Miracle-dz/UniFAR-FTUs huggingface dataset:
Consistency-aware scheduling and load balancing in multi-server distributed virtual environments

A distributed virtual environment (DVE) is a kind of distributed interactive application which allows a group of participants connected via a network to interact with a shared application state. DVEs have been widely used in many areas such as military training, collaborative design, e-learning and networked multi-user games. Zone-based multi-server DVEs (MSDVEs) have been shown to have good scalability to support a large population of users. The fundamental goal of a DVE is to create a consistent view of the virtual world among participants. However, resource saturations may occur at servers due to huge resource demand and imbalanced workload distribution. Due to resource limitations, some state updates cannot be timely disseminated to the relevant participants, and thus inconsistency is likely to occur. The inconsistency can have a great influence on the experiences of clients and the problem will become even more serious as the scale of the DVE grows. In this thesis, we study the position update scheduling and load balancing issues for improving consistency in MSDVEs with a set of servers constrained by upload bandwidth. In the update scheduling issue, we aim at investigating update schedules for minimizing the overall inconsistency in a MSDVE. Depending on how inconsistency is measured, we first formulate the update scheduling problems as inequality constrained optimization problems. Then, the update scheduling algorithms are investigated for minimizing the overall inconsistency in practical systems. The performance of the proposed algorithms is experimentally evaluated. The results show that our proposed algorithms generally outperform other algorithms under the same experimental settings. In the load balancing issue, we consider zone mapping and client assignment to minimize the total time-space inconsistency in a MSDVE. Zone mapping refers to determining which zone is maintained by which server while client assignment refers to determining which client is connected to which server. Similarly, the zone mapping and client assignment problem is first formulated as a mix-integer programming problem. Then, a centralized algorithm and a distributed adaptive tuning algorithm
Interval Tree-Based Task Scheduling Method for Mobile Crowd Sensing Systems

Nowadays there is an increasing demand to provide a real-time environmental information. So, the growing number of mobile devices carried by users establish a new and fastgrowing sensing paradigm to satisfy this need, which is called Mobile Crowd Sensing (MCS). The MCS uses different sensing abilities to acquire local knowledge through enhanced mobile devices. In MCS, it is very important to collect high-quality sensory data that satisfies the needs of all assigned tasks and the task organizers with a minimum cost for the participants. One of the most important factors which affect the MCS cost is how to schedule different sensing tasks which must be assigned to a smartphone with the objective of minimizing sensing energy consumption while ensuring high-quality sensory data. In this paper, the problem of task scheduling the which have mutual sensor is formulated and a scheduling method to minimize the energy consumption by reducing the sensor utilization is proposed. The proposed method will incentive the users to participate in multiple tasks at the same time, which minimizes the total cost of the performed tasks and increases his rewards. The experimental results by using synthetic and real data show that the proposed scheduling method can minimize the energy consumption and preserve the task requirements compared to existing algorithms.
The KIBS paradox and structural holes

Abstract A first aim of this paper is to discuss the problem of vertical competition between suppliers of knowledge-intensive business services (KIBS) and their customers. The problem stems from the fact that the former transfer knowledge to the latter, thereby placing the latter in a position to produce the previously-purchased services by themselves (the KIBS paradox). A second aim is to demonstrate how KIBS deal with this problem. A literature review provides the elements we need to identify the competitive issue that these organisations face. Then we try to explain how KIBS deal with this issue, drawing on two theoretical approaches: (i) Burt’s social structural theory of competition; and (ii) the concepts developed in the knowledge management literature relating to intra- and inter-organisational knowledge processes. The results of this theoretical work provide an avenue for further research on KIBS and relevant implications for practitioners.
Single Shot TextSpotter with Explicit Alignment and Attention

Text detection and recognition in natural images have long been considered as two separate tasks that are processed sequentially. Training of two tasks in a unified framework is non-trivial due to significant dif- ferences in optimisation difficulties. In this work, we present a conceptually simple yet efficient framework that simultaneously processes the two tasks in one shot. Our main contributions are three-fold: 1) we propose a novel text-alignment layer that allows it to precisely compute convolutional features of a text instance in ar- bitrary orientation, which is the key to boost the per- formance; 2) a character attention mechanism is introduced by using character spatial information as explicit supervision, leading to large improvements in recognition; 3) two technologies, together with a new RNN branch for word recognition, are integrated seamlessly into a single model which is end-to-end trainable. This allows the two tasks to work collaboratively by shar- ing convolutional features, which is critical to identify challenging text instances. Our model achieves impressive results in end-to-end recognition on the ICDAR2015 dataset, significantly advancing most recent results, with improvements of F-measure from (0.54, 0.51, 0.47) to (0.82, 0.77, 0.63), by using a strong, weak and generic lexicon respectively. Thanks to joint training, our method can also serve as a good detec- tor by achieving a new state-of-the-art detection performance on two datasets.
Contributions à la modélisation des déplacements en transport collectif

