Search

To effectively guarantee the safety and efficiency of space activities, and to meet the needs of different levels of users for space object situation (SOS) knowledge, formalized and standardized SOS knowledge is needed to assist cognition. A knowledge graph (KG), as a suitable technology that can formally express knowledge and construct a standardized knowledge base, can provide knowledge generation, representation and intelligent service of SOS. In this paper, an SOS information service based on the KG (SOSKG) was constructed. Aimed at different scales of components in SOS, a multi-granularity knowledge structure was constructed, and a formalized expression of SOSKG was realized. To incorporate the complex relations into the SOSKG, a multi-level semantic relation parsing model and formalized representation were proposed from the three aspects of basic relations, spatial relations, and temporal relations. Additionally, a multi-element knowledge construction model was constructed to address the dynamic characteristics of various relations in SOSKG under a complex spatio-temporal environment. This study constructed different cases and scenarios to test the service, and the results show that the proposed service can effectively organize and express SOS knowledge in a complex spatio-temporal environment, provide an intermediate bridge between users and SOS knowledge, and promote users’ cognition of SOS knowledge.

Recent advances in head-mounted eye-tracking technology have allowed researchers to monitor eye movements during locomotion in real-world environments, increasing the ecological validity of research on human gaze behavior. While collecting eye-tracking data is becoming more accessible, visual analytics of eye-tracking data remains difficult and time-consuming. As such, there is a significant need for developing efficient visualization and analysis tools for large-scale eye-tracking data. This work develops a first-of-its-kind eye-tracking data visualization and analysis system that allows for automatic recognition of independent objects within field-of-vision, using deep-learning-based semantic segmentation. This system recolors the fixated objects-of-interest by integrating gaze fixation information with semantic maps. The system effectively allows researchers to automatically infer what objects users view and for how long in dynamic contexts. The contributions are 1) a data visualization and analysis system that uses deep-learning technology along with eye-tracking data to automatically recognize objects-of-interest from head-mounted eye-tracking video recordings, and 2) a graphical user interface that presents objects-of-interest annotation along with eye-tracking data information. The architecture is tested with an outdoor case study of users walking around the Tufts University campus as part of a navigation study, which was administered by a team of research psychologists.

This paper presents a novel approach, called MALDIVE, to support tax administrations in the tax risk assessment for discovering tax evasion and tax avoidance. MALDIVE relies on a network model describing several kinds of relationships among taxpayers. Our approach suitably combines various data mining and visual analytics methods to support public officers in identifying risky taxpayers. MALDIVE consists of a 4-step pipeline: (i) A social network is built from the taxpayers data and several features of this network are extracted by computing both classical social network indexes and domain-specific indexes; (ii) an initial set of risky taxpayers is identified by applying machine learning algorithms; (iii) the set of risky taxpayers is possibly enlarged by means of an information diffusion strategy and the output is shown to the analyst through a network visualization system; (iv) a visual inspection of the network is performed by the analyst in order to validate and refine the set of risky taxpayers. We discuss the effectiveness of the MALDIVE approach through both quantitative analyses and case studies performed on real data in collaboration with the Italian Revenue Agency.

The human sense of touch is an integral part of daily life. For tasks involving grasping and manipulation of objects, force feedback is a key requirement. Most of the systems give contact point or complete grasping force feedback; for precision grasping and other physical interactions, finger awareness and force feedback from independent fingers is essential. In this study a novel, wearable proprioceptive rehabilitation system is designed which restores the ability of identifying and distinguishing between individual fingers of a prosthetic hand or an exoskeleton in a non-invasive manner. Moreover, it provides different levels of force feedback from every finger as well, which enables the user to distinguish and control force in precision grasping activities. For testing the system accuracy, classical psychophysical methods were used on a group of 14 voluntary disabled subjects. The tests were conducted in both, ideal and real-world conditions i.e. without and with distractions and accuracies were calculated accordingly. A p-test was also conducted to observe significance between the samples of with and without distraction datasets. The system performed with an overall accuracy of 82.04% which was well above the min. performance measure of 60%. Vi-HaB is standalone system and can be mounted on any upper limb rehabilitation (prosthesis, exoskeleton) system for finger awareness and force feedback.

