Reviewerly/exHarmony huggingface dataset:
Compressed Communication Complexity of Hamming Distance

We consider the communication complexity of the Hamming distance of two strings. Bille et al. [SPIRE 2018] considered the communication complexity of the longest common prefix (LCP) problem in the setting where the two parties have their strings in a compressed form, i.e., represented by the Lempel-Ziv 77 factorization (LZ77) with/without self-references. We present a randomized public-coin protocol for a joint computation of the Hamming distance of two strings represented by LZ77 without self-references. Although our scheme is heavily based on Bille et al.’s LCP protocol, our complexity analysis is original which uses Crochemore’s C-factorization and Rytter’s AVL-grammar. As a byproduct, we also show that LZ77 with/without self-references are not monotonic in the sense that their sizes can increase by a factor of 4/3 when a prefix of the string is removed.
Information security decisions of firms considering security risk interdependency

Information security management becomes more challenging nowadays due to the diverse security risk interdependency between firms. Prior researches rarely consider the impact of risk interdependency on security decisions. This paper comprehensively considers two types of security risk interdependency caused by the nature of information assets and the technical similarity. We find that it is necessary to distinguish the complementary and substitutable information assets since they have different effects on the firm’s investment incentive. As for the risk interdependency caused by the nature of the information assets, although both the high complementation degree and high substitution degree inhibit firms’ incentives to invest, the underlying reasons are different. Besides, for another risk interdependency, the technical similarity enhances the investment incentive of the complementary firms but suppresses that of the substitutable firms. Moreover, the free-riding problem is unavoidable when the firm makes security decisions independently. Thus, we propose two efficient mechanisms to coordinate the firm’s investment incentive: the effort-based mechanism and the liability-based mechanism. The effort-based mechanism demands the firm obtain a reward from its cooperative firm according to its security effort level. The liability-based mechanism demands the breached firm take the liability by compensating the non-breached firm. We find that both two mechanisms are efficient, and could guide firms to solve the problem of opportunism and shirking responsibility in practice. Finally, for generality, we extend our model to an asymmetric case and find that most of the results are robust.
Privacy-Preserving Analytics for Data Markets Using MPC

Data markets have the potential to foster new data-driven applications and help growing data-driven businesses. When building and deploying such markets in practice, regulations such as the European Union's General Data Protection Regulation (GDPR) impose constraints and restrictions on these markets especially when dealing with personal or privacy-sensitive data. In this paper, we present a candidate architecture for a privacy-preserving personal data market, relying on cryptographic primitives such as multi-party computation (MPC) capabl e of performing privacy-preserving computations on the data. Besides specifying the architecture of such a data market, we also present a privacy-risk analysis of the market following the LINDDUN methodology.
Lossy Medical Image Compression using Residual Learning-based Dual Autoencoder Model

In this work, we propose a two-stage autoencoder based compressor-decompressor framework for compressing malaria RBC cell image patches. We know that the medical images used for disease diagnosis are around multiple gigabytes size, which is quite huge. The proposed residual-based dual autoencoder network is trained to extract the unique features which are then used to reconstruct the original image through the decompressor module. The two latent space representations (first for the original image and second for the residual image) are used to rebuild the final original image. Color-SSIM has been exclusively used to check the quality of the chrominance part of the cell images after decompression. The empirical results indicate that the proposed work outperformed other neural network related compression technique for medical images by approximately 35%, 10% and 5% in PSNR, Color SSIM and MS-SSIM respectively. The algorithm exhibits a significant improvement in bit savings of 76%, 78%, 75% & 74% over JPEG-LS, JP2K-LM, CALIC and recent neural network approach respectively, making it a good compression-decompression technique.
Variable selection with false discovery rate control in deep neural networks

Deep neural networks are famous for their high prediction accuracy, but they are also known for their black-box nature and poor interpretability. We consider the problem of variable selection, that is, selecting the input variables that have significant predictive power on the output, in deep neural networks. Most existing variable selection methods for neural networks are only applicable to shallow networks or are computationally infeasible on large datasets; moreover, they lack a control on the quality of selected variables. Here we propose a backward elimination procedure called SurvNet, which is based on a new measure of variable importance that applies to a wide variety of networks. More importantly, SurvNet is able to estimate and control the false discovery rate of selected variables empirically. Further, SurvNet adaptively determines how many variables to eliminate at each step in order to maximize the selection efficiency. The validity and efficiency of SurvNet are shown on various simulated and real datasets, and its performance is compared with other methods. Especially, a systematic comparison with knockoff-based methods shows that although they have more rigorous false discovery rate control on data with strong variable correlation, SurvNet usually has higher power. Identifying salient input features can be a challenge in neural networks. The authors developed a variable selection procedure with false discovery rate control that works on classification or regression problems, one or multiple output neurons, and deep or shallow neural networks.
Securing Organization’s Data: A Role-Based Authorized Keyword Search Scheme With Efficient Decryption

