Real-time Situation Research Articles

Relatively Inefficient Integration of Metaphorical Semantics in Autistic Adults Without Intellectual Impairment.

Individuals diagnosed with autism are often thought to face challenges in comprehensive metaphors, even for the individuals without intellectual impairment. This study is to investigate the features and mechanisms of metaphor integration in the process of metaphor comprehension in real-time and context-free situations in autism, as well as the influence of the mental complexity of metaphor. Twenty autistic adults and twenty typically developing peers carried out a Lexical Decision Task and a Recognition Task. The results of the study showed that, there are deficiencies in real-time metaphor comprehension in autistic adults without intellectual impairment. It may caused by their relatively inefficient integration of metaphor semantics. This mechanism was equally pronounced in the metaphors with different mental complexity.

Real-Time Context-Aware Recommendation System for Tourism.

Recently, the tourism trend has been shifting towards the Tourism 2.0 paradigm due to increased travel experiences and the increase in acquiring and sharing information through the Internet. The Tourism 2.0 paradigm requires developing intelligent tourism service tools for positive effects such as time savings and marketing utilization. Existing tourism service tools recommend tourist destinations based on the relationship between tourists and tourist destinations or tourism patterns, so it is difficult to make recommendations in situations where information is insufficient or changes in real time. In this paper, we propose a real-time recommendation system for tourism (R2Tour) that responds to changing situations in real time, such as external factors and distance information, and recommends customized tourist destinations according to the type of tourist. R2Tour trains a machine learning model with situational information such as temperature and precipitation and tourist profiles such as gender and age to recommend the top five nearby tourist destinations. To verify the recommendation performance of R2Tour, six machine learning models, including K-NN and SVM, and information on tourist attractions in Jeju Island were used. As a result of the experiment, R2Tour was verified with accuracy of 77.3%, micro-F1 0.773, and macro-F1 0.415. Since R2Tour trains tourism patterns based on situational information, it is possible to recommend new tourist destinations and respond to changing situations in real time. In the future, R2Tour can be installed in vehicles to recommend nearby tourist destinations or expanded to tasks in the tourism industry, such as a smart target advertising system.

Effect of Heading a Soccer Ball as an External Focus During a Drop Vertical Jump Task.

Research has demonstrated that performing a secondary task during a drop vertical jump (DVJ) may affect landing kinetics and kinematics. To examine the differences in the trunk and lower extremity biomechanics associated with anterior cruciate ligament (ACL) injury risk factors between a standard DVJ and a DVJ while heading a soccer ball (header DVJ). Descriptive laboratory study. Participants comprised 24 college-level soccer players (18 female and 6 male; mean ± SD age, 20.04 ± 1.12 years; height, 165.75 ± 7.25 cm; weight, 60.95 ± 8.47 kg). Each participant completed a standard DVJ and a header DVJ, and biomechanics were recorded using an electromagnetic tracking system and force plate. The difference (Δ) in 3-dimensional trunk, hip, knee, and ankle biomechanics between the tasks was analyzed. In addition, for each biomechanical variable, the correlation between the data from the 2 tasks was calculated. Compared to the standard DVJ, performing the header DVJ led to significantly reduced peak knee flexion angle (Δ = 5.35°; P = .002), knee flexion displacement (Δ = 3.89°; P = .015), hip flexion angle at initial contact (Δ = -2.84°; P = .001), peak trunk flexion angle (Δ = 13.11°; P = .006), and center of mass vertical displacement (Δ = -0.02m; P = .010), and increased peak anterior tibial shear force (Δ = -0.72 N/kg; P = .020), trunk lateral flexion angle at initial contact (Δ = 1.55°; P < .0001), peak trunk lateral flexion angle (Δ = 1.34°; P = .003), knee joint stiffness (Δ = 0.002 N*m/kg/deg; P = .017), and leg stiffness (Δ = 8.46 N/kg/m; P = .046) compared to those in standard DVJs. In addition, individuals' data for these variables were highly and positively correlated between conditions (r = 0.632-0.908; P < .001). The header DVJ task showed kinetic and kinematic parameters that suggested increased risk of ACL injury as compared with the standard DVJ task. Athletes may benefit from acquiring the ability to safely perform header DVJs to prevent ACL injury. To simulate real-time competition situations, coaches and athletic trainers should incorporate such dual tasks in ACL injury prevention programs.

