Reviewerly/exHarmony huggingface dataset:
SealFS: Storage-based tamper-evident logging

Log analysis is essential for a forensic investigation.Upon intrusion, log files are usually forged in order to hide or fake evidence.If the system is completely compromised, malicious code can be executed in kernel or hypervisor mode making even signed log files vulnerable.As a countermeasure, some systems archive the log files on another system through the network.This solution is not always suitable or desirable and it just shifts the problem elsewhere.The log files need to be preserved on another networked machine which may itself be attacked.In this paper, we present a simple scheme to authenticate local log files based on a forward integrity model.The scheme is based on a realistic assumption: nowadays, storage is very cheap.We can authenticate the logged data generated, starting from boot time to the instant that the malicious code elevates privileges.This tamper-evident scheme does not depend on special security hardware or securing a distributed system.We also present a prototype implementation of this scheme, SealFS.Our implementation, which showcases this approach, is a novel stackable file system for Linux.It 1 enables tamper-evident logging to all existing applications, provides backwards compatibility and instant deployability.Last, we present a performance evaluation of this prototype that shows the viability of this approach.
Self-constructing graph neural networks to model long-range pixel dependencies for semantic segmentation of remote sensing images

Capturing global contextual representations in remote sensing images by exploiting long-range pixel–pixel dependencies has been shown to improve segmentation performance. However, how to do this efficiently is an open question as current approaches of utilizing attention schemes, or very deep models to increase the field of view, increases complexity and memory consumption. Inspired by recent work on graph neural networks, we propose the Self-Constructing Graph (SCG) module that learns a long-range dependency graph directly from the image data and uses it to capture global contextual information efficiently to improve semantic segmentation. The SCG module provides a high degree of flexibility for constructing segmentation networks that seamlessly make use of the benefits of variants of graph neural networks (GNN) and convolutional neural networks (CNN). Our SCG-GCN model, a variant of SCG-Net built upon graph convolutional networks (GCN), performs semantic segmentation in an end-to-end manner with competitive performance on the publicly available ISPRS Potsdam and Vaihingen datasets, achieving a mean F1-scores of 92.0% and 89.8%, respectively. We conclude that the SCG-Net is an attractive architecture for semantic segmentation of remote sensing images since it achieves competitive performance with much fewer parameters and lower computational cost compared to related models based on convolutional neural networks.
Fairness-Aware Online Meta-learning

In contrast to offline working fashions, two research paradigms are devised for online learning: (1) Online Meta-Learning (OML)[6, 20, 26] learns good priors over model parameters (or learning to learn) in a sequential setting where tasks are revealed one after another. Although it provides a sub-linear regret bound, such techniques completely ignore the importance of learning with fairness which is a significant hallmark of human intelligence. (2) Online Fairness-Aware Learning [1, 8, 21]. This setting captures many classification problems for which fairness is a concern. But it aims to attain zero-shot generalization without any task-specific adaptation. This therefore limits the capability of a model to adapt onto newly arrived data. To overcome such issues and bridge the gap, in this paper for the first time we proposed a novel online meta-learning algorithm, namely FFML, which is under the setting of unfairness prevention. The key part of FFML is to learn good priors of an online fair classification model's primal and dual parameters that are associated with the model's accuracy and fairness, respectively. The problem is formulated in the form of a bi-level convex-concave optimization. The theoretic analysis provides sub-linear upper bounds O(log T)for loss regret and O(√log T)violation of cumulative fairness constraints. Our experiments demonstrate the versatility of FFML by applying it to classification on three real-world datasets and show substantial improvements over the best prior work on the tradeoff between fairness and classification accuracy.
No-Reference Deep Quality Assessment of Compressed Light Field Images

Unlike traditional 2D image quality assessment, the structural relationship among sub-aperture images (SAIs) is an essential factor affecting the quality evaluation of light field (LF) images, where the labeled datasets are also not sufficient for improving learning performances. To solve these problems, we present a novel deep neural network-based approach to accurately predict the quality of compressed LF images without pristine images. Two modules dubbed SAI-Fusion and Global Context Perception (GCP) are proposed to obtain the relationship among SAIs. For effective training, we compress LF images from EPFL and HCI datasets and propose a ranking-based method to generate pseudo-labels as equivalents of Mean Opinion Score (MOS), i.e., Ranking-MOS. Therefore, we can pre-train our quality assessment network on compressed LF images with Ranking-MOS, and then fine-tune the model at small-scale datasets with real labels. Experiments demonstrate that the proposed method achieves state-of-the-art performance on compressed LF images of Win5-LID dataset.
Deep Learning RN-BCNN Model for Breast Cancer BI-RADS Classification

