Miracle-dz/UniFAR-FTUs huggingface dataset:
Demystifying the Mysteries of Security Vulnerability Discussions on Developer Q&A Sites

Detection and mitigation of Security Vulnerabilities (SVs) are integral tasks in software development and maintenance. Software developers often explore developer Question and Answer (Q&A) websites to find solutions for securing their software. However, there is empirically little known about the on-going SV-related discussions and how the Q&A sites are supporting such discussions. To demystify such mysteries, we conduct large-scale qualitative and quantitative experiments to study the characteristics of 67,864 SV-related posts on Stack Overflow (SO) and Security StackExchange (SSE). We first find that the existing SV categorization of formal security sources is not frequently used on Q&A sites. Therefore, we use Latent Dirichlet Allocation topic modeling to extract a new taxonomy of thirteen SV discussion topics on Q&A sites. We then study the characteristics of such SV topics. Brute-force/Timing Attacks and Vulnerability Testing are found the most popular and difficult topics, respectively. We discover that despite having higher user expertise than other domains, the difficult SV topics do not gain as much attention from experienced users as the more popular ones. Seven types of answers to SV-related questions are also identified on Q&A sites, in which SO usually gives instructions and code, while SSE provides more explanations and/or experience-based advice. Our findings can help practitioners and researchers to utilize Q&A sites more effectively to learn and share SV knowledge.
Bilevel Learning Model Towards Industrial Scheduling

Automatic industrial scheduling, aiming at optimizing the sequence of jobs over limited resources, is widely needed in manufacturing industries. However, existing scheduling systems heavily rely on heuristic algorithms, which either generate ineffective solutions or compute inefficiently when job scale increases. Thus, it is of great importance to develop new large-scale algorithms that are not only efficient and effective, but also capable of satisfying complex constraints in practice. In this paper, we propose a Bilevel Deep reinforcement learning Scheduler, \textit{BDS}, in which the higher level is responsible for exploring an initial global sequence, whereas the lower level is aiming at exploitation for partial sequence refinements, and the two levels are connected by a sliding-window sampling mechanism. In the implementation, a Double Deep Q Network (DDQN) is used in the upper level and Graph Pointer Network (GPN) lies within the lower level. After the theoretical guarantee for the convergence of BDS, we evaluate it in an industrial automatic warehouse scenario, with job number up to $5000$ in each production line. It is shown that our proposed BDS significantly outperforms two most used heuristics, three strong deep networks, and another bilevel baseline approach. In particular, compared with the most used greedy-based heuristic algorithm in real world which takes nearly an hour, our BDS can decrease the makespan by 27.5\%, 28.6\% and 22.1\% for 3 largest datasets respectively, with computational time less than 200 seconds.
Effect of Feeding Low-Oil Ddgs to Laying Hens and Broiler Chickens on Performance and Egg Yolk and Skin Pigmentation

Two experiments were conducted to evaluate the nutritional quality of two sources of low-oil distiller's dried grains with solubles (DDGS) and their pigmenting ability for broiler chicken skin and egg yolks. In Experiment 1, 360 Bovan-White hens between 69 and 77 weeks of age were randomly assigned to five dietary treatments with 6 replicates of 12 hens each. In Experiment 2, 375 Ross 308 broiler chickens were randomly assigned to five treatments with three replicates of 25 birds each. The chickens were fed the experimental diets from one to 42 d of age. In both experiments, treatments consisted of a basal diet with no DDGS, and diets with 6% or 12% inclusion of DDGS from two sources. In Experiment 1, no significant differences in performance were detected among treatments (p> 0.05). Egg yolk pigmentation, according to CR-400 Minolta Colorimeter redness (a) and yellowness (b), linearly increased (p 0.05) were detected among treatments in growth performance, carcass yield, or abdominal fat at 42 d of age. Yellowness linearly increased (p<0.05) in the skin and abdominal fat of the birds that consumed diets with DDGS. The results of the current study indicate that feeding two sources of low-oil DDGS to broiler chicks or laying hens does not negatively affect egg production or growth performance while improves egg yolk and skin yellowness.
Low-Light Maritime Image Enhancement with Regularized Illumination Optimization and Deep Noise Suppression

