Response Time Analysis Research Articles

The response time analysis of queuing model in cloud computing environment

In this paper, the queuing theory is used to model cloud computing environment. The aim of this job is to analyze the response time as a measure of the Quality of Service (QoS) of computer services. In the cloud computing, multi resources need to be allocated simultaneously to multiple customers. When a customer requests for a service, if servers are busy, the requested job enters into the waiting line until a server completes its service. So, this may lead to a bottleneck in the network. By modeling cloud platforms by a queuing network, it can be easy to determine and to measure the QoS. The arrival rate and the service rate of processing servers are two main parameters that can affect the performance of the model; so, they are used to analyze the performance of the model. This paper proposes a queuing model which is applied at multiple servers in order to analyze the response time and also to improve the network performance and QoS effectively in a cloud computing environment.

Response time analysis of memory-bandwidth-regulated multiframe mixed-criticality systems

The multiframe mixed-criticality task model eliminates the pessimism in many systems where the worst-case execution times (WCETs) of successive jobs vary greatly by design, in a known pattern. Existing feasibility analysis techniques for multiframe mixed-criticality tasks are shared-resource-oblivious, hence unsafe for commercial-off-the-shelf (COTS) multicore platforms with a memory controller shared among all cores. Conversely, the feasibility analyses that account for the interference on shared resource(s) in COTS platforms do not leverage the WCET variation in multiframe tasks. This paper extends the state-of-the-art by presenting an analysis that incorporates the memory access stall in memory-bandwidth-regulated multiframe mixed-criticality multicore systems. An exhaustive enumeration approach is proposed for this analysis to further enhance the schedulability success ratio. The running time of the exhaustive analysis is improved by proposing a pruning mechanism that eliminates the combinations of interfering job sequences that subsume others. Experimental evaluation, using synthetic task sets, demonstrates up to 72% improvement in terms of schedulability success ratio, compared to frame-agnostic analysis.

Effects of Gaze Fixation on the Performance of a Motor Imagery-Based Brain-Computer Interface.

Motor imagery-based brain-computer interfaces (BCIs) have been studied without controlling subjects’ gaze fixation position previously. The effect of gaze fixation and covert attention on the behavioral performance of BCI is still unknown. This study designed a gaze fixation controlled experiment. Subjects were required to conduct a secondary task of gaze fixation when performing the primary task of motor imagination. Subjects’ performance was analyzed according to the relationship between motor imagery target and the gaze fixation position, resulting in three BCI control conditions, i.e., congruent, incongruent, and center cross trials. A group of fourteen subjects was recruited. The average group performances of three different conditions did not show statistically significant differences in terms of BCI control accuracy, feedback duration, and trajectory length. Further analysis of gaze shift response time revealed a significantly shorter response time for congruent trials compared to incongruent trials. Meanwhile, the parietal occipital cortex also showed active neural activities for congruent and incongruent trials, and this was revealed by a contrast analysis of R-square values and lateralization index. However, the lateralization index computed from the parietal and occipital areas was not correlated with the BCI behavioral performance. Subjects’ BCI behavioral performance was not affected by the position of gaze fixation and covert attention. This indicated that motor imagery-based BCI could be used freely in robotic arm control without sacrificing performance.

Exact Response Time Analysis of Preemptive Priority Scheduling with Switching Overhead

The study of preemptive scheduling is essential to computer systems [15, 12, 3, 4]. Motivated by this, decades of queueing theory research have been done on the subject [19, 18, 16, 13, 21, 8, 17, 2, 11, 20, 10, 1]. However, almost all queuing theoretic literature on preemptive scheduling concerns systems without switching overhead - pausing or resuming a job is assumed to be instant. Practically speaking, switching in computer systems incurs some overhead [14], which causes a divide between models in research and the real world.

Computing exact WCRT for typed DAG tasks on heterogeneous multi-core processors

Heterogeneous multi-core architectures achieve high performance and energy efficiency in real-time domain towards various applications. Most real-time parallel applications on heterogeneous multi-cores can be modeled as a typed directed acyclic graph (DAG) task, where the workload of each vertex is only allowed to execute on a particular type of core. Traditional worst-case response time (WCRT) analysis methods for these applications modeled as DAG are very pessimistic and impractical, i.e., there is a big gap between existing WCRT bounds and the exact WCRT. In this paper, we propose a satisfiability modulo theories-based method to exactly analyze the WCRT of the typed DAG task scheduled upon heterogeneous multi-cores. Experimental results show that our method can significantly improve the precision of the WCRT, and thus, dramatically increase the acceptance rate in the schedulability analysis.

