Reviewerly/exHarmony huggingface dataset:
Toward an Optimal Countermeasure for Cache Side-Channel Attacks

In the last 15 years, we have witnessed a never-ending arm’s race between the attacker and the defender with respect to cache-based side-channel attacks. We have seen a slew of attacks, countermeasures (CMs), counterattacks, counter-CMs, and so on. We analyze the evolution of this area, propose three necessary conditions for designing a successful CM, and then analyze timing and address-based CMs for popular algorithms, such as AES and PRESENT. We show that an optimal yet trivial solution for timing-based CMs is possible. Furthermore, address-based CMs are inferior to timing-based CMs, and they can be broken in <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$n^{O(\log (\log (n)))}$ </tex-math></inline-formula> time.
DefOff: Defensive/Offensive System based on Hiding Technologies

Data encryption and steganography algorithms are heavily used in current systems to achieve data confidentiality. This paper presents a comprehensive Defensive/Offensive (DefOff) system that offers different hiding technologies and encryption algorithms, whether symmetric or asymmetric, to ensure the secrecy of the transmitted data, whether this data is benign or malicious. Furthermore, DefOff gives the developers and researchers a choice to hide the data only or encrypt then hide to add another level of security. The main contributions of DefOff are (a) the ability to integrate any hiding or encryption technology within the same platform, (b) embedding any type of secret data, (c) providing comprehensive evaluation metrics to assess the performance of the executed algorithms, (d) choosing the best combination of techniques for users' solutions based on their requirements, (e) offering DefOff services in terms of a user-friendly Python-based graphical user interface (GUI) tool. As a case study, this paper presents image steganography hiding scheme based on Least Significant Bit (LSB) and Pixel Value Differencing (PVD) algorithms. DefOff shows how LSB and PVD can hide the secret data within digital color images without encryption or after applying symmetric or asymmetric encryption. Additionally, a recent hiding approach has also been integrated with DefOff for comparison purposes. Different objective evaluation metrics were calculated to test the performance of all deployed hiding approaches. The experiment results obtained from DefOff prove the superior performance of the hiding systems to achieve high security, robustness, and imperceptibility.
Recommendation System Design for Social Media using Reinforcement Learning

Recommendation System plays an important role in capturing consumers’ preference. In order to better utilize the effectiveness of the recommendation system, many social media platforms have developed algorithms to improve their performance. However, there are still a lot of platforms that keep recommending the same but useless contents to the customers, which even reduce the customers’ interest in using these social media platforms. In response to this problem, we propose to transplant reinforcement learning to build efficient and effective recommendation system. Specifically, we decide to update our recommendation system frequently by taking all customers’ behaviors into consideration to improve accuracy of the system’s recommendation output. The experiment results show that our system can gain more than 100% profits and can converge to the optimal result quickly.
Linear-Algebraic Models of Linear Logic as Categories of Modules over Σ-Semirings✱

A number of models of linear logic are based on or closely related to linear algebra, in the sense that morphisms are “matrices” over appropriate coefficient sets. Examples include models based on coherence spaces, finiteness spaces and probabilistic coherence spaces, as well as the relational and weighted relational models. This paper introduces a unified framework based on module theory, making the linear algebraic aspect of the above models more explicit. Specifically we consider modules over Σ-semirings R, which are ring-like structures with partially-defined countable sums, and show that morphisms in the above models are actually R-linear maps in the standard algebraic sense for appropriate R. An advantage of our algebraic treatment is that the category of R-modules is locally presentable, from which it easily follows that this category becomes a model of intuitionistic linear logic with the cofree exponential. We then discuss constructions of classical models and show that the above-mentioned models are examples of our constructions.
Multi-view 3D human pose reconstruction based on spatial confidence point group for jump analysis in figure skating