For better data availability and accessibility while ensuring data secrecy, organizations often tend to outsource their encrypted data to the cloud storage servers, thus bringing the challenge of keyword search over encrypted data. In this article, we propose a novel authorized keyword search scheme using Role-Based Encryption (RBE) technique in a cloud environment. The contributions of this article are multi-fold. First, it presents a keyword search scheme which enables only authorized users, having properly assigned roles, to delegate keyword-based data search capabilities over encrypted data to the cloud providers without disclosing any sensitive information. Second, it supports a multi-organization cloud environment, where the users can be associated with more than one organization. Third, the proposed scheme provides efficient decryption, conjunctive keyword search and revocation mechanisms. Fourth, the proposed scheme outsources expensive cryptographic operations in decryption to the cloud in a secure manner. Fifth, we have provided a formal security analysis to prove that the proposed scheme is semantically secure against Chosen Plaintext and Chosen Keyword Attacks. Finally, our performance analysis shows that the proposed scheme is suitable for practical applications.
Characters, Weil sums and c-differential uniformity with an application to the perturbed Gold function

Building upon the observation that the newly defined Ellingsen, et al. (2020) concept of c-differential uniformity is not invariant under EA or CCZ-equivalence Hasan et al. (2021), we showed in Stănică and Geary (2021) that adding some appropriate linearized monomials increases the c-differential uniformity of the inverse function, significantly, for some c. We continue that investigation here. First, by analyzing the involved equations, we find bounds for the uniformity of the Gold function perturbed by a single monomial, exhibiting the discrepancy we previously observed on the inverse function. Secondly, to treat the general case of perturbations via any linearized polynomial, we use characters in the finite field to express all entries in the c-Differential Distribution Table (DDT) of an (n, n)-function on the finite field ${\mathbb {F}}_{p^{n}}$ , and further, we use that method to find explicit expressions for all entries of the c-DDT of the perturbed Gold function (via an arbitrary linearized polynomial).
Abacus: Precise Side-Channel Analysis

Side-channel attacks allow adversaries to infer sensitive information from non-functional characteristics. Prior side-channel detection work is able to identify numerous potential vulnerabilities. However, in practice, many such vulnerabilities leak a negligible amount of sensitive information, and thus developers are often reluctant to address them. Existing tools do not provide information to evaluate a leak's severity, such as the number of leaked bits. To address this issue, we propose a new program analysis method to precisely quantify the leaked information in a single-trace attack through side-channels. It can identify covert information flows in programs that expose confidential information and can reason about security flaws that would otherwise be difficult, if not impossible, for a developer to find. We model an attacker's observation of each leakage site as a constraint. We use symbolic execution to generate these constraints and then run Monte Carlo sampling to estimate the number of leaked bits for each leakage site. By applying the Central Limit Theorem, we provide an error bound for these estimations. We have implemented the technique in a tool called Abacus, which not only finds very fine-grained side-channel vulnerabilities but also estimates how many bits are leaked. Abacus outperforms existing dynamic side-channel detection tools in performance and accuracy. We evaluate Abacus on OpenSSL, mbedTLS, Libgcrypt, and Monocypher. Our results demonstrate that most reported vulnerabilities are difficult to exploit in practice and should be de-prioritized by developers. We also find several sensitive vulnerabilities that are missed by the existing tools. We confirm those vulnerabilities with manual checks and by contacting the developers.
Who Filters the Filters

Ad and tracking blocking extensions are popular tools for improving web performance, privacy and aesthetics. Content blocking extensions generally rely on filter lists to decide whether a web request is associated with tracking or advertising, and so should be blocked. Millions of web users rely on filter lists to protect their privacy and improve their browsing experience. Despite their importance, the growth and health of filter lists are poorly understood. Filter lists are maintained by a small number of contributors who use undocumented heuristics and intuitions to determine what rules should be included. Lists quickly accumulate rules, and rules are rarely removed. As a result, users' browsing experiences are degraded as the number of stale, dead or otherwise not useful rules increasingly dwarf the number of useful rules, with no attenuating benefit. An accumulation of "dead weight" rules also makes it difficult to apply filter lists on resource-limited mobile devices. This paper improves the understanding of crowdsourced filter lists by studying EasyList, the most popular filter list. We measure how EasyList affects web browsing by applying EasyList to a sam- ple of 10,000 websites. We find that 90.16% of the resource blocking rules in EasyList provide no benefit to users in common browsing scenarios. We use our measurements of rule application rates to taxonomies ways advertisers evade EasyList rules. Finally, we propose optimizations for popular ad-blocking tools that (i) allow EasyList to be applied on performance constrained mobile devices and (ii) improve desktop performance by 62.5%, while preserving over 99% of blocking coverage. We expect these optimizations to be most useful for users in non-English locals, who rely on supplemental filter lists for effective blocking and protections.
Research on Multi-Microgrids Scheduling Strategy Considering Dynamic Electricity Price Based on Blockchain

