Reviewerly/exHarmony huggingface dataset:
Entity Similarity-Based Negative Sampling for Knowledge Graph Embedding

Knowledge graph embedding (KGE) models optimize loss functions to maximize the total plausibility of positive triples and minimize the plausibility of negative triples. Negative samples are essential in KGE training since they are not as observable as positive samples. Currently, most negative sampling methods apply different techniques to keep track of negative samples with high scores that are regarded as quality negative samples. While, we found entities with similar semantic contexts are easier to be deceptive and misclassified, contributing to quality negative samples. This is not considered in most negative sampling approaches. Besides, the unequal effectiveness of quality negative samples in different loss functions is usually ignored. In this paper, we propose an Entity Similarity-based Negative Sampling framework (ESNS). The framework takes semantic similarities among entities into consideration with a shift-based logistic loss function. Comprehensive experiments on the five benchmark datasets have been conducted, and the experimental results demonstrate that ESNS outperforms the state-of-the-art negative sampling methods in the link prediction task.
Faithful Embeddings for $$\mathcal{E}\mathcal{L}^{++}$$ Knowledge Bases

Recently, increasing efforts are put into learning continual representations for symbolic knowledge bases (KBs). However, these approaches either only embed the data-level knowledge (ABox) or suffer from inherent limitations when dealing with concept-level knowledge (TBox), i.e., they cannot faithfully model the logical structure present in the KBs. We present BoxEL, a geometric KB embedding approach that allows for better capturing the logical structure (i.e., ABox and TBox axioms) in the description logic $$\mathcal{E}\mathcal{L}^{++}$$ . BoxEL models concepts in a KB as axis-parallel boxes that are suitable for modeling concept intersection, entities as points inside boxes, and relations between concepts/entities as affine transformations. We show theoretical guarantees (soundness) of BoxEL for preserving logical structure. Namely, the learned model of BoxEL embedding with loss 0 is a (logical) model of the KB. Experimental results on (plausible) subsumption reasonings and a real-world application–protein-protein prediction show that BoxEL outperforms traditional knowledge graph embedding methods as well as state-of-the-art $$\mathcal{E}\mathcal{L}^{++}$$ embedding approaches.
Issues in the adoption of the scaled agile framework

Agile methods were originally introduced for small sized, co-located teams. Their successful products immediately brought up the issue of adapting the methods also for large and distributed organizations engaged in projects to build major, complex products. Currently the most popular multi-teams agile method is the Scaled Agile Framework (SAFe) which, however, is subject to criticism: it appears to be quite demanding and expensive in terms of human resource and project management practices. Moreover, SAFe allegedly goes against some of the principles of agility.
Semantic Segmentation of High-Resolution Remote Sensing Images Using an Improved Transformer

Semantic segmentation has been widely researched for high level analysis of High Spatial Resolution (HSR) remote sensing images, where Convolutional Neural Network (CNN) is the mainstream method. However, the transformer with attention mechanism has its unique capacity of extracting global information which is generally ignored by CNN models. In this paper, a Swin Transformer with UPer head (STUP) is proposed to tackle with semantic segmentation problem on a challenging remote sensing land-cover dataset called LoveDA, which owns complex background samples and inconsistent classes distributions. The proposed STUP combines the Swin Transformer with Uper Head in the form of an encoder-decoder structure, to extract features of HSR images for segmentation. Furthermore, Focal Loss is adopted to handle the unbalanced distribution problem in the training step. Experimental results demonstrate that the proposed STUP clearly outperforms several state-of-the-art models.
APRIL: Finding the Achilles' Heel on Privacy for Vision Transformers

Federated learning frameworks typically require collaborators to share their local gradient updates of a common model instead of sharing training data to preserve privacy. However, prior works on Gradient Leakage Attacks showed that private training data can be revealed from gradients. So far almost all relevant works base their attacks on fully-connected or convolutional neural networks. Given the recent overwhelmingly rising trend of adapting Transformers to solve multifarious vision tasks, it is highly valuable to investigate the privacy risk of vision transformers. In this paper, we analyse the gradient leakage risk of self-attention based mechanism in both theoretical and practical manners. Particularly, we propose APRIL - Attention PRIvacy Leakage, which poses a strong threat to self-attention inspired models such as ViT. Showing how vision Transformers are at the risk of privacy leakage via gradients, we urge the significance of designing privacy-safer Transformer models and defending schemes.
Toward Practical Entanglement-Based Satellite FSO/QKD Systems Using Dual-Threshold/ Direct Detection

