Reviewerly/exHarmony huggingface dataset:
Boundary Attack++: Query-Efficient Decision-Based Adversarial Attack.

Decision-based adversarial attack studies the generation of adversarialexamples that solely rely on output labels of a target model. In this paper,decision-based adversarial attack was formulated as an optimization problem.Motivated by zeroth-order optimization, we develop Boundary Attack++, a familyof algorithms based on a novel estimate of gradient direction using binaryinformation at the decision boundary. By switching between two types ofprojection operators, our algorithms are capable of optimizing $L_2$ and$L_\infty$ distances respectively. Experiments show Boundary Attack++ requiressignificantly fewer model queries than Boundary Attack. We also show ouralgorithm achieves superior performance compared to state-of-the-art white-boxalgorithms in attacking adversarially trained models on MNIST.
Discrete symbiotic organism search with excellence coefficients and self-escape for traveling salesman problem

Traveling salesman problem (TSP) is one of the well-known NP-hard problems in combinatorial optimization. The optimal solution of large-scale TSP is difficult to find with exact algorithms in acceptable computation time. Thus, heuristic algorithms are widely studied to find the near-optimal or satisfactory solutions. The discrete symbiotic organism search (DSOS) is enhanced with excellence coefficients and self-escape strategy (ECSDSOS) for TSP. The excellence coefficients let ECSDSOS choose shorter edges (routes) for generating better local paths. The local optimization capability is improved so that ECSDSOS is accelerated for finding satisfactory solutions. The self-escape strategy improves the organism versatility in order to prevent ECSDSOS from being trapped into local minima. The premature convergence is constrained and the global optimization capability is improved. ECSDSOS is tested with certain real-world TSP instances in TSPLIB. The results are compared with those computed with basic DSOS and several other heuristic algorithms. Under the same preconditions, ECSDSOS shows competitive optimization capacities for computing better solutions for TSP than basic DSOS and some other heuristic algorithms.
A scalable model for building context-aware applications for noncommunicable diseases prevention

Healthy diet and physical activity practice are well-known required behaviors for preventing noncommunicable diseases. However, sometimes it is difficult for people to change life-styles by themselves, once they are subject to environment and social networks influences. Nowadays plenty of devices and applications are offered promising to help patients, but the solely use and spreading of ubiquitous computing devices are not enough to help patients to reach their health goals. Strategies are needed to integrate people, technology and the data generated by them, to patients overcome their health issues. So, this article presents an scalable model that aims to integrate people, data and technology for improving health care. This model was designed with noncommunicable diseases prevention in mind, but it can be generalized to other domains. Furthermore, available current technologies can be used to implement the proposed model.
Explainable deep learning based medical diagnostic system

Recently, many researchers have conducted data mining over medical data to uncover hidden patterns and use them to learn prediction models for clinical decision making and personalized medicine. While such healthcare learning models can achieve encouraging results, they seldom incorporate existing expert knowledge into their frameworks and hence prediction accuracy for individual patients can still be improved. However, expert knowledge spans across various websites and multiple databases with heterogeneous representations and hence is difficult to harness for improving learning models. In addition, patients' queries at medical consult websites are often ambiguous in their specified terms and hence the returned responses may not contain the information they seek. To tackle these problems, we first design a knowledge extraction framework that can generate an aggregated dataset to characterize diseases by integrating heterogeneous medical data sources. Then, based on the integrated dataset, we propose an end-to-end deep learning based medical diagnosis system (DL-MDS) to provide disease diagnosis for authorized users. We also provide explanations for the diagnose results. Evaluations on real-world data demonstrate that our proposed system achieves good performance on diseases diagnosis with a diverse set of patients' queries.
Alternating minimization algorithm for hybrid regularized variational image dehazing

Imaging quality is often significantly degraded under hazy weather condition. The purpose of this paper is to recover the latent sharp image from its hazy version. It is well known that the accurate estimation of depth information could assist in improving dehazing performance. In this paper, a hybrid regularized variational framework was proposed to simultaneously estimate depth map and haze-free image. In particular, the second-order total generalized variation (TGV) regularizer was introduced to constrain the estimation of depth map, which commonly contains the piecewise smooth regions separated by sharp edges. To take full advantage of the similar properties between three different color channels, we proposed to exploit the multichannel total variation (MTV) regularizer to guarantee the robust restoration of haze-free image. The resulting nonsmooth optimization problem was effectively handled using the alternating direction method of multipliers (ADMM)-based numerical optimization algorithm. A two-step correction mechanism was further introduced to improve visual image quality in the presence of large sky region. Numerous experiments have been implemented based on both synthetic and realistic hazy images. Dehazing results demonstrated that our method was competitive with or even outperformed current state-of-the-art methods under different imaging conditions.
TEEv: virtualizing trusted execution environments on mobile platforms

