Reviewerly/exHarmony huggingface dataset:
Distilled Split Deep Neural Networks for Edge-Assisted Real-Time Systems

Offloading the execution of complex Deep Neural Networks (DNNs) models to compute-capable devices at the network edge, that is, edge servers, can significantly reduce capture-to-output delay. However, the communication link between the mobile devices and edge servers can become the bottleneck when channel conditions are poor. We propose a framework to split DNNs for image processing and minimize capture-to-output delay in a wide range of network conditions and computing parameters. The core idea is to split the DNN models into head and tail models, where the two sections are deployed at the mobile device and edge server, respectively. Different from prior literature presenting DNN splitting frameworks, we distill the architecture of the head DNN to reduce its computational complexity and introduce a bottleneck, thus minimizing processing load at the mobile device as well as the amount of wirelessly transferred data. Our results show 98% reduction in used bandwidth and 85% in computation load compared to straightforward splitting.
Image-and-Spatial Transformer Networks for Structure-Guided Image Registration

Image registration with deep neural networks has become an active field of research and exciting avenue for a long standing problem in medical imaging. The goal is to learn a complex function that maps the appearance of input image pairs to parameters of a spatial transformation in order to align corresponding anatomical structures. We argue and show that the current direct, non-iterative approaches are sub-optimal, in particular if we seek accurate alignment of Structures-of-Interest (SoI). Information about SoI is often available at training time, for example, in form of segmentations or landmarks. We introduce a novel, generic framework, Image-and-Spatial Transformer Networks (ISTNs), to leverage SoI information allowing us to learn new image representations that are optimised for the downstream registration task. Thanks to these representations we can employ a test-specific, iterative refinement over the transformation parameters which yields highly accurate registration even with very limited training data. Performance is demonstrated on pairwise 3D brain registration and illustrative synthetic data.
Threats on the horizon: understanding security threats in the era of cyber-physical systems

Abstract Disruptive innovations of the last few decades, such as smart cities and Industry 4.0, were made possible by higher integration of physical and digital elements. In today’s pervasive cyber-physical systems, connecting more devices introduces new vulnerabilities and security threats. With increasing cybersecurity incidents, cybersecurity professionals are becoming incapable of addressing what has become the greatest threat climate than ever before. This research investigates the spectrum of risk of a cybersecurity incident taking place in the cyber-physical-enabled world using the VERIS Community Database. The findings were that the majority of known actors were from the US and Russia, most victims were from western states and geographic origin tended to reflect global affairs. The most commonly targeted asset was information, with the majority of attack modes relying on privilege abuse. The key feature observed was extensive internal security breaches, most often a result of human error. This tends to show that access in any form appears to be the source of vulnerability rather than incident specifics due to a fundamental trade-off between usability and security in the design of computer systems. This provides fundamental evidence of the need for a major reevaluation of the founding principles in cybersecurity.
Geometric Knowledge Embedding for unsupervised domain adaptation

Domain adaptation aims to transfer auxiliary knowledge from a source domain to enhance the learning performance on a target domain. Recent studies have suggested that deep networks are able to achieve promising results for domain adaptation problems. However, deep neural networks cannot reveal the underlying geometric information from input data. Indeed, such geometric information is very useful for describing the relationship between the samples from source and target domains. In this paper, we propose a novel learning algorithm named GKE, which stands for Geometric Knowledge Embedding. In GKE, we use a graph-based model to explore the underlying geometric structure of the input source and target data based on their similarities. Concretely, we develop a graph convolutional network to learn discriminative representations based on the constructed graph. To obtain effective transferable representations, we match source and target domains by reducing the Maximum Mean Discrepancy (MMD) between their learned representations. Extensive experiments on real-world data sets demonstrate that the proposed method outperforms existing domain adaption methods.
Evaluating Robustness of Deep Image Super-Resolution Against Adversarial Attacks

Single-image super-resolution aims to generate a high-resolution version of a low-resolution image, which serves as an essential component in many image processing applications. This paper investigates the robustness of deep learning-based super-resolution methods against adversarial attacks, which can significantly deteriorate the super-resolved images without noticeable distortion in the attacked low-resolution images. It is demonstrated that state-of-the-art deep super-resolution methods are highly vulnerable to adversarial attacks. Different levels of robustness of different methods are analyzed theoretically and experimentally. We also present analysis on transferability of attacks, and feasibility of targeted attacks and universal attacks.
Energy efficient multi-objective scheduling of tasks with interval type-2 fuzzy timing constraints in an Industry 4.0 ecosystem

