Miracle-dz/UniFAR-FTUs huggingface dataset:
On Local and Global Centrality in Large Scale Networks

Estimating influential nodes in large scale networks including but not limited to social networks, biological networks, communication networks, emerging smart grids etc. is a topic of fundamental interest. To understand influences of nodes in a network, a classical metric is centrality within which there are multiple specific instances including degree centrality, closeness centrality, betweenness centrality and more. As of today, existing algorithms to identify nodes with high centrality measures operate upon the entire (or rather global) network, resulting in high computational complexity. In this paper, we design efficient algorithms for determining the betweenness centrality in large scale networks by taking advantage of the modular topology exhibited by most of these large scale networks. Very briefly, modular topologies are those wherein the entire network appears partitioned into distinct modules (or clusters or communities), wherein nodes within the module (that likely share highly similar profiles) have dense connections between them, while connections across modules are relatively sparse. Using a novel adaptation of Dijkstra's shortest path algorithm, and executing it over local modules and over sparse edges between modules, we design algorithms that can correctly compute the betweenness centrality much faster than existing algorithms. To the best of our knowledge, ours is the first work that leverage modular topologies of large scale networks to address the centrality problem, though here we mostly limit our discussions to social networks. We also provide more insights on centrality in general, and also how our algorithms can be used to determine other centrality measures.
Behavioral and Endocrine Consequences of Simultaneous Exposure to Two Different Stressors in Rats: Interaction or Independence?

Although behavioral and endocrine consequences of acute exposure to stressors have been extensively studied, little is known about how simultaneous exposure to two different stressors interacts to induce short- and long-term effects. In the present experiment we studied this interaction in adult male rats exposed to cat fur odor (impregnated cloth) or immobilization on boards either separately or simultaneously. We reasoned that exposure to the odor of a potential predator while immobilized, may potentiate its negative consequences as compared to exposure to only one of the stressors. Exposure to cat odor elicited the expected reduction of activity and avoidance of the area where the impregnated cloth was located. The endocrine response (plasma levels of ACTH and corticosterone, as a measure of the hypothalamic-pituitary-adrenal axis, HPA) was markedly greater after immobilization than after cat fur odor and no additive effects were found by simultaneous exposure to both stressors. Cat odor, but not immobilization, increased anxiety-like behavior as evaluated in the elevated plus-maze 7 days after the stressors, with no evidence of enhanced HPA activation. In addition, cat odor exposure resulted in long-lasting (8 days later) fear conditioning to the box containing a clean cloth, which was reflected by hypoactivity, avoidance of the cloth area and enhanced HPA activation. All these effects were similarly observed in rats exposed simultaneously to cat odor and immobilization. In rats only exposed to immobilization, only some weak behavioral signs of fear conditioning were found, but HPA activation in response to the context paired to immobilization was enhanced to the same extent as in cat odor-exposed animals, supporting a certain degree of endocrine conditioning. The present results did not reveal important behavioral interactions between the two stressors when animals experienced both simultaneously, whereas some interactions were found regarding HPA activation. Theoretical implications are discussed.
New Circuit Design Architecture for a 300-MHz 40 nm 1 Mb Embedded STT-MRAM with Great Immunity to PVT Variation

A 1Mb STT-MRAM implemented in 2-cell per bit architecture is demonstrated in 40nm technology. By using newly developed sense amplifier and word-line driver as well, 300MHz access speed can be acquired and, compared to one cell per bit using conventional sensing scheme, huge immunity to PVT variation can be achieved. This paper shows an effective architecture to overcome the large deviation in TMR and MTJ resistance due to hard control on smoothness of MgO thin film and MTJ uniformity in 40nm or 28nm technology.
Faster SGD Using Sketched Conditioning

We propose a novel method for speeding up stochastic optimization algorithms via sketching methods, which recently became a powerful tool for accelerating algorithms for numerical linear algebra. We revisit the method of conditioning for accelerating first-order methods and suggest the use of sketching methods for constructing a cheap conditioner that attains a significant speedup with respect to the Stochastic Gradient Descent (SGD) algorithm. While our theoretical guarantees assume convexity, we discuss the applicability of our method to deep neural networks, and experimentally demonstrate its merits.
LINEARIZED INVERSE SCA ' ITERING PROBLEMS IN ELASTICITY