The most efficient and ordinarily used early detection method of breast cancer is screening mammography and deep learning is widely employed in the medical imaging domain. But In the medical circumstances, large data size for training is a significant difficulty and the second thing is very less work on six levels of breast cancer BI-RADS classification. In this research, for the purpose of six levels of BI-RADS classification, we proposed the methodology with a ResNet-based customized neural network (RN-BCNN) as compared to the traditional ConvNet model, using the data augmentation and pyramid of scales techniques on an imbalanced dataset. We obtained the outcomes from the INbreast dataset of mammograms. Therefore, we used the elastic deformation technique for increasing the training dataset size which aid in the improvement of outcomes. Moreover, the proposed methodology improves the accuracy of up to 85.9% because the customized model and elastic deformation took important roles in the efficiency of the proposed strategy.
Privacy-Preserving Distributed Multi-Agent Cooperative Optimization—Paradigm Design and Privacy Analysis

Large-scale multi-agent cooperative control problems have materially enjoyed the scalability, adaptivity, and flexibility of distributed optimization. However, due to the mandatory iterative communications between the agents and the system operator, the distributed architecture is vulnerable to malicious attacks and privacy breaches. Current research on privacy preservation of both agents and the system operator in cooperative distributed optimization with strongly coupled objective functions and constraints is still primitive. To fill in the gaps, this letter proposes a novel privacy-preserving distributed optimization paradigm based on Paillier cryptosystem. The proposed paradigm achieves ideal correctness and security, as well as resists attacks from a range of adversaries. The efficacy and efficiency of the proposed approach are verified via numerical simulations and a real-world physical platform.
Deep Performance Factors Analysis for Knowledge Tracing

Knowledge tracing is the task of dynamically tracking a student's mastery of skills based on their assessment performances and learning-related information (e.g., time spent answering a particular question). Traditional approaches (e.g., Bayesian knowledge tracing [BKT] and performance factors analysis [PFA]), are easy to interpret. Modern approaches (e.g., deep knowledge tracing [DKT] and dynamic key-value memory networks [DKVMN]) usually produce superior performance on certain datasets, but their model complexity causes difficulty in scaling and linking them to existing educational measurement studies. In this paper, we present a simple but effective model, deep performance factors analysis (DPFA) (Source code is available at https://github.com/scott-pu-pennstate/dpfa), to resolve this problem. DPFA consistently outperforms PFA and DKT and has results comparable to those of DKVMN when tested on widely used public datasets. In addition, DPFA's light weight in parameters makes it easy to scale. Finally, we demonstrate a straightforward approach to enhance the base DPFA by incorporating features from the educational measurement literature. The enhanced DPFA showed superior performance than DKVMN.
A spatially constrained asymmetric Gaussian mixture model for image segmentation

The finite Gaussian mixture model (GMM) is a flexible and powerful tool for addressing many computer vision and pattern recognition problems. The Gaussian distribution is a probability distribution that is symmetric with respect to the mean. However, in many segmentation applications, the observed data obey an asymmetric distribution. Furthermore, the GMM is sensitive to imaging noise. To alleviate these issues, a new finite anisotropic asymmetric normal mixture model is presented in this paper. Note that GMM is a degraded case of our proposed model. First, the proposed model employs anisotropic spatial information to reduce the effect of imaging noise while preserving the details, such as edges, corners and slim structure objects. Second, the anisotropic spatial information is coupled into the skew normal distribution to fit the observed data obeying an asymmetric distribution. Then the modeling and estimation of the object intensity probability density function are proposed by using the anisotropic skew normal mixture model. The proposed model not only has the capability to fit the observed data obeying a non-symmetric distribution, but also can reduce the effect of noise while preserving the objects details. Finally, expectation maximization (EM) algorithm is adopted to estimate the model parameters in order to maximize the log-likelihood function. The experiment results on synthetic images and natural grayscale images demonstrate the superior performance of the proposed model compared with other state-of-the-art segmentation methods.
Avoidance Critical Probabilistic Roadmaps for Motion Planning in Dynamic Environments