Routing and Navigation Solutions for Emergency Vehicles in Urban Emergency Management

Urban population and expansion continue to rise, as have emergencies, and emergency response times are becoming longer in the city. Response times are critical in urban emergency management. Traditional navigation systems do not consider the preferential road usage rights and requirements crucial for emergency teams such as ambulances, fire departments, and police. In traditional navigation systems, features such as safety lanes and road width are not incorporated as routing criteria. Past emergency navigation studies do not contain many variables and this causes delays in emergency response processes. As a result, applications that process real-time traffic information and provide guidance according to different vehicle types are needed for emergency management.. This study aims to develop an application capable of routing for different vehicles according to criteria such as working hours, holidays, weather conditions, and seasonal effects. The application is developed with a novel approach in this context using machine learning methods and produces alternate routes with GPS navigation based on the real-time traffic situation. Moreover, the software also provides routing solutions for trucks, freight distribution, and automobiles and recommends unique routes for emergency vehicles based on factors including safety lanes, road width, corner turning angles, and right of way. The expected outcomes upon implementing this application include easy and fast access to the emergency locations, reduced response times, and improvement in urban emergency management services.

Traffic Signal Scheduling using Machine Learning

Within the past few years the number of vehicles increased drastically and therefore the traffic of vehicles became a major issue in urban as well as in rural areas. Major traffic is happening in the area where many roads do intersect with each other. Our existing traffic signal is not real-time and it is run according to how it is programmed earlier irrespective of traffic. To avoid traffic, traffic signals should give the priority to the road that has the maximum density of vehicles. By doing this we can pass the maximum number of vehicles in a certain period of time. This type of signal acts according to the real-time situation, and take a decision smartly. Hence this system is also called a smart traffic light system. The purpose of this study is to get the traffic situation on the roads in real-time and acts accordingly. Using a web camera that should be mounted on the signals, we can get real-time footage of the roads and by using image processing methods, we can determine the densities of vehicles on each road. Signals which are programmed priorly or wrong signal scheduling was found to play the greatest role in causing vehicle traffic. This smart traffic signal scheduling system is definitely a better option in comparison with existing traffic signal scheduling as it is taking the decision according to the traffic situations.

Emotica.AI - A Customer feedback system using AI

Our lives are being significantly impacted by the rapid development of wire- less technology and mobile gadgets on this day. The digital economy demands that services be developed almost instantly while also paying close attention to client feedback. It becomes difficult to manage and analyse the informa- tion gathered about products from customers. Successful businesses typically gather reasonable input on customer behaviour, comprehend their clients, and maintain ongoing contact with them. But it’s not an easy task to keep a record of each and every customer’ feedback on a daily basis. Also, everyone is not intended to provide clear feedback whether the product was satisfactory or not. It is a very difficult and time-consuming task to analyse the data collected man- ually. Companies need automation of customer feedback processing in order to quickly use the data that has been collected and analyse consumer feedback. To proceed with the problem and through much research we came across a solution, Emotica.AI, an emotion recognition system which can overcome this situation in real time. Emotion recognition plays an important role in building interpersonal relationships. Speaking, making facial expressions, gesturing, or writing are all ways that people directly or indirectly convey their feelings. Now that AI has mastered the power of learning, it is capable of treating any- thing just like a human would. The proposed model is built with Haar-Cascade Algorithm and classified with CNN and is able to recognise the emotions of multiple faces in a real time scenario. Accuracy of this model is around 76% is achieved for seven emotions on a real -time basis. Our goal is to develop a real time implementation of an emotion detection system with better accuracy and make it more reliable for businesses and other purposes.

