Reviewerly/exHarmony huggingface dataset:
Efficient and DoS-resistant Consensus for Permissioned Blockchains

Existing permissioned blockchain systems designate a fixed and explicit group of committee nodes to run a consensus protocol that confirms the same sequence of blocks among all nodes. Unfortunately, when such a permissioned blockchain runs on a large scale on the Internet, these explicit committee nodes can be easily turned down by denial-of-service (DoS) or network partition attacks. Although recent studies proposed scalable BFT protocols that run on a larger number of committee nodes, these protocols' efficiency drops dramatically when only a small number of nodes are attacked. In this paper, we propose a novel protocol named Eges that leverages hardware trusted execution environments (e.g., Intel SGX) to develop a new abstraction called "stealth committee", which effectively hides a committee into a large pool of fake committee nodes. Eges selects a different stealth committee for each block and confirms the same blocks among all nodes with overwhelming probability. Our evaluation shows that Eges is the first efficient permissioned blockchain's consensus protocol, which simultaneously satisfies two important metrics: (1) Eges can tolerate tough DoS and network partition attacks; and (2) Eges achieves comparable throughput and latency as existing fastest permissioned blockchains' consensus protocols. Eges's source code is available on http://github.com/hku-systems/eges.
Physicality As an Anchor for Coordination: Examining Collocated Collaboration in Physical and Mobile Augmented Reality Settings

Design and co-creation activities around 3D artifacts often require close collocated coordination between multiple users. Augmented reality (AR) technology can support collocated work enabling users to flexibly work with digital objects while still being able to use the physical space for coordination. With most of current research focusing on remote AR collaboration, less is known about collocated collaboration in AR, particularly in relation to interpersonal dynamics between the collocated collaborators. Our study aims at understanding how shared augmented reality facilitated by mobile devices (mobile augmented reality or MAR) affects collocated users' coordination. We compare the coordination behaviors that emerged in a MAR setting with those in a comparable fully physical setting by simulating the same task -of the shared physical dimension for participants' ability to coordinate in the context of collaborative co-creation. Namely, participants working in a fully physical setting were better able to leverage the work artifact itself for their coordination needs, working in a mode that we term artifact-oriented coordination. Conversely, participants collaborating around an AR artifact leveraged the shared physical workspace for their coordination needs, working in what we refer to as space-oriented coordination. We discuss implications for a AR-based collaboration and propose directions for designers of AR tools.
Trajectory Optimisation in Learned Multimodal Dynamical Systems via Latent-ODE Collocation

This paper presents a two-stage method to perform trajectory optimisation in multimodal dynamical systems with unknown nonlinear stochastic transition dynamics. The method finds trajectories that remain in a preferred dynamics mode where possible and in regions of the transition dynamics model that have been observed and can be predicted confidently. The first stage leverages a Mixture of Gaussian Process Experts method to learn a predictive dynamics model from historical data. Importantly, this model learns a gating function that indicates the probability of being in a particular dynamics mode at a given state location. This gating function acts as a coordinate map for a latent Riemannian manifold on which shortest trajectories are solutions to our trajectory optimisation problem. Based on this intuition, the second stage formulates a geometric cost function, which it then implicitly minimises by projecting the trajectory optimisation onto the second-order geodesic ODE; a classic result of Riemannian geometry. A set of collocation constraints are derived that ensure trajectories are solutions to this ODE, implicitly solving the trajectory optimisation problem.
The Service Worker Hiding in Your Browser: The Next Web Attack Target?

In recent years, service workers are gaining attention from both web developers and attackers due to the unique features they provide. Recent findings have shown that an attacker can register a malicious service worker to take advantage of the victim such as by turning the victim’s device into a crypto-currency miner. However, the possibility of benign service workers being leveraged is not well studied.
Coeus

Given a private string q and a remote server that holds a set of public documents D, how can one of the K most relevant documents to q in D be selected and viewed without anyone (not even the server) learning anything about q or the document? This is the oblivious document ranking and retrieval problem. In this paper, we describe Coeus, a system that solves this problem. At a high level, Coeus composes two cryptographic primitives: secure matrix-vector product for scoring document relevance using the widely-used term frequency-inverse document frequency (tf-idf) method, and private information retrieval (PIR) for obliviously retrieving documents. However, Coeus reduces the time to run these protocols, thereby improving the user-perceived latency, which is a key performance metric. Coeus first reduces the PIR overhead by separating out private metadata retrieval from document retrieval, and it then scales secure matrix-vector product to tf-idf matrices with several hundred billion elements through a series of novel cryptographic refinements. For a corpus of English Wikipedia containing 5 million documents, a keyword dictionary with 64K keywords, and on a cluster of 143 machines on AWS, Coeus enables a user to obliviously rank and retrieve a document in 3.9 seconds---a 24x improvement over a baseline system.
ENMC: Extreme Near-Memory Classification via Approximate Screening

