Miracle-dz/UniFAR-FTUs huggingface dataset:
Increasing Lifetime and Security of Phase-Change Memory with Endurance Variation

Phase Change Memory (PCM) has emerged as a promising candidate for building the future main memory systems. However, the limited write endurance is one of the major obstacles for PCM to be practically applied. Traditional wear-leveling techniques try to uniformly balance the write traffics under both general applications and malicious attacks to enhance the PCM lifetime. However, these techniques fail to consider the endurance variation in PCM chips, and result in severe lifespan degradation since uniform write distribution leads to the weakest cell to be worn out much earlier. In this paper, we propose a weight-based algebraic wear-leveling (WAWL) scheme to balance wear rates (i.e., write traffics/endurance) in a secure manner according to the endurance distribution. In WAWL, the entire memory space is divided into multiple regions. When the number of the writes to a region reaches a threshold (i.e., swapping interval), the region is swapped with a randomly chosen region. The basic idea behind WAWL is that the swapping interval and the chosen probability of each region are variable and associated with the endurance metric of the region. By deploying suitable swapping interval and chosen probability, WAWL achieves uniform wear-rate distribution across the entire memory in an undetectable way. In addition, to reduce space consumption and alleviate performance degradation during region swapping, we propose a fine-grained swapping scheme which migrates the lines one-by-one between the candidate regions. Experimental evaluation driven by the various attacks demonstrates that WAWL significantly increases the PCM lifespan and improves security with slight performance degradation and affordable hardware overhead.
4 Bit Comparator Design Based on Reversible Logic Gates

Today, reversible logic circuits has attracted considerable attention in improving some fields like nanotechnology, quantum computing, and low power design. In this paper 4 bit reversible comparator based on classical logic circuit is represented which uses existing reversible gates. In this design we try to reduce optimization parameters like number of constant inputs, garbage outputs, and quantum cost. The results show that, the proposed comparator has 4 quantum cost and one constant input less than the prior design. 1
General BBV Model of Weighted Complex Networks

An evolving model is proposed to characterize weighted complex networks. Our model is based on the weighted network model proposed by Alain Barrat et al. (BBV for shot), emergences of new links according to triad formation and random selection mechanism, interactions more easily occur between neighbors (friends of friends) in our model. The evolution of degree and strength exhibit the power-law distributions. The average strength displays scale-free property with degree. The nontrivial clustering coefficient is investigated depending on parameters of model. A weighted scale-free network with high clustering coefficient can be obtained by adjusting two parameters of our model.
Projected and Observed Aridity and Climate Change in the East Coast of South India under RCP 4.5

In the purview of global warming, the present study attempts to project changes in climate and quantify the changes in aridity of two coastal districts in south India under the RCP 4.5 trajectory. Projected climate change output generated by RegCM 4.4 model, pertaining to 14 grid points located within the study area, was analyzed and processed for this purpose. The meteorological parameters temperature and precipitations were used to create De Martonne Aridity Index, to assess the spatial distribution of aridity. The original index values ranged from 13.7 to 16.4 mm/°C, characterizing this area as a semidry climate. The outcome from the changed scenario analysis under RCP 4.5 showed that, during the end of the 21st century, the aridity may be increased more as the index values tend to reduce. The increasing trend in the drying phenomenon may be attributed to the rising of mean annual temperatures.
Segmentation of cerebrospinal fluid from 3D CT brain scans using modified Fuzzy C-Means based on super-voxels

In this paper, the problem of segmentation of 3D Computed Tomography (CT) brain datasets is addressed using the fuzzy logic rules. In particular, a new method which combines Fuzzy C-Means clustering and the idea of super-voxels is introduced. Firstly, the method applies the extended Simple Linear Iterative Clustering (SLIC) method to divide image into super-voxels, which are next clustered by Modified Fuzzy C-Means algorithm. The method deals with 3D images and performs fully three dimensional image segmentation. Ten samples are supplied proving that our Modified Fuzzy C-Means (MFCM) together with super-voxels are apt to take into account a large diversity of special domains that appear and which are inappropriate solved adopting classical Fuzzy C-Means approach. The results of applying the introduced method to segmentation of the Cerebro-Spinal Fluid (CSF) from the brain ventricles are presented and discussed.
Aligning Environment Web Data to the ISO 15926 Oil and Gas Ontology

