Reviewerly/exHarmony huggingface dataset:
Effects of virtual environment and self-representations on perception and physical performance in redirected jumping

Redirected jumping (RDJ) allows users to explore virtual environments (VEs) naturally by scaling a small real-world jump to a larger virtual jump with virtual camera motion manipulation, thereby addressing the problem of limited physical space in VR applications. Previous RDJ studies have mainly focused on detection threshold estimation. However, the effect VE or selfrepresentation (SR) has on the perception or performance of RDJs remains unclear. In this paper, we report experiments to measure the perception (detection thresholds for gains, presence, embodiment, intrinsic motivation, and cybersickness) and physical performance (heart rate intensity, preparation time, and actual jumping distance) of redirected forward jumping under six different combinations of VE (low and high visual richness) and SRs (invisible, shoes, and human-like). Our results indicated that the detection threshold ranges for horizontal translation gains were significantly smaller in the VE with high rather than low visual richness. When different SRs were applied, our results did not suggest significant differences in detection thresholds, but it did report longer actual jumping distances in the invisible body case compared with the other two SRs. In the high visual richness VE, the preparation time for jumping with a human-like avatar was significantly longer than that with other SRs. Finally, some correlations were found between perception and physical performance measures. All these findings suggest that both VE and SRs influence users' perception and performance in RDJ and must be considered when designing locomotion techniques.
Hyperparameter Configuration Learning for Ship Detection From Synthetic Aperture Radar Images

Detecting ships from synthetic aperture radar (SAR) images is inherently subject to its imaging mechanism. With the development of deep learning, advanced learning-based techniques have been migrated from optical images to SAR images. However, the default hyperparameters (e.g., learning rate, size of the anchor box) predefined by a heuristic strategy on optical images might be suboptimal for SAR datasets. In addition, the low-quality imaging in SAR images further reduces the portability of hyperparameters. To solve this problem, a new optimization method, named reinforcement learning and hyperband (RLH), is proposed to dynamically learn hyperparameter configurations by deep reinforcement learning (DRL), where a neural network is adopted to capture the relationship between different configurations and predict new configurations to further improve the performance. Hyperparameter configuration is able to be automatically learned to accommodate various SAR image datasets, and experiments on two SAR image datasets demonstrate the effectiveness and advantage of the proposed approach.
Mind Relaxation Chatbot for University Students by Using Dense Neural Network

Relaxation is the emotional state of a living being, of low tension, in which there is an absence of arousal that could come from sources such as anger, anxiety, or fear. Technology can be used to mind relaxation. Chatbot is an automated computer software program capable of having intelligent live conversations with people. It is a technology that provides a new way to interact with computer systems. Nowadays, chatbots are very popular in a large scale of applications, especially in systems that provide intelligence support to the user. It is one of the technologies that has been used successfully in many fields such as education, marketing and health field. This paper aims to develop such a chatbot for the mind relaxation of university students using Natural Language processing techniques and Dense Neural Network. The chatbot tool was trained with a series of counseling conversations in text form. Training phases included intents, tags, patterns and responses. Primary function of chatbot have to play is to understand the intents of students and to respond to them appropriately. Input and Output are in the form of the text.
Design and Optimization of a Neuro-Fuzzy System for the Control of an Electromechanical Plant

One characteristic of neuro-fuzzy systems is the possibility of incorporating preliminary information in their structure as well as being able to establish an initial configuration to carry out the training. In this regard, the strategy to establish the configuration of the fuzzy system is a relevant aspect. This document displays the design and implementation of a neuro-fuzzy controller based on Boolean relations to regulate the angular position in an electromechanical plant, composed by a motor coupled to inertia with friction (a widely studied plant that serves to show the control system design process). The structure of fuzzy systems based on Boolean relations considers the operation of sensors and actuators present in the control system. In this way, the initial configuration of fuzzy controller can be determined. In order to perform the optimization of the neuro-fuzzy controller, the continuous plant model is converted to discrete time to be included in the closed-loop controller training equations. For the design process, first the optimization of a Proportional Integral (PI) linear controller is carried out. Thus, linear controller parameters are employed to establish the structure and initial configuration of the neuro-fuzzy controller. The optimization process also includes weighting factors for error and control action in such a way that allows having different system responses. Considering the structure of the control system, the optimization algorithm (training algorithm) employed is dynamic back propagation. The results via simulations show that optimization is achieved in the linear and neuro-fuzzy controllers using different weighting values for the error signal and control action. It is also observed that the proposed control strategy allows disturbance rejection.
FP-nets as novel deep networks inspired by vision

