Miracle-dz/UniFAR-FTUs huggingface dataset:
Ingress Rate Control in Resilient Packet Rings

THOMBARE, ASAVARI ANIL. Ingress Rate Control in Resilient Packet Rings. (Under the direction of Dr. Ioannis Viniotis.) Resilient Packet Ring (RPR) Protocol is becoming popular in Metro Networks due to its rich features. One of these features is the Fairness and Flow Control mechanism. RPR is standardized as IEEE 802.17 and claims that there will be no frame loss inside the network under normal operation. This is achieved through feedback flow control messages. Traffic rate is controlled at the ingress itself based on these messages. We concentrate on the Ingress Rate Control scheme where the local station adds traffic to the ringlet based on the allocated rate limits and FAIR rate limits to achieve fairness in the ring network. The research presented in the thesis tries to achieve three different goals. We implement the Rate Control scheme in software to identify early implementation level issues and then implement it in hardware. We achieve a speed of 10 Gbps with our hardware implementation. We also achieve a full throughput at minimum frame size of 24 bytes. The hardware implementation is tested for compliance with the standard IEEE 802.17. In the simulation study part of the research, we observe the response of individual shapers under compliance testing. We concentrate on the feature of bandwidth reclamation in detail and show that the standard implementation is able to provide bandwidth reclamation under different scenarios. We also study the effect of alternative methods for implementation of Fairness Eligible Shaper. The implementation and simulation parts of the thesis concentrate on the Single Transit Queue Implementation. In the third part we perform analysis of the buffer size requirement under worst case conditions in Dual Transit Queue Architecture. We compare our analysis with the standard and identify various components which affect the buffer size. The buffer size is chosen such that there will be no frame loss due to overflow of the Secondary Transit Queue. INGRESS RATE CONTROL IN RESILIENT PACKET RINGS
Annotation précise du français en sémantique de rôles par projection cross-linguistique

Dans le paradigme FrameNet, cet article aborde le problème de l’annotation précise et automatique de rôles sémantiques dans une langue sans lexique FrameNet existant. Nous évaluons la méthode proposée par Padó et Lapata (2005, 2006), fondée sur la projection de rôles et appliquée initialement à la paire anglais-allemand. Nous testons sa généralisabilité du point de vue (a) des langues, en l’appliquant à la paire (anglais-français) et (b) de la qualité de la source, en utilisant une annotation automatique du côté anglais. Les expériences montrent des résultats à la hauteur de ceux obtenus pour l’allemand, nous permettant de conclure que cette approche présente un grand potentiel pour réduire la quantité de travail nécessaire à la création de telles ressources dans de nombreuses langues.
Visualization, Data Analyzing and Energy Usage Analysis in Wireless Sensor Network Based on TinyOs and PowerTossimZ

Design and implement thousand of sensor nodes for develop and test new algorithm in realistic is difficult and sometimes is impossible. Cost of time and cost of money cause for using simulations. Hence many kind of simulation are available such PowerTossim, NS2 and many more. These simulations are very helpful for reduce the costs. But some times lack and weak point in simulation can cause many difiiculty for user. SmartSim has been develop for solve many problems in PowerTossim. It has many useful features such as user graphical interface, compile the configuration file automatic, very fast report generator and so on. In this paper produce and develop some unique feature in plotting based on PowerTossim and SmartSim. This module can help to SmartSim to be unique all-in-one simulation for TinyOs and Tossim.
Interdependent Algorithm Design of Angle Tracking and Velocity Tracking for Radar Seeker

Active radar seeker technique has shown its mainstream status in modern tactical missile,while angle tracking and velocity tracking algorithms are important parts of radar tracking system.Tracking precision determines shoot precision.However,once radar seeker was disturbed by velocity dragging jam,the measured value of velocity channel becomes unbelievable.Therefore,in order to detect velocity dragging jam in time for system target acquisition or target tracking phase,in it,algorithm of combined interactive angle/velocity tracking was designed.Angle channel information can be processed for velocity channel information correction,the tracking stability in disruption environment can be guaranteed.Whole algorithms were implemented in intact radar seeker system,whose validity and efficiency have been proved in simulation and infield test.
Reducing simulation runtime in wireless sensor networks: A simulation framework to reduce wsn simulation runtime by using multiple simultaneous instances

Wireless Sensor Networks (WSNs) can be deployed using available hardware and software. The Contiki is an operative system compatible with a wide range of WSN hardware. A Contiki development environment named InstantContiki is also available and includes the Cooja simulator, useful to test WSN simulation scenarios prior to their deployment. Cooja can provide realistic results since it uses the full Contiki’s source code and some motes can be emulated at the hardware level. However this implies extending the simulation runtime, which is heightened since the Cooja is single threaded, i.e, it makes use of a single core per instant of time, not taking advantage of the current multi-core processors. This chapter presents a framework to automate the configuration and execution of Cooja simulations. When a multi-core processor is available, this framework runs multiple simultaneous Cooja instances to reduce simulations runtime in exchange of higher CPU load and RAM usage. Reducing Simulation Runtime in Wireless Sensor Networks: A Simulation Framework to Reduce WSN Simulation Runtime by Using Multiple Simultaneous Instances
Probabilistic modeling in dynamic information retrieval