Extreme classification (XC) is the essential component of large-scale Deep Learning Systems for a wide range of application domains, including image recognition, language modeling, and recommendation. As classification categories keep scaling in real-world applications, the classifier’s parameters could reach several thousands of Gigabytes, way exceed the on-chip memory capacity. With the advent of near-memory processing (NMP) architectures, offloading the XC component onto NMP units could alleviate the memory-intensive problem. However, naive NMP design with limited area and power budget cannot afford the computational complexity of full classification. To tackle the problem, we first propose a novel screening method to reduce the computation and memory consumption by efficiently approximating the classification output and identifying a small portion of key candidates that require accurate results. Then, we design a new extreme-classification-tailored NMP architecture, namely ENMC, to support both screening and candidates-only classification. Overall, our approximate screening method achieves 7.3 × speedup over the CPU baseline, and ENMC further improves the performance by 7.4 × and demonstrates 2.7 × speedup compared with the state-of-the-art NMP baseline.
Creating Resilient Supply Chains Using Combination of Blockchain Technology and Different 4.0 Technologies

The purpose of the paper is to assess the state of the art in the co-existence of blockchain technology with other 4.0 technologies that are used for supply chain management and to identify examples of solutions that can support the construction of resilient chains. The paper also aims to identify future research directions that are extensions of the primary objective. Therefore, a coalescence of coexisting 4.0 technologies that can support the construction of resilient supply chains is proposed.
Transfer Meta-Learning: Information- Theoretic Bounds and Information Meta-Risk Minimization

Meta-learning automatically infers an inductive bias by observing data from a number of related tasks. The inductive bias is encoded by hyperparameters that determine aspects of the model class or training algorithm, such as initialization or learning rate. Meta-learning assumes that the learning tasks belong to a task environment, and that tasks are drawn from the same task environment both during meta-training and meta-testing. This, however, may not hold true in practice. In this paper, we introduce the problem of transfer meta-learning, in which tasks are drawn from a target task environment during meta-testing that may differ from the source task environment observed during meta-training. Novel information-theoretic upper bounds are obtained on the transfer meta-generalization gap, which measures the difference between the meta-training loss, available at the meta-learner, and the average loss on meta-test data from a new, randomly selected, task in the target task environment. The first bound, on the average transfer meta-generalization gap, captures the meta-environment shift between source and target task environments via the KL divergence between source and target data distributions. The second, PAC-Bayesian bound, and the third, single-draw bound, account for this shift via the log-likelihood ratio between source and target task distributions. Furthermore, two transfer meta-learning solutions are introduced. For the first, termed Empirical Meta-Risk Minimization (EMRM), we derive bounds on the average optimality gap. The second, referred to as Information Meta-Risk Minimization (IMRM), is obtained by minimizing the PAC-Bayesian bound. IMRM is shown via experiments to potentially outperform EMRM.
Approximate Solutions to a Class of Reachability Games

In this paper, we present a method for finding approximate Nash equilibria in a broad class of reachability games. These games are often used to formulate both collision avoidance and goal satisfaction. Our method is computationally efficient, running in real-time for scenarios involving multiple players and more than ten state dimensions. The proposed approach forms a family of increasingly exact approximations to the original game. Our results characterize the quality of these approximations and show operation in a receding horizon, minimally-invasive control context. Additionally, as a special case, our method reduces to local gradient-based optimization in the single-player (optimal control) setting, for which a wide variety of efficient algorithms exist.
Graph signal processing based underwater image enhancement techniques

This paper presents two new methods based on graph signal processing (GSP) techniques to enhance underwater images. The proposed schemes utilize the graph Fourier transform (GFT) and graph wavelet filterbanks in place of the conventional Fourier and wavelet transforms. Initially, the raw images are represented on a chosen graph structure, and then the root-filtering is applied in the GFT and graph wavelet transform (GWT) domains in the present work. It is observed that the root-filtering parameter plays a crucial role in the image enhancement process. The root filtering method highlights the high frequency terms, which pertain to edges and textural elements with smaller amplitudes, while de-emphasizing the higher magnitude frequency components. As a result, the high frequency components are accentuated in contrast to the lower frequencies, resulting in a sharper and more visually appealing image. The simulation results of the proposed techniques are compared with the existing enhancement methods. It is shown that the proposed approaches offer more improvement in the enhanced image quality than the current methods considered in this paper. It is also seen that the root-filtering parameter in the range of 0.75 to 0.95 and Mexican hat wavelet in the proposed graph wavelet filterbank based approach with N = 5 improve the quality of enhancement of underwater images.
Learning Structure Affinity for Video Depth Estimation