Trusted Execution Environments (TEE) are widely deployed, especially on smartphones. A recent trend in TEE development is the transition from vendor-controlled, single-purpose TEEs to open TEEs that host Trusted Applications (TAs) from multiple sources with independent tasks. This transition is expected to create a TA ecosystem needed for providing stronger and customized security to apps and OS running in the Rich Execution Environment (REE). However, the transition also poses two security challenges: enlarged attack surface resulted from the increased complexity of TAs and TEEs; the lack of trust (or isolation) among TAs and the TEE.
A Lightweight Payment Verification Protocol for Blockchain Transactions on IoT Devices

The Internet of Things (IoT) promises technical solutions to many important societal problems relating to the environment, healthcare, transportation, etc. One approach to making IoT solutions more cost-effective is the idea of horizontal integration, where sensors and actuators are shared between different applications. In such environments, economic transactions between system components are used to pay for data and services. Blockchain-based overlay networks have been developed to provide such financial mechanism for IoT devices with sufficient processing power and operating system support. However, no practical solutions for low-end devices exist. In addition, existing solutions also require setting up multiple blockchain accounts for a single administrative domain, which incurs high fees when consolidating those accounts. In this paper, we propose a Ticket-Based Verification Protocol, which defines two separate logical entities, contract manager and transaction verifier. Our design limits the need for high-performance embedded systems to only one unit per administrative domain and reduces the processing and networking overhead other IoT devices in that domain.
Recognizing online handwritten Chinese characters using RNNs with new computing architectures

Although online handwritten Chinese characters recognition has been explored for decades, it is still a challenging task to recognize handwritten Chinese characters accurately. In this paper, we propose an end-to-end recognizer for online handwritten Chinese characters recognition based on a new recurrent neural network (RNN). In the system, two new computing architectures are proposed based on traditional RNN system. One is the variance constraint, and the other is attention weight vector. The variance constraint is used to increase the representation ability of RNN, and the attention weight vector is used to describe the importance of hidden layer states at different time steps. Benefited from the two innovations, the recognition system obtains higher recognition accuracy with fewer parameters. Experiments are carried out on two handwritten Chinese character datasets, IAHCC-UCAS2016 dataset and ICDAR-2013 competition database. The experimental results show that the two innovations are effective, and the proposed end-to-end recognizer obtains better performance than the state-of-the-art methods.
Analysis of Reverberation via Teager Energy Features for Replay Spoof Speech Detection

The Automatic Speaker Verification (ASV) systems are vulnerable to spoofing attacks. Detecting replay attack is the challenging Spoof Speech Detection (SSD) task, as several factors are involved during replay mechanism. Hence, it is important to analyze these factors for effective SSD task. This paper introduces the analysis of the replay speech focusing only on the effect of reverberation on the replay speech. The reverberation introduces delay and change in amplitude producing close copies of natural signal that makes natural components inseparable from the replay components and hence, fails to classify the replay speech signal. To that effect, we propose use of Teager Energy Operator (TEO) to compute running estimate of subband energies for replay vs. natural signal. These subband energies are mapped to cepstraldomain to get proposed Teager Energy Cepstral Coefficients (TECC) for replay SSD task. With the TECC feature set, we analyzed the individual performance for all the Relay Configurations (RC) with Gaussian Mixture Model (GMM) as classifier. The experimental results gave lower Equal Error Rate (EER) of 11.73 % with TECC features and further reduced to 10.30 % with score-level fusion of LFCC and TECC features on evaluation dataset of ASVspoof 2017 challenge version 2.0 database.
Survey of optimal motion planning

Optimal motion planning becomes more and more important these days and is critical for motion planning algorithms developed in the academia to be applicable to real-world applications. The objective of this article is to survey the current state-of-the-art on optimal motion planning algorithms in terms of their three main components: the decision variables and problem formulation, constraints, and objectives. A selection of proposed techniques is reviewed along with a discussion of their effectiveness and limitations. The side-by-side comparison presented in this survey helps to gain insight into the strengths and limitations of the reviewed approaches and assists with system-level design choices.
Systemic Barriers and Equitable Interventions to Improve Vegetable and Fruit Intake in Children: Interviews with National Food System Actors

