Miracle-dz/UniFAR-FTUs huggingface dataset:
Report on the CLEF-IP 2012 Experiments: Search of Topically Organized Patents

This technical report presents the work which has been carried out using Distributed Information Retrieval methods for federated search of patent documents for the passage retrieval starting from claims (patentability or novelty search) task. Patent documents produced worldwide have manuallyassigned classification codes which in our work are used to cluster, distribute and index patents through hundreds or thousands of sub-collections. We tested different combinations of source selection (CORI, BordaFuse, Reciprocal Rank) and results merging algorithms (SSL, CORI). We also tested different combinations of the number of collections requested and documents retrieved from each collection. One of the aims of the experiments was to test older DIR methods that characterize different collections using collection statistics like term frequencies and how they perform in patent search. Also to experiment with newer DIR methods which focus on explicitly estimating the number of relevant documents in each collection and usually attain improvements in precision over previous approaches, but their recall is usually lower. However, the most important aim was to examine how DIR methods will perform if patents are topically organized using their IPC and if DIR methods can approximate the performance of a centralized index approach. We submitted 8 runs. According to PRES @100 our best DIR approach ranked 7 across 31 submitted results, however our best DIR (not submitted) run outperforms all submitted runs.
Detecting Abnormal Behaviors in Crowded Scenes

Situational awareness is a basic function of the human visual system, which is attracting a lot of research attention in machine vision and related research communities. There is an increasing demand for smarter video surveillance of public and private space using intelligent vision systems which can distinguish what is semantically meaningful to the human observer as 'normal' and 'abnormal' behaviors. In this study we propose a novel robust behavior descriptor for encoding the intrinsic local and global behavior signatures in crowded scenes. Crowd scenes transitioning from normal to abnormal behaviors such as "rush", "scatter" and "herding" were modeled and detected. The descriptor uses features that encode both local and global signatures of crowd interactions. Bayesian topic modeling is used to capture the intrinsic structure of atomic activity in the video frames and used to detect the transition from normal to abnormal behavior. Experimental results and analysis of the proposed framework on two publicly available crowd behavior datasets show the effectiveness of this method compared to other methods for anomaly detection in crowds with a very good detection accuracy rates.
Trypanosoma evansi: A clinical, parasitological and immunological evaluation of trypanosomosis using a chronic rabbit model

We evaluated the clinical, parasitological and immunological effects of a Venezuelan strain of Trypanosoma evansi (T. evansi) throughout in experimentally inoculated rabbits over the course of infection and compared them with the same aspect in healthy animals. Body temperature was recorded in degrees Celsius, animal weight in kilograms, serum proteins in g/dl using a refractometer, haematocrit percentage by capillary centrifugation and the anti-T. evansi IgG titer by indirect ELISA immunoassay, from both infected animals and controls for 95 days. Infected animals showed a higher body temperature, total serum protein and anti- T. evansi antibody titer, and a lower haematocrit and weight gain than controls. These differences were related to the presence of the parasites in the blood as detected micro-haematocrit centrifugation technique (MHCT) and direct microscopic examination (DME). This study confirms the usefulness of rabbits as a model for the study of trypanosomosis; the clinical features of the disease can be observed and the three characteristic stages, prepatent period, acute and chronic phase clearly defined over the course of the infection.
A Constraint-Based Multi-Dimensional Data Exception Mining Approach

Data exceptions often reflect potential problems or dangers in the management of corporation. Analysts often need to identify these exceptions from large amount of data. A recent proposed approach automatically detects and marks the exceptions for the user and reduces the reliance on manual discovery. However, the efficiency and scalability of this method are not so satisfying. According to these disadvantages, the optimizations are investigated to improve it. A new method that pushes several constraints into the mining process is proposed in this paper. By enforcing several user-defined constraints, this method first restricts the multidimensional space to a small constrained-cube and then mines exceptions on it. Experimental results show that this method is efficient and scalable.
Publicly Verifiable Outsourced Computation with a Key Distribution Centre

The combination of software-as-a-service and the increasing use of mobile devices gives rise to a considerable difference in computational power between servers and clients. Thus, there is a desire for clients to outsource the evaluation of complex functions to a server and to be able to verify that the resulting value is correct. Previous work in this area of Publicly Verifiable Outsourced Computation (PVC) requires a costly pre-processing stage. However, in many practical situations multiple clients will be interested in the same set of core functions and will make use of the same servers. Thus, the pre-processing phase may be performed many more times than is necessary. In this paper we introduce a Key Distribution Center (KDC) that handles the generation and distribution of the keys that are required to support PVC, thereby eliminating this redundancy. We define a number of new security models and functionalities that arise with the introduction of the KDC, and present a construction of such a scheme built upon Key-Policy Attribute-based Encryption.
Instabilities in elastomers and in soft tissues

