Reviewerly/exHarmony huggingface dataset:
Privacy of DNS-over-HTTPS: Requiem for a Dream?

The recently proposed DNS-over-HTTPS (DoH) protocol is becoming increasingly popular in addressing the privacy concerns of exchanging plain-text DNS messages over potentially malicious transit networks (e.g., mass surveillance at ISPs). By employing HTTPS to encrypt DNS communications, DoH traffic inherently becomes indistinguishable from regular encrypted Web traffic, rendering active disruption (e.g., downgrading to the plain-text DNS) by transit networks extremely hard. In this work, we investigate whether DoH traffic is indeed indistinguishable from encrypted Web traffic. To this end, we collect several DoH traffic traces corresponding to 25 resolvers (including major ones, e.g., Google and Cloudftare) by visiting thousands of domains in Alexa's list of top-ranked websites at different geographical locations and environments. Based on the collected traffic, we train a machine learning model to classify HTTPS traffic as either Web or DoH. With our DoH identification model in place, we show that an authoritarian ISP can identify ∼97.4% (∼90%) of the DoH packets correctly in a closed-world (open-world) setting while only misclassifying 1 in 10,000 Web packets. To counter this DoH identification model, we propose an effective mitigation technique, making the identification model impractical for ISPs to filter and consequently downgrade DoH to plain-text DNS communications.
Non-Markovian dynamics of open quantum system with memory

In this paper, non-Markovian generalization of master equation for open quantum system is proposed. Non-Markovian dynamics of two-level quantum system with memory and interaction with environment is described. To describe this system, the Gorini–Kossakowski–Sudarshan equation for open quantum states is generalized by taking into account power-law fading memory. The non-Markovian quantum processes with power-law memory are described by using integration and differentiation of non-integer orders. Complete positivity and bi-positivity in non-Markovian quantum dynamics with memory are described. An example of two-level quantum systems with power-law memory is suggested. Exact solution of the non-Markovian master equations of two-level open quantum systems with memory is derived.
Loss function search for person re-identification

In recent years, person re-identification, which learns discriminative features for the specific person retrieval problem across non-overlapping cameras, has attracted extensive attention. One of the main challenges in person re-identification with deep neural networks is the design of the loss function, which plays a vital role in improving the discrimination of the learned features. However, most existing models utilize the hand-designed loss functions, which are usually sub-optimal and time-consuming. The search spaces of the two existing AutoML-based methods are either too complicated or too simple to include various forms of loss functions. In order to solve the irrationality of the above search spaces, in this paper, we propose a method of AutoML for loss function search named LFS-ReID for person ReID in the framework of the margin-based softmax loss function. Specifically, we first analyze the margin-based softmax loss function and conclude four key properties. Then we carefully design a sampling distribution based on the non-independent truncated Gaussian distributions to sample the loss function, which conforms to the above four properties. Finally, a method based on reinforcement learning is adopted to optimize the sampling distribution dynamically. Experimental results demonstrate that the proposed method achieves state-of-the-art performance on four commonly used datasets.
Facilitating Vulnerability Assessment through PoC Migration

Recent research shows that, even for vulnerability reports archived by MITRE/NIST, they usually contain incomplete information about the software's vulnerable versions, making users of under-reported vulnerable versions at risk. In this work, we address this problem by introducing a fuzzing-based method. Technically, this approach first collects the crashing trace on the reference version of the software. Then, it utilizes the trace to guide the mutation of the PoC input so that the target version could follow the trace similar to the one observed on the reference version. Under the mutated input, we argue that the target version's execution could have a higher chance of triggering the bug and demonstrating the vulnerability's existence. We implement this idea as an automated tool, named VulScope. Using 30 real-world CVEs on 470 versions of software, VulScope is demonstrated to introduce no false positives and only 7.9% false negatives while migrating PoC from one version to another. Besides, we also compare our method with two representative fuzzing tools AFL and AFLGO. We find VulScope outperforms both of these existing techniques while taking the task of PoC migration. Finally, by using VulScope, we identify 330 versions of software that MITRE/NIST fails to report as vulnerable.
QDCT-Based Blind Color Image Watermarking With Aid of GWO and DnCNN for Performance Improvement

Artificial intelligence (AI) is of great potential for improving the performance of image processing and applications. In this study, we incorporate two AI techniques, namely, the grey wolf optimizer (GWO) and denoising convolutional neural network (DnCNN), within a framework developed based on the quaternion discrete cosine transform (QDCT). Binary embedding is formulated according to the attribute of each QDCT component and the distinctive properties of available modulation schemes. The GWO is responsible for performance optimization, while the DnCNN makes the extracted binary watermark more visually recognizable. Experiment results demonstrate the efficacy of the proposed scheme for resisting a variety of image processing attacks. The proposed scheme outperforms existing ones in terms of the robustness and intelligibility of the retrieved watermarks under the same payload capacity.
Cert-RNN: Towards Certifying the Robustness of Recurrent Neural Networks

