Reviewerly/exHarmony huggingface dataset:
Robot Use for Older Adults – Attitudes, Wishes and Concerns. First Results from Switzerland

Due to demographic change, the increasing shortage of skilled professionals in the care sector, and the desire of older adults to stay independent as long as possible, it is predicted that robots will be used more often in the future to support older adults. To achieve accepted solutions, the opinions and concerns of the population need to be considered. Studies so far have shown different results, focus on a specific type of robot, and there is little knowledge about the attitudes and concerns of the population regarding the general use of robots for older adults. Based on established models of technology and robot acceptance, a questionnaire was compiled. To gain nationwide representative data, the survey includes questions on different factors of robot acceptance, preferred appearance, preferred functions and field of application, as well as ethical considerations. Here, data for Switzerland is published for the first time and evaluated descriptively. So far, 189 participants answered the survey, 57.6% of them women. Most participants had a positive attitude towards robots and would personally use a robot. But some functions are judged to be more conceivable than others. The main ethical concerns were a lack of interpersonal contact or problems with data security. The data collection will continue to achieve representativeness and to provide an overview of the attitudes and concerns about the implementation of robots for older adults in Switzerland and will be the basis for later in-depth analyses.
Jacamo-Web is on the Fly: An Interactive Multi-Agent System IDE

This paper presents jacamo-web, an interactive programming IDE for developing Multi-Agent Systems (MAS). The standard programming method usually follows the sequence of compile, link and execute the application. Alternatively, the so-called interactive programming provides a way to modify a system while it is running. Besides saving developing time, it maintains the system’s availability since the application is kept running while it is being changed. To illustrate jacamo-web interactive functions, we have developed a MAS for the financial market. It checks data of companies and applies known formulae to suggest whether to buy assets or not.
Application of Fuzzy Logic and PID Controller for Mobile Robot Navigation

Mobile Robot navigation in real environment, detecting and avoiding static and dynamic obstacles is still a challenging task even counting new technology development. This consists, on methods and algorithms applied in order to achieve most optimal results. We present application of Fuzzy Logic Controller (FLC) and PID controller, which will deal with, signal processing of three sensors as inputs of FLC and two outputs which are left and right motors as actuators. Outputs from FLC will be sent to PID controller in order to get better and more accurate results for mobile robot navigation respectively more exact robot movement. In this paper is presented, a navigation of the mobile robot via FLC and PID controller.
Optimal Camera Point Selection Toward the Most Preferable View of 3-D Human Pose

Answering the question “what is the most preferable view of a three-dimensional (3-D) human model?” is a challenge in computer vision, computer graphics, and cinematography applications because the appearance of a human, for a given pose, relies on the viewpoint of the user. Currently, to the best of the authors’ knowledge, solid research on the most preferable viewing angle for obtaining numerical subjective evaluation scores has not been conducted. In this study, we investigate a metric that can be used to quantify the view of a 3-D human model, whose value is maximized at the most favorable camera angle in accordance with subjective assessments done by users. For an objective assessment in a numerical form, in this study, we define three view selection metrics: the 1) <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">normalized limb length sum</i> ; 2) <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">normalized area of a two-dimensional bounding box</i> ; and 3) <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">normalized visible area of a 3-D bounding box</i> . Finally, we formulate a viewpoint optimization problem whose objective function is the sum of the metrics. However, the objective function is nonconcave, and the solution set of the constraint is nonconvex. To overcome this difficulty, we employ decomposition and penalty methods. From the simulation results, it is verified that the average of the viewpoint selection error between the ground truth viewpoint and the optimal viewpoint obtained by the proposed algorithm is very close to the lower bound of the viewpoint selection error.
Deploying Wifi, RF and BLE sensors for pervasive monitoring and control

This paper presents the structure and the main functionalities of a cyber-physical-system, denoted as CPSc, to monitor and control common applications in the field of domotics and healthcare. From the features to be owned by such CPSc, the paper derives some guidelines for their implementation. In particular, the paper suggests: i) the use of the secured version of the Message Queuing Telemetry Transport (MQTT) protocol to collect data taken by the sensors and to send commands to the actuators, ii) how to use the main types of devices, i.e., Wifi, RF and Bluetooth Low Energy (BLE), for pervasive monitoring and control, and iii) how to use an automation system integrated with MQTT to react timely to undesired events. The use of smartphones for data visualization and not for bridging control services and sensors/actuators is widely discussed. Also, the paper illustrates how CPSc may be implemented and some code examples are provided to contribute to the implementation. Alternative solutions to be compared in further studies are outlined. The proposed architecture is under testing in the I-HOSP project, an initiative supported by EC Regional Funds aiming to deploy a healthcare system to assist remotely patients affected by risky pathologies.
Wigner function and photon number distribution of a superradiant state in semiconductor heterostructures