Neural Substrates of the Drift-Diffusion Model in Brain Disorders.

Many studies on the drift-diffusion model (DDM) explain decision-making based on a unified analysis of both accuracy and response times. This review provides an in-depth account of the recent advances in DDM research which ground different DDM parameters on several brain areas, including the cortex and basal ganglia. Furthermore, we discuss the changes in DDM parameters due to structural and functional impairments in several clinical disorders, including Parkinson's disease, Attention Deficit Hyperactivity Disorder (ADHD), Autism Spectrum Disorders, Obsessive-Compulsive Disorder (OCD), and schizophrenia. This review thus uses DDM to provide a theoretical understanding of different brain disorders.

Interval-Valued Invehicular Latency Characterization for Risk/Fault Management Purposes

Guaranteeing the reliability of Intelligent Transportation Systems (ITSs) is an overlapping issue. Not only the in-road risks must be mastered, but also the internal threats that emerge from the ITS material aspects should be addressed carefully. Most of the safety verification solutions ignore the limitations issued from the ITS onboard embedded structure, such as the invehicular communication delays. In this work, a novel Response Time Analysis (RTA) method is introduced to bridge the gap between the risk/failure management issues and the material aspects (the invehicular communication delays in particular) for ITSs. Aside of its simplicity, the suggested RTA reveals all potential latency scenarios by returning an interval estimation of delays. The interval results enclose certainly the exact delay that may occur in runtime. As a proof of concept of the introduced RTA algorithm, the Hardware-in-the-Loop (HIL) validation technique is adopted. The HIL platform is realized thanks to a model of a modern industrial automotive system. The validation work proves the consistency and efficiency of the proposed RTA solution to ensure ITS safety assurance.

The impact of decoys on a genetic oscillator based on coupled positive-negative feedbacks

Within cells, transcription factors (TFs) bind to a wide range of nonspecific genomic sites in addition to their target sites. Binding to such high affinity “decoys” has been shown to qualitatively alter the dynamics of gene regulatory circuits. Analyzing simple gene expression models with decoy binding we derive formulas for the TF response time as a function of the number of decoys, binding affinity, and stability of the decoy-bound TF. Our results show that while on one hand, decoys make the response sluggish whenever decoy binding stabilizes the TF, on the other hand, decoys can accelerate responses by destabilizing the bound TF. We apply these results in the context of a genetic oscillator based on an activator-repressor motif, where sustained oscillations result from a rapid activator-mediated positive feedback working in conjunction with a slow repressor-mediated negative feedback. Consistent with our response time analysis, we find that activator binding to decoy sites can destroy oscillations in the case of a stable decoy-activator complex that functions to slow down the positive feedback. In contrast, an unstable decoy-activator complex can expand the oscillatory parameter regime. In conclusion, our response time analysis provides intuitive insights into the emergence of sustained oscillations.

Effects of color–emotion association on facial expression judgments

Color and emotion are metaphorically associated in the human mind. This color–emotion association affects perceptual judgment. For example, stimuli representing colors can affect judgment of facial expressions. The present study examined whether colors associated with happiness (e.g., yellow) and sadness (e.g., blue and gray) facilitate judgments of the associated emotions in facial expressions. We also examined whether temporal proximity between color and facial stimuli interacts with any of these effects. Participants were presented with pictures of a happy or sad face against a yellow-, blue-, or gray-colored background and asked to judge whether the face represented happiness or sadness as quickly as possible. The face stimulus was presented simultaneously (Experiment 1) or preceded for one second by the colored background (Experiment 2). The analysis of response time showed that yellow facilitated happiness judgment, while neither blue nor gray facilitated sadness judgment. Moreover, the effect was found only when the face and color stimuli were presented simultaneously. The results imply that the association of sadness with blue and gray is weak and, consequently, does not affect emotional judgment. Our results also suggest that temporal proximity is critical for the effect of the color–emotion association (e.g., yellow–happiness) on emotional judgment.