We propose a new implementation of the BBM92 protocol for satellite continuous-variable quantum key distribution (CV-QKD) systems using the dual-threshold/direct detection (DT/DD) scheme. The proposed system is less complex and therefore possibly cheaper than current discrete-variable (DV) and CV-QKD ones using coherent detection receivers. We model and analyze the performance of the proposed system in the context that a satellite distributes secret keys to two legitimate users. The analytical results are derived by considering channel loss, atmospheric turbulence-induced fading, and receiver noises. The Gaussian beam model is considered to evaluate the impact of geometric spread on the signal received by legitimate users and the possibility of being eavesdropped. Based on the design criteria for the system and analytical results, we investigate the feasibility of a case study for the Japan QKD network using the existing low-Earth orbit (LEO) satellite constellation.
On-Demand Reconfigurable Incoherent Optical Matrix Operator for Real-Time Video Image Display

For emerging application fields such as autonomous driving and computing vision, which demands the real-time response of massive information, advanced image processing techniques on artificial neural networks (ANNs) are crucial. However, with increasing task complexity, the electrical hardware limited by the well-known von Neumann bottleneck is inadequate to meet these extreme requirements in computing speed and computing power. We propose an incoherent optical matrix operator by dynamically manipulating the parameters of the lightwave in terms of the wavelength, time and space to implement the on-demand reconfiguration both in the optical perceptron and optical convolutional processor. For the optical perceptron, it is conducted to perform the case-sensitive classification of 52 English letters. Furthermore, by adjusting the spectral characteristics and output ports, an optical convolution processor is constructed to realize multitask convolution processing for both the image and real-time video. Based on multikernels parallel computing in an optical convolutional layer coupled with an electrically fully connected layer, a convolutional neural network is built to recognize ‘0–9’ handwritten digital images from the Modified National Institute of Standards and Technology (MNIST) database. Owing to the excellent performance of the incoherent beam in terms of the spectral purity, wavelength stability and side-mode suppression ratio, the experimental classification accuracy up to 96.01% greatly matches the theoretical value of 96.67%. Moreover, incoherent information processing is synchronous with data transmission, which releases the requirement of the global clock in digital counterparts. The high-fidelity incoherent convolution processor offers a novel computing framework that serves the next-generation ANN platform.
Distributed Reptile Algorithm for Meta-Learning Over Multi-Agent Systems

Learning a good initialization model from distributed data sources over multi-agent systems is highly promising, in which the tasks or data are distributed stored and not accessible to all agents. This paper focuses on the distributed meta-learning problem and proposes a distributed Reptile meta-learning algorithm. In the proposed algorithm, each agent approximates the global model through a bi-level optimization scheme, where the inner step employs a stochastic gradient descent on a specific task, and the outer step utilizes information from neighbors and a gradient-like updating. The suggested algorithm avoids calculating the Hessian-vector products during training, reducing the computational complexity and affording memory miniaturization. We further analyze the convergence properties of the proposed algorithm under the convexity assumption. Finally, we demonstrate the effectiveness of the proposed algorithm on regression and classification tasks. The results show that our algorithm approximates a centralized solution and outperforms the non-cooperative algorithm.
Distributed Attribute-Based Signature With Attribute Dynamic Update for Smart Grid

Smart grid is gaining more and more attention as one of the typical applications of Internet of Things. However, in a distributed environment, how to guarantee the privacy of users in electricity trading while ensuring the efficiency of the transactions is one of the urgent issues to be solved. In this article, we propose a distributed attribute-based signature (DABS) scheme for distributed electricity trading, which can support users' free choice of trade objects without revealing their real identities. We construct a signature generation and verification method by taking advantage of the open and hard-to-tamper properties of blockchain to achieve signature verifiability independent of dynamic changes in attributes.To improve the update efficiency, we propose an improved scheme that enables the update complexity to be reduced from <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$O(n)$</tex-math></inline-formula> to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$O(\log n)$</tex-math></inline-formula> , where <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$n$</tex-math></inline-formula> is the number of users. Finally, performance analysis and simulation experiments demonstrate the security and practicality of the DABS.
Semantic Segmentation in Underwater Ship Inspections: Benchmark and Data Set

In this article, we present the first large-scale data set for underwater ship lifecycle inspection, analysis and condition information (LIACI). It contains 1893 images with pixel annotations for ten object categories: defects, corrosion, paint peel, marine growth, sea chest gratings, overboard valves, propeller, anodes, bilge keel and ship hull. The images have been collected during underwater ship inspections and annotated by human domain experts. We also present a benchmark evaluation of state-of-the-art semantic segmentation approaches based on standard performance metrics. Consequently, we propose to use U-Net with a MobileNetV2 backbone for the segmentation task due to its balanced tradeoff between performance and computational efficiency, which is essential if used for real-time evaluation. Also, we demonstrate its benefits for in-water inspections by providing quantitative evaluations of the inspection findings. With a variety of use cases, the proposed segmentation pipeline and the LIACI data set create new promising opportunities for future research in underwater ship inspections.
Accurate Human Pose Estimation using RF Signals

