Reviewerly/exHarmony huggingface dataset:
CrossVul: a cross-language vulnerability dataset with commit data

Examining the characteristics of software vulnerabilities and the code that contains them can lead to the development of more secure software. We present a dataset (∼1.4 GB) containing vulnerable source code files together with the corresponding, patched versions. Contrary to other existing vulnerability datasets, ours includes vulnerable files written in more than 40 programming languages. Each file is associated to (1) a Common Vulnerability Exposures identifier (CVE ID) and (2) the repository it came from. Further, our dataset can be the basis for machine learning applications that identify defects, as we show in specific examples. We also present a supporting dataset that contains commit messages derived from Git commits that serve as security patches. This dataset can be used to train ML models that in turn, can be used to detect security patch commits as we highlight in a specific use case.
Depth-aware total variation regularization for underwater image dehazing

Underwater images often show severe quality degradation due to the light absorption and scattering effects in water medium. This paper introduces a scene depth regularized underwater image dehazing method to obtain high-quality underwater images. Unlike previous underwater image dehazing methods that usually calculate a transmission map or a scene depth map using priors, we construct an exponential relationship between transmission map and normalized scene depth map. An initial scene depth is first estimated by the difference between color channels. Then it is refined by total variation regularization to keep structures while smoothing excessive details. An alternating direction algorithm is given to solve the optimization problem. Extensive experiments demonstrate that the proposed method can effectively improve the visual quality of degraded underwater images, and yields high-quality results comparative to the state-of-the-art underwater image enhancement methods quantitatively and qualitatively.
Intelligent Symmetric Cryptography With Chaotic Map and Quantum Based Key Generator for Medical Images Infosecurity

Medical images can be constructed in two-dimensional (2D) or three-dimensional (3D) view imaging to be applied in disease detection and diagnosis inside the body, such as cancer/tumor, heart, or lung-related diseases.These images may contain patients' privacy information and clinical diagnosis records.Hence, these images need to ensure authorization demands among hospitals, medical service organizations, or physicians in a picture archiving and communication system.This study presents an intelligent symmetric cryptography with a chaotic map and quantum-based key generator (KG) for medical image encryption and decryption.Overall scheme processes include (1) random cipher code generation, (2) training gray relational analysis (GRA)-based encryptor and decryptor, and (3) decrypted image evaluation.The hybrid chaotic map and quantum-based KG are used to increase the chaotic complexity and unpredictable levels to produce cipher codes for changing pixel values (substitution method) in a 2D image by 256 key-space cipher codes.The first and second GRA models are used to train the cipher codes to achieve an encryptor and a decryptor, respectively.Through the methodology validation using a chest X-ray database, the structural similarity index measurement is employed to evaluate the decryption quality between the plain image and decrypted image.The encrypted images show a visual uncorrelation with the plain images, and experimental results indicate higher confidences against the passive eavesdropper.
3D Surface reconstruction of transparent objects using laser scanning with LTFtF method

Three-dimensional (3D) vision plays an important role in robotics and industrial automation, wherein the reconstruction of transparent objects poses a great challenge. In this paper, we present a scheme for reconstructing the 3D surfaces of transparent objects based on a combination of stereo matching and laser scanning. The traditional laser scanning method can achieve good results in the 3D reconstruction of opaque objects. However, this method will fail for transparent and semitransparent objects because both the front and back surfaces of these objects will reflect the feature laser line. It is difficult to determine the reflection surface of the feature lines captured by the camera. To distinguish the laser reflected by the front surface from the distracting laser reflections by the other surfaces, a laser tracking frame to frame (LTFtF) method is proposed. The laser lines reflected from the front surface of the target are then reconstructed using stereo vision. The entire 3D surface is obtained through laser scanning. In addition, we improve the matching accuracy and point cloud density through an internal pixel matching method. Various experiments were conducted to verify the capability of the proposed method, which includes the reconstruction of transparent and semitransparent objects of various shapes. The proposed method was compared with traditional laser scanning algorithms.
TMFNet: Three-Input Multilevel Fusion Network for Detecting Salient Objects in RGB-D Images

