Execution Metrics Research Articles

AI Based Mental Health Prediction System

Abstract: The most common mood disorder in the world, depression has a considerable negative influence on health and functionality as well as profound personal, familial, and society implications. The correct and timely identification of depressionrelated symptoms may have numerous advantages for both doctors and those who are affected. The current work aimed to develop and clinically test a system capable of identifying visual signs of melancholy and supporting physician decisions. Programmable suffering assessment based on visible signals is a rapidly expanding research area. Picture handling and AI computations are the focus of the current thorough evaluation of existing approaches as described in more than sixty distributions during the last 10 years. The current datasets, various information-gathering methods, and visual cues of misery are compiled. The survey depicts estimates for visual element extraction, dimensionality reduction, layout and relapse choosing options, as well as numerous combination techniques. Incorporating a quantitative meta-analysis of announced results based on execution metrics risk-tolerant, it identifies general trends and significant irksome issues to be taken into account in ongoing investigations of programmed sadness appraisal using visible signs either alone or in combination with obvious signals. Additionally, the proposed work used deep learning to predict the level of the downturn as shown by the contribution of current face photographs.

Evaluation of Hybrid Control and Palpation Assistance for Situational Awareness in Telemanipulated Task Execution

The use of intelligent feedback modalities to control and react to interaction forces during surgical procedures is an important factor in enabling safe and precise surgery. We explore the use of a model-mediated telemanipulation framework to enhance a user’s situational awareness using assistive virtual fixtures and semi-automated task execution for safe and intuitive environment interaction during robotic laparoscopic surgery. The framework allows stiffness mapping with semi-autonomous excitation, hybrid position-force control, and model updates during soft geometry contact. A 24-person study was carried out at 3 sites in simulated ablation and palpation of phantom anatomy. Compared to methods lacking intelligent feedback and guidance, the proposed framework improved task execution metrics (force regulation, completion time, path-following error) and reduced user effort.

SHASTA

A key requirement for efficient general purpose approximate computing is an amalgamation of flexible hardware design and intelligent application tuning, which together can leverage the appropriate amount of approximation that the applications engender and reap the best efficiency gains from them. To achieve this, we have identified three important features to build better general-purpose cross-layer approximation systems: ① individual per-operation (“spatio-temporally fine-grained”) approximation, ② hardware-cognizant application tuning for approximation, ③ systemwide approximation-synergy. We build an efficient general purpose approximation system called SHASTA: Synergic HW-SW Architecture for Spatio-Temporal Approximation, to achieve these goals. 1 First, in terms of hardware, SHASTA approximates both compute and memory—SHASTA proposes (a) a form of timing approximation called Slack-control Approximation, which controls the computation timing of each approximation operation and (b) a Dynamic Pre-L1 Load Approximation mechanism to approximate loads prior to cache access. These hardware mechanisms are designed to achieve fine-grained spatio-temporally diverse approximation. Next, SHASTA proposes a Hardware-cognizant Approximation Tuning mechanism to tune an application’s approximation to achieve the optimum execution efficiency under the prescribed error tolerance. The tuning mechanism is implemented atop a gradient descent algorithm and, thus, the application’s approximation is tuned along the steepest error vs. execution efficiency gradient. Finally, SHASTA is designed with a full-system perspective, which achieves Synergic benefits across its optimizations, building a closer-to-ideal general purpose approximation system. SHASTA is implemented on top of an OOO core and achieves mean speedups/energy savings of 20%–40% over a non-approximate baseline for greater than 90% accuracy—these benefits are substantial for applications executing on a traditional general purpose processing system. SHASTA can be tuned to specific accuracy constraints and execution metrics and is quantitatively shown to achieve 2–15× higher benefits, in terms of performance and energy, compared to prior work.