Fruit and vegetable (FV) intake is declining in New Zealand, and over half of New Zealand's children do not meet the recommendation of two serves of fruit and three serves of vegetables daily (with even lower adherence among children in high-deprivation neighbourhoods). The aim of this study was to map the potential causal pathways explaining this decline and possible actions to reverse it. Semi-structured interviews were held in April-May 2018 with 22 national actors from the produce industry, food distribution and retail sector, government, and NGO health organisations. The qualitative systems dynamics method of cognitive mapping was used to explore causal relationships within the food system that result in low FV intake among children. Barriers and solutions identified by participants were analysed using thematic analysis and according to a public health intervention framework. Participants were in agreement with the goal of improving FV intake for health and economic outcomes, and that health promotion strategies had been ineffectual to date due to multiple systemic barriers. Common barriers discussed were poverty, high food prices, low skills/knowledge, unhealthy food environments, climate change, and urbanization. Solutions with the strongest evidence of efficacy identified by the participants were subsidizing FVs and early childhood interventions to improve FV exposure.
Distance-Based Analysis of Ordinal Data and Ordinal Time Series

The dissimilarity of ordinal categories can be expressed with a distance measure. A unified approach relying on expected distances is proposed to obtain well-interpretable measures of location, dispersion, or symmetry of random variables, as well as measures of serial dependence within a given process. For special types of distance, these analytic tools lead to known approaches for ordinal or real-valued random variables. We also analyze the sample counterparts of the proposed measures and derive asymptotic results for practically important cases in ordinal data and time series analysis. Two real applications about the economic situation in Germany and the credit rating of European countries are presented. Supplementary materials for this article are available online.
Attractiveness of augmented reality to consumers

Augmented reality (AR) may be capable of shaping the competitive advantage of companies by providing various benefits for the consumers. The aim of this article is to assess if AR is attractive to consumers and in which areas it can create the most value. Hence, we have sought to identify the most useful areas of AR applications, as well as factors influencing AR’s attractiveness. Our study finds that consumers regard AR as the most useful in the areas of education, medicine and tourism. Furthermore, we found a gap between the supply and needs of customers in terms of types of AR application offered on the market. We identified advantages and disadvantages of AR applications over their traditional counterparts impacting its adoption. Our research confirms that both hedonic and utilitarian aspects of the user experience are important for AR’s adoption.
Audio Replay Spoof Attack Detection Using Segment-based Hybrid Feature and DenseNet-LSTM Network

At present, most automatic speaker verification (ASV) systems are vulnerable to replay spoof attacks. Therefore, this paper proposes a new approach for the detection of audio replay spoof attacks. Here, a segment-based hybrid feature extraction method is used, which includes the Mel-frequency cepstral coefficient (MFCC) features and Constant-Q cepstral coefficients (CQCC) features. Then, hybrid features are trained using a variety of deep learning networks, including DenseNet, LSTM, and DenseNet-LSTM hybrid architectures. Experiments using the DenseNet-LSTM model with mixed features framework achieves the best performance. Compared to the baseline system built on the CQCC and Gaussian mixture model (GMM), the proposed method achieved 64.31% relative improvement.
A framework for generating condensed co-location sets from spatial databases

Spatial co-location mining is a useful tool for discovering spatial association patterns of feature sets which are frequently observed together in nearby geographic space. Most of co-location mining techniques aim to find all prevalent co-located feature sets which satisfy a given prevalence thresh old. However the result is often large, especially when the prevalence threshold is set low, or long co-location patterns present. Moreover the output has many redundant information which makes it difficult for users to filter useful patterns. This work introduces the problem of mining reduced sets of co-location patterns in order to concisely represent interesting spatial relationship patterns. With aiming two such outputs in the form of maximal and closed co-locations, this paper proposes an algorithmic framework to discover maximal co-location patterns and closed co-location patterns as well as all prevalent co-location patterns, and presents the algorithm details for each pattern discovery. The developed algorithms are correct and complete in finding maximal co-locations and closed co-locations. The experiment result shows that the framework reduces candidate feature sets effectively and finds co-location patterns efficiently.
Cloud detection on small satellites based on lightweight U-net and image compression

An effective onboard cloud detection method in small satellites will greatly improve the downlink data transmission efficiency and reduce the platform memory cost. A methodology combining a convolutional neural network and wavelet image compression is proposed to explore the possibility of onboard cloud detection. A lightweight neural network based on U-net architecture is established and evaluated. The red, green, blue, and infrared waveband images from the Landsat-8 dataset are trained and tested to estimate the performance of the mythology. Then a LeGall-5/3 wavelet transform is applied on the dataset to accelerate the neural network and improve the feasibility of the onboard implementation. The experiment results on advanced RISC machines-based embedded platform illustrate that by taking advantage of a mature image compression system in small satellites; the time cost and peak memory cost required by the neural network will be reduced significantly while the segment accuracy is only slightly decreased. The proposed method provides a good possibility of onboard cloud detection for small satellites.
Augmented reality in science classroom