Summary Biological soft tissues exhibit elastic properties that can be dramatically different from rubber-type materials (elastomers). To gain a better understanding of the role of constitutive relationships in determining material responses under loads we compare three different types of instabilities (two in compression, one in extension) in hyperelasticity for various forms of strain-energy functions typically used for elastomers and for soft tissues. Surprisingly, we find that the strain-hardening property of soft tissues does not always stabilize the material. In particular we show that the stability analyses for a compressed half-space and for a compressed spherical thick shell can lead to opposite conclusions: a soft tissue material is more stable than an elastomer in the former case and less stable in the latter case.
The Social Signal Interpretation Framework (SSI) for Real Time Signal Processing and Recognition

The construction of systems for recording, processing and recognising a human’s social and affective signals is a challenging effort that includes numerous but necessary sub-tasks to be dealt with. In this article, we introduce our Social Signal Interpretation (SSI) tool, a framework dedicated to support the development of such systems. It provides a flexible architecture to construct pipelines to handle multiple modalities like audio or video and establishing onand offline recognition tasks. The plug-in system of SSI encourages developers to integrate external code, while a XML interface allows anyone to write own applications with a simple text editor. Furthermore, data recording, annotation and classification can be done using a straightforward graphical user interface, allowing simple access to inexperienced users.
Noncritical superstrings in a Ramond-Ramond background

We use the recently found matrix description of noncritical superstring theory of type-0A to compute tachyon scattering amplitudes in a background with a RR flux. We find that after the string coupling is multiplicatively renormalized, the amplitudes in any genus become polynomial in the RR flux. We propose that in the limit where both the string coupling and the RR flux go to infinity, the theory has a weakly-coupled description in terms of another superstring theory with a vanishingly small RR flux. This duality exchanges the inverse string coupling and the 0-brane charge. The dual superstring theory must have a peculiar property that its only field-theoretic degree of freedom is a massless RR scalar.
Robust consensus for uncertain multi‐agent systems with discrete‐time dynamics

This paper investigates robust consensus for multi‐agent systems with discrete‐time dynamics affected by uncertainty. In particular, the paper considers multi‐agent systems with single and double integrators, where the weighted adjacency matrix is a polynomial function of uncertain parameters constrained into a semialgebraic set. Firstly, necessary and sufficient conditions are provided for robust consensus based on the existence of a Lyapunov function polynomially dependent on the uncertainty. In particular, an upper bound on the degree required for achieving necessity is provided. Secondly, a necessary and sufficient condition is provided for robust consensus with single integrator and nonnegative weighted adjacency matrices based on the zeros of a polynomial. Lastly, it is shown how these conditions can be investigated through convex programming by exploiting linear matrix inequalities and sums of squares of polynomials. Some numerical examples illustrate the proposed results. Copyright © 2013 John Wiley & Sons, Ltd.
Combining Adaptive and Dynamic Local Search for Satisfiability

In this paper we describe a stochastic local search (SLS) procedure for flnding models of satisflable propositional formulae. This new algorithm, gNovelty + , draws on the features of two other WalkSAT family algorithms: AdaptNovelty + and G 2 WSAT, while also successfully employing a hybrid clause weighting heuristic based on the features of two dynamic local search (DLS) algorithms: PAWS and (R)SAPS. gNovelty + was a Gold Medal winner in the random category of the 2007 SAT competition. In this paper we present a detailed description of the algorithm and extend the SAT competition results via an empirical study of the efiects of problem structure, parameter tuning and resolution preprocessors on the performance of gNovelty + . The study compares gNovelty + with three of the most representative WalkSAT-based solvers: AdaptG 2 WSAT0, G 2 WSAT and AdaptNovelty + ; and two of the most representative DLS solvers: RSAPS and PAWS. Our new results augment the SAT competition results and show that gNovelty + is also highly competitive in the domain of solving structured satisflability problems in comparison with other SLS techniques.
Diagnostic performance of intracoronary gradient-based methods by coronary computed tomography angiography for the evaluation of physiologically significant coronary artery stenoses: a validation study with fractional flow reserve.

