Reviewerly/exHarmony huggingface dataset:
VGRAPH: A Robust Vulnerable Code Clone Detection System Using Code Property Triplets

Software vulnerabilities are a common attack vector for cyber adversaries. This problem has been exacerbated by the wealth of open-source software projects, as code is often copy-pasted to new locations. This causes a serious problem when a new security vulnerability is discovered in a particular software project, as it may potentially affect many others. Discovering vulnerable code reuse in source code is known as vulnerable code clone detection. This is a very challenging problem as the cloned code has the potential to be modified, sometimes significantly, from the original code, while still retaining the underlying vulnerability. Existing vulnerable clone detection techniques are either too strict, missing vulnerabilities when they have subtle modifications, or are too narrow, applicable only to a small number of vulnerability types. In this work we present VGRAPH, a technique for identifying vulnerable code clones, which is more robust to code modification, while still remaining generic to all vulnerability types. VGRAPHs are representations of vulnerable source code comprising three graph-based components representing code property relationships extracted from the contextual code, the vulnerable code, and the patched code. We develop a matching algorithm utilizing these three graph-based components which is able to identify vulnerable code clones with a precision of 98% and recall of 97%. Even for highly modified code clones, we are able to identify over 100 more vulnerable clones than the best performing comparison work ReDeBug. When we apply our technique to several versions of popular software packages (e.g., FFMpeg, OpenSSL), we are able to identify 10 vulnerabilities which were silently patched and are not listed in the National Vulnerability Database.
Generalized Embedding Regression: A Framework for Supervised Feature Extraction

Sparse discriminative projection learning has attracted much attention due to its good performance in recognition tasks. In this article, a framework called generalized embedding regression (GER) is proposed, which can simultaneously perform low-dimensional embedding and sparse projection learning in a joint objective function with a generalized orthogonal constraint. Moreover, the label information is integrated into the model to preserve the global structure of data, and a rank constraint is imposed on the regression matrix to explore the underlying correlation structure of classes. Theoretical analysis shows that GER can obtain the same or approximate solution as some related methods with special settings. By utilizing this framework as a general platform, we design a novel supervised feature extraction approach called jointly sparse embedding regression (JSER). In JSER, we construct an intrinsic graph to characterize the intraclass similarity and a penalty graph to indicate the interclass separability. Then, the penalty graph Laplacian is used as the constraint matrix in the generalized orthogonal constraint to deal with interclass marginal points. Moreover, the L2,1 -norm is imposed on the regression terms for robustness to outliers and data's variations and the regularization term for jointly sparse projection learning, leading to interesting semantic interpretability. An effective iterative algorithm is elaborately designed to solve the optimization problem of JSER. Theoretically, we prove that the subproblem of JSER is essentially an unbalanced Procrustes problem and can be solved iteratively. The convergence of the designed algorithm is also proved. Experimental results on six well-known data sets indicate the competitive performance and latent properties of JSER.
Intelligent Resource Scheduling Based on Locality Principle in Data Center Networks

Cloud computing is developing rapidly and playing an increasingly important role in many fields. Resource management of data center networks (DCNs) is critical to the performance of cloud computing. Due to the large-scale, distributed, and cross-regional characteristics of DCNs, there is insufficient resource utilization. In this article, we discuss the latest developments in the resource management and optimization of DCNs. We also designed an intelligent resource search algorithm to efficiently search excellent resources in massive computing resources. We propose a novel intelligent resource scheduling based on the locality principle algorithm (RSLP). The proposed strategy can search computing resources dynamically and efficiently, and allocate large resources to large tasks efficiently. Furthermore, we outline critical demands and guidance for researchers and designers to achieve the convergence of artificial intelligence and data centers.
Quantum detector tomography of a 2×2 multi-pixel array of superconducting nanowire single photon detectors

We demonstrate quantum detector tomography of a commercial 2×2 array of superconducting nanowire single photon detectors. We show that detector-specific figures of merit including efficiency, dark-count and cross-talk probabilities can be directly extracted, without recourse to the underlying detector physics. These figures of merit are directly identified from just four elements of the reconstructed positive operator valued measure (POVM) of the device. We show that the values for efficiency and dark-count probability extracted by detector tomography show excellent agreement with independent measurements of these quantities, and we provide an intuitive operational definition for cross-talk probability. Finally, we show that parameters required for the reconstruction must be carefully chosen to avoid oversmoothing the data.
On the Compressed-Oracle Technique, and Post-Quantum Security of Proofs of Sequential Work