Industrial systems usually draw huge energy to run various machines. The amount of energy requirement has again increased due to the automation of the industrial plants to make them Industry 4.0 compliant. As a result, demand of energy is on the rise in almost all manufacturing and industrial plants. The necessity of critical and smart manufacturing processes in Industry 4.0 and its increased energy requirements force us to look for energy efficient techniques for running the deployed computing systems, which are often embedded and integrated within larger machines and have to function under time constraints. Computational efficiency of these real-time embedded systems (RTESs) depends solely on the timely completion of tasks. Task execution with less energy consumption within critical timing constraints is a challenging issue for the designers of RTESs. Thus, task scheduling in these systems require sophisticated energy efficient mechanisms. However, energy efficiency and timeliness are two mutually contradictory objectives, since the former is achieved only with a significant compromise of the later. In this paper, we propose a novel approach, based on the popular multi-objective evolutionary algorithm, Non-dominated sorting genetic algorithm-II, to solve this problem. Moreover, in RTESs, precise prediction of timing constraints is difficult before runtime which causes a form of imprecision or uncertainty in the system. Therefore, we use type-2 fuzzy sets (T2 FSs) to model the timing constraints in RTESs and introduce novel algorithms for membership function generation and calculation of fuzzy earliness. Numerical as well as real-life examples are included to demonstrate our proposed technique.
Impossibility of Full Decentralization in Permissionless Blockchains

Bitcoin uses the proof-of-work (PoW) mechanism where nodes earn rewards in return for the use of their computing resources. Although this incentive system has attracted many participants, power has, at the same time, been significantly biased towards a few nodes, called mining pools. In addition, poor decentralization appears not only in PoW-based coins but also in coins that adopt proof-of-stake (PoS) and delegated proof-of-stake (DPoS) mechanisms.
Application of matrix semi-tensor product in chaotic image encryption

This paper proposes a new image encryption method based on matrix semi-tensor product theory. Using Hyperchaotic Lorenz system to generate chaotic sequences, and then using this chaotic sequence to generate two fixed scrambling matrices. The plain image is scrambled by chaotic positioning sort scrambled method. Analogous to a chemical reaction, the plain image scrambled image is one of the reactant, then add a matrix which is related to the plain image information and extracted from the chaotic sequence (this matrix can be differ in size from the plain image matrix, different matrices can be intercepted and generated according to different plain image). This matrix is scrambled as another reactant, Apply the method of semi-tensor product to carry on the reaction (diffusion), finally get the product (encryption image). This method breaks the shackles of the traditional matrix operation and makes the reaction matrix have more forms. Compared with other experimental results, the proposed algorithm is more secure and available.
Phishing URL Detection via CNN and Attention-Based Hierarchical RNN

Phishing websites have long been a serious threat to cyber security. For decades, many researchers have been devoted to developing novel techniques to detect phishing websites automatically. While state-of-the-art solutions can achieve superior performances, they require substantial manual feature engineering and are not adept at detecting newly emerging phishing attacks. Therefore, developing techniques that can detect phishing websites automatically and handle zero-day phishing attacks swiftly is still an open challenge in this area. In this work, we propose PhishingNet, a deep learning-based approach for timely detection of phishing Uniform Resource Locators (URLs). Specifically, we use a Convolutional Neural Network (CNN) module to extract character-level spatial feature representations of URLs; meanwhile, we employ an attention-based hierarchical Recurrent Neural Network(RNN) module to extract word-level temporal feature representations of URLs. We then fuse these feature representations via a three-layer CNN to build accurate feature representations of URLs, on which we train a phishing URL classifier. Extensive experiments on a verified dataset collected from the Internet demonstrate that the feature representations extracted automatically are conducive to the improvement of the generalization ability of our approach on newly emerging URLs, which makes our approach achieve competitive performance against other state-of-the-art approaches.
Toward Realistic Image Compositing With Adversarial Learning

Compositing a realistic image is a challenging task and usually requires considerable human supervision using professional image editing software. In this work we propose a generative adversarial network (GAN) architecture for automatic image compositing. The proposed model consists of four sub-networks: a transformation network that improves the geometric and color consistency of the composite image, a refinement network that polishes the boundary of the composite image, and a pair of discriminator network and a segmentation network for adversarial learning. Experimental results on both synthesized images and real images show that our model, Geometrically and Color Consistent GANs (GCC-GANs), can automatically generate realistic composite images compared to several state-of-the-art methods, and does not require any manual effort.
A Single Layer Neural Network Implemented by a $4\times 4$ MZI-Based Optical Processor