Depth estimation is a structure learning problem. The affinity among neighbouring pixels plays an important role in inferring depth values. In this paper, we propose to learn structure affinity in both spatial and temporal domain for accurate depth estimation from monocular videos. Specifically, we first propose a convolutional spatial temporal propagation network (CSTPN) that learns affinity among neighbouring video frames. Secondly, we employ a structure knowledge distillation scheme that transfers the spatial temporal affinity learned by cumbersome network to compact network. By calculating pixel-wise similarities between neighboring frames and neighbouring sequences, our knowledge distillation scheme efficiently captures both short-term and long-term spatial temporal affinity. Finally, we apply a warping loss based on optical flow between video frames to further enforce the temporal affinity. Experiment results show that our proposed depth estimation approach outperform the state-of-the-art methods on both indoor and outdoor benchmark datasets.
Ada-VSR: Adaptive Video Super-Resolution with Meta-Learning

Most of the existing works in supervised spatio-temporal video super-resolution (STVSR) heavily rely on a large-scale external dataset consisting of paired low-resolution low-frame rate (LR-LFR) and high-resolution high-frame-rate (HR-HFR) videos. Despite their remarkable performance, these methods make a prior assumption that the low-resolution video is obtained by down-scaling the high-resolution video using a known degradation kernel, which does not hold in practical settings. Another problem with these methods is that they cannot exploit instance-specific internal information of a video at testing time. Recently, deep internal learning approaches have gained attention due to their ability to utilize the instance-specific statistics of a video. However, these methods have a large inference time as they require thousands of gradient updates to learn the intrinsic structure of the data. In this work, we present Adaptive VideoSuper-Resolution (Ada-VSR) which leverages external, as well as internal, information through meta-transfer learning and internal learning, respectively. Specifically, meta-learning is employed to obtain adaptive parameters, using a large-scale external dataset, that can adapt quickly to the novel condition (degradation model) of the given test video during the internal learning task, thereby exploiting external and internal information of a video for super-resolution. The model trained using our approach can quickly adapt to a specific video condition with only a few gradient updates, which reduces the inference time significantly. Extensive experiments on standard datasets demonstrate that our method performs favorably against various state-of-the-art approaches.
SalS-GAN

Many GAN inversion methods have emerged to embed a given real image into the latent space of GAN for real image editing. These methods usually use a latent space composed of a series of one-dimensional vectors as an optimization space to reconstruct real images such as W+ latent space. However, the reconstructed image of these methods is usually difficult to maintain the rich detailed information in the real image. How to better preserve details in the real image is still a challenge. To solve this problem, we propose a spatially-adaptive latent space, called SA latent space, and adopt it as the optimization latent space in GAN inversion task. In particular, we use the affine transformation parameters of each convolutional layer in the generator to form the SA latent space and change affine transformation parameters from a one-dimensional vector to a spatially-adaptive three-dimensional tensor. With the more expressive latent space, we can better reconstruct the details of the real image. Extensive experiments suggest that the image reconstruction quality can be significantly improved while maintaining the semantic disentanglement ability of latent code. The code is available at https://github.com/zhang-lingyun/SalS-GAN.
Information Retrieval from web with Faster R-CNN Deep Learning Networks: A New Perspective

With advancement in artificial intelligence and machine learning, web information retrieval has evolved from manual to automated to intelligent systems making significant strides in building tools and techniques to enable automated information retrieval from the websites. In this paper, an automated web information retrieval framework with Faster region based convolutional neural network (R-CNN) is proposed. The framework automatically navigates to the specific product page in a retail website and implements leading edge open-source tools for automated retrieval of product details from the retail website. Information retrieval by using partial tree alignment (DEPTA) machine learning technique is used as reference for comparison. The results reveal an improved precision and recall as compared to DEPTA. The future research in web information retrieval space will to design and develop self-healing capability of web information retrieval framework to adapt to dynamic changes in the website layout.
Anonymization of Network Traces Data through Condensation-based Differential Privacy