The Environment Web (EW) is the official database for emissions and discharges from the offshore oil and gas industry in Norway. Currently we are working on a project to describe and align terms in the EW application according to the ISO 15926 Reference Data Library (RDL). One of our goals is to ensure that the EW terms in the RDL can be used as a reference point for all systems using EW terms. Therefore the definitions of the EW constructs in the RDL need to be unambiguous. Moreover, another goal is to enrich the ISO 15926 ontology by adding terms from the EW domain.We briefly describe our experiences and challenges faced so far when mapping terms from the EW database to the ISO15926 Ontology.
Neural Correlates of Sex/Gender Differences in Humor Processing for Different Joke Types

Humor operates through a variety of techniques, which first generate surprise and then amusement and laughter once the unexpected incongruity is resolved. As different types of jokes use different techniques, the corresponding humor processes also differ. The present study builds on the framework of the ‘tri-component theory of humor,’ which details the mechanisms involved in cognition (comprehension), affect (appreciation), and laughter (expression). This study seeks to identify differences among joke types and between sexes/genders in the neural mechanisms underlying humor processing. Three types of verbal jokes, bridging-inference jokes (BJs), exaggeration jokes (EJs), and ambiguity jokes (AJs), were used as stimuli. The findings revealed differences in brain activity for an interaction between sex/gender and joke type. For BJs, women displayed greater activation in the temporoparietal–mesocortical-motor network than men, demonstrating the importance of the temporoparietal junction (TPJ) presumably for ‘theory of mind’ processing, the orbitofrontal cortex for motivational functions and reward coding, and the supplementary motor area for laughter. Women also showed greater activation than men in the frontal-mesolimbic network associated with EJs, including the anterior (frontopolar) prefrontal cortex (aPFC, BA 10) for executive control processes, and the amygdala and midbrain for reward anticipation and salience processes. Conversely, AJs elicited greater activation in men than women in the frontal-paralimbic network, including the dorsal prefrontal cortex (dPFC) and parahippocampal gyrus. All joke types elicited greater activation in the aPFC of women than of men, whereas men showed greater activation than women in the dPFC. To confirm the findings related to sex/gender differences, random group analysis and within group variance analysis were also performed. These findings help further establish the mechanisms underlying the processing of different joke types for the sexes/genders and provide a neural foundation for a theory of sex/gender differences in humor.
Real Economic Convergence and the Impact of Monetary Policy on Economic Growth of the EU Countries: The Analysis of Time Stability and the Identification of Major Turning Points Based on the Bayesian Methods

This study analyzes the time stability of both the GDP beta convergence and the impact of monetary policy variables on economic growth in EU27 countries during 1993-2010 and EU15 during 1972-2010. To address the problem of variables’ selection, Bayesian model pooling (BMP) is used while choosing the appropriate variables for the regression. To loosen the assumption of stability overtime, interaction terms of particular regressors are introduced with time dummies (the whole sample is divided into time intervals and is allowed for structural breaks). To address the problem of potential inconsistency of ‘typical’ estimators, the study employs Blundell and Bond’s GMM system estimator. All the calculations are based on annual data and 3-year intervals. The main findings are: (1) The EU27 countries converged at the rate of about 5-6% per annum while the EU15 countries – at 3% p.a. (2) The pure mechanism of conditional convergence of the countries under study was rather constant over time. (3) The considered economic growth determinants, including all the variables related with monetary policy, exhibited mixed impact on economic growth, especially based on annual data. (4) These results have been obtained for the following turning points: 2004, 1998, and – in the case of EU15 countries – 1989.
Approximate 2-Degree-Of-Freedom control of a class-D amplifier using FPGA

In recent years, it is desired that the bandwidth of a class-D amplifier is widened using sampling frequencies as low as possible. For example, it is expected in the application of the power amplifier of vibration generator that the bandwidth is kept 5[kHz] within the load impedance ∞~2 [Ω]. In this paper, it is shown that this demand to the class-D amplifier can be attained by an A2DOF (Approximate 2-Degree-Of-Freedom) digital controller with 200[kHz] switching and sampling frequencies. The controller is implemented by a FPGA and it is shown from experiments that this demand is attained enough.
Partners for the optimal organisation of the healthcare continuum for high users of health and social services: protocol of a developmental evaluation case study design

