Miracle-dz/UniFAR-FTUs huggingface dataset:
Building an Ontology-Based Framework For Semantic Information Retrieval: Application To Breast Cancer

In this paper, we will present IR framework for introducing ontologies in information retrieval. The main hypothesis is that the inclusion of conceptual knowledge such as ontologies in the information retrieval process can contribute to the solution of major problems currently faced by in information retrieval. Current information retrieval systems mostly use keyword search, which is unsatisfactory option because of its low precision and recall. In this paper, we consider concept- based information retrieval model as a new and promising way of improving search on the Web. Informally, concept- based information retrieval is search for documents based on their meaning rather than on the presence of the keywords in the object. Much more "smartness" should be embedded to search tools to manage effectively search, retrieval, filtering and presenting relevant information. The paper is organized as follows. Section 1 introduces the problem of current information retrieval systems. Section 2 presents an overview of the ontology-based information retrieval techniques. Section 3 presents our framework of IR. Section 4 describes an example of using ontologies for searching information about breast cancer and section 5 draws conclusions.
Memory Access Optimization for Computations on Unstructured Meshes

Graphs have been found an effective tool in modeling the real world from the road network to the 3D volumetric description of a human vein. Numerical algorithms that come from these models are often computationally expensive, thus these tasks have to be divided. This division is defined by the solution of the graph partitioning problem. The partitioning aims for the best possible parallelism with maximal resource utilization. In the age of slow interconnections and single-core processors, the optimal solution was clearly the one with minimal communication need and identically sized parts. However, the current processor architectures have thousands of processing elements, and the bandwidth of their interconnections is just one order of magnitude slower than the bandwidth of their main memory. The goal of my research is to investigate the properties of optimal partitions in the case of these novel processor architectures and naturally, I want to also give methods to generate these partitions. In this work, I show the best processor architectures available today and through them the general problem of memory bandwidth limitation. I present in details Dataflow Machines (DM) which provides the best solution to this problem in my view. These specific processors can be realized in FPGA or custom VLSI. The most important property of DMs is streaming, that is, the processing elements connected to a maximal bandwidth data flow and the intermediate results propagated the same way to other processing elements on-chip and finally the result is streamed back to the main memory. DMs utilize the whole memory bandwidth, but only a small portion of the data flow can be accessed on-chip at the same time. The operands of an operation have to be close to each other in the serialized data stream, otherwise, the operation can not be executed (one of the operands has not arrived when an other operand must be thrown out from the cache). The maximal distance of connected data elements is called data locality. DOI:10.15774/PPKE.ITK. 2016.007
Massively Parallel Density Functional Theory Calculations of Large Transition Metal Clusters

We report on ab initio density functional theory (DFT) calculations of structural properties of large elementary transition metal clusters with up to 561 atoms, corresponding to a diameter of about 2.5 nm, which is a relevant size for practical applications. The calculations were carried out on an IBM Blue Gene/L supercomputer, showing that reasonable scaling up to 1024 processors and beyond can be achieved with modern pseudopotential plane wave codes.
Bearings based robot homing with robust landmark matching and limited horizon view

Robot navigation is a well studied but unsolved problem in the general case. One particularly interesting behavior is homing, which can be defined as returning to a base position after traversing an arbitrary path. Particularly interesting are implementations of this behavior which do not rely on a metric estimate of the robot’s location, which is susceptible to monotonically increasing error. This paper seeks to expand upon previous work with visual homing using a panoramic camera by applying more robust methods of data association, working in natural, outdoor environments. Offline experimental results on image data with accurate ground truth show that the SIFT object recognition framework and a camera setup which cannot view the entire horizon provide adequate information for a bearings-only homing technique.
A complete operational architecture of alert correlation

To defend against various attacks, many security systems such as intrusion detection systems are deployed into hosts and networks to better protect digital assets. However, there are well-known problems related to the current intrusion detection systems. To better understand security threats from various sources and take appropriate response, it is necessary to perform alert correlation. This paper proposes a general alert correlation architecture, including four important components: log management, alert correlation, incident response and knowledge base system. The focus is to describe most important operations in alert correlation component. The proposed architecture includes anomaly-based analysis in alert correlation component. Different techniques for alert correlation are reviewed and compared. This study proposes that a hybrid model of multiple techniques leads to better performance of alert correlation engine.
The conditions for the successful use of research results by teachers: reflections on some innovations in France