Network traces are considered a primary source of information to researchers, who use them to investigate research problems such as identifying user behavior, analyzing network hierarchy, maintaining network security, classifying packet flows, and much more. However, most organizations are reluctant to share their data with a third party or the public due to privacy concerns. Therefore, data anonymization prior to sharing becomes a convenient solution to both organizations and researchers. Although several anonymization algorithms are available, few of them allow sufficient privacy (organization need), acceptable data utility (researcher need), and efficient data analysis at the same time. This article introduces a condensation-based differential privacy anonymization approach that achieves an improved tradeoff between privacy and utility compared to existing techniques and produces anonymized network trace data that can be shared publicly without lowering its utility value. Our solution also does not incur extra computation overhead for the data analyzer. A prototype system has been implemented, and experiments have shown that the proposed approach preserves privacy and allows data analysis without revealing the original data even when injection attacks are launched against it. When anonymized datasets are given as input to graph-based intrusion detection techniques, they yield almost identical intrusion detection rates as the original datasets with only a negligible impact.
Detecting Compiler Warning Defects Via Diversity-Guided Program Mutation

Compiler diagnostic warnings help developers identify potential programming mistakes during program compilation. However, these warnings could be erroneous due to the defects of compiler warning diagnostics. Although the existing technique (i.e., Epiphron) can automatically generate test programs for compiler warning defect detection, the effectiveness of Epiphron on defect-finding is still limited, due to the limitation for generating warning-sensitive test program structures. Therefore, in this paper, we propose a DIversity-guided PROgram Mutation approach, called DIPROM, to construct diverse warning-sensitive programs for effective compiler warning defect detection. Given a seed test program, DIPROM first removes its dead code to reduce false positive warning defects. Then, the abstract syntax tree (AST) of the test program is constructed; DIPROM iteratively mutates the structures of the AST to generate warning-sensitive program variants. To effectively construct diverse warning-sensitive structures, DIPROM applies a novel diversity-guided strategy to generate program variants in each iteration. With the generated program variants, differential testing is conducted to detect warning defects in different compilers. In the experiments, we evaluate DIPROM with two popular C compilers (i.e., GCC and Clang). Experimental results show that DIPROM significantly outperforms three state-of-the-art approaches (i.e., HiCOND, Epiphron, and Hermes) by up to 18.93% <inline-formula><tex-math notation="LaTeX">$\sim$</tex-math></inline-formula> 76.74% in terms of the bug-finding capability on average. Meanwhile, DIPROM is efficient, which spends less time on finding the same average number of warning defects. We at last applied DIPROM to the latest development versions of GCC and Clang. After two months’ running, we reported 8 new warning defects; 5 of them have been confirmed/fixed by developers.
Extending a physics-based constitutive model using genetic programming

In material science, models are derived to predict emergent material properties (e.g. elasticity, strength, conductivity) and their relations to processing conditions. A major drawback is the calibration of model parameters that depend on processing conditions. Currently, these parameters must be optimized to fit measured data since their relations to processing conditions (e.g. deformation temperature, strain rate) are not fully understood. We present a new approach that identifies the functional dependency of calibration parameters from processing conditions based on genetic programming. We propose two (explicit and implicit) methods to identify these dependencies and generate short interpretable expressions. The approach is used to extend a physics-based constitutive model for deformation processes. This constitutive model operates with internal material variables such as a dislocation density and contains a number of parameters, among them three calibration parameters. The derived expressions extend the constitutive model and replace the calibration parameters. Thus, interpolation between various processing parameters is enabled. Our results show that the implicit method is computationally more expensive than the explicit approach but also produces significantly better results.
Multistage Attack–Defense Graph Game Analysis for Protection Resources Allocation Optimization Against Cyber Attacks Considering Rationality Evolution

Cyber vulnerabilities become ever more critical in modern industrial systems since the attacker can utilize the vulnerabilities to degrade their performance or even cause disasters. In 2015, a series of sequential and well-organized cyber attacks intruded into the Ukrainian power grid, compromised access to the control system, and interrupted the power supply system, finally causing a widespread power outage. To assist the defender, e.g., power grid operator, to allocate protection resources against cyber attacks, existing studies have devoted considerable efforts to risk and reliability analysis and interaction analysis using game theory. The defender's protection strategy includes preevent defense strategy and postevent repair strategy. The strategy spaces of both players were static in previous studies. However, facing Ukrainian-style cyber attacks, the strategy spaces could variate during the attacker-defender confrontation. In other words, the vulnerability compromised by the attacker in one stage could expose the subsequential vulnerabilities, leading to the change of strategy spaces. In this work, a multistage attack-defense graph game model is proposed to assist the defender in allocating protection resources optimally against sequential cyber attacks during multiple stages. In addition, we consider the existence of the rationality evolution of the attacker, which mainly results from asymmetric information, capacity limitation, and progressive learning during the confrontation. Compared to previous studies based on static strategy spaces and static rationalities, our model is more practical and effective in dealing with Ukrainian-style cyber attacks. The simulation results show the superiority of our approach, and some notable observations and practical suggestions are summarized for the defender.