Abstract Competitive figure skaters perform successful jumps with critical parameters, which are valuable for jump analysis in athlete training. Driven by recent computer vision applications, recovering 3D pose of figure skater to obtain the meaningful variables has become increasingly important. However, conventional works have suffered from getting 3D information based on the corresponding 2D information directly or leaving the specificity of sports out of consideration. Issues such as self-occlusion, abnormal pose, limitation of venue and so on will result in poor results. Motivated by these problems, this paper proposes a multi-task architecture based on a calibrated multi-camera system to facilitate jointly 3D jump pose of figure skater. The proposed methods consist of three key components: Likelihood distribution and temporal smoothness- based discrete probability points selection filter out the most valuable 2D information; Multi-perspective and combinations unification-based large-scale venue 3D reconstruction is proposed to deal with the multi-camera; multi-constraint-based human skeleton estimation decides the final 3D coordinate from the candidates. This work is proved can be applied to 3D animated display and motion capture of the figure skating competition. The success rate of the independent joint is: 93.38% of 70 mm error range, 92.57% of 50 mm error range and 91.55% of 30 mm error range.
Improving the phase reconstruction accuracy of simultaneous phase-shifted lateral shearing interferometry using a polarization redundant sub-region interpolation method

In a simultaneous phase-shifted lateral shearing interferometry, a division of focal plane polarization camera is generally used as the phase-shifting device. However, acquiring simultaneous phase-shift interferograms in a single frame suffers from a lack of spatial resolution, significantly affecting the phase reconstruction accuracy. A polarization redundant sub-region interpolation (PRSI) method is proposed to solve this problem. This interpolation method distinguishes smooth regions from stripe fringe regions by calculating the polarization redundancy error of the synchronous phase shift interferogram. After sub-regional processing, resolution reconstruction is performed in the smoothed area using a fast convolutional bilinear interpolation method. In the streak detail region, the resolution reconstruction is performed based on the strength of the correlation between the orthogonal and non-orthogonal polarization channels crossing the streak region. The PRSI method can quickly reconstruct the lost pixels and accurately recover the stripe detail information. Experiment results show that the proposed interpolation method outperforms the existing dominant methods in terms of visual reconstruction effect and quantitative index of phase reconstruction.
Partitions and Ewens Distributions in element-free Probability Theory

This article redevelops and deepens the probability theory of Ewens and others from the 1970s in population biology. At the heart of this theory are the so-called Ewens distributions describing biolological mutations. These distributions have a particularly rich (and beautiful) mathematical structure. The original work is formulated in terms of partitions, which are special multisets on natural numbers. The current redevelopment starts from multisets on arbitrary sets, with partitions as a special form that captures only the multiplicities of multiplicities, without naming the elements themselves. This ‘element-free’ approach will be developed in parallel to the usual element-based theory. Ewens’ famous sampling formula describes a cone of (parametrised) distributions on partitions. Another cone for this chain is described in terms of new (element-free) multinomials. They are well-defined because of a novel ‘partitions multinomial theorem’ that extends the familiar multinomial theorem. This is based on a new concept of ‘division’, as element-free distribution, in terms of multisets of probabilities that add up to one.
Improving Image Compression With Adjacent Attention and Refinement Block

Recently, learned image compression algorithms have shown incredible performance compared to classic hand-crafted image codecs.Despite its considerable achievements, the fundamental disadvantage is not optimized for retaining local redundancies, particularly non-repetitive patterns, which have a detrimental influence on the reconstruction quality.This paper introduces the autoencoder-style network-based efficient image compression method, which contains three novel blocks, i.e., adjacent attention block, Gaussian merge block, and decoded image refinement block, to improve the overall image compression performance.The adjacent attention block allocates the additional bits required to capture spatial correlations (both vertical and horizontal) and effectively remove worthless information.The Gaussian merge block assists the rate-distortion optimization performance, while the decoded image refinement block improves the defects in low-resolution reconstructed images.A comprehensive ablation study analyzes and evaluates the qualitative and quantitative capabilities of the proposed model.Experimental results on two publicly available datasets reveal that our method outperforms the state-of-the-art methods on the KODAK dataset (by around 4dB and 5dB) and CLIC dataset (by about 4dB and 3dB) in terms of PSNR and MS-SSIM.
Video Super-Resolution for Wireless Capsule Endoscopy Imaging Sensor

