Reviewerly/exHarmony huggingface dataset:
Definition of dynamic characteristics of pointer measuring devices on the basis of automatic indications determination

The article presents the method and algorithm of automatic pointer measuring devices (voltmeter, manometer, metronomes etc.) indications determination in order to determine their dynamic characteristics with the help of web-camera and personal computer.The results of testing and experimental research of developed tool for determining the dynamic characteristics of pointer measuring devices are given.Using this method, the algorithm and the software developed, the process of determining the dynamic characteristics of the pointer measuring devices was automated.The time of recognition and calculation of one measured value for a dual-core processor and webcam with a resolution of 0.3 Mp averages 250-330 ms.
Understanding user representations, a new development path for supporting Smart City policy: Evaluation of the electric car use in Lorraine Region

Today electric car and Smart Grid are potential elements of any 'Smart City' initiative. According to several recent studies, French citizens are ready to purchase an electric car. Despite of these positive figures, sales of the electric cars are not really taking-off, but slightly increasing. Although, previous studies have identified motivation and barriers for the diffusion of electric cars (EC), we hypothesise, that the analysis of the a priori knowledge of the actors, in particular the users, can be enriched and it will provide a new understanding of the spread of EC. However, there is no studies explaining the types of representations people have of the EC, particularly for non-users, in order to have a better consideration of individuals, not so much as consumers or users, but rather as citizens. Thus, this paper explores the determining factors that determine how social groups perceive the emerging technology of EC and thus provide elements about a potential acceptability enabling its dissemination. In order to achieve this, this paper proposes a new approach based on the use of the AttrakDiff-2 questionnaire, a specific User eXperience (UX) method. This method assesses four different but complementary aspects of the social representation: the possibility of action of the subject on the system; the stimulation, caused, by the system; how the user will identify with the system and, the overall attraction. Data about the social representations of electric car for 131 potential users in the Lorraine Region of France were collected and analysed. Results show that there are differences of user representation regarding the socio-demographic variables. The proposed approach will provide relevant inputs to the infrastructure network design and Smart City policy decision makers.
On the Bruhat order of labeled graphs

We investigate two Bruhat (partial) orders on graphs with vertices labeled 1 , 2 , … , n and with a specified degree sequence R , equivalently, symmetric ( 0 , 1 ) -matrices with zero trace and a specified row sum vector R (adjacency matrices of such graphs). One is motivated by the classical Bruhat order on permutations while the other one, more restrictive, is defined by a switch of a pair of disjoint edges. In the Bruhat order, one seeks to concentrate the edges of a graph with a given degree sequence among the vertices with smallest labels, thereby producing a minimal graph in this order. We begin with a discussion of graphs whose isomorphism class does not change under a switch. Then we are interested in when the two Bruhat orders are identical. For labeled graphs of regular degree k , we show that the two orders are identical for k ≤ 2 but not for k = 3 .
A formal model for Multi Software Product Lines

A Software Product Line (SPL) is a family of similar programs generated from a common artifact base. A Multi SPL (MPL) is a set of interdependent SPLs that are typically managed and developed in a decentralized fashion. Delta-Oriented Programming (DOP) is a flexible and modular approach to implement SPLs. This paper presents new concepts that extend DOP to support the implementation of MPLs. These extensions aim to accommodate compositional analyses. They are presented by means of a core calculus for delta-oriented MPLs of Java programs. Suitability for MPL compositional analyses is demonstrated by compositional reuse of existing SPL analysis techniques.
Utilizing multi-source data in popularity prediction for shop-type recommendation

It is important for an investor to determine the most suitable shop type (e.g., restaurant, cafe) given a location. Traditionally, investors determine shop types based on their subjective judgments and perceptions. However, insufficient information and cognitive limitation often lead to flawed decisions and increase investment risks. With advances in information technology, multi-source heterogeneous information and big data analytics can be utilized to provide support for making such a decision. In this paper, we propose a novel shop-type recommendation method that suggests a suitable shop type based on multi-source information collected from a business review site, a location-based navigation system and a mobile carrier. Specifically, our method first constructs the location-type matrix to alleviate the problem of incomplete data and models the location profile by considering internal and external features simultaneously. In particular, a hybrid similarity model is proposed to integrate the location profile and the commercial structure into a unified framework. Then, a location-based collaborative filtering method is developed to predict shop popularity and suggest a suitable shop type. Finally, we demonstrate the effectiveness of our method compared to several benchmark methods by applying it to a real-world dataset from China.
Towards an Approach for Modelling Uncertain Theory of Mind in Multi-Agent Systems

