Miracle-dz/UniFAR-FTUs huggingface dataset:
A Pragmatic Approach for Predicting the Scalability of Parallel Applications

Predicting the scalability of parallel applications is becoming crucial now that the number of cores in modern CPUs doubles roughly every two years. Traditional ways to get some understanding of the scalability of a parallel application rely on extensive experiments or detailed application models. Both are very time consuming and often hard to use. This paper presents PreSca, a pragmatic system for predicting the scalability of parallel applications. PreSca uses function approximation techniques to model scalability with an analytical performance function extracted from a set of measurements. By considering the application as a black-box without requiring any knowledge about its internals, PreSca can be applied with little ef- fort to any parallel application. We show how PreSca can be used statically to predict the scalability of a given application and decide which synchronization primitive scales best for it as well as how it can be used on-line to dynamically assist scheduling decisions and adjust core assignment. In some sense, PreSca shows, for the first time, how function approximation can be used to predict the scalability of parallel applications in a completely general way. We extensively evaluated PreSca using a large number of parallel benchmarks, including some that use locks and some that use transactional memory. We also consider two different multi- core systems. Our evaluation shows that PreSca produces accurate results. More specifically: (1) PreSca’s interpolations based on only 8 measurements have 90th percentile of error lower than 15%, (2) PreSca’s extrapolations using measurements with up to m cores predict the performance for n <= 2m cores with errors lower than 20% in most cases, and (3) PreSca’s on-line scheduler determines the optimal thread count using fewer than 7 measurements with errors lower than 3% on average.
A VQ speaker identification system in car environment for personalized infotainment

Car applications demand more and more the use of speech technologies. Drivers must concentrate on controlling the car and the non-use of hands makes the voice a valuable tool. Here we analyze the possibility of identifying the user of a car through her/his voice in order to develop some useful applications, and establish preferences, some of them related to music. The identification will be done in parallel to speech commands which will be given to devices in the car in the future. Once the user is identified, the system loads a personal profile. It includes music preferences which can be downloaded from the Internet databases using e.g. MPEG-7 .
Design and optimization of a web 3D Massively Multiplayer Online Game engine

The biggest problem in transforming C/S MMOGs (Massively Multiplayer Online Games) into web games is how to load the game resources fluently without waiting for downloading during game time. Thus we propose following methods: 1) Reduce the response time of game resource server by using a shared memory database to buffer and manage the game resources; 2) Improve game resource download speed by cutting game resource into fixed-size fragments and transmitting them with UDP instead of HTTP; 3) Reduce waiting time in client by progressively loading and rendering. To optimize game performance and effects in clients, we adopt classical graphics libraries to render in browser using plugin techniques. To optimize the performance of service program in MMOG servers, we avoid using sync tools (e.g. semaphores) to save CPU cycles. Overall, we optimize the key points that limit the performance of web 3D game engines in both client and server.
An Estimation of Distribution Algorithm based on the Natural Gradient and the Boltzmann Distribution

This paper introduces an Estimation of Distribution Algorithm (EDA), in which the parameters of the search distribution are updated by the natural gradient technique. The parameter updating is guided via the Kullback-Leibler divergence between the multivariate Normal and the Boltzmann densities. This approach makes sense because it is well-known that the Boltzmann function yields a reliable model to simulate particles near to optimum locations. Three main contributions are presented here in order to build an effective EDA. The first one is a natural gradient formula which allows for an update of the parameters of a density function. These equations are related to an exponential parametrization of the search distribution. The second contribution involves the approximation of the developed gradient formula and its connection to the importance sampling method. The third contribution is a parameter update rule which is designed to control the exploration and exploitation phases of the algorithm. The proposed EDA is tested on a benchmark of 16 problems and compared versus the XNES and iAMaLGaM algorithms. The statistical results show that the performance of the proposed method is competitive and it is the winner in several problems.
Modelling the Effects of Immigration on Regional Economic Performance and the Wage Distribution: A Cge Analysis of Three EU Regions

The paper uses a regional Computable General Equilibrium (CGE) model to analyse the effects of immigration on three small remote EU regions located within Scotland, Greece and Latvia. Two migration scenarios are assessed. In the first, total labour supply is affected. In the second, the importance of migratory flows by differential labour skill types is investigated. The results indicate significant differences in the extent to which regional economies are affected by immigration. They also suggest that remote regions are highly vulnerable to the out-migration of skilled workers ('brain-drain') while the in-migration of unskilled workers leads to widening wage inequality.
Assessing the Interaction between Real Estate and Equity in Households Portfolio Choice

