Reviewerly/exHarmony huggingface dataset:
Randomly generating three-dimensional realistic schistous sand particles using deep learning: Variational autoencoder implementation

Nanjing sand, a type of greenish-grey schistous sand, is rich in weathered mica fragments, making it anisotropic and significantly differ from the round-grained quartz sand in terms of composition, grading, and mechanical properties. In this paper, the variational-autoencoder (VAE), a generative deep-learning model, was used to randomly generate realistic three-dimensional (3D) schistous sand particles. A training set was developed based on a micro-computed tomography scanned image sequence of the specimens of Nanjing sand. A total of 30,000 particles were chosen as the training set. Two thousand realistic 3D schistous sand particles were randomly generated by the trained VAE model and statistically compared with all the natural sand particles. The agreement of the statistical distribution and parameters between the generated and natural schistous sand particles confirmed the generative fidelity of the trained VAE model. Also, the validity and controllability of the generation is testified by the addition of Gaussian random noise and conducting linear interpolation to the latent variables. This methodology not only can be used for the clump template of the DEM, but also holds the potential of generating other geological entities and modeling complicated engineering geological processes.
To Augment or Not to Augment? A Comparative Study on Text Augmentation Techniques for Low-Resource NLP

Abstract Data-hungry deep neural networks have established themselves as the de facto standard for many NLP tasks, including the traditional sequence tagging ones. Despite their state-of-the-art performance on high-resource languages, they still fall behind their statistical counterparts in low-resource scenarios. One methodology to counterattack this problem is text augmentation, that is, generating new synthetic training data points from existing data. Although NLP has recently witnessed several new textual augmentation techniques, the field still lacks a systematic performance analysis on a diverse set of languages and sequence tagging tasks. To fill this gap, we investigate three categories of text augmentation methodologies that perform changes on the syntax (e.g., cropping sub-sentences), token (e.g., random word insertion), and character (e.g., character swapping) levels. We systematically compare the methods on part-of-speech tagging, dependency parsing, and semantic role labeling for a diverse set of language families using various models, including the architectures that rely on pretrained multilingual contextualized language models such as mBERT. Augmentation most significantly improves dependency parsing, followed by part-of-speech tagging and semantic role labeling. We find the experimented techniques to be effective on morphologically rich languages in general rather than analytic languages such as Vietnamese. Our results suggest that the augmentation techniques can further improve over strong baselines based on mBERT, especially for dependency parsing. We identify the character-level methods as the most consistent performers, while synonym replacement and syntactic augmenters provide inconsistent improvements. Finally, we discuss that the results most heavily depend on the task, language pair (e.g., syntactic-level techniques mostly benefit higher-level tasks and morphologically richer languages), and model type (e.g., token-level augmentation provides significant improvements for BPE, while character-level ones give generally higher scores for char and mBERT based models).
Interpreting and Mitigating Leakage-Abuse Attacks in Searchable Symmetric Encryption

Searchable symmetric encryption (SSE) enables users to make confidential queries over always encrypted data while confining information disclosure to pre-defined leakage profiles. Despite the well-understood performance and potentially broad applications of SSE, recent leakage-abuse attacks (LAAs) are questioning its real-world security implications. They show that a passive adversary with certain prior information of a database can recover queries by exploiting the legitimately admitted leakage. While several countermeasures have been proposed, they are insufficient for either security, i.e., handling only specific leakage like query volume, or efficiency, i.e., incurring large storage and bandwidth overhead. We aim to fill this gap by advancing the understanding of LAAs from a fundamental algebraic perspective. Our investigation starts by revealing that the index matrices of a plaintext database and its encrypted image can be linked by linear transformation. The invariant characteristics preserved under the transformation encompass and surpass the information exploited by previous LAAs. They allow one to unambiguously link encrypted queries with corresponding keywords, even with only partial knowledge of the database. Accordingly, we devise a new powerful attack and conduct a series of experiments to show its effectiveness. In response, we propose a new security notion to thwart LAAs in general, inspired by the principle of local differential privacy (LDP). Under the notion, we further develop a practical countermeasure with tunable privacy and efficiency guarantee. Experiment results on representative real-world datasets show that our countermeasure can reduce the query recovery rate of LAAs, including our own.
Content-Based Recommendation Using Machine Learning