Mathematical model of spatial and temporal processing of broadband signals in satellite radio systems of broadband access and radio navigation

To effectively receive a useful broadband signal, it is necessary to compensate for distortions caused by the complex frequency-dependent coefficient of the receiving and transmitting antenna, multiplicative interference and noise from the heterogeneity of the propagation medium, additive interference and noise. Using only correlation processing under certain conditions will not allow receiving a useful broadband signal with the required quality. The article develops a mathematical model of spatio-temporal processing of broadband signals in satellite broadband access systems, showing that spatio-temporal processing should be carried out in two stages. At the first stage, space-time processing is carried out in the channel adaptively to the antenna array based on the formation of a complex frequency-dependent vector of weighting coefficients, which should change with a change in the signal-interference situation in real time, taking into account the change in the direction of radiation sources. At the second stage, correlation processing of the broadband signal is performed based on the use of long-length code sequences. This model is the basis for the development of methods for the formation and correlation (temporal) processing of broadband signals and methods of spatio-temporal processing of broadband signals under conditions of intentional and unintentional exposure. Based on the presented mathematical model of spatio-temporal processing of broadband signals, the main directions of increasing the noise immunity of radio-electronic systems are determined.

Location- and Physical-Activity-Based Application for Japanese Vocabulary Acquisition for Non-Japanese Speakers

There are various mobile applications to support foreign-language learning. While providing interactive designs and playful games to keep learners interested, these applications do not focus on motivating learners to continue learning after a long time. Our goal for this study was to develop an application that guides learners to achieve small goals by creating small lessons that are related to their real-life situations, with a main focus on vocabulary acquisition. Therefore, we present MiniHongo, a smartphone application that recognizes learners’ current locations and activities to compose lessons that comprise words that are strongly related to the learners’ real-time situations and can be studied in a short time period, thereby improving user motivation. MiniHongo uses a cloud service for its database and public application programming interfaces for location tracking. A between-subject experiment was conducted to evaluate MiniHongo, which involved comparing it to two other versions of itself. One composed lessons without location recognition, and the other composed lessons without location and activity recognition. The experimental results indicate that users have a strong interest in learning Japanese with MiniHongo, and some difference was found in how well users could memorize what they learned via the application. It is also suggested that the application requires improvements.

A dynamic rescheduling and speed management approach for high-speed trains with uncertain time-delay

This paper studies the problem of rescheduling trains with speed management in uncertain disruptions that lead to temporary speed restrictions (TSRs) in high-speed railway systems. The disruption uncertainties are that the coverage and speed limit value of TSRs change randomly over time, and the start and end times of TSRs are unknown in advance. First, a mixed-integer linear programming model is formulated to reduce the total train traveling times and improve the passenger comfortability. The solutions of the model provide simultaneously the optimal train rescheduling strategies and train speed control strategies. Second, a rolling horizon algorithm is applied in consideration of the disruption uncertainties. The rolling horizon algorithm updates and solves the model in every time horizon, according to the disruption information newly detected. Therefore, the train rescheduling orders and speed control orders can be adjusted according to the real-time situation to guarantee effectiveness. The rolling horizon approach is terminated automatically once the disruption ends. Finally, based on the practical data of the Beijing-Tianjin intercity high-speed railway line, numerical experiments are carried out to test the effectiveness and efficiency of the proposed approach.

Why Police Ethics Matter

When police abuse their duties, it undermines the state's internal security. It creates a crisis of legitimacy of police because people detest them for their abuse and tyranny. In 1957, IACP (the International Association of Chiefs of Police) developed an ethics tool Code of Ethics for law enforcement. Nevertheless, training has been focused and emphasized on techniques and tactics of policing. Ethics is not the part of presell of the training. The Code of Ethics is pronounced once in a life only during pass-out day. Police face dilemmas in real-time situations with people and society. This is why, this article is concerned with ethics a tool, and procedures to reduce police abuse.

