Miracle-dz/UniFAR-FTUs huggingface dataset:
Unfolding Off-the-shelf IR Systems for Reproducibility

In this position paper, we discuss the issue of how to ensure reproducibility of the results when off-the-shelf open source Information Retrieval (IR) systems are used. These systems provided a great advancement to the field but they rely on many configurations parameters which are often implicit or hidden in the documentation and/or source code. If not fully understood and made explicit, these parameters may make it difficult to reproduce results or even to understand why a system is not behaving as expected. The paper provides examples of the effects of hidden parameters in off-the-shelf IR systems, describes the enabling technologies needed to embody the approach, and show how these issues can be addressed in the broader context of component based IR evaluation. We propose a solution for systematically unfolding the configuration details of off-the-shelf IR systems and understanding whether a particular instance of a system using is behaving as expected. The proposal requires to: 1) build a taxonomy of components used by off-the-shelf systems, 2) uniquely identify them and their combination in a given configuration, 3) run each configuration on standard test collections, 4) compute the expected performance measures for each run, 4) and publish on a Web portal all the gathered information in order to make accessible and comparable for everybody how an off-the-shelf system with a given configuration is expected to behave.
Video Delivery Architecture for Hierarchical HetNet: HH-D2D Caching

ABSTRACT Over the last few years, smart devices and mobile devices drive the wide growth in mobile data communications. This increase is a major challenge for mobile network operators (MNOs). The MNOs are forced to operate with their maximum capacity limits and this growth needs mobile data offloading to accomplish the capacity demands. The mobile data offloading through device-to-device (D2D) communication can help to reduce the network congestion at low operational and capital expenses. The data offloading through D2D attracts the mobile users to participate, by paying them proper incentives in returns of their services. The offloading requires mutual agreement and benefits both the parties, MNOs and mobile users. In this paper, we categorize the smart devices (SDs) on the basis of their storage and methoding capabilities. Furthermore, we create the hierarchy of SDs to utilize the available resources efficiently with minimum cost and higher benefits. We present a main node–friend node (MNFN) selection and matching mechanism, which efficiently selects and utilizes available high-end SDs (HSDs) on the basis of available resources for offloading purpose. It also decides the optimal pricing policy for the selected SDs. The efficiency of MNFN has been realized in terms of bandwidth and storage demand. Subsequently, we implement caching in MNs, and FNs with the help of our novel hierarchical heterogeneous D2D (HH-D2D) caching-based video dissemination architecture to solve the bit rate issues in the mobile network. The particular focus of our work is on video data, due to high demands. Simulation results show that FN caching increases bit rate up to ∼26 times, while MNFN caching improves bit rate up to ∼40 times.
Directional-based Beacon Packet Mobility Prediction Scheme for Wireless Link Breakdown in MANET Position-Based Routing Protocols

Mobile Ad-hoc NETwork (MANET) is a collection of wireless devices forming network in the absence of any fixed infrastructure. The devices use wireless transmissions to communicate with each other within their transmission ranges. The absence of fixed infrastructure makes the problem of data routing and forwarding a challenging task in such network. To carry out this task, several position-based routing protocols have been proposed that utilize the position of the wireless devices to make routing decision. In position-based routing protocols, the sender routes the data packet to a neighbour node, whose geographical position is the closest to the destination among all the sender's neighbour nodes. However, the selected neighbour node is closer to the edge of the maximum of the sender's transmission range and thus the wireless link between the sender node, and its routing neighbour node may breakdown. Several mobility predication schemes have been proposed in literature to overcome the wireless link breakdown problem. In this paper, we propose a directional- basedbeacon packet mobility prediction scheme to mitigate the drawback observed in literature mobility prediction schemes. The proposed scheme brings better performance for the network performance in term of end-to-end delay.
Pushing the boundaries of deep parsing

I examine the application of deep parsing techniques to a range of Natural Language Processing tasks as well as methods to improve their performance. Focussing specifically on the English Resource Grammar, a hand-crafted grammar of English based on the Head-Driven Phrase Structure Grammar formalism, I examine some techniques for improving parsing accuracy in diverse domains and methods for evaluating these improvements. I also evaluate the utility of the in-depth linguistic analyses available from this grammar for some specific NLP applications such as biomedical information extraction, as well as investigating other applications of the semantic output available from this grammar.
Some armchair worries about wheeled behavior