Abstract Although numerous claims are made about the necessity for research to improve teaching and learning practices, links between research and teaching are still tenuous. Therefore we argue that the issue of linking research to practice should be tackled in a different way than in the past. Research knowledge in education should consider teachers and school leaders prior to being disseminated, in order to be shaped according to the complexity of educational practice. We illustrate our theory with several experiences of the French Institute of Education, including the organisation of a range of consensus conferences to better link research results, practices and policy.
Compressed Sensing Image Reconstruction Algorithm by Dictionary Learning

It is a challenging task to reconstruct images from compressed sensing measurement due to its implicit ill-posed property. In this paper, we propose an image reconstruction algorithm for compressed sensing image application based on the adaptive dictionary, which is learned from the reconstructed image itself. The sparsity level is enhanced since the sparse coding of overlapping image patches takes into account the local image features, and accordingly the quality of the reconstructed image is improved. In addition, linearization technique is also exploited to remove the computation of matrix inversion. Numerical experiments are conducted on several test images with a variety of sampling ratios. The results demonstrate that our proposed algorithm can efficiently reconstruct images from compressed sensing measurements and achieve more than 3dB gain averagely over the current state-of-art compressed sensing reconstruction algorithms.
Nonlinear effects in LMS adaptive equalizers

An adaptive transversal equalizer based on the least-mean-square (LMS) algorithm, operating in an environment with a temporally correlated interference, can exhibit better steady-state mean-square-error (MSE) performance than the corresponding Wiener filter. This phenomenon is a result of the nonlinear nature of the LMS algorithm and is obscured by traditional analysis approaches that utilize the independence assumption (current filter weight vector assumed to be statistically independent of the current data vector). To analyze this equalizer problem, we use a transfer function approach to develop approximate analytical expressions of the LMS MSE for sinusoidal and autoregressive interference processes. We demonstrate that the degree to which LMS may outperform the corresponding Wiener filter is dependent on system parameters such as signal-to-noise ratio (SNR), signal-to-interference ratio (SIR), equalizer length, and the step-size parameter.
IR-Tree: An Efficient Index for Geographic Document Search

Given a geographic query that is composed of query keywords and a location, a geographic search engine retrieves documents that are the most textually and spatially relevant to the query keywords and the location, respectively, and ranks the retrieved documents according to their joint textual and spatial relevances to the query. The lack of an efficient index that can simultaneously handle both the textual and spatial aspects of the documents makes existing geographic search engines inefficient in answering geographic queries. In this paper, we propose an efficient index, called IR-tree, that together with a top-k document search algorithm facilitates four major tasks in document searches, namely, 1) spatial filtering, 2) textual filtering, 3) relevance computation, and 4) document ranking in a fully integrated manner. In addition, IR-tree allows searches to adopt different weights on textual and spatial relevance of documents at the runtime and thus caters for a wide variety of applications. A set of comprehensive experiments over a wide range of scenarios has been conducted and the experiment results demonstrate that IR-tree outperforms the state-of-the-art approaches for geographic document searches.
Application of Electromagnetic Bandgaps to the Design of Ultra-Wide Bandpass Filters With Good Out-of-Band Performance

In this study, a new technique for the design of ultra-wide bandpass filters with spurious suppression over a very wide band is presented. The method consists on the combination of a well-known analytical design approach to achieve wide bandwidths with an electromagnetic bandgap structure, which is fundamental for spurious suppression. To illustrate the technique, a microstrip of ultra-wide bandpass filter centered at 3.4 GHz with a bandwidth covering 4.8 GHz is implemented in an Arlon substrate (permittivity epsivr=2.4, thickness h=0.675 mm). Measured filter characteristics are good with in-band insertion losses below 0.90 dB and return losses better than 10 dB. Out-of-band performance is also good with spurious passband attenuation higher than 30 dB up to at least 20 GHz
Action Recognition in the Rhino Cooperative Framework

The University of Rochester's Rhino system is a locally organized cooperative agent architecture that uses observation as its primary means of inter-agent coordination. Observing agents recognize ongoing individual actions, and from sequences of actions reason about likely ongoing plans. Sets of individual plans are combined to form hypothesized group plans. A key element in the group plan inference process is the recognition of individual agents' actions. Action recognition is the problem of describing an observed agent's activity as goal-directed behavior. Atomic actions are those whose eeects occur instantaneously; composite actions take place over time. This paper proposes a knowledge-based, Bayesian technique for describing, detecting and classifying actions. To recognize atomic actions, we use Bayes nets to detect the corresponding instantaneous changes in world state. Composite actions are recognized by hidden Markov models, which describe them as sequences of atomic actions. Previous approaches to action recognition have required tedious knowledge engineering, and have been unreliable in the face of sensor error. Given an action-library, Rhino's approach automatically constructs action-recognizers that are robust to sensor error.
On selfish behavior using asymmetric carrier sensing in IEEE 802.11 wireless networks

