Reviewerly/exHarmony huggingface dataset:
Lightweight Block Cipher Security Evaluation Based on Machine Learning Classifiers and Active S-Boxes

Machine learning has recently started to gain the attention of cryptographic researchers, notably in block cipher cryptanalysis.Most of these machine learning-based approaches are black box attacks that are cipher-specific.Thus, more research is required to understand the capabilities and limitations of machine learning when being used to evaluate block cipher security.We contribute to this body of knowledge by investigating the capability of linear and nonlinear machine learning classifiers in evaluating block cipher security.We frame block cipher security evaluation as a classification problem, whereby the machine learning models attempt to classify a given block cipher output as secure or insecure based on the number of active S-boxes.We also train the machine learning models with common block cipher features such as truncated differences, the number of rounds, and permutation pattern.Various experiments were performed on small-scale (4-branch) generalized Feistel ciphers to identify the best performing machine learning model for the given security evaluation problem.Results show that nonlinear machine learning models outperform linear models, achieving a prediction accuracy of up to 93% when evaluating inputs from ciphers that they have seen before during training.When evaluating inputs from other unseen ciphers, nonlinear models again outperformed linear models with an accuracy of up to 71%.We then showcase the feasibility of our approach when used to evaluate a real-world 16-branch generalized Feistel cipher, TWINE.By training the best performing nonlinear classifiers (k-nearest neighbour and decision tree) using data from other similar ciphers, the nonlinear classifiers achieved a 74% accuracy when evaluating differential data generated from TWINE.In addition, the trained classifiers were capable of generalizing to a larger number of rounds than they were trained for.Our findings showcase the feasibility of using simple machine learning classifiers as a security evaluation tool to assess block cipher security.
Occam: A Secure and Adaptive Scaling Scheme for Permissionless Blockchain

Blockchain scalability is one of the most desired properties for permissionless blockchain. Many recent blockchain protocols have focused on increasing the transaction throughput. However, existing protocols cannot dynamically scale the throughput to meet transaction demand. In this paper, we propose Occam, a secure and adaptive scaling scheme. Occam adaptively changes the transaction throughput by expanding and shrinking according to the transaction demand in the network. We introduce a dynamic adjustment mechanism of mining difficulty and a mining power load balancing mechanism to resist various attacks. Furthermore, we implement Occam on Amazon EC2 cluster with 1000 full nodes. Experimental results show that Occam can greatly increase the throughput of the blockchain and the mining power utilization.
An Explainable Multi-Modal Hierarchical Attention Model for Developing Phishing Threat Intelligence

Phishing website attack, as one of the most persistent forms of cyber threats, evolves and remains a major cyber threat. Various detection methods (e.g., lookup systems, fraud cue-based methods) have been proposed to identify phishing websites. The limitations of lookup systems (e.g., failing to address newly created attacks) and the fraud cue-based methods (e.g., relying on feature engineering) motivated the development of deep representation-based methods capable of learning deep fraud cues for enhanced anti-phishing capacity. Focusing mostly on URLs, these methods fail to analyze other two important modalities of website content: textual information and visual design. Moreover, the interpretability of these deep learning based methods is limited, reducing model trustworthiness and preventing relevant and actionable intelligence. We propose a multi-modal hierarchical attention model (MMHAM) which jointly learns the deep fraud cues from the three major modalities of website content for phishing website detection. Specifically, MMHAM features an innovative shared dictionary learning approach for aligning representations from different modalities in the attention mechanism. In evaluation experiments, the proposed MMHAM not only learned improved deep cues for enhanced phishing detection, but provided a hierarchical interpretability system from which we could develop phishing threat intelligence to inform phishing websites detection at different levels.
Multi-Modal Mutual Information (MuMMI) Training for Robust Self-Supervised Deep Reinforcement Learning

This work focuses on learning useful and robust deep world models using multiple, possibly unreliable, sensors. We find that current methods do not sufficiently encourage a shared representation between modalities; this can cause poor performance on downstream tasks and over-reliance on specific sensors. As a solution, we contribute a new multi-modal deep latent state-space model, trained using a mutual information lower-bound. The key innovation is a specially-designed density ratio estimator that encourages consistency between the latent codes of each modality. We tasked our method to learn policies (in a self-supervised manner) on multi-modal Natural MuJoCo benchmarks and a challenging Table Wiping task. Experiments show our method significantly outperforms state-of-the-art deep reinforcement learning methods, particularly in the presence of missing observations.
Verification of blind quantum computation with entanglement witnesses