Feature-product networks (FP-nets) are inspired by end-stopped cortical cells with FP-units that multiply the outputs of two filters. We enhance state-of-the-art deep networks, such as the ResNet and MobileNet, with FP-units and show that the resulting FP-nets perform better on the Cifar-10 and ImageNet benchmarks. Moreover, we analyze the hyperselectivity of the FP-net model neurons and show that this property makes FP-nets less sensitive to adversarial attacks and JPEG artifacts. We then show that the learned model neurons are end-stopped to different degrees and that they provide sparse representations with an entropy that decreases with hyperselectivity.
A Security Review of a Portuguese Hospital Using the Cyber Security Framework: A Case Study

The objective of the study reported by the present paper was to assess the cyber resilience state of a Portuguese hospital. For the study implementation, the Cyber Security Framework (CSF), proposed by the National Institute of Standards and Technology (NIST), was used in conjunction with the Cyber Resilience Review (CRR) tool. The results point to satisfactory levels of cyber resilience of the healthcare entity being studied, but several features need to be optimized. The results also show that the use of CSF and CRR report, generates a large quantity of objective information, which provides an exhaustive identification of aspects that should be improved.
Application of Heterogeneous Network Oriented to NoSQL Database in Optimal Postevaluation Indexes of Construction Projects

With the advent of the Internet Web 2.0 era, storage devices used to store website data are developing at an ever-increasing high-growth rate and a diversified trend. The focus on the structured data storage model has reduced the responsiveness of traditional relational databases to data changes. NoSQL database is scalable, has a powerful and flexible data model and a large amount of data, and has an increasing application potential in the memory field. Heterogeneous networks are composed of third-party computers, network equipment, and systems. Network types are usually used for other protocols to support other functions and applications. The research on heterogeneous networks can be traced back to the BARWAN project that started in 1995 at the University of California, Berkeley. The project leader RHKatz merged multiple types of nested networks for the first time to form heterogeneous network requirements for various future terminal services. Construction engineering refers to an engineering entity formed by installing pipelines and equipment that support the construction of various houses and ancillary facilities. “House construction” refers to projects with roofs, beams, columns, walls, and foundations that can form internal spaces to meet people’s needs in production, living, learning, and public activities. Among them, the engineering evaluation index is a statistical index used to evaluate and compare the quality and effects of social and economic activities through the use of equipment, such as capital turnover rate and employee labor efficiency. It is the exchange of corporate performance evaluation content and the expression of corporate performance evaluation content.
An Improved Method for Red Segmentation Based Traffic Sign Detection

The red color segmentation is widely used in image processing especially for traffic sign detection. The color segmentation under variety of light and weather conditions is a challenging task. These conditions make modifications on traffic sign colors. In this paper, we aim to improve the precision of a state-of-the-art method in RGB and HSV spaces. Although the efficiency of this state-of-the-art method, the red segmentation in RGB space has some false positive detections especially at night, and the red segmentation in HSV space detects the black pixels with red pixels as a region of interest. In order to improve the precision of this method, we propose in this paper to combine the two segmentations. We also propose changing the threshold of the red segmentation in HSV to remove non-red pixels and to extract red pixels with low saturation. In addition, we make some modifications on the threshold of the red segmentation in RGB space to improve its performance. The results obtained in experiments show that our method detects more accurately red color of traffic signs under variety of light and weather conditions compared to the state-of-the-art method.
Machine Transfer Learning Deep Softmax Regression Neural Network for Image Classification