Maritime images captured under low-light imaging condition easily suffer from low visibility and unexpected noise, leading to negative effects on maritime traffic supervision and management. To promote imaging performance, it is necessary to restore the important visual information from degraded low-light images. In this paper, we propose to enhance the low-light images through regularized illumination optimization and deep noise suppression. In particular, a hybrid regularized variational model, which combines L0-norm gradient sparsity prior with structure-aware regularization, is presented to refine the coarse illumination map originally estimated using Max-RGB. The adaptive gamma correction method is then introduced to adjust the refined illumination map. Based on the assumption of Retinex theory, a guided filter-based detail boosting method is introduced to optimize the reflection map. The adjusted illumination and optimized reflection maps are finally combined to generate the enhanced maritime images. To suppress the effect of unwanted noise on imaging performance, a deep learning-based blind denoising framework is further introduced to promote the visual quality of enhanced image. In particular, this framework is composed of two sub-networks, i.e., E-Net and D-Net adopted for noise level estimation and non-blind noise reduction, respectively. The main benefit of our image enhancement method is that it takes full advantage of the regularized illumination optimization and deep blind denoising. Comprehensive experiments have been conducted on both synthetic and realistic maritime images to compare our proposed method with several state-of-the-art imaging methods. Experimental results have illustrated its superior performance in terms of both quantitative and qualitative evaluations.
A natural boundary of dark matter halo revealed at the minimum bias and maximum infall location.

We introduce a new boundary of dark matter halo which is naturally defined in the halo bias profile as the location of the bias minimum in the quasi-linear scale. This boundary is shown to correspond to the maximum infall location in the halo velocity profile, demarcating the transition between a growing halo and the environment being depleted. Using cosmological N-body simulations we show that this depletion radius depends the most on halo mass and environment. The dependence on formation time is also clear, with likely non-redundant dependencies on other properties such as halo shape and spin. We show that this depletion boundary is approximately proportional to the conventionally defined splashback radius, and can be interpreted as the radius enclosing a highly complete population of splashback orbits. For low mass haloes that have stopped mass growth, this depletion radius approaches the turnaround radius, while for massive haloes it is smaller than the turnaround. The depletion radius is approximately 2 times the virial radius and encloses an average density of ~10 times the critical density of the universe, independent on halo mass but dependent on other halo properties. This radius can also be interpreted as a natural radius for halo exclusion in halo models of the large scale structure.
On criticality theory for elliptic mixed boundary value problems in divergence form

The paper is devoted to the study of positive solutions of a second-order linear elliptic equation in divergence form in a domain $D\subseteq \mathbb{R}^n$ that satisfy an oblique boundary condition on a portion of $\partial D$. First, we study the degenerate mixed boundary value problem $$ \begin{cases} Pu=f & \text{in } D, \\ Bu = 0 & \text{on } \partial D_{\mathrm{Rob}}, \\ u=0& \text{on } \partial D_{\mathrm{Dir}}, \end{cases} $$ where $D$ is a bounded Lipschitz domain, $\partial D_{\mathrm{Rob}}$ is a relatively open portion of $\partial D$, $\partial D_{\mathrm{Dir}}$ is a closed set of $\partial D$, and $B$ is an oblique (Robin) boundary operator defined on $\partial D_{\mathrm{Rob}}$. In particular, we discuss the unique solvability of the above problem, the existence of a principal eigenvalue, and the existence of a positive minimal Green function. Then we establish a criticality theory for positive weak solutions of the operator $(P,B)$ in a general domain with no boundary condition on $\partial D_{\mathrm{Dir}}$ and no growth condition at infinity. The paper generalizes and extends results obtained by Pinchover and Saadon (2002) for classical solutions of such a problem, where stronger regularity assumptions on the coefficients of $(P,B)$, and $\partial D_{\mathrm{Rob}}$.
Performance Analysis of Priority-Aware NoCs with Deflection Routing under Traffic Congestion

Priority-aware networks-on-chip (NoCs) are used in industry to achieve predictable latency under different workload conditions. These NoCs incorporate deflection routing to minimize queuing resources within routers and achieve low latency during low traffic load. However, deflected packets can exacerbate congestion during high traffic load since they consume the NoC bandwidth. State-of-the-art analytical models for priority-aware NoCs ignore deflected traffic despite its significant latency impact during congestion. This paper proposes a novel analytical approach to estimate end-to-end latency of priority-aware NoCs with deflection routing under bursty and heavy traffic scenarios. Experimental evaluations show that the proposed technique outperforms alternative approaches and estimates the average latency for real applications with less than 8% error compared to cycle-accurate simulations.
SemEval-2020 Task 9: Overview of Sentiment Analysis of Code-Mixed Tweets

In this paper, we present the results of the SemEval-2020 Task 9 on Sentiment Analysis of Code-Mixed Tweets (SentiMix 2020). We also release and describe our Hinglish (Hindi-English)and Spanglish (Spanish-English) corpora annotated with word-level language identification and sentence-level sentiment labels. These corpora are comprised of 20K and 19K examples, respectively. The sentiment labels are - Positive, Negative, and Neutral. SentiMix attracted 89 submissions in total including 61 teams that participated in the Hinglish contest and 28 submitted systems to the Spanglish competition. The best performance achieved was 75.0% F1 score for Hinglish and 80.6% F1 for Spanglish. We observe that BERT-like models and ensemble methods are the most common and successful approaches among the participants.
Toric Newton-Okounkov functions with an application to the rationality of certain Seshadri constants on surfaces

We initiate a combinatorial study of Newton-Okounkov functions on toric varieties with an eye on the rationality of asymptotic invariants of line bundles. In the course of our efforts we identify a combinatorial condition which ensures a controlled behavior of the appropriate Newton-Okounkov function on a toric surface. Our approach yields the rationality of many Seshadri constants that have not been settled before.
How Trustworthy are the Existing Performance Evaluations for Basic Vision Tasks?

