Miracle-dz/UniFAR-FTUs huggingface dataset:
SINAI-GIR System. University of Jaén at GeoCLEF 2008

This paper describes the third participation of the SINAI research group from Uni- versity of Jaen in GeoCLEF track. We have tried to improve the system proposed last year in GeoCLEF 2007. The main developments are related to the use of query reformulation, keywords recognition, hyponyms extraction and query geo-expansion. On the other hand, new rules have been applied in the Validator subsystem in order to fllter the documents recovered by the IR subsystem. We have run several experiments, combining these developments in order to resolve the monolingual and bilingual tasks. The results obtained shown that flltering does not reach yet to improve the baseline case. However, the use of keywords and hyponyms in the re-ranking process seems to improve the flltering results. On the other hand, the use of query reformulation and geo-expansion does not improve the baseline case either.
Protecting against cyber threats in networked information systems

This paper provides an overview of our efforts in detecting cyber attacks in networked information systems. Traditional signature based techniques for detecting cyber attacks can only detect previously known intrusions and are useless against novel attacks and emerging threats. Our current research at the University of Minnesota is focused on developing data mining techniques to automatically detect attacks against computer networks and systems. This research is being conducted as a part of MINDS (Minnesota Intrusion Detection System) project at the University of Minnesota. Experimental results on live network traffic at the University of Minnesota show that the new techniques show great promise in detecting novel intrusions. In particular, during the past few months our techniques have been successful in automatically identifying several novel intrusions that could not be detected using state-of-the-art tools such as SNORT.
Distance-based Classification of Structures within a Connec- tionist Framework

We present a novel self-organizing classification network for structured objects. The system consists of two layers, an upper layer which serves as a classifier and a lower layer as a feature extractor. The classifier is an inhibitory winner-take-all network where each unit corresponds to a unique class while the lower layer consists of Hopfield-style networks solving the problem of finding maximum common parts of the input structure and the particular prototypes. The self-organizing architecture is able to outperform traditional classification models using a maximum selector as a classifier. The improvements arise from deactivating networks in the lower layer which indicate large dissimilarities between the input structure and the corresponding prototypes.
Optimum Supply Voltages for Minimization of Leakage Currents in SRAM in Stand-by Mode

An effective technique for reducing leakage currents in an SRAM cell in standby mode is by reducing effective supply voltage across the cell. It is shown that raising of negative supply voltage level (Vss) is more effective in reducing subthreshold leakage currents, while reduction in positive supply voltage level (Vdd) has relatively larger impact on gate leakage currents. As a result, for a fixed effective supply voltage across the cell in standby mode, there is an optimum combination of supply rails that minimizes leakage currents depending on the relative importance of subthreshold and gate leakage currents. Simulation results based on BPTM (Berkeley Predictive Technology Model) are presented that show that for a net voltage across the cell of 0.5V, supply voltages (VDD, VSS) of (0.35, 0.85), (0.25, 0.75) and (0.1, 0.6) yield minimum leakage currents in 90nm, 65nm and 45nm technology nodes respectively
Finding Sparse Solutions for the Index Coding Problem

The Index Coding problem has recently attracted a significant attention from the research community. In this problem, a server needs to deliver data to a set of wireless clients over the broadcast channel. Each client requires one or more packets, but it might have access to the packets requested by other clients as side information. The goal is to deliver the required data to each client with minimum number of transmissions. In this paper, we focus on finding sparse solutions to the Index Coding problem. In a sparse solution each transmitted packet is a linear combination of at most two original packets. We focus both on scalar and vector versions of the problem. For the scalar case, we present a polynomial time algorithm that achieves an approximation ratio of 2-(1/¿an). For the vector case, we present a polynomial time algorithm that identifies an optimal solution to the problem. Our simulation studies demonstrate that our algorithms achieve good performance in practical scenarios.
Photosynthetic light acclimation in peach leaves: importance of changes in mass:area ratio, nitrogen concentration, and leaf nitrogen partitioning.

Photosynthetic light acclimation of leaves can result from (i) changes in mass-based leaf nitrogen concentration, Nm, (ii) changes in leaf mass:area ratio, Ma, and (iii) partitioning of total leaf nitrogen among different pools of the photosynthetic machinery. We studied variations in Nm and Ma within the crowns of two peach (Prunus persica L. Batsch) trees grown in an orchard in Portugal, and one peach tree grown in an orchard in France. Each crown was digitized and a 3-D radiation transfer model was used to quantify the intra-crown variations in time-integrated leaf irradiance, . Nitrogen concentration, leaf mass:area ratio, chlorophyll concentration, and photosynthetic capacity were also measured on leaves sampled on five additional peach trees in the orchard in Portugal. The data were used to compute the coefficients of leaf nitrogen partitioning among carboxylation, bioenergetics, and light harvesting pools. Leaf mass:area ratio and area-based leaf nitrogen concentration, Na, were nonlinearly related to , and photosynthetic capacity was linearly related to Na. Photosynthetic light acclimation resulted mainly from changes in Ma and leaf nitrogen partitioning, and to a lesser extent from changes in Nm. This behavior contrasts with photosynthetic light acclimation observed in other tree species like walnut (Juglans regia L.) in which acclimation results primarily from changes in Ma.
Domain-specific augmentations for High-Level Synthesis