Dynamic modeling is used to design systems that are adaptive to their changing environment and is currently poorly understood in information retrieval systems. Common elements in the information retrieval methodology, such as documents, relevance, users and tasks, are dynamic entities that may evolve over the course of several interactions, which is increasingly captured in search log datasets. Conventional frameworks and models in information retrieval treat these elements as static, or only consider local interactivity, without consideration for the optimisation of all potential interactions. Further to this, advances in information retrieval interface, contextual personalization and ad display demand models that can intelligently react to users over time. This thesis proposes a new area of information retrieval research called Dynamic Information Retrieval. The term dynamics is defined and what it means within the context of information retrieval. Three examples of current areas of research in information retrieval which can be described as dynamic are covered: multi-page search, online learning to rank and session search. A probabilistic model for dynamic information retrieval is introduced and analysed, and applied in practical algorithms throughout. This framework is based on the partially observable Markov decision process model, and solved using dynamic programming and the Bellman equation. Comparisons are made against well-established techniques that show improvements in ranking quality and in particular, document diversification. The limitations of this approach are explored and appropriate approximation techniques are investigated, resulting in the development of an efficient multi-armed bandit based ranking algorithm. Finally, the extraction of dynamic behaviour from search logs is also demonstrated as an application, showing that dynamic information retrieval modeling is an effective and versatile tool in state of the art information retrieval research.
Probabilistic Outputs for Multiclass Support Vector Machines

Abstract Support Vector Machines (SVM) have been successfully applied in many classification tasks with great generalization performance. However, the output function of SVMs gives an uncalibrated value, impairing the post-processing and making the combination of several classifiers inefficient, as in the case of multiclass SVMs. Some methods of transforming the binary SVM output in a calibrated posterior probability have been proposed, notably the sigmoid fitting method by Platt. This paper proposes an extension of the Platt’s model for multiclass SVMs, by combining the optimization procedures of all sigmoid functions. Experimental results are presented and confirm the efficiency of the proposed method.
RotationNet: Joint Learning of Object Classification and Viewpoint Estimation using Unaligned 3D Object Dataset

We propose a Convolutional Neural Network (CNN)-based model"RotationNet,"which takes multi-view images of an object as input and estimates both its pose and object category. Unlike previous approaches that use known pose labels for training, our method treats the pose labels as latent variables, which are optimized to self-align in an unsupervised manner during the training using an unaligned dataset. RotationNet is designed to use only a partial set of multi-view images for inference, and this property makes it useful in real-world scenarios where only partial views are available. Moreover, our pose alignment strategy enables one to obtain view-specific feature representations shared across classes, which is important to maintain high accuracy both in object categorization and pose estimation. Effectiveness of RotationNet is demonstrated by its comparable performance to the state-of-the-art methods of 3D object classification on 10- and 40-class object benchmark datasets. We also show that RotationNet, with only a few partial views, achieves comparable performance to the multi-view CNN method that uses 80 different view images. The code is available on https://github.com/kanezaki/rotationnet.
The Assessment of Time-Domain Features for Detecting Symptoms of Diabetic Retinopathy

Diabetes affects the capillary vessels in retina and causes vision loss. This disorder of retina due to diabetes is named as Diabetic Retinopathy (DR). Diagnosing the stages of DR is performed on a publicly available database (DiaraetDB1) via detecting the symptoms of this disease. Time-domain features are extracted and selected to classify a fundus image. Fisher’s Linear Discriminant Analysis (FLDA), Linear Bayes Normal Classifier (LDC), Decision Tree (DT) and k-Nearest Neighbor (k-NN) are used as the classification methods in the experimental benchmarking. The recognition accuracies are obtained using all features (68 features) and selected features separately. k-NN is observed as the best classification method for without feature selection case and it gives averagely 92.22% accuracy. For feature selection case, LDC gives the best average accuracy as 92.45% with maximum 7 carefully chosen features.
Using Multilevel Models to Explore Predictors of High School Students’ Nonresponse in Experience Sampling Method (ESM) Studies

