Reviewerly/exHarmony huggingface dataset:
Hybrid image encryption algorithm based on 3D chaotic system and choquet fuzzy integral

In this paper, a novel image encryption algorithm based on 3D hybrid chaotic system and choquet fuzzy integral is introduced. To construct an algorithm with high sensitivity and least losing, we introduce a new type of 3D hybrid chaos map which has uniformly distributed, and apply choquet fuzzy integral using B-spline functions as membership grades to confuse and diffuse the pixels. Finally, simulation results and various security analysis illustrate that this algorithm possess a high security and resist various attacks.
Deep Graph Laplacian Regularization for Robust Denoising of Real Images

Recent developments in deep learning have revolutionized the paradigm of image restoration. However, its applications on real image denoising are still limited, due to its sensitivity to training data and the complex nature of real image noise. In this work, we combine the robustness merit of model-based approaches and the learning power of data-driven approaches for real image denoising. Specifically, by integrating graph Laplacian regularization as a trainable module into a deep learning framework, we are less susceptible to overfitting than pure CNN-based approaches, achieving higher robustness to small datasets and cross-domain denoising. First, a sparse neighborhood graph is built from the output of a convolutional neural network (CNN). Then the image is restored by solving an unconstrained quadratic programming problem, using a corresponding graph Laplacian regularizer as a prior term. The proposed restoration pipeline is fully differentiable and hence can be end-to-end trained. Experimental results demonstrate that our work is less prone to overfitting given small training data. It is also endowed with strong cross-domain generalization power, outperforming the state-of-the-art approaches by a remarkable margin.
Proximity Human-Robot Interaction Using Pointing Gestures and a Wrist-mounted IMU

We present a system for interaction between co-located humans and mobile robots, which uses pointing gestures sensed by a wrist-mounted IMU. The operator begins by pointing, for a short time, at a moving robot. The system thus simultaneously determines: that the operator wants to interact; the robot they want to interact with; and the relative pose among the two. Then, the system can reconstruct pointed locations in the robot's own reference frame, and provide real-time feedback about them so that the user can adapt to misalignments. We discuss the challenges to be solved to implement such a system and propose practical solutions, including variants for fast flying robots and slow ground robots. We report different experiments with real robots and untrained users, validating the individual components and the system as a whole.
Thermal Image SuperResolution Through Deep Convolutional Neural Network

Due to the lack of thermal image datasets, a new dataset has been acquired for proposed a super-resolution approach using a Deep Convolution Neural Network schema. In order to achieve this image enhancement process, a new thermal images dataset is used. Different experiments have been carried out, firstly, the proposed architecture has been trained using only images of the visible spectrum, and later it has been trained with images of the thermal spectrum, the results showed that with the network trained with thermal images, better results are obtained in the process of enhancing the images, maintaining the image details and perspective. The thermal dataset is available at http://www.cidis.espol.edu.ec/es/dataset .
Mitigating power side channels during compilation

The code generation modules inside modern compilers, which use a limited number of CPU registers to store a large number of program variables, may introduce side-channel leaks even in software equipped with state-of-the-art countermeasures. We propose a program analysis and transformation based method to eliminate such leaks. Our method has a type-based technique for detecting leaks, which leverages Datalog-based declarative analysis and domain-specific optimizations to achieve high efficiency and accuracy. It also has a mitigation technique for the compiler's backend, more specifically the register allocation modules, to ensure that leaky intermediate computation results are stored in different CPU registers or memory locations. We have implemented and evaluated our method in LLVM for the x86 instruction set architecture. Our experiments on cryptographic software show that the method is effective in removing the side channel while being efficient, i.e., our mitigated code is more compact and runs faster than code mitigated using state-of-the-art techniques.
Socksdirect

Communication intensive applications in hosts with multi-core CPU and high speed networking hardware often put considerable stress on the native socket system in an OS. Existing socket replacements often leave significant performance on the table, as well have limitations on compatibility and isolation.
Scalable Gaussian Process Computations Using Hierarchical Matrices

Christopher J. Geogaa* , Mihai Anitescuab & Michael L. Steinba Mathematics and Computer Science Division, Argonne National Laboratory, Lemont, IL; b Department of Statistics, University of Chicago, Chicago, IL
Single Wearable Accelerometer-Based Human Activity Recognition via Kernel Discriminant Analysis and QPSO-KELM Classifier