The use of depth information, acquired by depth sensors, for salient object detection (SOD) is being explored. Despite the remarkable results from recent deep learning approaches for RGB-D SOD, they fail to fully incorporate original and accurate information to express details of RGB-D images in salient objects. Here, we propose an RGB-D SOD model using a three-input multilevel fusion network (TMFNet), which differs from existing methods based on double-stream networks. In addition to RGB input (first input) and depth input (second input), the RGB image and depth map are combined into a four-channel representation (RGBD input) that constitutes the third input to the TMFNet. The RGBD input generates multilevel features that reflect details of the RGB-D image. In addition, the proposed TMFNet aggregates diverse region-based contextual information without discarding RGB and depth features. Thus, we introduce a cross-fusion module, and benefiting from rich low- and high-level information from salient features, feature fusion enables the improvement of localization of salient objects. The proposed TMFNet achieves state-of-the-art performance on six benchmark datasets for SOD.
Modeling Functional Similarity in Source Code With Graph-Based Siamese Networks

Code clones are duplicate code fragments that share (nearly) similar syntax or semantics. Code clone detection plays an important role in software maintenance, code refactoring, and reuse. A substantial amount of research has been conducted in the past to detect clones. A majority of these approaches use lexical and syntactic information to detect clones. However, only a few of them target semantic clones. Recently, motivated by the success of deep learning models in other fields, including natural language processing and computer vision, researchers have attempted to adopt deep learning techniques to detect code clones. These approaches use lexical information (tokens) and(or) syntactic structures like abstract syntax trees (ASTs) to detect code clones. However, they do not make sufficient use of the available structural and semantic information, hence limiting their capabilities. This paper addresses the problem of semantic code clone detection using program dependency graphs and geometric neural networks, leveraging the structured syntactic and semantic information. We have developed a prototype tool <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Holmes</small> , based on our novel approach and empirically evaluated it on popular code clone benchmarks. Our results show that <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Holmes</small> performs considerably better than the other state-of-the-art tool, TBCCD. We also assessed <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Holmes</small> on unseen projects and performed cross dataset experiments to evaluate the generalizability of <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Holmes</small> . Our results affirm that <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Holmes</small> outperforms TBCCD since most of the pairs that <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Holmes</small> detected were either undetected or suboptimally reported by TBCCD.
Exploring Intuitive Lifelog Retrieval and Interaction Modes in Virtual Reality with vitrivr-VR

The multimodal nature of lifelog data collections poses unique challenges for multimedia management and retrieval systems. The Lifelog Search Challenge (LSC) offers an annual evaluation platform for such interactive retrieval systems. They compete against one another in finding items of interest within a set time frame.
Spatio-Temporal association rule based deep annotation-free clustering (STAR-DAC) for unsupervised person re-identification

Multi-camera video surveillance environment has a variety of emerging research problems among, which person re-identification is the premier one. Unsupervised person re-identification has been explored less in literature than the supervised approach. Images acquired from the video surveillance systems are unlabeled, which denotes that it is naturally an unsupervised learning problem. The state-of-the-art unsupervised methods seek external annotations support such as incorporating transfer learning techniques, partial labeling of train images, etc., which makes them not purely unsupervised and unsuitable for practical real-world surveillance settings. Identity mismatch happens due to the similar costumes and complex environmental factors. To resolve this issue, we introduce a new framework named Spatio-Temporal Association Rule based Deep Annotation-free Clustering (STAR-DAC) which incrementally clusters the unlabeled person re-identification images based on visual features and performs cluster fine-tuning through the mined spatio-temporal association rules. STAR formulations leveraged upto 75% of images for reliable sample selection through cluster fine-tuning. STAR based fine-tune algorithm aims to attain ground-truth labels of an unlabeled dataset and eliminate cluster outliers to stabilize the evaluation. Experiments are performed on image and video-based benchmark person re-identification datasets such as DukeMTMC re-ID, Market1501, MSMT17, CUHK03, GRID and Dukevideo re-ID, iLIDSVid, ViPer respectively. Experimental results clearly show that the proposed STAR-DAC framework outperforms the state-of-the-art methods in case of large scale datasets with multiple cameras.
The Mediating Role of Big Data to Influence Practitioners to Use Forensic Accounting for Fraud Detection