Abstract Advanced quantum technologies require sources of non-Gaussian and non-classical light. For the understanding of properties of quantum light it is necessary to reconstruct its quantum state. Here, we use time-domain optical homodyne tomography for the quantum state recognition and reconstruction of the femtosecond optical field from a nonequilibrium superradiant coherent electron–hole state formed in a semiconductor GaAs/AlGaAs heterostructure. We observe severe deviations from the Poissonian statistics of the photons associated with the coherent state when the transformation from lasing to superradiance occurs. The estimated Mandel parameter Q of the superradiant states is in the range of 1.08–1.89. The reconstructed Wigner functions show large areas of negative values, a characteristic sign of non-classicality, demonstrating the quantum nature of the generated superradiant emission. The photon number distribution and Wigner function of the superradiant state are very similar to those of the displaced Fock state.
Challenging software developers: dialectic as a foundation for security assurance techniques

Abstract Development teams are increasingly expected to deliver secure code, but how can they best achieve this? Traditional security practice, which emphasizes ‘telling developers what to do’ using checklists, processes and errors to avoid, has proved difficult to introduce. From analysis of industry interviews with a dozen experts in app development security, we find that secure development requires ‘dialectic’: a challenging dialog between the developers and a range of counterparties, continued throughout the development cycle. Analysing a further survey of 16 industry developer security advocates, we identify the six assurance techniques that are most effective at achieving this dialectic in existing development teams, and conclude that the introduction of these techniques is best driven by the developers themselves. Concentrating on these six assurance techniques, and the dialectical interactions they involve, has the potential to increase the security of development activities and thus improve software security for everyone.
SMSS: Secure Member Selection Strategy in Federated Learning

Data security and user privacy-issue have become an important field. As federated learning (FL) could solve the problems from data security and privacy-issue, it starts to be applied in many different applied machine learning tasks. However, FL does not verify the quality of the data from different parties in the system. Hence, the low-quality datasets with fewer common entities can be cotrained with others. This could result in a huge amount of computing-resources waste, and the attack on the FL model from malicious clients as federal members. To solve this problem, this article proposes a secure member selection strategy (SMSS), which can evaluate the data qualities of members before training. With SMSS, only datasets share more common entities than a certain threshold can be selected for learning, whereas malicious clients with fewer common objects cannot acquire any information about the model. This article implements SMSS, and evaluate its performance via several extensive experiments. Experimental results demonstrate that SMSS is safe, efficient, and effective.
O-Flocking: Optimized Flocking Model on Autonomous Navigation for Robotic Swarm

Flocking model has been widely used in robotic swarm control. However, the traditional model still has some problems such as manually adjusted parameters, poor stability and low adaptability when dealing with autonomous navigation tasks in large-scale groups and complex environments. Therefore, it is an important and meaningful research problem to automatically generate Optimized Flocking model (O-flocking) with better performance and portability. To solve this problem, we design Comprehensive Flocking (C-flocking) model which can meet the requirements of formation keeping, collision avoidance of convex and non-convex obstacles and directional movement. At the same time, Genetic Optimization Framework for Flocking Model (GF) is proposed. The important parameters of C-flocking model are extracted as seeds to initialize the population, and the offspring are generated through operations such as crossover and mutation. The offspring model is input into the experimental scene of autonomous navigation for robotic swarms, and the comprehensive fitness function value is obtained. The model with smallest value is selected as the new seed to continue evolution repeatedly, which finally generates the O-flocking model. The extended simulation experiments are carried out in more complex scenes, and the O-flocking and C-flocking are compared. Simulation results show that the O-flocking model can be migrated and applied to large-scale and complex scenes, and its performance is better than that of C-flocking model in most aspects.
Image Defogging Algorithm Based on Sparse Representation