A Decision-Aware Ambient Assisted Living System with IoT Embedded Device for In-Home Monitoring of Older Adults

Older adults' independent life is compromised due to various problems, such as memory impairments and decision-making difficulties. This work initially proposes an integrated conceptual model for assisted living systems capable of providing helping means for older adults with mild memory impairments and their caregivers. The proposed model has four main components: (1) an indoor location and heading measurement unit in the local fog layer, (2) an augmented reality (AR) application to make interactions with the user, (3) an IoT-based fuzzy decision-making system to handle the direct and environmental interactions with the user, and (4) a user interface for caregivers to monitor the situation in real time and send reminders once required. Then, a preliminary proof-of-concept implementation is performed to evaluate the suggested mode's feasibility. Functional experiments are carried out based on various factual scenarios, which validate the effectiveness of the proposed approach. The accuracy and response time of the proposed proof-of-concept system are further examined. The results suggest that implementing such a system is feasible and has the potential to promote assisted living. The suggested system has the potential to promote scalable and customizable assisted living systems to reduce the challenges of independent living for older adults.

A VGG16 Based Hybrid Deep Convolutional Neural Network Based Real-Time Video Frame Emotion Detection System for Affective Human Computer Interaction

Abstract: Facial emotion recognition is field of computer vision that deals with recognizing the emotions of the user by facial expression analysis. The development of facial emotion recognition technology has the potential to enhance human-computer interaction by making machines more responsive to human emotions. For recognizing facial emotions, a variety of machine learning methods have been used, such as deep neural networks, support vector machines, decision trees, and among others, Convolutional neural networks (CNNs), recurrent neural networks (RNNs), and their combinations have all been suggested as designs, which have proven especially effective in this field. However, despite substantial advancements in the area, there are still difficulties in attaining accurate and consistent facial expression identification in real-time everyday situations. The effectiveness and usability of face expression detection systems for diverse applications must therefore be improved and efficient systems be developed to expand the focus on further applications of emotions in achieving affective Human computer interaction. My main objective in this paper will be to focus on being able to accurately identify user’s emotion in a real world scenario. I will be a designing a hybrid deep learning CNN architectures for the real world implementation of the real-time emotion recognition system for improving Human Computer Interaction

A fast and precise double-diode model for predicting photovoltaic panel electrical behavior in variable environmental conditions

A precise understanding of photovoltaic output behaviour leads to better organising of investment resources and more efficient development planning. Besides under standard test conditions, the presented model can predict the output of a photovoltaic module under variable real-time situations. The proposed hybrid analytical-numerical method declined the calculation and running time while offering an acceptable predicting accuracy. Models’ predictions were validated with the information reported by the manufacturer and experimental analysis and real-time measurements. The dust impacts were also investigated. The model accuracy was compared with a single diode model under the same conditions and several other similar works. Finally, the model was used to investigate the effect of any variation in cell temperature and irradiation levels on the output of photovoltaic modules. The proposed hybrid model can be a benchmark for future studies. It can be modified and developed to foresee the electrical output of a photovoltaic system in other regions if the system specifications and weather conditions and diversities are applied accordingly.

Vulnerability analysis of urban road networks based on traffic situation

Traffic congestion is a global issue, which occurs during rush hour but also in situations of emergency causing massive congestion. This paper proposes a method for building a weighted road network by using the real-time traffic situation and the inherent characteristics of Urban Road Networks (URNs). To research the cascading failure vulnerability of URNs three kinds of node importance indexes are constructed from the structure, function, and traffic flow characteristics. Then the feasibility and validity of the proposed model are verified by taking Shanghai road networks (SRNs) as an example. The results indicate that the highest betweenness node-based attack causes the most damage to the SRNS of different types of attacks, and the SRNS cascade fails with the greatest speed and scale. Furthermore, we explore that the correlations between network vulnerability indicators, and suggest significant differences at different times during cascading failures of the weighted road network.

A novel cyber‐attack modelling and detection in overcurrent protection relays based on wavelet signature analysis

AbstractCyber attacks can disrupt the digital protection relays and change their output command to the circuit breaker. The aim of this research is to provide new condition monitoring for overcurrent relays during cyber treatments. This research is modeled three common types of attacks with randomize concept and its detection method via MATLAB/SIMULINK software into IEEE 3‐machine 9‐bus model that is applicable in real time situation. Finally a new index has been defined for detection that employs wavelet analysis of the substation currents and its energy which has been called cyber attack detection index (CADI). Proposed index is robust to variation of sampling signal, sampling rate, cyber attack type and current transformer saturation. The results show that the index has an acceptable performance with accuracy of 100% without any false trigger. This method is not dependent on operating point, relay protection scheme and attack type and also could distinguish the abnormal actions of breakers due to unexpected cyber contingencies caused by natural disasters or software malfunctions rather than cyber attacks in a certain operating points. The level of CADI curve in cyber attacks is always higher than symmetrical fault and current signal detects them more quickly and clearly than other signals.

