Reviewerly/exHarmony huggingface dataset:
An Integrated Traveling Salesman and Coverage Path Planning Problem for Unmanned Aircraft Systems

Unmanned aircraft systems (UASs) have gained great popularity in land monitoring, 3-D mapping, search and rescue, among others. Existing studies on UAS path planning in these missions consider only a single region to be examined. However, it is frequently encountered that multiple regions need to be considered while performing a real life mission. How to design the optimal path for the UAS to cover multiple regions is then critical. From a strategical point of view, such a problem can be considered as a variant of the traveling salesman problem (TSP) combined and enhanced with the coverage path planning (CPP) problem. Although TSP and CPP have been studied extensively, its combination, which here is given the name TSP-CPP, hasn't received any attention. In this letter, a preliminary study on how to formulate and solve this problem is conducted. Two novel approaches including a grid-based approach and a dynamic programming based approach are introduced to find the (near) optimal solution. Both numerical analysis and simulation studies are conducted to prove and illustrate the optimality and efficiency of the proposed TSP-CPP approaches.
Neural-net-induced Gaussian process regression for function approximation and PDE solution

Neural-net-induced Gaussian process (NNGP) regression inherits both the high expressivity of deep neural networks (deep NNs) as well as the uncertainty quantification property of Gaussian processes (GPs). We generalize the current NNGP to first include a larger number of hyperparameters and subsequently train the model by maximum likelihood estimation. Unlike previous works on NNGP that targeted classification, here we apply the generalized NNGP to function approximation and to solving partial differential equations (PDEs). Specifically, we develop an analytical iteration formula to compute the covariance function of GP induced by deep NN with an error-function nonlinearity. We compare the performance of the generalized NNGP for function approximations and PDE solutions with those of GPs and fully-connected NNs. We observe that for smooth functions the generalized NNGP can yield the same order of accuracy with GP, while both NNGP and GP outperform deep NN. For non-smooth functions, the generalized NNGP is superior to GP and comparable or superior to deep NN.
Releasing Correlated Trajectories: Towards High Utility and Optimal Differential Privacy

A mutual correlation between trajectories of two users is very helpful to real-life applications such as product recommendation and social media. While providing tremendous benefits, the releasing of correlated trajectories may leak sensitive social relations, due to potential links between mutual correlations and social relations. To the best of our knowledge, we take the first step to propose a mathematically rigorous n-body Laplace framework, satisfying "-differential privacy, which efficiently prevents a social relation inference through the mutual correlation between n-node trajectories of two users. The problem is mathematically formulated by defining a trajectory correlation score to measure the social relation between two users. Then, under the n-body Laplace framework, we propose two Lagrange Multiplier-based Differentially Private (LMDP) approaches to optimize the privacy budgets, for the data utility measured by location distances and the data utility measured by location correlations, i.e., UD-LMDP and UC-LMDP. Also, we present detailed analyses of privacy, data utility, adversary knowledge and the constrained optimizations. Finally, we perform experimental studies with real-life data. Our experimental results show that our proposed approaches achieve better privacy and data utility than the existing approaches.
The role of planning in complex problem solving

Complex problem solving (CPS) is a highly transversal competence needed in educational and vocational settings as well as everyday life. The assessment of CPS is often computer-based, and therefore provides data regarding not only the outcome but also the process of CPS. However, research addressing this issue is scarce. In this article we investigated planning activities in the process of complex problem solving. We operationalized planning through three behavioral measures indicating the duration of the longest planning interval, the delay of the longest planning interval and the variance of intervals between each two successive interactions. We found a significant negative average effect for our delay indicator, indicating that early planning in CPS is more beneficial. However, we also found effects depending on task and interaction effects for all three indicators, suggesting that the effects of different planning behaviors on CPS are highly intertwined.
eQASM: An Executable Quantum Instruction Set Architecture

A widely-used quantum programming paradigm comprises of both the data How and control How. Existing quantum hardware cannot well support the control How, significantly limiting the range of quantum software executable on the hardware. By analyzing the constraints in the control microarchitecture, we found that existing quantum assembly languages are either too high-level or too restricted to support comprehensive How control on the hardware. Also, as observed with the quantum microinstruction set QuMIS [1], the quantum instruction set architecture (QISA) design may suffer from limited scalability and Hexibility because of microarchitectural constraints. It is an open challenge to design a scalable and Hexible QISA which provides a comprehensive abstraction of the quantum hardware. In this paper, we propose an executable QISA, called eQASM, that can be translated from quantum assembly language (QASM), supports comprehensive quantum program How control, and is executed on a quantum control microarchitecture. With efficient timing specification, single-operation-multiple-qubit execution, and a very-long-instruction-word architecture, eQASM presents better scalability than QuMIS. The definition of eQASM focuses on the assembly level to be expressive. Quantum operations are configured at compile time instead of being defined at QISA design time. We instantiate eQASM into a 32-bit instruction set targeting a seven-qubit superconducting quantum processor. We validate our design by performing several experiments on a two-qubit quantum processor.
Seeded transfer learning for regression problems with deep learning