Image classification is a major topic in image processing. Conventional algorithm had the major drawback of vanishing gradient problem with stochastic descent algorithm; to overcome this problem, a new approach called deep Softmax regression classifier neural network (DSRCNN) was developed. The proposed DSRCNN technique developed layer-wise learning that is applied to restricted Boltzmann machine for accurate image classification. Restricted Boltzmann machine contains visible unit and hidden unit to carry the image classification on CIFAR-10 and CIFAR-100 dataset. The datasets of each input image is preprocessed to reduce the noise then that are fed to the visible unit. The visible and hidden units are trained by the extracted images and forward into Softmax regression to accurately classifying an input image into multiple classes. The simulation of the proposed, DSRCNN technique metrics’ accuracy and false-positive ratio performance are compared with the other state-of-the-art methods.
Comparative Analysis of Supervised Learning Techniques of Machine Learning for Software Defect Prediction

Software Bug prediction or defect prediction is very important for the organizations to detect the bugs in the early stage of Software development process because software developers can know the vulnerable areas where the defects may be present.In this research paper we have compared different statistical techniques like Linear Regression, Naïve Bayes, Random Forest, Decision Tree, Artificial Neural Networks etc. and come up with the best among them for the Bug prediction. Comparison is made using Performance Measures like Accuracy, precision, recall and F-measure.
A Comparative Study of Different Chaotic Systems in Path Planning for Surveillance Applications

This paper compares the performance of four different chaotic systems in path planning for surveillance applications. The four investigated systems are Lorenz, Arneodo, Liu, and Chen. While the Lorenz system was employed in a similar application before, Arneodo, Liu, and Chen systems are newly introduced in this paper. A bounded-grid chaotic path planner is proposed based on the mirror mapping technique, which keeps the robot bounded in the terrain and prevents it from going outside. The effect of using different state variables of each chaotic system to control the motion angle of the robot is discussed and shown to have a significant impact on the robot’s performance. The obtained trajectory and several performance metrics show promising results of the chaotic path planner for the four systems.
Evaluation of photovoltaic potential application in urban environments using GIS-based method: the particular case of Baghdad / Iraq

Abstract This paper aims to develop a multi-criteria technique that originated in Geo-Information Systems ( GIS), horizontal radiation data and Light Detection and Ranging (LIDAR) to discover the installation potential of photovoltaic systems in an urban area and assess the output of yearly generation of electricity. In a small area in the center of Baghdad (Iraq), data that were obtained from LIDAR provide a precise representation of the urban environments through the development of a Digital Surface Model (DSM) that was utilized for computing the degree of the roofs of building local inclination and orientation using the impact of shadow for the different elements (other construction and trees). The paper introduces the possibility of distinguishing two forms of roof: for the first one, PV panels were situated parallel with the roofs, while for the second, the optimum position was obtained through the utilization of the structures to mount them. For the second case, the self-shading was considered. Incident radiation on the surface of panels is computed using a geometric technique that depends on horizontal radiation per hour, including direct and diffuse components. Finally, the evaluation of the effective production also includes panels efficiency and various sources of waste, particularly temperature.
Formation Control Strategy of Multi-agent System with Improved Probabilistic Roadmap Method Applied in Restricted Environment

In order to solve the obstacle avoidance adaptability problem of multi-agent swarm, a formation control strategy of dynamic barrier avoidance and static collision-free path was proposed. We introduce Probabilistic Roadmap Method (PRM) to solve the problems of track planning and formation strategy in restricted environment. First of all, a multi-agent formation model with fixed undirected communicative topology was established, the virtual leader was set up as the virtual control center of the swarm. Formation and trajectory planning were established around the virtual control center, then we verified the consistency. Secondly, the improved PRM sampling method was designed and optimized to ensure a reasonable sampling density in the restricted environment, so as to improve the quality of trajectory planning. Finally, through the dynamic-static collaborative barrier avoidance method, combined with the lack of dynamic artificial potential field barrier avoidance, the formation control strategy of dynamic adjustment was constructed. In order to solve the multiagent to avoid other agents and obstacles, the problem may occur that the established formation can not adapt to the surrounding environment, resulting in swarm deadlock or agent out of formation. The simulation results show that the multi-agent swarm can adjust the formation strategy in time and avoid obstacles effectively, and then pass through the waypoint to reach the target point.
Object-Aware Adaptive Convolution Kernel Attention Mechanism in Siamese Network for Visual Tracking