P367 Significant impact on health care utilization upon implementation of an electronic IBD care management program.

Abstract Background The management of Inflammatory Bowel Diseases (IBD) remains to be a significant challenge because of its unpredictable clinical course and high associated costs. Recently, we implemented an electronic IBD care management program, provided by DEARhealth BV, which offers care-pathways that configurate according to individual IBD risk profiles, called THINK. High-risk (HR) IBD patients (current biochemical disease activity) will receive more intense monitoring and provider interactions compared to Low-risk (LR) (&amp;gt;3 years in remission) and Intermediate-risk (IR) (other patients) individuals (figure 1). In this project, before implementation of THINK, we aimed to assess whether 1. Current healthcare utilisation (HCU) (consultations, lab tests, calprotectin) differs between risk groups and 2, what the effect of implementing THINK would be on HCU (focused on consultations and laboratory tests) in a retrospective-analysed population. Methods 161 consecutive IBD patients in hospital A, categorized in either the HR or IR group, were studied prospectively during 1 year before implementation of THINK. HCU was captured using an electronical medical record (EMR) and included consultations, blood tests and calprotectin. Furthermore, HCU in the years 2018-2019 in the IBD population of hospital B was studied (extraction of EMR). This historical data was compared to the THINK care path (see figure 1) when optimal implemented. The analysis focused on the activities present in de THINK care pathway (consultations and laboratory tests) to uncover the potential of the electronic IBD care management program. Results Baseline characteristics for hospital A cohort (N=161) are summed in Table 1. We followed 102 patients (60 Crohn) in the IR group and 59 patients in the HR group (29 Crohn). As expected, significant more HCU was present in the HR group (table 2). In hospital B, the utilization impact assessment was performed in the IBD cohort of 543 Crohn and 495 colitis patients. A difference in clinic consults was observed of -52,7% (HR), -53.5% (IR) and -29,3% (LR). The difference in laboratory tests utilization was found -54,0% (HR), -27.8% (IR) and -3,1% (LR). Conclusion Our initial experiences with a novel IBD management program demonstrated the feasibility of accurate remote disease activity monitoring and significant impact on utilization patterns due to the risk-based care pathway configurations. However, the analysis shows the ideal fictive situation when using standardized care pathways which cannot fully be considered the real-time situation. In this analysis no unexpected events are included. Future research should investigate the realization of these predictions.

RANKING OF CRITERIA FOR ORDER ALLOCATION IN A RUBBER GLOVE MANUFACTURING FACTORY USING ANALYTIC HIERARCHY PROCESS (AHP)

Malaysia is the world’s leading producer of rubber gloves, among over 150 manufacturers worldwide. Based on current practice among the manufacturer of rubber gloves, there is no fixed guideline in planning for the orders based on various criteria as each criterion has its importance, and the orders are planned based on the real-time situation. Therefore, in this study, the criteria to be considered for order allocation to factories and their importance were determined using Analytical Hierarchical Process (AHP) technique. Six criteria, namely quality, cost, lead time, capacity, special requirement, and regulation compliance, were identified based on the literature search of past studies in the field and supported by the expert’s opinion. Later, the experts ranked the importance of each criterion using a specifically designed questionnaire employing the AHP method. The pairwise comparison matrix was consistent with a consistency ratio (CR) value of 0.0495. Thus, the six criteria by ranking top to bottom with respective weightage are quality (25.81%), cost (21.7%), lead time (20.73%), regulation compliance (16.86%), special requirement (7.86%), and capacity (7.04%). In summary, the objectives of this research have been successfully met, according to the findings, and the criteria ranking can be used as a guideline by rubber glove manufacturers in planning for order allocation.