Currently, the user profile based online recommender system has become a hit both in research and engineering domain. Accurately capturing users' profile is the key of recommendation. Recently, lots of researches on user profile extraction have been launched, including content-based recommendation. To better capture users' profiles, a three-step profiling method is adopted in this work. (1) Purchase item prediction is made based on Logistic Regression. (2) Purchase category prediction is made based on support vector machine (SVM), and (3) User's rating prediction is made based on convolutional neural network (CNN) and Long Short-Term Memory (LSTM). This work outperformed the baseline model on the user dataset collected from Amazon. So, in conclusion, the work has the ability of giving reasonable recommendation for users who would like to purchase online. In the future, the video signal processing techniques will also be taken under consideration to capture users' face expression for better recommendation.
SyncAttack: Double-spending in Bitcoin Without Mining Power

The existing Bitcoin security research has mainly followed the security models in [22, 35], which stipulate that an adversary controls some mining power in order to violate the blockchain consistency property (i.e., through a double-spend attack). These models, however, largely overlooked the impact of the realistic network synchronization, which can be manipulated given the permissionless nature of the network. In this paper, we revisit the security of Bitcoin blockchain by incorporating the network synchronization into the security model and evaluating that in practice. Towards this goal, we propose the ideal functionality for the Bitcoin network synchronization and specify bounds on the network outdegree and the block propagation delay in order to preserve the consistency property. By contrasting the ideal functionality against measurements, we find deteriorating network synchronization reported by Bitnodes and a notable churn rate with 10% of the nodes arriving and departing from the network daily.
Contrastive adversarial knowledge distillation for deep model compression in time-series regression tasks

Knowledge distillation (KD) attempts to compress a deep teacher model into a shallow student model by letting the student mimic the teacher’s outputs. However, conventional KD approaches can have the following shortcomings. First, existing KD approaches align the global distribution between teacher and student models and overlook the fine-grained features. Second, most of existing approaches focus on classification tasks and require the architecture of teacher and student models to be similar. To address these limitations, we propose a contrastive adversarial knowledge distillation called CAKD for time series regression tasks where the student and teacher are using different architectures. Specifically, we first propose adversarial adaptation to automatically align the feature distribution between student and teacher networks respectively. Yet, adversarial adaptation can only align the global feature distribution without considering the fine-grained features. To mitigate this issue, we employ a novel contrastive loss for instance-wise alignment between the student and teacher. Particularly, we maximize similarity between teacher and student features that originate from the same sample. Lastly, a KD loss is used to for the knowledge distillation where the teacher and student have two different architectures. We used a turbofan engine dataset that consists of four sub-datasets to evaluate the model performance. The results show that the proposed CAKD method consistently outperforms state-of-the-art methods in terms of two different metrics.
Anti-UAV: A Large-Scale Benchmark for Vision-Based UAV Tracking

Unmanned Aerial Vehicles (UAV) have many applications in both commerce and recreation. However, irresponsibly operated UAVs will pose a threat to public safety. Therefore, developing our understanding of UAVs and their uses is of particular interest. This paper considers tracking UAVs, which provide multifaceted information around location, paths and trajectories. To facilitate research on this topic, we introduce a new benchmark, herein referred to as Anti-UAV, which provides a novel direction for UAV tracking with more than 300 video pairs containing over 580 k manually annotated bounding boxes. Addressing anti-UAV research challenges could help to design anti-UAV systems, which in turn may improve surveillance. Accordingly, we have proposed a simple yet effective approach, called dual-flow semantic consistency (DFSC) is proposed for UAV tracking. Modulated by the semantic flow across video sequences, tracker learns more robust class-level semantic information and obtains more discriminative instance-level features. Experiments highlight significant performance gain with the proposed approach over state-of-the-art trackers and the challenging aspects of Anti-UAV. The Anti-UAV benchmark and the code for the proposed approach have been made publicly available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/ucas-vg/Anti-UAV</uri> and <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/ZhaoJ9014/Anti-UAV</uri> .
A systematic review of the automatic kidney segmentation methods in abdominal images

The precise kidney segmentation is very helpful for diagnosis and treatment planning in urology, by giving information about malformation in the shape and size of the kidney. Kidney segmentation in abdominal computed tomography (CT) images provides support for the efficient and effortless detection of kidney tumors or cancers. Manual kidney segmentation is time-consuming and not reproducible. To overcome this problem, computer-aided automatic approach is used for kidney segmentation. The purpose of presenting this review paper is to analyze different automatic kidney segmentation methods in abdominal CT scans. PRISMA guidelines were used to conduct the systematic review. To acquire related articles, three online open source databases were used and a query was formed with relevant keywords. On the basis of inclusion and exclusion criteria, relevant papers were selected from the search results for finding answers to the four evolved research questions. The results reported in the different studies were analyzed based on the formulated research questions. The challenges of these studies were listed to overcome in the future. Many performance parameters representing the results like Hausdorff Distance (HD) and Dice Similarity Coefficient (DSC) were compared among the relevant studies. The systematic review article consists of the essence of the several computer-aided kidney segmentation methods using abdominal CT images, which are dedicated to answering the evolved research questions like various methods, accuracy, datasets size, various challenges, and the effect of pathological kidney on the performance of segmentation method had been discussed.
Rapid Extraction of the Spatial Distribution of Physical Saliency and Semantic Informativeness from Natural Scenes in the Human Brain

