Miracle-dz/UniFAR-FTUs huggingface dataset:
Development of a Software Repository for the Precise Search and Extract Retrieval of the Component

The recent progress in the component based engineering has offered many facilities in the field of software reuse. However, this advancement has also brought the challenge such as designing repositories and developing efficient search of software components. But the problem that is generally encountered is the efficient search and the exact retrieval of the software components from the software repositories as they are not properly represented yet. Thus, this paper deals with the construction of the software repository, construction of the operational semantic keyword based retrieval technique for evaluating the best results and at the end comparison between the techniques for retrieval of the software components is made on the basis of precision and recall.
Color Image Information Hiding Based on Perceptual Color Clustering

This paper proposes a color image information hiding algorithm based on perceptual color clustering. In the procedures of hiding information, the first step is merging carrier's each color into specified number of color clustering according to the relation from RGB to HVC and the conception of NBS distance in HVC color space; and then the information is hidden by replacing each hiding-place's color with the color in the same color clustering which is selected according to the hiding information, and the hiding capability of each pixel depends on the number of colors in which color cluster it belongs to. In order to extract the hidden information, the index of pixel color is looked up from its color cluster. In the end, the experimental results of several images using this algorithm are presented.
The Double-Edged Sword of Backward Compatibility: The Adoption of Multigenerational Platforms in the Presence of Intergenerational Services

We investigate the impact of the intergenerational nature of services, via backward compatibility, on the adoption of multi-generational platforms. We consider a mobile Internet platform that has evolved over several generations and for which users download complementary services from third party providers. These services are often intergenerational: newer platform generations are backward compatible with respect to services released under earlier generation platforms. In this paper, we propose a model to identify the main drivers of consumers’ choice of platform generation, accounting for (i) the migration from older to newer platform generations, (ii) the indirect network effect on platform adoption due to same-generation services, and (iii) the effect on platform adoption due to the consumption of intergenerational services via backward compatibility. Using data on mobile Internet platform adoption and services consumption for the time period of 2001 – 2007 from a major wireless carrier in an Asian country, we estimate the three effects noted above. We show that both the migration from older to newer platform generations and the indirect network effects are significant. The surprising finding is that intergenerational services that connect subsequent generations of platforms essentially engender backward compatibility with two opposing effects. While an intergenerational service may accelerate the migration to the subsequent platform generations, it may also, perhaps unintentionally, provide a fresh lease on life for earlier generation platforms due to the continued use of earlier generation services on newer platform generations.
Counting Mycobacteria in Infected Human Cells and Mouse Tissue: A Comparison between qPCR and CFU

Due to the slow growth rate and pathogenicity of mycobacteria, enumeration by traditional reference methods like colony counting is notoriously time-consuming, inconvenient and biohazardous. Thus, novel methods that rapidly and reliably quantify mycobacteria are warranted in experimental models to facilitate basic research, development of vaccines and anti-mycobacterial drugs. In this study we have developed quantitative polymerase chain reaction (qPCR) assays for simultaneous quantification of mycobacterial and host DNA in infected human macrophage cultures and in mouse tissues. The qPCR method cannot discriminate live from dead bacteria and found a 10- to 100-fold excess of mycobacterial genomes, relative to colony formation. However, good linear correlations were observed between viable colony counts and qPCR results from infected macrophage cultures (Pearson correlation coefficient [r] for M. tuberculosis = 0.82; M. a. avium = 0.95; M. a. paratuberculosis = 0.91). Regression models that predict colony counts from qPCR data in infected macrophages were validated empirically and showed a high degree of agreement with observed counts. Similar correlation results were also obtained in liver and spleen homogenates of M. a. avium infected mice, although the correlations were distinct for the early phase (<day 9 post-infection) and later phase (≥day 20 post-infection) liver r = 0.94 and r = 0.91; spleen r = 0.91 and r = 0.87, respectively. Interestingly, in the mouse model the number of live bacteria as determined by colony counts constituted a much higher proportion of the total genomic qPCR count in the early phase (geometric mean ratio of 0.37 and 0.34 in spleen and liver, respectively), as compared to later phase of infection (geometric mean ratio of 0.01 in both spleen and liver). Overall, qPCR methods offer advantages in biosafety, time-saving, assay range and reproducibility compared to colony counting. Additionally, the duplex format allows enumeration of bacteria per host cell, an advantage in experiments where variable cell death can give misleading colony counts.
Weather Data Mining using Artificial Neural Network