High-Level Synthesis (HLS) has become a very popular instrument to facilitate rapid development of production-ready implementations for FPGAs. Ever increasing flexibility of the frameworks, however, demands a very high level of domain-specific knowledge from the designer. Examples for such knowledge in window-based image processing are median computation and border handling. Depending on the size of the considered window, writing the code to perform such operations may become overwhelming even at very high abstraction levels. To increase productivity and to make the underlying architecture accessible to non-experts, we propose to combine HLS with domain-specific augmentations. Specifically, we propose a new language extension in form of a reduction for sorting and median computation. Furthermore, we introduce a new high-level transformation to perform multiple kinds of border treatment automatically. Both augmentations may reduce the required amount of code lines considerably. The increase in productivity is analyzed by comparing the lines of code necessary to specify a median filter for HLS in PAULA for synthesis using PARO and in C++ for synthesis using a commercial HLS tool.
Learning the Strength of the Factors Influencing User Behavior in Online Social Networks

As social networking is moving into the web, the study and exploitation of social correlation has emerged as a hot research topic. Most of these work consider binary social relations, called "friendships". However, online users tend to establish many friendships of varying degree of strength, e.g., relatives, friends, co-workers, and acquaintances. We argue that, due to their different degree of strength, different friend relationships will have greatly varying degrees of correlation and should be distinguished. Besides, social correlation is not the only factor driving user behavior. In this paper, we address the problem of learning the strength of the social correlation, user, item, and sparsity factors in online social networks. We propose a probabilistic model, Factor Weight Model, for learning these strengths which maximize the joint probability of the observed user behavior, i.e., actions on items. Different from existing methods, our model considers not only social correlation, but it also considers the other factors affecting user behavior. We have conducted experiments on four real life data sets from Epinions, Flixster, Flickr, and Digg. Our experiments prove the superiority of our model over a state-of-the-art method in terms of action prediction. We also analyze the contributions of the various factors for the prediction performance.
Designing a pattern recognition system on GPU for discriminating between patients with micro-ischaemic and multiple sclerosis lesions, using MRI images

The aim of this study was to employ state-of-art graphics processing unit (GPU) technology and CUDA parallel programming to design and implement a stand-alone pattern recognition (PR) system to discriminate between patients with micro-ischaemic (mIS) and multiple sclerosis (MS) lesions. The dataset comprised MRI image series of 32 patients with mIS and 19 with MS lesions. The probabilistic neural network classifier and 40 textural features, calculated from lesions in the magnetic resonance imaging (MRI) images, were used to design the PR system. The highest classification accuracy was 90.2%, employing six textural features. It took about 135 minutes to design the PR system on a desktop CPU (Intel Core 2 Quad Q9550), using sequential programming, against 250 seconds on the Nvidia 8800GT GPU card, using parallel programming. The proposed PR system may be redesigned on site, when new verified data are incorporated in its depository, and it may serve as a second opinion tool in a clinical environment.
Reinforced Similarity Integration in Image-Rich Information Networks

Social multimedia sharing and hosting websites, such as Flickr and Facebook, contain billions of user-submitted images. Popular Internet commerce websites such as Amazon.com are also furnished with tremendous amounts of product-related images. In addition, images in such social networks are also accompanied by annotations, comments, and other information, thus forming heterogeneous image-rich information networks. In this paper, we introduce the concept of (heterogeneous) image-rich information network and the problem of how to perform information retrieval and recommendation in such networks. We propose a fast algorithm heterogeneous minimum order k-SimRank (HMok-SimRank) to compute link-based similarity in weighted heterogeneous information networks. Then, we propose an algorithm Integrated Weighted Similarity Learning (IWSL) to account for both link-based and content-based similarities by considering the network structure and mutually reinforcing link similarity and feature weight learning. Both local and global feature learning methods are designed. Experimental results on Flickr and Amazon data sets show that our approach is significantly better than traditional methods in terms of both relevance and speed. A new product search and recommendation system for e-commerce has been implemented based on our algorithm.
Balancing Self-Compassion with Self-Advocacy: A New Approach for Persons with Disabilities