Aiming at the problems of low contrast and low definition of fog degraded image, this paper proposes an image defogging algorithm based on sparse representation. Firstly, the algorithm transforms image from RGB space to HSI space and uses two-level wavelet transform extract features of image brightness components. Then, it uses the K-SVD algorithm training dictionary and learns the sparse features of the fog-free image to reconstructed I-components of the fog image. Using the nonlinear stretching approach for saturation component improves the brightness of the image. Finally, convert from HSI space to RGB color space to get the defog image. Experimental results show that the algorithm can effectively improve the contrast and visual effect of the image. Compared with several common defog algorithms, the percentage of image saturation pixels is better than the comparison algorithm.
A Context-Aware Assistance Framework for Implicit Interaction with an Augmented Human

The automotive industry is currently facing massive challenges. Shorter product life cycles together with mass customization lead to a high complexity for manual assembly tasks. This induces the need for effective manual assembly assistances which guide the worker faultlessly through different assembly steps while simultaneously decrease their completion time and cognitive load. While in the literature a simulation-based assistance visualizing an augmented digital human was proposed, it lacks the ability to incorporate knowledge about the context of an assembly scenario through arbitrary sensor data. Within this paper, a general framework for the modular acquisition, interpretation and management of context is presented. Furthermore, a novel context-aware assistance application in augmented reality is introduced which enhances the previously proposed simulation-based assistance method by several context-aware features. Finally, a preliminary study (N = 6) is conducted to give a first insight into the effectiveness of context-awareness for the simulation-based assistance with respect to subjective perception criteria. The results suggest that the user experience is improved by context-awareness in general and the developed context-aware features were overall perceived as useful in terms of error, time and cognitive load reduction as well as motivational increase. However, the developed software architecture offers potential for improvement and future research considering performance parameters is mandatory.
Reconfigurable Convolution Architecture for Heterogeneous Systems-on-Chip

The image convolution is a crucial task in several computer vision applications. Modern Convolutional Neural Networks (CNNs) involve 2D convolution filters of different sizes over a set of input images with various resolutions. Unfortunately, state-of-the-art hardware accelerators do not well fit the radical variations that may occur within a CNN algorithm. This paper presents a novel reconfigurable convolution architecture purposely designed to support runtime variable kernel and image sizes. Results, obtained by integrating the proposed accelerator within a Zynq Ultrascale-based embedded system targeted for inference of CNNs, show that significantly higher performance-resource and performance-power efficiencies are reached in comparison with several existing hardware designs.
Enabling Situational Awareness via Augmented Reality of Autonomous Robot-Based Environmental Change Detection

Accurately detecting changes in one's environment is an important ability for many application domains, but can be challenging for humans. Autonomous robots can easily be made to autonomously detect metric changes in the environment, but unlike humans, understanding context can be challenging for robots. We present a novel system that uses an autonomous robot performing point cloud-based change detection to facilitate information-gathering tasks and provides enhanced situational awareness. The robotic system communicates detected changes via augmented reality to a human teammate for evaluation. We present results from a fielded system using two differently-equipped robots to examine implementation questions of point cloud density and its effect on visualization of changes. Our results show that there are trade-offs between implementations that we believe will be constructive towards similar systems in the future.
An Arabic Dataset for Disease Named Entity Recognition with Multi-Annotation Schemes

This article outlines a novel data descriptor that provides the Arabic natural language processing community with a dataset dedicated to named entity recognition tasks for diseases. The dataset comprises more than 60 thousand words, which were annotated manually by two independent annotators using the inside–outside (IO) annotation scheme. To ensure the reliability of the annotation process, the inter-annotator agreements rate was calculated, and it scored 95.14%. Due to the lack of research efforts in the literature dedicated to studying Arabic multi-annotation schemes, a distinguishing and a novel aspect of this dataset is the inclusion of six more annotation schemes that will bridge the gap by allowing researchers to explore and compare the effects of these schemes on the performance of the Arabic named entity recognizers. These annotation schemes are IOE, IOB, BIES, IOBES, IE, and BI. Additionally, five linguistic features, including part-of-speech tags, stopwords, gazetteers, lexical markers, and the presence of the definite article, are provided for each record in the dataset.
E-ProSRNet: An enhanced progressive single image super-resolution approach

