident. However, majority of the recommendation systems are primarily learned in supervised fashion on empirical data like user ratings. This approach has certain short comings, specifically the cold start problem and the data sparsity problem. Moreover, existing recommendation systems do not utilize multiple information associated with videos/movies like their textual summary, meta-data information, audio and video signals etc. With the increase in the multi-modal information processing in different fields of artificial intelligence, we have proposed the task of multi-modal recommendation system in the current study. For representing the movie/video, feature vectors generated from text modality, meta-data modality, audio and video modalities are concatenated. A novel knowledge graph based approach is applied for generating feature vectors from text modality. Finally, a Siamese architecture based deep learning technique is proposed to perform regression over similarities using multi-modal user-item embeddings. As there was no data set available for solving the task of multi-modal recommendation, we have also enhanced the existing benchmark Movie-lens 100k dataset with text, video, audio information and utilized that for performing experimentation. Experimental results establish the efficacy of using multi-modal information for movie embedding generation
Local Perception-Aware Transformer for Aerial Tracking

Transformer-based visual object tracking has been utilized extensively. However, the Transformer structure is lack of enough inductive bias. In addition, only focusing on encoding the global feature does harm to modeling local details, which restricts the capability of tracking in aerial robots. Specifically, with local-modeling to global-search mechanism, the proposed tracker replaces the global encoder by a novel local-recognition encoder. In the employed encoder, a local-recognition attention and a local element correction network are carefully designed for reducing the global redundant information interference and increasing local inductive bias. Meanwhile, the latter can model local object details precisely under aerial view through detail-inquiry net. The proposed method achieves competitive accuracy and robustness in several authoritative aerial benchmarks with 316 sequences in total. The proposed tracker's practicability and efficiency have been validated by the real-world tests. The source code is available at https://github.com/vision4robotics/LPAT.
KXNet: A Model-Driven Deep Neural Network for Blind Super-Resolution

Although current deep learning-based methods have gained promising performance in the blind single image super-resolution (SISR) task, most of them mainly focus on heuristically constructing diverse network architectures and put less emphasis on the explicit embedding of the physical generation mechanism between blur kernels and high-resolution (HR) images. To alleviate this issue, we propose a model-driven deep neural network, called KXNet, for blind SISR. Specifically, to solve the classical SISR model, we propose a simple-yet-effective iterative algorithm. Then by unfolding the involved iterative steps into the corresponding network module, we naturally construct the KXNet. The main specificity of the proposed KXNet is that the entire learning process is fully and explicitly integrated with the inherent physical mechanism underlying this SISR task. Thus, the learned blur kernel has clear physical patterns and the mutually iterative process between blur kernel and HR image can soundly guide the KXNet to be evolved in the right direction. Extensive experiments on synthetic and real data finely demonstrate the superior accuracy and generality of our method beyond the current representative state-of-the-art blind SISR methods. Code is available at: https://github.com/jiahong-fu/KXNet .
An exploratory study on code attention in BERT

Many recent models in software engineering introduced deep neural models based on the Transformer architecture or use transformer-based Pre-trained Language Models (PLM) trained on code. Although these models achieve the state of the arts results in many downstream tasks such as code summarization and bug detection, they are based on Transformer and PLM, which are mainly studied in the Natural Language Processing (NLP) field. The current studies rely on the reasoning and practices from NLP for these models in code, despite the differences between natural languages and programming languages. There is also limited literature on explaining how code is modeled.
A Comprehensive Investigation of the Impact of Class Overlap on Software Defect Prediction

Software Defect Prediction (SDP) is one of the most vital and cost-efficient operations to ensure the software quality. However, there exists the phenomenon of class overlap in the SDP datasets (i.e., defective and non-defective modules are similar in terms of values of metrics), which hinders the performance as well as the use of SDP models. Even though efforts have been made to investigate the impact of removing overlapping technique on the performance of SDP, many open issues are still challenging yet unknown. Therefore, we conduct an empirical study to comprehensively investigate the impact of class overlap on SDP. Specifically, we first propose an overlapping instances identification approach by analyzing the class distribution in the local neighborhood of a given instance. We then investigate the impact of class overlap and two common overlapping instance handling techniques on the performance and the interpretation of seven representative SDP models. Through an extensive case study on 230 diversity datasets, we observe that: i) 70.0% of SDP datasets contain overlapping instances; ii) different levels of class overlap have different impacts on the performance of SDP models; iii) class overlap affects the rank of the important feature list of SDP models, particularly the feature lists at the top 2 and top 3 ranks; IV) Class overlap handling techniques could statistically significantly improve the performance of SDP models trained on datasets with over 12.5% overlap ratios. We suggest that future work should apply our KNN method to identify the overlap ratios of datasets before building SDP models.