Hybrid Model for Antipattern Analysis in Web Services

Performance of service based systems depends on the architectural design of the web service architecture. Presence of Antipatterns in the system lead to maintainability issues and impact the overall quality of service computing architecture. Quality measurement of the service based architecture can be done using metric analysis using well defined thresholds. A variety of static metrics based detection approaches are defined in the literature but detection using dynamic metrics is still in infancy. The main objective of this paper is to propose a heuristic based hybrid model for Antipattern detection using static, interface and runtime execution metrics. A comparison of the result generated by the newly proposed model is done with the existing algorithm. The results generated using the proposed model clearly depicts that performance of the proposed hybrid model is better in terms of precision and recall rate. Along with that the threshold estimation technique used in the paper leads to generation of more precise threshold which ultimately optimizes the generated results.

Towards a Novel Framework for Automatic Big Data Detection

Big data is a ”relative” concept. It is the combination of data, application, and platform properties. Recently, big data specific technologies have emerged, including software frameworks, databases, hardware accelerators, storage technologies, etc. However, the automatic selection of these solutions for big data computations remains a non-trivial task. Presently, the big data tools are selected by analyzing the problem manually, or by using several performance prediction techniques. The manual identification is based on the data properties only, whereas the performance predictors only estimate basic execution metrics without linking them with big data (3Vs) thresholds. Hence, both ways of identification are mostly incorrect, which can lead to inefficient use of 3Vs optimizations, resulting into global inefficiency, reduced system performance, increasing power consumption, requiring greater effort on the part of the programming team, and misallocation of the hardware resources required for the task. In this regard, a novel framework has been proposed for automatic detection of 3Vs (Volume, Velocity, Variety) of big data, using machine learning. The detection is done through static code features, data, and platform properties, leading to relevant tool selection, and code generation, with minimal overheads, lesser programmer interventions, higher usability, and portability. Instead of handling each application with big data specialized solutions, or manually identifying the 3Vs, the framework can automatically detect and link the 3Vs to the relevant optimizations. Several standard applications have been tested using the proposed framework. In the case of volume, the average detection accuracy is up to 97.8% for seen and 95.9% for unseen applications. In the case of velocity, the average detection accuracy is up to 97.3% for seen and 92.6% for unseen applications. There is no margin of error in variety detection, as it has straightforward computations without any predictions. Furthermore, an airline recommendation system case study strengthens the effectiveness of the proposed approach.

GC-Wise: A Self-adaptive approach for memory-performance efficiency in Java VMs

Abstract High-level language runtimes are ubiquitous in every cloud deployment. From the geo-distributed heavy resources cloud provider to the new Fog and Edge deployment paradigms, all rely on these runtimes for portability, isolation and resource management. Across these clouds, an efficient resource management of several managed runtimes involves limiting the heap size of some VMs so that extra memory can be assigned to higher priority workloads. The challenges in this approach rely on the potential scale of systems and the need to make decisions in an application-driven way, because performance degradation can be severe, and therefore it should be minimized. Also, each tenant tends to repeat the execution of applications with similar memory-usage patterns, giving opportunity to reuse parameters known to work well for a given workload. This paper presents GC-Wise, a system to determine, at run-time, the best values for critical heap management parameters of the OpenJDK JVM, aiming to maximize memory-performance efficiency. GC-Wise comprises two main phases: 1) a training phase where it collects, with different heap resizing policies, representative execution metrics during the lifespan of a workload; and 2) an execution phase where an oracle matches the execution parameters of new workloads against those of already seen workloads, and enforces the best heap resizing policy. Distinctly from other works, the oracle can also decide upon unknown workloads. Using representative applications and different hardware setting (a resourceful server and a fog-like device), we show that our approach can lead to significant memory savings with low-impact on the throughput of applications. Furthermore, we show that we can predict with high accuracy the best heap resizing configuration in a relatively short period of time.

