Reviewerly/exHarmony huggingface dataset:
VULDEFF: Vulnerability detection method based on function fingerprints and code differences

The significant increase in Open Source Software has led to a sharp increase in code cloning vulnerabilities. Code similarity detection methods are usually used to detect these vulnerabilities. However, cloned code often modifies the original code to varying degrees, and the difference between vulnerable code and patch code can be very small. Traditional code similarity detection methods cannot effectively detect common mutation patterns in code cloning and distinguish vulnerable code from patch code. The paper proposes a vulnerability detection method named VULDEFF based on function fingerprints and code differences. This paper designs a lightweight function fingerprint method based on the Context Triggered Piecewise Hashing algorithm, which can characterize the basic syntax features of function source code. In particular, the fingerprint of the vulnerable function contains the syntax features, vulnerability features, and patch features of the vulnerable function. VULDEFF detects whether target function has vulnerabilities by searching target function fingerprint in the vulnerable function fingerprint library. Compared with five advanced software vulnerability detection tools, VULDEFF significantly reduces the false positive and false negative rates while ensuring the scalability of vulnerability detection. VULDEFF discovered 111 new vulnerabilities in 10 open source projects.
Loss reweight in scale dimension: A simple while effective feature selection strategy for anchor-free detectors

Feature selection plays an important role during training in object detection and it is becoming a hotspot these years. Here we present a simple yet effective feature selection strategy called Loss Reweight in Scale Dimension(LRSD) and train a single-stage anchor-free detector, termed LRD. To optimize the heuristic-guided feature selection process across FPN levels, for each instance, LRD dynamically reweights the training loss of positive samples from selected top-k feature levels by introducing a reweight function. Because of its rigorous and nonlinear mathematical properties, a precise sample loss reweighting procedure across scale dimension could be done. Without adding extra meta-nets or branches, LRD improves detection performance economically without sacrificing inference speed. Moreover, our detection framework can be further improved by recently proposed transformer-based feature extraction networks such as swin-transformer. Extensive experiments show that LRD achieves 40.4% AP at a speed of 16.7 fps with ResNet-50 as the backbone and helps to improve detection performance by around 1.9% ∼ 2.6% compared with our baseline, symbolic one-stage anchor-free detector Foveabox using ResNet-101 as the backbone. Codes are released at (https://github.com/PanffeeReal/LRSD-LRD).
Low-light image enhancement and denoising via dual-constrained Retinex model

For low-light image enhancement, Retinex-based models have been acknowledged as a representative technique, yet they frequently amplify noise hidden in dark image. There have been some attempts to noise reduction. Because they assume a uniform level of noise, the background seems blurry. In this paper, a dual constraint is developed to perform Retinex decomposition and de-noising. First, a patch-aware low-rank model (PALR) is proposed to reject noise. In contrast to the global noise intensity assumption, PALR can measure noise intensity for each image patch, and flexibly control the regularization extent to achieve a trade-off between noise removal and details preservation. Second, noise looks like image background texture instead of image structure. Relative total variation (RTV) is used to simulate the visual difference and works well in enlarging image gradients for highlighting details. By imposing PALR constraint on clean scene image, enforcing visual difference constraint on the illumination and reflectance, a dual-constrained Retinex decomposition model (DCRD) is proposed, which can remove noise during decomposition. DCRD can be solved by an alternating optimization algorithm. Experiments on commonly tested low-light image datasets demonstrate the competing performance of our proposed model in comparison with the state-of-the-art methods.
Application of 2‑gram and 3‑gram to Obtain Factor Scores of Statements Posted at Q&amp;A Sites

Abstract With a view to solving the mismatches between the ideas of questioners and respondents of Question and Answer (Q&amp;A) sites, impression evaluation experiments have resulted in obtaining nine factors of impressions. Then through multiple regression analysis factor scores have been estimated by utilizing the feature values of statements, such as syntactic information, etc. Those factor scores calculated were subsequently employed for inspecting their potential to detect respondents who are expected and likely to appropriately answer a newly posted question. Nevertheless, our method so far has largely depended on the syntactic information extracted through morphological analysis. Moreover, the number of explanatory variables utilized for obtaining factor scores has been appreciably extravagant and complex. Thus, instead of morphological analysis, 2-gram was applied to the explanatory variables to estimate factor scores. The analysis result with the application of 2-gram has led to greater estimation accuracy than the case of morphological analysis for all nine factors. For further perception and comparison, in this paper, 3-gram was applied to the feature values in place of 2-gram or morphological analysis, in a similar fashion as the previous analysis using 2-gram. Further analysis has shown that 2-gram and 3-gram outperform morphological analysis in terms of estimation accuracy. Comparing the results for the nine factors, 2-gram showed the best results. It could also be suggested that a mere 2-gram or 3-gram would be sufficient in applying N-gram as syntactic information of the feature values to estimate factor scores.
Revisiting Online Data Markets in 2022

