During software development, vulnerabilities have posed a significant threat to users. Patches are the most effective way to combat vulnerabilities. In a large-scale software system, testing the presence of a security patch in every affected binary is crucial to ensure system security. Identifying whether a binary has been patched for a known vulnerability is challenging, as there may only be small differences between patched and vulnerable versions. Existing approaches mainly focus on detecting patches that are compiled in the same compiler options. However, it is common for developers to compile programs with very different compiler options in different situations, which causes inaccuracy for existing methods. In this paper, we propose a new approach named PS3, referring to precise patch presence test based on semantic-level symbolic signature. PS3 exploits symbolic emulation to extract signatures that are stable under different compiler options. Then PS3 can precisely test the presence of the patch by comparing the signatures between the reference and the target at semantic level.

Patch presence test is critical in software security to ensure that binary files have been patched for known vulnerabilities. It is challenging due to the semantic gap between the source code and the binary, and the small and subtle nature of patches. In this paper, we propose React, the first patch presence test approach on IR-level. Based on the IR code compiled from the source code and the IR code lifted from the binary, we first extract four types of feature (return value, condition, function call, and memory store) by executing the program symbolically. Then, we refine the features from the source code and rank them. Finally, we match the features to determine the presence of a patch with an SMT solver to check the equivalence of features at the semantic level.

At a time when effective tools for monitoring malaria control and eradication efforts are crucial, the increasing availability of molecular data motivates their application to epidemiology. The multiplicity of infection (MOI), defined as the number of genetically distinct parasite strains co-infecting a host, is one key epidemiological parameter for evaluating malaria interventions. Estimating MOI remains a challenge for high-transmission settings where individuals typically carry multiple co-occurring infections. Several quantitative approaches have been developed to estimate MOI, including two cost-effective ones relying on molecular data: i) THE REAL McCOIL method is based on putatively neutral single nucleotide polymorphism loci, and ii) the varcoding method is a fingerprinting approach that relies on the diversity and limited repertoire overlap of the var multigene family encoding the major Plasmodium falciparum blood-stage antigen PfEMP1 and is therefore under selection. In this study, we assess the robustness of the MOI estimates generated with these two approaches by simulating P. falciparum malaria dynamics under three transmission conditions using an extension of a previously developed stochastic agent-based model. We demonstrate that these approaches are complementary and best considered across distinct transmission intensities. While varcoding can underestimate MOI, it allows robust estimation, especially under high transmission where repertoire overlap is extremely limited from frequency-dependent selection. In contrast, THE REAL McCOIL often considerably overestimates MOI, but still provides reasonable estimates for low and moderate transmission. Regardless of transmission intensity, results for THE REAL McCOIL indicate that an inaccurate tail at high MOI values is generated, and that at high transmission, an apparently reasonable estimated MOI distribution can arise from some degree of compensation between overestimation and underestimation. As many countries pursue malaria elimination targets, defining the most suitable approach to estimate MOI based on sample size and local transmission intensity is highly recommended for monitoring the impact of intervention programs.

During software development, developers introduce code clones by reusing existing code to improve programming productivity. Considering the detrimental effects on software maintenance and evolution, many techniques are proposed to detect code clones. Existing approaches are mainly used to detect clones written in the same programming language. However, it is common to develop programs with the same functionality but in different programming languages to support various platforms. In this paper, we propose a new approach named C4, referring to Contrastive Cross-language Code Clone detection model. It can detect cross-language clones with learned representations effectively. C4 exploits the pre-trained model CodeBERT to convert programs in different languages into high-dimensional vector representations. In addition, we fine tune the C4 model through a constrastive learning objective that can effectively recognize clone pairs and non-clone pairs. To evaluate the effectiveness of our approach, we conduct extensive experiments on the dataset proposed by CLCDSA. Experimental results show that C4 achieves scores of 0.94, 0.90, and 0.92 in terms of precision, recall and F-measure and substantially outperforms the state-of-the-art baselines.

To achieve an objective and efficient depression detection system, we propose a cognitive psychology experimental paradigm based on the attentional bias theory and eye movements in this paper. We select images of three different emotions (positive, neutral, and negative) as experimental stimulus. Comparing with the traditional free viewing paradigm, the paradigm we proposed adds a stage of frame tracking to analyse the process of attention disengagement. Based on extracted psychological features from eye movement data, we train a mental state classifier of Support Vector Machine to classify people with depression and normal controls, and the model achieves 77.0% of accuracy, which achieve state-of-the-art under the same data condition. Our model is interpretable and our results demonstrate the theory of attention bias.