In order to improve the economy and safety of multi-microgrids (MMGs) scheduling, this paper proposes a research on MMGs scheduling strategy that takes into account dynamic electricity prices based on the blockchain.We first introduce the principle of blockchain, analyze the security and economy of the combination of blockchain and MMGs scheduling, and design the scheduling architecture and process based on the blockchain platform.Second, we set a dynamic electricity price model according to the total power supply and demand of MMGs, and set a load optimization model.Finally, we take optimal system economy and minimum environmental pollution as the objective function, then use the linear programming method and the improved krill herd algorithm (KHA) with nonlinear changes in weights to solve the problem.The simulation results show that: (1) The dynamic electricity prices can reflect the power supply and demand of microgrids and optimize the load; (2) Comparing the three scheduling schemes, the strategy in this paper can improve the economic and environmental protection of MMGs by 37.33% and 39.34%, while reduce the interactive power between the microgrid and the distribution network by 56.28%, and the curtailment rate by 63.22%; (3) The improved krill herd algorithm has higher convergence speed and convergence accuracy;(4) The blockchain technology can ensure the security of scheduling data.
A New Method to Determine the Annual Energy Output of Liquid-Based Solar Collectors

One of the most effective ways to reduce greenhouse gas emissions is the widespread use of solar panels. A new approach to estimating the annual energy output ESC of liquid-based solar thermal collectors is presented in this paper. The main parameters of the surrounding environment that affect the energy yield from solar water heating (SWH) systems are total solar radiation ES and outdoor air temperature θO. Here, two functions are developed that allow the specification of the ESC value based on these two previously determined factors. The article presents the subsequent stages that led to the development of this method. In the first step, the computer model of a solar domestic hot water (SDHW) system was built in the EnergyPlus environment. An object of the modeling procedure was the real solar installation located at Bialystok University of Technology. The database containing the annual energy output of the solar collectors, total solar radiation, and outdoor air temperature was developed based on the results of simulations made for 24 Polish cities. Then, the multiple regression method (implemented in Statistica v.13.1) was used to correlate these variables. Two contour graphs were also developed to determine the energy yield from solar collectors.
Robustness of 2 × N × M entangled states against qubit loss

Entanglement in quantum systems is usually degraded by interaction with the environment. From time to time, some parties of a multipartite entangled system may become decoherent with other parties of the system due to the interference with the environment. In this situation, it is interesting to know how much information the residual system would keep on carrying. In this paper, as a starting point for any entangled system, we investigate the property of the 2×N×M state with qubit being disentangled, which is characterized by the measurement of robustness.
An intelligent unsupervised technique for fraud detection in health care systems

Healthcare is an essential part of people’s lives, particularly for the elderly population, and also should be economical. Medicare is one particular healthcare plan. Claims fraud is a significant contributor to increased healthcare expenses, though the effect of it could be lessened by fraud detection. In this paper, an analysis of various machine learning techniques was done to identify Medicare fraud. The isolated forest an unsupervised machine learning algorithm which improves overall performance while detecting fraud based upon outliers. The goal of this specific paper is generally to show probable dishonest providers on the ground of their allegations. Obtained results were found more promising compared to existing techniques. Around 98.76% accuracy is obtained using an isolated forest algorithm.
Hand Pose Understanding With Large-Scale Photo-Realistic Rendering Dataset

Hand pose understanding is essential to applications such as human computer interaction and augmented reality. Recently, deep learning based methods achieve great progress in this problem. However, the lack of high-quality and large-scale dataset prevents the further improvement of hand pose related tasks such as 2D/3D hand pose from color and depth from color. In this paper, we develop a large-scale and high-quality synthetic dataset, PBRHand. The dataset contains millions of photo-realistic rendered hand images and various ground truths including pose, semantic segmentation, and depth. Based on the dataset, we firstly investigate the effect of rendering methods and used databases on the performance of three hand pose related tasks: 2D/3D hand pose from color, depth from color and 3D hand pose from depth. This study provides insights that photo-realistic rendering dataset is worthy of synthesizing and shows that our new dataset can improve the performance of the state-of-the-art on these tasks. This synthetic data also enables us to explore multi-task learning, while it is expensive to have all the ground truth available on real data. Evaluations show that our approach can achieve state-of-the-art or competitive performance on several public datasets.
High-Capacity Embedding Method Based on Double-Layer Octagon-Shaped Shell Matrix