We revisit the so-called compressed oracle technique, introduced by Zhandry for analyzing quantum algorithms in the quantum random oracle model (QROM). To start off with, we offer a concise exposition of the technique, which easily extends to the parallel-query QROM, where in each query-round the considered algorithm may make several queries to the QROM in parallel. This variant of the QROM allows for a more fine-grained query-complexity analysis. Our main technical contribution is a framework that simplifies the use of (the parallel-query generalization of) the compressed oracle technique for proving query complexity results. With our framework in place, whenever applicable, it is possible to prove quantum query complexity lower bounds by means of purely classical reasoning. More than that, for typical examples the crucial classical observations that give rise to the classical bounds are sufficient to conclude the corresponding quantum bounds. We demonstrate this on a few examples, recovering known results but also obtaining new results. Our main target is the hardness of finding a q-chain with fewer than q parallel queries, i.e., a sequence $$x_0, x_1,\ldots , x_q$$ with $$x_i = H(x_{i-1})$$ for all $$1 \le i \le q$$ . The above problem of finding a hash chain is of fundamental importance in the context of proofs of sequential work. Indeed, as a concrete cryptographic application of our techniques, we prove quantum security of the "Simple Proofs of Sequential Work" by Cohen and Pietrzak .
TagBleed: Breaking KASLR on the Isolated Kernel Address Space using Tagged TLBs

Kernel Address Space Layout Randomization (KASLR) has been repeatedly targeted by side-channel attacks that exploit a typical unified user/kernel address space organization to disclose randomized kernel addresses. The community has responded with kernel address space isolation techniques that separate user and kernel address spaces (and associated resources) to eradicate all existing side-channel attacks. In this paper, we show that kernel address space isolation is insufficient to harden KASLR against practical side-channel attacks on modern tagged TLB architectures. While tagged TLBs have been praised for optimizing the performance of kernel address space isolation, we show that they also silently break its original security guarantees and open up opportunities for new derandomization attacks. As a concrete demonstration, we present TagBleed, a new side-channel attack that abuses tagged TLBs and residual translation information to break KASLR even in the face of state-of-the-art mitigations. TagBleed is practical and shows that implementing secure address space isolation requires deep partitioning of microarchitectural resources and a more generous performance budget than previously assumed.
No-Reference Image Contrast Evaluation by Generating Bidirectional Pseudoreferences

This article proposes a simple yet reliable no-reference image contrast evaluator (NICE) by generating bidirectional pseudoreferences (BPR). Different from the existing no-reference metrics that only operate on the contrast distorted image (CDI) itself, our proposed NICE-BPR measures the deviations of a CDI to its corresponding aggravated and enhanced counterparts (i.e., BPRs) in a hybrid feature space. Given a CDI, we first perform contrast aggravation and contrast enhancement using gamma correction and histogram equalization, respectively. Then, hybrid contrast-aware features are, respectively, extracted from the CDI and its corresponding BPRs via the analysis of histogram, entropy, and structure. The features obtained from the CDI are one-by-one compared with those from the BPRs to derive the bidirectional feature deviation vector. Finally, a quality predictor is built by learning a regression model to fuse the feature vector into a continuous quality score. Extensive experiments on several databases well-demonstrate the superiority of NICE-BPR.
Yet Another Intermediate-Level Attack

The transferability of adversarial examples across deep neural network (DNN) models is the crux of a spectrum of black-box attacks. In this paper, we propose a novel method to enhance the black-box transferability of baseline adversarial examples. By establishing a linear mapping of the intermediate-level discrepancies (between a set of adversarial inputs and their benign counterparts) for predicting the evoked adversarial loss, we aim to take full advantage of the optimization procedure of mulch-step baseline attacks. We conducted extensive experiments to verify the effectiveness of our method on CIFAR-100 and ImageNet. Experimental results demonstrate that it outperforms previous state-of-the-arts considerably. Our code is at https://github.com/qizhangli/ila-plus-plus.
Is postgraduate English academic writing more clausal or phrasal? Syntactic complexification at the crossroads of genre, proficiency, and statistical modelling

This study uses a novel approach to describe modern English academic texts in terms of the amount and distribution of syntactic subordination, coordination and phrasal structures. Inconsistent results have been reported in previous scholarship regarding the trajectory of syntactic complexification based on English language backgrounds and linguistic proficiency of writers. For the first time, a combination of two predictive statistical modelling methods of mixed-effects modelling and supervised machine learning modelling with random forest is used to examine the extent to which the type, amount, and distribution of these syntactic structures can be attributed to a text-intrinsic feature (sub-genres or rhetorical sections of academic register) and a text-extrinsic factor (English language background based on academic context) in a corpus of master’s dissertations. To revisit the theories of English L1 vs. L2 texts, strong predictors of syntactic complexity across academic writings of EFL, ESL, and English L1 postgraduate students were identified. The findings show that EFL texts are dominantly subordinate in nature, English L1’s dominantly phrasal, and the ESL dissertations exhibited similar amounts of subordination and phrasal structures. Distinct syntactic structures also characterise rhetorical sections: abstracts are predominantly phrasal, literature reviews distinctly subordinate, and conclusion sections have noticeable amounts of verb phrases.
SpectreRewind