As a preferred diagnostic imaging sensor for small bowel diseases, wireless capsule endoscopy (WCE) suffers from poor video quality, which can be mitigated via video super-resolution (VSR) techniques. However, the promise of applying VSR into WCE applications has been offset by two significant challenges. Firstly, high-resolution (HR) counterparts of real endoscopic frames are unavailable for supervised learning. Secondly, utilizing temporal correlations to enhance recovery performance becomes difficult due to the poor video quality. To tackle these two challenges, we develop a novel training dataset and an effective alignment network for VSR on practical WCE applications. Specifically, clean natural videos and synthetic endoscopic videos are collected as the ground truth of the training dataset. Then, a set of complex degradation models is designed to generate their low-resolution (LR) counterparts for supervised learning. Although real HR endoscopic videos remain unknown during training, this complicated dataset enables the trained network to have a strong generalization capacity so that it can make reasonable inferences on real endoscopic videos. To utilize temporal correlations from WCE videos, a block-based temporal attention alignment network (BTAAN) is proposed to build accurate and robust correspondences among consecutive frames under poor video quality. This network exploits the global information from neighboring frames to capture large-scale motions, and suppresses the effect of unreliable points by a temporal attention mechanism. Moreover, its block-wise characteristic reduces the computational complexity significantly, which makes it hardware-friendly. Extensive experiments demonstrate the superiority of the proposed method over other state-of-the-art approaches on practical WCE applications.
Blockchain-Based Distributed Renewable Energy Management Framework

Most of the world's countries are concerned with reducing harmful gas emissions.Some governments have made considerable attempts to address this problem.Saudi Arabia, for example, has taken significant steps to utilize renewable energy (RE) sources in addition to oil and gas.Consumers are encouraged to build small RE systems.These small grids will help people meet their daily electrical energy requirements.They can sell the excess to other customers.One of the major issues is managing the distribution and sale of RE.Blockchain-based peer-to-peer (P2P) networks can help overcome several obstacles to the implementation of a distributed RE management system (DREMS).However, several impediments may still stand in the way of its execution.Scalability and productivity are two of the most important considerations.The number of transactions made to the Blockchain network will increase in lockstep with the number of energy consumers.This will result in a significant lag in response.Understanding the renewable distributed energy system will aid in minimizing the effects of these roadblocks.Therefore, this research identifies the RE systems installation approaches, and how Blockchain technology can be utilized.It provides the solution requirements of any DREMS.Moreover, it proposes a new Blockchain-based framework for DREMS.And designs selective protocols of the proposed framework.The designed protocols are evaluated through a comparative analysis with the state of the art identified requirements.
Visual control through narrow passages for an omnidirectional wheeled robot

Robotic systems are gradually replacing human intervention in dangerous facilities to improve human safety and prevent risky situations. In this domain, our work addresses the problem of autonomous crossing narrow passages in a semi-structured (i.e., partially-known) environment. In particular, we focus on the CERN’s Super Proton Synchrotron particle accelerator, where a mobile robot platform is equipped with a lightweight arm to perform measurements, inspection, and maintenance operations. The proposed approach leverages an image-based visual servoing strategy that exploits computer vision to detect and track known geometries defining narrow passage gates. The effectiveness of the proposed approach has been demonstrated in a realistic mock-up.
Control Barrier Navigation Functions for STL Motion Planning

This paper describes an approach to constructing control barrier functions that realize planning and control objectives that are expressed in a fragment of signal temporal logic. The particular construction is based on the navigation function method for robot motion planning and is attractive because it offers a straightforward way to design the robot control law that implements the signal temporal logic specification. The efficacy of the reported method is illustrated through simulation examples.
A hybrid adversarial training for deep learning model and denoising network resistant to adversarial examples

Abstract Deep neural networks (DNNs) are vulnerable to adversarial attacks that generate adversarial examples by adding small perturbations to the clean images. To combat adversarial attacks, the two main defense methods used are denoising and adversarial training. However, both methods result in the DNN having lower classification accuracy for clean images than conventionally trained DNN models. To overcome this problem, we propose a hybrid adversarial training (HAT) method that trains the denoising network and DNN model simultaneously. The proposed HAT method uses both clean images and adversarial examples denoised by the denoising network and non-denoised clean images and adversarial examples to train the DNN model. The results of experiments conducted on the MNIST, CIFAR-10, CIFAR-100, and GTSRB datasets show that the HAT method results in a higher classification accuracy than both conventional training with a denoising network and previous adversarial training methods. They also indicate that training with the HAT method results in average improvements in robustness of 0.84%, 27.33%, 28.99%, and 17.61% against adversarial attacks compared with several state-of-the-art adversarial training methods on the MNIST, CIFAR-10, CIFAR-100, and GTSRB datasets, respectively. Thus, the proposed HAT method results in improved robustness for DNNs against a wider range of adversarial attacks.
It's not just about accuracy: An investigation of the human factors in users' reliance on anti-phishing tools