Well-functioning data markets match sellers with buyers to allocate data effectively. Although most of today's data markets fall short of this ideal, there is a renewed interest in online data marketplaces that may fulfill the promise of data markets. In this paper, we survey participants of some of the most common data marketplaces to understand the platforms' upsides and downsides. We find that buyers and sellers spend the majority of their time and effort in price negotiations. Although the markets work as an effective storefront that lets buyers find useful data fast, the high transaction costs required to negotiate price and circumvent the information asymmetry that exists between buyers and sellers indicates that today's marketplaces are still far from offering an effective solution to data trading. We draw on the results of the interviews to present potential opportunities for improvement and future research.
Walk This Beam: Impact of Different Balance Assistance Strategies and Height Exposure on Performance and Physiological Arousal in VR

Dynamic balance is an essential skill for the human upright gait; therefore, regular balance training can improve postural control and reduce the risk of injury. Even slight variations in walking conditions like height or ground conditions can significantly impact walking performance. Virtual reality is used as a helpful tool to simulate such challenging situations. However, there is no agreement on design strategies for balance training in virtual reality under stressful environmental conditions such as height exposure. We investigate how two different training strategies, imitation learning, and gamified learning, can help dynamic balance control performance across different stress conditions. Moreover, we evaluate the stress response as indexed by peripheral physiological measures of stress, perceived workload, and user experience. Both approaches were tested against a baseline of no instructions and against each other. Thereby, we show that a learning-by-imitation approach immediately helps dynamic balance control, decreases stress, improves attention focus, and diminishes perceived workload. A gamified approach can lead to users being overwhelmed by the additional task. Finally, we discuss how our approaches could be adapted for balance training and applied to injury rehabilitation and prevention.
PORTAL: Portal Widget for Remote Target Acquisition and Control in Immersive Virtual Environments

This paper introduces PORTAL (POrtal widget for Remote Target Acquisition and controL) that allows the user to interact with out-of-reach objects in a virtual environment. We describe the PORTAL interaction technique for placing a portal widget and interacting with target objects through the portal. We conduct two formal user studies to evaluate PORTAL for selection and manipulation functionalities. The results show PORTAL supports participants to interact with remote objects successfully and precisely. Following that, we discuss its potential and limitations, and future works.
Will Metaverse become a more exciting place to listen to music streaming for mental health?

In this article, the author provides several points of view regarding some of the advantages of the existence of Metaverse on music streaming, especially as a healing medicine for mental health. It is hoped that it can provide another viewpoint from the five key themes, or a series of criticisms that have been discussed in the previous article.
DxFormer: a decoupled automatic diagnostic system based on decoder–encoder transformer with dense symptom representations

Symptom-based automatic diagnostic system queries the patient's potential symptoms through continuous interaction with the patient and makes predictions about possible diseases. A few studies use reinforcement learning (RL) to learn the optimal policy from the joint action space of symptoms and diseases. However, existing RL (or Non-RL) methods focus on disease diagnosis while ignoring the importance of symptom inquiry. Although these systems have achieved considerable diagnostic accuracy, they are still far below its performance upper bound due to few turns of interaction with patients and insufficient performance of symptom inquiry. To address this problem, we propose a new automatic diagnostic framework called DxFormer, which decouples symptom inquiry and disease diagnosis, so that these two modules can be independently optimized. The transition from symptom inquiry to disease diagnosis is parametrically determined by the stopping criteria. In DxFormer, we treat each symptom as a token, and formalize the symptom inquiry and disease diagnosis to a language generation model and a sequence classification model, respectively. We use the inverted version of Transformer, i.e. the decoder-encoder structure, to learn the representation of symptoms by jointly optimizing the reinforce reward and cross-entropy loss.We conduct experiments on three real-world medical dialogue datasets, and the experimental results verify the feasibility of increasing diagnostic accuracy by improving symptom recall. Our model overcomes the shortcomings of previous RL-based methods. By decoupling symptom query from the process of diagnosis, DxFormer greatly improves the symptom recall and achieves the state-of-the-art diagnostic accuracy.Both code and data are available at https://github.com/lemuria-wchen/DxFormer.Supplementary data are available at Bioinformatics online.
#FleeingWuhan: Legitimation and Delegitimation Strategies in Hostile Online Discourse