In this paper, we provide a new empirical analysis of the dynamic portfolio decisions of households by simultaneously considering their stock market participation and home tenure choices. There is already a huge body of literature on housing status (own/rent) decisions and many contributions doc- umented the low stock market participation rate of US households. Although some papers evidenced that the home status (modeled as an exogenous variable) has an impact on the stock proportion in portfolio, our paper is the rst one to allow both decisions (home and stock) to be simultaneous and endogenous. We estimate a dynamic bivariate logistic panel data model on Panel Study of income Dynamics data from 1999 to 2007 controlling for sample selection bias and time-invariant unobserved heterogeneity. We rst evidence that our original joint setup outperforms a standard one (with two distinct equations for stock holdings and for home tenure), i.e. marginal odds ratios are signi cant. Using these estimates, we are able to simulate individual paths of stock and home equity positions over the life cycle according to households attributes. Ceteris Paribus, we show that households taking positions in one asset (home or stock) encounter a positive position in the other asset at an earlier stage in their life cycle, i.e. some households appear to be locked in a no-stock-and-renter position.
Towards Static Type-checking for Jolie

Static verification of source code correctness is a major milestone towards software reliability. The dynamic type system of the Jolie programming language, at the moment, allows avoidable run-time errors. A static type system for the language has been exhaustively and formally defined on paper, but still lacks an implementation. In this paper, we describe our steps toward a prototypical implementation of a static type checker for Jolie, which employs a technique based on a SMT solver.
Robust jointly optimized multistage vector quantization for speech coding

In this paper, a novel channel–optimized multistage vector quantization (COMSVQ) codec is presented in which the stage codebooks are jointly designed. The proposed codec uses a signal source and channel–dependent distortion measure to encode line spectral frequencies derived from segments of a speech signal. Simulation results are provided to demonstrate the consistent reduction in the spectral distortion obtained using the proposed codec as compared to the conventional sequentially–designed channel–matched multistage vector quantizer.
Inhibitory Effect of Methyleugenol on IgE-Mediated Allergic Inflammation in RBL-2H3 Cells

Allergic diseases, such as asthma and allergic rhinitis, are common. Therefore, the discovery of therapeutic drugs for these conditions is essential. Methyleugenol (ME) is a natural compound with antiallergic, antianaphylactic, antinociceptive, and anti-inflammatory effects. This study examined the antiallergic effect of ME on IgE-mediated inflammatory responses and its antiallergy mechanism in the mast cell line, RBL-2H3. We found that ME significantly inhibited the release of β-hexosaminidase, tumor necrosis factor- (TNF-) α, and interleukin- (IL-) 4, and was not cytotoxic at the tested concentrations (0–100 μM). Additionally, ME markedly reduced the production of the proinflammatory lipid mediators prostaglandin E2 (PGE2), prostaglandin D2 (PGD2), leukotriene B4 (LTB4), and leukotriene C4 (LTC4). We further evaluated the effect of ME on the early stages of the FcεRI cascade. ME significantly inhibited Syk phosphorylation and expression but had no effect on Lyn. Furthermore, it suppressed ERK1/2, p38, and JNK phosphorylation, which is implicated in proinflammatory cytokine expression. ME also decreased cytosolic phospholipase A2 (cPLA2) and 5-lipoxygenase (5-LO) phosphorylation and cyclooxygenase-2 (COX-2) expression. These results suggest that ME inhibits allergic response by suppressing the activation of Syk, ERK1/2, p38, JNK, cPLA2, and 5-LO. Furthermore, the strong inhibition of COX-2 expression may also contribute to the antiallergic action of ME. Our study provides further information about the biological functions of ME.
Elaborating sensor data using temporal and spatial commonsense reasoning

Ubiquitous computing has established a vision of computation where computers are so deeply integrated into our lives that they become both invisible and everywhere. In order to have computers out of sight and out of mind, they will need a deeper understanding of human life. LifeNet (Singh and Williams, 2003) is a model that functions as a computational model of human life that attempts to anticipate and predict what humans do in the world from a first-person point of view. LifeNet utilizes a general knowledge storage (Singh, 2002) gathered from assertions about the world input by the web community at large. In this work, we extend this general knowledge with sensor data gathered in vivo. By adding these sensor-network data to LifeNet, we are enabling a bidirectional learning process: both bottom-up segregation of sensor data and top-down conceptual constraint propagation, thus correcting current metric assumptions in the LifeNet conceptual model by using sensor measurements. Also, in addition to having LifeNet learning general common sense metrics of physical time and space, it will also learn metrics to a specific lab space and chances for recognizing specific individual human activities, and thus be able to make both general and specific spatial/temporal inferences, such as predicting how many people are in a given room and what they might be doing. This paper discusses the following topics: (1) details of the LifeNet probabilistic human model, (2) a description of the plug sensor network used in this research, and (3) a description of an experimental design for evaluation of the LifeNet learning method
Dynamic Reconfiguration through a Generic Connector