Technology in education has evolved over the decades and has provided more opportunities for technologies such as Augmented Reality to find its way into the pedagogy of teachers in science. In this paper, an AR app was used in facilitating a lesson on chemical bonding and simple compounds. The effects of such innovation were studied using observation and various data gathering methods in an action research design. Results show that AR is a tool that enhances visualization of concepts through elements of virtual and real images, allows students to map physical characteristics easily and aids them in developing mental images for further discourse. Results also show that AR does not only improve motivation but triggers more student-student interaction and teacher-student interaction as well. Students learning attitudes towards chemistry improved in terms of their appreciation of the subject, their perception of its relation to real life and of their meaning-making processes. The teacher indicated that AR apps can innovate the classroom and can provide opportunities for implementation as they are easy to use. However, findings also show that teachers may have a hard time looking for free resources of apps that will meet their needs, and that there is challenge in ensuring that misconceptions are avoided from AR apps' oversimplification of visualization. This study also presents some action plans for further use of AR apps in teaching science.
FP+DIC for low-cost 3D full-field experimental modal analysis in industrial components

High Speed 3D Digital Image Correlation has reached notorious popularity in dynamic characterisations because of the full-field non-invasive performance. It is being especially relevant for experimental modal analysis due to the importance of the modal parameters to define the dynamic behaviour. However, the economic cost of a stereoscopic system of two high speed cameras is a main concern regarding traditional instrumentations. In the search for low-cost alternatives, different methodologies have arisen that employ a single camera for 3D measurements. One approach consists in obtaining a stereo-vision in a single image using, for instance, a mirror system. These approaches involve important field-of-view restrictions. An interesting approach employs the full resolution by combining Fringe Projection and 2D-DIC for out-of-plane and in-plane displacements, respectively. In this study, this technique is explored to perform experimental modal analysis in a large non-flat aeronautical panel. The validation of the results has been performed using accelerometer measurements during an impact hammer test. The results of the study stand this technique as an attractive low-cost technique for 3D full-field modal characterisations of complex components.
Learning Motion Disfluencies for Automatic Sign Language Segmentation

We introduce a novel technique for the automatic detection of word boundaries within continuous sentence expressions in Japanese Sign Language from three-dimensional body joint positions. First, the flow of signed sentence data within a temporal neighborhood is determined utilizing the spatial correlations between line segments of inter-joint pairs. Next, a frame-wise binary random forest classifier is trained to distinguish word and non-word frame content based on the extracted spatio-temporal features. The output of the classifier is used to propose an automatic word synthesis that achieves reliable and accurate sentence segmentation with an average frame-wise F1 score of 0.89. Evaluation with a baseline data set furthermore shows that the proposed approach can easily be adapted to distinguish between motion transitions and motion primitives for a coarse-action domain.
A High-Performance Elliptic Curve Cryptographic Processor of SM2 over GF(p)

Elliptic curve cryptography (ECC) is widely used in practical applications because ECC has far fewer bits for operands at the same level of security than other public-key cryptosystems such as RSA. The performance of an ECC processor is usually determined by modular multiplication (MM) and point multiplication (PM) operations. For recommended prime field, MM operation can consist of multiplication and fast reduction operations. In this paper, a 256-bit multiplication operation is implemented by a 129-bit (half-word) multiplier using Karatsuba–Ofman multiplication algorithm. The fast reduction is a modulo operation, which gets 512-bit input data from multiplication and outputs a 256-bit result ( 0 ≤ Z &lt; p ) . We propose a two-stage fast reduction algorithm (TSFR) over SCA-256 prime field, which can obtain an intermediate result of 0 ≤ Z &lt; 2 p instead of 0 ≤ Z &lt; 14 p in traditional algorithm, avoiding a lot of repetitive subtraction operations. The PM operation is implemented in width nonadjacent form (NAF) algorithm and its operational schedules are improved to increase the parallelism of multiplication and fast reduction operations. Synthesized with a 0.13 μ m complementary metal oxide semiconductor (CMOS) standard cell library, the proposed processor costs an area of 280 k gates and PM operation takes 0.057 ms at the frequency of 250 MHz. The design is also implemented on Xilinx Virtex-6 platform, which consumes 27.655 k LUTs and takes 0.37 ms to perform one 256-bit PM operation, attaining six times speed-up over the state-of-the-art. The processor makes a tradeoff between area and performance, thus it is better than other methods.