Reviewerly/exHarmony huggingface dataset:
Sample Complexity of Device-Independently Certified “Quantum Supremacy”

Results on the hardness of approximate sampling are seen as important stepping stones towards a convincing demonstration of the superior computational power of quantum devices. The most prominent suggestions for such experiments include boson sampling, IQP circuit sampling, and universal random circuit sampling. A key challenge for any such demonstration is to certify the correct implementation. For all these examples, and in fact for all sufficiently flat distributions, we show that any non-interactive certification from classical samples and a description of the target distribution requires exponentially many uses of the device. Our proofs rely on the same property that is a central ingredient for the approximate hardness results: namely, that the sampling distributions, as random variables depending on the random unitaries defining the problem instances, have small second moments.
Multidimensional membership functions in T–S fuzzy models for modelling and identification of nonlinear multivariable systems using genetic algorithms

In this work, a new method for Takagi–Sugeno (T–S) fuzzy modelling based on multidimensional membership functions (MDMFs) is proposed. It is verified that the fuzzy inference method of one-dimensional membership functions (1DMFs) may place the fuzzy rules in inappropriate locations for modelling of nonlinear multivariable systems, while the application of MDMFs allows a better identification through a smaller number of fuzzy rules. The proposed method uses a genetic algorithm (GA) for the adjustment of the MDMFs and the T–S method for modelling and identification of the nonlinear system. As a validation example, a nonlinear multivariable system, a coupled tanks system, is chosen. The results show that the proposed method presents less identification error than the T–S method, with less number of fuzzy rules.
Geographic ontology for major disasters: Methodology and implementation

During a catastrophic event, the International Charter1 "Space and Major Disasters" is regularly activated and provides the rescue teams damage maps prepared by a photo-interpreter team basing on pre and post-disaster satellite images. A satellite image manual processing must be accomplished in most cases to build these maps, a complex and demanding process. Given the importance of time in such critical situations, automatic or semiautomatic tools are highly recommended. Despite the quick treatment presented by automatic processing, it usually presents a semantic gap issue. Our aim is to express expert knowledge using a well-defined knowledge representation method: ontologies and make semantics explicit in geographic and remote sensing applications by taking the ontology advantages in knowledge representation, expression, and knowledge discovery. This research focuses on the design and implementation of a comprehensive geographic ontology in the case of major disasters, that we named GEO-MD, and illustrates its application in the case of Haiti 2010 earthquake. Results show how the ontology integration reduces the semantic gap and improves the automatic classification accuracy.
AItalk: a tutorial to implement AI as IoT devices

In one of the recent trends of Internet of Things (IoT), the IoT data are manipulated by Artificial Intelligence (AI) techniques for smart applications. By including AI into existing IoT application programs, significant coding effort is required. This paper proposes a solution called AItalk to resolve this issue. Unlike traditional AI-based IoT applications that tightly integrate the AI mechanism within the network applications, the novel idea of AItalk is to treat the machine learning mechanism as a cyber IoT device. Our solution allows seamless inclusion of machine learning capability to the existing IoT applications without any programming effort. The advantage of this approach is that we can decompose a complex AI application into simplified distributed modules connected by using the IoT technology, and therefore the AI solution can be built more effectively. Also, in our approach, data can be easily processed in real time for an AI application. Supervised machine learning naturally fits the IoT applications, where the sensors provide the features to the AI algorithms, and the remote controllers serve as the labels. We show an example that the overhead of the IoT communication in AItalk is less than 30 ms and the AI prediction time is less than 2 ms.
A recursive ensemble organ segmentation (REOS) framework: application in brain radiotherapy

The aim of this work is to develop a novel recursive ensemble OARs segmentation (REOS) framework for accurate organs-at-risk (OARs) automatic segmentation. The REOS recursively segment individual OARs by ensembling images features extracted from an organ localization module and a contour detection module. Both modules are based on a 3D U-Net architecture. The organ localization module is trained for rough segmentation to localize a region of interest (ROI) that encompasses the to-be-delineated OAR, while the contour detection module is trained to segment the OAR within the identified ROI. In this study, the developed REOS framework is applied for brain radiotherapy on segmenting six OARs including the eyes, the brainstem (BS), the optical nerves and the chiasm. Eighty T1-weighted magnetic resonance images (MRI) from 80 brain cancer patients' cases with OARs' gold standard contours were collected for training and testing REOS. On 20 testing cases, the REOS achieve a high segmentation accuracy with Dice similarity coefficient (DSC) mean and standard deviation of 93.9% ± 1.4%, 94.5% ± 2.0%, 90.6% ± 2.7%, on the left and right eyes and the BS, respectively. On small and segmentation-challenging organs, the left and right optical nerves and the chiasm, the REOS achieves DSC of 78.0% ± 10.5%, 82.2% ± 5.9% and 71.1% ± 9.1%. The satisfactory performances demonstrated the effectiveness of the REOS in OARs segmentation.
Efficient Local Secret Sharing for Distributed Blockchain Systems