Implementing any linear transformation matrix through the optical channels of an on-chip reconfigurable multiport interferometer has been emerging as a promising technique for various fields of study, such as information processing and optical communication systems. Recently, the use of multiport optical interferometric-based linear structures in neural networks has attracted a great deal of attention. Optical neural networks have proven to be promising in terms of computational speed and power efficiency, allowing for the increasingly large neural networks that are being created today. This paper demonstrates the experimental analysis of programming a 4 × 4 reconfigurable optical processor using a unitary transformation matrix implemented by a single layer neural network. To this end, the Mach-Zehnder interferometers (MZIs) in the structure are first experimentally calibrated to circumvent the random phase errors originating from fabrication process variations. The linear transformation matrix of the given application can be implemented by the successive multiplications of the unitary transformation matrices of the constituent MZIs in the optical structure. The required phase shifts to construct the linear transformation matrix by means of the optical processor are determined theoretically. Using this method, a single layer neural network is trained to classify a synthetic linearly separable multivariate Gaussian dataset on a conventional computer using a stochastic optimization algorithm. Additionally, the effect of the phase errors and uncertainties caused by the experimental equipment inaccuracies and the device components imperfections is also analyzed and simulated. Finally, the optical processor is experimentally programmed by applying the obtained phase shifts from the matrix decomposition process to the corresponding phase shifters in the device. The experimental results show that the optical processor achieves 72% classification accuracy compared to the 98.9% of the simulated optical neural network on a digital computer.
Effects of sanctions, moral beliefs, and neutralization on information security policy violations across cultures

A principal concern of organizations is the failure of employees to comply with information security policies (ISPs). Deterrence theory is one of the most frequently used theories for examining ISP violations, yet studies using this theory have produced mixed results. Past research has indicated that cultural differences may be one reason for these inconsistent findings and have hence called for cross-cultural research on deterrence in information security. To address this gap, we formulated a model including deterrence, moral beliefs, shame, and neutralization techniques and tested it with the employees from 48 countries working for a large multinational company.
Learning to Answer Complex Questions over Knowledge Bases with Query Composition

Recent years have seen a surge of knowledge-based question answering (KB-QA) systems which provide crisp answers to user-issued questions by translating them to precise structured queries over a knowledge base (KB). A major challenge in KB-QA is bridging the gap between natural language expressions and the complex schema of the KB. As a result, existing methods focus on simple questions answerable with one main relation path in the KB and struggle with complex questions that require joining multiple relations. We propose a KB-QA system, TextRay, which answers complex questions using a novel decompose-execute-join approach. It constructs complex query patterns using a set of simple queries. It uses a semantic matching model which is able to learn simple queries using implicit supervision from question-answer pairs, thus eliminating the need for complex query patterns. Our proposed system significantly outperforms existing KB-QA systems on complex questions while achieving comparable results on simple questions.
ALIME: Autoencoder Based Approach for Local Interpretability

Machine learning and especially deep learning have garnered tremendous popularity in recent years due to their increased performance over other methods. The availability of large amount of data has aided in the progress of deep learning. Nevertheless, deep learning models are opaque and often seen as black boxes. Thus, there is an inherent need to make the models interpretable, especially so in the medical domain. In this work, we propose a locally interpretable method, which is inspired by one of the recent tools that has gained a lot of interest, called local interpretable model-agnostic explanations (LIME). LIME generates single instance level explanation by artificially generating a dataset around the instance (by randomly sampling and using perturbations) and then training a local linear interpretable model. One of the major issues in LIME is the instability in the generated explanation, which is caused due to the randomly generated dataset. Another issue in these kind of local interpretable models is the local fidelity. We propose novel modifications to LIME by employing an autoencoder, which serves as a better weighting function for the local model. We perform extensive comparisons with different datasets and show that our proposed method results in both improved stability, as well as local fidelity.
LiKe: Lightweight Certificateless Key Agreement for Secure IoT Communications