Certifiable robustness, the functionality of verifying whether the given region surrounding a data point admits any adversarial example, provides guaranteed security for neural networks deployed in adversarial environments. A plethora of work has been proposed to certify the robustness of feed-forward networks, e.g., FCNs and CNNs. Yet, most existing methods cannot be directly applied to recurrent neural networks (RNNs), due to their sequential inputs and unique operations.
Burn After Reading: Adaptively Secure Puncturable Identity-Based Proxy Re-Encryption Scheme for Securing Group Message

Puncturable proxy re-encryption (PPRE) is envisioned to provide secure access control delegation and fine-grained forward security for asynchronous group messaging systems. Nevertheless, the existing PPRE scheme not only suffers from the burden of certificate management but also merely achieves selective security based on the nonstandard assumption. In this article, a puncturable identity-based PRE (P-IB-PRE) scheme is proposed to efficiently protect the security and privacy of the group message. The proposed scheme introduces a message server as the proxy to transform ciphertext for each participant in the group; thus, the heavy computation overhead is delegated to the message server with abundant resources. Most importantly, our scheme enables the recipient to revoke its private key’s decryption capability of the specific messages without affecting other messages. Moreover, the identity-based mechanism eliminates the burden of certificate management as well as improves efficiency. The proposed scheme achieves adaptive security under the standard decisional bilinear Diffie–Hellman (DBDH) assumption. Eventually, theoretical and experimental analyses demonstrate that the proposed scheme has an excellent performance in efficiency and practicality.
BlockGNN: Towards Efficient GNN Acceleration Using Block-Circulant Weight Matrices

In recent years, Graph Neural Networks (GNNs) appear to be state-of-the-art algorithms for analyzing non-euclidean graph data. By applying deep-learning to extract high-level representations from graph structures, GNNs achieve extraordinary accuracy and great generalization ability in various tasks. However, with the ever-increasing graph sizes, more and more complicated GNN layers, and higher feature dimensions, the computational complexity of GNNs grows exponentially. How to inference GNNs in real time has become a challenging problem, especially for some resource-limited edge-computing platforms.To tackle this challenge, we propose BlockGNN, a software-hardware co-design approach to realize efficient GNN acceleration. At the algorithm level, we propose to leverage block-circulant weight matrices to greatly reduce the complexity of various GNN models. At the hardware design level, we propose a pipelined CirCore architecture, which supports efficient block-circulant matrices computation. Basing on CirCore, we present a novel BlockGNN accelerator to compute various GNNs with low latency. Moreover, to determine the optimal configurations for diverse deployed tasks, we also introduce a performance and resource model that helps choose the optimal hardware parameters automatically. Comprehensive experiments on the ZC706 FPGA platform demonstrate that on various GNN tasks, BlockGNN achieves up to 8.3× speedup compared to the baseline HyGCN architecture and 111.9× energy reduction compared to the Intel Xeon CPU platform.
Revisiting Nakamoto Consensus in Asynchronous Networks: A Comprehensive Analysis of Bitcoin Safety and ChainQuality

The Bitcoin blockchain safety relies on strong network synchrony. Therefore, violating the blockchain safety requires strong adversaries that control a mining pool with 51% hash rate. In this paper, we show that the network synchrony does not hold in the real world Bitcoin network which can be exploited to lower the cost of various attacks that violate the blockchain safety and chain quality. Towards that, first we construct the Bitcoin ideal functionality to formally specify its ideal execution model in a synchronous network. We then develop a large-scale data collection system through which we connect with more than 36K IP addresses of the Bitcoin nodes and identify 359 mining nodes. We contrast the ideal functionality against the real world measurements to expose the network anomalies that can be exploited to optimize the existing attacks. Particularly, we observe a non-uniform block propagation pattern among the mining nodes showing that the Bitcoin network is asynchronous in practice.
Online Mapping and Motion Planning Under Uncertainty for Safe Navigation in Unknown Environments