The difference in data distributions among related, but different domains is a long standing problem for knowledge adaptation. A new method to transform the source domain knowledge to fit the target domain is proposed in this work. The proposed method uses deep learning method and limited number of samples from target domain to transform the source domain dataset. It treats the limited samples of target domain as seeds for initiating the transfer of source knowledge. Comprehensive experiments are conducted using different computational intelligence models and different datasets. Obtained results reveal that prediction models trained using the proposed method demonstrate the best performance in comparison with the same models trained with only source knowledge or deep learned features. Experiments show that models trained using proposed method have outperformed the baseline methods by at least 50% in 14 experiments out of a total of 18.
PedX: Benchmark Dataset for Metric 3-D Pose Estimation of Pedestrians in Complex Urban Intersections

This letter presents a novel dataset titled PedX, a large-scale multimodal collection of pedestrians at complex urban intersections. PedX consists of more than 5 000 pairs of high-resolution (12MP) stereo images and LiDAR data along with providing two-dimensional (2-D) image labels and 3-D labels of pedestrians in a global coordinate frame. Data were captured at three four-way stop intersections with heavy pedestrian-vehicle interaction. We also present a 3-D model fitting algorithm for automatic labeling harnessing constraints across different modalities and novel shape and temporal priors. All annotated 3-D pedestrians are localized into the real-world metric space, and the generated 3-D models are validated using a motion capture system configured in a controlled outdoor environment to simulate pedestrians in urban intersections. We also show that the manual 2-D image labels can be replaced by state-of-the-art automated labeling approaches, thereby facilitating automatic generation of large scale datasets.
Focal Loss in 3D Object Detection

3D object detection is still an open problem in autonomous driving scenes. When recognizing and localizing key objects from sparse 3D inputs, autonomous vehicles suffer from a larger continuous searching space and higher fore-background imbalance compared to image-based object detection. In this letter, we aim to solve this fore-background imbalance in 3D object detection. Inspired by the recent use of focal loss in image-based object detection, we extend this hard-mining improvement of binary cross entropy to point-cloud-based object detection and conduct experiments to show its performance based on two different 3D detectors: 3D-FCN and VoxelNet. The evaluation results show up to 11.2AP gains through the focal loss in a wide range of hyperparameters for 3D object detection.
Dense Multi-path U-Net for Ischemic Stroke Lesion Segmentation in Multiple Image Modalities

Delineating infarcted tissue in ischemic stroke lesions is crucial to determine the extend of damage and optimal treatment for this life-threatening condition. However, this problem remains challenging due to high variability of ischemic strokes’ location and shape.
Publicly verifiable privacy-preserving aggregation and its application in IoT

With the development of smart devices, the Internet of Things (IoT) has found wide applications and extended various services of Internet. On intermediate nodes and edge nodes of the IoT network, the aggregation primitive is a basic function for forwarding data, which takes data sources of other nodes as input. To protect sensitive information of source nodes while enabling the aggregation computations, some works presented corresponding secure protocols. However, the aggregation node could return invalid results due to transition failures, software bugs, or computation delays. Thus, how to verify the results is a big challenge of secure aggregation protocols. In this paper, we focus on the verification problem, and propose a new publicly verifiable scheme for the aggregation operation. Different from existing solutions, our scheme enables a public verifier to test an aggregation result on the data of source nodes while protecting the data privacy. Security analysis shows that the proposed scheme can achieve the security properties. Finally, we provide the experimental evaluation that demonstrates the effectiveness of our scheme.
Detecting task demand via an eye tracking machine learning system

Computerized systems play a significant role in today's fast-paced digital economy. Because task demand is a major factor that influences how computerized systems are used to make decisions, identifying task demand automatically provides an opportunity for designing advanced decision support systems that can respond to user needs at a personalized level. A first step for designing such advanced decision tools is to investigate possibilities for developing automatic task load detectors. Grounded in decision making, eye tracking, and machine learning literature, we argue that task demand can be detected automatically, reliably, and unobtrusively using eye movements only. To investigate this possibility, we developed an eye tracking task load detection system and tested its effectiveness. Our results revealed that our task load detection system reliably predicted increased task demand from users' eye movement data. These results and their implications for research and practice are discussed.
Automatic superpixel-based segmentation method for breast ultrasound images