Autonomous agents research aims to understand adaptive behavior by building models that exhibit such behavior. In this paper, I describe some theoretical worries about the tendency to simplify the sensory-motor control of these behaving models as much as possible. Wheeled robots provide a good example of this tendency. The worries derive from the idea that a complex sensory-motor system is a necessary requirement for reliable functional behavior in a natural environment. The evidence on which I base this conviction is three-fold: (a) when animals, but not wheeled robots, move across a surface this results from variable spatio-temporal patterns across a complex neuro-musculo-skeletal system, (b) a theoretical analysis according to which the stability of distal behavior arises as a result of the variability of proximal behavior, and (c) the possibility to interpret the behavior of animals, but not that of wheeled robots, as a process of self-organization. The use of wheeled robots therefore seems to side step a core problem for understanding adaptive behavior.
A hardware and software architecture for efficient datacenters

Datacenters host an increasing amount of the world's compute, powering a diverse set of applications that range from scientific computing and business analytics to massive online services such as social media and online maps. Despite their growing importance, however, datacenters suffer from low resource and energy efficiency, using only 10-30% of their compute capacity on average. This overprovisioning adds billions of dollars annually to datacenter equipment costs, and wastes significant energy. This low efficiency stems from two sources. First, latency-critical applications, which form the backbone of user-facing, interactive services, need guaranteed low response times, often a few tens of milliseconds or less. By contrast, current systems are architected to maximize long-term, average performance (e.g., throughput over a period of seconds), and cannot provide the short-term performance guarantees needed by these applications. The stringent performance requirements of latency-critical applications make power management challenging, and make it hard to colocate them with other applications, as interference in shared resources hurts their responsiveness. Second, throughput-oriented batch applications, while easier to colocate, experience performance degradation as multiple colocated applications compete for shared resources on servers. This thesis presents novel hardware and software techniques that improve resource and energy efficiency for both classes of applications. First, Ubik is a dynamic cache partitioning technique that allows latency-critical and batch applications to safely share the last-level cache, maximizing batch throughput while providing latency guarantees for latency-critical applications. Ubik accurately predicts the transients that result when caches are reconfigured, and can thus mitigate latency degradation due to performance inertia, i.e., the loss of performance as an application transitions between steady states. Second, Rubik is a fine-grain voltage and frequency scaling scheme that quickly and accurately adapts to short-term load variations in latency-critical applications to minimize dynamic power consumption without hurting latency. Rubik uses a novel, lightweight statistical model that accurately predicts queued work, and accounts for variations in per-request compute requirements as well as queuing delays. Further, Rubik improves system utilization by allowing latency-critical and batch applications to safely share cores, using frequency scaling to mitigate performance degradation due to interference in per-core resources such as private caches.
Requêtes XPath bipolaires et évaluation

Le concept de requetes bipolaires (on parle aussi de requetes avec preferences) a emerge dans la communaute des Bases de Donnees Relationnelles, pour permettre aux utilisateurs d'obtenir des reponses beaucoup plus pertinentes a leurs preoccupations, exprimees via des requetes dites avec preferences. De telles requetes ont generalement deux parties : la premiere permet d'exprimer les contraintes strictes et la seconde, des preferences ou souhaits. Toute reponse a une requete avec preferences doit necessairement satisfaire la premiere partie et preferentiellement la seconde. Toutefois, s'il existe au moins une reponse satisfaisant la seconde partie, toutes les reponses ne satisfaisant que la premiere partie seront exclues du resultat final : elles sont dominees. Dans ce papier, nous explorons une approche d'importation de ce concept dans les Bases de Donnees XML via le langage XPath. Pour ce faire, nous proposons le langage PrefSXPath, une extension du langage XPath permettant d'exprimer les requetes XPath avec preferences structurelles, puis, nous presentons un algorithme d'evaluation des requetes PrefSXPath a l'aide des automates. ABSTRACT. The concept of bipolar queries (also call preferences queries) emerged in the Relational Databases community, allowing users to get much more relevant responses to their requests, expressed via queries say with preferences. Such requests usually have two parts: the first is used to express the strict constraints and the second, preferences or wishes. Any response to a query with preferences must necessarily satisfy the first part and preferably the latter. However, if there is at least a satisfactory answer of the second part, those satisfying only the first part will be excluded from the final result: they are dominated. In this paper, we explore an approach of importation of this concept in a XML Database via XPath language. To do this, we propose PrefSXPath language, an extension of XPath in order to express XPath queries with structural preferences, then we present a query evaluation algorithm of PrefSXPath using automata.
Extracting conceptual structures from multiple sources

