Miracle-dz/UniFAR-FTUs huggingface dataset:
A Data Analysis Driven Streaming Framework for Body Sensor Area Networks

Streaming and analysis of time series body sensor data have been well investigated recently for various applications, especially on health data monitoring. However, existing strategies work independently from each other. Obviously, lacking appropriate information sharing, feedback, and interaction mechanisms, these strategies, even in combination, do not provide an efficient and effective solution for real time body sensor data collection, transmission, and analysis. In this work, we propose a data analysis driven framework with feedback for efficient streaming of body sensor data. The core idea of this framework is based on a data analysis algorithm specific variance threshold that identifies the data reliability requirement. Then, a reliability index is generated and sent from the data aggregator to sensors as a feedback to guide the streaming protocol. At the sensor side, a data importance ranking and grouping strategy is designed so that samples that affect data analysis most significantly are given higher priority for transmission.
Localized advantage in a global economy: The case of Bangalore

This article examines how and why firms in Bangalore, a city in southern India, have achieved success in the global software industry. We use Porter's "diamond framework" to analyze information obtained from secondary sources and interviews with engineers, managers, and top executives from software firms and officials involved in Bangalore's development. While we found some aspects of the case conform to Porter's framework, many other elements tend to diverge from the model. Thus, the article contributes to the Porter's diamond model literature by extending its application to assessing the development of successful regions in knowledge-based industries in developing economies. The discussion in the article would be of value to officials interested in creating such successful regions, as well as international business executives interested in the opportunities afforded by locating in these regions.
Modeling the demand for factors of production in the mechanical engineering industry of Northern Ireland, 1954-79

here have been few attempts to model the demand for factors of pro-JL duction at a sub-national level, even though the main emphasis of regional policy in most countries has been an attempt to increase the demand for labour and capital through the availability of factor subsidies. Estimates of the parameters of production functions are more readily available with regard to the US and Japanese regional economiesalthough many of these have used cross-sectional data (two particular exceptions are Alperovich, 1980; and Carlino, 1982), while Harris (1982a) has provided a comprehensive set of parameter results using time-series data for the regions of the UK (previous UK studies have been hampered through not having access to regional capital stock data, e.g., O'Donnell and Swales, 1979). Factor demand models have been estimated using Irish data (Higgins, 1981;Boyle and Sloane, 1982), while a recent paper by Hanseman (1986) estimated a quarterly factor demand model for Cincinnati (USA) using a Nadiri and Rosen (1969) type approach. This paper estimates an econometric model of the demand for the factors of production using data
A Roots Method in GI/PH/1 Queueing Model and Its Application

In this paper, we introduce a roots method that uses the roots inside the unit circle of the associated characteristics equation to evaluate the steady-state system-length distribution at three epochs (pre-arrival, arbitrary, and postdeparture) and sojourn-time distribution in GI/PH/1 queueing model. It is very important for an air base to inspect airplane oil because low-quality oil leads to drop or breakdown of an airplane. Since airplane oil inspection is composed of several inspection steps, it sometimes causes train congestion and delay of inventory replenishments. We analyzed interarrival time and inspection (service) time of oil supply from the actual data which is given from one of the ROKAF’s (Republic of Korea Air Force) bases. We found that interarrival time of oil follows a normal distribution with a small deviation, and the service time follows phase-type distribution, which was first introduced by Neuts to deal with the shortfalls of exponential distributions. Finally, we applied the GI/PH/1 queueing model to the oil train congestion problem and analyzed the distributions of the number of customers (oil trains) in the queue and their mean sojourn-time using the roots method suggested by Chaudhry for the model GI/C-MSP/1.
Unsupervised Learning of Predictors from Unpaired Input-Output Samples