Physically salient objects are thought to attract attention in natural scenes. However, research has shown that meaning maps, which capture the spatial distribution of semantically informative scene features, trump physical saliency in predicting the pattern of eye moments in natural scene viewing. Meaning maps even predict the fastest eye movements, suggesting that the brain extracts the spatial distribution of potentially meaningful scene regions very rapidly. To test this hypothesis, we applied representational similarity analysis to ERP data. The ERPs were obtained from human participants (N = 32, male and female) who viewed a series of 50 different natural scenes while performing a modified 1-back task. For each scene, we obtained a physical saliency map from a computational model and a meaning map from crowd-sourced ratings. We then used representational similarity analysis to assess the extent to which the representational geometry of physical saliency maps and meaning maps can predict the representational geometry of the neural response (the ERP scalp distribution) at each moment in time following scene onset. We found that a link between physical saliency and the ERPs emerged first (∼78 ms after stimulus onset), with a link to semantic informativeness emerging soon afterward (∼87 ms after stimulus onset). These findings are in line with previous evidence indicating that saliency is computed rapidly, while also indicating that information related to the spatial distribution of semantically informative scene elements is computed shortly thereafter, early enough to potentially exert an influence on eye movements.SIGNIFICANCE STATEMENT Attention may be attracted by physically salient objects, such as flashing lights, but humans must also be able to direct their attention to meaningful parts of scenes. Understanding how we direct attention to meaningful scene regions will be important for developing treatments for disorders of attention and for designing roadways, cockpits, and computer user interfaces. Information about saliency appears to be extracted rapidly by the brain, but little is known about the mechanisms that determine the locations of meaningful information. To address this gap, we showed people photographs of real-world scenes and measured brain activity. We found that information related to the locations of meaningful scene elements was extracted rapidly, shortly after the emergence of saliency-related information.
Evaluation of augmented-reality based navigation for brain tumor surgery

To date, several researchers have introduced augmented reality navigation (ARN) into neurological surgery. While its application in brain tumor surgery seems promising, reports on its utility have been limited, thus warranting further evaluation. To clarify the stages and approaches in which ARN is useful and assess the effect of presurgical discussion with surgeons, we assessed usefulness using a hand-held ARN system we had developed, which displays three-dimensional (3D) virtual structures overlaid on a real-time image of the surgical field via a tablet PC monitor. The system was tested in 20 patients undergoing various procedures, with the first 10 consecutive cases being unselected and the following 10 cases being selected, for whom 3D models were prepared per the surgeons' request. Thereafter, the surgeons ranked its usefulness during each stage of surgery. Consequently, case selection and presurgical discussions with surgeons considerably improved the usefulness, with the "useful" gradings improving from 50% to 88% across all surgical stages. Accordingly, usefulness improved from 50% to 90%, 67% to 100%, and 40% to 80% during the skin incision and craniotomy, dura incision, and intradural procedure stages, respectively. ARN was useful for superficial tumor resection, but less so for deep-seated tumor resection, except when using the transcortical and interhemispheric approaches. In conclusion, a tablet-type ARN can be useful during skin incisions, craniotomy and dura incisions, superficial tumor resections, and transcortical and interhemispheric approaches for deep-seated tumors. Case selection and presurgical discussions with surgeons were essential for the efficacy of ARN.
Carrier fringe pattern analysis: Links between methods

Introducing a carrier into a fringe pattern makes the phase retrieval problem well-posed, and has become one of the pillar techniques in optical metrology. The carrier fringe pattern analysis for high-accuracy phase retrieval is hence important, for which, many well-known methods have been developed, such as the Fourier transform method, the windowed Fourier transform based methods (the windowed Fourier filtering and the windowed Fourier ridges), the spatial carrier phase-shifting method, and the sampling moiré method. These methods were developed independently and seemingly different. The purpose of this paper is to reveal the close relationships between them, and to connect these methods into a tight-knit methodology family.
Improving the Robustness of JPEG Steganography With Robustness Cost