Blockchain systems store transaction data in the form of a distributed ledger where each peer is to maintain an identical copy. Blockchain systems resemble repetition codes, incurring high storage cost. Recently, distributed storage blockchain (DSB) systems have been proposed to improve storage efficiency by incorporating secret sharing, private key encryption, and information dispersal algorithms. However, the DSB results in significant communication cost when peer failures occur due to denial of service attacks. In this letter, we propose a new DSB approach based on a local secret sharing (LSS) scheme with a hierarchical secret structure of one global secret and several local secrets. The proposed DSB approach with LSS improves the storage and recovery communication costs.
Recursive Discriminative Subspace Learning With $\ell_{1}$ -Norm Distance Constraint

In feature learning tasks, one of the most enormous challenges is to generate an efficient discriminative subspace. In this paper, we propose a novel subspace learning method, named recursive discriminative subspace learning with an l1 -norm distance constraint (RDSL). RDSL can robustly extract features from the contaminated images and learn a discriminative subspace. With the use of an inequation-based l1 -norm distance metric constraint, the minimized l1 -norm distance metric objective function with slack variables induces samples in the same class to cluster as close as possible, meanwhile samples from different classes can be separated from each other as far as possible. By utilizing l1 -norm items in both the objective function and the constraint, RDSL can well handle the noisy data and outliers. In addition, the large margin formulation makes the proposed method insensitive to initializations. We describe two approaches to solve RDSL with a recursive strategy. Experimental results on six benchmark datasets, including the original data and the contaminated data, demonstrate that RDSL outperforms the state-of-the-art methods.
Akaike and Bayesian Information Criteria for Hidden Markov Models

We propose the Bayesian information criterion (BIC) and the Akaike information criterion (AIC) for model selection in hidden Markov models (HMM) when the number of states is unknown. The exact solutions exploit the properties of HMM that allow tractable forms of both criteria to be obtained while transgressing the common assumption in AIC and BIC model selection approaches on the independence of data. The proposed algorithm is presented and evaluated in application to blind channel estimation and symbol detection when the channel length is assumed unknown.
On modeling left-truncated loss data using mixtures of distributions

A new statistical methodology is developed for fitting left-truncated loss data by using the G-component finite mixture model with any combination of Gamma, Lognormal, and Weibull distributions. The EM algorithm, along with the emEM initialization strategy, is employed for model fitting. We propose a new grid map which considers the model selection criterion (AIC or BIC) and risk measures at the same time, by using the entire space of models under consideration. A simulation study validates our proposed approach. The application of the proposed methodology and use of new grid maps are illustrated through analyzing a real data set that includes left-truncated insurance losses.
Generalized models for estimation of global solar radiation based on sunshine duration and detailed comparison with the existing: A case study for India

The main objectives of this work are the comparison and the development of models for estimating mean monthly global solar radiation in India. Meteorological data for a 15-year period (1986–2000) are used for 23 locations across India from Indian Meteorological Department, Pune. Quality control of data is performed and appropriate limits were established. From the literature, 104 models were nominated which correlate clearness index to relative sunshine duration. Models were subsequently applied to the used data. Furthermore, 7 new 'generalized' models are developed using all data. Comparison and performance assessment of all the models is conducted using some well-known statistical indicators. Moreover, statistical indicators were scaled to obtain Global Performance Indicator which is subsequently used to rank the models. According to the results, the Global Performance Indicator takes values between −7.2627 and 0.9065. The developed generalized models present superior performance compared to those nominated from literature. Top ranking models are used to assess solar radiation for six stations under different climates and it is established that these models have strong estimation capabilities. So, this study determines the most precise model demonstrating generalized behavior to assess global solar radiation anywhere in the region.
A Wearable Activity Recognition Device Using Air-Pressure and IMU Sensors

Human activity recognition (HAR) has received a lot of attention due to its wide applications in recent years, while the improvement of recognition accuracy is seemingly considered to be one of the great challenges in this field.In this paper, a novel wearable device for improving the activity recognition accuracy is proposed based on the different multiple sensors, which simultaneously collects the muscle activity and motion information.The muscular activity is monitored by measuring the air pressure in an air bladder contacting the targeted muscle, while the motion information, such as three-axis accelerations and angular velocities of body movement, is collected via the on-body inertial measurement unit (IMU) sensor.The performance of the air-pressure sensor is verified by comparing with the electromyography and the IMU sensors.To implement our device, we collect the labeled activities data from eight subjects as they perform 11 daily activities.Some commonly used features from raw data are calculated, and five popular classification techniques are evaluated in terms of the accuracy, recall, precision, and F-measure.The experimental results indicate that the proposed wearable device can improve the performance of HAR system.Particularly, the usage of air-pressure sensor can eliminate the confusions among dynamic activities, such as walking and going upstairs, which is an open problem in HAR.
Associating working memory capacity and code change ordering with code review performance