Existing prescriptive compression strategies used in hearing aid fitting are designed based on gain averages from a group of users which may not be necessarily optimal for a specific user. Nearly half of hearing aid users prefer settings that differ from the commonly prescribed settings. This paper presents a human-in-the-loop deep reinforcement learning approach that personalizes hearing aid compression to achieve improved hearing perception. The developed approach is designed to learn a specific user’s hearing preferences in order to optimize compression based on the user’s feedbacks. Both simulation and subject testing results are reported. These results demonstrate the proof-of-concept of achieving personalized compression via human-in-the-loop deep reinforcement learning.

International audience; The alarming morbidity of COVID-19 has drawn the attention to the social role of hygiene rules, with a particular focus on the importance of limiting face-touch occurrences. To deal with this aspect, we present No Face-Touch, a system able to estimate hand proximity to face and notify the user whenever a face-touch movement is detected. In its complete setup, the system consists of an application running on the smartwatch and a wearable accessory. Its ease of implementation allows this solution to be ready-to-use and large-scale deployable. We developed two gesture detection approaches compatible with sensors embedded in recent smartwatches, i.e. inertial and magnetic sensors. After preliminary tests to tune target gesture parameters, we tested the two approaches and compared their accuracy. The final phase of this project consisted in exploiting the most robust approach in a daily living scenario during a 6-days campaign. Experimental results revealed the effectiveness of the proposed system, demonstrating its impact in reducing the number of face-touches and their duration.

The paralyzed population is increasing worldwide due to stroke, spinal code injury, post-polio, and other related diseases. Different assistive technologies are used to improve the physical and mental health of the affected patients. Exoskeletons have emerged as one of the most promising technology to provide movement and rehabilitation for the paralyzed. But exoskeletons are limited by the constraints of weight, flexibility, and adaptability. To resolve these issues, we propose an adaptive and flexible Brain Energized Full Body Exoskeleton (BFBE) for assisting the paralyzed people. This paper describes the design, control, and testing of BFBE with 15 degrees of freedom (DoF) for assisting the users in their daily activities. The flexibility is incorporated into the system by a modular design approach. The brain signals captured by the Electroencephalogram (EEG) sensors are used for controlling the movements of BFBE. The processing happens at the edge, reducing delay in decision making and the system is further integrated with an IoT module that helps to send an alert message to multiple caregivers in case of an emergency. The potential energy harvesting is used in the system to solve the power issues related to the exoskeleton. The stability in the gait cycle is ensured by using adaptive sensory feedback. The system validation is done by using six natural movements on ten different paralyzed persons. The system recognizes human intensions with an accuracy of 85%. The result shows that BFBE can be an efficient method for providing assistance and rehabilitation for paralyzed patients.

The designers’ tendency to adhere to a specific mental set and heavy emotional investment in their initial ideas often limit their ability to innovate during the design ideation process. The shrinking time-to-market and the growing diversity of users’ needs further exacerbate this gap. Recent advances in deep generative models have created new possibilities to overcome the cognitive obstacles of designers through automated generation or editing of design concepts. This article explores the capabilities of generative adversarial networks (GAN) for automated, attribute-aware generative design of the visual attributes of a product. Specifically, a design attribute GAN (DA-GAN) model is developed for automated generation of fashion product images with the desired visual attributes. Experiments on a large fashion dataset signify the potentials of GAN for attribute-aware generative design, verify the ability of editing attributes with relatively higher accuracy and uncover several key challenges and research questions for future work.

Due to the recent progress in Deep Neural Networks, Reinforcement Learning (RL) has become one of the most important and useful technology. It is a learning method where a software agent interacts with an unknown environment, selects actions, and progressively discovers the environment dynamics. RL has been effectively applied in many important areas of real life. This article intends to provide an in-depth introduction of the Markov Decision Process, RL and its algorithms. Moreover, we present a literature review of the application of RL to a variety of fields, including robotics and autonomous control, communication and networking, natural language processing, games and self-organized system, scheduling management and configuration of resources, and computer vision.