This thesis extracts conceptual structures from multiple sources: Wordnet, Web Corpora and Wikipedia. The conceptual structures extracted from Wordnet and Web Corpora are inspired by the feature norm effort in cognitive psychology. The conceptual structure extracted from Wikipedia makes the transition between feature norm structures and theory like structures. The main contribution of this thesis can be grouped in two categories: 1. Novel methods for the extraction of conceptual structures. More precisely, there are three new methods we developed: (a) Conceptual structure extraction from Wordnet. We devise a procedure for property extraction from Wordnet using the notion of semantic neighborhood. The procedure exploits the main relations organizing the nouns, the information in glosses and the inheritance of properties principle. (b) Feature Norms like extraction from corpora. We propose a method to acquire feature norm like structures from corpora using weakly supervised methods. (c) Conceptual Structure from Wikipedia. A novel unsupervised method for the extraction of conceptual structures from Wikipedia entries of similar concepts is put forward. The main idea we follow is that similar concepts (i.e. those classied under the same node in a taxonomy) are described in a comparable way in Wikipedia. Moreover, to understand the kind of information extracted from Wikipedia we annotate this knowledge with a set of property types. 2. Evaluation. We evaluate Wordnet as a model of semantic memory and suggest the addition of new semantic relations. We also assess the properties extracted from all sources for a unified test set, in a clustering experiment.
Formal verification of documentation by means of self-organizing map

By using background knowledge of the general and speciﬁc domains and by processing new natural language corpus experts are able to produce a conceptual model for some speciﬁc domain. In this paper we present a model that tries to capture some aspects of this conceptual modeling process. This model is functionally organized into two information processing streams: one reﬂects the process of formal concept lattice generation from domain conceptual model, and the another one reﬂects the process of formal concept lattice generation from the domain documentation. It is expected that similarity between those concept lat-tices reﬂects similarity between documentation and conceptual model.In addition to this process of documentation formal veriﬁcation the set of natural language processing artifacts are created. Those artifacts then can be used for the development of information systems natural language interfaces. To demonstrate it, an experiment for the concepts identiﬁcation form natural language queries is provided at the end of this paper.
The incorporation of bubbles into a computer graphics fluid simulation

The Incorporation of Bubbles into a Computer Graphics Fluid Simulation. (May 2004) Shannon Thomas Greenwood, B.S., Texas A&M University Chair of Advisory Committee: Dr. Donald House We present methods for incorporating bubbles into a photorealistc fluid simulation. Previous methods of fluid simulation in computer graphics do not include bubbles. Our system automatically creates bubbles, which are simulated on top of the fluid simulation. These bubbles are approximated by spheres and are rendered with the fluid to appear as one continuous surface. This enhances the overall realism of the appearance of a splashing fluid for computer graphics. Our methods leverage the particle level set representation of the fluid surface. We create bubbles from escaped marker particles from the outside to the inside. These marker particles might represent air that has been trapped within the fluid surface. Further, we detect when air is trapped in the fluid and create bubbles within this space. This gives the impression that the air pocket has become bubbles and is an inexpensive way to simulate the air trapped in air pockets. The results of the simulation are rendered with a raytracer that includes caustics. This allows the creation of photorealistic images. These images support our position that the simple addition of bubbles included in a fluid simulation creates results that are much more true to life.
Big data challenges, an insight into the GAIA Hadoop solution

Large data sets, so-called big data, are no longer only a problem for web companies. Advances in data acquisition and telecommunications have created huge amounts of data. Storing, managing and processing this data are critical issues today. Gaia, an ESA mission for which CNES is a data processing center, will face the challenge of dealing with an end mission volume of one Petabyte stored in tables containing eighty billion rows. Unlike other big databases already in operation, the project involves complex data accesses and intensive computing. In this paper we will briefly present the studied products. Then we will describe the data management system based on Hadoop we have designed, which is a batch execution framework (MapReduce paradigm) with an underlying file system (HDFS) optimized for task execution. We will conclude by explaining how we have solved the many key questions raised by this conceptual migration: data access, workflow orchestration, designing a powerful Hadoop cluster taking into account its integration and the budget.
Biometric System using Gait Feature Analysis and Comparison

:- The world today is making rapid progress in its quest to realize the dream of a creating a user friendly, customer caring ambience. With every new dream comes the nightmare of a security of the system lapse which may allow the misuse of the system. A major success in trying to bridge the advent of a security lapse is the use of biometrics. Biometric systems are becoming increasingly important, since they provide more reliable and efficient means of identity verification. Biometric gait recognition (i.e. recognizing people from the way they walk) is one of the recent attractive topics in biometric research. This paper presents biometric user recognition based on gait. Most real-life biometric systems are still unimodal. Unimodal biometric systems perform person recognition based on a single source of biometric information. Such systems are often affected by some problems such as noisy sensor data, non universality and spoof attacks. Multi biometrics overcomes these problems. Multi biometric systems represent the fusion of two or more unimodal biometric systems. Such systems are expected to be more reliable due to the presence of multiple independent pieces of evidence. So , gait can be used alone as a biometric identification or it can be used with many other biometric trait .
Parallel processing for the load flow of power systems: the approach and applications