Self-advocacy and self-compassion are not typically seen as related concepts, or as skills made more valuable when put together. Learning to self-advocate can be a challenging task for many persons with disabilities. Often, people are not afforded access to information on how to advocate for themselves, and as a result, many must struggle to figure out if, when, and how to self-advocate. In an effort to change this trend, this article argues that self-compassion can empower people to advocate for themselves, while also reinforcing the message that individuals do not need to be rescued and are not victims of their environments. Learning to self-advocate is a complex process, and this article provides information on self-advocacy, self-compassion, and the potential these can generate when combined.
Relational hopes: A study of the lived experience of hope in some patients hospitalized for intentional self-harm

Hopelessness is a well-established predictor of suicide, and inspiring hope is an important goal in mental health care, but there are few studies of hope among persons with suicidal behavior. The aim of this study was to interpret the lived experience of hope in some patients hospitalized for intentional self-harm. Twelve persons that had engaged in suicidal behavior by ingesting an overdose of medication were interviewed shortly after hospitalization and asked to narrate about their hopes. The transcripts were analyzed using a phenomenological hermeneutic method inspired by Ricoeur's theory of interpretation. The naïve reading was one of hope being relational. The structural analysis identified three themes: hopes for life, hopes for death, and the act of hoping. We interpreted the common theme of the interviews as being definite and indefinite relational hopes for life and death. For clinicians, expressions of indefinite hopes may raise concerns about the low likelihood of fulfillment. However, the expression of indefinite hope may serve to avoid experiencing failure, disappointment, and hopelessness.
Associations between parenting and adolescent conflict resolution styles: Moderation by adolescent personality

Disagreements are an essential part of human interactions. Especially in adolescence, parentadolescent quarreling about everyday issues is common (Smetana, 1989). Although conflicts might have a negative impact on relationships, they are not necessarily detrimental for adolescent development. Hence, acquiring constructive conflict management skills is crucial during adolescence. How one handles such conflicts is a learning process which is determined by both contextual and individual factors.
A rule-based platform for distributed real-time SOA with application in defence systems

Modularity has been a key issue in the design and development of modern embedded Real-Time Software Systems (RTS), where modularity enables flexibility with respect to changes in platform, environment, and requirements, as well as reuse. In distributed RTS, similar ideas have led to the adoption of COTS components integrated via Service-Oriented Architecture (SOA) principles and technologies that are already well-established in business-oriented information systems. However, current SOA implementations, with respect to service activation and orchestration, do not meet strict time-dependent constraints on scalability and latency required by RTS. We present a novel approach to RTS development where the orchestration of real-time processes is distributed among the services within a fully distributed data-driven process framework. Our framework wraps around COTS components implementing individual processing steps in a distributed real-time process. Our execution model is configurable through message routing policies distributed as a knowledge base containing rule sets, thus maintaining real-time constraints, and therefore dispenses with the need for a central orchestration component which could easily become a bottleneck. Deterministic behaviour can therefore be achieved through the validation of the rule-sets and the use of Modular Performance Analysis.
Motivating People to Be Physically Active

Motivating People to Be Physically Active addresses a topic of critical im- portance in modern society—assisting diverse segments of the population to in- crease their ongoing levels of physical activity to establish optimal levels of health. As emphasized by The Surgeon General's Call to Action to Prevent and Decrease Overweight and Obesity (U.S. Department of Health and Human Services, 2001), increasing physical activity is a topic of crucial importance in developed nations. An overall goal of the authors is to facilitate the reader's work in helping people to be physically active. Exercise behavior is an integral part of health promotion and disease prevention. This practical handbook is written for exercise and health professionals who direct physical activity programs. The book also is of value to researchers, espe- cially for those who have little background in the stage theory of motivation. Re- searchers who are interested in exploring exercise factors related to stage theory will find practical introductory information. Motivating People to Be Physically Active is a compact, easy-to-read book and is almost exclusively based on the stage model of exercise promotion. This focus is a simultaneous strength and a weakness. The single model is a strength because it enables the authors to provide a focused examination of the multiple roles of the stages model in exercise settings. It is a weakness, however, because uninformed readers will need to read additional books such as McElroy's (2002) Resistance to Exercise: A Social Analysis of Inactivity and Kimiecik's (2002) The Intrinsic Exerciser: Discovering the Joy of Exercise. Additional sources such as these are needed to provide insights into the complex exercise dilemma of so many benefits, so few participants. The book easily fulfills its stated purpose of translating stage theory and concepts from behavioral science research into a handbook that is useful for per- sonal trainers, employees at health clubs, and other health/exercise professionals. The 11 chapters range in length between 6 and 12 pages of text. The chapters are brief and yet are complete enough to introduce readers to basic concepts and prin- ciples and to provide useful questionnaires for promoting physical activity. The later chapters also provide guidelines for applying the stages model of motiva- tional readiness in a multitude of exercise settings. As emphasized throughout Motivating People, the stages model of motiva- tional readiness for change includes five well-established stages through which participants in physical activity proceed. The nonsequential stages that the authors describe are the (a) "inactive and not thinking about becoming physically active"
Enabling High Performance Computing for Semantic Web Applications by Means of Open MPI Java Bindings