AIMS We validated and compared transluminal attenuation gradient (TAG) and corrected coronary opacification (CCO) of coronary computed tomography angiography (CCTA) with invasively measured fractional flow reserve (FFR). BACKGROUND One of the major limitations of CCTA is the discrepancy between angiographical stenosis and ischaemia-causing stenosis. Recently two new CCTA analysis methods, TAG and CCO, have been attempted to overcome this limitation but without physiological validation. METHODS AND RESULTS We measured TAG and CCO of 97 major epicardial coronary arteries from 63 patients who underwent CCTA and followed by invasive coronary angiography and FFR. Diagnostic performance of TAG and CCO was assessed using FFR <0.80 as the reference standard. The overall diagnostic performance of TAG and CCO on a per-vessel basis was moderate and similar (c-statistic = 0.696 vs. 0.637, P = 0.29). The sensitivity, specificity, positive, and negative predictive values of TAG cut-off ≤-0.654 for FFR <0.80 were 47.5, 91.2, 79.2, and 71.2%, and those of CCO cut-off >0.063 were 65.0, 61.4, 54.2, and 71.4%. TAG showed an incremental value to the diagnostic performance of CCTA but CCO did not (c-statistic =0.726 vs. 0.809, P = 0.025; c-statistic =0.726 vs. 0.784, P = 0.09). In net reclassification improvement (NRI) analysis, addition of TAG to CCTA did not result in significant reclassification (NRI = 1.0%, P = 0.41) and addition of CCO to CCTA resulted in negative reclassification (NRI = -9.3%, P = 0.036). CONCLUSION Intracoronary attenuation-based CCTA analyses, TAG and CCO, showed moderate correlation with physiological coronary artery stenosis. The incremental value of TAG or CCO to the evaluation of haemodynamically stenosis by CCTA seemed to be limited.
Enhanced statistical stability in coherent interferometric imaging

We analyze the resolution and statistical fluctuations of images when the ambient medium is random and scattering can be modeled primarily by wavefront distortion. We compare the coherent interferometric imaging method to the widely used Kirchhoff migration and show how the latter loses statistical stability at an exponential rate with the distance of propagation. In Kirchhoff migration we form images by superposing the array data back-propagated to the image domain. In coherent interferometry, we back-propagate local cross-correlations of the array data. This is a denoising process that enhances the signal-to-noise ratio of images but also reduces the resolution. We quantify analytically the trade-off between enhanced stability and reduced resolution in coherent interferometric imaging.
Proximal Remote Sensing Buggies and Potential Applications for Field-Based Phenotyping

The achievements made in genomic technology in recent decades are yet to be matched by fast and accurate crop phenotyping methods. Such crop phenotyping methods are required for crop improvement efforts to meet expected demand for food and fibre in the future. This review evaluates the role of proximal remote sensing buggies for field-based phenotyping with a particular focus on the application of currently available sensor technology for large-scale field phenotyping. To illustrate the potential for the development of high throughput phenotyping techniques, a case study is presented with sample data sets obtained from a ground-based proximal remote sensing buggy mounted with the following sensors: LiDAR, RGB camera, thermal infra-red camera and imaging spectroradiometer. The development of such techniques for routine deployment in commercial-scale breeding and pre-breeding operations will require a multidisciplinary approach to leverage the recent technological advances realised in computer science, image analysis, proximal remote sensing and robotics.
Enhancing the K-means Clustering Algorithm by Using a O(n logn) Heuristic Method for Finding Better Initial Centroids

With the advent of modern techniques for scientific data collection, large quantities of data are getting accumulated at various databases. Systematic data analysis methods are necessary to extract useful information from rapidly growing data banks. Cluster analysis is one of the major data mining methods and the k-means clustering algorithm is widely used for many practical applications. But the original k-means algorithm is computationally expensive and the quality of the resulting clusters substantially relies on the choice of initial centroids. Several methods have been proposed in the literature for improving the performance of the k-means algorithm. This paper proposes an improvement on the classic k-means algorithm to produce more accurate clusters. The proposed algorithm comprises of a O(n logn) heuristic method, based on sorting and partitioning the input data, for finding the initial centroids in accordance with the data distribution. Experimental results show that the proposed algorithm produces better clusters in less computation time.
Reverse Code Engineering — State of the Art and Countermeasures

Abstract Reverse Code Engineering (RCE) is, loosely speaking, the process of analyzing a piece of code in order to understand it. RCE is often used to analyze proprietary, binary programs, and in the last few years this research area has evolved a lot. In this article, we survey and structure the area of reverse code engineering. We focus on different techniques to recover both the control and data flow of a given binary program, for which no source code is available. Furthermore, we also discuss analysis techniques for malicious software (short: malware), which is commonly protected to resist analysis. We present the current state of the art of such protection techniques, while dividing them into active and passive measures. Our survey focusses on reverse engineering of binary native code for the Intel/AMD x86 architecture, and we thus disregard analysis of byte-code like Java or . NET. Nevertheless, most of the techniques presented in this article can be transferred to other architectures and operating system as well. Zusammenfassung Reverse Code Engineering (RCE) ist die Analyse von Binärprogrammen mit dem Ziel, deren Semantik zu verstehen. Traditionell wird dabei vor allem proprietäre Software untersucht, für die kein Sourcecode verfügbar ist. In letzter Zeit hat es jedoch eine enorme Ausweitung auch auf andere Einsatzgebiete gegeben. In diesem Bericht werden die verschiedenen Bereiche und Einsatzgebiete vorgestellt und eine Strukturierung vorgenommen. Dabei wird im ersten Teil auf die verschiedenen Methoden zur Rückgewinnung von Kontroll- und Datenfluss unbekannter Software eingegangen. Im zweiten Teil werden ausführlich die verschiedenen Schutzmaßnahmen behandelt, die von Programmen eingesetzt werden, um sich einer solchen Analyse zu entziehen. Der Fokus liegt dabei in der Analyse von Binärcode für die Intel/AMD x86 Architektur. Daher wird das Gebiet der heutigen Bytecode-Sprachen wie Java or . NET vernachlässigt. Die vorgestellten Methoden und Verfahren lassen sich jedoch problemlos auch auf andere Hardware- und Software-Architekturen übertragen.
Sampling social streams for hot social events analytics

