Miracle-dz/UniFAR-FTUs huggingface dataset:
DOS: A Spatial System Offering Extremely High-Throughput Road Distance Computations

Large analytic applications on road networks including simulations, logistics, location-based advertisement, and transportation planning require shortest distance/time methods that provide high throughput (i.e., distance/time computations per second). Our previous work discussed how to process graph distance computations in a PostgreSQL database on a large road network, e.g., 60 K distance computations per second per machine, how to “scale out” by using a Spark cluster to achieve 73 . 8 K distance computations per second per machine, and how to obtain a extremely high-throughput so-lution in memory for city-sized road networks, e.g., 6 . 7 M distance computations per second. However, there is no solution that could achieve more than 1 M throughput for large road networks. In an industrial setting, most state-of-the-art solutions yield 5 K − 10 K shortest distance computations per second per machine even with multi-threads. In this paper, we propose a new distance oracle system (DOS) for large road networks. It can solve most spatial analytic queries, and its throughput achieves 5 M distance computations per second even on the whole USA road network. For example, a 10 K × 10 K origin-distance (OD) matrix can be computed in 20 seconds.
Impact of Non-stationary Workload on Resource Reservation Based Slack Reclamation Technical Report CU-CS-1053-09 May 2009

In open real-time systems with a non-stationary workload, tasks can be dynamically mapped to servers. If a server-based, slack reclamation scheduler is used, each task reserves a fraction of the bandwidth based on its resource estimation. However, the slack time scheduler makes no guarantee to a task beyond its processor capacity reserve, i.e., the scheduler cannot guarantee that a soft real-time task will receive a higher quality of service than is specified by its resource reservation. Nevertheless users often assume that a desired quality of service (QoS) can be achieved using an optimistic resource reservation. Even though existing slack time schedulers perform well in a closed real-time system (with well-behaved applications, stationary workloads, or excessive processor speed), their performance can drop significantly when a task changes its workload. We identify resource underbooking and overbooking problems in non-stationary workloads, resulting in a denial of service (DoS) phenomenon. We solve these problems by preventing DoS attack on the slack time scheduler; we also allow any server to donate slack time even when it is in an idle state. Finally, we introduce a forward donation algorithm that helps existing slack time schedulers reclaim extra slack time. By increasing the robustness of the scheduler, it can be used with broader spectrum of applications in an open real-time system.
AutoStyle : An Autoencoder Based Architecture for Fast & Real Time Audio Style Transfer

Recently, there has been great interest in the field of audio style transfer, where a stylized audio is generated by imposing the style of a reference audio on the content of a target audio. We improve on the current approaches which use neural networks to extract the content and the style of the audio signal and propose a new autoencoder based architecture for the task. This network generates a stylized audio for a content audio in a single forward pass. The proposed network architecture proves to be advantageous over the quality of audio produced and the time taken to train the network. The network is experimented on speech signals to confirm the validity of our proposal.
INCENTIVE-BASED ROBUST REPUTATION MECHANISM FOR P2P SERVICES EMMANUELLE ANCEAUME AND AINA RAVOAJA

In this paper, we address the problem of designing a robust reputation mechanism for peer-to-peer services. The mechanism we propose achieves high robustness against malicious peers (from individual or collusive ones) and provides incentive for participation. We show that the quality of the reputation value of trustworthy and participating peers is always better than the one of cheating and non participating ones. Finally we formally prove that, even when a high fraction of peers of the system exhibits a collusive behavior, a correct peer can still compute an accurate reputation mechanism towards a server, at the expense of a reasonable convergence time. Key-words: peer-to-peer, reputation, ballot stu ng, bad mouthing, opinions, incentive
Principal Investigator: A comparative study of graduate thesis language policy in Canadian universities SSHRC Knowledge Synthesis Grant (2016) $25,000 Co-investigator: Ethical relationality, Canadian applied linguists and Indigenous language revitalization President’s Fund and SSHRC General Research

The development, maintenance and proper functioning of Tand B-cells are essential for the survival of mammals in a pathogen-ridden environment. Their absence results in inherited or acquired immunodeficiency, the latter of which is the basis of a growing health crisis. Conversely, deregulated growth and development can lead to cancer of the immune system. Leukemia and Lymphoma are the most common cancers among children.
Energy-Efﬁcient Resource Allocation for Heterogeneous Network with Grouping D2D