Weather Data Mining is a form of Data mining concerned with finding hidden patterns inside largely available meteorological data, so that the information retrieved can be transformed into usable knowledge. A variety of data mining tools and techniques are available in the industry, but they have been used in a very limited way for meteorological data. In this paper, a neural network-based algorithm for predicting the atmosphere for a future time and a given location is presented. We have used Back Propagation Neural (BPN) Network for initial modelling. The results obtained by BPN model are fed to a Hopfield Network. The performance of our proposed ANN-based method (BPN and Hopfield Network based combined approach) tested on 3 years weather data set comprising 15000 records containing attributes like temperature, humidity and wind speed. The prediction error is found to be very less and the learning converges very sharply. The main focus of this paper is based on predictive data mining by which we can extract interesting (non-trivial, implicit, previously unknown and potentially useful) patterns or knowledge from huge amount of meteorological data.
Data Stream Clustering for Online Anomaly Detection in Cloud Applications

This paper introduces a new approach for the online detection of performance anomalies in cloud virtual machines (VMs). It is designed for cloud infrastructure providers to detect during runtime unknown anomalies that may still be observed in complex modern systems hosted on VMs. The approach is drawn on data stream clustering of per-VM monitoring data and detects at a fine granularity where anomalies occur. Its operations are independent of the types of applications deployed over VMs. Moreover it deals with frequent changes in systems normal behaviors during runtime. The parallel analyses of each VM makes this approach scalable to a large number of VMs composing an application. The approach consists of two online steps: 1) the incremental update of sets of clusters by means of data stream clustering, and 2) the computation of two attributes characterizing the global clusters evolution. We validate our approach over a VMware vSphere testbed. It hosts a typical cloud application, MongoDB, that we study in normal behavior contexts and in presence of anomalies.
Millimeter wave beamforming based on WiFi fingerprinting in indoor environment

Millimeter Wave (mm-w), especially the 60 GHz band, has been receiving much attention as a key enabler for the 5G cellular networks. Beamforming (BF) is tremendously used with mm-w transmissions to enhance the link quality and overcome the channel impairments. The current mm-w BF mechanism, proposed by the IEEE 802.11ad standard, is mainly based on exhaustive searching the best transmit (TX) and receive (RX) antenna beams. This BF mechanism requires a very high setup time, which makes it difficult to coordinate a multiple number of mm-w Access Points (APs) in mobile channel conditions as a 5G requirement. In this paper, we propose a mm-w BF mechanism, which enables a mm-w AP to estimate the best beam to communicate with a User Equipment (UE) using statistical learning. In this scheme, the fingerprints of the UE WiFi signal and mm-w best beam identification (ID) are collected in an offline phase on a grid of arbitrary learning points (LPs) in target environments. Therefore, by just comparing the current UE WiFi signal with the pre-stored UE WiFi fingerprints, the mm-w AP can immediately estimate the best beam to communicate with the UE at its current position. The proposed mm-w BF can estimate the best beam, using a very small setup time, with a comparable performance to the exhaustive search BF.
The Albedo, Size, and Density of Binary Kuiper Belt Object (47171) 1999 TC36

We measured the system-integrated thermal emission of the binary Kuiper Belt object (47171) 1999 TC36 at wavelengths near 24 and 70 μm using the Spitzer Space Telescope. We fit these data and the visual magnitude using both the standard thermal model and thermophysical models. We find that the effective diameter of the binary is 405 km, with a range of 350-470 km, and the effective visible geometric albedo for the system is 0.079, with a range of 0.055-0.11. The binary orbit, magnitude contrast between the components, and system mass have been determined from HST data studied by Margot et al. Our effective diameter, combined with that system mass, indicates an average density for the objects of 0.5 g cm-3, with a range 0.3-0.8 g cm-3. This density is low compared to that of materials expected to be abundant in solid bodies in the trans-Neptunian region, requiring 50%-75% of the interior of (47171) 1999 TC36 be taken up by void space. This conclusion is not greatly affected if (47171) 1999 TC36 is "differentiated" (in the sense of having either a rocky or just a nonporous core). If the primary is itself a binary, the average density of that (hypothetical) triple system would be in the range 0.4-1.1 g cm-3, with a porosity in the range 15%-70%.
Tight Integration of Non-Ground Answer Set Programming and Satisfiability Modulo Theories

Non-Boolean variables are important primitives in logical modeling. For instance, in Answer Set Programming (ASP), they are used as plac e holders for constants and more complex ground terms. This is essential for com pact and uniform encodings used in ASP although variables are removed in a grou nding phase preceding the search for answer sets. On the other hand, in theo ries in the Satisfiability Modulo Theories (SMT) framework, variables are realize d as constants that have a free interpretation over a specific domain such as in tegers or reals. The goal of this paper is to propose an approach to integrating the la nguages employed in ASP and SMT so that non-Boolean variables of the kinds abov e can appear in the same program. The resulting formalism ASP(SMT) is rule-b as d and extended by theory atoms from SMT dialects. We illustrate the use of the n ew language and its advantages from the modeling perspective. Moreover , we show how existing off-the-shelf ASP and SMT technology can be used to impleme nt grounding and search for answer sets for this class of programs.
Part-of-speech tagging guidelines for the penn treebank project