The IEEE 802.11 utilizes carrier sensing mechanism to support multiple users effectively with small number of collisions. However, if there are hidden nodes, IEEE 802.11 nodes suffer from consecutive collisions and experience extremely low throughput. Due to the nature of non-cooperative property, every 802.11 node tries to maximize its own throughput irrespective of total network performance. In this paper, we show that 802.11 node can achieve higher throughput by intentionally reducing its carrier sensing range in some cases. Based on simulations, we also show that adjusting carrier sense threshold may bring about undesirable Nash equilibrium in terms of total network performance in game-theoretical model.
Combined adaptive beamforming techniques for noise reduction in changing environments

This paper introduces two novel adaptive beam-forming techniques for noise reduction applications. The proposed architectures derive from the generalized sidelobe canceller (GSC); the novelty relies on the use of a combined adaptive sidelobe cancelling structure which allows the system to achieve robustness in nonstationary environments. This structure is composed of a convex combination of two multiple-input singleoutput (MISO) adaptive systems with complementary capabilities which is able to preserve the best properties of each system. The difference between the two schemes lies in the way of combining the two MISO systems. The effectiveness of the proposed systems is assessed in a typical context of a hands-free teleconferencing application in a noisy environment. The experiments show that the proposed beamforming system is capable of improving performance of conventional techniques.
Anticipatory balance control

A hallmark of many skilled motions is the anticipatory nature of the balance-related adjustments that happen in preparation for the expected evolution of forces during the motion. This can shape simulated and animated motions in subtle-but-important ways, help lend physical credence to the motion, and help signal the character's intent. In this paper, we investigate how center of mass reference trajectories (CMRTs) can be learned in order to achieve anticipatory balance control with a state-of-the-art reactive balancing system. This enables the design of physics-based motion simulations that involve fast pose transitions as well as force-based interactions with the environment, such as punches, pushes, and catching heavy objects. We demonstrate the results on planar human models, and show that CMRTs can generalize across parameterized versions of a motion. We illustrate that they are also effective at conveying a mismatch between a character's expectations and reality, e.g., thinking that an object is heavier than it is.
An Architecture of Intelligent Virtual Avatars for E-Business

An intelligent virtual avatar is very useful in an e-business website, it can attract users to explore more business information, how to construct the architecture of an intelligent virtual avatar is a very important topic for a friendly interface of e-business. Based on current research on cognitive science, architecture of an intelligent virtual avatar is presented, and a computational cognitive model that integrates outer stimuli and inner mental variables (motivation, emotion, personality) is proposed, the result can support a quantitative method to control the behaviors of an intelligent virtual avatar.
Synchronization enhancement of a scale-free highly clustered network

We investigate synchronization of a scale-free highly clustered echo state network with Hindmarsh-Rose equations. A new method, inspired by the connectivity between functional cortical columns, is proposed to improve the network in reservoir and enhance its synchrony. The experimental results show that synchronization can be achieved and kept stable with the increase of nodes. This modified complex network, characterized by nature-growing, preferential attachment, and synchrony, is critical to echo state network, and will help study the synchronization of real large-scale complex systems.
Can NIC Memory in InﬁniBand Beneﬁt Communication Performance? — A Study with Mellanox Adapter (cid:0)

This paper presents a comprehensive micro-benchmark performance evaluation on using NIC memory in the Mel-lanox InﬁniBand adapter. Three main beneﬁts have been explored, including non-blocking and high performance host/NIC data movement, trafﬁc reduction of the local interconnect, and avoidance of the local interconnect bottle-neck. Two case studies have been carried out to show how these beneﬁts can be utilized by applications. In the ﬁrst case in which the NIC memory is used as intermediate communication buffer for non-contiguous data communication, lower CPU overhead and better latency are attained. In the second case, a common communication building block, communication forwarding chain , has been studied. Our re-sults show that using the NIC memory can achieve a factor of up to 2.2 improvement over the conventional approach. To the best of our knowledge, this is the ﬁrst such study to demonstrate the beneﬁts of NIC memory in InﬁniBand adapter.
Exploiting short query expansion for images retrieval