SAR image enhancement plays an important role in the process of SAR image processing and information interpretation. Super-resolution reconstruction is a widely adopted enhancement method. However, it is difficult to achieve a decent tradeoff between reconstruction effectiveness and the convergence speed for existing methods. To combat such problem, this paper proposed a novel solution to it, we started from the modeling of SAR image degradation principle, applying the adaptive line search strategy to the SAR image super-resolution reconstruction process, and redefined the step size selection in the reconstruction process, made it possible to achieve both reconstruction effectiveness and convergence speed. Compared with the existing empirical setting or iterative selection, the proposed method can reduce the number of iterations while guarantee the reconstruction results.

Digital reconstruction of neuronal structures is very important to neuroscience research. Many existing reconstruction algorithms require a set of good seed points. 3D neuron critical points, including terminations, branch points and cross-over points, are good candidates for such seed points. However, a method that can simultaneously detect all types of critical points has barely been explored. In this work, we present a method to simultaneously detect all 3 types of 3D critical points in neuron microscopy images, based on a spherical-patches extraction (SPE) method and a 2D multi-stream convolutional neural network (CNN). SPE uses a set of concentric spherical surfaces centered at a given critical point candidate to extract intensity distribution features around the point. Then, a group of 2D spherical patches is generated by projecting the surfaces into 2D rectangular image patches according to the orders of the azimuth and the polar angles. Finally, a 2D multi-stream CNN, in which each stream receives one spherical patch as input, is designed to learn the intensity distribution features from those spherical patches and classify the given critical point candidate into one of four classes: termination, branch point, cross-over point or non-critical point. Experimental results confirm that the proposed method outperforms other state-of-the-art critical points detection methods. The critical points based neuron reconstruction results demonstrate the potential of the detected neuron critical points to be good seed points for neuron reconstruction. Additionally, we have established a public dataset dedicated for neuron critical points detection, which has been released along with this article.

In this paper, a dynamic codebook selection scheme is proposed for beam sweeping at millimeter wave receiver. The codebook selection problem is formulated as a Partially Observed Markov Decision Process (POMDP) and Q-learning method is used to learn a policy to dynamically select an appropriate codebook from a pre-defined codebook set in each beam sweeping period. A hierarchical structure codebook set design method based on Lloyd's method is proposed to generate a proper codebook set with good coverage and high average beamforming gain. The proposed scheme offers more than 1 dB gain in terms of block error rate (BLER) performance compared with the existing methods which use a fixed codebook in all beam sweeping periods.

Conventional medical image registration relies on time-consuming iterative optimization. We propose a two-stage unsupervised learning method for 3D medical image registration. In the first stage, we learn a global image-wise affine map by a deep network. In the second stage, we learn a local voxel-wise deformation vector field by an encoder-decoder architecture. The final registered image is acquired by applying the local deformation field to the moved image of the first stage. The two networks are trained in an unsupervised manner by maximizing global and local normalized cross-correlations between the fixed image and moved images in the two stages respectively. The well-trained networks can be directly adopted to register images through forward propagation without iteration. We do not need ground-truth in training stage and aligning images in preprocess step. Experiments on four brain MRI datasets demonstrate that the proposed approach outperforms several state-of-the-art methods in terms of accuracy and efficiency.

The emerging millimeter-wave (mm-wave) MIMO systems are subject to strong radio frequency (RF) distortions and their compensation is crucial to realize such systems. In the existing literature, several estimation and compensation schemes have been proposed for RF distortions, such as carrier frequency offset, phase noise, and in-phase and quadrature amplitude and phase imbalance (IQI). However, in-phase and quadrature timing mismatch (IQTM) is largely ignored. This paper investigates the effect of the IQTM on mm-wave system performance and reveals that IQTM causes a specific image rejection ratio characteristic, which is substantially different from the regular frequency-dependent IQI and it substantially prolongs the effective channel length. If not compensated, the IQTM can degrade system performance significantly. As a solution to this IQTM problem, this paper proposes novel pilot designs for transmit and receive IQTM estimation, and develops corresponding estimators, transmission protocol, and compensation schemes. MIMO averaging is also proposed, which substantially enhances the IQTM estimation performance. Simulation results show that our proposed pilot designs and estimators offer an efficient solution to the IQTM problem.

Deformable Part Model (DPM) is one of the best algorithms for image-based object detection. However, the high computation intensity leads to relatively long detecting time. Even with the powerful CPU or GPU computing system, it is still too slow for practical applications. To solve this problem, this paper proposes a high performance DPM accelerator based on FPGA, where a dedicated JPEG decoder is integrated to process the images with the 1080p JPEG format. Pipelined architecture and data reuse strategies are developed to achieve the high throughput and energy efficiency. The proposed accelerator can process input images with 22 fps at the frequency of 156MHz on Xilinx VC709 board, which outperforms previous approaches.