In recent years, the convolutional neural networks (CNNs) have been successfully applied to single image super-resolution (SISR) task. However, most of the CNN based SISR methods obtain better performance with a huge amount of training parameters which increases the computational complexity of their SISR models. Such SR networks suffer from a heavy burden on computational assets and as a result, they are no longer appropriate for many real-world applications. Hence, in the computer vision community, it is an interest to endorse an SR approach which makes use of less number of training parameters with better SR performance. In this paper, we propose a computationally efficient SR approach called enhanced progressive super-resolution network i.e., E-ProSRNet. This approach is the enhanced version of our base proposed model called ProSRNet. In E-ProSRNet model, we propose a novel enhanced parallel densely connected residual network (E-PDRN) which helps to extract rich features in the low-resolution (LR) observation. The SR performance of proposed E-ProSRNet model is better than that of ProSRNet and it uses a less number of training parameters when compared to that of ProSRNet model. The experimental analysis on common testing benchmark datasets shows that the proposed E-ProSRNet sets new state-of-the-art performance on SISR task for upscaling factor ×4. The E-ProSRNet method obtains better SR performance when compared to that obtained using proposed ProSRNet as well as the other state-of-the-art methods with significant reduction in the computational complexity.
Parallelization implementation of the m<scp>ulti‐scale</scp> retinex i<scp>mage‐enhancement</scp> algorithm based on a <scp>many integrated core</scp> platform

Summary Image‐enhancement algorithms, for example, median filtering algorithms, Gaussian filtering algorithms, and the multiscale Retinex (MSR) algorithm, are widely used in unmanned aerial vehicle (UAV) image processing to resolve the problems of poor clarity, insufficient contrast, and weak adaptability of the aerial images. Aiming to improve the low efficiency of processing a large volume of UAV images using the MSR algorithm, this research realized a parallel MSR algorithm using the OpenMP programming model based on Intel's many integrated core (MIC) platform. First, the principle and serial implementation of the MSR algorithm were reviewed in detail, and the algorithm's hotspots were determined with the Intel VTune tool. Then, the corresponding parallel algorithm was designed and implemented. After checking the correctness of the parallel algorithm, systematical experiments on UAV images of different sizes were carried out. According to the experiments performed in the course of this work, the parallel MSR interpolation algorithm attained a speedup factor of 32 on two Intel MIC acceleration cards with 60 cores, which indicated that the parallel algorithm greatly reduced processing time and maximized speed and performance.
Innovation of Business Model for Electrical Household Appliance Enterprises to Deploy IoT+AI and IoT+5G

Based on the future development trend of the Internet of Things, this article proposes a business model for the electrical household appliance enterprises to deploy IoT+AI and IoT+5G. The business model proposes that electrical household appliance enterprises should cooperate with high-tech companies that research AI and 5G, add online live on their official online website and use AI+ IoT and 5G + IoT technologies to improve online platforms, improve total quality management and warranty of smart home appliances in AI+IoT system and create new brands and products to expand the market of smart home appliances based on customers' feedback. This paper aims at providing new ideas of reform and innovation for electrical household appliances enterprises to seize the opportunity of Internet of Things in E-commerce era.
An Analytical Review of Quantum Neural Network Models and Relevant Research

Quantum Machine Learning is gaining prominence with the advent of real quantum processors. The quantum analogue of one of the most important and popular model of machine learning, namely Artificial Neural Network is studied and reviewed here in this paper. Various approaches to design and implement a quantum neural network has been studied. Most of the works done in this field involved a translation of classical neural network components into the language of quantum physics. But direct translation cannot be a solution as not every classical component has a quantum analogue. Non-linear activation functions are good examples of it. Quantum operators need to be linear and therefore finding alternate solutions to activation functions is an active area of research. Nevertheless, its advantages over the classical models are profound. Quantum superposition gives exponential storage capacity to a quantum network. Quantum parallelism can be put to use and to train the network with multiple inputs in one go. Despite being a new field, many real world applications of a quantum neural network has also been theorised. These models, their advantages, applications and limitations has been discussed in this paper.
Riding the Wave of Misclassification: How We End up with Extreme YouTube Content

Content recommendations on big internet platforms such as YouTube are supported by crowd-based recommender systems. Users are presented with what other users in comparable situations spent time on. There are several examples, however, where sequences of suggested content quickly degenerated towards only slightly related, sometimes problematic content that we define as contextually inappropriate. We try to identify the basis of this effect from both the user and the technical side, and set up a simple simulation system in order to better understand the interactions. Simulation results provide evidence that autoplay is an especially problematic feature, but that completely preventing inappropriate suggestions is technically very hard if not infeasible because it is the nature of a recommendation system to take into account feedback from the user and adapt to it. We also propose some possible measures to mitigate the problem.