This study uses multilevel generalized linear models to examine predictors of high school students’ nonresponse when using the experience sampling method (ESM), a form of momentary data collection that captures participants’ situational thoughts, feelings, and emotions. Because ESM approaches often seek to generalize and compare participants’ emotional states across days and times, it is important to understand how and when participants may miss response opportunities, and further to explore how this response bias may limit generalizability of findings. Results from this study, conducted in three mid-Michigan high schools in 2013–2014 with a sample of 141 students, indicate that time of day and day of week are significantly related to a given participant’s odds of nonresponse. Specifically, ESM “prompts” occurring after school and over the weekend had much higher odds of being missed by participants, even after controlling for other covariates such as race/ethnicity, gender, and person-level emotional trends. These findings demonstrate that day and time contextual factors are significantly related to odds of nonresponse, and researchers using these approaches to compare widely different time contexts should be mindful of possible generalizability concerns.
Clustering by heterogeneous data fusion : framework and applications

Clustering is an important problem with many applications, and a number of different algorithms and methods have emerged over the years. The goal of clustering is to group data points into homogeneous groups, where the homogeneity is usually measured by distances or similarities among data points. Recently, may applications face the requirement of clustering by data fusion. This is because that information contained in single data source is limited by its specific observation, therefore, combining multiple observations might facilitate the comprehensive understanding of the problem. For instance, in order to investigate memory persistence (long term or short term memory) of bacteria, a bacterium is observed at different experimental conditions and evolutional times [11]. Then the multiple observations are categorized by clustering algorithms. In scientometrics, a strategy has been proposed to combine text mining data and bibliometrics data (hybrid clustering) to explore the structure mapping of journal sets [8]. In bioinformatics, high throughput techniques produce numerous genomic data. The challenge to endow clustering algorithm with the ability to retrieve correlated or complementary information about the underlying functional partitions of genes and proteins has attracted many interests [2,14]. Unfortunately, though the machine learning community has already focused on data fusion for classification [7] and novelty detection [5], the extension to unsupervised learning such as clustering, is still an unresolved and ongoing problem.
CS322 Fall 2003: Programming Language Design -Lecture Notes-

This report contains the complete lecture notes for CS322, Programming Language Design, taught by Grigore Rosu in the Fall 2003 semester at the University of Illinois at Urbana Champaign. This large PDF document has been generated automatically from the CS322's website at: http://fsl.cs. uiuc.edu/~grosu/classes/2003/fall/cs322/. Of particular importance may be the novel technique for defining concurrent languages that starts at page 673, based on a first-order representation of computations (called "continuations" for simplicity, though only their tail is an actual "continuation structure"). Grigore Rosu Fall 2003 CS322 CS322 Programming Language Design (Fall 2003) Students enrolled in this class are expected to check this web page regularly. Complete lecture notes will be posted here.
Automated high level synthesis of data conversation systems

This chapter considers a flexible framework for the automatic synthesis of data conversion architectures employing binary-weighted C-arrays which can meet a broad range of input specifications, both for A/D and D/A conversion, and perform to a high degree of precision even when considering the non-idealities of the circuit components such as offset, charge injection, noise and component mismatch errors. This included not only the description of the complete architecture design and verification automation processes but also the discussion of an efficient top-down flow of design information to access a variety of lower level design environments including cell libraries, parametrized circuit module generators and circuit level compilers. By ensuring such design flexibility at the lower level, this framework also renders it easier to meet even the stringent specifications for implementation of a data conversion system employing binary-weighted C-arrays.
Maths Information Retrieval for Digital Libraries

Užitecnost digitalnich knihoven zavisi na jejich schopnosti poskytnout ctenaři veskerý svůj obsah, o který by mohl mit zajem. Propracovane techniky a technologie vyvinute pro „textove“ digitalni knihovny nanestěsti nejsou plně použitelne pro specificke potřeby matematickeho obsahu matematických digitalnich knihoven. Důležitou chybějici funkci je robustni implementace matematiku zohledňujiciho plnotextoveho vyhledavace. Mým cilem je využit existence systemu Math Indexer and Searcher (MIaS) a přislusne sady dat k vývoji, implementaci a vyhodnoceni technik pro vylepseni matematiku zohledňujiciho plnotextoveho vyhledavace pro digitalni matematicke knihovny. Prvni kroky směrem k vylepseni hledani podobnosti MathML byly uciněny pomoci kanonizace Presentation MathML. Dalsi povedou k normalizaci Content MathML. Zvlastni pozornost byla věnovana možnosti využiti kontextu hledani pro vylepseni relevance výsledků a poskytnuti uživatelske zpětne vazby o možných dalsich upravach dotazu. Dalsiho vylepseni při hledani podobnosti matematických výrazů může být dosaženo zapojenim specializovaneho matematickeho software. Opticke rozpoznavani znaků využivane pro zpracovani starých publikaci nas pak přivedlo na myslenku experimentů s hledanim podobných formuli na zakladě podobnosti obrazků jejich tistěne podoby. Výsledna implementace bude ověřena na významně velke kolekci realných dat. Výsledky budou dokumentovany v textu me disertacni prace a publikovany na recenzovaných mezinarodnich forech.
Scaffolding students’ assignments