Abstract Following the first coronavirus case reported to the World Health Organization in Wuhan in 2019 and the ensuing city-wide lockdown that was imposed, many people attempted to leave the city, culminating in a vigorous discourse on the dominant Chinese microblogging site, Weibo. This study seeks to examine how online participants discursively delegitimated and legitimated people who left Wuhan before the lockdown. Weibo posts with the hashtag #逃离武汉 (‘Fleeing Wuhan’) were collected, and delegitimation and legitimation strategies deployed by users were identified. My findings reveal that the delegitimators exploited moral evaluation and impersonal authority to highlight the construed unethicality and shamelessness of people who left Wuhan, whereas the legitimators used an array of strategies, including explanation and definition, to normalize their intentions and counter linguistic hostility. These findings also provide implications vis-à-vis the clustering of delegitimation strategies as well as their linkages with emotional appeals in online discourse.
Parallel learned generative adversarial network with multi-path subspaces for cross-modal retrieval

Cross-modal retrieval aims to narrow the heterogeneity gap between different modalities, such as retrieving images through texts or vice versa. One of the key challenges of cross-modal retrieval is the inconsistent distribution across diverse modalities. Most existing methods tend to construct a common representation subspace to overcome the challenge. However, the supervision information is not fully explored in most single-path cross-modal learning approaches. In this paper, we present a novel Parallel Learned generative adversarial network with Multi-path Subspaces (PLMS) for cross-modal retrieval. PLMS is a parallel learned architecture that aims to capture more effective information in an end-to-end trained cross-modal retrieval model. To be specific, a dual-branch network is constructed in the modality-specific generator, thereby the overall framework learns two common subspaces to emphasize discrepant supervision information and preserve more effective transformed features. We further design two objective functions for the training of the dual branches in generators. Through joint training, the feature representations generated by dual branches in a specific modality are fused for similarity measurement between modalities. To avoid redundancy and overlap during fusion, a Multi-source Domain Balancing (MDB) mechanism is presented to explore the contribution of the two specific-task branches. Extensive experiments show that our proposed method is effective and achieves state-of-the-art results on four widely-used databases.
tBART: Abstractive summarization based on the joining of Topic modeling and BART

Topic information has been helpful to direct semantics in text summarization. In this paper, we present a study on a novel and efficient method to incorporate the topic information with the BART model for abstractive summarization, called the tBART. The proposed model inherits the advantages of the BART, learns latent topics, and transfers the topic vector of tokens to context space by an align function. The experimental results illustrate the effectiveness of our proposed method, which significantly outperforms previous methods on two benchmark datasets: XSUM and CNN/DAILY MAIL.
A Novel Label Enhancement Algorithm Based on Manifold Learning

We propose a label enhancement model to solve the multi-label learning (MLL) problem by using the incremental subspace learning to enrich the label space and to improve the ability of label recognition. In particular, we use the incremental estimation of the feature function representing the manifold structure to guide the construction of the label space and to transform the local topology from the feature space to the label space. First, we build a recursive form for incremental estimation of the feature function representing the feature space information. Second, the label propagation is used to obtain the hidden supervisory information of labels in the data. Finally, an enhanced maximum entropy model based on conditional random field is established as the objective, to obtain the predicted label distribution. The enriched label information in the manifold space obtained in first step and the estimated label distributions provided in second step are employed to train this enhanced maximum entropy model by a gradient-descent iterative optimization to obtain the label distribution predictor’s parameters with enhanced accuracy. We evaluate our method on 24 real-world datasets. Experimental results demonstrate that our label enhancement manifold learning model has advantages in predictive performance over the latest MLL methods.
A unified deep semi-supervised graph learning scheme based on nodes re-weighting and manifold regularization

