Search results for "Task analysis"
showing 10 items of 86 documents
Semiautomatic Behavioral Change-Point Detection: A Case Study Analyzing Children Interactions With a Social Agent
2021
The study of human behaviors in cognitive sciences provides clues to understand and describe people’s personal and interpersonal functioning. In particular, the temporal analysis of behavioral dynamics can be a powerful tool to reveal events, correlations and causalities but also to discover abnormal behaviors. However, the annotation of these dynamics can be expensive in terms of temporal and human resources. To tackle this challenge, this paper proposes a methodology to semi-automatically annotate behavioral data. Behavioral dynamics can be expressed as sequences of simple dynamical processes: transitions between such processes are generally known as change-points. This paper describes th…
Continuous energy-efficient monitoring model for mobile ad hoc networks
2021
The monitoring of mobile ad hoc networks is an observation task that consists of analysing the operational status of these networks while evaluating their functionalities. In order to allow the whole network and applications to work properly, the monitoring task has become of considerable importance. It must be carried out in real-time by performing measurements, logs, configurations, etc. However, achieving continuous energy-efficient monitoring in mobile wireless networks is very challenging considering the environment features as well as the unpredictable behavior of the participating nodes. This paper outlines the challenges of continuous energy-efficient monitoring over mobile ad hoc n…
CrowdVAS-Net: A Deep-CNN Based Framework to Detect Abnormal Crowd-Motion Behavior in Videos for Predicting Crowd Disaster
2019
With the increased occurrences of crowd disasters like human stampedes, crowd management and their safety during mass gathering events like concerts, congregation or political rally, etc., are vital tasks for the security personnel. In this paper, we propose a framework named as CrowdVAS-Net for crowd-motion analysis that considers velocity, acceleration and saliency features in the video frames of a moving crowd. CrowdVAS-Net relies on a deep convolutional neural network (DCNN) for extracting motion and appearance feature representations from the video frames that help us in classifying the crowd-motion behavior as abnormal or normal from a short video clip. These feature representations a…
Comprehensive Experimental Analysis of Handcrafted Descriptors for Face Recognition
2018
Over the past few decades, LBP descriptor, which shown its high robustness in extracting discriminative features from an image, has been successfully applied in diverse challenging computer vision applications including face recognition. The efficiency and usability of the LBP operator and its success in various real world applications has inspired the development of much new powerful LBP variants. Indeed, after the appearance of the LBP operator, several renowned extensions and modifications of LBP have been proposed in the literature to the point that it can be difficult to recognize their respective LBP-related strategies, strengths and weaknesses according to a given application, and th…
Context-Awareness in Ensemble Recommender System Framework
2021
Recommender systems that provide recommendations based uniquely on information over users and items may not be very accurate in some situations. Therefore, adding contextual information to recommendations may be a good choice resulting in a system with increased precision. In an early work, we proposed an Ensemble Variational Autoencoders (EnsVAE) framework for recommendation. EnsVAE is adjusted to output interest probabilities by learning the distribution of each item's ratings and attempts to provide diverse novel items that are pertinent to users. In this paper, we propose and investigate a context awareness framework based on the Ensemblist Variational Autoencoders model with integratin…
Taking on the “Dark Side”–Coping With Technostress
2020
Technostress is stress that individuals experience due to their use of information technology. It is associated with critical workplace consequences including reduced productivity. While the negative consequences are well known, what is less understood is how individuals can cope with technostress to alleviate them. We report on two studies that explain how organizational IT users can cope with technostress. The first is a qualitative study conducted in the U.K., by interviewing thirty executives/knowledge workers. Here, we identified seven coping behaviors that individuals engage in, in response to technostress. The second is a survey of 846 U.S. employees who use IT in their workplace. He…
Online Scheduling of Task Graphs on Heterogeneous Platforms
2020
Modern computing platforms commonly include accelerators. We target the problem of scheduling applications modeled as task graphs on hybrid platforms made of two types of resources, such as CPUs and GPUs. We consider that task graphs are uncovered dynamically, and that the scheduler has information only on the available tasks, i.e., tasks whose predecessors have all been completed. Each task can be processed by either a CPU or a GPU, and the corresponding processing times are known. Our study extends a previous $4\sqrt{m/k}$ 4 m / k -competitive online algorithm by Amaris et al. [1] , where $m$ m is the number of CPUs and $k$ k the number of GPUs ( $m\geq k$ m ≥ k ). We prove that no online…
Use of Supercomputing towards the Generation of a Process Execution Plan in Distributed Real-Time Systems
2010
This work consider the scheduling of periodic tasks or processes with real-time constraints in a distributed environment. Each task must be executed meeting deadlines, precedence relationships and resources constraints. The problem of scheduling tasks on a distributed environment can be viewed as a problem of assigning processes to the processors but keeping the schedulability in local environments. Thus, the problem can be divided in two phases: the first phase is assigning processes to processors and the second is to schedule assigned processes in each processor in the distributed environment. This paper focuses in the first phase. It introduces a heuristic mechanism for assigning process…
Individual differences for self-regulating task-oriented reading activities.
2010
The goal of this study is to analyze the self-regulation processes present in task-oriented reading activities. In the 1st experiment, we examined the following self-regulation processes in the context of answering questions about an available text: (a) monitoring the comprehension of the question, (b) self-regulating the search process, and (c) monitoring the decision to search. Skilled and less skilled comprehenders from 7th and 8th grades read 2 texts and answered 16 questions while all their actions were recorded on a computer. We hypothesized that skilled comprehenders would differ from less skilled comprehenders on the 1st 2 processes on the basis of their general comprehension skills…
ASR performance prediction on unseen broadcast programs using convolutional neural networks
2018
In this paper, we address a relatively new task: prediction of ASR performance on unseen broadcast programs. We first propose an heterogenous French corpus dedicated to this task. Two prediction approaches are compared: a state-of-the-art performance prediction based on regression (engineered features) and a new strategy based on convolutional neural networks (learnt features). We particularly focus on the combination of both textual (ASR transcription) and signal inputs. While the joint use of textual and signal features did not work for the regression baseline, the combination of inputs for CNNs leads to the best WER prediction performance. We also show that our CNN prediction remarkably …