Performance evaluation is indispensable to the advancement of machine vision, yet its consistency and rigour have not received proportionate attention. This paper examines performance evaluation criteria for basic vision tasks namely, object detection, instance-level segmentation and multi-object tracking. Specifically, we advocate the use of criteria that are (i) consistent with mathematical requirements such as the metric properties, (ii) contextually meaningful in sanity tests, and (iii) robust to hyper-parameters for reliability. We show that many widely used performance criteria do not fulfill these requirements. Moreover, we explore alternative criteria for detection, segmentation, and tracking, using metrics for sets of shapes, and assess them against these requirements.
Optimizing Co-flows Scheduling and Routing in Data Centre Networks for Big Data Applications

This paper optimizes the scheduling and routing of the co-flows of MapReduce shuffling phase in state-of-the-art and proposed Passive Optical Networking (PON)-based Data Centre Network (DCN) architectures. A time-slotted Mixed Integer Linear Programming (MILP) model is developed and used for the optimization with the objective of minimizing either the total energy consumption or the completion time. The DCN architectures include four state-of-the-art electronic switching architectures which are spine-leaf, Fat-tree, BCube, and DCell data centres. The proposed PON-based DCN architectures include two designs that utilize ports in Optical Line Terminal (OLT) line cards for inter and possibly intra data centre networking in addition to passive interconnects for the intra data centre networking between different PON groups (i.e. racks) within a PON cell (i.e. number of PON groups connected to a single OLT port). The first design is a switch-centric design that uses two Arrayed Waveguide Grating Routers (AWGRs) and the second is a server-centric design. The study also considers different traffic patterns defined according to the distribution of map and reduce tasks in the servers and data skewness.
Why to "grow" and "harvest" deep learning models?

Current expectations from training deep learning models with gradient-based methods include: 1) transparency; 2) high convergence rates; 3) high inductive biases. While the state-of-art methods with adaptive learning rate schedules are fast, they still fail to meet the other two requirements. We suggest reconsidering neural network models in terms of single-species population dynamics where adaptation comes naturally from open-ended processes of "growth" and "harvesting". We show that the stochastic gradient descent (SGD) with two balanced pre-defined values of per capita growth and harvesting rates outperform the most common adaptive gradient methods in all of the three requirements.
Deep Reinforcement Learning with Label Embedding Reward for Supervised Image Hashing

Deep hashing has shown promising results in image retrieval and recognition. Despite its success, most existing deep hashing approaches are rather similar: either multi-layer perceptron or CNN is applied to extract image feature, followed by different binarization activation functions such as sigmoid, tanh or autoencoder to generate binary code. In this work, we introduce a novel decision-making approach for deep supervised hashing. We formulate the hashing problem as travelling across the vertices in the binary code space, and learn a deep Q-network with a novel label embedding reward defined by Bose-Chaudhuri-Hocquenghem (BCH) codes to explore the best path. Extensive experiments and analysis on the CIFAR-10 and NUS-WIDE dataset show that our approach outperforms state-of-the-art supervised hashing methods under various code lengths.
Labels Are Not Perfect: Improving Probabilistic Object Detection via Label Uncertainty

Reliable uncertainty estimation is crucial for robust object detection in autonomous driving. However, previous works on probabilistic object detection either learn predictive probability for bounding box regression in an un-supervised manner, or use simple heuristics to do uncertainty regularization. This leads to unstable training or suboptimal detection performance. In this work, we leverage our previously proposed method for estimating uncertainty inherent in ground truth bounding box parameters (which we call label uncertainty) to improve the detection accuracy of a probabilistic LiDAR-based object detector. Experimental results on the KITTI dataset show that our method surpasses both the baseline model and the models based on simple heuristics by up to 3.6% in terms of Average Precision.
Knowing Depth Quality In Advance: A Depth Quality Assessment Method For RGB-D Salient Object Detection