Radio-frequency (RF) based human sensing technologies, due to their great practical value in various applications and privacy-preserving nature, have gained tremendous attention in recent years. However, without fully exploiting the characteristics of radio signals, the performance of existing methods are still limited. First, RF features of the moving human body have different representations in dimensions such as channel and scale, which is challenging when performing feature fusion. Besides, the human body is specularly reflective with respect to the radar, which means the human body cannot be fully captured by a single RF snapshot. Therefore, the radar signal reflected by the human body is sparse and incomplete, which is difficult to extract high-quality features for 3D human pose estimation. In this paper, we present the RF-based Pose Machines (RPM), a novel framework which can generate 3D skeletons from RF signals. Considering the characteristics of RF signals, RPM includes several modules to overcome the challenges. Firstly, a Multidimensional Feature Fusion (MFF) backbone is designed to effectively fuse radio signals based on the channels' correlation and maintain high-quality feature via a multi-scale fusion block. A Spatio-Temporal Attention network is then designed to reconstruct 3D skeletons by modeling the non-local spatio-temporal relationships. To evaluate the performance of our RPM framework, we construct a large-scale dataset of synchronized 3D skeletons and RF signals, RFSkeleton3D. Our experimental results show that RPM locates 3D key points of the human body with an average error of 5.71cm and maintains its performance in new environments with occlusion or bad illumination. The dataset and codes will be made in public.
Evaluating the Benefits of Explicit and Semi-Automated Clusters for Immersive Sensemaking

Immersive spaces have great potential to support analysts in complex sensemaking tasks, but the use of only manual interactions for organizing data elements can become tedious. We analyzed the user interactions to support cluster formation in an immersive sensemaking system, and we designed a semi-automated cluster creation technique that determines the user’s intent to create a cluster based on object proximity. We present the results of a user study comparing this proximity-based technique with a manual clustering technique and a baseline immersive workspace with no explicit clustering support. We found that semi-automated clustering was faster and preferred, while manual clustering gave greater control to users. These results provide support for the approach of adding intelligent semantic interactions to aid the users of immersive analytics systems.
The threat of industrial espionage for SME in the age of digitalization

The digital revolution has been gathering pace for years. Companies benefit significantly from digital data and the fast processing of high storage volumes. However, it is also necessary to consider the threats to companies that arise from digital espionage attempts. The aim of this study is to identify determinants with a significant impact on the threat potential of industrial espionage in SME. It is based on a qualitative approach and a content analysis according to Mayring. The study proves that employee training plays a major role in preventing industrial espionage. In addition, the degree of innovation and the nature of trade secrets also play a major role. It should also be contemplated whether companies are targeted victims or are more at risk from large-scale automated attacks. Based on the findings a revised conceptual model has been created.
Attention-based Single Image Dehazing Using Improved CycleGAN

Single image dehazing is a popular research topic among the researchers in computer vision, machine learning, image processing, and graphics. Most of the recent methods for single image dehazing are based upon supervised learning set up. However, supervised methods require annotation of the data, which often makes the dehazing methods biased towards the manual annotation errors. Unsupervised methods are more likely to produce realistic, clear images. However, fewer efforts are found in the literature for single image dehazing in unsupervised set up. We propose an enhanced CycleGAN architecture for Unpaired single image dehazing, with an attention-based transformer architecture embedded in the generator. The proposed transformer comprises three components: 1) A Feature Attention (FA) block combining channel attention and pixel attention mechanism, 2) A Dynamic feature enhancement block for dynamically capturing the spatial structured features and 3) An adaptive mix-up module to preserve the flow of shallow features from downsampling. Experiments on the benchmark datasets show the efficacy of the proposed method. Codes for this work are available in the link: https://github.com/rsjai47/Attention-Based-CycleDehaze.
On Time-Space Tradeoffs for Bounded-Length Collisions in Merkle-Damgård Hashing