Verifiable blind quantum computation provides a cloud scenario for scalable quantum information processing. However, constructing one resource-efficient verification protocol is still an open problem. In this paper, the context of verification we consider is the measurement-based model, in which the client receives the graph state prepared by the server and performs single-qubit measurements on it to drive the computation. We first utilize three entanglement witnesses to estimate the fidelity of the prepared graph state. Applying entanglement witnesses to design the test phase, we propose verification protocols. Our protocol requires overhead in terms of copies of the graph state that scales as $O({n}^{2}logn)$, where $n$ is the number of qubits of the graph state. Furthermore, the soundness of our protocol is improved. The advantages of our protocol are derived from the fact that each entanglement witness can be implemented by the client with a fixed number of measurement settings.
Learning Visual Shape Control of Novel 3D Deformable Objects from Partial-View Point Clouds

If robots could reliably manipulate the shape of 3D deformable objects, they could find applications in fields ranging from home care to warehouse fulfillment to surgical assistance. Analytic models of elastic, 3D deformable objects require numerous parameters to describe the potentially infinite degrees of freedom present in determining the object's shape. Previous attempts at performing 3D shape control rely on hand-crafted features to represent the object shape and require training of object-specific control models. We overcome these issues through the use of our novel DeformerNet neural network architecture, which operates on a partial-view point cloud of the object being manipulated and a point cloud of the goal shape to learn a low-dimensional representation of the object shape. This shape embedding enables the robot to learn to define a visual servo controller that provides Cartesian pose changes to the robot end-effector causing the object to deform towards its target shape. Crucially, we demonstrate both in simulation and on a physical robot that DeformerNet reliably generalizes to object shapes and material stiffness not seen during training and outperforms comparison methods for both the generic shape control and the surgical task of retraction.
Conv1D Energy-Aware Path Planner for Mobile Robots in Unstructured Environments

Driving energy consumption plays a major role in the navigation of mobile robots in challenging environments, especially if they are left to operate unattended under limited on-board power. This paper reports on first results of an energy-aware path planner, which can provide estimates of the driving energy consumption and energy recovery of a robot traversing complex uneven terrains. Energy is estimated over trajectories making use of a self-supervised learning approach, in which the robot autonomously learns how to correlate perceived terrain point clouds to energy consumption and recovery. A novel feature of the method is the use of 1D convolutional neural network to analyse the terrain sequentially in the same temporal order as it would be experienced by the robot when moving. The performance of the proposed approach is assessed in simulation over several digital terrain models collected from real natural scenarios, and is compared with a heuristic inclination-based energy model. We show evidence of the benefit of our method to increase the overall prediction r2 score by 66.8% and to reduce the driving energy consumption over planned paths by 5.5%.
Textual Backdoor Attack for the Text Classification System

Deep neural networks provide good performance for image recognition, speech recognition, text recognition, and pattern recognition. However, such networks are vulnerable to backdoor attacks. In a backdoor attack, normal data that do not include a specific trigger are correctly classified by the target model, but backdoor data that include the trigger are incorrectly classified by the target model. One advantage of a backdoor attack is that the attacker can use a specific trigger to attack at a desired time. In this study, we propose a backdoor attack targeting the BERT model, which is a classification system designed for use in the text domain. Under the proposed method, the model is additionally trained on a backdoor sentence that includes a specific trigger, and afterward, if the trigger is attached before or after an original sentence, it will be misclassified by the model. In our experimental evaluation, we used two movie review datasets (MR and IMDB). The results show that using the trigger word “ATTACK” at the beginning of an original sentence, the proposed backdoor method had a 100% attack success rate when approximately 1.0% and 0.9% of the training data consisted of backdoor samples, and it allowed the model to maintain an accuracy of 86.88% and 90.80% on the original samples in the MR and IMDB datasets, respectively.
Explainable Artificial Intelligence for Predictive Maintenance Applications using a Local Surrogate Model

This paper provides an explanatory interface using Local Interpretable Model-agnostic Explanations (LIME) for a predictive maintenance dataset. The explanations are evaluated and the explanatory quality of the model is compared to two previous explainable models for the same dataset.
Towards Heat-Recirculation-Aware Virtual Machine Placement in Data Centers