Previous RGB-D salient object detection (SOD) methods have widely adopted deep learning tools to automatically strike a trade-off between RGB and D (depth), whose key rationale is to take full advantage of their complementary nature, aiming for a much-improved SOD performance than that of using either of them solely. However, such fully automatic fusions may not always be helpful for the SOD task because the D quality itself usually varies from scene to scene. It may easily lead to a suboptimal fusion result if the D quality is not considered beforehand. Moreover, as an objective factor, the D quality has long been overlooked by previous work. As a result, it is becoming a clear performance bottleneck. Thus, we propose a simple yet effective scheme to measure D quality in advance, the key idea of which is to devise a series of features in accordance with the common attributes of high-quality D regions. To be more concrete, we conduct D quality assessments for each image region, following a multi-scale methodology that includes low-level edge consistency, mid-level regional uncertainty and high-level model variance. All these components will be computed independently and then be assembled with RGB and D features, applied as implicit indicators, to guide the selective fusion. Compared with the state-of-the-art fusion schemes, our method can achieve a more reasonable fusion status between RGB and D. Specifically, the proposed D quality measurement method achieves steady performance improvements for almost 2.0\% in general.
Largely Enhanced Photogalvanic Effects in a Phosphorene Photodetector by Strain-Increased Device Asymmetry

Photogalvanic effect (PGE) occurring in noncentrosymmetric materials enables the generation of the open-circuit voltage that is much larger than the bandgap, making it rather attractive in solar cells. However, the magnitude of the PGE photocurrent is usually small, which severely hampers its practical application. Here we propose a mechanism to largely enhance the PGE photocurrent by mechanical strain based on the quantum transport simulations for the two-dimensional nickel-phosphorene-nickel photodetector. Broadband PGE photocurrent governed by the Cs noncentrosymmetry is generated at zero bias under the illumination of linearly polarized light. The photocurrent depends linearly on the device asymmetry, while nonlinearly on the optical absorption. By applying the appropriate mechanical tension stress on the phosphorene, the photocurrent can be substantially enhanced by up to 3 orders of magnitude, which is primarily ascribed to the largely increased device asymmetry. The change in the optical absorption in some cases can also play a critical role in tuning the photocurrent due to the nonlinear dependence. Moreover, the photocurrent can even be further enhanced by the mechanical bending, mainly owing to the considerably enhanced device asymmetry. Our results reveal the dependence of the PGE photocurrent on the device asymmetry and absorption in transport process through a device, and also explore the potentials of the PGE in the self-powered low-dimensional flexible optoelectronics.
NASB: Neural Architecture Search for Binary Convolutional Neural Networks

Binary Convolutional Neural Networks (CNNs) have significantly reduced the number of arithmetic operations and the size of memory storage needed for CNNs, which makes their deployment on mobile and embedded systems more feasible. However, after binarization, the CNN architecture has to be redesigned and refined significantly due to two reasons: 1. the large accumulation error of binarization in the forward propagation, and 2. the severe gradient mismatch problem of binarization in the backward propagation. Even though substantial effort has been invested in designing architectures for single and multiple binary CNNs, it is still difficult to find an optimized architecture for binary CNNs. In this paper, we propose a strategy, named NASB, which adapts Neural Architecture Search (NAS) to find an optimized architecture for the binarization of CNNs. In the NASB strategy, the operations and their connections define a unique searching space and the training and binarization of the network progress in the three-stage training algorithm. 1 Due to the flexibility of this automated strategy, the obtained architecture is not only suitable for binarization but also has low overhead, achieving a better trade-off between the accuracy and computational complexity compared to hand-optimized binary CNNs. The implementation of the NASB strategy is evaluated on the ImageNet dataset and demonstrated as a better solution compared to existing quantized CNNs. With insignificant overhead increase, NASB outperforms existing single and multiple binary CNNs by up to 4.0% and 1.0% Top-1 accuracy respectively, bringing them closer to the precision of their full precision counterpart.
Navigating Language Models with Synthetic Agents

Modern natural language models such as the GPT-2/GPT-3 contain tremendous amounts of information about human belief in a consistently interrogatable form. If these models could be shown to accurately reflect the underlying beliefs of the human beings that produced the data used to train these models, then such models become a powerful sociological tool in ways that are distinct from traditional methods, such as interviews and surveys. In this study, We train a version of the GPT-2 on a corpora of historical chess games, and then compare the learned relationships of words in the model to the known ground truth of the chess board, move legality, and historical patterns of play. We find that the percentages of moves by piece using the model are substantially similar from human patterns. We further find that the model creates an accurate latent representation of the chessboard, and that it is possible to plot trajectories of legal moves across the board using this knowledge.
Formation of singularity for the rotating shallow water system

In this paper, we investigate the formation of singularity for general two dimensional and radially symmetric solutions for rotating shallow water system from different aspects. First, the formation of singularity is proved via the study for the associated moments for two dimensional solutions. For the radial symmetric solutions, the formation of singularity is established for the initial data with compact support. Finally, the global existence or formation of singularity for the radial symmetric solutions of the rotating shallow water system are analyzed in detail when the solutions are of the form with separated variables.