Applying Theory of Mind to multi-agent systems enables agents to model and reason about other agents' minds. Recent work shows that this ability could increase the performance of agents, making them more efficient than agents that lack this ability. However, modelling others agents' minds is a difficult task, given that it involves many factors of uncertainty, e.g., the uncertainty of the communication channel, the uncertainty of reading other agents correctly, and the uncertainty of trust in other agents. In this paper, we explore how agents acquire and update Theory of Mind under conditions of uncertainty. To represent uncertain Theory of Mind, we add probability estimation on a formal semantics model for agent communication based on the BDI architecture and agent communication languages.
PSDSD-A Superpixel Generating Method Based on Pixel Saliency Difference and Spatial Distance for SAR Images

Superpixel methods are widely used in the processing of synthetic aperture radar (SAR) images. In recent years, a number of superpixel algorithms for SAR images have been proposed, and have achieved acceptable results despite the inherent speckle noise of SAR images. However, it is still difficult for existing algorithms to obtain satisfactory results in the inhomogeneous edge and texture areas. To overcome those problems, we propose a superpixel generating method based on pixel saliency difference and spatial distance for SAR images in this article. Firstly, a saliency map is calculated based on the Gaussian kernel function weighted local contrast measure, which can not only effectively suppress the speckle noise, but also enhance the fuzzy edges and regions with intensity inhomogeneity. Secondly, superpixels are generated by the local k-means clustering method based on the proposed distance measure, which can efficiently sort pixels to different clusters. In this step, the distance measure is calculated by combining the saliency difference and spatial distance with a proposed adaptive local compactness parameter. Thirdly, post-processing is utilized to clean up small segments. The evaluation experiments on the simulated SAR image demonstrate that our proposed method dramatically outperforms four state-of-the-art methods in terms of boundary recall, under-segmentation error, and achievable segmentation accuracy under almost all of the experimental parameters at a moderate segment speed. The experiments on real-world SAR images of different sceneries validate the superiority of our method. The superpixel results of the proposed method adhere well to the contour of targets, and correctly reflect the boundaries of texture details for the inhomogeneous regions.
A trend study on the impact of social media in decision making

Social media has grown steadily during the last decade and it is now considered as a new opportunity to use for different purposes such as decision making.The primary objective of this paper is to review articles related to social media and decision making using manual and bibliometrics analysis methods, and to identify top themes in these articles.We have reviewed the papers published between 2008 and the first month of 2019 in Scopus where 1,159 articles were published in this period.These articles come from 733 sources and 3,459 authors.According to our survey, United States is the most productive country.Moreover, most collaborations occurred between two countries of United States and United Kingdom as well as between United States and China.The bibliometrics analysis examines global research in this field from the different point of views.
Calibration of position and orientation for point light source synchronously with single image in photometric stereo

In this paper, a calibration method for non-isotropic point light source is developed, which is capable of calibrating position and orientation of the point light source with a single image synchronously. Based on the bidirectional reflectance distribution function (BRDF), the relationship between the shape of observed surface and the recorded grayscale image is firstly investigated in a more accurate way. Based on this model, a cost function is proposed to evaluate the estimation error, and a new cumulative, error-free iteration method is designed to recursively estimate the point light source parameters. The average simulation rebuilding root mean square error (RMSE) is below 0.9. The experiments are performed by calibrating a point light source in a photometric stereo measurement system, and the corresponding RMSE is below 1.2 in the unit of grayscale. Further analysis shows one image is enough to achieve the precision calibration using our proposed method.
Theoretical evidence for adversarial robustness through randomization: the case of the Exponential family.

This paper investigates the theory of robustness against adversarial attacks. It focuses on the family of randomization techniques that consist in injecting noise in the network at inference time. These techniques have proven effective in many contexts, but lack theoretical arguments. We close this gap by presenting a theoretical analysis of these approaches, hence explaining why they perform well in practice. More precisely, we provide the first result relating the randomization rate to robustness to adversarial attacks. This result applies for the general family of exponential distributions, and thus extends and unifies the previous approaches. We support our theoretical claims with a set of experiments.
On the Formal Semantics of Theory of Mind in Agent Communication

Recent studies have shown that applying Theory of Mind to agent technologies enables agents to model and reason about other agents' minds, making them more efficient than agents that do not have this ability or agents that have a more limited ability of modelling others' minds. Apart from the interesting results of combining Theory of Mind and agent technologies, an important premise has not been yet fully investigated in the AI literature: how do agents acquire and update their models of others' minds? In the context of multi-agent systems, one of the most natural ways in which agents can acquire models of other agents' mental attitudes is through communication. In this work, we propose an operational semantics for agents to update Theory of Mind through communication. We not only make our formalisation broadly applicable by defining a formal semantics based on components from the BDI architecture, but we also implement our approach in an agent-oriented programming language that is based on that architecture.
Programming quantum computers