As customers take virtual machines (VMs) as their demands, high-efficient placement of VMs is required to reduce the energy consumption in data centers. Existing Virtual Machine placement (VMP) strategies can minimize energy consumption of data centers by optimizing resource allocation in terms of multiple physical resources (e.g., memory, bandwidth, CPU, etc.). However, these strategies ignore the role of heat recirculation in the data center, which can cause a huge energy waste in cooling. To address this problem, we propose a heat-recirculation-aware VMP strategy for reducing the energy consumption of data centers. This novel VMP strategy takes into account heat recirculation coupled with multiple physical resource allocation to reduce the energy consumption of data centers. We design a simulated annealing based algorithm called SABA to lower the energy consumption of data centers where multiple VMs are deployed. SABA remarkably cuts down the energy consumption of physical resources through two salient features. First, it obtains an approximation of the optimum with much fewer iterations than simulated annealing algorithm (SA). Second, it reduces the number of activated servers required for VM tasks. We quantitatively evaluate the performance of SABA in terms of algorithm efficiency, the number of activated servers and the energy-saving. We compare the performance of SABA with state-of-art XINT-GA, PPVMP, TSTD and SA algorithms. Moreover, we evaluate the efficiency of SABA by leveraging a real-world 50 hours trace from practical IBM cloud data centers. Experimental results indicate that our heat-recirculation-aware VM placement strategy provides a powerful solution for improving the energy efficiency of data centers (SABA improves energy efficiency of cooling by up to 13.2% over TSTD, 13.8% over XINT-GA and 45% over PPVMP algorithm).
Efficient Reachability Analysis of Closed-Loop Systems with Neural Network Controllers

Neural Networks (NNs) can provide major empirical performance improvements for robotic systems, but they also introduce challenges in formally analyzing those systems’ safety properties. In particular, this work focuses on estimating the forward reachable set of closed-loop systems with NN controllers. Recent work provides bounds on these reachable sets, yet the computationally efficient approaches provide overly conservative bounds (thus cannot be used to verify useful properties), whereas tighter methods are too intensive for online computation. This work bridges the gap by formulating a convex optimization problem for reachability analysis for closed-loop systems with NN controllers. While the solutions are less tight than prior semidefinite program-based methods, they are substantially faster to compute, and some of the available computation time can be used to refine the bounds through input set partitioning, which more than overcomes the tightness gap. The proposed framework further considers systems with measurement and process noise, thus being applicable to realistic systems with uncertainty. Finally, numerical comparisons show that our approach based on linear programming and partitioning can give 10× reduction in conservatism in $\frac{1}{2}$ of the computation time compared to the state-of-the-art, and the ability to handle various sources of uncertainty is highlighted on a quadrotor model.
Fully Functional Image Manipulation Using Scene Graphs in A Bounding-Box Free Way

Recently, performing semantic editing of an image by modifying a scene graph has been proposed to support high-level image manipulation, and plays an important role for image generation. However, existing methods are all based on bounding boxes, and they suffer from the bounding box constraint. First, a bounding box often involves other instances (e.g, objects or environments) which do not need to be modified, but existing methods manipulate all the contents included in the bounding box. Secondly, prior methods fail to support adding instances when the bounding box of the target instance cannot be provided. To address the two issues above, we propose a novel bounding box free approach, which consists of two parts: a Local Bounding Box Free (Local-BBox-Free) Mask Generation and a Global Bounding Box Free (Global-BBox-Free) Instance Generation. The first part relieves the model of reliance on bounding boxes by generating the mask of the target instance to be manipulated without using the target instance bounding box. This enables our method to be the first to support fully functional image manipulation using scene graphs, including adding, removing, replacing and repositing instances. The second part is designed to synthesize the target instance directly from the generated mask and then paste it back to the inpainted original image using the generated mask, which preserves the unchanged part to the largest extent and precisely controls the target instance generation. Extensive experiments on Visual Genome and COCO-Stuff demonstrate that our model significantly surpasses the state-of-the-art both quantitatively and qualitatively.
ABCD: Attentive Bilateral Convolutional Network for Robust Depth Completion

We propose a point-cloud-centric depth completion method called attention bilateral convolutional network for depth completion (ABCD). The proposed method uses LiDAR data and camera data to improve the resolution of the sparse depth information. Color images, which have been seen as fundamental to depth completion tasks, are inevitably sensitive to light and weather conditions. We designed an attentive bilateral convolutional layer (ABCL) to build a robust depth completion network under diverse environmental conditions. An ABCL efficiently learns geometric characteristics by directly leveraging a 3D point cloud and enhances the representation capability of sparse depth information by highlighting the core while suppressing clutter. The ABCD, with an ABCL as a building block, stably fills the void in sparse depth images even under unfamiliar conditions with minimum dependency on unstable camera sensors. Therefore, the proposed method is expected to be a solution to depth completion problems caused by changes in the environment in which images are captured. Through comparative experiments with other methods using the KITTI <xref ref-type="bibr" rid="ref1" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[1]</xref> and VirtualKITTI2 <xref ref-type="bibr" rid="ref2" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[2]</xref> datasets, we demonstrated the outstanding performance of the proposed method in diverse driving environments.
Semantic-aware Transfer with Instance-adaptive Parsing for Crowded Scenes Pose Estimation