Safe autonomous navigation is an essential and challenging problem for robots operating in highly unstructured or completely unknown environments. Under these conditions, not only robotic systems must deal with limited localization information but also their maneuverability is constrained by their dynamics and often suffers from uncertainty. In order to cope with these constraints, this article proposes an uncertainty-based framework for mapping and planning feasible motions online with probabilistic safety guarantees. The proposed approach deals with the motion, probabilistic safety, and online computation constraints by: 1) incrementally mapping the surroundings to build an uncertainty-aware representation of the environment and 2) iteratively (re)planning trajectories to goal that is kinodynamically feasible and probabilistically safe through a multilayered sampling-based planner in the belief space. In-depth empirical analyses illustrate some important properties of this approach, namely: 1) the multilayered planning strategy enables rapid exploration of the high-dimensional belief space while preserving asymptotic optimality and completeness guarantees and 2) the proposed routine for probabilistic collision checking results in tighter probability bounds in comparison to other uncertainty-aware planners in the literature. Furthermore, real-world in-water experimental evaluation on a nonholonomic torpedo-shaped autonomous underwater vehicle and simulated trials in an urban environment on an unmanned aerial vehicle demonstrate the efficacy of the method and its suitability for systems with limited onboard computational power. Note to Practitioners—Emergent robotic applications require operating in previously unmapped scenarios. This article presents a unified mapping–planning strategy that enables robots to navigate autonomously and safely in harsh environments.
Software Defect Prediction Using Wrapper Feature Selection Based on Dynamic Re-Ranking Strategy

Finding defects early in a software system is a crucial task, as it creates adequate time for fixing such defects using available resources. Strategies such as symmetric testing have proven useful; however, its inability in differentiating incorrect implementations from correct ones is a drawback. Software defect prediction (SDP) is another feasible method that can be used for detecting defects early. Additionally, high dimensionality, a data quality problem, has a detrimental effect on the predictive capability of SDP models. Feature selection (FS) has been used as a feasible solution for solving the high dimensionality issue in SDP. According to current literature, the two basic forms of FS approaches are filter-based feature selection (FFS) and wrapper-based feature selection (WFS). Between the two, WFS approaches have been deemed to be superior. However, WFS methods have a high computational cost due to the unknown number of executions available for feature subset search, evaluation, and selection. This characteristic of WFS often leads to overfitting of classifier models due to its easy trapping in local maxima. The trapping of the WFS subset evaluator in local maxima can be overcome by using an effective search method in the evaluator process. Hence, this study proposes an enhanced WFS method that dynamically and iteratively selects features. The proposed enhanced WFS (EWFS) method is based on incrementally selecting features while considering previously selected features in its search space. The novelty of EWFS is based on the enhancement of the subset evaluation process of WFS methods by deploying a dynamic re-ranking strategy that iteratively selects germane features with a low subset evaluation cycle while not compromising the prediction performance of the ensuing model. For evaluation, EWFS was deployed with Decision Tree (DT) and Naïve Bayes classifiers on software defect datasets with varying granularities. The experimental findings revealed that EWFS outperformed existing metaheuristics and sequential search-based WFS approaches established in this work. Additionally, EWFS selected fewer features with less computational time as compared with existing metaheuristics and sequential search-based WFS methods.
The Astounding Relationship: Middleware, Frameworks, and API

Complex software is taking over the current existence of the technology. With the furtherance of software, the deepest levels of constituents have originated. Aspects like time, expenses, and, error solving has been expounded to at most levels of convolution. The purpose of Software is to plunge deep into the concepts and then perceive the enduring and deep-rooted solutions. The paper focuses on three major characteristics of software: middleware, frameworks, and APIs. The reinforcement of these strands has escalated the development of new magnitudes in this field. We will assimilate about Middleware which is software whose function is to bridge together the operating system or database with the applications over a network. Another predominant module is Framework that incorporates various libraries, compiler, and supplementary programs that can be used to fabricate unique applications. Beyond Middleware and Framework, APIs i.e. Application Programming Interface facilitates interactivity between two applications. It has been thoroughly explained about the conjunction of these emerging technologies into new applications and how they can change the working of the software.
A new clustering algorithm based on a radar scanning strategy with applications to machine learning data

In this paper, we propose a novel density-based radar scanning clustering algorithm. Its main objective is to quickly discover and accurately extract individual clusters by employing the radar scanning strategy. By using this algorithm, the number of clusters does not need to be specified beforehand. Two techniques are utilized in our proposed method. First, we use a fast mean-shift algorithm with adaptive radius and active subsets to effectively locate the centers, reducing the computational time significantly. Second, we employ the shape of the probability density function of the distribution of distances between a selected point and the other points in the data set. This is performed to determine the critical parameters of the radiuses of the fast mean-shift algorithm and radiuses of clusters. The new algorithm has four merits. It reduces the computational complexity, overcomes problems caused by high dimensionality, is capable of dealing with heterogeneous spherical data sets, and lastly, is robust to noise and outliers. After applying our proposed method to several kinds of synthetic and real-world data sets, the results indicate that the density-based radar scanning algorithm is efficient and accurate.
State-of-the-Art Challenges and Perspectives in Multi-Organ Cancer Diagnosis via Deep Learning-Based Methods