Public Transit (PT) is an essential component of urban mobility in the actual context of sustainable development. PT planning contains all efforts for descriptive analysis and modeling. This thesis contributes more specifically to PT modeling. This thesis objective is to enhance the models in a way that they represent the reality of mobility behavior. Two types of simplifications are present in modeling procedure: Theoretical construction and mathematical formalization. Considering these simplifications, this research contributes in enhancing the modeling procedure in three different ways, each presented as a published or submitted paper in scientific journals. The first part consists of demographic analysis of route choice for PT. The results are presented in a paper titled “Demographic Analysis of Route Choice for Public Transit”, and published in the journal of Transportation Research Record in 2011. By considering six sociodemographic categories, made up of two genders and three age cohorts, a route choice model for PT is developed and examined. The main contribution of this paper is the development of a discrete choice model for PT route choice based on sociodemographic characteristics. In this model, each choice alternative is represented by a list of independent variables describing a PT route. Several sociodemographic profiles together with a complete model treating the whole population are examined. The results confirmed that considering sociodemographic profiles for analysing PT route choice yields better results compared to the complete model. Some enhancements for calculating access time based on different walk speeds for different sociodemographic profiles are also presented. The second part is the development of an integrated intervening opportunities model for PT trip generation-distribution. The findings are presented in a paper titled “Integrated Intervening Opportunities Model for Public Transit Trip Generation-Distribution: A Supply-dependent Approach”, which is published in the journal of Transportation Research Record in 2013. The main advantage of the intervening opportunities model is its behavioral basis. In this research, three forms of basic, hierarchical and integrated intervening opportunities model are developed. The performance of this model was analyzed and results were compared with the gravity model typically used for trip distribution. The comparison confirmed that the integrated form of intervening opportunities model could better reproduce an observed OD matrix for PT. Due to sociodemographic, socioeconomic and PT Level-Of-Service (LOS)
NIST workshop on ontology evaluation

The National Institute for Standards and Technology sponsored a workshop in October, 2007, on the subject of ontology evaluation. An international group of invited experts met for two days to discuss problems in measuring ontology quality. The workshop highlighted several divisions among ontology developers regarding approaches to ontology evaluation. These divisions were generally reflective of the opinions of the participants. However, the workshop documented a paucity of empirical evidence in support of any particular position. Given the importance of ontologies to every knowledge-intensive human activity, there is an urgent need for research to develop an empirically derived knowledge base of best practices in ontology engineering and methods for assuring ontology quality over time. This is a report of the workshop discussion and brainstorming by the participants about what such a research program might look like.
Sentiment Classification of Movie Reviews Using Korean Sentiment Dictionary

While there exists a large volume of research on sentiment classification of English customer reviews using English sentiment dictionaries, there are few researches on classifying sentiment of Korean customer reviews using Korean sentiment dictionaries. We use more than a thirteen thousandword movie domain Korean sentiment dictionary to classify positive/negative sentiment of online movie reviews written in Korean. The binary sentiment classification performance of the constructed sentiment dictionary was 80.7% confirming the effectiveness of the Korean sentiment dictionary and the dictionary-based sentiment classification method.
Acceleration of the Anisotropic PSPI Imaging Algorithm with Dataflow Engines

SUMMARY Although time-domain depth migration techniques have been successfully ported to run on modern hardware accelerators, their ultimate obstacle is the I/O overhead present during the imaging step. Frequency-domain depth migration algorithms overcome this limitation and can exploit the full potential of new computing technologies. In particular, our implementation of the Phase Shift Plus Interpolation (PSPI) method is characterized by fast running time, good quality results under low signal-to-noise ratio conditions and excellent results for steep dips. We provide a novel computational dataflow scheme to perform acceleration of PSPI on a generic dataflow engine. We present speedup results obtained on the state-of-theart dataflow technology for synthetic VTI datasets. Our measurements indicate that a dataflow approach can achieve high speedups despite larger and larger computational domains, the increased complexity of the anisotropic approach and the I/O overhead during angle-gathers calculation.
Assessing Writing in MOOCs: Automated Essay Scoring and Calibrated Peer Review™.