This tutorial provides a hands-on introduction to quantum computing. It will feature the three pillars, architectures, programming, and algorithms/applications of quantum computing. Its focus is on the applicability of problems to quantum computing from a practical point, with only the necessary foundational coverage of the physics and theoretical aspects to understand quantum computing. Simulation software will be utilized complemented by access to actual quantum computers to prototype problem solutions. This should develop a better understanding of how problems are transformed into quantum algorithms and what programming language support is best suited for a given application area. As a first of its kind, to the best of our knowledge, the tutorial includes hands-on programming experience with IBM Q and D-Wave hardware.
AE and EA robustness of interval circulant matrices in max-min algebra

Max-min algebra is defined as a linearly ordered set with two binary operations. Classical addition and multiplication are replaced by maximum and minimum, respectively. A square matrix is robust, if its eigenspace is achieved starting at arbitrary vector and it is X-robust if its eigenspace is achieved starting at each vector from a given interval vector X. The EA and AE robustness and X-robustness of interval circulant matrices over max-min algebra are defined. Polynomial algorithms for checking these types of robustness and X-robustness are given.
Adaptive Condensed Nearest Neighbor for Imbalance Data Classification

Classification over numerous real-world datasets has a peculiar drawback called unstructured class problem.A dataset is said to be unstructured when the majority of the class has more samples insignificantly than the minor class.Such drawbacks result in an ineffective performance of data classification techniques.Classification is a supervised learning method which acquires a training dataset to form its model for classifying unseen examples.However, in unstructured data classification, the class boundary learned by standard machine learning algorithms can be severely skewed toward the target class.As a result, the false-negative rate can be excessively high.The researches focus on the unstructured data classification using uncertain Nearest Neighbor (NN) decision rule and also found the major issues face by k-Nearest Neighbor (k-NN).In any case, given a dataset, prediction of accuracy is a monotonous task to improve the execution of kNN by tuning .Because of class imbalance, the performance of kNN decreases and this situation is represented by dissimilar characteristic from various classes.This paper addresses the issues faced by kNN by developing Adaptive-Condensed NN (Ada-CNN).The Ada-CNN classifier utilizes the distribution and density of test point's neighborhood and learn an appropriate point-explicit by using artificial neural systems.Ada-CNN performed well compared to kNN and other well-known classifiers.The experimental results showed that Ada-CNN achieved nearly 94% accuracy for Diabetes dataset and 100% accuracy in pop-failure compared to kNN for imbalanced classification.
Estimation of Pedestrian Pose Orientation Using Soft Target Training Based on Teacher–Student Framework

Semi-supervised learning is known to achieve better generalisation than a model learned solely from labelled data. Therefore, we propose a new method for estimating a pedestrian pose orientation using a soft-target method, which is a type of semi-supervised learning method. Because a convolutional neural network (CNN) based pose orientation estimation requires large numbers of parameters and operations, we apply the teacher–student algorithm to generate a compressed student model with high accuracy and compactness resembling that of the teacher model by combining a deep network with a random forest. After the teacher model is generated using hard target data, the softened outputs (soft-target data) of the teacher model are used for training the student model. Moreover, the orientation of the pedestrian has specific shape patterns, and a wavelet transform is applied to the input image as a pre-processing step owing to its good spatial frequency localisation property and the ability to preserve both the spatial information and gradient information of an image. For a benchmark dataset considering real driving situations based on a single camera, we used the TUD and KITTI datasets. We applied the proposed algorithm to various driving images in the datasets, and the results indicate that its classification performance with regard to the pose orientation is better than that of other state-of-the-art methods based on a CNN. In addition, the computational speed of the proposed student model is faster than that of other deep CNNs owing to the shorter model structure with a smaller number of parameters.
Optimization and Technical Validation of the AIDE-MOI Fall Detection Algorithm in a Real-Life Setting with Older Adults