Worst-Case Response Time Analysis of Multitype DAG Tasks Based on Reconstruction

With the wide application of heterogeneous multi-core processor real-time systems, the existing analysis methods of worst-case response time (WCRT) overestimate the blocking information among tasks, resulting in a rather pessimistic estimation. To improve the accuracy of the WCRT, we propose a reconstruction-based WCRT analysis method for multi-type directed acyclic graph (DAG) tasks scheduling algorithm(RMDS). The RMDS algorithm comprises the following steps: First, we unitize all task nodes in the multi-type DAG task; Then, we use key factors as task priorities to schedule tasks and reconstruct the DAG task model into a parallel node segment model; Finally, we estimate the WCRT of multi-type DAG tasks according to the parallel node segment model to assess task schedulability. To verify the performance of our algorithm, we compared it with traditional algorithms. RDMS showed an acceptance rate 6.13% higher and its overall performance increased by 25.95% in comparison with traditional algorithms.

High-Capacity Real-Time Face Retrieval Recognition Algorithm Based on Task Scheduling Model for the Treatment Area of Hospital

This paper presents an in-depth study of face detection, face feature extraction, and face classification from three important components of a high-capacity face recognition system for the treatment area of hospital and a study of a high-capacity real-time face retrieval and recognition algorithm for the treatment area of hospital based on a task scheduling model. Considering the real-time nature of our system, our face feature extraction network is modeled by DeepID, and the network is slightly improved by introducing a central loss verification signal to train a DeepID-like network model using central loss and use it to extract face features. To further investigate and optimize the schedulability analysis problem of the directed graph real-time task model, this paper proposes a rigorous and approximate response time analysis method for the directed graph real-time task model with an arbitrary time frame. Based on the theoretical results of the greatly additive algebra, it is shown that the coherent qualifying function is linearly periodic, i.e., the function can be represented by a finite nonperiodic part and an infinitely repeated periodic part, thus calculating the coherent qualifying function independent of the magnitude of the interval time. The algorithm for high-capacity real-time face retrieval and recognition in the treatment area of hospital based on the task scheduling model is further investigated, and a face database is established by using the PCA dimensionality reduction technique. Based on the internal architecture of the processor, image preprocessing and IP core packaging are implemented, and the hardware engineering of the high-capacity real-time face recognition system for hospital visits is built using the IP-based design concept. The performance tests of the face detection model and feature extraction network show that the face detection model has a significant reduction in false-positive rate, better fitting of border regression, and improved time performance. The face feature extraction network has no overfitting, and the features are highly discriminative with small feature extraction time consumption. The high-capacity real-time face recognition system for the treatment area of hospital combined with the optimized directed graph task scheduling model can approach 25 fps, which meets the real-time requirements, and the face recognition rate surpasses that of real people. It realizes the intelligence, self-help, and autonomy of medical services and satisfies the medical needs of users in all aspects.

The influence of location, ownership, and the presence of a coactor on the processing of objects.

Humans operate in complex environments where social interactions require individuals to constantly attend to people and objects around them. Despite the complexity of these interactions from a visuomotor perspective, humans can engage and thrive in social settings. The purpose of the current study was to examine the simultaneous influence of multiple social cues (i.e., ownership and the presence of a coactor) on the processing of objects. Participants performed an object-based compatibility task in the presence and absence of a coacting confederate. Participants indicated whether pictures of mugs (that were either self-owned or unowned) were upright or inverted. The pictures appeared at one of 2 locations (a near or far location relative to the participant) on a computer screen laid flat on (parallel to) the tabletop. When present, the coactor stood on the opposite side of the screen/table. Analysis of response times (RTs) indicated that the processing of objects was influenced by the object's ownership status, the presence of the coactor, and where the object was located on the screen. Specifically, RTs for pictures of self-owned mugs were shorter than unowned mugs, but only when the pictures were located at the near location. Further, the presence of a confederate resulted in shorter RTs for pictures located at the near but not the far location. These findings suggest that when objects were placed at the far location, the additional social cues of ownership and social context did not influence visuomotor processing of the objects. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

Neural response time analysis: Explainable artificial intelligence using only a stopwatch