This article discusses scaffolding in typical student assignments in mother tongue learning materials in upper secondary education in Denmark and the United Kingdom. It has been determined that assignments do not have sufficient scaffolding end features to help pupils understand concepts and build objects. The article presents the results of empirical research on tasks given in Danish and British learning materials. This work is based on a further development of my PhD thesis: “Learning materials in the subject of Danish” (Slot 2010). The main focus is how cognitive models (and subsidiary explicit learning goals) can help students structure their argumentative and communicative learning processes, and how various multimodal representations can give more open-ended learning possibilities for collaboration. The article presents a short introduction of the skills for 21st century learning and defines, on the shoulders of linguistics research, results of “assignmentdidactics” as a useful but also very tentative term in the context of the assessment of design principles for the design of assignments. Based on empirical studies, I discuss cognition, multi-modality and collaboration as crucial types of scaffolding in students’ assignments. Finally there will be a discussion of learning materials.
Isolation and molecular characterization of IBDV from Qualubyia governorate, Egypt, 2015

Infectious bursal disease virus (IBDV) remerged frequently in Qualubyia was the cause of serious economic losses. Twenty field samples were collected from broiler chick farms in different localities of the governorate in January 2015, subjected for isolation on specific pathogen free-embryonated chicken eggs (SPF-ECE), detection using AGID and RT-PCR. Ten samples showed positive results with egg isolation, AGID and RT-PCR. Sequence analysis and characterization of the variable region of VP2 gene purified from the PCR product showed many amino acid substitutions that may affect IBDV antigenicity and virulence. Comparative phylogenetic analysis based on the sequence of the hypervariable region of VP2 gene clustered the isolated IBDV at a distance from the other reference IBDV from Egypt, Europe and other vaccinal strains. It was concluded that substitution mutations observed with the hypervariable region of VP2 gene of the isolated IBDV could affect the virus antigenicity and further studies on the efficacy of vaccines used locally against this variant IBDV strain have to be investigated.
Proceedings of the third conference on Applied natural language processing

The focus of the Third Conference on Applied Natural Language Processing is on the application of natural language processing techniques to real world problems with the aim of featuring the best work presently available in the world. It is the first time that this conference of the Association for Computational Linguistics has been held outside of the United States: it follows on from ones held in Santa Monica, California in 1983 and in Austin, Texas in 1988.Thirty papers and eighteen posters were selected from one hundred submissions to the program committee from four continents. A rich demonstration program of about thirty system demonstrations together with a video program, show Concretely the level of achievement of applied natural language processing research.
Medical image steganography using priority queue with Elgamal cryptosystem

This paper describes a methodology for hiding medical records and patient's identity information inside their scan medical image files using the concept of dynamic priority queue least significant bits (LSBs) substitution. Elgamal cryptosystem is used to encrypt patient's sensitive data. The outcome of the cryptosystem is organised in various priority queue blocks and sub blocks based on a secret data length of each block. The cipher text is obtained from priority queue as highest priority (weight) data will be retrieved first. Now embed the cipher text in a medical cover image at 7th bit or 8th bit position using heap-LSB technique. Authorised receiver can retrieve the correct plain text using private keys. Performance analysis is observed using various performance measure parameters between various medical stego and cover images. Results are analysed and compared with many of existing steganography algorithms.
Queuing Schemes for Wireless Sensor Nodes Transmitting Prioritized Data

Wireless sensor networks (WSNs) are networks that use low powered devices interacting via radio signals. These networks are popular as platforms that provide monitoring and control services to an environment due to their portability and mobility. In order to acquire a comprehensive picture of a monitored environment, wireless nodes are deployed with mounted sensors to collect data from multiple heterogeneous sources, then transmitting these to receiving stations for processing. Sensors exhibit varying data characteristics in terms of transmission delay tolerance and reliable delivery requirements. If a wireless node is installed with multiple sensors, data from these sensors must be prioritized and queued accordingly for transmission. Critical data must be delivered as quickly and as reliably as possible. Low priority data, on the other hand, may be delayed or dropped during high traffic situations; but are still valuable to a monitoring application. As such, their delivery must not be completely omitted even if the wireless node has much data to transmit. This study provides a comparative study of data queuing techniques that may be used in WSNs that carry heterogeneous data traffic. To evaluate techniques, the study simulates a wireless node that acquires data with three priority levels: high, mid and low. These data are then run through test cases that vary the queuing technique used, as well as the ratio of high, mid, and low priority that the node must transmit. Based on results, Fair Preemptive and Aging- based Multilevel queuing provided the best performances in data delivery ratio. Both techniques allowed critical data to be quickly transmitted, while still providing opportunity for low priority data to be transmitted in high traffic scenarios.