Using the single-scattering approximation we invert for the material parameters of an acoustic two-parameter medium and then for a three-parameter isotropic elastic medium. Our procedure is related to various methods of depth migration in seismics, i.e. methods for locating major discontinuities in the subsurface material without specifying which quantities are discontinuous or by how much they jump. Our asymptotic multiparameter inversion makes use of amplitude information to reconstruct the size of the jumps in the parameters describing the medium. We allow spatially varying background parameters (both vertically and laterally) and an almost arbitrary source-receiver configuration. The computation is performed in the time domain and we use all available data even if it is redundant. This ability to incorporate the redundant information in a natural way is based upon a formula for double integrals over spheres. We solve for perturbations in different parameters treating separately P-to-P, P-to-S, S-to-P, and S-to-S data. It turns out that one may invert using subsets of the data, or all of it together. We also describe modifications to our scheme which allow us to use the Kirchhoff instead of the Born approximation for the forward problem when the scatterers are smooth surfaces of discontinuity.
INDUCING VALUABLE RULES FROM IMBALANCED DATA: THE CASE OF AN IRANIAN BANK EXPORT LOANS

Credit scoring is a classification problem leading to introducing numeroustechniques to deal with itsuch as support vector machines, neural networks and rule-based classifiers. Rule bases are the top priority in credit decision making because of their ability to explicitly distinguish between good and bad applicants. In a creditscoring context, imbalanced data sets frequently occur as the number of good loans in a portfolio, which is usually much higher than the number of loans that default.The paper is to explore the suitability of RIPPER, OneR, Decision table, PART and C4.5 for loandefault prediction rule extraction. A real database of one of Iranian banks export loans is used, and class imbalance issues are investigated in its loan database by random oversampling the minority class of defaulters along with three sampling of majority in nondefaulters class. The performance criterion chosen to measure such an effect isthe area under the receiver operating characteristic curve (AUC), accuracy measure and number of rules. Friedman’s statistic is used to test significant differences between techniques and datasets. Theresults shows that PART is the best classifier in all of balanced and imbalanced datasets.
SEEM: a scalable visualization for comparing multiple large sets of attributes for malware analysis

Recently, the number of observed malware samples has rapidly increased, expanding the workload for malware analysts. Most of these samples are not truly unique, but are related through shared attributes. Identifying these attributes can enable analysts to reuse analysis and reduce their workload. Visualizing malware attributes as sets could enable analysts to better understand the similarities and differences between malware. However, existing set visualizations have difficulty displaying hundreds of sets with thousands of elements, and are not designed to compare different types of elements between sets, such as the imported DLLs and callback domains across malware samples. Such analysis might help analysts, for example, to understand if a group of malware samples are behaviorally different or merely changing where they send data. To support comparisons between malware samples' attributes we developed the Similarity Evidence Explorer for Malware (SEEM), a scalable visualization tool for simultaneously comparing a large corpus of malware across multiple sets of attributes (such as the sets of printable strings and function calls). SEEM's novel design breaks down malware attributes into sets of meaningful categories to compare across malware samples, and further incorporates set comparison overviews and dynamic filtering to allow SEEM to scale to hundreds of malware samples while still allowing analysts to compare thousands of attributes between samples. We demonstrate how to use SEEM by analyzing a malware sample from the Mandiant APT1 New York Times intrusion dataset. Furthermore, we describe a user study with five cyber security researchers who used SEEM to rapidly and successfully gain insight into malware after only 15 minutes of training.
A new global policy regime founded on invalid statistics? Hanushek, Woessmann, PISA, and economic growth

ABSTRACT Several recent, highly influential comparative studies have made strong statistical claims that improvements on global learning assessments such as PISA will lead to higher GDP growth rates. These claims have provided the primary source of legitimation for policy reforms championed by leading international organisations, most notably the World Bank and OECD. To date there have been several critiques but these have been too limited to challenge the validity of the claims. The consequence is continued utilisation and citation of these strong claims, resulting in a growing aura of scientific truth and concrete policy reforms. In this piece we report findings from two original studies that invalidate these statistical claims. Our intent is to contribute to a more rigorous global discussion on education policy, as well as call attention to the fact that the new global policy regime is founded on flawed statistics.
Consensus in Ad Hoc WSNs With Noisy Links—Part I: Distributed Estimation of Deterministic Signals

We deal with distributed estimation of deterministic vector parameters using ad hoc wireless sensor networks (WSNs). We cast the decentralized estimation problem as the solution of multiple constrained convex optimization subproblems. Using the method of multipliers in conjunction with a block coordinate descent approach we demonstrate how the resultant algorithm can be decomposed into a set of simpler tasks suitable for distributed implementation. Different from existing alternatives, our approach does not require the centralized estimator to be expressible in a separable closed form in terms of averages, thus allowing for decentralized computation even of nonlinear estimators, including maximum likelihood estimators (MLE) in nonlinear and non-Gaussian data models. We prove that these algorithms have guaranteed convergence to the desired estimator when the sensor links are assumed ideal. Furthermore, our decentralized algorithms exhibit resilience in the presence of receiver and/or quantization noise. In particular, we introduce a decentralized scheme for least-squares and best linear unbiased estimation (BLUE) and establish its convergence in the presence of communication noise. Our algorithms also exhibit potential for higher convergence rate with respect to existing schemes. Corroborating simulations demonstrate the merits of the novel distributed estimation algorithms.
Light curves of five type Ia supernovae at intermediate redshift