In recent years, semi-supervised learning on graphs has gained importance in many fields and applications. The goal is to use both partially labeled data (labeled examples) and a large amount of unlabeled data to build more effective predictive models. Deep Graph Neural Networks (GNNs) are very useful in both unsupervised and semi-supervised learning problems. As a special class of GNNs, Graph Convolutional Networks (GCNs) aim to obtain data representation through graph-based node smoothing and layer-wise neural network transformations. However, GCNs have some weaknesses when applied to semi-supervised graph learning: (1) it ignores the manifold structure implicitly encoded by the graph; (2) it uses a fixed neighborhood graph and focuses only on the convolution of a graph, but pays little attention to graph construction; (3) it rarely considers the problem of topological imbalance. To overcome the above shortcomings, in this paper, we propose a novel semi-supervised learning method called Re-weight Nodes and Graph Learning Convolutional Network with Manifold Regularization (ReNode-GLCNMR). Our proposed method simultaneously integrates graph learning and graph convolution into a unified network architecture, which also enforces label smoothing through an unsupervised loss term. At the same time, it addresses the problem of imbalance in graph topology by adaptively reweighting the influence of labeled nodes based on their distances to the class boundaries. Experiments on 8 benchmark datasets show that ReNode-GLCNMR significantly outperforms the state-of-the-art semi-supervised GNN methods.1.
Pareto optimization of wavelet filter design for partial discharge detection in electrical machines

A reliable detection of partial discharges (PDs) in the insulation system of electrical machines is essential to prevent premature breakdowns. Wavelet filters (WFs) are often recommended for denoising this PD measurement data. However, filtering performance strongly depends on numerous settings, which impedes an optimum filter choice. We propose an approach to systematically select the best filter for denoising PD signals. First, representative PD pulses are determined from measurement data by an averaging and a singular value decomposition method. Next, realistic benchmark signals are generated for filter assessment. Further, all possible WF combinations from a set of different mother wavelet selection methods, decomposition trees, and thresholding approaches are evaluated. On the basis of two contradicting objectives, namely mean square error of noise intervals and pulse amplitude distortion, Pareto optimization is applied to choose optimal WF settings. The corresponding filters dominate several common WF proposals from previous literature.
Group-based bi-directional recurrent wavelet neural network for efficient video super-resolution (VSR)

Video super-resolution (VSR) is an important technology for enhancing the quality of video frames. The recurrent neural network (RNN)-based approach is suitable for sequential data because it can use accumulated temporal information. However, since existing methods only tend to capture slow and symmetrical motion with low frame rate, there are still limitations to restore the missing details for more dynamic motion. Most of the previous methods using spatial information treat different types of the spatial features identically. It leads to lack of obtaining meaningful information and enhancing the fine details. We propose a group-based bi-directional recurrent wavelet neural network (GBR-WNN) to exploit spatio-temporal information effectively. The proposed group-based bi-directional RNN (GBR) framework is built on the well-structured process with the group of pictures (GOP). In a GOP, we resolves the low-resolution (LR) frames from border frames to center target frame. Because super-resolved features in a GOP are cumulative, neighboring features are improved progressively and asymmetrical motion can be dealt with. Also, we propose a temporal wavelet attention (TWA) adopting attention module for both spatial and temporal features simultaneously based on discrete wavelet transform. Experiments show that the proposed scheme achieves superior performance compared with state-of-the-art methods.
Research on the digital economy promoting the high-quality development of trade in the central and western regions under the background of big data technology

In order to improve the effect of the digital economy in promoting the development of trade in the central and western regions, this paper combines the intelligent digital economy identification algorithm to construct a trade analysis model, and proposes a collaborative cache strategy in the hybrid mobile opportunistic network scenario. By placing the AP access point at the center of the network, the disadvantage of relying only on node mobility for opportunistic data access is effectively compensated. For different communication scenarios, this paper divides the data transmission stage into D2D communication stage and D2A communication stage according to different relay choices in the data transmission process. Moreover, this paper conducts modeling analysis on them respectively, and discusses the access delay of nodes to data in the network. Finally, this paper verifies that the algorithm in this paper has a certain effect through experimental research, and puts forward suggestions for promoting the high-quality development of trade in the central and western regions.
Research on the application of virtual reality technology in 3D animation creation

In order to improve the effect of three-dimensional animation creation, this paper combines the process of three-dimensional animation creation with virtual reality technology, and performs simulation research through the digitization of character movements. The animated image model controls the skin of the model through the controlled vertices. In order to achieve the control of the controlled vertices, we introduce a data structure that saves the information of the controlled vertices. In addition, this paper combines the needs of animation creation to construct a three-dimensional animation creation system, and uses case design to verify the effect. Through experimental research, it is concluded that the virtual reality creation technology in the three-dimensional animation creation proposed in this paper has a good effect, and it has a certain effect on promoting the development of virtual technology and the progress of animation technology.