Various analytical methods are applied on social media data for opinion mining, user recommondation, product advertising, and etc. They share the common requirement on collecting massive social media data, among which messages on hotspot social events are mostly valuable for understanding users' intensions. Apparently, sampling the global timeline evenly cannot meet the requirement, because it may miss important messages. In this paper, a sampling method for social streams, named as RS3, for Real-time Social-Stream Sampler, is introduced. It adaptively samples the global timeline as well as hotspotting messages. Preliminary empirical study shows the effectiveness of RS3. We also show its application in a prototype system for social event analytics.
Performance Analysis of Hadoop for Query Processing

Query processing using mostly various NoSQL languages becomes a significant application area for Hadoop. Despite significant work on performance improvement of these languages the performance dependence on basic configuration parameters seems not to be fully considered. In this paper we present a relatively comprehensive study into influence the basic configuration parameters have on performance of typical types of queries. We choose three queries from Lehigh University Benchmark that can represent the most typical challenges and we analyze their dependence on parameters such as: dataset size, number of nodes, number of reducers and loading overhead. The results indicate strong dependence on the amount of reducers and IO performance of the cluster, which proves the common opinion that MapReduce is IO bound. These results can help to compare performance behavior of different languages and serve as a basis for understanding the influence of configuration parameters on the final performance.
Robust Visual Tracking Using Structure-Preserving Sparse Learning

Even though numerous visual tracking methods have been proposed to deal with image streams, it is a still challenging problem to facilitate a tracking method to accurately distinguish the target from the background without drifting under the severe appearance variation of target caused by distortion of local structures. For preserving local structures of target template datasets, we present a novel structure-preserving sparse learning algorithm by obtaining sparse coefficients under maximum margin projection-based subspace representation and by updating the sparse codes under multiple task feature selection framework. To reinforce local structures of targets, we adopted a novel optimization process using an accelerated proximal gradient shrinkage operation and an efficient stopping criterion. Experimental results demonstrate that the proposed method outperforms existing state-of-the-art tracking methods.
Improved approximation algorithm for vertex cover problem using articulation points

There has been many vertex cover algorithms proposed for the solution of well-known NP-complete class problem of vertex cover. The Vertex Cover problem is important to address in graphs as it has various real world applications viz. Wireless Communication Network, Airline Communication Network, Terrorist Communication Network etc. In this paper, we propose a new algorithm based on Articulation Point, which reduces the vertex cover computation problem in polynomial time and yield solution nearer to an optimal solution, better than the classical approach. We also present a Graphical Visualization Tool that allows the automatic application of the Improved Articulation Point based Approximation Algorithm to process large graphs and finds their articulation points for minimal vertex cover computation. The tool is currently under development.
The Isomorphism Between Expansion Proofs and Multi-Focused Sequent Proofs

The sequent calculus is often criticized for requiring proofs to contain large amounts of low-level syntactic details that can obscure the essence of a given proof. Because each inference rule introduces only a single connective, sequent proofs can separate closely related steps—such as instantiating a block of quantifiers—by irrelevant noise. Moreover, the sequential nature of sequent proofs forces proof steps that are syntactically non-interfering and permutable to nevertheless be written in some arbitrary order. The sequent calculus thus lacks a notion of canonicity : proofs that should be considered essentially the same may not have a common syntactic form. To fix this problem, many researchers have proposed replacing the sequent calculus with proof structures that are more parallel or geometric. Proof-nets, matings, and atomic flows are examples of such revolutionary formalisms. We propose, instead, an evolutionary approach to recover canonicity within the sequent calculus, which we illustrate for classical first-order logic. The essential element of our approach is the use of a multi-focused sequent calculus as the means for abstracting away low-level details from classical cut-free sequent proofs. We show that, among the multi-focused proofs, the maximally multi-focused proofs that collect together all possible parallel foci are canonical. Moreover, if we start with a certain focused sequent proof system, such proofs are isomorphic to expansion proofs—a well known, minimalistic, and parallel generalization of Herbrand disjunctions—for classical first-order logic. This technique appears to be a systematic way to recover the essence of sequent calculus proofs.