Joshua Brown, Adam Mikeal, and Alysha Clark provided substantial feedback that greatly enhanced the value and clarity of the information in this article. Abstract Two of the largest Massive Open Online Course (MOOC) organizations have chosen different methods for the way they will score and provide feedback on essays students submit. EdX, MIT and Harvard’s non-profit MOOC federation, recently announced that they will use a machine-based Automated Essay Scoring (AES) application to assess written work in their MOOCs. Coursera, a Stanford startup for MOOCs, has been skeptical of AES applications and therefore has held that it will use some form of human-based “calibrated peer review” to score and provide feedback on student writing. This essay reviews the relevant literature on AES and UCLA’s Calibrated Peer ReviewTM (CPR) product at a high level, outlines the capabilities and limitations of both AES and CPR, and provides a table and framework for comparing these forms of assessment of student writing in MOOCs. Stephen Balfour is an instructional associate professor of psychology and the Director of Information Technology for the College of Liberal Arts at Texas A&M University.
Dynamic Modeling and Simulation of 4-Wheel Skid-Steering Mobile Robot with Considering Tires Longitudinal and Lateral Slips

Skid-steered vehicles are regularly considered for outdoor activities due to their structural robustness and extraordinary maneuverability. Dynamic model of ground vehicles should predict a faithful motion in unknown environments and ability to design a proper controller. The purpose of this paper is to develop a nonlinear model for a 4-wheel skid-steering mobile robot considering dynamic model of tires with all slides. Unlike previous friction models which are considering only frictional forces without tire's slips, the proposed model consists of longitudinal and lateral tires' slips and will be used to predict a real robot's (P3-AT) motion in some specified situations such as moving straight on horizontal and ascent planes and moving on a curve on a horizontal plane. For validation purposes, obtained results will be compared to similar simulations on an Adams/View model. This comparison shows the accuracy of the model. Finally, advantage and disadvantage of the proposed model will be discussed.
Noise processing methods for Media Processor SOC

Images taken with both digital cameras and conventional film cameras will pick up noise from a variety of sources. Many further uses of these images require that the noise will be (partially) removed for aesthetic purposes as in artistic work or marketing, or for practical purposes such as computer vision. Various types of noises include improper black level, salt-and-pepper noise, speckle noise, etc. These noises can be removed with the help of a media processor SOC. The approach mentioned here explains the methods that can be used to remove the above mentioned noises. Each method is explained in detail with necessary diagrams.
Conditional techniques for stream processing kernels

The stream programming model casts applications as a set of sequential data streams that are operated on by data-parallel computation kernels. Previous work has shown that this model is a powerful representation for media processing applications, such as image- and signal-processing, because it captures the locality and concurrency inherent to an application. This careful handling of important application properties results in kernels that are compute-intensive—i.e., kernels that perform a large number of arithmetic operations per unit of inter-kernel communication bandwidth. Furthermore, the stream model can be implemented with efficient VLSI designs, such as the Imagine Programmable Stream Processor. The Imagine chip supports 48 ALUs on a single die, operating at over 200MHz. This large number of ALUs provides a high peak performance, but makes efficiently executing kernels with conditional code a challenge. We will introduce two techniques for efficiently supporting kernel conditionals, such as if-statements, case-statements, and while-loops: conditional routing and conditional streams. Conditional routing is a code transformation that exploits the trade-off of increasing inter-kernel communication in order to increase the performance of kernel inner-loops containing conditional code. The second technique we will discuss is the use of a conditional stream, which is a mechanism to reduce the amount of load-imbalance that arises between parallel processing clusters on a single stream processor chip. Load-imbalance results when different conditional paths are taken on different processing clusters, and causes one or more of the clusters to wait idle for the others to complete a kernel. We will also present a case study of the impact of these techniques on a programmable polygon rendering pipeline that contains many unpredictable conditionals. We show that our techniques improve the performance of this application by 1.9×.
Freenet : A Distributed Anonymous Information Storage and Retrieval System in Designing Privacy Enhancing Technologies

We describe Freenet a peer to peer network application that permits the publication replication and retrieval of data while protecting the anonymity of both authors and readers Freenet operates as a network of identical nodes that collectively pool their storage space to store data les and cooperate to route requests to the most likely physical location of data No broadcast search or centralized location index is employed Files are referred to in a location independent manner and are dynamically replicated in locations near requestors and deleted from locations where there is no interest It is infeasible to discover the true origin or destination of a le passing through the network and di cult for a node operator to determine or be held responsible for the actual physical contents of her own node
Ensemble of Surrogates: a Framework based on Minimization of the Mean Integrated Square Error