: In this paper, a new communication model is built named grouping D2D (GD2D). Different from the traditional D2D coordination, we proposed GD2D communication in licensed and unlicensed spectrum simultaneously. We formulate a resource allocation problem, which aims at maximizing the energy ef-ﬁciency (EE) of the system while guaranteeing the quality-of-service (Qos) of users. To efﬁciently solve this problem, the non-convex optimization problem is ﬁrst transformed into a convex optimization problem. By transforming the fractional-form problem into an equivalent subtractive-form problem, an iterative power allocation algorithm is proposed to maximize the system EE. Moreover, the optimal closed-form power allocation expressions are derived by the Lagrangian approach. Simulation results show that our algorithm achieves higher EE performance than the traditional D2D communication scheme.
Petri-Net based Performance Modeling for Effective DVFS for Multithreaded Programs

Chip Multiprocessors (CMP’s) have made their way into servers, desktop systems and even embedded systems. While power dissipation limits performance of CMP’s and affect system reliability, energy efficiency is becoming increasingly important for bringing down the significant contribution of energy in organizations’ budgets and the environment, besides enhancing battery life. Dynamic Voltage and Frequency Scaling (DVFS) is a very effective tool for designing energy vs. performance and power vs. performance tradeoffs. Several CMPs available in the market [1, 2] support voltage and frequency scaling. While most of them support system-wide voltage-frequency settings, a few of them allow independent voltage and frequency settings for subsystems on the chip. It is important that the potential benefits DVFS offers are realized. There have been several efforts that have used DVFS to meet peformance demands, while conforming to power constraints. However, most of them use local aggregate performance indicators such as MIPS/IPC or queue occupancies to effect DVFS decisions, ignoring the properties of the multithreaded applications. In this paper, we use a formal petri net based program performance model that directly captures both the application and system properties, in finding energy efficient voltage-frequency settings for CMP systems, that satisfy a given performance constraint, for multithreaded programs. Experimental evaluation on subsets of SPECOMP, NAS Parallel Benchmarks, and Splash2 benchmarks show that we achieve, on an average, 16% and 28% energy savings for storage and non-storage subsystems on the chip, while meeting the performance constraints. We track the energy savings achieved by the optimal scheme in five out of the seven smaller program segments of these benchmarks.
Using Policy Gradient Reinforcement Learning on Autonomous Robot Controllers

Robot programmers can often quickly program a robot to approximately execute a task under specific environment conditions. However, achieving robust performance under more general conditions is significantly more difficult. We propose a framework that starts with an existing control system and uses reinforcement feedback from the environment to autonomously improve the controller’s performance. We use the Policy Gradient Reinforcement Learning (PGRL) framework, which estimates a gradient (in controller space) of improved reward, allowing the controller parameters to be incrementally updated to autonomously achieve locally optimal performance. Our approach is experimentally verified on a Cye robot executing a room entry and observation task, showing significant reduction in task execution time and robustness with respect to un-modelled changes in the environment.
Human Postures Recognition Based on D-S Evidence Theory and Multi-sensor Data Fusion

Body Sensor Networks (BSNs) are conveying notable attention due to their capabilities in supporting humans in their daily life. In particular, real-time and noninvasive monitoring of assisted livings is having great potential in many application domains, such as health care, sport/fitness, e-entertainment, social interaction and e-factory. And the basic as well as crucial feature characterizing such systems is the ability of detecting human actions and behaviors. In this paper, a novel approach for human posture recognition is proposed. Our BSN system relies on an information fusion method based on the D-S Evidence Theory, which is applied on the accelerometer data coming from multiple wearable sensors. Experimental results demonstrate that the developed prototype system is able to achieve a recognition accuracy between 98.5% and 100% for basic postures (standing, sitting, lying, squatting). Keywords-Body sensor networks, D-S Evidence Theory, wearable sensors, human postures recognition
Syntactic Simpli(cid:2)cation and Text Cohesion

: Syntactic simpli(cid:2)cation is the process of reducing the grammatical complexity of a text, while retaining its information content and meaning. The aim of syntactic simpli(cid:2)cation is to make text easier to comprehend for human readers, or process by programs. In this paper, we formalise the interactions that take place between syntax and discourse during the simpli(cid:2)cation process. This is important because the usefulness of syntactic simpli(cid:2)cation in making a text accessible to a wider audience can be undermined if the rewritten text lacks cohesion. We describe how various generation issues like sentence ordering, cue-word selection, referring-expression generation, determiner choice and pronominal use can be resolved so as to preserve conjunctive and anaphoric cohesive relations during syntactic simpli(cid:2)cation and present the results of an evaluation of our syntactic simpli(cid:2)cation system.
Systematic approach to outcome assessment from coded electronic healthcare records in the DaRe2THINK NHS-embedded randomised trial