Unsupervised learning is the most challenging problem in machine learning and especially in deep learning. Among many scenarios, we study an unsupervised learning problem of high economic value --- learning to predict without costly pairing of input data and corresponding labels. Part of the difficulty in this problem is a lack of solid evaluation measures. In this paper, we take a practical approach to grounding unsupervised learning by using the same success criterion as for supervised learning in prediction tasks but we do not require the presence of paired input-output training data. In particular, we propose an objective function that aims to make the predicted outputs fit well the structure of the output while preserving the correlation between the input and the predicted output. We experiment with a synthetic structural prediction problem and show that even with simple linear classifiers, the objective function is already highly non-convex. We further demonstrate the nature of this non-convex optimization problem as well as potential solutions. In particular, we show that with regularization via a generative model, learning with the proposed unsupervised objective function converges to an optimal solution.
Using Semantic Networks to Identify Temporal Expressions from Semantic Roles

Nowadays, the temporal aspects of natural language are receiving a great research interest. TimeML has been adopted as a standard for temporal information annotation by a large number of researchers. Available TimeML resources are very limited in size and in diversity of languages. This paper analyzes a combination of semantic roles and semantic networks information for improving this situation. An automatic approach using semantic networks to convert temporal semantic roles into TimeML TIMEX3 elements is presented. This approach has been quantitatively evaluated for English and Spanish. The results point out that the presented approach can help in a semi-automatic creation of TimeML resources for the evaluated languages and could be also valid for other European languages.
Resolving Temporal Conflicts in Inconsistent RDF Knowledge Bases

Recent trends in information extraction have allowed us to not only extract large semantic knowledge bases from structured or loosely structured Web sources, but to also extract additional annotations along with the RDF facts these knowledge bases contain. Among the most important types of annotations are spatial and temporal annotations. In particular the latter temporal annotations help us to reflect that a majority of facts is not static but highly ephemeral in the real world, i.e., facts are valid for only a limited amount of time, or multiple facts stand in temporal dependencies with each other. In this paper, we present a declarative reasoning framework to express and process temporal consistency constraints and queries via first-order logical predicates. We define a subclass of first-order constraints with temporal predicates for which the knowledge base is guaranteed to be satisfiable. Moreover, we devise efficient grounding and approximation algorithms for this class of first order constraints, which can be solved within our framework. Specifically, we reduce the problem of finding a consistent subset of time-annotated facts to a scheduling problem and give an approximation algorithm for it. Experiments over a large temporal knowledge base (T-YAGO) demonstrate the scalability and excellent approximation performance of our framework.
Better short-seed extractors against quantum knowledge

We construct a strong extractor against quantum storage that works for every min-entropy k, has logarithmic seed length, and outputs Ω(k) bits, provided that the quantum adversary has at most βk qubits of memory, for any β < 12 . Previous constructions required poly-logarithmic seed length to output such a fraction of the entropy and, in addition, required super-logarithmic seed length for small values of k. The construction works by first condensing the source (with minimal entropy-loss) and then applying an extractor that works well against quantum adversaries, when the source is close to uniform. We also obtain an improved construction of a strong extractor against quantum knowledge, in the high guessing entropy regime. Specifically, we construct an extractor that uses a logarithmic seed length and extracts Ω(n) bits from any source over {0, 1}, provided that the guessing entropy of the source conditioned on the quantum adversary’s state is at least (1−β)n, for any β < 12 . Previous constructions required poly-logarithmic seed length to output Ω(n) bits from such sources.
Knowledge extraction model for power load characteristics of special days and extreme weather

Load forecasting is a traditional research field of power system, under normal circumstance, there are great similarities between the similar days, but under the condition of special days and extreme weather, load prediction has some uncertainty. Extraction model for load characteristics of special days and extreme weather was proposed in this paper, coding strategy and genetic operation of traditional genetic algorithm were redefined in the model, the relational rules about relation between power load special days and extreme weather were extracted. At the same time, the credibility of these rules was proven by practice.
Quantification of Cell-Free DNA in Normal and Complicated Pregnancies: Overcoming Biological and Technical Issues