AbstractHow would you describe the features that a deep learning model composes if you were restricted to measuring observable behaviours? Explainable artificial intelligence (XAI) methods rely on privileged access to model architecture and parameters that is not always feasible for most users, practitioners and regulators. Inspired by cognitive psychology research on humans, we present a case for measuring response times (RTs) of a forward pass using only the system clock as a technique for XAI. Our method applies to the growing class of models that use input‐adaptive dynamic inference and we also extend our approach to standard models that are converted to dynamic inference post hoc. The experimental logic is simple: If the researcher can contrive a stimulus set where variability among input features is tightly controlled, differences in RT for those inputs can be attributed to the way the model composes those features. First, we show that RT is sensitive to difficult, complex features by comparing RTs from ObjectNet and ImageNet. Next, we make specific a priori predictions about RT for abstract features present in the SCEGRAM data set, where object recognition in humans depends on complex intrascene object‐object relationships. Finally, we show that RT profiles bear specificity for class identity and therefore the features that define classes. These results cast light on the model's feature space without opening the black box.

Beyond the limitations of real-time scheduling theory: a unified scheduling theory for the analysis of real-time systems

We investigate the mathematical properties of event bound functions as they are used in the worst-case response time analysis and utilization tests. We figure out the differences and similarities between the two approaches. Based on this analysis, we derive a more general form do describe events and event bounds. This new unified approach gives clear new insights in the investigation of real-time systems, simplifies the models and will support algebraic proofs in future work. In the end, we present a unified analysis which allows the algebraic definition of any scheduler. Introducing such functions to the real-time scheduling theory will lead two a more systematic way to integrate new concepts and applications to the theory. Last but not least, we show how the response time analysis in dynamic scheduling can be improved.

Evaluation of Fire Disaster Emergency Response Capacity using Open-source Data and Response Time Analysis in Ilorin Metropolis

This study evaluates the response capacity to fire disaster emergency response system in Ilorin metropolis using Open-source data and response time analysis. Road and street information were obtained from Geofabrik. In addition, coordinates of fire service stations and fire disaster risk spots, specifically fuel and gas stations were acquired using Garmin 76X handheld GPS. Using the relationship of the length of road segments and speed, the travel time was computed in ArcGIS 10.4 environment. With the Network analyst tool, the response capability of the fire stations was evaluated at different response times (1, 2, and 3 minutes) based on service area coverage. The results showed that the fire stations could only cover 0.24%, 0.68%, and 1.22% of the service area within 1-, 2- and 3-minute response time, respectively, whereas 97.86% of the metropolis requires longer time (>3 minutes). Finding from this study has revealed the inadequacy of the existing fire disaster emergency response system to effectively cover the city. This will be useful for local and state governments in policy directives on strengthening fire disaster emergency response structure.

EFFECT OF DIFFERING EXERCISE INTENSITIES ON THE RESPONSE TIME OF GYMNASTS AND NON-GYMNASTS IN 3D CUBE MENTAL ROTATION TASK

The purpose of the present study was to examine the effect of different levels of exercise intensity on mental rotation performance in gymnasts vs. non-gymnasts'. Forty-one participants (18 females; mean age 20.94±0.89 years, height 1.65±0.03 m, body mass58.94±5.67 kg, and 23 males; mean age 21.26±0.99 years, height 1.70±0.05 m, body mass 66.87±4.52 kg) divided into two groups (i.e., gymnasts and non-gymnast) voluntarily took part in the present study. The two groups performed a 3D cube mental rotation task at rest, and then performed the same task preceded by short bouts of intense exercise at 60%, 80%, 100% and 120% of their maximum aerobic speed (MAS). The analyses of response times showed that gymnasts performed the mental rotation task faster following bouts of intense exercise than in rest condition, especially in 60% and 80% of MAS, whereas non-gymnasts increased their response times after moderate exercises (i.e., 60 et 80 % of MAS) and stabilized their performance (i.e., equally at the rest) after intense exercises. This finding highlights the specific physical expertise as a variable that can affect the influence of exercise on cognitive processing.