Change-based code review is a software quality assurance technique that is widely used in practice. Therefore, better understanding what influences performance in code reviews and finding ways to improve it can have a large impact. In this study, we examine the association of working memory capacity and cognitive load with code review performance and we test the predictions of a recent theory regarding improved code review efficiency with certain code change part orders. We perform a confirmatory experiment with 50 participants, mostly professional software developers. The participants performed code reviews on one small and two larger code changes from an open source software system to which we had seeded additional defects. We measured their efficiency and effectiveness in defect detection, their working memory capacity, and several potential confounding factors. We find that there is a moderate association between working memory capacity and the effectiveness of finding delocalized defects, influenced by other factors, whereas the association with other defect types is almost non-existing. We also confirm that the effectiveness of reviews is significantly larger for small code changes. We cannot conclude reliably whether the order of presenting the code change parts influences the efficiency of code review.
General decomposition of fuzzy relations: Semi-tensor product approach

This paper focuses on the general decomposition problem of fuzzy relations, which can be regarded as an extension of the common one, modeled by fuzzy relational equation (FRE) X∘X=R. The considered decomposition problem is described by an FRE X∘Y=R. For the purpose of deriving all solutions to the decomposition problem, the structure of solutions to the corresponding FRE is investigated first, and a necessary and sufficient condition for the existence of solutions is obtained. Then, based on semi-tensor product (STP) of matrices, an algebraic approach is proposed. Furthermore, all solutions to the general decomposition problem can be computed via the proposed method. In order to demonstrate the effectiveness of the presented approach, two numerical examples are given.
Cost-Effective Vehicle Type Recognition in Surveillance Images With Deep Active Learning and Web Data

Recently, vehicle type recognition in surveillance images with deep learning has received significant attention in various applications of intelligent transportation systems. However, annotating large-scale images from many surveillance images is tedious and time-consuming, which impedes its application in the real world. This paper aims to resolve this problem by reducing manual labeling in surveillance images, and then maximizing the effect of the few tagged data. Thus, a deep active learning method with a new query strategy is proposed in this paper for vehicle type recognition in surveillance images. First, the proposed method constructs a memory space using a large-scale fully labeled auxiliary dataset collected from the Internet. Subsequently, two metrics, the similarity measurement in memory space and the entropy, are used to simultaneously emphasize the diversity and uncertainty in the query strategy. Moreover, an additional label-consistent term apart from the hyper-parameters is used to adaptively adjust the combination of the two principles in active learning. The proposed method was evaluated on the Comprehensive Cars dataset. The experimental results demonstrated that the proposed method could effectively reduce the annotation cost by up to 40% in surveillance vehicle type recognition compared with the random selection method.
When Correlation Filters Meet Siamese Networks for Real-Time Complementary Tracking

Discriminative correlation filter (DCF)-based trackers have recently exhibited high efficiency and impressive robustness to challenging factors, such as illumination change and partial occlusion. However, in cases with fast motion and full occlusion, these trackers drift off soon and can hardly re-detect the target from the restricted search region due to the boundary effect. On the contrary, recent work using a fully convolutional Siamese network (Siamfc) locates the exemplar image within a large search image but suffers from coarse location and distractors. In this paper, we propose a real-time complementary tracker (RCT) by integrating DCF and Siamfc into a two-stage tracking framework where DCF and Siamfc share mutual advantages and complement each other. In the first stage of this framework, RCT locates the target coarsely but robustly with Siamfc. In the second stage, the derived coarse location is refined by DCF for higher accuracy. For efficiency reasons, Siamfc in the first stage is activated occasionally based on the tracking status inferred from the correlation response map of DCF in the second stage. Comprehensive experiments are performed on three popular benchmark datasets: OTB2013, OTB2015, and VOT2016. On OTB2013, RCT runs with over 40 f/s and achieves an absolute gain of 4.8% and 5.2% in mean overlap precision compared with two base trackers (Staple and Siamfc). On VOT2016, RCT makes a good balance between performance and efficiency, ranking fifth in EAO and first in EFO compared with the top five trackers.
Rotated Sphere Haar Wavelet and Deep Contractive Auto-Encoder Network With Fuzzy Gaussian SVM for Pilot’s Pupil Center Detection

