Detecting Textual Adversarial Examples through Randomized Substitution and Vote

A line of work has shown that natural text processing models are vulnerable to adversarial examples. Correspondingly, various defense methods are proposed to mitigate the threat of textual adversarial examples, eg, adversarial training, input transformations, detection, etc. In this work, we treat the optimization process for synonym substitution based textual adversarial attacks as a specific sequence of word replacement, in which each word mutually influences other words. We identify that we could destroy such mutual interaction and eliminate the adversarial perturbation by randomly substituting a word with its synonyms. Based on this observation, we propose a novel textual adversarial example detection method, termed Randomized Substitution and Vote (RS&V), which votes the prediction label by accumulating the logits of k samples generated by randomly substituting the words in the input text with synonyms. The proposed RS&V is generally applicable to any existing neural networks without modification on the architecture or extra training, and it is orthogonal to prior work on making the classification network itself more robust. Empirical evaluations on three benchmark datasets demonstrate that our RS&V could detect the textual adversarial examples more successfully than the existing detection methods while maintaining the high classification accuracy on benign samples.Comment: Accepted by UAI 2022, code is avaliable at https://github.com/JHL-HUST/RS

Inference of high-dimensional linear models with time-varying coefficients

In part 1, we propose a pointwise inference algorithm for high-dimensional linear models with time-varying coefficients and dependent error processes. The method is based on a novel combination of the nonparametric kernel smoothing technique and a Lasso bias-corrected ridge regression estimator using a bias-variance decomposition to address non-stationarity in the model. A hypothesis testing setup with familywise error control is presented alongside synthetic data and a real application to fMRI data for Parkinson's disease. In part 2, we propose an algorithm for covariance and precision matrix estimation high-dimensional transpose-able data. The method is based on a Kronecker product approximation of the graphical lasso and the application of the alternating directions method of multipliers minimization. A simulation example is provided

SCK: A sparse coding based key-point detector

All current popular hand-crafted key-point detectors such as Harris corner, MSER, SIFT, SURF... rely on some specific pre-designed structures for the detection of corners, blobs, or junctions in an image. In this paper, a novel sparse coding based key-point detector which requires no particular pre-designed structures is presented. The key-point detector is based on measuring the complexity level of each block in an image to decide where a key-point should be. The complexity level of a block is defined as the total number of non-zero components of a sparse representation of that block. Generally, a block constructed with more components is more complex and has greater potential to be a good key-point. Experimental results on Webcam and EF datasets [1, 2] show that the proposed detector achieves significantly high repeatability compared to hand-crafted features, and even outperforms the matching scores of the state-of-the-art learning based detector.Comment: Manuscript accepted for presentation at 2018 IEEE International Conference on Image Processing, October 7-10, 2018, Athens, Greece. Patent applied. If you use any techniques, claims, images in this manuscript, please cite the corresponding pape

Species invasions threaten the antiquity of China's freshwater fish fauna

AimHuman-mediated species introductions and extirpations have resulted in the homogenization of biotas over time. However, there remains considerable uncertainty in our understanding of homogenization process for megadiverse regions of the world. Here, we investigate the consequences of widespread species invasions and extirpations for the biogeography of China&#39;s unique freshwater fish fauna. LocationChina. MethodsBy assembling a comprehensive dataset for distribution of Chinese freshwater fishes, we quantify how non-native fish species, from both overseas introductions and domestic translocations, has led to taxonomic homogenization of fish faunas at watershed, basin, ecoregion and country scales. We explore how the observed patterns in homogenization vary geographically, and identify those species most responsible for the faunal changes. Lastly, we simulate how China&#39;s fish fauna may continue to homogenize according to different scenarios of anticipated species introductions and extirpations. ResultsWe demonstrate that species introductions and extirpations have homogenized freshwater fish faunas across China. Overall compositional similarity of watersheds increased by 7.0% (from a historical 14.9% to 21.9% in the present day; SOrensen index). Compositional similarity of 96 of 103 (93.2%) watersheds increased, with western basins exhibiting the highest magnitude. Translocated non-native species associated with aquaculture practices contributed the most to faunal homogenization when compared to alien species (7.3% and 0.4%, respectively). Furthermore, faunal homogenization is predicted to intensify an additional 0.5-4.2% with increasing numbers of new non-native species introductions and the extirpation of native species. Main conclusionsSpecies introductions and extirpations have resulted in the significant impoverishment, and thus the loss of antiquity, of China&#39;s freshwater fish fauna over the past century. In the light of the growing realization that species composition (not richness) defines the role that biodiversity plays in maintaining ecosystem function, our study highlights the need for conservation strategies in China that consider changing patterns of diversity.</p