Transient execution attacks use microarchitectural covert channels to leak secrets that should not have been accessible during logical program execution. Commonly used micro-architectural covert channels are those that leave lasting footprints in the micro-architectural state, for example, a cache state change, from which the secret is recovered after the transient execution is completed.
Improving traffic sign recognition results in urban areas by overcoming the impact of scale and rotation

Automatic detection and recognition of traffic signs have many applications. However, some problems can affect the accuracy of the existing algorithms, such as changes in environmental light conditions, shadows, the presence of objects of the same colour, significant changes in scale and rotation, as well as obstacles in front of the traffic signs. To overcome these difficulties, a reference image database is usually used that includes different modes of appearing the traffic signs in the images. In order to overcome the effects of scale and rotation, in this paper a new method is presented in which only one reference image is needed for each sign to recognise the traffic sign in an image. In the proposed method, imaging is done in stereo. Using the captured image pair, a virtual image is generated which is then used to recognise the sign. As a result, the recognition is carried out with a minimum number of reference images. Experiments show that the proposed algorithm significantly improves recognition results. The traffic signs are recognised with 93.1% accuracy that enjoys a 4.9% improvement over traditional methods.
Learning Diverse Features with Part-Level Resolution for Person Re-identification

Learning diverse features is key to the success of person re-identification. Various part-based methods have been extensively proposed for learning local representations, which, however, are still inferior to the best-performing methods for person re-identification. This paper proposes to construct a strong lightweight network architecture, termed PLR-OSNet, based on the idea of Part-Level feature Resolution over the Omni-Scale Network (OSNet) for achieving feature diversity. The proposed PLR-OSNet has two branches, one branch for global feature representation and the other branch for local feature representation. The local branch employs a uniform partition strategy for part-level feature resolution but produces only a single identity-prediction loss, which is in sharp contrast to the existing part-based methods. Empirical evidence demonstrates that the proposed PLR-OSNet achieves state-of-the-art performance on popular person Re-ID datasets, including Market1501, DukeMTMC-reID and CUHK03, despite its small model size.
Dense Scene Multiple Object Tracking with Box-Plane Matching

Multiple Object Tracking (MOT) is an important task in computer vision. MOT is still challenging due to the occlusion problem, especially in dense scenes. Following the tracking-by-detection framework, we propose the Box-Plane Matching (BPM) method to improve the MOT performacne in dense scenes. First, we design the Layer-wise Aggregation Discriminative Model (LADM) to filter the noisy detections. Then, to associate remaining detections correctly, we introduce the Global Attention Feature Model (GAFM) to extract appearance feature and use it to calculate the appearance similarity between history tracklets and current detections. Finally, we propose the Box-Plane Matching strategy to achieve data association according to the motion similarity and appearance similarity between tracklets and detections. With the effectiveness of the three modules, our team achieves the 1st place on the Track-1 leaderboard in the ACM MM Grand Challenge HiEve 2020.
A Comparative Study of Different Types of Convolutional Neural Networks for Breast Cancer Histopathological Image Classification

Recently, for image classification and detection convolutional neural networks have shown breakthrough performance. The main reason behind their impressive performance is that they are inspired by the mammal's visual cortex. In this paper, we have investigated the performance of five convolutional neural network architectures that are LeNet-5, AlexNet, VGG-16, ResNet-50, and Inception-vl on the basis of test accuracy, AUC, precision, recall, f1-score. All of these models are tested on the BreaKHis dataset. In the BreaKHis dataset the Inception-vl network has achieved the highest test accuracy of 89%, 92%, 94% and 90% respectively at 40X, 100X, 200X and 400X magnification factor binary classification. The inception-v l network also has achieved the highest AUC of 85%, 91%, 94% and 88%, highest precision of 86%, 91%, 92% and 89%, highest recall of 85%, 91%, 94% and 88% and highest f1-score of 85%, 91%, 93% and 89% respectively at 40X, 100X, 200X and 400X magniflcation factor.
A Battery-Free Long-Range Wireless Smart Camera for Face Detection