With the tremendous revolutions in the digital technology and ease of use in the results large volumes of images generated. In such types gigantic image corpus it is hardly to find the relevant images. Different researchers have defined techniques to retrieve images with high accuracy but still not cope in the semantic gap and lexical ambiguity. This paper focused on short query expansion i.e. expands user query with ConceptNet and WordNet knowledge bases. It retrieved the images, even if they are labeled with different words. The experiments showed the performance of this proposed algorithm, that retrieves images effectively and efficiently from LabelMe dataset.
Performance and energy evaluation of spark applications on low-power SoCs

Microservers have recently gained attention as low-cost, low power, reduced footprint servers that are mainly based on energy efficient processors such as the ones used in embedded systems. Microservers based on low-power embedded processors are mainly targeting lightweight applications or parallel applications that benefit most from individual low-power servers with sufficient I/O between nodes rather than high performance processors. In this paper we evaluate the mapping of Apache Spark on low-power SoC-based processors. Apache Spark is one of the most widely framework in cloud computing for batch and streaming data analytics. We evaluate the energy efficiency of low-power SoCs that are used in embedded system for the execution of several Spark applications. The performance evaluation shows that low-power SoCs have the potential to offer up to 3x higher energy efficiency compared to high performance processors typically used in data centers.
Engaging stigmatized communities through solutions journalism: Residents of South Los Angeles respond

In many communities across the United States, substantive local news is a rare commodity. For areas long stigmatized and associated with high levels of violence, crime, and poverty, negative reporting may be the only local news available. Drawing from communication infrastructure theory and literature on local news audiences and civic journalism, this study explores how a local solutions journalism project is received by members of an underrepresented and stigmatized community. Solutions journalism stories focus on responses to social problems, usually exploring problem-solving efforts that have the potential to be scaled. This case examines how participants in six focus groups with 48 African-American and Latino South Los Angeles residents responded to solutions-oriented stories produced by a local media project. Study findings illustrate how residents navigate and critically interpret local media coverage, and how their response to ‘solutions journalism’ is largely positive but tempered by concerns regarding structural inequalities.
The transplantation of tumors to the brains of heterologous species.

The transfer of heterologous tumor tissue to the brain was reported by Shirai in 1921 (5) and con firmed by Murphy and Sturm in 1923 (4). Despite early recognition, however, the ability of the brain to support heterologous growth has not been uti lized in experimental work, nor has its application to the general problems of transplantation been explored. One phase of a study undertaken in this labora tory to determine the factors allowing heterol ogous growth in the anterior chamber of the eye has been a systematic investigation of other bodily regions, including the brain, with respect to trans plantation reactions. The Brown-Pearce rabbit tumor has been successfully transplanted to the subcutaneous space and testicle of the mouse (1), and several mouse tumors have been grown intra muscularly in hamsters and rats; but, in general, tumors will not survive transfer to such regions in alien hosts. In contrast, the brain has proved an excellent nidus for heterologous tissues, rivaling the anterior chamber as a growth site, and in one case providing a medium for growth after con sistent failure of eye transfer. Although in individual cases special conditions inherent in the region many act to produce differ ences in the incidence and rate of growth, the basic factors determining the success or failure of brain transfer appear to be identical with those con cerned in anterior chamber transplantation. Adult, embryonic, and cancer tissues grow on homologous transfer, while benign tumors and precancerous tissues fail to survive, and heterologous transfer is successful only in the case of embryonic tissue and cancer. The object of the present paper is to report the heterologous growth of tumors with particular reference to those of human origin. Experiments concerned with embryonic tissues will be described at a later date.
Tracking Articulated Motion with Piecewise Learned Dynamical Models

We present a novel approach to modelling the non-linear and time- varying dynamics of human motion, using statistical methods to capture the char- acteristic motion patterns that exist in typical human activities. Our method is based on automatically clustering the body pose space into connected regions ex- hibiting similar dynamical characteristics, modelling the dynamics in each region as a Gaussian autoregressive process. Activities that would require large numbers of exemplars in example based methods are covered by comparatively few mo- tion models. Different regions correspond roughly to different action-fragments and our class inference scheme allows for smooth transitions between these, thus making it useful for activity recognition tasks. The method is used to track activi- ties including walking, running, etc., using a planar 2D body model. Its effective- ness is demonstrated by its success in tracking complicated motions like turns, without any key frames or 3D information.