Motion planning among dynamic obstacles is an essential capability towards navigation in the real-world. Sampling-based motion planning algorithms find solutions by approximating the robot’s configuration space through a graph representation, predicting or computing obstacles’ trajectories, and finding feasible paths via a pathfinding algorithm. In this work, we seek to improve the performance of these subproblems by identifying regions critical to dynamic environment navigation and leveraging them to construct sparse probabilistic roadmaps. Motion planning and pathfinding algorithms should allow robots to prevent encounters with obstacles, irrespective of their trajectories, by being conscious of spatial context cues such as the location of chokepoints (e.g., doorways). Thus, we propose a self-supervised methodology for learning to identify regions frequently used for obstacle avoidance from local environment features. As an application of this concept, we leverage a neural network to generate hierarchical probabilistic roadmaps termed Avoidance Critical Probabilistic Roadmaps (ACPRM). These roadmaps contain motion structures that enable efficient obstacle avoidance, reduce the search and planning space, and increase a roadmap’s reusability and coverage. ACPRMs are demonstrated to achieve up to five orders of magnitude improvement over grid-sampling in the multi-agent setting and up to ten orders of magnitude over a competitive baseline in the multi-query setting.
Improving Teamwork in Agile Software Engineering Education: The ASEST+ Framework

<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Contribution:</i> This article presents agile software engineers stick together (ASEST+), an improved version of a framework called ASEST that aims to develop team cohesion, leading to better team learning and software engineering student teams. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Background:</i> Effective teamwork is crucial for agile software development’s success and is, therefore, a key topic of current software engineering education. In the previous work, a preliminary proposal for ASEST+ was presented. Here, an improved version, more suitable for agile practice education and considering cohesion antecedents, is described. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Intended Outcome:</i> A teaching-learning framework to support teamwork in agile software education. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Application Design:</i> ASEST+ is built around Scrum teams and combines learning strategies to train students in collaborative and technical agile practices. ASEST+ establishes policies for role allocation and team rule agreements to regulate communication and address conflict management agile practices. ASEST+ addresses personality traits, conflict resolution, and task interdependence as the antecedents identified as the most important. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Findings:</i> A quasiexperiment showed that the use of ASEST+ significantly increases the students’ positive perceptions on team cohesion, team performance, and team learning compared with the control group.
A path-based relation networks model for knowledge graph completion

We consider the problem of learning and inference in a large-scale knowledge graph containing incomplete knowledge. We show that a simple neural network module for relational reasoning through the path extracted from the knowledge base can be used to reliably infer new facts for the missing link. In our work, we used path ranking algorithm to extract the relation path from knowledge graph and use it to build train data. In order to learn the characteristics of relation, a detour path between nodes was created as training data using the extracted relation path. Using this, we trained a model that can predict whether a given triple (Head entity, relation, tail entity) is valid or not. Experiments show that our model obtains better link prediction, relation prediction and triple classification results than previous state-of-the-art models on benchmark datasets WN18RR, FB15k-237, WN11 and FB13.
Toward practical autoencoder-based side-channel analysis evaluations

In the field of side-channel analysis, profiled attacks are one of the most powerful types of attacks. Nevertheless, major issues with profiled attacks are in sensitivity to noise and the high-dimensional nature of the signals used for training, generating a less efficient classifier to conduct the attack phase. Consequently, evaluating the security of cryptographic implementation in hardware devices like IoT becomes more complex as side-channel analysis evaluation easily falls into false-positive results. This paper assesses the efficacy of applying a feature reduction process to deal with high-dimensional signals. We propose a practical procedure to conduct feature reduction using autoencoders for profiled side-channel leakage evaluations. Two autoencoder architectures are compared while performing feature reduction showing that our proposed architecture keeps most of the relevant information. Our proposal is tested on the ASCAD random key database with a high desynchronization value and produced results that outperform other state-of-the-art techniques. The guessing entropy value converges to 1 after around 500 leakage traces.
Taking Care of the Discretization Problem: A Comprehensive Study of the Discretization Problem and a Black-Box Adversarial Attack in Discrete Integer Domain

Neural network based (NN-based) classifiers are known vulnerable against adversarial examples, namely, adding slight perturbations to a benign image cause a classifier to make a false prediction. To evaluate the robustness of NN-based classifiers against adversarial examples, numerous adversarial attacks with high success rates have been proposed recently. NN-based image classifiers usually normalize valid images (e.g., RGB image where the value at each coordinate is an integer between 0 and 255) into a real continuous domain (e.g., 3-dimensional matrix where the value at each coordinate is a real number between 0 and 1) and make classification decisions on the normalized images. However, adversarial examples crafted in a real continuous domain may become benign once they are denormalized back into the corresponding discrete integer domain, known as the discretization problem. This problem has been mentioned in some prior works but received relatively limited attention. In this work, we report the first comprehensive study of existing works to understand the impacts of the discretization problem. By analyzing 35 representative methods and empirically studying 20 representative open source tools, we found 29/35 (theoretically) and 14/20 (empirically) are affected by the discretization problem, e.g., the success rate could dramatically drop from 100 to 10 percent after the domain transformation. As the first step towards addressing this problem in a black-box scenario, we propose a novel derivative-free optimization method, which can directly craft adversarial examples in the discrete integer domain. Experimental results show that the method achieves nearly 100 percent attack success rates for both targeted and untargeted attacks, comparable to the most popular white-box methods (FGSM, BIM and C&W), and significantly outperforms representative black-box methods (ZOO, AutoZOOM, NES-PGD, Bandits, FD, FD-PSO and GenAttack). Our results suggest that the discretization problem should be treated more seriously, and the discrete optimization algorithms show a promising future in crafting effective black-box attacks.
Hand Palm Tracking in Monocular Images by Fuzzy Rule-Based Fusion of Explainable Fuzzy Features With Robot Imitation Application