Phishing attacks pose substantial threats to the security of individuals and organizations. Although current anti-phishing tools achieve high accuracy rates and present a potential solution to this problem, users are often reluctant to rely on the predictions of these competent tools. However, we continue to lack a means of resolving this reluctance—or even an explanation for it. To address this need and advance toward a solution, we investigate the factors that influence users' reliance on anti-phishing tools. Over the course of two studies, we test the effects of tool attributes (i.e., accuracy and frequency of phishing email predictions) and develop a model based on the notions of trust and distrust. Countering the common conjecture that tools are not accurate enough, we find that users' under-reliance is not an artifact of the insufficient accuracy of tools, as even in a 100% accuracy condition, users were under-reliant on tools. Rather, we find that while accuracy increases users' trust in tools, full reliance is inhibited by users' distrust, which is driven by a lack of transparency regarding tools' functionalities and the quantity of predictions provided. Thus, overall, our study shows the limits of accuracy in engendering reliance and explains the under-reliance phenomenon by showing that due to lack of knowledge or understanding, some users prefer to rely on their own inferior judgment instead of trusting and relying on the predictions provided by highly accurate tools.
Mechanisms and techniques to enhance the security of big data analytic framework with MongoDB and Linux Containers

The frequency and scale of unauthorized access cases and misuses of data access privileges are a growing concern of many organizations. The protection of confidential data, such as social security numbers, financial information, etc., of the customers and/or employees is among the key responsibilities of any organization, and damage to such sensitive data can easily pose a threat to the future of a business and the security of the customers. Therefore, this paper proposes and implements some security mechanisms and techniques, such as secure authentication, secure authorization, and encryption, to assure the overall security of a big data analytic framework with MongoDB free community edition. This paper presents the fourth phase of our continuous research where in the first phase we proposed a data analytic framework with MongoDB and Linux Containers (LXCs) with basic security requirements. Next, in the second phase we proposed a vulnerability analysis testbed to find vulnerabilities associated with the system. Finally, in the third phase we discussed in detail root causes and some prevention techniques of vulnerabilities found in the system. In addition, this paper introduces a new security mechanism for privacy preserving data handling with MongoDB to ensure the privacy of the data before being processed. Our results show, with our initial model of the analytic framework, how well our newly introduced security mechanisms work and how these security mechanisms and techniques can be used to assure the confidentiality, integrity, and availability (CIA) of any data science project conducted on our proposed analytic framework. In addition, these security mechanisms and techniques help us to strengthen the current system against zero-day attacks where attacks on vulnerabilities that have not been patched or made public yet. Therefore, our vulnerability analysis testbed which is proposed in the second phase of this research will not be able to finds vulnerabilities related to zero-day attacks.
This Looks Like That There: Interpretable Neural Networks for Image Tasks When Location Matters

Abstract We develop and demonstrate a new interpretable deep learning model specifically designed for image analysis in Earth system science applications. The neural network is designed to be inherently interpretable, rather than explained via post hoc methods. This is achieved by training the network to identify parts of training images that act as prototypes for correctly classifying unseen images. The new network architecture extends the interpretable prototype architecture of a previous study in computer science to incorporate absolute location. This is useful for Earth system science where images are typically the result of physics-based processes, and the information is often geolocated. Although the network is constrained to only learn via similarities to a small number of learned prototypes, it can be trained to exhibit only a minimal reduction in accuracy relative to noninterpretable architectures. We apply the new model to two Earth science use cases: a synthetic dataset that loosely represents atmospheric high and low pressure systems, and atmospheric reanalysis fields to identify the state of tropical convective activity associated with the Madden–Julian oscillation. In both cases, we demonstrate that considering absolute location greatly improves testing accuracies when compared with a location-agnostic method. Furthermore, the network architecture identifies specific historical dates that capture multivariate, prototypical behavior of tropical climate variability. Significance Statement Machine learning models are incredibly powerful predictors but are often opaque “black boxes.” The how-and-why the model makes its predictions is inscrutable—the model is not interpretable. We introduce a new machine learning model specifically designed for image analysis in Earth system science applications. The model is designed to be inherently interpretable and extends previous work in computer science to incorporate location information. This is important because images in Earth system science are typically the result of physics-based processes, and the information is often map based. We demonstrate its use for two Earth science use cases and show that the interpretable network exhibits only a small reduction in accuracy relative to black-box models.