An enclosed-type magnetic disc recording and/or reproducing apparatus according to this invention, having a pressure chamber to enclose a bearing unit of a flange rotating along with a magnetic disc, introduces an air pressure at a high-pressure portion, created by an air flow produced by rotation of the magnetic disc and the flange, into the pressure chamber so as to raise the pressure within the pressure chamber to a level higher than 1 atm. , thereby preventing the lubricant evaporated from the bearing member from sticking to the magnetic disc or head to lower the faculty thereof.
) Denial of Service attacks via 4 G / LTE network

Long-Term Evolution (LTE) is the fourth generation of mobile telecommunications technology. (Ab)using the anonymity that prepaid cards provide and the high speed of 4G networks, (D)DoS attacks via 4G networks could be just as harmful as wired connections, but with the added risk that anonymity can be bought. Common (D)DoS attacks are divided in three categories; Bandwidth, Resource and Distributed Amplification -Attacks. Via LTE network measurements, the most promising attack for each category is chosen. For each attack a practical implementation on the Android operating system is provided. Tests with these applications show that Vodafone mitigates two out of three attacks, were other providers mitigate less. LTE network measurements indicate that T-Mobile provides the best network characteristics for a (D)DoS attack over LTE. Furthermore they provide information on the necessary phones needed for a 100 Mb/s bandwidth attack per provider, for which T-mobile needs the least amount. This research also discusses LTE specific mitigation techniques which might result in a safer network infrastructure.
Properties of Bayesian Dirichlet Scores to Learn Bayesian Network Structures

This paper addresses exact learning of Bayesian network structure from data based on the Bayesian Dirichlet score function and its derivations. We describe useful properties that strongly reduce the computational costs of many known methods without losing global optimality guarantees. We show empirically the advantages of the properties in terms of time and memory consumptions, demonstrating that state-of-the-art methods, with the use of such properties, might handle larger data sets than those currently possible.
Another look at data

We do not, it seems, have a very clear and commonly agreed upon set of notions about data---either what they are, how they should be fed and cared for, or their relation to the design of programming languages and operating systems. This paper sketches a theory of data which may serve to clarify these questions. It is based on a number of old ideas and may, as a result, seem obvious. Be that as it may, some of these old ideas are not common currency in our field, either separately or in combination; it is hoped that rehashing them in a somewhat new form may prove to be at least suggestive.
An Approach for Evaluating the Effectiveness of Design Patterns in Software Evolution

In recent years, the influences of design patterns on software quality have attracted increasing attention in the area of software engineering, as design patterns encapsulate valuable knowledge to resolve design problems, and more importantly to improve the design quality. Numerous design patterns improve design quality by means of providing a more flexible architecture to enhance the maintainability. Therefore, many systems applied design patterns in the early design and expect the patterns will contribute to the system evolution. However, many systems applied wrong patterns- the patterns are never applied in the future evolution. In this case, we call these patterns are "effective-less in the evolution". In this paper, we will propose an evaluation approach to evaluate the effectiveness of a pattern in an evolution. We also develop a tool and to evaluate some well-known open source systems in this research.
REM-II : A Model of the Developmental Co-Evolution of Episodic Memory and Semantic Knowledge

Episodic memories are formed from the interpretation of events by semantic knowledge, while semantic knowledge is formed by the accumulation of episodic memories. Through this two-way process, our extensive episodic memory for events in the past co-evolves with our vast knowledge about the world. We present REM-II, a new bayesian account of episodic and semantic memory that explicitly models the development of these two aspects of our long-term memory. REM-II encodes episodic traces as sets of features with different values, and semantic knowledge as a set of co-occurrences of these features. The use of feature co-occurrence allows polysemy and connotation of meaning to be encoded within a single structure, and begins to approach the complexity of human knowledge. We demonstrate knowledge formation in REM-II and show the emergence of semantic spaces through experience and the resultant polysemy and biasing of encoding that REM-II produces. Appearing in the Proceedings of the International Conference on Development and Learning, 5, 2006.
Using Hierarchical Decomposition to Speed Up Average Consensus