Improving Streaming Capacity in Multi-Channel P2P VoD Systems via Intra-Channel and Cross-Channel Resource Allocation

Multi-channel Peer-to-Peer (P2P) Video-on-Demand (VoD) systems can be categorized into independent-channel P2P VoD systems and correlated-channel P2P VoD systems. Streaming capacity for a channel is defined as the maximal streaming rate that can be received by every user of the channel. In this paper, we study the streaming capacity problem in multi-channel P2P VoD systems. In an independent-channel P2P VoD system, there is no resource correlation among channels. Therefore, we can find the average streaming capacity for the independent-channel P2P VoD system by finding the streaming capacity for each individual channel, respectively. We propose a distributed algorithm to solve the streaming capacity problem for a single channel in an independent-channel P2P VoD system. The average streaming capacity for a correlated-channel P2P VoD system depends on both the intra-channel and cross-channel resource allocation. To better utilize the cross-channel resources, we first optimize the server upload allocation among channels to maximize the average streaming capacity and then propose cross-channel helpers to enable cross-channel sharing of peer upload bandwidths. We demonstrate in the simulations that the correlated-channel P2P VoD systems with both intra-channel and cross-channel resource allocation can obtain a higher average streaming capacity compared to the independent-channel P2P VoD systems with only intra-channel resource allocation

Research on the fracture evolution law and combined bearing structure load of shallow buried coal seam group

The high-intensity mining of shallow-buried coal seams leads to the fracture of the bearing strata to form a combined bearing structure, which complicates the distribution of cracks in the overlying rock and causes serious damage to the surface. In order to study the relationship between the evolution law of the overburden fissures and the bearing structure of the bearing rock in the mining of the shallow coal seam group, and the working resistance value of the support to keep the bearing rock stable, the coal mining of Hanjiawan Coal Mine 2−2 and 3−1 in the Shenfu mining area in northern Shaanxi was studied. In the background, through field observation and similar simulation experiments, the evolution law of coal seam group mining cracks, the combined bearing structure of bearing rock layers and the relationship between the two were obtained. The mechanical model of combined bearing structure was established by theoretical calculation method, and the support work of the combined bearing rock layer to maintain stability was studied. resistance. The research shows that the evolution process of overlying fissures in coal seam mining can be divided into four stages: the upper coal seam mining rapid growth stage, the upper coal seam mining steady growth stage, the lower coal seam mining rapid growth stage, and the lower coal seam mining stable growth stage. This leads to different fissure evolution forms and subsidence characteristics on the surface. The surface of the “step rock beam” structure subsides in steps, and the surface of the “hinge rock beam” structure subsides continuously; The fractured structure of the bearing rock formation in the lower coal seam is obtained from the filling rate and mining height of the interlayer, and it is revealed that the fractured combined structure of the bearing rock formation in the shallow buried coal seam mining group is “step-hinged” structure, “hinged-hinged” structure, “hinged-hinged” structure. The combined form of the “step” structure and the “step-step” structure. Based on the fractured composite structure of the bearing rock, the mechanical model of the bearing rock bearing structure of the shallow buried coal seam group mining is established, and the calculation method of the working resistance of the bearing rock to maintain the stability of the support is obtained, and the field measurement results are verified