Reviewerly/exHarmony huggingface dataset:
CB-PS: An Efficient Short-Certificate-Based Proxy Signature Scheme for UAVs

Recently, the deployment and usage of unmanned aerial vehicles (UAVs) to monitor and track a geographical region have been increased exponentially. The network infrastructure designed to deploy UAVs consists of sensors, actuators, and other communication smart devices for data collection and processing. As security and privacy is one of the challenging tasks in such an environment, so to design an efficient and secure data dissemination scheme is a challenging task due to limited storage and computation power of UAVs. To mitigate various attacks in such an environment, several efficient signature schemes were proposed in the literature. However, none of these schemes work well due to high computation and communication costs. To overcome these challenges, this paper presents a short proxy signature (CB-PS) scheme in certificate-based setting. Due to implicit usage of certificates, CB-PS solves inherent key escrow of ID-based designated verifier proxy signature. It also eliminates the necessity of secret key distribution in ID-based schemes. The proposed CB-PS scheme is adaptively unforgeable in a random oracle model. It is also efficient with respect to both signature length and computational complexity. The comparison of computational costs reveals that the proposed scheme consumes the least cost 15.45 ms in comparison to the costs of existing competitive schemes.
Widening siamese architectures for stereo matching

Computational stereo is one of the classical problems in computer vision. Numerous algorithms and solutions have been reported in recent years focusing on developing methods for computing similarity, aggregating it to obtain spatial support and finally optimizing an energy function to find the final disparity. In this paper, we focus on the feature extraction component of stereo matching architecture and we show standard CNNs operation can be used to improve the quality of the features used to find point correspondences. Furthermore, we use a simple space aggregation that hugely simplifies the correlation learning problem, allowing us to better evaluate the quality of the features extracted. Our results on benchmark data are compelling and show promising potential even without refining the solution.
Part-Aware Region Proposal for Vehicle Detection in High Occlusion Environment

Visual-based vehicle detection has been extensively applied for autonomous driving systems and advanced driving assistant systems, however, it faces great challenges as a partial observation regularly happens owing to occlusion from infrastructure or dynamic objects or a limited vision field. This paper presents a two-stage detector based on Faster R-CNN for high occluded vehicle detection, in which we integrate a part-aware region proposal network to sense global and local visual knowledge among different vehicle attributes. That entails the model simultaneously generating partial-level proposals and instance-level proposals at the first stage. Then, different parts belong to the same vehicle are encoded and reconfigured into a compositional entire proposal through a part affinity fields, allowing the model to generate integral candidates and mitigate the impact of occlusion challenge to the utmost extent. Extensive experiments conducted on KITTI benchmark exhibit that our method outperforms most machine-learning-based vehicle detection methods and achieves high recall in the severely occluded application scenario.
Deterministic conversions between Greenberger-Horne-Zeilinger states and <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>W</mml:mi></mml:math> states of spin qubits via Lie-transform-based inverse Hamiltonian engineering

In this paper, we propose a protocol to realize the conversions between Greenberger-Horne-Zeilinger (GHZ) states and $W$ states of spin qubits. By analyzing and simplifying the dynamics of the system, the control fields are designed via the inverse Hamiltonian engineering based on the Lie transforms. Moreover, the states' conversions between multitype GHZ states and $W$ states can be executed deterministically and reversibly, which makes the protocol resource saving and flexible. We show in numerical simulations that the state conversions are robust against the systematic errors, random noise, and frequency mismatching of the control fields. Therefore, the protocol may provide some useful prospectives to the research of quantum mechanics and applications of quantum information processing based on GHZ states and $W$ states.
SparCE: Sparsity Aware General-Purpose Core Extensions to Accelerate Deep Neural Networks