In recent years, sensor-based human activity recognition (HAR) has gained tremendous attention around the world with a range of applications.Instead of using body sensor network-based recognition systems which are intrusive and increase equipment cost, we focus on the development of efficient HAR approach based on a single triaxial accelerometer.In order to improve the recognition accuracy of the system, a novel recognition approach based on kernel discriminant analysis (KDA) and quantum-behaved particle swarm optimization-based kernel extreme learning machine (QPSO-KELM) is proposed.KDA is utilized to extract more meaningful features and enhance the discrimination between different activities.To verify the effectiveness of KDA, three kinds of features including original features, linear discriminant analysis (LDA) features and KDA features are extracted and compared for activity recognition.In addition, QPSO-KELM is compared with two existing classification methods: support vector machine (SVM) and extreme learning machine (ELM), which are commonly utilized in activity recognition.Meanwhile, two comparative optimization methods for KELM are also discussed in the experiment.The experimental results demonstrate the superiority of the proposed approach.
Auditable Protocols for Fair Payment and Physical Asset Delivery Based on Smart Contracts

With the rapid development of electronic information technology, online transaction will gradually surpass traditional market transaction, among which online payment and asset delivery become the focus of attention.But in fact, due to the incomplete third-party payment mechanism and the intrusion risk of various charging Trojan, it is easy to cause a trust crisis.The existing centralized framework often leads to information asymmetry between the two parties.Therefore, how to realize the fairness of payment and the auditability of assets in the distributed system is a challenging problem.The emerging blockchain technology provides a new method with its openness, transparency and verifiability.Existing researches do not provide a complete shopping model for consumers, most of which focuses on payments or only on asset delivery.In this paper, we propose an auditable fair payment and physical asset delivery protocol based on smart contracts.Three types of smart contracts are designed to achieve reliable and fair payment among merchants, consumers and logistics companies.The traceability and auditability of blockchain provide an effective method to audit assets and data sharing in the whole transportation.In view of the phenomenon of goods being switched, the way of "pre-verification" is added.In order to prevent the illegal elements to fake pickup code, induce consumers to conduct illegal operations, cause property loss, in our system the pickup codes are generated by consumers to reduce the risk of fraud.In addition, our plan designs a complete return process for the first time, providing better service experience and higher efficiency for consumers.Finally, all the contracts involved in the scheme are implemented and deployed on the ethereum test network.The results of security analysis and evaluation showed that our scheme was improved in cost, with high security and availability.
Grid-Based Coverage Path Planning With Minimum Energy Over Irregular-Shaped Areas With Uavs

Grid-based methods have been proposed to solve the Coverage Path Planning problem using Unmanned Aerial Vehicles in irregular-shaped areas since simple geometric flight patterns, such as the back-and-forth, are inefficient in this type of scenario. However, the grid-based methods usually apply simplistic cost functions and demand high computational time leading to inefficient and expensive paths, making them not usable in real-world scenarios. This paper introduces an energy-aware grid-based approach aimed at minimizing energy consumption during mapping missions over irregular-shaped areas. Our work was built upon a previously proposed grid-based approach. Here we introduce an energy-aware cost function based on an accurate energy model. The proposed approach was able to save up to 17% of energy in real flight experiments, proving that the original cost function was not capable of finding the optimal solution in terms of real energy measurements. Additional simulation experiments were also performed to state the energy savings in different irregular-shaped scenarios. As a further contribution, we also applied two pruning techniques to the original approach dropping the computation time up to 99%.
HD Map Change Detection with a Boosted Particle Filter

In this paper, we present a change detection algorithm that can run in real time as part of a backend-based stream processing pipeline. It can process the floating car data collected by series-production vehicles to detect changes in an automotive high definition digital (HD) map used for automated driving. The algorithm uses a particle filter approach with odometry, GNSS and landmark readings to localize the vehicle within the digital map. While all particles together represent the probability distribution for the vehicle’s position at a given time, each individual particle also serves as a hypothesis about the vehicle’s position. This is used to compute various metrics for how well the current sensor readings match the world model encoded in the HD map. The different metrics are evaluated by a number of weak classifiers that are used as input for a trained Adaboost classifier. The achievable detection rate of a single vehicle is then compared to that of a simple crowd-based approach, where each vehicle votes on whether or not the current section of the road has changed.
A Subjective Comparison of AV1 and HEVC for Adaptive Video Streaming

In this paper we compare the performance of two state-of-the-art competing codecs, AV1 and HEVC, in the context of adaptive streaming. We specifically consider a Dynamic Optimizer (DO) methodology that is content-aware and selects the resolution of the video sequence after constructing the convex hull of the Rate-Quality curves of all considered resolutions. We start with an objective evaluation of the Dynamic Optimizer, based on both PSNR and VMAF quality metrics. The Rate-VMAF curves show an average of 6.3% BD-Rate gain of AV1 over HEVC, while the Rate-PSNR curves an show an average BD-Rate loss of 1.8%. We then report subjective tests which evaluate the perceived quality of the selected bitstreams generated by the two codecs. In this case it was found that, for most rate points, the difference in the perceived quality between HEVC and AV1 is not significant.
Machine Learning for In-Region Location Verification in Wireless Networks