Data hiding is a technique that embeds a secret message into a cover medium and transfers the hidden information in the secret message to the recipient. In the past, several data hiding methods based on magic matrix have used various geometrical shapes to transmit secret data. The embedding capacity achieved in these methods was often limited due to simple geometrical layouts. This paper proposes a data hiding scheme based on a double-layer octagon-shaped shell matrix. Compared to previous octagon-shaped data hiding methods, the proposed method embeds a total of 7 bits in each pixel pair, reaching an embedding capacity of 3.5 bits per pixel (bpp). Experimental results show that the proposed scheme has a higher embedding capacity compared to other irreversible data hiding schemes. Using the proposed method, it is possible to maintain the Peak Signal to Noise Ratio (PSNR) within an acceptable range with the embedding time less than 2 s.
An Investigation of Critical Issues in Bias Mitigation Techniques

A critical problem in deep learning is that systems learn inappropriate biases, resulting in their inability to perform well on minority groups. This has led to the creation of multiple algorithms that endeavor to mitigate bias. However, it is not clear how effective these methods are. This is because study protocols differ among papers, systems are tested on datasets that fail to test many forms of bias, and systems have access to hidden knowledge or are tuned specifically to the test set. To address this, we introduce an improved evaluation protocol, sensible metrics, and a new dataset, which enables us to ask and answer critical questions about bias mitigation algorithms. We evaluate seven state-of-the-art algorithms using the same network architecture and hyperparameter selection policy across three benchmark datasets. We introduce a new dataset called Biased MNIST that enables assessment of robustness to multiple bias sources. We use Biased MNIST and a visual question answering (VQA) benchmark to assess robustness to hidden biases. Rather than only tuning to the test set distribution, we study robustness across different tuning distributions, which is critical because for many applications the test distribution may not be known during development. We find that algorithms exploit hidden biases, are unable to scale to multiple forms of bias, and are highly sensitive to the choice of tuning set. Based on our findings, we implore the community to adopt more rigorous assessment of future bias mitigation methods. All data, code, and results are publicly available <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> .
DAP: Detection-Aware Pre-training with Weak Supervision

This paper presents a detection-aware pre-training (DAP) approach, which leverages only weakly-labeled classification-style datasets (e.g., ImageNet) for pretraining, but is specifically tailored to benefit object detection tasks. In contrast to the widely used image classification-based pre-training (e.g., on ImageNet), which does not include any location-related training tasks, we transform a classification dataset into a detection dataset through a weakly supervised object localization method based on Class Activation Maps to directly pre-train a detector, making the pre-trained model location-aware and capable of predicting bounding boxes. We show that DAP can outperform the traditional classification pre-training in terms of both sample efficiency and convergence speed in downstream detection tasks including VOC and COCO. In particular, DAP boosts the detection accuracy by a large margin when the number of examples in the downstream task is small.
Forecasting Transactional Amount in Bitcoin Network Using Temporal GNN Approach

Financial institutions such as banks regularly forecast the amount of finances an individual will have in his/her account in the near future. This can help banks in categorizing their customers so that banks can recommend financial products that matches the needs of their customers. In this work, we explored the historical financial transactions for predicting the amount a customer will receive through his/her transacting partners at a specific time. In particular, we use the Bitcoin transactional dataset, which has two main characteristics: i) network, and ii) temporal. This paper contributes by exploiting a specific kind of Graph Neural Network approach called Temporal-Graph Convolutional Network (T-GCN) for predicting the amount of Bitcoins received by a customer at a particular timestamp. The lower errors obtained using T-GCN approach compared to 11 baseline approaches (such as Support Vector Regression (SVR), Random Forest Regression (RFR), Vector Auto-Regressive (VAR), Long Short-Term Memory (LSTM), etc.) clearly demonstrate the effectiveness of T-GCN approach. In addition, our findings reveal that time is an important feature for such kind of predictive tasks.
A survey on bandwidth-aware geo-distributed frameworks for big-data analytics

Abstract In the era of global-scale services, organisations produce huge volumes of data, often distributed across multiple data centres, separated by vast geographical distances. While cluster computing applications, such as MapReduce and Spark, have been widely deployed in data centres to support commercial applications and scientific research, they are not designed for running jobs across geo-distributed data centres. The necessity to utilise such infrastructure introduces new challenges in the data analytics process due to bandwidth limitations of the inter-data-centre communication. In this article, we discuss challenges and survey the latest geo-distributed big-data analytics frameworks and schedulers (based on MapReduce and Spark) with WAN-bandwidth awareness.