Globally, the financial industry in the recent times has witnessed various forms of fraudulent activities in the financial markets creating dilemma for the professionals, and the auditors who owns responsibility of ensuring accuracy and transparency.This article aims at finding the emergence of Big Data technology to fraud and forensic accounting by practitioner accountants in in India.A research model and hypotheses has been developed to examine the relationship between the awareness level of forensic accounting, Big Data and intentions to use it for fraud detection using structural equation modeling.Results indicate that awareness of forensic accounting has a positive influence on practitioners' intentions to its use for fraud detection.Big data technologies mediate the relationship between awareness and intentions to use for fraud detection.The results of the study are useful in implementation of Big Data technologies into the forensic accounting domain that can facilitate combating fraud.
Factors affecting motivation in online courses during the COVID-19 pandemic: the experiences of students at a Romanian public university

Following the outbreak of the COVID-19 pandemic, faculty at higher education institutions across the world quickly converted courses to an online format. With that swift switch, a major challenge experienced by many students has been the loss of motivation, due primarily to the social isolation experienced during the pandemic. This study examined factors that affect student motivation at a Romanian public university. As such, the study contributes to the literature on motivation, by focusing on a context that has rarely been addressed by other studies – universities that began experiencing with online education for the first time during the pandemic. The study highlights commonalities with prior literature by demonstrating that motivation is affected by instructors' use of effective online instructional practices, perceived course quality, and issues with technology. However, important contextual differences exist between universities with established online programmes and universities that experimented with online learning for the first time during the pandemic. Specifically, instructors at the examined university were more likely than faculty in established online programmes to experience issues with technology and less likely to use effective online teaching practices. Consequently, students' perceived quality of the online courses was lower.
Video Pose Distillation for Few-Shot, Fine-Grained Sports Action Recognition

Human pose is a useful feature for fine-grained sports action understanding. However, pose estimators are often unreliable when run on sports video due to domain shift and factors such as motion blur and occlusions. This leads to poor accuracy when downstream tasks, such as action recognition, depend on pose. End-to-end learning circumvents pose, but requires more labels to generalize.We introduce Video Pose Distillation (VPD), a weakly-supervised technique to learn features for new video domains, such as individual sports that challenge pose estimation. Under VPD, a student network learns to extract robust pose features from RGB frames in the sports video, such that, whenever pose is considered reliable, the features match the output of a pretrained teacher pose detector. Our strategy retains the best of both pose and end-to-end worlds, exploiting the rich visual patterns in raw video frames, while learning features that agree with the athletes’ pose and motion in the target video domain to avoid over-fitting to patterns unrelated to athletes’ motion.VPD features improve performance on few-shot, fine-grained action recognition, retrieval, and detection tasks in four real-world sports video datasets, without requiring additional ground-truth pose annotations.
AE-Net: Fine-grained sketch-based image retrieval via attention-enhanced network

In this paper, we investigate the task of Fine-grained Sketch-based Image Retrieval (FG-SBIR), which uses hand-drawn sketches as input queries to retrieve the relevant images at the fine-grained instance level. The sketches and images come from different modalities, thus the similarity computation needs to consider both fine-grained and cross-modal characteristics. Existing solutions only focus on fine-grained details or spatial contexts, while ignoring the channel context and spatial sequence information. To mitigate such challenging problems, we propose a novel deep FG-SBIR model, which aims at inferring attention maps along channel dimension and spatial dimension, improving modules of channel attention and spatial attention, and exploring Transformer to enhance the model's ability for constructing and understanding spatial sequence information. We focus not only on the correlation information between two modalities of sketch and image, but also on the discrimination information inside the single modality. Mutual Loss is especially proposed to enhance the traditional triplet loss, and promote the internal discrimination ability of the model on a single modality. Extensive experiments show that our AE-Net obtains promising results on Sketchy, which is the largest public dataset available for FG-SBIR at present.
Generalizable model-agnostic semantic segmentation via target-specific normalization