In-region location verification (IRLV) aims at verifying whether a user is inside a region of interest (ROI). In wireless networks, IRLV can exploit the features of the channel between the user and a set of trusted access points. In practice, the channel feature statistics is not available and we resort to machine learning (ML) solutions for IRLV. We first show that solutions based on either neural networks (NNs) or support vector machines (SVMs) with typical loss functions are Neyman-Pearson (N-P)-optimal at learning convergence for sufficiently complex learning machines and large training datasets. For a finite training, ML solutions are more accurate than the N-P test based on estimated channel statistics. Then, as estimating channel features outside the ROI may be difficult, we consider one-class classifiers, namely auto-encoder NNs and one-class SVMs which, however, are not equivalent to the generalized likelihood ratio test (GLRT), typically replacing the N-P test in the one-class problem. Numerical examples support the results in realistic wireless networks, with channel models including path-loss, shadowing, and fading.
Watermarking Public-Key Cryptographic Primitives

A software watermarking scheme enables users to embed a message or mark within a program while preserving its functionality. Moreover, it is difficult for an adversary to remove a watermark from a marked program without corrupting its behavior. Existing constructions of software watermarking from standard assumptions have focused exclusively on watermarking pseudorandom functions (PRFs). In this work, we study watermarking public-key primitives such as the signing key of a digital signature scheme or the decryption key of a public-key (predicate) encryption scheme. While watermarking public-key primitives might intuitively seem more challenging than watermarking PRFs, our constructions only rely on simple assumptions. Our watermarkable signature scheme can be built from the minimal assumption of one-way functions while our watermarkable public-key encryption scheme can be built from most standard algebraic assumptions that imply public-key encryption (e.g., factoring, discrete log, or lattice assumptions). Our schemes also satisfy a number of appealing properties: public marking, public mark-extraction, and collusion resistance. Our schemes are the first to simultaneously achieve all of these properties. The key enabler of our new constructions is a relaxed notion of functionality-preserving. While traditionally, we require that a marked program (approximately) preserve the input/output behavior of the original program, in the public-key setting, preserving the "functionality" does not necessarily require preserving the exact input/output behavior. For instance, if we want to mark a signing algorithm, it suffices that the marked algorithm still output valid signatures (even if those signatures might be different from the ones output by the unmarked algorithm). Similarly, if we want to mark a decryption algorithm, it suffices that the marked algorithm correctly decrypt all valid ciphertexts (but may behave differently from the unmarked algorithm on invalid or malformed ciphertexts). Our relaxed notion of functionality-preserving captures the essence of watermarking and still supports the traditional applications, but provides additional flexibility to enable new and simple realizations of this powerful cryptographic notion.
Analysis of Deep Fusion Strategies for Multi-Modal Gesture Recognition

Video-based gesture recognition has a wide spectrum of applications, ranging from sign language understanding to driver monitoring in autonomous cars. As different sensors suffer from their individual limitations, combining multiple sources has strong potential to improve the results. A number of deep architectures have been proposed to recognize gestures from e.g. both color and depth data. However, these models conventionally comprise separate networks for each modality, which are then combined in the final layer (e.g. via simple score averaging). In this work, we take a closer look at different fusion strategies for gesture recognition especially focusing on the information exchange in the intermediate layers. We compare three fusion strategies on the widely used C3D architecture: 1) late fusion, combining the streams in the final layer; 2) information exchange in an intermediate layer using an additional convolution layer; and 3) linking information at multiple layers simultaneously using the cross-stitch units, originally designed for multi-task learning. Our proposed C3D-Stitch model achieves the best recognition rate, demonstrating the effectiveness of sharing information at earlier stages.
Communication-Efficient Unconditional MPC with Guaranteed Output Delivery

We study the communication complexity of unconditionally secure MPC with guaranteed output delivery over point-to-point channels for corruption threshold $$t < n/3$$ . We ask the question: "is it possible to construct MPC in this setting s.t. the communication complexity per multiplication gate is linear in the number of parties?" While a number of works have focused on reducing the communication complexity in this setting, the answer to the above question has remained elusive for over a decade. We resolve the above question in the affirmative by providing an MPC with communication complexity $$O(Cn\kappa + n^3\kappa )$$ where $$\kappa $$ is the size of an element in the field, C is the size of the (arithmetic) circuit, and, n is the number of parties. This represents a strict improvement over the previously best known communication complexity of $$O(Cn\kappa +D_Mn^2\kappa +n^3\kappa )$$ where $$D_M$$ is the multiplicative depth of the circuit. To obtain this result, we introduce a novel technique called 4-consistent tuples of sharings which we believe to be of independent interest.
Deep Attention Neural Network for Multi-Label Classification in Unmanned Aerial Vehicle Imagery