We studyGhoshal,Ashrujit Komargodski,Ilan the power of preprocessing adversaries in finding bounded-length collisions in the widely used Merkle-Damgård (MD) hashing in the random oracle model. Specifically, we consider adversaries with arbitrary S-bit advice about the random oracle and can make at most T queries to it. Our goal is to characterize the advantage of such adversaries in finding a B-block collision in an MD hash function constructed using the random oracle with range size N as the compression function (given a random salt). The answer to this question is completely understood for very large values of B (essentially $$\varOmega (T)$$ ) as well as for $$B=1,2$$ . For $$B\approx T$$ , Coretti et al. (EUROCRYPT ’18) gave matching upper and lower bounds of $$\tilde{\varTheta }(ST^2/N)$$ . Akshima et al. (CRYPTO ’20) observed that the attack of Coretti et al. could be adapted to work for any value of $$B>1$$ , giving an attack with advantage $$\tilde{\varOmega }(STB/N + T^2/N)$$ . Unfortunately, they could only prove that this attack is optimal for $$B=2$$ . Their proof involves a compression argument with exhaustive case analysis and, as they claim, a naive attempt to generalize their bound to larger values of B (even for $$B=3$$ ) would lead to an explosion in the number of cases needed to be analyzed, making it unmanageable. With the lack of a more general upper bound, they formulated the STB conjecture, stating that the best-possible advantage is $$\tilde{O}(STB/N + T^2/N)$$ for any $$B>1$$ . In this work, we confirm the STB conjecture in many new parameter settings. For instance, in one result, we show that the conjecture holds for all constant values of B. Further, using combinatorial properties of graphs, we are able to confirm the conjecture even for super constant values of B, as long as some restriction is made on S. For instance, we confirm the conjecture for all $$B \leqslant T^{1/4}$$ as long as $$S \leqslant T^{1/8}$$ . Technically, we develop structural characterizations for bounded-length collisions in MD hashing that allow us to give a compression argument in which the number of cases needed to be handled does not explode.
Credit Card Fraud Identification Using Logistic Regression and Local Outlier Factor

Credit cards are a crucial financial tool that enables its users to make purchases and pay at a later date. Issued by financial customs, credit cards give users a pre-agreed credit limit that they can use for their purchases. MasterCard extortion is a type of data fraud where crooks make buys utilising a Visa account which doesn't have a place with them. The two primary tactics for reducing frauds and losses caused by fraudulent conduct are fraud detection systems and fraud prevention. Fraud detection is tracking the behaviours of large groups of people in order to estimate, perceive, or identify obnoxious activity, such as fraud, intrusion, or defaulting. The Local Outlier Factor is a technique for detecting aberrant data points by comparing a data point's local variability to that of its neighbours. Under the Supervised Learning approach, one of the most prominent Machine Learning algorithms is logistic regression
A Multi-Shuffler Framework to Establish Mutual Confidence for Secure Federated Learning

Albeit the popularity of federated learning (FL), recently emerging model-inversion and poisoning attacks arouse extensive concerns towards privacy or model integrity, which catalyzes the developments of secure federated learning (SFL) methods. Nonetheless, the collisions between its privacy and integrity, two equally crucial elements in collaborative learning scenarios, are relatively underexplored. Individuals’ wish to “hide in the crowd” for privacy frequently clashes with aggregators’ need to resist abnormal participants for integrity (i.e., the incompatibility between Byzantine robustness and differential privacy). The dilemma prompts researchers to reflect on how to build mutual confidence between individuals and aggregators. Against the backdrop, this paper proposes a multi-shuffler secure federated learning (MSFL) framework, based on which we further propound three modules (hierarchical shuffling mechanism, malice evaluation module, and composite defense strategy) to jointly guarantee strong privacy protection, efficient poisoning resistance, and agile adversary elimination. Extensive experiments on standard datasets exhibited the method's effectiveness in thwarting different FL poisoning attack paradigms with a minimal cost of privacy breaches.
Boosting Robustness Verification of Semantic Feature Neighborhoods

Deep neural networks have been shown to be vulnerable to adversarial attacks that perturb inputs based on semantic features. Existing robustness analyzers can reason about semantic feature neighborhoods to increase the networks’ reliability. However, despite the significant progress in these techniques, they still struggle to scale to deep networks and large neighborhoods. In this work, we introduce VeeP, an active learning approach that splits the verification process into a series of smaller verification steps, each is submitted to an existing robustness analyzer. The key idea is to build on prior steps to predict the next optimal step. The optimal step is predicted by estimating the robustness analyzer’s velocity and sensitivity via parametric regression. We evaluate VeeP on MNIST, Fashion-MNIST, CIFAR-10 and ImageNet and show that it can analyze neighborhoods of various features: brightness, contrast, hue, saturation, and lightness. We show that, on average, given a 90 minute timeout, VeeP verifies 96% of the maximally certifiable neighborhoods within 29 minutes, while existing splitting approaches verify, on average, 73% of the maximally certifiable neighborhoods within 58 minutes.