Thus far, the most common cause of death in the world is cancer. It consists of abnormally expanding areas that are threatening to human survival. Hence, the timely detection of cancer is important to expanding the survival rate of patients. In this survey, we analyze the state-of-the-art approaches for multi-organ cancer detection, segmentation, and classification. This article promptly reviews the present-day works in the breast, brain, lung, and skin cancer domain. Afterwards, we analytically compared the existing approaches to provide insight into the ongoing trends and future challenges. This review also provides an objective description of widely employed imaging techniques, imaging modality, gold standard database, and related literature on each cancer in 2016–2021. The main goal is to systematically examine the cancer diagnosis systems for multi-organs of the human body as mentioned. Our critical survey analysis reveals that greater than 70% of deep learning researchers attain promising results with CNN-based approaches for the early diagnosis of multi-organ cancer. This survey includes the extensive discussion part along with current research challenges, possible solutions, and prospects. This research will endow novice researchers with valuable information to deepen their knowledge and also provide the room to develop new robust computer-aid diagnosis systems, which assist health professionals in bridging the gap between rapid diagnosis and treatment planning for cancer patients.
Efficient Implementation of Finite Field Arithmetic for Binary Ring-LWE Post-Quantum Cryptography Through a Novel Lookup-Table-Like Method

The recent advance in the post-quantum cryptography (PQC) field has gradually shifted from the theory to the implementation of the cryptosystem, especially on the hardware platforms. Following this trend, in this paper, we aim to present efficient implementations of the finite field arithmetic (key component) for the binary Ring-Learning-with-Errors (Ring-LWE) PQC through a novel lookup-table (LUT)-like method. In total, we have carried out four stages of interdependent efforts: (i) an algorithm-hardware co-design driven derivation of the proposed LUT-like method is provided detailedly for the key arithmetic of the BRLWE scheme; (ii) the proposed hardware architecture is then presented along with the internal structural description; (iii) we have also presented a novel hybrid size structure suitable for flexible operation, which is the first report in the literature; (iv) the final implementation and comparison processes have also been given, demonstrating that our proposed structures deliver significant improved performance over the state-of-the-art solutions. The proposed designs are highly efficient and are expected to be employed in many emerging applications.
Multi-spectral multi-image super-resolution of Sentinel-2 with radiometric consistency losses and its effect on building delineation

High resolution remote sensing imagery is used in a broad range of tasks, including detection and classification of objects. High-resolution imagery is however expensive to obtain, while lower resolution imagery is often freely available and can be used for a range of social good applications. To that end, we curate a multi-spectral multi-image dataset for super-resolution of satellite images. We use PlanetScope imagery from the SpaceNet-7 challenge as the high resolution reference and multiple Sentinel-2 revisits of the same location as the low-resolution imagery. We present the first results of applying multi-image super-resolution (MISR) to multi-spectral remote sensing imagery. We, additionally, introduce a radiometric-consistency module into the MISR model to preserve the high radiometric resolution and quality of the Sentinel-2 sensor. We show that MISR is superior to single-image super-resolution (SISR) and other baselines on a range of image fidelity metrics. Furthermore, we present the first assessment of the utility of multi-image super-resolution on a semantic and instance segmentation – common remote sensing tasks – showing that utilizing multiple images results in better performance in these downstream tasks, but MISR pre-processing is non-essential.
Privacy and efficiency guaranteed social subgraph matching

Due to the increasing cost of data storage and computation, more and more graphs (e.g., web graphs, social networks) are outsourced and analyzed in the cloud. However, there is growing concern on the privacy of these outsourced graphs at the hands of untrusted cloud providers. Unfortunately, simple label anonymization cannot protect nodes from being re-identified by adversary who knows the graph structure. To address this issue, existing works adopt the k-automorphism model, which constructs $$(k-1)$$ symmetric vertices for each vertex. It has two disadvantages. First, it significantly enlarges the graphs, which makes graph mining tasks such as subgraph matching extremely inefficient and sometimes infeasible even in the cloud. Second, it cannot protect the privacy of attributes in each node. In this paper, we propose a new privacy model (k, t)-privacy that combines the k-automorphism model for graph structure with the t-closeness privacy model for node label generalization. Besides a stronger privacy guarantee, the paper also optimizes the matching efficiency by (1) an approximate label generalization algorithm TOGGLE with $$(1+\epsilon )$$ approximation ratio and (2) a new subgraph matching algorithm PGP on succinct k-automorphic graphs without decomposing the query graph.
The Moral Risks of Online Shaming

Abstract Shaming behaviour on social media has been the cause of concern in recent public discourse. Supporters of online shaming argue that it is an important tool in helping to make social media and online communities safer and more welcoming to traditionally marginalized groups. Objections to shaming often sound like high-minded calls for civility, but this chapter argues that shaming behaviour poses serious risks. It identifies the moral and political risks of online shaming. In particular, shaming threatens to undermine our commitment to the co-deliberative practices of morality. As a result, online shaming can undermine the very goals it is supposed to accomplish.