Reduced gamma oscillation during visual processing of the mother's face in children with autism spectrum disorder: A pilot study.

This study aimed to investigate gamma oscillations related to face processing of children with autism spectrum disorders and typically developed children using magnetoencephalography. We developed stimuli that included naturalistic real-time eye-gaze situations between participants and their mothers. Eighteen young children with autism spectrum disorders (62-97 months) and 24 typically developed children (61-79 months) were included. The magnetoencephalography data were analyzed in the bilateral banks of the superior temporal sulcus, fusiform gyrus, and pericalcarine cortex for frequency ranges 30-59 and 61-90 Hz. The gamma oscillation normalized values were calculated to compare the face condition (children gazing at mother's face) and control measurements (baseline) using the following formula: (face - control)/(face + control). The results revealed significant differences in gamma oscillation normalized values in the low gamma band (30-59 Hz) in the right banks of the superior temporal sulcus, right fusiform gyrus, and right pericalcarine cortex between children with autism spectrum disorders and typically developed children. Furthermore, there were significant differences in gamma oscillation normalized values in the high gamma band (61-90 Hz) in the right banks of the superior temporal sulcus, bilateral fusiform gyrus, and bilateral pericalcarine cortex between the groups. This report is the first magnetoencephalography study revealing atypical face processing in young children with autism spectrum disorders using relevant stimuli between participants and their mothers. Our naturalistic paradigm provides a useful assessment of social communication traits and a valuable insight into the underlying neural mechanisms in children with autism spectrum disorders.

Disentangled Dynamic Deviation Transformer Networks for Multivariate Time Series Anomaly Detection.

Graph neural networks have been widely used by multivariate time series-based anomaly detection algorithms to model the dependencies of system sensors. Previous studies have focused on learning the fixed dependency patterns between sensors. However, they ignore that the inter-sensor and temporal dependencies of time series are highly nonlinear and dynamic, leading to inevitable false alarms. In this paper, we propose a novel disentangled dynamic deviation transformer network (D3TN) for anomaly detection of multivariate time series, which jointly exploits multiscale dynamic inter-sensor dependencies and long-term temporal dependencies to improve the accuracy of multivariate time series prediction. Specifically, to disentangle the multiscale graph convolution, we design a novel disentangled multiscale aggregation scheme to better represent the hidden dependencies between sensors to learn fixed inter-sensor dependencies based on static topology. To capture dynamic inter-sensor dependencies determined by real-time monitoring situations and unexpected anomalies, we introduce a self-attention mechanism to model dynamic directed interactions in various potential subspaces influenced by various factors. In addition, complex temporal correlations across multiple time steps are simulated by processing the time series in parallel. Experiments on three real datasets show that the proposed D3TN significantly outperforms the state-of-the-art methods.

Diffusion of White-Hat Botnet Using Lifespan with Controllable Ripple Effect for Malware Removal in IoT Networks

Self-propagating malware has been infecting thousands of IoT devices and causing security breaches worldwide. Mitigating and cleaning self-propagating malware is important but challenging because they propagate unpredictably. White-hat botnets have been used to combat self-propagating malware with the concept of fight fire-with-fire. However, white-hat botnets can also overpopulate and consume the resource of IoT devices. Later, lifespan was introduced as a self-destruct measure to restrain white-hat botnets' overpopulation, but unable to change based on real-time situations. This paper proposes a method for diffusing white-hat botnets by controlling lifespan. The main contribution of this paper is that the method uses a dynamic lifespan that increases and decreases based on the congregation's situation of the self-propagating malware and white-hat botnets. The method tackles the problem of overpopulation of white-hat botnets since they can self-propagate by controlling the ripple effect that widens the white-hat botnet's diffusion area but suppresses the number of white-hat botnets to achieve a 'zero-botnet' situation. The effectiveness in reducing the overpopulation rate was confirmed. The experiment result showed that the ripple effect could reduce the number of white-hat botnets in the network by around 80%, depending on different control parameters.