—The volume of data available in the Semantic Web has already reached the order of magnitude of billions of triples and is expected to further grow in the future. The availability of such an amount of data makes it attractive for Semantic Web applications to exploit High Performance Computing (HPC) infrastructures to effectively process such data. Unfortunately, most Semantic Web applications are written in the Java programming language, whereas current frameworks that make the most out of HPC infrastructures, such as the Message Passing Interface (MPI), only target C or Fortran applications. Attempts to port existing parallelization frameworks to the Java language prove to be very inefﬁcient in terms of the performance beneﬁts for applications. This paper presents an efﬁcient porting based on the Open MPI framework.
Parsing Outdoor Scenes from Streamed 3D Laser Data Using Online Clustering and Incremental Belief Updates

In this paper, we address the problem of continually parsing a stream of 3D point cloud data acquired from a laser sensor mounted on a road vehicle. We leverage an online star clustering algorithm coupled with an incremental belief update in an evolving undirected graphical model. The fusion of these techniques allows the robot to parse streamed data and to continually improve its understanding of the world. The core competency produced is an ability to infer object classes from similarities based on appearance and shape features, and to concurrently combine that with a spatial smoothing algorithm incorporating geometric consistency. This formulation of feature-space star clustering modulating the potentials of a spatial graphical model is entirely novel. In our method, the two sources of information: feature similarity and geometrical consistency are fed continu- ally into the system, improving the belief over the class distributions as new data arrives. The algorithm obviates the need for hand-labeled training data and makes no apriori assumptions on the number or characteristics of object categories. Rather, they are learnt incrementally over time from streamed input data. In experiments per- formed on real 3D laser data from an outdoor scene, we show that our approach is capable of obtaining an ever- improving unsupervised scene categorization.
SALT: Semantically Annotated LATEX

Machine-understandable data constitutes the basis for the Semantic Desktop. We provide in this paper means to author and annotate Semantic Documents on the Desktop. In our approach, the PDF file format is the basis for semantic documents, which store both a document and the related metadata in a single file. To achieve this we provide a framework, SALT that extends the Latex writing environment and supports the creation of metadata for scientific publications. SALT lets the scientific author create metadata while putting together the content of a research paper. We discuss some of the requirements one has to meet when developing such an ontology-based writing environment and we describe a usage scenario.
Roles of YqjH and YqjW, Homologs of the Escherichiacoli UmuC/DinB or Y Superfamily of DNA Polymerases, in Stationary-Phase Mutagenesis and UV-Induced Mutagenesis of Bacillussubtilis

ABSTRACT YqjH and YqjW are Bacillussubtilis homologs of the UmuC/DinB or Y superfamily of DNA polymerases that are involved in SOS-induced mutagenesis in Escherichiacoli. While the functions of YqjH and YqjW in B. subtilis are still unclear, the comparisons of protein structures demonstrate that YqjH has 36% identity to E. coli DNA polymerase IV (DinB protein), and YqjW has 26% identity to E. coli DNA polymerase V (UmuC protein). In this report, we demonstrate that both YqjH and the products of the yqjW operon are involved in UV-induced mutagenesis in this bacterium. Furthermore, resistance to UV-induced damage is significantly reduced in cells lacking a functional YqjH protein. Analysis of stationary-phase mutagenesis indicates that absences of YqjH, but not that of YqjW, decreases the ability of B. subtilis to generate revertants at the hisC952 allele via this system. These data suggest a role for YqjH in the generation of at least some types of stationary-phase-induced mutagenesis.
Building the record keeping system (RKS). Process improvement triggered by management of archival documents

The paper results from a research project at Tilburg University in which organization, information and archival studies have been integrated. We argue that the archivistic concept of the record keeping system (RKS) can be used as an instrument for improving the performance of the document flow in a business process, and as a result of the process. Archival documents contain information related to the result of an activity, to the circumstances of their creation and to organization and business processes. The elements of a RKS are context, quality, appraisal, warehousing and logistics. The translation of our conceptual model is the process-specific archival document-file, a meta-file that operates as an engine managing document management. The approach was tested in a case study. It became clear that our approach leads to considerable improvements in the flow of documents and thus in the primary processes supported by these documents.