Falls are the primary contributors of accidents in elderly people. An important factor of fall severity is the amount of time that people lie on the ground. To minimize consequences through a short reaction time, the motion sensor “AIDE-MOI” was developed. “AIDE-MOI” senses acceleration data and analyzes if an event is a fall. The threshold-based fall detection algorithm was developed using motion data of young subjects collected in a lab setup. The aim of this study was to improve and validate the existing fall detection algorithm. In the two-phase study, twenty subjects (age 86.25 ± 6.66 years) with a high risk of fall (Morse &gt; 65 points) were recruited to record motion data in real-time using the AIDE-MOI sensor. The data collected in the first phase (59 days) was used to optimize the existing algorithm. The optimized second-generation algorithm was evaluated in a second phase (66 days). The data collected in the two phases, which recorded 31 real falls, was split-up into one-minute chunks for labelling as “fall” or “non-fall”. The sensitivity and specificity of the threshold-based algorithm improved significantly from 27.3% to 80.0% and 99.9957% (0.43) to 99.9978% (0.17 false alarms per week and subject), respectively.
Segmentation of pulmonary vascular tree by incorporating vessel enhancement filter and variational region-growing

BACKGROUND:Automatic segmentation of pulmonary vascular tree in the thoracic computed tomography (CT) image is a promising but challenging task with great clinical potential values. It is difficult to segment the whole vascular tree in reasonable time and acceptable accuracy. OBJECTIVE:To develop a novel pulmonary vessel segmentation approach by incorporating vessel enhancement filters and the anisotropic diffusion filter with the variational region growing. METHODS:First, the airway wall from the lung lobes is eliminated from CT images by using multi-scale morphological operations. Second, a Hessian-based multi-scale vesselness filter and medialness filter are applied to detect and enhance the potential vessel. Third, an anisotropic diffusion filter is used to remove noise and enhance the tube-like structures in CT images. Last, the vascular tree is segmented by applying variational region growing algorithm. RESULTS:Applying to the CT images collected from the entire dataset of VESSEL12 challenge, we achieved an average sensitivity of 92.9%, specificity of 91.6% and the area under the ROC curve of AUC = 0.972. CONCLUSIONS:This study demonstrated feasibility of segmenting the pulmonary vessel effectively by incorporating vessel enhancement filters and the anisotropic diffusion filter with the variational region growing algorithm. Our method cannot only segment both large and peripheral vessels, but also distinguish the vessels from the adjacent tissues, especially the airway walls.
Optimal Image-Based Guidance of Mobile Manipulators using Direct Visual Servoing

This paper presents a direct image-based controller to perform the guidance of a mobile manipulator using image-based control. An eye-in-hand camera is employed to perform the guidance of a mobile differential platform with a seven degrees-of-freedom robot arm. The presented approach is based on an optimal control framework and it is employed to control mobile manipulators during the tracking of image trajectories taking into account robot dynamics. The direct approach allows us to take both the manipulator and base dynamics into account. The proposed image-based controllers consider the optimization of the motor signals sent to the mobile manipulator during the tracking of image trajectories by minimizing the control force and torque. As the results show, the proposed direct visual servoing system uses the eye-in-hand camera images for concurrently controlling both the base platform and robot arm. The use of the optimal framework allows us to derive different visual controllers with different dynamical behaviors during the tracking of image trajectories.
Effects of display technologies on operation performances and visual fatigue

The use of 3D stereoscopic display technology in all aspects of life has attracted considerable research attention. However, only few studies have simultaneously considered the effects of stereoscopic display technologies on visual fatigue and operating performances. This study with 36 participants (50% females; mean age = 23.17 years, SD = 3.3) analyzed the effects of display technologies, task complexity, and use time on operation performances, subjective and objective visual fatigue. Results indicated that display technology did not significantly affect the operation performances, whereas task complexity did. However, no interaction took place between them. Display technology had no significant effects on subjective visual fatigue and near-point accommodation (NPA) but affected critical fusion frequency (CFF) significantly. Use time had significant effects on both subjective and objective visual fatigue.
A multisource fusion framework driven by user-defined knowledge for egocentric activity recognition

Recently, egocentric activity recognition has attracted considerable attention in the pattern recognition and artificial intelligence communities because of its widespread applicability to human systems, including the evaluation of dietary and physical activity and the monitoring of patients and older adults. In this paper, we present a knowledge-driven multisource fusion framework for the recognition of egocentric activities in daily living (ADL). This framework employs Dezert-Smarandache theory across three information sources: the wearer's knowledge, images acquired by a wearable camera, and sensor data from wearable inertial measurement units and GPS. A simple likelihood table is designed to provide routine ADL information for each individual. A well-trained convolutional neural network is then used to produce a set of textual tags that, along with routine information and other sensor data, are used to recognize ADLs based on information theory-based statistics and a support vector machine. Our experiments show that the proposed method accurately recognizes 15 predefined ADL classes, including a variety of sedentary activities that have previously been difficult to recognize. When applied to real-life data recorded using a self-constructed wearable device, our method outperforms previous approaches, and an average accuracy of 85.4% is achieved for the 15 ADLs.