As a classic framework for visual object tracking, the Siamese convolutional neural network has received widespread attention from the research community. This method uses a convolutional neural network to obtain the object features and to match them with the search area features to achieve object tracking. In this work, we observe that the contribution of each convolution kernel in the convolutional neural network for object tracking tasks is different. We propose an object-aware convolution kernel attention mechanism. Based on the characteristics of each object, the convolution kernel features are dynamically weighted to improve the expression ability of object features. The experiments performed using OTB and VOT benchmark datasets show that the performance of the tracking method fused with the convolution kernel attention mechanism is significantly better compared with the original method. Moreover, the attention mechanism can also be integrated with other tracking frameworks as an independent module to improve the performance.
Distribution Privacy Under Function Recoverability

A user generates <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula> independent and identically distributed data random variables with a probability mass function that must be guarded from a querier. The querier must recover, with a prescribed accuracy, a given function of the data from each of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula> independent and identically distributed query responses upon eliciting them from the user. The user chooses the data probability mass function and devises the random query responses to maximize distribution privacy as gauged by the (Kullback-Leibler) divergence between the former and the querier’s best estimate of it based on the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula> query responses. Considering an arbitrary function, a basic achievable lower bound for distribution privacy is provided that does not depend on <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula> and corresponds to worst-case privacy. Worst-case privacy equals the logsum cardinalities of inverse atoms under the given function, with the number of summands decreasing as the querier recovers the function with improving accuracy. Next, upper (converse) and lower (achievability) bounds for distribution privacy, dependent on <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula> , are developed. The former improves upon worst-case privacy and the latter does so under suitable assumptions; both converge to it as <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula> grows. The converse and achievability proofs identify explicit strategies for the user and the querier.
Smart Devices and Large Scale Reasoning via ASP: Tools and Applications

In the last few years, we have been witnessing the spread of computing devices getting smaller and smaller (e.g., Smartphones, Smart Devices, Raspberry, etc.), and the production and availability of data getting bigger and bigger. In this work we introduce DLV Large Scale (DLV-LS), a framework based on Answer Set Programming (ASP) for performing declarative-based reasoning tasks over data-intensive applications, possibly on Smart Devices. The framework encompasses DLV Mobile Edition (DLV-ME), an ASP based solver for Android systems and Raspberry devices, and DLV Enterprise Edition (DLV-EE), an ASP-based platform, accessible by REST interfaces, for large-scale reasoning over Big Data, classical relational database systems, and NoSQL databases. DLV-LS enables Smart Devices to both locally perform reasoning over data generated by their own sensors and properly interact with DLV-EE when more computational power is needed for harder tasks, possibly over bigger centralized data. We present also a real-world application of DLV-LS; the use case consists of a tourist navigator that calculates the best routes and optimizes a tour of a tourist under custom-defined time constraints.
Bridging the Invisible and Visible World: Translation between RGB and IR Images through Contour Cycle GAN

Infrared Radiation (IR) images that capture the emitted IR signals from surrounding environment have been widely applied to pedestrian detection and video surveillance. However, there are not many textures that appeared on thermal images as compared to RGB images, which brings enormous challenges and difficulties in various tasks. Visible images cannot capture scenes in the dark and night environment due to the lack of light. In this paper, we propose a Contour GAN-based framework to learn the cross-domain representation and also map IR images with visible images. In contrast to existing structures of image translation that focus on spectral consistency, our framework also introduces strong spatial constraints, with further spectral enhancement by illuminance contrast and consistency constraints. Designating our method for IR and RGB image translation, it can generate high-quality translated images. Extensive experiments on near IR (NIR) and far IR (thermal) datasets demonstrate superior performance for quantitative and visual results.