Characteristics of agent-based hierarchical diff-EDF schedulability over heterogeneous real-time Packet networks

Packet networks are currently enabling the integration of heterogeneous traffic with a wide range of characteristics that extend from video traffic with stringent QoS requirements to best-effort traffic requiring no guarantees. QoS guarantees can be provided in packet networks by the use of proper packet scheduling algorithms. In this paper, we propose a new priority assignment scheduling algorithm, Hierarchical Diff-EDF, which can meet the real-time needs while continuing to provide best effort service over heterogeneous network traffic environment. The Hierarchical Diff-EDF service meets the flow miss rate requirements through the combination of single step hierarchal scheduling for the different network flows and the admission control mechanism that detects the overload conditions to adjust packets' priorities. To examine the proposed scheduler, we introduced an attempt to provide an exact analytical solution. The attempt showed that the solution was apparently very complicated due to the high interdependences between the system queues' service. Hence, the use of simulation seems inevitable. A multi-agent simulation that takes the inspiration from object-oriented programming is adopted. The simulation itself is aimed to the construction of a set of elements which, when fully elaborated, define an agent system specification. When evaluating our proposed scheduler, it was extremely obvious that the Hierarchical Diff-EDF scheduler performs over both of the EDF and Diff-EDF schedulers

Exploration and analysis of On-Surface and In-Air handwriting attributes to improve dysgraphia disorder diagnosis in children based on machine learning methods

Dysgraphia is a type of learning disorder that affects children’s writing skills. Poor writing skills can obstruct students’ academic growth if it is undiagnosed and untreated properly in the early stages. The irregularity in the symptoms and varying levels of difficulty at each age level made the dysgraphia diagnosis task quite complex. This work focuses on developing machine learning-based automated methods to build the dysgraphia screening tool for children. The proposed work analyzes the various attributes of online handwritten data recorded by digitizing tablets during On-Surface (when the pen is on the tablet’s surface) and In-Air activity (when the pen is away from the tablet’s surface). The proposed work has considered feature extraction from the whole handwriting data in a combined manner instead of feature extraction from task-specific (word, letter, sentence, etc.) handwritten data separately to reduce the number of features. This approach has significantly reduced the number of features by about 85%. Extracted features are used to train and evaluate multiple machine learning classifiers such as K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Random forest, and AdaBoost. Evaluation in a publicly available dataset indicates that the AdaBoost classifier achieved a classification accuracy of 80.8%, which is 1.3% more than the state-of-the-art method. Moreover, a deep analysis of different characteristics (kinematic, dynamic, temporal, spatial, etc.) of online handwriting is conducted to examine their significance in distinguishing normal and abnormal handwritten data. The analysis can help psychologists determine what attributes and methods should be considered for effective treatment.This publication was supported by Qatar University Graduate Assistant Grant . The contents of this publication are solely the responsibility of the authors and do not necessarily represent the official views of Qatar University

Impact of safety-related dose reductions or discontinuations on sustained virologic response in HCV-infected patients: Results from the GUARD-C Cohort

BACKGROUND: Despite the introduction of direct-acting antiviral agents for chronic hepatitis C virus (HCV) infection, peginterferon alfa/ribavirin remains relevant in many resource-constrained settings. The non-randomized GUARD-C cohort investigated baseline predictors of safety-related dose reductions or discontinuations (sr-RD) and their impact on sustained virologic response (SVR) in patients receiving peginterferon alfa/ribavirin in routine practice. METHODS: A total of 3181 HCV-mono-infected treatment-naive patients were assigned to 24 or 48 weeks of peginterferon alfa/ribavirin by their physician. Patients were categorized by time-to-first sr-RD (Week 4/12). Detailed analyses of the impact of sr-RD on SVR24 (HCV RNA <50 IU/mL) were conducted in 951 Caucasian, noncirrhotic genotype (G)1 patients assigned to peginterferon alfa-2a/ribavirin for 48 weeks. The probability of SVR24 was identified by a baseline scoring system (range: 0-9 points) on which scores of 5 to 9 and <5 represent high and low probability of SVR24, respectively. RESULTS: SVR24 rates were 46.1% (754/1634), 77.1% (279/362), 68.0% (514/756), and 51.3% (203/396), respectively, in G1, 2, 3, and 4 patients. Overall, 16.9% and 21.8% patients experienced 651 sr-RD for peginterferon alfa and ribavirin, respectively. Among Caucasian noncirrhotic G1 patients: female sex, lower body mass index, pre-existing cardiovascular/pulmonary disease, and low hematological indices were prognostic factors of sr-RD; SVR24 was lower in patients with 651 vs. no sr-RD by Week 4 (37.9% vs. 54.4%; P = 0.0046) and Week 12 (41.7% vs. 55.3%; P = 0.0016); sr-RD by Week 4/12 significantly reduced SVR24 in patients with scores <5 but not 655. CONCLUSIONS: In conclusion, sr-RD to peginterferon alfa-2a/ribavirin significantly impacts on SVR24 rates in treatment-naive G1 noncirrhotic Caucasian patients. Baseline characteristics can help select patients with a high probability of SVR24 and a low probability of sr-RD with peginterferon alfa-2a/ribavirin

Towards adaptive multimedia system for assisting children with Arabic learning difficulties

Children with learning difficulties (LD) are increasing dramatically in the Arab world. Such children require quick intervention especially during the early childhood years. This paper presents a dynamic multimedia system for helping children with LD to overcome their learning problems. We introduce an approach to automatically convert modern standard Arabic children's stories to the finest representative images that can efficiently illustrate the meaning of words. Specifically, first, we apply natural language processing techniques to analyze the text in stories and we extract keywords of all characters and events in each sentence. Second, we apply an image captioning process through a pre-trained deep learning model for all retrieved images from our multimedia database as well as the Google search engine. Third, using sentence similarities, most significant images are retrieved back by selecting top highest similarity values. The proposed system aims to better enhance understanding, communications, and thinking skills for children with LD in elementary schools and special education centers.NPRP grant #10-0205-170346 from the Qatar National Research Fund (a member of Qatar Foundation)