The application of virtual reality technology in science experiment education is a research with practical significance and value in human-computer interaction. However, in some existing education tools based on virtual reality, due to the single interaction mode, the complexity of user intention and the non-physical interaction characteristics brought by virtualization, their experimental teaching ability is limited, resulting in the lack of practical value and popularity. In order to solve these problems, a multimodal interaction model is constructed by fusing gesture, speech and pressure information. Specifically, our tasks include: 1) collecting user input information and time series information to construct basic data input tuples. 2) The basic interaction information is used to identify the user’s basic intention, and the correlation degree between the user’s intentions is considered to determine the correctness of the current identification intention. 3) It allows users to alternate between multi-channel and single channel interaction. Based on this model, we build a multi-modal intelligent interactive virtual experiment platform (MIIVEP), and design and implement a kind of dropper with strong perception ability, which has been verified, tested, evaluated and applied in the intelligent virtual experiment system. In addition, in order to evaluate this work more effectively, we developed a fair scoring criterion for the virtual experimental system (Evaluation scale of virtual experiment system, ESVES), and invited middle school teachers and students to participate in the verification of the results of this work. Through the user’s actual use effect verification and result research, we prove the effectiveness of the proposed model and the corresponding implementation.

Smart sensing devices are furnished with an array of sensors, including locomotion sensors, which enable continuous and passive monitoring of human activities for the ambient assisted living. As a result, sensor-based human activity recognition has earned significant popularity in the past few years. A lot of successful research studies have been conducted in this regard. However, the accurate recognition of in-the-wild human activities in real-time is still a fundamental challenge to be addressed as human physical activity patterns are adversely affected by their behavioral contexts. Moreover, it is essential to infer a user's behavioral context along with the physical activity to enable context-aware and knowledge-driven applications in real-time. Therefore, this research work presents “C2FHAR”, a novel approach for coarse-to-fine human activity recognition in-the-wild, which explicitly models the user's behavioral contexts with activities of daily living to learn and recognize the fine-grained human activities. For addressing real-time activity recognition challenges, the proposed scheme utilizes a multi-label classification model for identifying in-the-wild human activities at two different levels, i.e., coarse or fine-grained, depending upon the real-time use-cases. The proposed scheme is validated with extensive experiments using heterogeneous sensors, which demonstrate its efficacy.

Recognizing the current activity of a software developer (e.g., debugging, reading, editing) can improve the effectiveness of recommendation systems that aim to reduce the cognitive load of information lookup during software development. Current recommendation systems based on developer activity detection focus primarily on a single dimension of developer behavior, e.g., both observing and recommending IDE commands or source code classes. In addition, the current state of the art techniques require that the number and type of activities exhibited by developers is pre-specified and that labeled interaction data is provided as input. In this article, we propose the use of an approach that eschews these requirements, leveraging an unsupervised statistical model that uses both IDE commands and source code accesses to discern latent developer activities. Our approach outperforms baseline supervised and unsupervised learning techniques on simulation-based evaluation of source code and command recommendations for most of the configurations we examined. We also show that our technique benefits from observing both commands and code accesses when identifying developer activity.

Many of the current online businesses base completely their revenue models in earnings from online advertisement. A problematic fact is that according to recent studies more than half of display ads are not being detected as viewable. The International Advertising Bureau (IAB) has defined a viewable impression as an impression that at least 50% of its pixels are rendered in the viewport during at least one continuous second. Although there is agreement on this definition for measuring viewable impressions in the industry, there is no systematic methodologies on how it should be implemented or the trustworthiness of these methods. In fact, the Media Rating Council (MRC) announced that there are inconsistencies across multiple reports attempting to measure this metric. In order to understand the magnitude of the problem, we conduct an analysis of different methods to track viewable impressions. Then, we test a subset of geometric and strong interaction methods in a webpage registered in the worldwide ad-network ExoClick, which currently serves over 7 billion geo-targeted ads a day to a global network of 65000 web/mobile publisher platforms. We find that the Intersection Observer API is the method that detects more viewable impressions given its robustness towards the technological constraints that face the rest of implementations available. The motivation of this work is to better understand the limitations and advantages of such methods, which can have an impact at a standardisation level in online advertising industry, as well as to provide guidelines for future research based on the lessons learned. This work was possible thanks to the support of “Plan de Doctorados Industriales de la Secretaría de Universidades e Investigación del Departamento de Empresa y Conocimiento de la Generalitat de Catalunya” and the Spanish Ministry of Economy and Competitiveness through the Juan de la Cierva Formación program (FJCI-2017-34926). We also want to thank ExoClick for their support in conducting thisresearch