Background: Improving the efficiency of clinical trials is key to their continued importance in directing evidence-based patient care. Digital innovations, in particular the use of electronic healthcare records (EHR), allow for large-scale screening and follow-up of participants. However, it is critical these developments are accompanied by robust and transparent methods that can support high quality and high clinical value research. Methods: The DaRe2THINK trial includes a series of novel processes, including nationwide pseudonymised pre-screening of the primary care EHR across England, digital enrolment, remote e-consent, and no-visit follow-up by linking all primary and secondary care health data with patient-reported outcomes. Findings: DaRe2THINK is a pragmatic, healthcare-embedded randomised trial testing whether earlier use of direct oral anticoagulants in patients with prior or current atrial fibrillation can prevent thromboembolic events and cognitive decline (www.birmingham.ac.uk/dare2think). This paper outlines the systematic approach and methodology employed to define patient information and outcome events. This includes transparency on all medical code lists and phenotypes used in the trial across a variety of national data sources, including Clinical Practice Research Datalink Aurum (primary care), Hospital Episode Statistics (secondary care) and the Office for National Statistics (mortality). Interpretation: Co-designed by a patient and public involvement team, DaRe2THINK presents an opportunity to transform the approach to randomised trials in the setting of routine healthcare, providing high-quality evidence generation in populations representative of the community at-risk.
Thesis subject: Reliability enhancement of severe accident computer codes

Context The frame of the proposed thesis work concerns the simulation of severe accidents in light water nuclear reactors. In an accident situation, nuclear fuel can melt and interact with surrounding structures to form a corium pool in the reactor core and then, after relocation, in the bottom of the vessel. In case of vessel failure, the corium can then relocate in the reactor pit, reach the basemat and interact with the concrete that constitutes it. In the presence of water, the interaction between corium and coolant can lead to a steam explosion, an energy event that may compromise the integrity of the containment.
Proceedings of the 21st Annual Conference of the European Association for Machine Translation, EAMT 2018, Alicante, Spain, May 28-30, 2018

We present an approach to neural machine translation (NMT) that supports multiple domains in a single model and allows switching between the domains when translating. The core idea is to treat text domains as distinct languages and use multilingual NMT methods to create multidomain translation systems; we show that this approach results in significant translation quality gains over fine-tuning. We also explore whether the knowledge of prespecified text domains is necessary; turns out that it is after all, but also that when it is not known quite high translation quality can be reached, and even higher than with known domains in some cases.
Proceedings of the 21 st Annual Conference of the European Association for Machine Translation 28 – 30 May 2018

The requirement for neural machine translation (NMT) models to use fixed-size input and output vocabularies plays an important role for their accuracy and generalization capability. The conventional approach to cope with this limitation is performing translation based on a vocabulary of sub-word units that are predicted using statistical word segmentation methods. However, these methods have recently shown to be prone to morphological errors, which lead to inaccurate translations. In this paper, we extend the source-language embedding layer of the NMT model with a bi-directional recurrent neural network that generates compositional representations of the source words from embeddings of character n-grams. Our model consistently outperforms conventional NMT with sub-word units on four translation directions with varying degrees of morphological complexity and data sparseness on the source side.
Non-Additive Measures: A Theoretical Approach to Medical Decision-Making

Informatics-based decision-making aids are becoming an essential component of clinical care from both a physician and patient perspective. Although additive approaches are used with a certain degree of success in a medical context, they often suffer from an inability to conveniently represent dependencies, which is certainly desirable in practice. To address this drawback, we present the concepts of non-additive measures
ASL Recognition Based on a Coupling Between HMMs and 3 D Motion Analysis

We present a framework for recognizing isolated and continuous American Sign Language (ASL) sentences from three-dimensional data. The data are obtained by using physics-based three-dimensional tracking methods and then presented as input to Hidden Markov Models (HMMs) for recognition. To improve recognition performance, we model context-dependent HMMs and present a novel method of coupling three-dimensional computer vision methods and HMMs by temporally segmenting the data stream with vision methods. We then use the geometric properties of the segments to constrain the HMM framework for recognition. We show in experiments with a 53 sign vocabulary that three-dimensional features outperform two-dimensional features in recognition performance. Furthermore, we demonstrate that context-dependent modeling and the coupling of vision methods and HMMs improve the accuracy of continuous ASL recognition. Comments University of Pennsylvania Department of Computer and Information Science Technical Report No. MSCIS-98-21. This technical report is available at ScholarlyCommons: http://repository.upenn.edu/cis_reports/165 ASL Recognition Based on a Coupling Between HMMs and 3D Motion Analysis Christian Vogler and Dimitris Metaxas University of Pennsylvania School of Engineering and Applied Science Computer and Information Science Department Philadelphia, PA 19104-6389 An earlier version of this paper appeared in the proceedings of the International Conference on Computer Vision, pp. 363-369, Mumbai, India, January 4-7,1998 ASL Recognition Based on a Coupling Between HMMs and 3D Motion Analysis Christian Vogler and Dimitris Metaxas Department of Computer and Information Science, University of Pennsylvania, Philadelphia, PA 19 104-6389 cvogler@gradient.cis. upenn.edu, dnm@central.cis. upenn.edu
Text-Based Ephemeral Clustering for Web Image Retrieval on Mobile Devices (version 1). (Partitionnement Éphémère pour la Recherche d'Images Web en Dispositifs Nomades (version 1))