Abstract We study the continuous-time consensus problem where nodes on a graph attempt to reach average consensus. We consider communication graphs that can be decomposed into a hierarchical structure and present a consensus scheme that exploits this hierarchical topology. The scheme consists of splitting the overall graph into layers of smaller connected subgraphs. Consensus is performed within the individual subgraphs starting with those of the lowest layer of the hierarchy and moving upwards. Certain “leader” nodes bridge the layers of the hierarchy. By exploiting the increased convergence speed of the smaller subgraphs, we show how this scheme can achieve faster overall convergence than the standard single-stage consensus algorithm running on the full graph topology. The result presents some fundamentals on how the communication architecture influences the global performance of a networked system. Analytical performance bounds are derived and simulations provided to illustrate the effectiveness of the scheme.
Innovation diffusion in time and space: effects of social information and of income inequality

In this paper we consider the spread of modern technological innovations. We contrast social learning and threshold heterogeneity models of innovation diffusion, and show how the typical temporal evolution of the distribution of adopters may be consistent with either explanation. Noting the likelihood that each model contains some useful independent explanatory power, we introduce a combined model. We also consider a spatially-structured population in which the spread of an innovation by social influence is modelled as a reaction-diffusion system, and show that the typical spatiotemporal evolution of the distribution is also consistent with a heterogeneity explanation. Additional contextual information is required to estimate the relative importance of social learning and of economic inequalities in observed adoption lags.
SENSORY SUBSTITUTION: LIMITS AND PERSPECTIVES

A quarter of a century ago, in the preface to Brain Mechanisms in Sensory« Substitution », Paul Bach y Rita wrote: "This monograph thus risks becoming outdated in a very short time since the development of refined sensory substitution systems should allow many of the question raised here to be answered, and some of the conclusions may appear naive to future readers. " (BACH Y RITA, 1972) As it turns out, this prediction is far from having been fulfilled: in spite of their scientific and social interest, their real effectiveness and a certain technological development, prosthetic devices employing the principle of "sensory substitution" are not widely used by the blind persons for whom they were originally destined. After a brief recall of the general principle of sensory substitution, we will advance several hypotheses to account for this situation. We will then identify some elements which may favour the conception and, especially, the usability of future devices. To this end, we will focus our analysis on the work of Bach-y-Rita, particularly well documented, devoted to TVSS (Tactile Vision Sensory Substitution) since the 1960's. This choice is motivated by the extensive and exemplary nature of this research, devoted to the rehabilitation of a handicapped population, as an enterprise which is both technical and scientific in character. We will also present the specific interest of substitution systems employing tactile stimulation, and we will emphasize the essential coordination of fundamental and technological research in this area. In addition, besides their direct utility for handicapped persons, these devices open broad experimental and theoretical perspectives on cognition in general (brain plasticity, perception, intentionality, etc.)
Urocortin, But Not Urocortin II, Protects Cultured Hippocampal Neurons from Oxidative and Excitotoxic Cell Death via Corticotropin-Releasing Hormone Receptor Type I

Urocortin and urocortin II are members of the corticotropin-releasing hormone (CRH) family of neuropeptides that function to regulate stress responses. Two high-affinity G-protein-coupled receptors have been identified that bind CRH and/or urocortin I and II, designated CRHR1 and CRHR2, both of which are present in hippocampal regions of mammalian brain. The hippocampus plays an important role in regulating stress responses and is a brain region in which neurons are vulnerable during disease and stress conditions, including cerebral ischemia, Alzheimer's disease, and anxiety disorders. Here we report that urocortin exerts a potent protective action in cultured rat hippocampal neurons with concentrations in the range of 0.5–5.0 pm, increasing the resistance of the cells to oxidative (amyloid β-peptide, 4-hydroxynonenal, ferrous sulfate) and excitotoxic (glutamate) insults. We observed that urocortin is 10-fold more potent than CRH in protecting hippocampal neurons from insult, whereas urocortin II is ineffective. RT-PCR and sequencing analyses revealed the presence of both CRHR1 and CRHR2 in the hippocampal cultures, with CRHR1 being expressed at much higher levels than CRHR2. Using subtype-selective CRH receptor antagonists, we provide evidence that the neuroprotective effect of exogenously added urocortin is mediated by CRHR1. Furthermore, we provide evidence that the signaling pathway that mediates the neuroprotective effect of urocortin involves cAMP-dependent protein kinase, protein kinase C, and mitogen-activated protein kinase. This is the first demonstration of a biological activity of urocortin in hippocampal neurons, suggesting a role for the peptide in adaptive responses of hippocampal neurons to potentially lethal oxidative and excitotoxic insults.
Scaling of SiGe BiCMOS Technologies for Applications above 100 GHz

This paper summarizes the technological developments carried out in STMicroelectronics to raise the fT / fMAX of SiGe HBTs up to ~ 300 GHz / 400 GHz. The noise and power performance in the W-band of different SiGe HBT generations are compared along with CML ring oscillators and circuit results up to the D band.