Introduction Case management allows us to respond to the complex needs of a vulnerable clientele through a structured approach that promotes enhanced interaction between partners. Syntheses on the subject converge towards a need for a better description of the relationships between programmes and their local context, as well as the characteristics of the clienteles and programmes that contribute to positive impacts. The purpose of this project is thus to describe and evaluate the case management programmes of four health and social services centres in the Saguenay-Lac- Saint-Jean region of Québec, Canada, in order to inform their improvement while creating knowledge on case management that can be useful in other contexts. Methods and analysis This research relies on a multiple embedded case study design based on a developmental evaluation approach. We will work with the case management programme for high users of hospital services of each centre. Three different units of analysis will be interwoven to obtain an in-depth understanding of each case, that is: (1) health and social services centre and local services network, (2) case management programme and (3) patients who are high users of services. Two strategies for programme evaluation (logic models and implementation analysis) will guide the mixed data collection based on qualitative and quantitative methods. This data collection will rely on: (1) individual interviews and focus groups; (2) participant observation; (3) document analysis; (4) clinical and administrative data and (5) questionnaires. Description and comparison of cases, and integration of qualitative and quantitative data will be used to guide the data analysis. Ethics and dissemination The study protocol was approved by the Ethics Research Boards of the four health and social services centres (HSSCs) involved. Findings will be disseminated by publications in peer-reviewed journals, conferences, and policy and practice partners in local and national government.
A novel detection approach using bio-inspired vision for enhanced object tracking in video

Video surveillance systems play an important role in many civilian and military applications, for the purposes of security and surveillance. Object detection is an important component in a video surveillance system, used to identify possible objects of interest and to generate data for tracking and analysis purposes. A video surveillance system is presented here which incorporates a novel motion detection approach using a biologically inspired vision system, within a hardware solution, for enhanced object tracking performance. This method is compared with a traditional object detection approach. This initial investigation determined that the biological vision system is robust in detecting objects representative of actual ground truth values under various complex lighting conditions. The system has shown enhanced capability by reducing false track confirmation rates (at least one order of magnitude) and achieving high confirmation rates of objects of interest within independent video data sets. An effective tracking solution has been achieved through the implementation of the biologically-inspired vision system as an enhancement to object detection.
Investigations of the ambiguity effect in the estimation of Doppler frequency and directions in channel sounding using switched Tx and Rx arrays

—The improved-search-and-initialization space- alternating generalized expectation-maximization (ISI-SAGE) algorithm is proposed in [1] and [2] for joint estimation of the relative delay, the direction (i.e. azimuth and co-elevation) of departure, the direction of arrival, the Doppler frequency (DF) as well as the complex weight of the propagation paths from the transmitter (Tx) site to the receiver (Rx) site in mobile radio environments. A recent study [3] shows that the ISI-SAGE algorithm combined with a switched multiple-element Tx and Rx antenna sounding technique makes it possible to estimate the DF with an absolute value up to half the rate with which the pairs of Tx and Rx elements are switched (switching rate), rather than half the rate with which any ﬁxed pair is switched (cycle rate) as commonly believed. This paper shows that so-called modulo-type switching modes (SMs) used with uniform linear and planar arrays lead to an ambiguity in the estimation of the DF and the directions, when the DF estimation range is extended from minus to plus half the switching rate. The SM of a switched array is the order with which the array elements are switched. Moreover, theoretical and experimental investigations show that the ambiguity problem can be avoided by using some speciﬁc SMs.
BOUNDED SOCIABILITY, RELATIONSHIP COSTS AND INTANGIBLE RESOURCES IN COMPLEX DIGITAL NETWORKS

A diverter for drilling mud is disclosed that has a packer housing mounted on the upper end of a riser through which drilling mud flows to the surface between the riser and the pipe string extending through the riser. A packer having a body of resilient material is located in the housing. Fluid pressure moves the body of resilient material into sealing engagement with the pipe string to divert drilling fluid laterally into a mud return line. A plurality of conduits extend vertically through the packer. Each conduit includes a flexible section extending through the body of resilient material to move with the resilient material as it is moved into and out of sealing engagement with the pipe string. Drilling mud is pumped through the conduits and nozzles attached to the lower ends of the conduits. The nozzles are positioned to divert the drilling fluid into the drilling fluid below the packer to break up large pieces of formation carried to the surface by the drilling mud.
Unsupervised Techniques for Extracting and Clustering Complex Events in News

Structured machine-readable representations of news articles can radically change the way we interact with information. One step towards obtaining these representations is event extraction - the identification of event triggers and arguments in text. With previous approaches mainly focusing on classifying events into a small set of predefined types, we analyze unsupervised techniques for complex event extraction. In addition to extracting event mentions in news articles, we aim at obtaining a more general representation by disambiguating to concepts defined in knowledge bases. These concepts are further used as features in a clustering application. Two evaluation settings highlight the advantages and shortcomings of the proposed approach.
An Empirical Relationship between Entrepreneurship and FDI. A Note.