A description is given of homotopy-based computational parallel algorithms for solving for all the roots of a system of algebraic polynomial equations. Also presented is a convenient polynomial representation of the load flow equations of power systems. The algorithm techniques are then applied to obtain all steady-state solutions of the load flow for five-bus and seven-bus power system networks. A special probability-one homotopy method is tailored for the load flow to reduce the computational complexity while still guaranteeing the finding of all solutions computationally. More importantly and practically, the numerical implementation of the solution procedures exploits inherent parallelism in the load flow equations to be efficiently executed on massively parallel distributed-memory multiprocessors. <<ETX>>
Online recognition of freely handwritten Japanese characters using directional feature densities

The authors propose an online handwritten Japanese character recognition method permitting both stroke number and stroke order variations. The method is based on the pattern matching technique. Matching is done by the multiple similarity method using directional feature densities, which are independent of both stroke number and stroke order. This method has achieved a good recognition rate, 91%, for 2965 freely written Japanese kanji characters. <<ETX>>
Deep learning: Using machine learning to study biological vision

Today many vision-science presentations employ machine learning, especially the version called “deep learning”. Many neuroscientists use machine learning to decode neural responses. Many perception scientists try to understand how living organisms recognize objects. To them, deep neural networks offer benchmark accuracies for recognition of learned stimuli. Originally machine learning was inspired by the brain. Today, machine learning is used as a statistical tool to decode brain activity. Tomorrow, deep neural networks might become our best model of brain function. This brief overview of the use of machine learning in biological vision touches on its strengths, weaknesses, milestones, controversies, and current directions. Here, we hope to help vision scientists assess what role machine learning should play in their research.
Brain Tumour Segmentation Using K-Means And Fuzzy C-Means Clustering Algorithm

Abstract — The image segmentation is performed to detect, extract and characterize the anatomical structure. Here, we apply two widely used algorithm for tumour detection (i) K-means clustering (ii) Fuzzy C Means clustering The segmentation algorithms are compared to estimate the efficiency by evaluating the execution time and accuracy of the algorithm. The result shows that the execution time is less in K-Means compared to Fuzzy C Means clustering technique, because the number of iterations of K-Means is less than Fuzzy C Means clustering . Further, the tumour area is calculated for accurate result Keywords — Image segmentation, K-Means clustering, Fuzzy C – means clustering techniques, Segmentation algorithms I. Introduction The recent survey [4] has concluded that brain tumour comes next to lung cancer around the world . Early detection and treatment can increase the rate of survival. Magnetic Resonance Imaging(MRI)depends on computer technology to generate and display digital images Segmentation is an important process in most medical image analysis[12],[14]. It is very difficult to conduct surgery without using image processing techniques. Structures like tumour, brain tissue cannot be identified without image segmentation [3]. Clustering to Magnetic Resonance (MR) brain tumours maintains efficiency. Clustering is suitable for biomedical image segmentation as the number of clusters is usually known for images of particular regions of the human anatomy Here, an object density-based image segmentation [7],[10] is used, which incorporates intensity-based, edge-based and texture-based segmentation techniques. The method consist of three main stages: pre processing, object segmentation and final segmentation. Image enhancement, noise reduction and layer-of-interest extraction are some of the task of pre processing The paper presents the efficient use of clustering algorithm to detect the tumour cells from MR images which may present the characteristic of severity of the tumour such that required evaluation and treatment can be facilitated. In this paper, we present a segmentation method which helps in identifying the affected region and also able to calculate the affected area
Block diagram grammar and signal flow graph grammar with their applications

A block diagram grammar and a signal flow graph grammar were previously defined as a theoretical and practical foundation for the computerized analysis and design of control systems and filters by using block diagrams and/or signal flow graphs. They are formalized on the basis of a context-sensitive graph grammar. In this research, a knowledge representation method in order to generate specific system structures such as series connections, feedback connections, and (non)recursive filters is formalized on the basis of the block diagram grammar and the signal flow graph grammar. For this purpose, we discuss the composition of the production copies of the context-sensitive graph grammar, its associativity property, and the confluence property. Then we define the composite production copies that serve as templates for generating those structures. We also describe our block diagram processing system that supports block diagram editing, parsing, system structure generation, and analysis based on the BDG. This work is the first attempt to formally represent analysis and design knowledge of control systems and filters on the basis of these grammars.