Automatic and accurate breast ultrasound (BUS) image segmentation is crucial to achieve effective ultrasound-based computer aided diagnosis (CAD) systems for breast cancer. However, segmenting the tumor in BUS images is often challenging due to several artifacts that degrade the quality of ultrasound images. In this study, a new two-phase method is proposed to enable automatic and accurate segmentation of BUS images by decomposing the image into superpixels with high boundary recall ratio and employing edge- and region-based information to outline the tumor. The first phase of the method obtains an initial outline of the tumor by decomposing the BUS image into coarse superpixels to enable effective estimation of their tumor likelihoods and employing a customized graph cuts algorithm to segment the superpixels. The segmentation of the superpixels is carried out using edge-based information that quantifies the image contour cue and region-based information that characterizes the texture content of the superpixels. In the second phase, the tumor outline is improved by decomposing the BUS image into fine superpixels that enable high boundary recall ratio and employing the customized graph cuts algorithm to segment the superpixel located around the initial tumor outline. Furthermore, an edge-based active contour model is used to smooth the tumor outline. The performance of the proposed method was evaluated using a database that includes 160 BUS images (86 benign and 74 malignant). The results indicate that the first phase of the proposed method was able to detect the tumor in all BUS images and obtain mean values of the true positive ratio (TPR), false positive ratio (FPR), false negative ratio (FNR), similarity ratio (SIR), Hausdorff error (HE), and mean absolute error (ME) equal to 91.68, 11.16, 8.32, 84.52, 17.59, and 4.67, respectively. In fact, the results obtained by the first phase of the proposed method outperform four existing BUS image segmentation algorithms. Moreover, the second phase of the proposed method was able to improve the tumor outlines of the first phase and achieve mean TPR, FPR, FNR, SIR, HE, and ME values of 96.04, 7.99, 3.96, 91.41, 11.66, and 3.65, respectively. These results suggest the feasibility of employing the proposed method, which enables automatic and accurate tumor segmentation in BUS images, to develop effective CAD systems for breast cancer.
Using Multilayer Fuzzy Cognitive Maps to diagnose Autism Spectrum Disorder

Autism Spectrum Disorder (ASD) is comprised of a group of heterogeneous neurodevelopmental conditions, typically characterized by a triad of symptoms consisting of (1) impaired communication, (2) restricted interests, and (3) repetitive and stereotypical behavior pattern. An accurate and early diagnosis of autism can provide the basis for an appropriate educational and treatment program. In this work, we propose a computational model using a Multilayer Fuzzy Cognitive Map (hereafter referred to as MFCM) based on standardized behavioral assessments diagnosing the ASD (MFCM-ASD). The two standards used in the model are: the Autism Diagnostic Observation Schedule, Second Edition (ADOS2), and the Autism Diagnostic Interview Revised (ADIR). The MFCM's are a soft computing technique characterized by robust properties that make it an effective technique for medical decision support systems. For the evaluation of the MFCM-ASD model, we have used real datasets of diagnosed cases, so as to compare against other method/approaches. Initial experiments demonstrated that the proposed model outperforms conventional Fuzzy Cognitive Maps (FCMs) for ASD diagnosis. Our MFCM-ASD model serves as a diagnostic tool required to support the medical decisions when determining the correct diagnosis of Autism in children with different cognitive characteristics.
Risk-DTRRT-Based Optimal Motion Planning Algorithm for Mobile Robots

In a human-robot coexisting environment, reaching the target place efficiently and safely is pivotal for a mobile service robot. In this paper, a Risk-based Dual-Tree Rapidly exploring Random Tree (Risk-DTRRT) algorithm is proposed for the robot motion planning in a dynamic environment, which provides a homotopy optimal trajectory on the basis of a heuristic trajectory. A dual-tree framework consisting of an RRT tree and a rewired tree is proposed for the trajectory searching. The RRT tree is a time-based tree, considering the future trajectory predictions of the pedestrians, and this tree is utilized to generate a heuristic trajectory. However, the heuristic trajectory is usually nonoptimal. Then, a line-of-sight (LoS) control checking algorithm is proposed to detect whether two time-based nodes can be rewired with the least cost. On the basis of the LoS control checking algorithm, a tree rewiring algorithm is proposed to optimize the heuristic trajectory. The tree generated in the tree rewiring process is called the rewired tree. The trajectory generated by the Risk-DTRRT algorithm proves to be optimal in the homotopy class of the heuristic trajectory. The navigation run time and the lengths of the planned trajectories are selected to demonstrate the effectiveness of the proposed algorithm. The experimental results in both simulation studies and real-world implementations reveal that our proposed method achieves convincing performance in both static and dynamic environments. Note to Practitioners-This paper is motivated by planning optimized trajectories for the mobile service robots in dynamic environments with pedestrians. In this area, the sampling-based motion planning algorithms have been widely used for their high efficiency and robustness. However, the real-time optimality of the motion planning cannot be guaranteed due to the challenges caused by the moving pedestrians. In this paper, we propose a dual-tree framework to solve this problem. First, a classic Rapidly exploring Random Tree (RRT) is constructed to generate a heuristic trajectory. Then, instead of reconnecting the nodes on the heuristic trajectory directly, a rewired tree is built to optimize the heuristic trajectory. This proposed dual-tree framework can fully exploit the information of the RRT tree and ensure the completeness of the motion planning. The proposed motion planning algorithm also considers the constraints of the nonholonomic mobile robots, and it can be applied in most mobile service robots to improve their motion planning quality.
Total variation and high-order total variation adaptive model for restoring blurred images with Cauchy noise