This paper presents a battery-free smart camera that combines tiny machine learning, long-range communication, power management, and energy harvesting. The smart sensor node has been implemented and evaluated in the field, showing both battery-less capabilities with a small-size photovoltaic panel and the energy efficiency of the proposed solution. We evaluated two different ARM Cortex-M4F microcontrollers, the Ambiq Apollo 3 that is an energy-efficient microcontroller, and a Microchip SAMD51 able to work in high radiation environments but with a higher power in active mode. Finally, a low power LoRa module provides the long-range wireless transmission capability. The tiny machine learning algorithm for face recognition has been optimized in terms of accuracy versus energy, achieving up to 97% accuracy recognizing five different faces. Experimental results demonstrated the capability of the developed sensor node to start from the cold start after 1 minute at a very low luminosity of 350 lux using a cm size flexible photovoltaic panels and work perpetually after the cold start.
Image Super-Resolution Using Sparse Representation And Novelty Noise Removal Super-Resolution

The reconstruction of a composite image with a super-resolution will produce a high-resolution image from a low-resolution picture. Since the super-resolution problem is not well answered, a narrow method of optimizing numeric algorithm is implemented to remove image noise. The total variation of the image is minimized subject to noise statistical constraints. The restrictions are imposed on the use of Lagrange multipliers. The solution is derived from the gradient projection method. It is the solution of a partial differential equation based on time based on several constraints described. As t--$\sim$0o, the solution converges into a continuous image. It is a fast, relatively easy numerical algorithm. The measurements are typically state of the art with very noisy videos. The process is non-invasive, which gives straight boundaries to the picture. A first step to the transfer can be translated. level range of the normal image at a pace equivalent to the level curvature separate from the magnitude of the feature gradient, and a second stage for the return to the boundary point of the picture.
Prediction and Recovery for Adaptive Low-Resolution Person Re-Identification

Low-resolution person re-identification (LR re-id) is a challenging task with low-resolution probes and high-resolution gallery images. To address the resolution mismatch, existing methods typically recover missing details for low-resolution probes by super-resolution. However, they usually pre-specify fixed scale factors for all images, and ignore the fact that choosing a preferable scale factor for certain image content probably greatly benefits the identification. In this paper, we propose a novel Prediction, Recovery and Identification (PRI) model for LR re-id, which adaptively recovers missing details by predicting a preferable scale factor based on the image content. To deal with the lack of ground-truth optimal scale factors, our model contains a self-supervised scale factor metric that automatically generates dynamic soft labels. The generated labels indicate probabilities that each scale factor is optimal, which are used as guidance to enhance the content-aware scale factor prediction. Consequently, our model can more accurately predict and recover the content-aware details, and achieve state-of-the-art performances on four LR re-id datasets.
Biases and differences in code review using medical imaging and eye-tracking: genders, humans, and machines

Code review is a critical step in modern software quality assurance, yet it is vulnerable to human biases. Previous studies have clarified the extent of the problem, particularly regarding biases against the authors of code,but no consensus understanding has emerged. Advances in medical imaging are increasingly applied to software engineering, supporting grounded neurobiological explorations of computing activities, including the review, reading, and writing of source code. In this paper, we present the results of a controlled experiment using both medical imaging and also eye tracking to investigate the neurological correlates of biases and differences between genders of humans and machines (e.g., automated program repair tools) in code review. We find that men and women conduct code reviews differently, in ways that are measurable and supported by behavioral, eye-tracking and medical imaging data. We also find biases in how humans review code as a function of its apparent author, when controlling for code quality. In addition to advancing our fundamental understanding of how cognitive biases relate to the code review process, the results may inform subsequent training and tool design to reduce bias.
Training Simplification and Model Simplification for Deep Learning : A Minimal Effort Back Propagation Method

We propose a simple yet effective technique to simplify the training and the resulting model of neural networks. In back propagation, only a small subset of the full gradient is computed to update the model parameters. The gradient vectors are sparsified in such a way that only the top-k elements (in terms of magnitude) are kept. As a result, only k rows or columns (depending on the layout) of the weight matrix are modified, leading to a linear reduction in the computational cost. Based on the sparsified gradients, we further simplify the model by eliminating the rows or columns that are seldom updated, which will reduce the computational cost both in the training and decoding, and potentially accelerate decoding in real-world applications. Surprisingly, experimental results demonstrate that most of time we only need to update fewer than 5% of the weights at each back propagation pass. More interestingly, the accuracy of the resulting models is actually improved rather than degraded, and a detailed analysis is given. The model simplification results show that we could adaptively simplify the model which could often be reduced by around 9x, without any loss on accuracy or even with improved accuracy. The codes, including the extension, are available at https://github.com/lancopku/meSimp