Cultural heritage (CH) artifacts, such as ceramics and clothes, reflect the unique characteristics of ancient cultures and have the potential to be sustainably employed in modern design and entertainment. In particular, the shape of ceramics reflects regional and historical characteristics, so datafication is a promising avenue to preserve these assets for future generations. However, design is a specialized domain that requires significant human (expert and novice) labor. This often tedious process decreases the labeler’s motivation to complete the task, and data consistency varies with the experience and motivation of the labeler. To increase engagement, we developed an image labeling platform with graphical icon-based labeling methods and introduced gamification. The robust labeling methods with gamification increased novices’ engagement and decreased the workload of expert and novice labelers, but decreased data agreement between experts and novices, so we consider opportunities for gamification within the specialized cultural heritage domain.

The traditional methods of urban landscape planning and design cannot integrate designs into the entire planning scenes planning processes, and cannot efficiently, intuitively and coordinately express planning effects of multiple schemes and large scenes. With the development of computer science and technology, virtual reality (VR) technology, which is based on similar theory, mathematical theory, control theory, and graphics processing theory, is playing an important role in many aspects of urban landscape planning and design. On the basis of summarizing and analyzing previous research works, this article expounded the research status and significances of three-dimensional (3D) visualized urban landscape planning and design, elaborated the development background, current status and future challenges of VR technology, introduced the methods and principles of spatial roaming sorting algorithm and evaluation indicator screening and system construction, established 3D database and virtual scene model of urban landscapes, performed 3D visualized urban landscape modeling, realized 3D presentation of virtual scene landscape, discussed the integration and optimization of landscape design schemes, analyzed the 3D modeling and interactive adjustment of the landscape planning and design schemes, and finally conducted simulation experiment and its result analysis. The results show that the VR technology provides designer with realistic information processing effects through intelligent drawing, full display and timely information, which greatly improves design efficiency and effectively promotes design quality; the 3D visualized technology can transmit a large number of information through graphics in a short time, enabling professionals to intuitively and quickly observe test results or interactively process these results.

Open social network (OSN) plays a more significant role in information propagation through the rapid developing of information technology. Since information diffusion is an essential process happens in OSN, it has been studied in many studies. Several models have been proposed to infer the diffusion process and reproduce diffusion network. However, these methods have two critical problems: 1) ignoring the effects of user social characteristics and 2) inaccuracy resulted from calculating the influence of different features independently. To address these limitations, a diffusion inferring method based on a recommender system (DIM-SPTF) was proposed. The DIM-SPTF method considers the propagation process between the users as the recommendation process of information and employs a recommender system to infer the propagation relationship. Through determining the propagation relations among all users in the observed topic data set, an information diffusion network can be finally obtained. Experimental results show that DIM-SPTF leads to improvements in performance compared with the state-of-the-art methods.

Modern mass customization production allows user interaction activities to be distributed in the full product life cycle via multiple information systems. Investigating user behaviors across the boundaries of different domains helps to deeply integrate isolated fragmental profiles into a comprehensive one, and therefore can provide multi-dimensional, high-quality and valuable services. However, traditional user behavior analysis models are based on individual user profile information derived from separate domains, such as requirement analysis, design, supply chain, logistics, marketing, etc., which have not considered the whole complexity of mass customization manufacturing. In this paper, we introduce the concept of multi-dimensional semantic activity space, where user behavior features are merged and represented as combined vectors. User behavior patterns are discovered by mining action data extracted from log files in different subsystems in the corresponding domains. We also identify distinct categories of user behaviors in various modules and subsystems in the context of an intelligent manufacturing environment. Experiment results show a strong indication that the proposed approach can be applied to reveal variations in typical behavioral aspects of cross-domain participants, in terms of patterns in resource access, operation tasks, performance assessment, etc.