Aims. We present multi-band light curves and distances for five type Ia supernovae at intermediate redshifts, 0.18 < z < 0.27. Methods. Three telescopes on the Canary Island of La Palma, INT, NOT, and JKT, were used for discovery and follow-up of type Ia supernovae in the g' and r' filters. Supernova fluxes were measured by simultaneously fitting a supernova and host galaxy model to the data, and then calibrated using star catalogues from the Sloan Digital Sky Survey. Results. The light curve shape and colour corrected peak luminosities are consistent with the expectations of a flat ΛCDM universe at the 1. 5σ level. One supernova in the sample, SN1999dr, shows surprisingly large reddening, considering both that it is located at a significant distance from the core of its host (~4 times the fitted exponential radius) and that the galaxy can be spectroscopically classified as early-type with no signs of ongoing star formation.
Secure data transfer in MANET using hybrid cryptosystem

Today networks are protected by many firewalls and encryption software's. But many of them protection mechanisms are not sufficient and effective. Therefore an intrusion detection system (IDS) is required to monitor the network that detects misbehavior or anomalies and intimate other nodes in the network to avoid the misbehaving nodes. Various schemes are available for Intrusion Detection systems in Mobile Ad hoc networks (MANET). MANET does not require a fixed network infrastructure; every node works as both a sender and receiver. The security solutions for wireless networks are to provide security services, such as authentication, confidentiality, integrity. The IDS (Intrusion Detection System) is suitable for networks, which detects nodes misbehavior, anomalies in packet forwarding, such as intermediate nodes dropping or delaying packets is Enhanced Adaptive Acknowledgment (EAACK). We propose a hybrid encryption technique to reduce the network overhead caused by digital signature in EAACK.
Shape gradients, shape warping and medical application to facial expression analysis. (Déformation de courbes et de surfaces, gradients généralisés et application à l'analyse d'expressions faciales chez des patients épileptiques)

Cette these porte sur le domaine des statistiques de formes. Une forme peut etre une courbe plane en 2D ou une surface en 3D. Afin de pouvoir definir ces statistiques (moyenne, modes de variation), nous avons etudie plus precisement, dans une premiere partie plutot theorique, le recalage et la mise en correspondance de deux formes entre elle. Cela consiste a developper des moyens de deformer une forme sur une autre. Des distances sont definies entre deux formes et une descente de gradient est effectuee pour deformer la premiere en la seconde. Nous avons donc defini la notion de gradient sur l'espace des formes et generalise cette definition pour definir des champs de deformations qui ne derivent plus d'un gradient. Cette notion a ete appliquee pour construire une methode permettant de deformer une courbe en une autre en etant guide par des points d'amers definissant des correspondances entre ces deux courbes. Dans une seconde partie, nous presentons une application de ces methodes a l'analyse d'expressions faciales de patients epileptiques en collaboration avec l'equipe du Professeur Patrick Chauvel a l'hopital de La Timone a Marseille. Nous avons developpe des techniques pour quantifier ces expressions faciales, et ainsi pouvoir les comparer entre elles. Nous avons ensuite etudie un moyen de mettre en relation ces expressions faciales (enregistrees pendant des crises d'epilepsies) avec le signal electrique enregistre simultanement dans le cerveau des patients. Cette mise en relation repond a une demande de l'equipe medicale qui se sert de cette information parmi d'autres pour affiner leur diagnostic.
Do National and State Assessments Converge for Educational Accountability? A Meta-Analytic Synthesis of Multiple Measures in Maine and Kentucky

Given the policy imperative of using multiple measures for state education accountability under the No Child Left Behind Act (NCLB), this study examines similarities and discrepancies between the National Assessment of Educational Progress (NAEP) and the states' own math assessment results in Kentucky and Maine, with a focus on 3 major academic performance indicators: proficiency level, achievement gap, and achievement gain. Using meta-analytic techniques, the study synthesizes multiple measures from the two states over the periods of 1992–1996 and 2000–2003. It pinpoints the areas and degrees of the discrepancies and explores contributing factors. It also reports emerging convergence of the NAEP and state assessments under the NCLB.
Using realistic simulation for performance analysis of mapreduce setups