The influence of sociophonetic knowledge on lexical encoding across dialects

The goal of this study was to determine if the strength of lexical encoding differed between regional dialects as a function of the explicit social knowledge listeners held about the dialects or talkers. Participants heard lexical items produced in the Midland, Northern, and Southern dialects of American English in an explicit lexical recognition memory task. Some participants were told the regional backgrounds of the talkers while others were not. It was predicted that encoding would be most robust for tokens produced in the ideologically standard Midland dialect and would be more robust for the socially stereotyped Southern dialect compared with the non-stereotyped Northern dialect. Being told regional information about the talkers was predicted to increase the robustness of encoding. Analysis of response times provided some evidence of a standard dialect benefit across listeners from the three regions, though this effect varied depending on the vowel contained in the stimulus item. Interestingly, being given regional information about the talkers did not increase the robustness with which tokens were encoded; for some listeners, it hindered performance. These results indicate that social knowledge about linguistic varieties can be highly integrated with speech processing, but that it is not facilitative in all situations.

Excluding Parallel Execution to Improve Global Fixed Priority Response Time Analysis

Response Time Analysis (RTA) is an effective method for testing the schedulability of real-time tasks on multiprocessor platforms. Existing RTAs for global fixed priority scheduling calculate the upper bound of the worst case response time of each task. Given a target task, existing RTAs first calculate the workload upper bound of each higher priority task (than the target task), and then calculate the interference on the target task by each higher priority task according to the obtained workload upper bounds. The workload of a task consists of three parts: carry-in, body and carry-out. The interference from all these three parts may be overestimated in existing RTAs. However, although the overestimation of the interference from body is the major factor that causes the low accuracy of existing RTAs, all existing work only focuses on how to reduce the overestimation of the interference from carry-in, and there is no method to reduce the overestimation of the interference from body or carry-out. In this work, we propose a method to calculate the lower bound of the accumulative time in which the target task and higher priority tasks are executed in parallel. By excluding the parallel execution time from the interference, we derive a new RTA test that can reduce the overestimation of the interference from all three parts of the workload. Extensive experiments are conducted to verify the superior performance of the proposed RTA test.

An Efficient Execution Framework of Two-Part Execution Scenario Analysis

Response Time Analysis ( RTA ) is an important and promising technique for analyzing the schedulability of real-time tasks under both Global Fixed-Priority ( G-FP ) scheduling and Global Earliest Deadline First ( G-EDF ) scheduling. Most existing RTA methods for tasks under global scheduling are dominated by partitioned scheduling, due to the pessimism of the -based interference calculation where is the number of processors. Two-part execution scenario is an effective technique that addresses this pessimism at the cost of efficiency. The major idea of two-part execution scenario is to calculate a more accurate upper bound of the interference by dividing the execution of the target job into two parts and calculating the interference on the target job in each part. This article proposes a novel RTA execution framework that improves two-part execution scenario by reducing some unnecessary calculation, without sacrificing accuracy of the schedulability test. The key observation is that, after the division of the execution of the target job, two-part execution scenario enumerates all possible execution time of the target job in the first part for calculating the final Worst-Case Response Time ( WCRT ). However, only some special execution time can cause the final result. A set of experiments is conducted to test the performance of the proposed execution framework and the result shows that the proposed execution framework can improve the efficiency of two-part execution scenario analysis by up to in terms of the execution time.

Yes! I love my mother as much as myself: Self- and mother-association effects in an Indian sample.

Information associated with the self is preferentially processed compared to information associated with others. However, cultural differences appear to exist in the way information is processed about those close to us like our mothers. In Eastern compared with Western cultures, information about the mother seems to be processed as well as our self. However, it is not clear whether this lack of difference is due to familiarity or would extend to processing arbitrary perceptual information associated with different categorical labels. The current study employs a perceptual association paradigm in which category labels like self, mother, and none are associated with arbitrary shapes to study self versus mother processing in an Indian sample. We hypothesised that there would be no difference between self and mother processing given the familial and collectivistic tendencies in India. Participants in two experiments performed a matching task between a shape and a pre-assigned category label, with self, mother, and none as categories in Experiment 1A and self, friend, and none as categories in Experiment 1B. As expected, analysis of response times (RT), accuracies, and signal detection theoretic measures showed that information about mother is processed as well as self in Experiment 1A, but this effect is not present with friend in Experiment 1B. Moreover, participants' processing for the self-associated information gets attenuated depending upon the other close person category used in the task (friend vs. mother) indicating that self-information processing is dynamically dependent on the categorical contexts in which such processing takes place. Our findings have implications for understanding the processing of self-associated information across cultures and contexts.