In the present note an effort will be made for a contribution to economic theory by estimating an econometric relationship between the foreign direct investment [FDI] and total economy’s entrepreneurship reward [rEM]. This note is based mainly on the next two papers. First, it will be based on the discussion paper “Understanding the Role of Entrepreneurship for Economic Growth. ” by Martin Carree and Roy Thurik, MPIoE, #2005, in which the authors show the importance of entrepreneurship to adapt to new technology, to develop capacity and reach economies of scale. Second, it will be based on the discussion paper “Does Entrepreneurship Create Enough Jobs in Europe? A Note. ” by Miltiades N. Georgiou, MPIoE, #0806, in which entrepreneurship reward [rEM] is measurable and consequently can be related with the other measurable economic variables like the [FDI]. Hence, in the present note a relationship between [FDI] and [rEM] can be numerically estimated. More specifically, it will be pointed out with panel data econometric analysis that in all Western European Countries and the United States [FDI] is positively related with [rEM], and that decisions about [FDI] are mainly affected by [rEM].
BitScope: Automatically Dissecting Malicious Binaries

Automatic analysis of malicious binaries is necessary in order to scale with the rapid development and recovery of malware found in the wild. The results of automatic analysis are useful for creating defense systems and understanding the current capabilities of attackers. We propose an approach for automatic dissection of malicious binaries which can answer fundamental questions such as what behavior they exhibit, what are the relationships between their inputs and outputs, and how an attacker may be using the binary. We implement our approach in a system called BitScope. At the core of BitScope is a system which allows us to execute binaries with symbolic inputs. Executing with symbolic inputs allows us to reason about code paths without constraining the analysis to a particula r input value.
A fast texture synthesis using Gene Expression Programming

In computer graphics, vision, and image processing, the texture synthesis occupies an important position. However, most of the existing methods are relatively inefficient. Thus, it is necessary to design a fast texture synthesis algorithm. This article describes an efficient algorithm for texture synthesis. The texture model of this algorithm is derived from the Markov random field. It uses Gene Expression Programming (GEP) to find the best function and the value of each pixel in the synthesized image determined by this function. In this way, this algorithm can avoid scanning all of the pixels, so as to improve the speed of texture synthesis. This algorithm is faster than the previous synthesis algorithm.
The Anytime Reliability of Constrained Packet Erasure Channels with Feedback

We study the anytime reliability of packet erasure channels where the erasures are iid and known to both the transmitter and receiver. The packets are allowed to carry a variable number of bits within them although only one packet may be transmitted at any given time step. For any moment constraint on the size of the packets, we show that the anytime reliability is constant at all rates up to the Shannon capacity of the channel and that this constant is essentially the logarithm of the probability of erasure. For cases where there is both a first moment constraint and a constraint on the maximum number of bits that a packet can carry, we show that the optimum anytime reliability is determined primarily by the peak-packet size, but drops abruptly to zero above the Shannon capacity. In order to show achievability of these reliabilities, we give schemes involving FIFO queues where the server can adjust its service rate based on the number of bits awaiting transmission. When many bits are waiting, larger packets are used.
All-pole modeling of wide-band speech using weighted sum of the LSP polynomials

The autocorrelation function of the all-pole ﬁlter given by the conventional linear prediction (LP) matches exactly the autocorrelation function of the input signal between indices 0 and m , when the prediction order equals m . This study describes a recently developed technique, Weighted-Sum Line Spectrum Pair (WLSP), where an all-pole ﬁlter is deﬁned by using a sum of weighted LSP (Line Spectrum Pair) polynomials. WLSP yields a stable all-pole ﬁlter of order m , whose autocorrelation function coincides to that of the input between indices 0 and m − 1 . By sacriﬁcing the exact matching of the autocorrelations at index m , WLSP models the autocorrelation of the input at the indices above m more accurately than conventional LP. In the current paper, the performance of WLSP in spectral modelling of wide-band speech is analysed. It is shown that WLSP yields all-pole spectra that model the formant structure of vowels more accurately than conventional LP of the same prediction order.
Utilizing the Open Movie Database API for Predicting the Review Class of Movies

In this paper, we present our contribution to the Linked Data Mining Challenge 2015. Our approach predicts the review class of movies using external data from the Open Movie Database API (OMDb-API). We select specific features, such as movie ratings and box office, that are very likely to describe the quality of a movie. With RapidMiner we utilize these features and apply three basic classification algorithms to train and validate the prediction model using a 10-fold cross-validation. The results of our evaluation are interesting in a two-fold way: (i) few movie ratings from professional critics provide a higher accuracy (accuracy ≈ 0.94) than many ratings from users (accuracy ≈ 0.7 ), and (ii) the Decision Tree classifier (accuracy ≈ 0.83) outperforms Naive Bayes (accuracy ≈ 0.73), whereas k-NN is not suitable at all (accuracy ≈ 0.53).