The characterization of cell-free DNA (cfDNA) originating from placental trophoblast in maternal plasma provides a powerful tool for non-invasive diagnosis of fetal and obstetrical complications. Due to its placental origin, the specific epigenetic features of this DNA (commonly known as cell-free fetal DNA) can be utilized in creating universal ‘fetal’ markers in maternal plasma, thus overcoming the limitations of gender- or rhesus-specific ones. The goal of this study was to compare the performance of relevant approaches and assays evaluating the amount of cfDNA in maternal plasma throughout gestation (7.2–39.5 weeks). Two fetal- or placental- specific duplex assays (RPP30/SRY and RASSF1A/β-Actin) were applied using two technologies, real-time quantitative PCR (qPCR) and droplet digital PCR (ddPCR). Both methods revealed similar performance parameters within the studied dynamic range. Data obtained using qPCR and ddPCR for these assays were positively correlated (total cfDNA (RPP30): R = 0.57, p = 0.001/placental cfDNA (SRY): R = 0.85, p<0.0001; placental cfDNA (RASSF1A): R = 0.75, p<0.0001). There was a significant correlation in SRY and RASSF1A results measured with qPCR (R = 0.68, p = 0.013) and ddPCR (R = 0.56, p = 0.039). Different approaches also gave comparable results with regard to the correlation of the placental cfDNA concentration with gestational age and pathological outcome. We conclude that ddPCR is a practical approach, adaptable to existing qPCR assays and well suited for analysis of cell-free DNA in plasma. However, it may need further optimization to surpass the performance of qPCR.
Face Recognition Based on Local Image Descriptor and Non-linear Features Extraction

In this paper, we introduce EABF (Extraction Analysis of Bsif Features) new method to face recognition based on extraction and analysis of binary sif features (BSIF). In our proposed algorithm, FABF eliminates some objections that led to many problems in the previous algorithms, such as a large query space and different quality and the size of images due to different time conditions for imaging and it removes the disadvantages of ELPDA (Nearby Local Discriminating Analysis) methods as a between-class-Scatter by using the Scatter matrix. This matrix introduces and updates the nearest neighbors to the outer class (K) through the samples. In addition, one of the advantages of the EABF is the high-speed face recognition by reducing the size of feature matrix and using NLPCA (Non-Linear Locality Preserving Analysis). Finally, the experiments results on the FERET data base indicate the impact of proposed method on the face recognition.
Production of Conjugated Linoleic and Conjugated α-Linolenic Acid in a Reconstituted Skim Milk-Based Medium by Bifidobacterial Strains Isolated from Human Breast Milk

Eight bifidobacterial strains isolated from human breast milk have been tested for their abilities to convert linoleic acid (LA) and α-linolenic acid (LNA) to conjugated linoleic acid (CLA) and conjugated α-linolenic acid (CLNA), respectively. These bioactive lipids display important properties that may contribute to the maintenance and improvement human health. Three selected Bifidobacterium breve strains produced CLA from LA and CLNA from LNA in MRS (160–170 and 210–230 μg mL−1, resp.) and, also, in reconstituted skim milk (75–95 and 210–244 μg mL−1, resp.). These bifidobacterial strains were also able to simultaneously produce both CLA (90–105 μg mL−1) and CLNA (290–320 μg mL−1) in reconstituted skim milk. Globally, our findings suggest that these bifidobacterial strains are potential candidates for the design of new fermented dairy products naturally containing very high concentrations of these bioactive lipids. To our knowledge, this is the first study describing CLNA production and coproduction of CLA and CLNA by Bifidobacterium breve strains isolated from human milk in reconstituted skim milk.
Sensitivity of aerosol and cloud effects on radiation to cloud types: comparison between deep convective clouds and warm stratiform clouds over one-day period