Crowded scenes human pose estimation remains challenging, which requires joint comprehension of multi-persons and their keypoints in a highly complex scenario. The top-down mechanism, which is a detect-then-estimate pipeline, has become the mainstream solution for general pose estimation and obtained impressive progress. However, simply applying this mechanism to crowded scenes pose estimation results in unsatisfactory performance due to several issues, in particular involving missing keypoints in crowds and ambiguously labeling during training. To tackle above two issues, we introduce a novel method named Semantic-aware Transfer with Instance-adaptive Parsing (STIP). Specifically, our STIP first enhances the discriminative power of pixel-level representations with a semantic-aware mechanism, where it smartly decides which pixels to enhance and what semantic embeddings to add. In this way, the missing keypoints detection can be alleviated.Secondly, instead of adopting a standard regressor with fixed parameters, we propose a new instance-adaptive parsing method, where it dynamically generates instance-specific parameters for reducing adverse effects caused by ambiguously labeling. Notably, STIP is designed in a plugin fashion and it can be integrated into any top-down models, such as HRNet. Extensive experiments on two challenging benchmarks, i.e., CrowdPose and MS-COCO, demonstrate the superiority and generalizability of our approach.
A Deep Learning Approach for Classifying Vulnerability Descriptions Using Self Attention Based Neural Network

Cyber threat intelligence (CTI) refers to essential knowledge used by organizations to prevent or mitigate against cyber attacks. Vulnerability databases such as CVE and NVD are crucial to cyber threat intelligence, but also provide information leveraged in hundreds of security products worldwide. However, previous studies have shown that these vulnerability databases sometimes contain errors and inconsistencies which have to be manually checked by security professionals. Such inconsistencies could threaten the integrity of security products and hamper attack mitigation efforts. Hence, to assist the security community with more accurate and time-saving validation of vulnerability data, we propose an automated vulnerability classification system based on deep learning. Our proposed system utilizes a self-attention deep neural network (SA-DNN) model and text mining approach to identify the vulnerability category from the description text contained within a report. The performance of the SA-DNN-based vulnerability classification system is evaluated using 134,091 vulnerability reports from the CVE details website.The experiments performed demonstrates the effectiveness of our approach, and shows that the SA-DNN model outperforms SVM and other deep learning methods i.e. CNN-LSTM and graph convolutional neural networks.
Design of Supply Chain System Based on Blockchain Technology

As the interaction between companies becomes more and more complex, the problems of asymmetric information, weak traceability, and low collaboration efficiency in the traditional centralized supply chain are becoming increasingly prominent. To solve these problems, this paper designs a supply chain system based on blockchain. With the help of trade chain and information chain platforms, an overall framework of the supply chain system is constructed. By formulating platform interaction rules, the system information exchange format is standardized to ensure the stability and efficiency of system interaction. Smart contracts are used to manage supply chain system transactions and information interactions to achieve efficient and convenient information sharing, ensuring the security and reliability of supply chain information. The comprehensive performance of the system is evaluated through experiments. Experimental results indicate that while the system realizes the basic functions of the supply chain, it can promote the sharing of information between participants and improve its efficiency.
Collaborative Filtering Recommendation Algorithm Based on TF-IDF and User Characteristics

The recommendation algorithm is a very important and challenging issue for a personal recommender system. The collaborative filtering recommendation algorithm is one of the most popular and effective recommendation algorithms. However, the traditional collaborative filtering recommendation algorithm does not fully consider the impact of popular items and user characteristics on the recommendation results. To solve these problems, an improved collaborative filtering algorithm is proposed, which is based on the Term Frequency-Inverse Document Frequency (TF-IDF) method and user characteristics. In the proposed algorithm, an improved TF-IDF method is used to calculate the user similarity on the basis of rating data first. Secondly, the multi-dimensional characteristics information of users is used to calculate the user similarity by a fuzzy membership method. Then, the above two user similarities are fused based on an adaptive weighted algorithm. Finally, some experiments are conducted on the movie public data set, and the experimental results show that the proposed method has better performance than that of the state of the art.
SelfDeco: Self-Supervised Monocular Depth Completion in Challenging Indoor Environments

We present a novel algorithm for self-supervised monocular depth completion. Our approach is based on training a neural network that requires only sparse depth measurements and corresponding monocular video sequences without dense depth labels. Our self-supervised algorithm is designed for challenging indoor environments with textureless regions, glossy and transparent surfaces, moving people, longer and diverse depth ranges and scenes captured by complex ego-motions. Our novel architecture leverages both deep stacks of sparse convolution blocks to extract sparse depth features and pixel-adaptive convolutions to fuse image and depth features. We compare with existing approaches in NYUv2, KITTI and NAVERLABS indoor datasets, and observe 5 - 34 % improvements in root- means-square error (RMSE) reduction.