Investigating drivers' mandatory lane change behavior on the weaving section of freeway with managed lanes: A driving simulator study

Managed lanes (MLs) system has been widely implemented on freeways to mitigate congestion and improve efficiency. MLs are usually designed as concurrent with general-purpose lanes (GPLs), and separated by a barrier or painted stripe with several at-grade ingresses and egresses. However, these kinds of designs may result in weaving segments between ingress (egress) and on-ramp (off-ramp). For example, vehicles from on-ramp must cross multiple GPLs to get access to the ML. This study aims to investigate the safety effects of weaving length, traffic condition, and driver characteristics on drivers’ mandatory lane change behavior based on a driving simulator study. Mixed factorial design with two within-subject factors (traffic volume: off-peak and peak; speed harmonization (SH): SH and Non-SH) and one between-subject factor (weaving length per lane change (LLC): 600 feet, 1000 feet, and 1400 feet) were employed in this study. Fifty-four licensed drivers were recruited to conduct this driving simulator experiment. Based on the experimental data, three lane change decision metrics (i.e., lane change merging gap, duration, and patience time), three lane change execution metrics (i.e., maximum longitudinal deceleration, lateral acceleration, and steering wheel angle), and two surrogate-safety metrics (i.e., number of conflicts and time exposed time-to-collision) were analyzed. Results indicated that for the ingress of MLs (entrance weaving segment), 1000 feet LLC would be recommended if the space is limited, otherwise 1400 feet LLC is preferable. For the egress of MLs (exit weaving segment), however, only 1000 feet LLC was recommended since 1400 feet LLC was found to be significantly more dangerous than 600 and 1000 feet LLC. Moreover, peak traffic condition could significantly increase the difficulty of lane change behavior on the weaving segments, and speed harmonization could significantly improve the lane change safety on the entrance weaving segments.

Brokel: Towards enabling multi-level cloud elasticity on publish/subscribe brokers

Internet of Things networks together with the data that flow between networked smart devices are growing at unprecedented rates. Often brokers, or intermediaries nodes, combined with the publish/subscribe communication model represent one of the most used strategies to enable Internet of Things applications. At scalability viewpoint, cloud computing and its main feature named resource elasticity appear as an alternative to solve the use of over-provisioned clusters, which normally present a fixed number of resources. However, we perceive that today the elasticity and Pub/Sub duet presents several limitations, mainly related to application rewrite, single cloud elasticity limited to one level and false-positive resource reorganization actions. Aiming at bypassing the aforesaid problems, this article proposes Brokel, a multi-level elasticity model for Pub/Sub brokers. Users, things, and applications use Brokel as a centralized messaging service broker, but in the back-end the middleware provides better performance and cost (used resources × performance) on message delivery using virtual machine (VM) replication. Our scientific contribution regards the multi-level, orchestrator, and broker, and the addition of a geolocation domain name system service to define the most suitable entry point in the Pub/Sub architecture. Different execution scenarios and metrics were employed to evaluate a Brokel prototype using VMs that encapsulate the functionalities of Mosquitto and RabbitMQ brokers. The obtained results were encouraging in terms of application time, message throughput, and cost (application time × resource usage) when comparing elastic and non-elastic executions.

Prediction of user preference over shared-control paradigms for a robotic wheelchair.

The design of intelligent powered wheelchairs has traditionally focused heavily on providing effective and efficient navigation assistance. Significantly less attention has been given to the end-user's preference between different assistance paradigms. It is possible to include these subjective evaluations in the design process, for example by soliciting feedback in post-experiment questionnaires. However, constantly querying the user for feedback during real-world operation is not practical. In this paper, we present a model that correlates objective performance metrics and subjective evaluations of autonomous wheelchair control paradigms. Using off-the-shelf machine learning techniques, we show that it is possible to build a model that can predict the most preferred shared-control method from task execution metrics such as effort, safety, performance and utilization. We further characterize the relative contributions of each of these metrics to the individual choice of most preferred assistance paradigm. Our evaluation includes Spinal Cord Injured (SCI) and uninjured subject groups. The results show that our proposed correlation model enables the continuous tracking of user preference and offers the possibility of autonomy that is customized to each user.