New media and communication technologies like mobile devices are nowadays widely used everywhere for providing rich functionalities and highly personalized services. However, using such a device in a driving environment is still very inconvenient and unsafe to be controlled by the driver. The touchscreen operations are one major obstacle since multi-touchscreen is optimized for hand-held usage scenarios. To overcome this limitation, we propose to replace some most used touch operations with gesture controls for mobile devices in a driving environment. Gesture control is simple, more flexible and requires less eye focus, which makes it more suitable for in-vehicle usages. In this paper, we design Givs, a fully functional gesture control system for mobile devices in a driving environment. Givs leverages the latest motion sensing technology to enable ubiquitous and driving-friendly gestures. Compared to other off-the-shelf gesture recognition solutions, Givs is optimized for in-vehicle use cases and is designed to overcome various limitations caused by real driving conditions, including bumpy road conditions, significant noise introduced by car vibration and technical limitations of motion sensors. Our extensive in-vehicle tests and participant experience experiments demonstrate that Givs well assists users in accomplishing various types of tasks and support human-machine interaction in driving environments such as personal vehicle and public transport, with high accuracy and fast responsiveness, while promoting drivnig convenience and safety.

This article describes Daphne, a virtual assistant for designing Earth observation distributed spacecraft missions. It is, to the best of our knowledge, the first virtual assistant for such application. The article provides a thorough description of Daphne, including its question answering system and the main features we have implemented to help system engineers design distributed spacecraft missions. In addition, the article describes a study performed at NASA's Jet Propulsion Laboratory (JPL) to assess the usefulness of Daphne in this use case. The study was conducted with N = 9 subjects from JPL, who were asked to work on a mission design task with two versions of Daphne, one that was fully featured implementing the cognitive assistance functions, and one that only had the features one would find in a traditional design space exploration tool. After the task, they filled out a standard user experience survey, completed a test to assess how much they learned about the task, and were asked a number of questions in a semi-structured exit interview. Results of the study suggest that Daphne can help improve performance during system design tasks compared to traditional tools, while keeping the system usable. However, the study also raises some concerns with respect to a potential reduction in human learning due to the use of the cognitive assistant. The article ends with a list of suggestions for future development of virtual assistants for space mission design.

This paper presents a software architecture for robots providing manipulation services autonomously in human environments. In an unstructured human environment, a service robot often needs to perform tasks even without human intervention and prior knowledge about tasks and environments. For autonomous execution of tasks, varied processes are necessary such as perceiving environments, representing knowledge, reasoning with the knowledge, and planning for task and motion. While developing each of the processes is important, integrating them into a working system for deployment is also important as a robotic system can bring tangible outcomes when it works in real world. However, such an architecture has been rarely realized in the literature owing to the difficulties of a full integration, deployment, understanding high-level goals without human interventions. In this work, we suggest a software architecture that integrates the components necessary to perform tasks by a real robot without human intervention. We show our architecture composed of deep learning based perception, symbolic reasoning, AI task planning, and geometric motion planning. We implement a deep neural network that produces information about the environment, which are then stored in a knowledge base. We implement a reasoner that processes the knowledge to use the result for task planning. We show our implementation of the symbolic task planner that generates a sequence of motion predicates. We implement an interface that computes geometric information necessary for motion planning to execute the symbolic task plans. We describe the deployment of the architecture through the result of lab tests and a public demonstration. The architecture is developed based on Robot Operating System (ROS) so compatible with any robot that is capable of object manipulation and mobile navigation running in ROS. We deploy the architecture to two different robot platforms to show the compatibility.

Visual navigation (vNavigation) is a key and fundamental technology for artificial agents' interaction with the environment to achieve advanced behaviors. Visual navigation for artificial agents with deep reinforcement learning (DRL) is a new research hotspot in artificial intelligence and robotics that incorporates the decision making of DRL into visual navigation. Visual navigation via DRL, an end-to-end method, directly receives the high-dimensional images and generates an optimal navigation policy. In this paper, we first present an overview on reinforcement learning (RL), deep learning (DL) and deep reinforcement learning (DRL). Then, we systematically describe five main categories of visual DRL navigation: direct DRL vNavigation, hierarchical DRL vNavigation, multi-task DRL vNavigation, memory-inference DRL vNavigation and vision-language DRL vNavigation. These visual DRL navigation algorithms are reviewed in detail. Finally, we discuss the challenges and some possible opportunities to visual DRL navigation for artificial agents.