Semantic segmentation in a supervised learning manner has achieved significant progress in recent years. However, its performance usually drops dramatically due to the data-distribution discrepancy between seen and unseen domains when we directly deploy the trained model to segment the images of unseen (or new coming) domains. To this end, we propose a novel domain generalization framework for the generalizable semantic segmentation task, which enhances the generalization ability of the model from two different views, including the training paradigm and the test strategy. Concretely, we exploit the model-agnostic learning to simulate the domain shift problem, which deals with the domain generalization from the training scheme perspective. Besides, considering the data-distribution discrepancy between seen source and unseen target domains, we develop the target-specific normalization scheme to enhance the generalization ability. Furthermore, when images come one by one in the test stage, we design the image-based memory bank (Image Bank in short) with style-based selection policy to select similar images to obtain more accurate statistics of normalization. Extensive experiments highlight that the proposed method produces state-of-the-art performance for the domain generalization of semantic segmentation on multiple benchmark segmentation datasets, i.e., Cityscapes, Mapillary.
From Contexts to Locality: Ultra-high Resolution Image Segmentation via Locality-aware Contextual Correlation

Ultra-high resolution image segmentation has raised increasing interests in recent years due to its realistic applications. In this paper, we innovate the widely used high-resolution image segmentation pipeline, in which an ultrahigh resolution image is partitioned into regular patches for local segmentation and then the local results are merged into a high-resolution semantic mask. In particular, we introduce a novel locality-aware contextual correlation based segmentation model to process local patches, where the relevance between local patch and its various contexts are jointly and complementarily utilized to handle the semantic regions with large variations. Additionally, we present a contextual semantics refinement network that associates the local segmentation result with its contextual semantics, and thus is endowed with the ability of reducing boundary artifacts and refining mask contours during the generation of final high-resolution mask. Furthermore, in comprehensive experiments, we demonstrate that our model outperforms other state-of-the-art methods in public benchmarks. Our released codes are available at https://github.com/liqiokkk/FCtL.
FaceInpainter: High Fidelity Face Adaptation to Heterogeneous Domains

In this work, we propose a novel two-stage framework named FaceInpainter to implement controllable Identity-Guided Face Inpainting (IGFI) under heterogeneous domains. Concretely, by explicitly disentangling foreground and background of the target face, the first stage focuses on adaptive face fitting to the fixed background via a Styled Face Inpainting Network (SFI-Net), with 3D priors and texture code of the target, as well as identity factor of the source face. It is challenging to deal with the inconsistency between the new identity of the source and the original background of the target, concerning the face shape and appearance on the fused boundary. The second stage consists of a Joint Refinement Network (JR-Net) to refine the swapped face. It leverages AdaIN considering identity and multi-scale texture codes, for feature transformation of the decoded face from SFI-Net with facial occlusions. We adopt the contextual loss to implicitly preserve the attributes, encouraging face deformation and fewer texture distortions. Experimental results demonstrate that our approach handles high-quality identity adaptation to heterogeneous domains, exhibiting the competitive performance compared with state-of-the-art methods concerning both attribute and identity fidelity.
Quantum computing methods for supervised learning

The last two decades have seen an explosive growth in the theory and practice of both quantum computing and machine learning. Modern machine learning systems process huge volumes of data and demand massive computational power. As silicon semiconductor miniaturization approaches its physics limits, quantum computing is increasingly being considered to cater to these computational needs in the future. Small-scale quantum computers and quantum annealers have been built and are already being sold commercially. Quantum computers can benefit machine learning research and application across all science and engineering domains. However, owing to its roots in quantum mechanics, research in this field has so far been confined within the purview of the physics community, and most work is not easily accessible to researchers from other disciplines. In this paper, we provide a background and summarize key results of quantum computing before exploring its application to supervised machine learning problems. By eschewing results from physics that have little bearing on quantum computation, we hope to make this introduction accessible to data scientists, machine learning practitioners, and researchers from across disciplines.
Neural Networks-Based Cryptography: A Survey

A current trend of research focuses on artificial intelligence based cryptography which although proposed almost thirty years ago could not attract much attention. Abadi and Anderson’s work on adversarial cryptography in 2016 rejuvenated the research area which now focuses in building neural networks that are able to learn cryptography using the idea from Generative Adversarial Networks (GANs). In this paper, we survey the most prominent research works that cover neural networks based cryptography from two main periods. The first period covers the oldest models that have been proposed shortly after 2000 and the second period covers the more recent models that have been proposed since 2016. We first discuss the implementation of the systems from the earlier era and the attacks mounted on them. After that, we focus on post 2016 era where more advanced techniques are utilized that rely on GANs in which neural networks compete with each other in order to achieve a goal e.g. learning to encrypt a communication. Finally, we discuss security analysis performed on adversarial cryptography models.