Certificateless public-key cryptography (CL-PKC) schemes are particularly robust against the leakage of secret information stored on a trusted third party (TTP). These security features are particularly relevant for Internet of Things (IoT) domains, where the devices are typically preconfigured with secret keys, usually stored locally on the TTP for following maintenance tasks. Despite some contributions already proposed for the adoption of CL-PKC schemes in constrained IoT devices, current solutions generally require high message overhead, are computationally demanding, and place a high toll on the energy budget. To close this gap, we propose LiKe, a lightweight pairing-free certificateless key agreement protocol suitable for integration in the latest ZigBee 3.0 protocol stack and constrained IoT devices. LiKe is an authenticated key agreement protocol characterized by: 1) ephemeral cryptographic materials; 2) support for intermittent connectivity with the TTP; 3) lightweight rekeying operations; and 4) robustness against impersonation attacks, even when information stored on the TTP is leaked. LiKe has been thoroughly described, and its security properties have been proved via formal tools. Moreover, we have implemented and tested it on real IoT devices, in networks with up to 11 nodes-the source code has been released as an open source. Results are striking: on the OpenMote-b hardware platform, LiKe requires a total time of 3.259 s to establish session keys on each participating device, and at most 0.258% of the overall battery capacity, emerging as a lightweight and energy-friendly solution. Finally, comparisons with competing solutions do show the superior quality and viability of our proposal.
Ensemble Selection based on Classifier Prediction Confidence

Ensemble selection is one of the most studied topics in ensemble learning because a selected subset of base classifiers may perform better than the whole ensemble system. In recent years, a great many ensemble selection methods have been introduced. However, many of these lack flexibility: either a fixed subset of classifiers is pre-selected for all test samples (static approach), or the selection of classifiers depends upon the performance of techniques that define the region of competence (dynamic approach). In this paper, we propose an ensemble selection method that takes into account each base classifier's confidence during classification and the overall credibility of the base classifier in the ensemble. In other words, a base classifier is selected to predict for a test sample if the confidence in its prediction is higher than its credibility threshold. The credibility thresholds of the base classifiers are found by minimizing the empirical 0–1 loss on the entire training observations. In this way, our approach integrates both the static and dynamic aspects of ensemble selection. Experiments on 62 datasets demonstrate that the proposed method achieves much better performance in comparison to some ensemble methods.
Dataflow Challenges in an Internet of Production

The Internet of Production (IoP) envisions the interconnection of previously isolated CPS in the area of manufacturing across institutional boundaries to realize benefits such as increased profit margins and product quality as well as reduced product development costs and time to market. This interconnection of CPS will lead to a plethora of new dataflows, especially between (partially) distrusting entities. In this paper, we identify and illustrate these envisioned inter-organizational dataflows and the participating entities alongside two real-world use cases from the production domain: a fine blanking line and a connected job shop.
Depth-map completion for large indoor scene reconstruction

Traditional Multi View Stereo (MVS) algorithms are often difficult to deal with large-scale indoor scene reconstruction, due to the photo-consistency measurement errors in weak textured regions, which are commonly exist in indoor scenes. To solve this limitation, in this paper we proposed a point cloud completion strategy that combines learning-based depth-map completion and geometry-based consistency filtering to fill large-area missing in depth-maps. The proposed method takes nonuniform and noisy MVS depth-map as input, and completes each depth-map individually. In the completion process, we first complete depth-maps using learning based method, and then filter each depth-map using depth consistency validation with its neighboring depth-maps. This depth-map completion and geometric filtering steps are performed iteratively until the number of depth points is converged. Experiments on large-scale indoor scenes and benchmark MVS datasets demonstrate the effectiveness of the proposed methods.
Shapeglot: Learning Language for Shape Differentiation

In this work we explore how fine-grained differences between the shapes of common objects are expressed in language, grounded on 2D and/or 3D object representations. We first build a large scale, carefully controlled dataset of human utterances each of which refers to a 2D rendering of a 3D CAD model so as to distinguish it from a set of shape-wise similar alternatives. Using this dataset, we develop neural language understanding (listening) and production (speaking) models that vary in their grounding (pure 3D forms via point-clouds vs. rendered 2D images), the degree of pragmatic reasoning captured (e.g. speakers that reason about a listener or not), and the neural architecture (e.g. with or without attention). We find models that perform well with both synthetic and human partners, and with held out utterances and objects. We also find that these models are capable of zero-shot transfer learning to novel object classes (e.g. transfer from training on chairs to testing on lamps), as well as to real-world images drawn from furniture catalogs. Lesion studies indicate that the neural listeners depend heavily on part-related words and associate these words correctly with visual parts of objects (without any explicit supervision on such parts), and that transfer to novel classes is most successful when known part-related words are available. This work illustrates a practical approach to language grounding, and provides a novel case study in the relationship between object shape and linguistic structure when it comes to object differentiation.