Cloud and aerosol effects on radiation in two contrasting cloud types, a deep mesoscale convective system (MCS) and warm stratocumulus clouds, are simulated and compared. At the top of the atmosphere, 45–81% of shortwave cloud forcing (SCF) is offset by longwave cloud forcing (LCF) in the MCS, whereas warm stratiform clouds show the offset of less than ~20%. 28% of increased negative SCF is offset by increased LCF with increasing aerosols in the MCS at the top of the atmosphere. However, the stratiform clouds show the offset of just around 2–5%. Ice clouds as well as liquid clouds play an important role in the larger offset in the MCS. Lower cloud-top height and cloud depth, characterizing cloud types, lead to the smaller offset of SCF by LCF and the offset of increased negative SCF by increased LCF at high aerosol in stratocumulus clouds than in the MCS. Supplementary simulations show that this dependence of modulation of LCF on cloud depth and cloud-top height is also simulated among different types of convective clouds.
Necessity to review the Brazilian regulation about fluoride toothpastes

The aim of this study was to evaluate the adequacy of the Brazilian legislation about fluoride toothpaste. A search was conducted in LILACS, Medline and SciELO databases about the fluoride concentration found in Brazilians toothpastes, using descriptors on health. Publications since 1981 have shown that some Brazilian toothpastes are not able to maintain, during their expiration time, a minimum of 1,000 ppm F of soluble fluoride in the formulation. However, the Brazilian regulation (ANVISA, Resolution 79, August 28, 2000) only sets the maximum total fluoride (0.15%; 1,500 ppm F) that a toothpaste may contain but not the minimum concentration of soluble fluoride that it should contain to have anticaries potential, which according to systematic reviews should be 1,000 ppm F. Therefore, the Brazilian regulation on fluoride toothpastes needs to be revised to assure the efficacy of those products for caries control.
Real-Time Network Intrusion Detection System Based on Neural Networks

Traditional Network Intrusion Detection Systems (NIDSs) use rules to detect intrusions, with these rules being updated manually by knowledgeable engineers. With today’s complex network environment, a new systematic method is desired to detect intrusions automatically. Data mining techniques can be used to add a systematic intrusion detection capability to NIDSs. Data Mining is concerned with uncovering patterns, associations, changes, anomalies, and statistically significant structures and events in data. This paper describes a Neural Network (NN) based NIDS architecture. We introduce our experiment on the data available from the 1998 DARPA Intrusion Detection Evaluation. We also present a real-time implementation of a NN based NIDS for Denial of Service (DoS) attacks using open source software.
Dolby Digital vs . DTS

Auditory masking describes the ability of one sound component to render another sound component inaudible to the listener. The effect of discarding to-be-masked sounds in a commercial application was evaluated by comparing Dolby Digital (DD) and DTS on the basis of perceived sound quality. No sounds are completely discarded when recorded by DTS. Whereas sounds predicted to be inaudible due to auditory masking are completely discarded when recorded by DD. On each trial, the listeners were presented with two paired sound samples in succession, with each half of the pair recorded by identical or different systems. They indicated which sample (first or second) they preferred on a rating scale. Signal Detection Theory (SDT) was employed as the analysis method. This article demonstrates, firstly, the results that can be gained through SDT, and its applicability towards research concerned with subjective sound quality. In this case, the results indicated that DD was more favorably rated than DTS. Secondly, while fully accepting the limitations of the present experimental paradigm, it can be concluded that the amount of digital data used to describe an audio signal is not necessarily a reliable metric for perceived sound quality. Walker & Shih: Comparing Two Cinema Sound Systems Page 3
A Tunable Body Biasing Scheme for Ultra-Low Power and High Speed CMOS Designs