In engineering problems, high-fidelity simulations are often time consuming and computationally expensive. Surrogate models, frequently chosen based on past experience, are commonly used to replace expensive simulations. However, when a single surrogate is needed, previous work has shown that it is a good strategy to fit a large set of different surrogates and pick one based on cross-validation errors (PRESS in particular). In addition, cross validation errors may also be used to create a weighted surrogate. In this paper, we investigate how well PRESS estimates the RMS errors, and whether to use the best PRESS solution or a weighted surrogate when a single surrogate is needed. We propose the minimization of the integrated square error as a way to compute the weights of the weighted average surrogate. The investigation is based on a set of standard test functions as well as an industrial example of the lift, drag and pitching moment coefficients of an airfoil. We confirm that it pays to generate a large set of different surrogates and then use PRESS as a criterion for selection. We find that with sufficient number of points (i) cross validation error vectors provide an excellent estimate of the RMS errors, and (ii) in general, PRESS is good for filtering out inaccurate surrogates, and especially in high dimensions, PRESS is also good for identifying the best surrogate of the set. However, it appears that the potential gains from using weighted surrogates diminish substantially in high dimensions. Surprisingly, this result holds even if we used perfect knowledge of the function to select the best possible weights. Additionally, we also found that PRESS as obtained through the k-fold strategy successfully estimates the RMS errors.
Orchestration of a large infrastructure of Remote Desktop Windows Servers

The CERN Windows Terminal Service infrastructure is an aggregation of multiple virtual servers running Remote Desktop Services, accessed by hundreds of users every day; it has two purposes: provide external access to the CERN network, and exercise access control to certain parts of the accelerator complex. Currently, the deployment and configuration of these servers and services requires some interaction by system administrators, although scripts and tools developed at CERN do contribute to alleviate the problem. Scaling up and down the infrastructure (i.e., adding or removing servers) is also an issue, since it’s done manually. However, recent changes in the infrastructure and the adoption of new software tools that automate software deployment and configuration open new possibilities to improve and orchestrate the current service. Automation and Orchestration will not only reduce the time and effort necessary to deploy new instances, but also simplify operations like patching, analysis and rebuilding of compromised nodes and will provide better performance in response to load increase. The goal of this CERN project, we’re now a part of, is to automate provisioning (and decommissioning) and scaling (up and down) of the infrastructure. Given the scope and magnitude of problems that must be solved, no single solution is capable of addressing all; therefore, multiple technologies are required. For deployment and configuration of Windows Server systems we resort to Puppet, while for orchestration tasks, Microsoft Service Management Automation will be used.
Automating the Generation of Interactive Applications

As user interfaces become more powerful and easier to use they are often harder to design and implement. This has caused a great demand for interface tools. While existing tools ease interface creation, they typically do not provide mechanisms to simplify application development and are too low-level. Furthermore, existing tools do not provide effective mechanisms to port interactive applications across user interfaces. While some tools provide limited mechanisms to port applications across user interfaces which belong to the same class (e.g., the class of all standard graphical direct-manipulation user interfaces), very few can provide the ability to port applications across different interface classes (e.g., command-line, hypermedia, speech recognition and voice synthesis, virtual reality, etc.). \n With my approach, the programmer uses an abstract model to describe the structure of the application including the information that the application must exchange with the user, rather than describing a user interface which realizes these characteristics. By specifying application semantics at a very high level of abstraction it is possible to obtain a much greater separation between the application and the user interface. Consequently, the resulting applications can be ported not only across user interfaces which belong to a common interface class, but across interfaces which belong to distinct classes. This can be realized through simple recompilation --- source code does not have to be modified. \n NAAG (Not Another Application Generator), a tool which embodies these ideas, enables programmers to create interactive applications with minimal effort. An application is modelled as a set of operations which manipulate objects belonging to user-defined object classes. The input to NAAG is a source program which describes classes, operations and their inputs and outputs, and the organization of operations within the application. Classes and operations are implemented as data structures and functions in a conventional programming language such as C. This model simplifies not only the specification and generation of the user interface, but the design and implementation of the underlying application. \n NAAG utilizes existing technology such as macro-preprocessors, compilers, make programs, and low-level interface tools, to reduce the programming task. An application that is modified by adding, removing, or reorganizing artifacts (classes, operations, and menus), can be regenerated with a single command. Traditionally, software maintenance has been a very difficult task as well. Due to the use of a simple abstract model, NAAG applications are also easier to maintain. Furthermore, this approach encourages software reuse: applications consisting of arbitrary collections of original and pre-existing artifacts can be composed easily; functions which implement abstract operations are independent of both, user interface aspects, and the context in which they are