In component-based programming, it is often desirable that applications can be modeled as sets of services executed by components selected at runtime. In order to support this requirement, we present the Generic Connector, a mechanism that searches for components that provide the services required in the application connguration program, activates each service and returns the results to the client. The Generic Connector explores Lua's and LuaOrb's intrinsic characteristics. Lua is a dynamically typed, interpreted language and LuaOrb is a binding between CORBA and Lua that provides dynamic access to CORBA objects. The Generic Connector allows the connguration of applications as sets of services without previous knowledge about the components that will actually provide them. As components are selected on-they , the Generic Connector turns out to be a powerful mechanism for dynamic reconnguration.
Mining Twitter for an Explanatory Model of Social Influence

The large-scale availability of online communication data offers an opportunity to learn about social influence on the individual level. Starting from an abstract cognitive definition, we iteratively build a predictive model of social influence upon the principle of locality of influence, which implies the decomposition of observed behavior into resistance to influence, and influence received via direct and indirect exposure to others’ behavior. After training the model on a 30,000 user dataset of the social network service Twitter, we find that direct exposure has much less explanatory value than expected, and sources of influence exhibit strong temporal variation. We identify two modes of communication on Twitter, differing in the manifestation of influence.
Financial innovation and Divisia monetary indices in Taiwan: a neural network approach

In this paper a weighted index measure of money using the ‘Divisia’ formulation is constructed for the Taiwan economy and its inflation forecasting potential is compared with that of its traditional simple sum counterpart. This research extends an earlier study by Gazely and Binner by examining the theory that rapid financial innovation, particularly during the financial liberalization of the 1980s, has been responsible for the poor performance of conventional simple sum monetary aggregates. The Divisia index is adjusted in two ways to allow for the major financial innovations that Taiwan has experienced since the 1970s. The technique of neural networks is used to allow a completely flexible mapping of the variables and a greater variety of functional form than is currently achievable using conventional econometric techniques. Results suggest that superior tracking of inflation is possible for networks that employ a Divisia M2 measure of money that has been adjusted to incorporate a learning mechanism to allow individuals to gradually alter their perceptions of the increased productivity of money. Divisia measures of money appear to offer advantages over their simple sum counter parts as macroeconomic indicators.
The POM Monoclonals: A Comprehensive Set of Antibodies to Non-Overlapping Prion Protein Epitopes

PrPSc, a misfolded and aggregated form of the cellular prion protein PrPC, is the only defined constituent of the transmissible agent causing prion diseases. Expression of PrPC in the host organism is necessary for prion replication and for prion neurotoxicity. Understanding prion diseases necessitates detailed structural insights into PrPC and PrPSc. Towards this goal, we have developed a comprehensive collection of monoclonal antibodies denoted POM1 to POM19 and directed against many different epitopes of mouse PrPC. Three epitopes are located within the N-terminal octarepeat region, one is situated within the central unstructured region, and four epitopes are discontinuous within the globular C-proximal domain of PrPC. Some of these antibodies recognize epitopes that are resilient to protease digestion in PrPSc. Other antibodies immunoprecipitate PrPC, but not PrPSc. A third group was found to immunoprecipitate both PrP isoforms. Some of the latter antibodies could be blocked with epitope-mimicking peptides, and incubation with an excess of these peptides allowed for immunochromatography of PrPC and PrPSc. Amino-proximal antibodies were found to react with repetitive PrPC epitopes, thereby vastly increasing their avidity. We have also created functional single-chain miniantibodies from selected POMs, which retained the binding characteristics despite their low molecular mass. The POM collection, thus, represents a unique set of reagents allowing for studies with a variety of techniques, including western blotting, ELISA, immunoprecipitation, conformation-dependent immunoassays, and plasmon surface plasmon resonance-based assays.
Assessment of User Experience with Responsive Web Applications using Expert Method and Cognitive Walkthrough - A Case Study

The paper presents a case study of Responsive web design GUI quality testing. The case study use specially designed web page and the adjusted testing procedure. The procedure is based on the expert method and cognitive walkthrough and is adjusted to web pages created using Responsive web design. The paper outlines contain the method description and results including questionnaire summary and experts opinion. The most important users remarks concerning GUI adjusting to different devices are also presented.
Tract-based spatial statistics (TBSS): Application to detecting white matter tract variation in mild hypoxic-ischemic neonates