With the world around us rapidly becoming smarter, an extremely relevant question is how “we humans” are going to cope with the onslaught of information coming at us. One plausible answer is to use similar technologies to evolve ourselves and to equip us with the necessary tools to interact with and to become an essential part of the smart world. Various wearable devices have been or are being developed for this purpose. However, their potential to create a whole new set of human experiences is still largely unexplored. To be more effective, functionality cannot be centralized and needs to be distributed to capture the right information at the right place. This requires a human Intranet, a platform that allows multiple distributed input–output and information processing functions to coalesce and form a single application. In addition, it needs the capabilities to understand, interpret, reason, and act on the obtained data under diverse and changing conditions, and to do so in concert with the human body and its computer, the brain. To this effect, this article explores the concept of human-centric computing, an approach that aspires to create a symbiotic convergence between biological and physical computing.

Multi-armed bandits achieve excellent long-term performance in practice and sublinear cumulative regret in theory. However, a real-world limitation of bandit learning is poor performance in early rounds due to the need for exploration—a phenomenon known as the cold-start problem. While this limitation may be necessary in the general classical stochastic setting, in practice where “pre-training” data or knowledge is available, it is natural to attempt to “warm-start” bandit learners. This paper provides a theoretical treatment of warm-start contextual bandit learning, adopting Linear Thompson Sampling as a principled framework for flexibly transferring domain knowledge as might be captured by bandit learning in a prior related task, a supervised pre-trained Bayesian posterior, or domain expert knowledge. Under standard conditions, we prove a general regret bound. We then apply our warm-start algorithmic technique to other common bandit learners—the $$\epsilon $$ ϵ -greedy and upper-confidence bound contextual learners. An upper regret bound is then provided for LinUCB. Our suite of warm-start learners are evaluated in experiments with both artificial and real-world datasets, including a motivating task of tuning a commercial database. A comprehensive range of experimental results are presented, highlighting the effect of different hyperparameters and quantities of pre-training data.

Metalearning has been widely applied for implementing few-shot learning and fast model adaptation. Particularly, existing metalearning methods have been exploited to learn the control mechanism for gradient descent processes, in an effort to facilitate gradient-based learning in gaining high speed and generalization ability. This article presents a novel method that controls the gradient descent process of the model parameters in a neural network, by limiting the model parameters within a low-dimensional latent space. The main challenge for implementing this idea is that a decoder with many parameters may be required. To tackle this problem, the article provides an alternative design of the decoder with a structure that shares certain weights, thereby reducing the number of required parameters. In addition, this work combines ensemble learning with the proposed approach to improve the overall learning performance. Systematic experimental studies demonstrate that the proposed approach offers results superior to the state of the art in performing the Omniglot classification and miniImageNet classification tasks.

Driver's emotion recognition is vital to improving driving safety, comfort, and acceptance of intelligent vehicles. This article presents a cognitive-feature-augmented driver emotion detection method that is based on emotional cognitive process theory and deep networks. Different from the traditional methods, both the driver's facial expression and cognitive process characteristics (age, gender, and driving age) were used as the inputs of the proposed model. Convolutional techniques were adopted to construct the model for driver's emotion detection simultaneously considering the driver's facial expression and cognitive process characteristics. A driver's emotion data collection was carried out to validate the performance of the proposed method. The collected dataset consists of 40 drivers' frontal facial videos, their cognitive process characteristics, and self-reported assessments of driver emotions. Another two deep networks were also used to compare recognition performance. The results prove that the proposed method can achieve well detection results for different databases on the discrete emotion model and dimensional emotion model, respectively.

Research in cognitive robotics has been traditionally carried out in high-end research robots. This poses a barrier to many research institutions that cannot handle the costs associated with purchasing and operating this type of robot. In this paper, the design of a low-cost and open-source robot called OSCAR is presented. This robot was purposely designed to adapt to manipulation tasks in the context of cognitive robotics. The morphology, sensors equipped and software architecture of the robot are explained. Moreover, an initial implementation based in the Gazebo simulator is used to carry out an experiment involving a manipulation task using the Multilevel Darwinist Brain cognitive architecture. Results show that OSCAR is capable of learning a task and to obtain general information of its environment when paired with a cognitive architecture.