Deep Neural Networks (DNNs) have emerged as the method of choice for solving a wide range of machine learning tasks. The enormous computational demand posed by DNNs is a key challenge for computing system designers and has most commonly been addressed through the design of DNN accelerators. However, these specialized accelerators utilize large quantities of multiply-accumulate units and on-chip memory and are prohibitive in area and cost constrained systems such as wearable devices and IoT sensors. In this work, we take a complementary approach and improve the performance of DNNs on general-purpose processor (GPP) cores. We do so by exploiting a key attribute of DNNs, viz. sparsity or the prevalence of zero values. We propose Sparsity-aware Core Extensions (SparCE) - a set of low-overhead micro-architectural and ISA extensions that dynamically detect whether an operand (e.g., the result of a load instruction) is zero and subsequently skip a set of future instructions that use it. To maximize performance benefits, SparCE ensures that the instructions to be skipped are prevented from even being fetched, as squashing instructions comes with a penalty (e.g., a pipeline stall). SparCE consists of 2 key micro-architectural enhancements. First, a Sparsity Register File (SpRF) is utilized to track registers that are zero. Next, a Sparsity-Aware Skip Address (SASA) Table is used to indicate instruction sequences that can be skipped, and to specify conditions on SpRF registers that trigger instruction skipping. When an instruction is fetched, SparCE dynamically pre-identifies whether the following instruction(s) can be skipped, and if so appropriately modifies the program counter, thereby skipping the redundant instructions and improving performance. We model SparCE using the gem5 architectural simulator, and evaluate our approach on 6 state-of-the-art image-recognition DNNs in the context of both training and inference using the Caffe deep learning framework. On a scalar microprocessor, SparCE achieves 1.11×-1.96× speedups across both convolution and fully-connected layers that exhibit 10-90 percent sparsity. These speedups translate to 19-31 percent reduction in execution time at the overall application-level. We also evaluate SparCE on a 4-way SIMD ARMv8 processor using the OpenBLAS library, and demonstrate that SparCE achieves 8-15 percent reduction in the application-level execution time.
On the graded acceptability of arguments in abstract and instantiated argumentation

The paper develops a formal theory of the degree of justification of arguments, which relies solely on the structure of an argumentation framework, and which can be successfully interfaced with approaches to instantiated argumentation. The theory is developed in three steps. First, the paper introduces a graded generalization of the two key notions underpinning Dung's semantics: self-defense and conflict-freeness. This leads to a natural generalization of Dung's semantics, whereby standard extensions are weakened or strengthened depending on the level of self-defense and conflict-freeness they meet. The paper investigates the fixpoint theory of these semantics, establishing existence results for them. Second, the paper shows how graded semantics readily provide an approach to argument rankings, offering a novel contribution to the recently growing research programme on ranking-based semantics. Third, this novel approach to argument ranking is applied and studied in the context of instantiated argumentation frameworks, and in so doing is shown to account for a simple form of accrual of arguments within the Dung paradigm. Finally, the theory is compared in detail with existing approaches.
Virtual intersecting location based UAV circumnavigation and bearings-only target-tracking techniques

The use of agents to circle a moving target and estimate its states with bearing measurements only is a kind of passive tracking technique, and has received wide attention in recent years. However, the performance of the existing estimation and control laws for moving agent are still not so satisfactory when the target position is not known beforehand. In this paper, an unbiased estimator to locate the position of the target and a controller to circumnavigate around the target at a prescribed circular distance are investigated for an unmanned aircraft vehicle (UAV). First, a rough target-state estimator and a rough two-stage saturation UAV circumnavigation strategy are introduced, and their convergence performance is analyzed. Second, a virtual intersecting localization algorithm (VILA) is proposed based on a rough estimation strategy, and then the accurate target-state estimation is obtained with a Kalman filter. Third, the UAV rough circumnavigating strategy is modified with the accurate target-state estimation. By numerical simulation, it is verified that the steady estimation error with VILA is asymptotically unbiased, and the estimation accuracy is much higher than that of the existing estimation methods. It is also proven that the control error by the proposed UAV circumnavigating strategy can asymptotically converge to zero by the modified controller, which is also superior to that for the existing circumnavigating controller.
Indoor Occupancy Detection and Estimation using Machine Learning and Measurements from an IoT LoRa-based Monitoring System

In this paper, we present results on the application of machine learning to the detection of human presence and estimation of the number of occupants in our offices using data from an IoT LoRa-based indoor environment monitoring system at Aalborg University, Denmark. We cast the problem as either binary or multi-class classification and apply a two-layer feed forward neural network to the data. The data used for training, validation and testing of the network comprises of environmental data from the IoT sensors and manual recordings of the door and window states. Results show that the classifier is able to correctly determine occupancy of our offices from the IoT sensor measurements with accuracy up to 94.6% and 91.5% for the binary (presence or absence of persons) and multi-class (no person, one person or two or more persons) problems, respectively. Our analysis also shows that occupancy detection with a network trained either in another room or with single environmental parameter is also possible but with less accuracy.
Beyond the Maximum Storage Capacity Limit in Hopfield Recurrent Neural Networks