The multi-label classification problem in Unmanned Aerial Vehicle (UAV) images is particularly challenging compared to single-label classification due to its combinatorial nature. To tackle this issue, we propose in this paper a deep learning approach based on encoder-decoder neural network architecture with channel and spatial attention mechanisms. Specifically, the encoder module which is based on a pre-trained convolutional neural network (CNN) has the task to transform the input image to a set of feature maps using an opportune feature combination. To improve the feature representation further, this module incorporates a squeeze excitation (SE) layer for modelling the interdependencies between the channels of the feature maps. The decoder module which is based on a long short terms memory (LSTM) network has the task of generating, in a sequential way, the classes present in the image. At each time step, it predicts the next class-label by aligning its hidden state to the corresponding region in the image by means of an adaptive spatial attention mechanism. The experiments carried out on two UAV datasets with a spatial resolution of 2-cm show that our method is promising in predicting the labels present in the image while attending the relevant objects in the image. Additionally, it is able to provide better classification results compared to state-of-the-art methods.
Sparse and collaborative representation based kernel pairwise linear regression for image set classification

As an important part of expert and intelligent systems, image set classification has been widely applied to many real-life scenarios including surveillance videos, multi-view camera networks and personal albums. Compared with single image based classification, it is more promising and therefore has attracted significant research attention in recent years. Traditional pairwise linear regression classification (PLRC) introduces the unrelated subspace to increase the discriminative information, and it shows a demonstrated better performance on image set classification. However, the unrelated subspace constructed by PLRC is not optimal and PLRC may fail for well classifying image sets that are not linear separable, or when the axes of linear regression of class-specific samples of different classes have an intersection. In this paper, two new unrelated subspace construction strategies are proposed based on sparse and collaborative representation, respectively. Then, based on them, a new image set classification framework, kernel pairwise linear regression classification (KPLRC) is developed. KPLRC is a nonlinear extension of PLRC and can overcome the drawback of PLRC. Specifically, KPLRC embeds the related and unrelated gallery sets and probe sets into the high-dimensional Hilbert space, and in the kernel space, the data become more linear separable. Extensive experiments on four well-known databases prove that the classification accuracies of KPLRC are better than that of PLRC and several state-of-the-art classifiers. These quantitative assessments reinforce the significance as well as the importance of embedding the proposed method in other intelligent systems application areas.
Adaptive Weight Decay for Deep Neural Networks

Regularization in the optimization of deep neural networks is often critical to avoid undesirable over-fitting leading to better generalization of model. One of the most popular regularization algorithms is to impose L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> penalty on the model parameters resulting in the decay of parameters, called weight-decay, and the decay rate is generally constant to all the model parameters in the course of optimization. In contrast to the previous approach based on the constant rate of weight-decay, we propose to consider the residual that measures dissimilarity between the current state of model and observations in the determination of the weight-decay for each parameter in an adaptive way, called adaptive weight-decay (AdaDecay) where the gradient norms are normalized within each layer and the degree of regularization for each parameter is determined in proportional to the magnitude of its gradient using the sigmoid function. We empirically demonstrate the effectiveness of AdaDecay in comparison to the state-of-the-art optimization algorithms using popular benchmark datasets: MNIST, Fashion-MNIST, and CIFAR-10 with conventional neural network models ranging from shallow to deep. The quantitative evaluation of our proposed algorithm indicates that AdaDecay improves generalization leading to better accuracy across all the datasets and models.
Structured Journalism and the Semantic Units of News

The growing influence of computation on news has intensified the need for an analytical framework that describes the common foundations of different computational approaches to journalism. This article proposes such a framework, founded on the concept of units of journalistic knowledge smaller than the article, expressed partially or completely as structured data and positioned along a continuum of news artifacts. The emerging practice of structured journalism and its use of "atomized" news is described and is presented as an embodiment of the framework. Different approaches to the use of computation within journalism are then positioned as specific instances of that practice. This conception, analogous to "semantic unit" paradigms currently emerging in other information-centric domains, is then used to reinterpret several of journalism's urgent problems. A research agenda for developing computational journalism as an editorial activity within an increasingly data-centric communication environment is proposed, and several implications of the conception are discussed.