In this thesis, we present a study about Web image results visualization on mobile devices. Our main findings were inspired by the recent advances in two main research areas - Information Retrieval and Natural Language Processing. In the former, we considered different topics such as search results clustering, Web mobile interfaces, query intent mining, to name but a few. In the latter, we were more focused in collocation measures, high order similarity metrics, etc. Particularly in order to validate our hypothesis, we performed a great deal of different experiments with task specific datasets. Many characteristics are evaluated in the proposed solutions. First, the clustering quality in which classical and recent evaluation metrics are considered. Secondly, the labeling quality of each cluster is evaluated to make sure that all possible query intents are covered. Thirdly and finally, we evaluate the user's effort in exploring the images in a gallery-based interface. An entire chapter is dedicated to each of these three aspects in which the datasets - some of them built to evaluate specific characteristics - are presented. For the final results, we can take into account two developed algorithms, two datasets and a SRC evaluation tool. From the algorithms, Dual $C$-means is our main product. It can be seen as a generalization of our previously developed algorithm, the $AGK$-means. Both are based in text-based similarity metrics. A new dataset for a complete evaluation of SRC algorithms is developed and presented. Similarly, a new Web image dataset is developed and used together with a new metric to measure the users effort when a set of Web images is explored. Finally, we developed an evaluation tool for the SRC problem, in which we have implemented several classical and recent SRC metrics. Our conclusions are drawn considering the numerous factors that were discussed in this thesis. However, additional studies could be motivated based in our findings. Some of them are discussed in the end of this study and preliminary analysis suggest that they are directions that have potential.
A Simple Classroom Management Addin for RStudio Server Professional

Both authors have taught courses at Bentley University in which they require students to do work in R (R Core Team 2016) using RStudio (RStudio Team 2015) and RMarkdown (Allaire, Cheng, Xie, McPherson, Chang, Allen, Wickham, Atkins, and Hyndman 2016). For Dr. Oury, this has included graduate-level courses in data mining, data science, and machine learning. For Dr. Carter, it includes a single undergraduate course in discrete mathematics. In these courses, many of the homework assignments, handouts, slides, and projects are given to students in RMarkdown format. Students are expected to submit work in the same format. This paper introduces a new RStudio addin, called “Simple Course Management System,” that facilitates the exchange, grading, and real-time supervision of student work in RStudio. It also discusses the results of using the addin in a course in Fall 2016, and suggests future extensions.
New Challenges in Systems Engineering and Architecting Conference on Systems Engineering Research ( CSER ) 2012 –

Cells of living organisms simultaneously operate hundreds or thousands of interconnected chemical reactions. Metabolic networks include these chemical reactions and the compounds participating in them. Metabolic engineering is a science centered on the analysis and purposeful modification of an organism's metabolic network toward a beneficial purpose, such as production of fuel or medicinal compounds in microorganisms. Unfortunately, there are problems with the design and visualization of modified metabolic networks due to lack of a standardized and fully developed visual modelling languages. The purposes of this paper are to propose a multi-level framework for the synthesis, analysis and design of metabolic systems, and then explore the extent to which abstractions from systems engineering (e.g., SysML) can complement and add value to the abstractions currently under development within the greater biological community (e.g. SBGN). The computational test-bed that accompanies this work is production of the anti-malarial drug artemisinin in genetically engineered Saccaharomyces cerevisiae (yeast). © 2012 Published by Elsevier Ltd. Selection
Self-organization and transition to turbulence in randomly forced isotropic fluid motion

We observe a symmetry-breaking transition to a self-organized state in direct numerical simulation of the Navier-Stokes equation with random, isotropic forcing at very low Reynolds number. In this state the kinetic energy is contained only in modes at the lowest resolved wavenumber, the skewness vanishes, and visualization of the flows shows a lack of small-scale structure, with the vorticity and velocity vectors becoming aligned (a Beltrami flow). We infer from this the existence of a lower critical Reynolds number for transition to isotropic turbulence.
Nonlinear system identification using wavelet networks

This paper presents an identification scheme using wavelet networks for nonlinear dynamical systems. Based on wavelet networks, the parameter adaptation laws of the wavelet network are developed using a Lyapunov synthesis approach. This guarantees the stability of the overall identification scheme and the convergence of both the parameters and the state errors, even in the presence of modelling errors. B-spline wavelets are used for realization of the wavelet network based identification. The identification scheme is illustrated using a simulated example.