The aim of this study is to employ tract-based spatial statistics (TBSS) to analyze the voxel-wise differences in DTI parameters between normal and mild hypoxic-ischemic (HI) neonatal brains. Forty-one full term neonates (24 normal controls and 17 with mild HI injury) and 31 preterm neonates (20 normal controls and 11 with mild HI injury) underwent T1 weighted imaging, T2 weighted imaging and diffusion tensor imaging (DTI) within 28 days after birth. The voxel differences of fractional anisotropy (FA), λ1, λ2, and λ3 values between mild HI group and control group were analyzed in preterm and full term neonates respectively. The significantly decreased FA with increased λ2, λ3 in corticospinal tract, genu of corpus callosum (GCC), external capsule (EC) and splenium of the corpus callosum (SCC) in mild HI neonates suggested deficits or delays in both myelination and premyelination. Such impaired corticospinal tract, in both preterm and term neonates, may directly lead to the subsequent poor motor performance. Impaired EC and SCC, the additional injured sites observed in full term neonates with mild HI injury, may be causally responsible for the dysfunction in coordination and integration. In conclusion, TBSS provides an objective, independent and sensitive method for DTI data analysis of neonatal white matter alterations after mild HI injury.
Sinhala-Tamil Machine Translation: Towards better Translation Quality

Statistical Machine Translation (SMT) is a well-known and well established datadriven approach used for language translation. The focus of this work is to develop a statistical machine translation system for Sri Lankan languages, Sinhala and Tamil language pair. This paper presents a systematic investigation of how SinhalaTamil SMT performance varies with the amount of parallel training data used, in order to find out the minimum needed to develop a machine translation system with acceptable performance.
Design and Implementation of Radar Signal Processing System Based on Design Patterns

Design patterns are a set of solutions in software development process, and have been successfully applied to the modern software development process, greatly improved the performance of the software systems, such as reusability, reliability, flexibility and scalability. The paper analysis the radar signal processing system and combines it with the design patterns, and divide the system into processing component, interaction component and communication component, and choose three kinds of patterns builder pattern, state pattern and single pattern to design and implement the radar signal processing system. By combination with the design patterns, it could improve the scalability, universality and reusability of the system and the designed signal processing system can be used in variety of radars.
Translating–transliterating named entities for multilingual information access

Named entities are major constituents of a document but are usually unknown words. This work proposes a systematic way of dealing with formulation, transformation, translation, and transliteration of multilingual-named entities. The rules and similarity matrices for translation and transliteration are learned automatically from parallel-named-entity corpora. The results are applied in cross-language access to collections of images with captions. Experimental results demonstrate that the similarity-based transliteration of named entities is effective, and runs in which transliteration is considered outperform the runs in which it is neglected. © 2006 Wiley Periodicals, Inc.
Energy-Efficient Circular Sector Sensing Coverage Model for Wireless Sensor Networks

Reducing the energy consumption in Wireless Sensor Networks (WSN) is an important goal, sought to extend the life-expectancy of the networks. One of the main problems in actual implementation is their inherently limited life-span, due to the (usually) not field-replaceable energy source. The energy consumption is due to the data and management communication, messages, sensing, processing (and storage) activities of the motes. The sensing may be local, e.g. temperature, radiation, humidity or similar physical measurement. There are a lot of applications, civilian or military alike, which implement some remote discovery, localization or tracking activities. In this case, the active sensing is performed mainly by emitting light, electromagnetic or sound waves. Obviously this imposes a heavy burden on the limited energy resources available to the mote, shortening his life and consequently the life of the WSN itself. Previously published sensing models use a circular coverage area around the motes, with fixed (unit-disk) or variable radius. Our work proposes a more energy-efficient sensing model, based on circular sectors with variable angles and radii, negotiated between the motes, as a function of their current energy capabilities. The performance of this model was checked vs. previously published schemes, using a proprietary simulation program, developed specifically for this purpose. The paper presents the results of simulations, showing significant energy savings. The obtained data confirm the viability and the advantages of this new sensing model. The possible implementation of such a WSN will significantly improve the energy-related performance of the WSN, allowing the development of new applications and improving the performance of existing ones.
ADAPTIVE EDGE DETECTION USING ADJUSTED ANT COLONY OPTIMIZATION

Edges contain important information in image and edge detection can be considered a low level process in image processing. Among different methods developed for this purpose traditional methods are simple and rather efficient. In Swarm Intelligent methods developed in last decade, ACO is more capable in this process. This paper uses traditional edge detection operators such as Sobel and Canny as input to ACO and turns overall process adaptive to application. Magnitude matrix or edge image can be used for initial pheromone and ant distribution. Image size reduction is proposed as an efficient smoothing method. A few parameters such as area and diameter of travelled path by ants are converted into rules in pheromone update process. All rules are normalized and final value is acquired by averaging.
Non-uniqueness of deep parameters and shocks in estimated DSGE models: a health warning

Estimation of dynamic stochastic general equilibrium (DSGE)models using state space methods implies vector autoregressive moving average (VARMA)representations of the observables. Following Lippi and Reichlin’s (1994)analysis of nonfundamentalness, this note highlights the potential dangers of end of non-uniqueness, both of estimates of deep parameters and of structural innovations.