This article proposes a new method for the tracking of three-dimensional (3-D) hand palms from the whole human standing body using fuzzy rule-based fusion of explainable fuzzy features from a monocular video. The characteristics of this method include visually and linguistically explainable fuzzy features and rules and computational efficiency. This article first tracks the 2-D palms using the following four fuzzy features: optical flows; the degree of a pixel in the foreground; skin color information; and the search area around a hand palm candidate from a segmented body. Afterward, a fuzzy system (FS) is proposed to fuse the four fuzzy features to estimate the 2D- palm positions. Localization of the elbows is based on the estimated palm locations, human body skeletons, and body contour. The 2-D palms and elbows are tracked using a modified particle filter. To estimate the depth of each palm, the locations of the palm and elbow are fed as inputs to a neural FS. The 3-D palm tracking result is applied to a robot upper-body imitation system. Experiments with comparisons of different hand palm tracking methods are performed to verify the real-time computational ability and accuracy of the proposed method.
A Novel Robust Watermarking Algorithm for Encrypted Medical Image Based on Bandelet-DCT

Digital watermarking technology is a good way to solve the problem of image information security. However, in previous studies, watermark is encrypted, but the host image is not encrypted, which causes the leakage of the private information of the images themselves. When the carrier image is a medical image, more attention should be paid. Therefore, a new robust zero-watermarking algorithm based on Bandelet-DCT (Bandelet Transform and Discrete Cosine Transform) for medical image in the encryption domain of DWT-DCT (Discrete Wavelet Transform and Discrete Cosine Transform) is proposed. Firstly, the original medical image is encrypted in a transform domain based on a Logistic chaotic map to enhance the security of the original medical image. Then Bandelet-DCT is applied to extract the feature sequences of encrypted medical images which were preprocessed by SIFT (Scale Invariant Feature Transform). In the stage of watermarking embedding and extraction, zero-watermarking technology is implemented to ensure that the region of interest of the medical image remains unchanged, and evaluate the robustness of the algorithm through the correlation coefficient between the original watermark and the attacked watermark. Experimental results show that the proposed algorithm has strong robustness and can effectively solve the problem of information leakage. It has a strong anti-attack ability, good robustness, and certain application prospects.
Shape-Adaptive Convolutional Operator for Breast Ultrasound Image Segmentation

Convolutional neural networks (CNNs) are widely used in medical image analysis, especially for breast ultrasound (BUS) image segmentation. Automatically encoding deep features is one of the most important reasons leading to the success of deep convolutional neural networks. There are a lot of studies on obtaining better convolutional features; how-ever, they do not discuss the higher-order information in the features. In this research, we propose a novel convolutional operator, a shape-adaptive convolutional operator, which can select pixels for calculating convolution rather than in the Euclidean space. The proposed operator is combined with the original convolutional operator to extract higher-order convolutional features. We conduct extensive experiments to evaluate the performance of the proposed operator for image segmentation using three datasets: two public BUS image datasets and one multi-category BUS image dataset. The proposed approach achieves state-of-the-art performance.
Personalized recommendation: an enhanced hybrid collaborative filtering

Commonly used similarity-based algorithms in memory-based collaborative filtering may provide unreliable and misleading results. In a cold start situation, users may find the most similar neighbors by relying on an insufficient number of ratings, resulting in low-quality recommendations. Such poor recommendations can also result from similarity metrics as they are incapable of capturing similarities among uncommon items. For example, when identical items between two users are popular items, and both users rated them with high scores, their different preferences toward other items are hidden from similarity metrics. In this paper, we propose a method that estimates the final ratings based on a combination of multiple ratings supplied by various similarity measures. Our experiments show that this combination benefits from the diversity within similarities and offers high-quality personalized suggestions to the target user.