In this paper, we propose a novel model to restore an image corrupted by blur and Cauchy noise. The model is composed of a data fidelity term and two regularization terms including total variation and high-order total variation. Total variation provides well-preserved edge features, but suffers from staircase effects in smooth regions, whereas high-order total variation can alleviate staircase effects. Moreover, we introduce a strategy for adaptively selecting regularization parameters. We develop an efficient alternating minimization algorithm for solving the proposed model. Numerical examples suggest that the proposed method has the advantages of better preserving edges and reducing staircase effects.
A Systematic Literature Review on big data for solar photovoltaic electricity generation forecasting

Solar power is expected to play a substantial role globally, due to it being one of the leading renewable electricity sources for future use. Even though the use of solar irradiation to generate electricity is currently at a fast deployment pace and technological evolution, its natural variability still presents an important barrier to overcome. Machine learning and data mining techniques arise as alternatives to aid solar electricity generation forecast reducing the impacts of its natural inconstant power supply. This paper presents a literature review on big data models for solar photovoltaic electricity generation forecasts, aiming to evaluate the most applicable and accurate state-of-art techniques to the problem, including the motivation behind each project proposal, the characteristics and quality of data used to address the problem, among other issues. A Systematic Literature Review (SLR) method was used, in which research questions were defined and translated into search strings. The search returned 38 papers for final evaluation, affirming that the use of these models to predict solar electricity generation is currently an ongoing academic research question. Machine learning is widely used, and neural networks is considered the most accurate algorithm. Extreme learning machine learning has reduced time and raised precision.
Facial expression recognition using iterative universum twin support vector machine

Facial expressions are one of the most important characteristics of human behaviour. They are very useful in applications on human computer interaction. To classify facial emotions, different feature extraction methods are used with machine learning techniques. In supervised learning, information about the distribution of data is given by data points not belonging to any of the classes. These data points are known as universum data. In this work, we use universum data to perform multiclass classification of facial emotions from human facial images. Moreover, the existing universum based models suffer from the drawback of high training cost, so we propose an iterative universum twin support vector machine (IUTWSVM) using Newton method. Our IUTWSVM gives good generalization performance with less computation cost. To solve the optimization problem of proposed IUTWSVM, no optimization toolbox is required. Further, improper selection of universum points always leads to degraded performance of the model. For generating better universum, a novel scheme is proposed in this work based on information entropy of data. To check the effectiveness of proposed IUTWSVM, several numerical experiments are performed on benchmark real world datasets. For multiclass classification of facial emotions, the performance of IUTWSVM is compared with existing algorithms using different feature extraction techniques. Our proposed algorithm shows better generalization performance with less training cost in both binary as well as multiclass classification problems.
A scalable and robust trust-based nonnegative matrix factorization recommender using the alternating direction method

Matrix Factorization (MF) has been proven to be an effective approach for the generation of a successful recommender system. However, most current MF-based recommenders cannot obtain high prediction accuracy due to the sparseness of the user–item matrix in collaborative filtering models. Moreover, they suffer from scalability issues when applied to large-scale real-world tasks. To tackle these issues, a social regularization method, called TrustANLF, is proposed, which incorporates users’ social trust information in a nonnegative matrix factorization framework. The proposed method integrates trust statements as an additional information source along with rating values into the recommendation model to deal with the data sparsity and cold-start issues. Moreover, the alternating direction optimization method is used for solving the trust-based nonnegative MF model in order to improve convergence speed as well as reduce computational and memory costs. To evaluate the effectiveness of the proposed method, several experiments are performed on three real-world datasets. The obtained results demonstrate the significant improvements of the proposed method over several state-of-the-art methods.