How to track the attention of the pilot is a huge challenge. We are able to capture the pupil status of the pilot and analyze their anomalies and judge the attention of the pilot. This paper proposes a new approach to solve this problem through the integration of spherical Haar wavelet transform and deep learning methods. First, considering the application limitations of Haar wavelet and other wavelets in spherical signal decomposition and reconstruction, a feature learning method based on the spherical Haar wavelet is proposed. In order to obtain the salient features of the spherical signal, a rotating spherical Haar wavelet is also proposed, which has a consistent scale in the same direction between the reconstructed image and the original image. Second, in order to find a better characteristic representation of the spherical signal, a higher contractive autoencoder (HCAE) is designed for the potential representation of the spherical Haar wavelet coefficients, which has two penalty items, respectively, from Jacobian and two order items from Taylor expansion of the point ${x}$ for the contract learning of sample space. Third, in order to improve the classification performance, this paper proposes a fuzzy Gaussian support vector machine (FGSVM) as the top classification tool of the deep learning model, which can punish some Gaussian noise from the output of the deep HCAE network (DHCAEN). Finally, a DHCAEN-FGSVM classifier is proposed to identify the location of the pupil center. The experimental results of the public data set and actual data show that our model is an effective method for spherical signal detection.
Chinese undergraduate students' perceptions of mobile learning: Conceptions, learning profiles, and approaches

Abstract Close links between students' conceptions of and approaches to learning were established in the past research. However, only a few quantitative studies investigated this relationship particularly with regard to mobile learning (m‐learning). The correlation between learners' conceptions and approaches to m‐learning was analysed using a partial least squares analysis applied to data obtained from a sample of 971 undergraduate students in China. The results indicated that students' conceptions of m‐learning could be classified into reproductive, transitional, and constructive levels. Students may hold multiple m‐learning applications than a predominant one; hence, examining m‐learning as one monolithic entity may provide limited information. Latent profile analysis identified four learning profiles based on students' preferred m‐learning applications: passive, mixed, surface‐supportive, and high‐engagement.. Moreover, a general trend was observed, whereby students with reproductive and surface‐supportive learning profiles showed a tendency to adopt surface approaches, whereas those expressing constructive and mixed learning profiles were more inclined to adopt deep approaches. Interestingly, students with transitional conceptions and high‐engagement learning profiles tended to take both surface and deep approaches.
Online Recognition of Incomplete Gesture Data to Interface Collaborative Robots

Online recognition of gestures is critical for intuitive human-robot interaction (HRI) and further push collaborative robotics into the market, making robots accessible to more people. The problem is that it is difficult to achieve accurate gesture recognition in real unstructured environments, often using distorted and incomplete multisensory data. This paper introduces an HRI framework to classify large vocabularies of interwoven static gestures (SGs) and dynamic gestures (DGs) captured with wearable sensors. DG features are obtained by applying data dimensionality reduction to raw data from sensors (resampling with cubic interpolation and principal component analysis). Experimental tests were conducted using the UC2017 hand gesture dataset with samples from eight different subjects. The classification models show an accuracy of 95.6% for a library of 24 SGs with a random forest and 99.3% for 10 DGs using artificial neural networks. These results compare equally or favorably with different commonly used classifiers. Long short-term memory deep networks achieved similar performance in online frame-by-frame classification using raw incomplete data, performing better in terms of accuracy than static models with specially crafted features, but worse in training and inference time. The recognized gestures are used to teleoperate a robot in a collaborative process that consists in preparing a breakfast meal.
Quality Enhancement Network via Multi-Reconstruction Recursive Residual Learning for Video Coding

Lossy compression algorithms introduce multiple compression artifacts that severely decrease visual quality. These compression artifacts are highly related to texture contents, and the hierarchical coding units decision structure also brings multi-scale similarity to these artifacts. Current loop filters fail to utilize these characteristics to comprehensively remove compression artifacts. To this end, this letter proposes a novel quality enhancement method by adopting a multi-reconstruction recurrent residual network (MRRN). In particular, a modified recursive residual structure is designed to capture the multi-scale similarity of compression artifact. To effectively enhance frames with uneven noise, a multi-reconstruction structure is proposed, which outputs images with different denoise ratios and adaptively fuses them. Experimental results show that the proposed MRRN can improve coding efficiency up to 15.1% compared with the original loop filter in high-efficiency video coding. Averagely, 6.7%, 7.8%, 7.6% BD-rate reduction is achieved for all intra, low-delay P, and low-delay B, respectively. Meanwhile, as a quality enhancement method performed at encoder side, MRRN also achieves a good balance between coding performance and computational complexity compared to the state-of-the-art methods.