Due to a large number of user-uploaded images, social networks have become secure channels for covert communication. However, the JPEG recompression of social networks changes the DCT coefficients of stego images, resulting in the failure of adaptive steganography. To achieve steganography in lossy channels, robust steganography has been proposed. In this letter, the ability against JPEG recompression of robust steganography is further improved by introducing a robustness cost function. For calculating the robustness cost, a robustness model based on the spatial domain calculated from DCT coefficients is firstly proposed. Then the robustness cost is acquired by measuring the distance between the spatial pixels calculated from modified DCT coefficients and the robustness model adjusted spatial pixels. Combining the distortion function and the robustness cost function, the method proposed has considerable robustness performance while maintaining satisfying security performance. Experimental results show that with the maximum reduction of 4.04% on security performance, the algorithm proposed has a significant improvement on robustness performance compared with state-of-the-art robust steganography.
Black-box adversarial attacks on XSS attack detection model

Cross-site scripting (XSS) has been extensively studied, although mitigating such attacks in web applications remains challenging. While there is an increasing number of XSS attack detection approaches designed based on machine learning and deep learning algorithms, it is important to study and evaluate the reliability and security of these approaches. In our study, focusing on machine / deep learning-based XSS attack detection approaches, we propose a fuzzing-based approach to realize "Black&White attack", in order to effectively improve the confidence coefficient of malicious samples. We also present an adversarial attack model based on Soft Q-learning, designed to generate adversarial attack examples for different XSS attack detection models using multiple strategies. Experimental results reveal that the proposed adversarial attack model generates adversarial attack examples against various XSS attack detection models, with an escape rate of over 85%. In other words, our research has implications on existing XSS attack detection models, for example in terms of effectiveness.
Face photo-sketch synthesis via full-scale identity supervision

Face photo-sketch synthesis refers transforming a face image between photo domain and sketch domain. It plays a crucial role in law enforcement and digital entertainment. A great deal of efforts have been devoted on face photo-sketch synthesis. However, limited by the weak identity supervision, existing methods mostly yield indistinct details or great deformation, resulting in poor perceptual appearance or low recognition accuracy. In the past several years, face identification achieved great progress, which represents the face images much more precisely than before. Considering the face image translation is also a type of face image re-representation, we attempt to introduce face recognition models to improve the synthesis performance. First, we applied existing synthesis models to augment the training set. Then, we proposed a full-scale identity supervision method to reduce redundant information introduced by these pseudo samples and take the valid information to enhance the intra-class variations. The proposed framework consists of two sub-networks: cross-domain translation (CT) network and intra-domain adaptation (IA) network. The CT network translates the input image from source domain to latent image of target domain, which overcomes the great gap between two domains with less structural deformation. The IA network adapts the perceptual appearance of latent image to target image by adversarial learning. Experimental results on CUHK Face Sketch Database and CUHK Face Sketch FERET Database demonstrate the proposed method preserved best perceptual appearance and more distinct details with less deformation.
Two End-to-End Quantum-Inspired Deep Neural Networks for Text Classification

In linguistics, the uncertainty of context due to polysemy is widespread, which attracts much attention.Quantum-inspired complex word embedding based on Hilbert space plays an important role in natural language processing (NLP), which fully leverages the similarity between quantum states and word tokens.A word containing multiple meanings could correspond to a single quantum particle which may exist in several possible states, and a sentence could be analogous to the quantum system where particles interfere with each other.Motivated by quantum-inspired complex word embedding, interpretable complex-valued word embedding (ICWE) is proposed to design two end-to-end quantum-inspired deep neural networks (ICWE-QNN and CICWE-QNN representing convolutional complexvalued neural network based on ICWE) for binary text classification.They have the proven feasibility and effectiveness in the application of NLP and can solve the problem of text information loss in CE-Mix [1] model caused by neglecting the important linguistic features of text, since linguistic feature extraction is presented in our model with deep learning algorithms, in which gated recurrent unit (GRU) extracts the sequence information of sentences, attention mechanism makes the model focus on important words in sentences and convolutional layer captures the local features of projected matrix.The model ICWE-QNN can avoid random combination of word tokens and CICWE-QNN fully considers textual features of the projected matrix.Experiments conducted on five benchmarking classification datasets demonstrate our proposed models have higher accuracy than the compared traditional models including CaptionRep BOW, DictRep BOW and Paragram-Phrase, and they also have great performance on F1-score.Eespecially, CICWE-QNN model has higher accuracy than the quantum-inspired model CE-Mix as well for four datasets including SST, SUBJ, CR and MPQA.It is a meaningful and effictive exploration to design quantum-inspired deep neural networks to promote the performance of text classification.