In a neural network, an autapse is a particular kind of synapse that links a neuron onto itself. Autapses are almost always not allowed neither in artificial nor in biological neural networks. Moreover, redundant or similar stored states tend to interact destructively. This paper shows how autapses together with stable state redundancy can improve the storage capacity of a recurrent neural network. Recent research shows how, in an N-node Hopfield neural network with autapses, the number of stored patterns (P) is not limited to the well known bound 0.14 N , as it is for networks without autapses. More precisely, it describes how, as the number of stored patterns increases well over the 0.14 N threshold, for P much greater than N, the retrieval error asymptotically approaches a value below the unit. Consequently, the reduction of retrieval errors allows a number of stored memories, which largely exceeds what was previously considered possible. Unfortunately, soon after, new results showed that, in the thermodynamic limit, given a network with autapses in this high-storage regime, the basin of attraction of the stored memories shrinks to a single state. This means that, for each stable state associated with a stored memory, even a single bit error in the initial pattern would lead the system to a stationary state associated with a different memory state. This thus limits the potential use of this kind of Hopfield network as an associative memory. This paper presents a strategy to overcome this limitation by improving the error correcting characteristics of the Hopfield neural network. The proposed strategy allows us to form what we call an absorbing-neighborhood of state surrounding each stored memory. An absorbing-neighborhood is a set defined by a Hamming distance surrounding a network state, which is an absorbing because, in the long-time limit, states inside it are absorbed by stable states in the set. We show that this strategy allows the network to store an exponential number of memory patterns, each surrounded with an absorbing-neighborhood with an exponentially growing size.
Deep Multi-class Adversarial Specularity Removal

We propose a novel learning approach, in the form of a fully-convolutional neural network (CNN), which automatically and consistently removes specular highlights from a single image by generating its diffuse component. To train the generative network, we define an adversarial loss on a discriminative network as in the GAN framework and combined it with a content loss. In contrast to existing GAN approaches, we implemented the discriminator to be a multi-class classifier instead of a binary one, to find more constraining features. This helps the network pinpoint the diffuse manifold by providing two more gradient terms. We also rendered a synthetic dataset designed to help the network generalize well. We show that our model performs well across various synthetic and real images and outperforms the state-of-the-art in consistency.
Privacy-preserving <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" id="d1e942" altimg="si666.svg"><mml:mi>k</mml:mi></mml:math> nearest neighbor query with authentication on road networks

k nearest neighbor (kNN) queries are frequently used in location-based services (LBSs), by which we wish to get k closest points of interest (POIs) given a certain point. Since the cloud computing is developing fast, LBS providers are tended to outsource spatial databases to the cloud. However, cloud servers are often untrusty, so that ensuring the spatial query integrity as well as the spatial query privacy is critical. We present a verifiable privacy-preserving kNN query scheme, which can be used on road networks. Our work makes use of the network Voronoi diagram and several cryptographic primitives including pseudo-random functions, Paillier cryptosystem, condensed RSA digital signature, and so on. It can simultaneously preserve the privacy of spatial data and kNN queries, and verify the reliability of query results. The effectiveness and practicability of our scheme are validated by our experimental results. We further analyzed the security of our scheme under the adaptive chosen-query attack via rigorous proof.
Trials and Tribulations of Novices Working with the Arduino

Physical computing has grown over the past decade leading to diverse experiences and tools for novices. Despite the variety of tools, Arduinos remain a leading choice in education. However, few studies examine how novices are learning about the programming and electronics concepts, and how tools impact their experience. The research presented reports on the qualitative analysis of a laboratory study in which 31 novices work with the Arduino for the first time. Video and audio recordings captured participants' actions and thoughts as they used the Arduino platform with a blocks-based programming environment, and two electronics prototyping tools-the standard Breadboard and a modular breadboard called BitBlox. The study presents three main contributions to the literature: first, it provides a codebook of the common breakdowns faced by novices; second, it offers insight into the work processes of novices; and third, it demonstrates ways that the tools used by novices can affect their experience.
White-light interferometer without mechanical scanning

We introduce an optical 3D sensor that can measure the shape of objects with rough surface. The proposed sensor is based on white-light interferometry but it does not require a mechanical movement of the measured object relative to the measuring device. The output of a fiber optical interferometer is used as the light source for the measuring interferometer. The built-in fiber stretcher changes the optical path difference and replaces the mechanical movement. A focus tunable lens is a part of the imaging system. This lens secures that the measured part of the surface is imaged sharply onto the camera during the measurement process.
Applying a new quantitative image analysis scheme based on global mammographic features to assist diagnosis of breast cancer