User Experience and Performance analysis of websites are key factors in their accessibility and usability assessment. According to the human-computer interaction standards and guidelines, the user experience and performance can be affected by several factors such as page speed, web design, responsiveness, usability, accessibility, and security. Recently, the user experience and performance were conducted using automatic quality assurance tools. In this research paper, we aim to evaluate and compare the user experience and performance of Palestinian universities' websites by using the ImmuniWeb software, and JMeter software automated tools, as well as to validate the result through traditional survey user experience evaluation tool. The study evaluated response rates and security performance on a sample of 4 Palestinian university websites. The websites were also assessed in terms of efficiency, effectiveness, and user satisfaction through a comprehensive survey conducted on a sample of 84 students. The results indicated that the universities' websites had several challenges in terms of efficiency, effectiveness, and user satisfaction. Thus, Universities must upgrade their security features, use automation tools to tests their performance, and enhance their overall usability. © 2021 IEEE.

With the rapid development of Virtual Reality (VR) technology, the research of User Interface (UI), especially menus, in the VR environment has attracted continued attention. However, it is very tedious for researchers to develop UI from scratch or modify existing functions and there are no easy-to-use tools for efficient development. This paper aims to present VRMenuDesigner, a novel, flexible and modular toolkit for automatically generating/modifying VR menus. This toolkit is provided as an open-source library and easy to extend to adapt to various requirements. The main contribution of this work is to organize the menus and functions with object-oriented thinking, which makes the system very understandable and extensible. VRMenuDesigner includes two key tools: Creator and Modifier for quickly generating and modifying elements. Moreover, we developed several built-in menus and discussed their usability. After a brief review and taxonomy of 3D menus, the architecture and implementation of the toolbox are introduced Refereed/Peer-reviewed

Flow is an aspect of player experience that has been often targeted in game studies. Still, similar to presence it can be ambiguous and difficult to quantify. In this study, we approach the concept of flow in cooperative gaming and frame the target of this study as ”collaborative flow”. Team-play and Flow relevant aspects of player experience, Flow, Co-presence, Sensory Immersion, Imaginative Immersion, Behavioral involvement, Social Presence, and Engagement, were mapped in an online survey with 75 participants. We noticed small significant differences between semi-professional gamers and hobbyists in some flow related aspects of player experience. In addition, weekly gaming time influenced the results. In this study we used existing validated questionnaires and can only state that better metrics and definitions for this aspect of player experience are needed for future research.

Interactions between people are of utmost magnitude for cross-device systems development. By using this kind of software, devices owned by those people end up interacting between themselves, and, therefore, making the system work. This work proposes to elaborate a toolkit that can detect and analyze those human interactions by using computer vision over videos showing them. All of these through the usage of 3D modeled test scenarios in addition to applying proxemics metrics and concepts of F -formations patterns so we can define them at various interaction types. To meet this goal, we used a previously trained human detection model in conjunction with two proposed concepts to estimate indispensable values: Distance between people, their body orientation, and relative position. To validate this tool, we tested it with a hundred test cases, each one having a set of different F -formation types so we could get the effectiveness of its detection functionality.

The analysis of human behavior has been attracting the attention of scientists for a long time. Prediction of user intents and actions when interacting with an information system and the corresponding adaptation of the system can significantly increase the efficiency of the joint work of the pair ""user - information system"". The paper proposes an approach to modeling the behavior of information system users based on the situation analysis with the aim of further behavior analysis via, for example, machine learning methods. The main idea of the approach is to capture and compare the situation described by a context before, at the moment of, and after an action made by the user. The applicability of the proposed approach is confirmed by the presented case study.

A human-swarm cooperative system, which mixes multiple robots and a human supervisor to form a mission team, has been widely used for emergent scenarios such as criminal tracking and victim assistance. These scenarios are related to human safety and require a robot team to quickly transit from the current undergoing task into the new emergent task. This sudden mission change brings difficulty in robot motion adjustment and increases the risk of performance degradation of the swarm. Trust in human-human collaboration reflects a general expectation of the collaboration; based on the trust humans mutually adjust their behaviors for better teamwork. Inspired by this, in this research, a trust-aware reflective control (Trust-R), was developed for a robot swarm to understand the collaborative mission and calibrate its motions accordingly for better emergency response. Typical emergent tasks “transit between area inspection tasks”, “response to emergent target - car accident” in social security with eight fault-related situations were designed to simulate robot deployments. A human user study with 50 volunteers was conducted to model trust and assess swarm performance. Trust-R's effectiveness in supporting a robot team for emergency response was validated by improved task performance and increased trust scores.