Interest in VLSI subthreshold design has recently increased due to the emergence of systems that require ultralow power operation. Further more the ever increasing leakage currents, now used to drive logic make subthreshold design an interesting prospect. Subthreshold sacrifices speed for power creating a clear divide between designing for high speed and ultra-low power. It might be beneficial to allow circuits designed for subthreshold operation to become operable at above threshold voltages (super-threshold), depending on processing needs. In this paper among other things; the feasibility of optimizing device sizes for both subthreshold and above threshold operations is considered. Three widely publicized body biasing techniques are examined and compared to a newly proposed tunable body biasing scheme. The comparison is in terms of power and energy dissipation as well as speed. A number of logic styles are simulated to draw conclusions on what logic style in conjunction with what body biasing scheme offer improved performance. The tunable body biasing scheme offers the ability to operate circuits in either the subthreshold region for ultra-low power, at above threshold voltages for high speed or at voltages that offer optimal speed-power operation (2∗Vt0 for example). Device sizing for circuits to span all the regions of operation is examined. The tunable body biasing approach leads to increased operating frequencies particularly in subthreshold operation and shows no performance degradation at voltages above threshold, hence bridging of the speed-power gap. Post layout simulations of circuits ranging from simple to more complex ones enable for effective evaluation of optimal device sizing and identifying the optimal power-speed operational region. Simulations have been performed at a modest 180 nm technology node and ring oscillator circuits show optimal operating regions ranging from 0.5 to 0.7 V. An 8-bit linear feedback shift register using the tunable body biasing scheme serves as a design example and expends 4.24 joules per second at super-threshold while dissipating 2.34 nJ per second at subthreshold voltages. The linear feedback shift register also shows optimal operating regions ranging from 0.5
Mergers, Acquisitions and Employment Duration

This paper uses a unique data set of linked employer-employee data in which asset transfers between firms can be identified to study the manner in which employment policy changes in the aftermath of a merger or acquisition (M&A). Using parametric duration models with unobserved heterogeneity and modeling the status of the employer with respect to M&A activity, it appear that the employment policy of the firm changes radically after an M&A with respect to the "steady state". Not only do various observed characteristics impact the probability of continued employment in a different manner after an M&A, but the distribution of unobserved characteristics that affect employment changes - reflecting differences in the stock of previous employees and the flow of new hires - as does the impact of this heterogeneity on employment durations.
Pragmatic use of Language by Adolescents who did not develop Schizophrenia until adulthood

Running Title: pragmatic use of language in pre-morbid adolescents Abstract At eleven years of age all children in a UK national birth cohort wrote short stories about the life they expected to be leading at age 25. Using a data linkage exercise, we identified those who later developed schizophrenia , affective psychosis , or other non-psychotic psychiatric disorders in later life based on the PSE CATEGO diagnostic system. The majority of these had completed the written essays. Controls from the reference population were selected , matched for gender, IQ and social and economic status. The essays were scored using well established methods for assessing pragmatic use of language, namely narrative coherence and linguistic cohesion. We hypothesised that children pre-morbid for schizophrenia (Pre-Scz) would obtain low scores on all these measures. However this general hypothesis was largely disproved by the data, although some unpredicted gender effects were found. It is concluded that once one controls for reduced general cognitive ability (nb IQ) thought appears to be organised in an unexceptional way, in adolescents prior to their developing schizophrenia. 3 1. Introduction Understanding the full meaning of what someone says is typically influenced by your perceptions of the speaker ie who it is, and the social context in which the utterance is made. 'I've had it' or ' I'm done' are all but meaningless. In the context of the speaker trying to complete a task , one infers a generic meaning ' S/he is ceasing to do a difficult task ' but the full meaning can be embellished further with great understanding of the context to include assumptions such as the speaker being too tired, bored, or frustrated to continue. This aspect of communication is referred to as 'pragmatics' since to understand the full meaning one has to appreciate the practical , or social, setting . The study of pragmatics in linguistics covers those aspects of communication that are refers to word use in spoken or written communication which is informed by the social context in which the spoken utterance, or written text, is produced. Pragmatics is just as relevant to written language too. We understand the notice in a shop window " Baby Sale-lots of bargains " to be about a clothes sale, not a sale of babies . 'I've had it' or ' I'm done' in the context of trying to complete a task , which means ' I am stopping my …