This study aims to develop and evaluate a unique global mammographic image feature analysis scheme to predict likelihood of a case depicting the detected suspicious breast mass being malignant for breast cancer.From the entire breast area depicting on the mammograms, 59 features were initially computed to characterize the breast tissue properties at both spatial and frequency domain. Given that each case consists of two cranio-caudal and two medio-lateral oblique view images of left and right breasts, two feature pools were built, which contain the computed features from either two positive images of one breast or all the four images of two breasts. Next, for each feature pool, a particle swarm optimization (PSO) method was applied to determine the optimal feature cluster followed by training a support vector machine (SVM) classifier to generate a final score for predicting likelihood of the case being malignant. To test the scheme, we assembled a dataset involving 275 patients who had biopsy due to the suspicious findings on mammograms. Among them, 134 are malignant and 141 are benign. A ten-fold cross validation method was used to train and test the scheme.The classification performance levels measured by the areas under ROC curves are 0.79 ± 0.07 and 0.75 ± 0.08 when applying the SVM classifiers trained using image features computed from two-view and four-view images, respectively.This study demonstrates feasibility of developing a new global mammographic image feature analysis-based scheme to predict the likelihood of case being malignant without lesion segmentation.
Development of Models in Resilient Computing

The article analyzes the concept of "Resilience" in relation to the development of computing. The strategy for reacting to perturbations in this process can be based either on "harsh Resistance" or "smarter Elasticity." Our "Models" are descriptive in defining the path of evolutionary development as structuring under the perturbations of the natural order and enable the analysis of the relationship among models, structures and factors of evolution. Among those, two features are critical: parallelism and "fuzziness", which to a large extent determine the rate of change of computing development, especially in critical applications. Both reversible and irreversible development processes related to elastic and resistant methods of problem solving are discussed. The sources of perturbations are located in vicinity of the resource boundaries, related to growing problem size with progress combined with the lack of computational "checkability" of resources i.e. data with inadequate models, methodologies and means. As a case study, the problem of hidden faults caused by the growth, the deficit of resources, and the checkability of digital circuits in critical applications is analyzed.
Applying Model Driven Engineering Techniques to the Development of Contiki-Based IoT Systems

The huge variety of smart devices and their communication models increases the development complexity of embedded software for the Internet of Things. As a consequence, development of these systems becomes more complex, error-prone, and costly. To tackle this problem, in this study, a model-driven approach is proposed for the development of Contiki-based IoT systems. To this end, the Contiki metamodel available in the literature is extended to include elements of WiFi connectivity modules (such as ESP8266), IoT Log Manager, and information processing components (such as Raspberry Pi). Based on this new metamodel, a domain-specific modeling environment is developed in which visual symbols are used and static semantics (representing system constraints) are defined. Also, the architectural code for the computing components of the IoT system such as Contiki, ESP8266, and RaspberryPi are generated from the developer's instance model. Finally, a Smart Fire Detection system is used to evaluate this study. By modeling the Contiki-based IoT system, we support model-driven development of the system, including WSN motes and sink nodes (with ContikiOS), WiFi modules and information processing components.
Sequential approximation optimization assisted particle swarm optimization for expensive problems

In this paper, an efficient sequential approximation optimization assisted particle swarm optimization algorithm is proposed for optimization of expensive problems. This algorithm makes a good balance between the search ability of particle swarm optimization and sequential approximation optimization. Specifically, the proposed algorithm uses the optima obtained by sequential approximation optimization in local regions to replace the personal historical best particles and then runs the basic particle swarm optimization procedures. Compared with particle swarm optimization, the proposed algorithm is more efficient because the optima provided by sequential approximation optimization can direct swarm particles to search in a more accurate way. In addition, a space partition strategy is proposed to constraint sequential approximation optimization in local regions. This strategy can enhance the swarm diversity and prevent the preconvergence of the proposed algorithm. In order to validate the proposed algorithm, a lot of numerical benchmark problems are tested. An overall comparison between the proposed algorithm and several other optimization algorithms has been made. Finally, the proposed algorithm is applied to an optimal design of bearings in an all-direction propeller. The results show that the proposed algorithm is efficient and promising for optimization of the expensive problems.
Dual Adversarial Transfer for Sequence Labeling

We propose a new architecture for addressing sequence labeling, termed Dual Adversarial Transfer Network (DATNet). Specifically, the proposed DATNet includes two variants, i.e., DATNet-F and DATNet-P, which are proposed to explore effective feature fusion between high and low resource. To address the noisy and imbalanced training data, we propose a novel Generalized Resource-Adversarial Discriminator (GRAD) and adopt adversarial training to boost model generalization. We investigate the effects of different components of DATNet across different domains and languages, and show that significant improvement can be obtained especially for low-resource data. Without augmenting any additional hand-crafted features, we achieve state-of-the-art performances on CoNLL, Twitter, PTB-WSJ, OntoNotes and Universal Dependencies with three popular sequence labeling tasks, i.e., Named entity recognition (NER), Part-of-Speech (POS) Tagging and Chunking.