Recently, there has been a huge growth in the amount of data processed by enterprises and the scientific computing community. Two promising trends ensure that applications will be able to deal with ever increasing data volumes: First, the emergence of cloud computing, which provides transparent access to a large number of compute, storage and networking resources; and second, the development of the MapReduce programming model, which provides a high-level abstraction for data-intensive computing. However, the design space of these systems has not been explored in detail. Specifically, the impact of various design choices and run-time parameters of a MapReduce system on application performance remains an open question. To this end, we embarked on systematically understanding the performance of MapReduce systems, but soon realized that understanding effects of parameter tweaking in a large-scale setup with many variables was impractical. Consequently, in this paper, we present the design of an accurate MapReduce simulator, MRPerf, for facilitating exploration of MapReduce design space. MRPerf captures various aspects of a MapReduce setup, and uses this information to predict expected application performance. In essence, MRPerf can serve as a design tool for MapReduce infrastructure, and as a planning tool for making MapReduce deployment far easier via reduction in the number of parameters that currently have to be hand-tuned using rules of thumb. Our validation of MRPerf using data from medium-scale production clusters shows that it is able to predict application performance accurately, and thus can be a useful tool in enabling cloud computing. Moreover, an initial application of MRPerf to our test clusters running Hadoop, revealed a performance bottleneck, fixing which resulted in up to 28.05% performance improvement.
Modelling temporality and subjectivity in ConML: Short paper

Conceptual modelling allows us to create simplified representations of reality that remove unnecessary detail in order to focus on what is relevant. However, this process of detail removal usually entails an unintended loss of information regarding the temporal variation of reality, the subjective perspectives that there may exist on it, and the different degrees of vagueness that we may have about it. Such “soft” aspects are highly informative about reality, and a conceptual modelling approach capable of capturing them would allow users to create more expressive and accurate models. This paper presents the mechanisms that have been implemented in ConML to cater for the modelling of temporality and subjectivity.
1 iMed : An Intelligent Medical Web Search Engine

Searching for medical information on the Web is becoming popular and important these days. However, medical search has its own unique requirements that are not well handled by existing medical Web search engines. In this paper, we present iMed, the first intelligent medical Web search engine that extensively uses domain-specific medical knowledge and questionnaire to facilitate ordinary Internet users to search for medical information. iMed uses several key techniques to improve its usability and the quality of search results. First, since ordinary Internet users often have difficulty in clearly describing their situations due to lack of medical background, iMed uses a questionnaire-based query interface to guide searchers to provide the most important information about their situations. Second, iMed uses medical knowledge to automatically form multiple queries from a searcher’ answers to the questions. Using these queries to perform search can significantly improve the quality of search results. Third, iMed simultaneously returns diversified search results for the multiple queries. This greatly increases the probability of finding useful information. Lastly, iMed suggests diversified, related medical phrases for the multiple queries concurrently. These medical phrases are extracted from the MeSH ontology and can help searchers quickly digest search results and refine their queries. We evaluated iMed under a wide range of medical scenarios. The results show that iMed is effective and efficient for medical search.
Towards AGV safety and navigation advancement obstacle detection using a TOF range camera

The performance evaluation of an obstacle detection and segmentation algorithm for automated guided vehicle (AGV) navigation using a 3D real-time range camera is the subject of this paper. Our approach has teen tested successfully on British safety standard recommended object sizes and materials placed on the vehicle path. The segmented (mapped) obstacles are then verified using absolute measurements obtained using a relatively accurate 2D scanning laser rangefinder. Sensor mounting and sensor modulation issues are also described through representative data sets
Energy Efficiency Performance Analysis of MAC Protocols in Wireless Sensor Networks

By analyzing the characteristics of energy consumption in wireless sensor networks, this study finds out that the pivotal aspect in controlling energy consumption is the media access control protocol, and introduces the media access control protocol of typical wireless sensor network in various categories, meanwhile, abstractly divides the context of use into three conditions. Based on the OPNET, this paper simulates different scenarios in terms of the energy consumption and delay performance under the varieties of protocol, generating the result that none of them could provide the optimal performance in the different scenarios. Therefore, for the sake of adapting into complex application scenarios, it is necessary to develop the media access control protocol with further improvements, in terms of the general applicability or business mode with adaptive ability.
Classification of Mental Tasks from Eeg Signals Using Extreme Learning Machine

In this paper, a recently developed machine learning algorithm referred to as Extreme Learning Machine (ELM) is used to classify five mental tasks from different subjects using electroencephalogram (EEG) signals available from a well-known database. Performance of ELM is compared in terms of training time and classification accuracy with a Backpropagation Neural Network (BPNN) classifier and also Support Vector Machines (SVMs). For SVMs, the comparisons have been made for both 1-against-1 and 1-against-all methods. Results show that ELM needs an order of magnitude less training time compared with SVMs and two orders of magnitude less compared with BPNN. The classification accuracy of ELM is similar to that of SVMs and BPNN. The study showed that smoothing of the classifiers' outputs can significantly improve their classification accuracies.