Derivation of optimal input parameters for minimizing execution time of matrix-based computations on a GPU

Input parameters are the size of the grid and blocks assigned for GPU execution.We formulate execution metrics as functions of input parameters and GPU hardware to represent GPU computational behavior.From the execution metrics, optimal input parameters are derived to minimize GPU execution time.Three GPU kernels are tested: matrix-vector multiplication, matrix-matrix multiplication, and convolution.The derived parameters yield minimum or near-minimum execution time for all three kernels. As GPUs are continually being utilized as coprocessors, the demand for optimally utilizing them for various computations continues to grow. The goal of this work is to derive input parameters which yield the minimum execution time for matrix-based computations executing on a GPU. Input parameters are defined as the dimensions of the grid and blocks assigned for execution on the GPU. Since input parameters inadequately represent the executional behavior of the GPU, execution metrics are formulated as functions of the input parameters to represent the behavior. The execution metrics are architecture independent and are utilized to derive optimal input parameters, which are input parameters that yield the minimum execution time. Optimal input parameters are derived for the following matrix-based computations: matrix-vector multiplication (Mv), matrix-matrix multiplication (MM), and convolution. The derivation allows for selection of optimal input parameters without executing code. Results, for all matrix-based computations and sizes tested, show that utilizing the derived optimal input parameters often yields the minimum execution time, and, at worst, execution time within 13.6% of the minimum.

Consumer Privacy Enforcement in Context-Aware Web Services

Privacy protection constitutes a genuine human right reflected both in the legislation and in different aspects of software engineering. Sensitive information needs to be protected in end-users interaction with Web Services especially in cases, where context-aware features are included. In this work the authors address the inclusion of consumer privacy preferences in the provision of context-aware Web Services. To achieve this the authors propose, on the one hand, a preferences language, where end-users can specify their privacy options, namely Consumer Privacy Language, and, on the other hand, a seamless enforcement mechanism that considers consumer preferences by intercepting and modifying appropriately Simple Object Access Protocol request and response messages. The enforcement approach has been evaluated based on various execution metrics for an example use case consisting of various Web Services and for different users configurations demonstrating the usefulness of the approach assisting towards the provision of privacy-aware environments.

Uncertain Classification of Fault-Prone Software Modules

Many development organizations try to minimize faults in software as a means for improving customer satisfaction. Assuring high software quality often entails time-consuming and costly development processes. A software quality model based on software metrics can be used to guide enhancement efforts by predicting which modules are fault-prone. This paper presents statistical techniques to determine which predictions by a classification tree should be considered uncertain. We conducted a case study of a large legacy telecommunications system. One release was the basis for the training dataset, and the subsequent release was the basis for the evaluation dataset. We built a classification tree using the TREEDISC algorithm, which is based on χ2 tests of contingency tables. The model predicted whether a module was likely to have faults discovered by customers, or not, based on software product, process, and execution metrics. We simulated practical use of the model by classifying the modules in the evaluation dataset. The model achieved useful accuracy, in spite of the very small proportion of fault-prone modules in the system. We assessed whether the classes assigned to the leaves were appropriate by statistical tests, and found sizable subsets of modules with uncertain classification. Discovering which modules have uncertain classifications allows sophisticated enhancement strategies to resolve uncertainties. Moreover, TREEDISC is especially well suited to identifying uncertain classifications.

Classification-tree models of software-quality over multiple releases

This paper presents an empirical study that evaluates software-quality models over several releases, to address the question, How long will a model yield useful predictions? The classification and regression trees (CART) algorithm is introduced, CART can achieve a preferred balance between the two types of misclassification rates. This is desirable because misclassification of fault-prone modules often has much more severe consequences than misclassification of those that are not fault-prone. The case-study developed 2 classification-tree models based on 4 consecutive releases of a very large legacy telecommunication system. Forty-two software product, process and execution metrics were candidate predictors. Model 1 used measurements of the first release as the training data set; this model had 11 important predictors. Model 2 used measurements of the second release as the training data set; this model had 15 important predictors. Measurements of subsequent releases were evaluation data sets. Analysis of the models' predictors yielded insights into various software development practices. Both models had accuracy that would be useful to developers. One might suppose that software-quality models lose their value very quickly over successive releases due to evolution of the product and the underlying development processes. The authors found the models remained useful over all the releases studied.