Miracle-dz/UniFAR-FTUs huggingface dataset:
LSS: A Tool for Large Scale Scenarios

Today's complex computational and embedded systems compute complex quantities from complex inputs, with behavior dependent upon the (distributed) state of the system and its environment. Describing the intended behavior of such a system is challenging. The most natural and commonly used approach is to give a collection of scenarios, where each scenario is a sequence of inputs and expected outputs. An analyst elicits scenarios from subject matter experts (SMEs) to document functional requirements. Scenarios can be represented in a variety of ways, from informal narratives through (formal) linear event sequences. Formal behavior scenarios are useful in many software engineering tasks, including requirements engineering, specification- and architectural-modeling, and test generation. Uses in modeling include model inference, validation, and measuring resource usage and reliability. Finally, scenarios are useful in discovering and documenting gaps in specifications. Existing scenario-based tools and methodologies break down when scenarios grow large. Elicitation becomes impractical, as it requires the human to specify all the steps. Even assuming one can capture thousands or more of input events, it is problematic for the human to describe the correct expected outputs at each point of interest. Finally, even if one can capture all inputs and outputs, the complexity of such an artifact gives one little confidence that it represents desirable behavior in detail, as the risk of undetected errors grows at least linearly with the size. Finally, the behavior of systems described by large scale scenarios typically requires a large and complex set of them, which creates the added difficulty of assuring oneself of covering a "representative" subset of a huge space
Software Effort Estimation in the Early Stages of the Software Life Cycle Using a Cascade Correlation Neural Network Model

Software cost estimation is a crucial element in project management. Failing to use a proper cost estimation method might lead to project failures. According to the Standish Chaos Report, 65% of software projects are delivered over budget or after the delivery deadline. Conducting software cost estimation in the early stages of the software life cycle is important and this would be helpful to project managers to bid on projects. In this paper, we propose a novel model to predict software effort from use case diagrams using a cascade correlation neural network approach. The proposed model was evaluated based on the MMER and PRED criteria using 214 industrial and 26 educational projects against a multiple linear regression model and the Use Case Point model. The results show that the proposed cascade correlation neural network can be used with promising results as an alternative approach to predict software effort.
Maximal protection: Lowering incident energy and arc blast elements by minimizing arcing time

Arcing faults are rare events, but they often lead to severe injuries. From the economic point of view, the consequences due to direct and indirect costs can be extremely high. There are various options to prevent arcing faults, but faults cannot be totally eliminated. This is why several approaches to mitigate the consequences of arcing faults have been introduced. Several manufacturers have started to produce arc flash protection relays based on optical detection of light energy from an arc. In most applications, the light information is confirmed by overcurrent information before a trip command is initiated to an upstream current breaking device. The tripping of a circuit breaker, for instance, occurs in only a few milliseconds. This seems to be the state-of-the-art technology, leading in most cases to very reasonable incident energy levels. However, it is essential to be able to minimize not only the thermal impact but the pressure wave as well. This paper investigates technology aimed at maximal protection.
Underground drainage system based on artificial intelligence control

By analyzing common artificial intelligence technologies, the paper introduced design of software and structure of a drainage system of coal mine underground based on artificial intelligence control. By detecting water level in sump and other parameters, the system controls pumps to work by turns and standby pump to start at duly time, so as to schedule dumps run reasonably, and it has function of fault alarming, which decreases labor intensity of workers greatly and improves utilization rate of devices. The system also has good expansibility and is fit for different fields.
Association between Sex, Age, Insulin Regimens and Glycemic Control in Children and Adolescents with Type 1 Diabetes

We examined the association between sex, age, insulin regimens and glycemic control in 133 Japanese children and adolescents, 42 males and 61 females aged 16.8 ± 7.0 yr, with type 1 diabetes mellitus (T1DM). The patients were divided into 5 age groups and were also classified according to the insulin regimen. The annual median HbA1c level in males (7.3 ± 0.2%) was similar to that in females (7.2 ± 0.2%). In regard to the age of the patients, the median HbA1c levels in patients aged 15–19 yr (7.9 ± 0.4%) was significantly higher than those aged 5–9 yr (7.2 ± 0.1%) and those aged 20≤ yr (6.6 ± 0.4%, p<0.05, respectively). On the other hand, there were no significant relationships between the HbA1c values and the insulin regimens. In conclusion, difficulty in management of diabetes due to emotional issues and endocrinological factors during adolescence may play a possible role in the deterioration of diabetes control. On the other hand, the insulin regimen does not seem to have a major impact on the metabolic outcome in young people with T1DM.
Sensing the "Health State" of a Community

Mobile phones are a pervasive platform for opportunistic sensing of behaviors and opinions. Three studies use location and communication sensors to model individual behaviors and symptoms, long-term health outcomes, and the diffusion of opinions in a community. These three analyses illustrate how mobile phones can unobtrusively monitor rich social interactions, because the underlying sensing technologies are now commonplace and readily available.
A Three-Terminal Spin-Wave Device for Logic Applications

We demonstrate a three-terminal spin wave-based device utilizing spin wave interference. The device consists of three coplanar transmission lines inductively coupled to the 100nm thick CoFe film. Two spin wave signals are excited by microwave fields produced by electric current in two sets of lines, and the output signal is detected by the third set. The initial phases of the spin wave signals are controlled by the direction of the current flow in the excitation set of lines. Experimental data show prominent output signal modulation as a function of the relative phase (in-phase and out-of phase) of two input signals. The micrometer scale device operates in the GHz frequency range and at room temperature. Our experimental results show that spin-wave devices exploiting spin wave interference may be scaled to micrometer and nanometer scales for potential logic circuit application.
Towards a Standard for Heterogeneous Ontology Integration and Interoperability

Even though ontologies are widely being used to enable interoperability in information-rich endeavours, there is currently no uniﬁed framework for ontology interoperability itself. Surprisingly little of the state of the art in modularity and structuring, e.g. in software engineering, has been applied to ontology engineering so far. However, application areas like Ambient Assisted Living (AAL), which require synchronisation and orchestration of interoperable services, are in dire need of safe and secure ontology interoperability. OntoIOp (Ontology Integration and Interoperability), a new international standard proposed in ISO/TC 37/SC 3, aims at ﬁlling this gap.
Sand and Sediment Transport in Desert Areas

In the ESA AO-3 project “Sand and sediment transport in desert areas” we use ERS Interferometric Synthetic Aperture Radar (InSAR) techniques to study wind erosion in four arid areas. Our approach is to use InSAR phase images to derive digital elevation models (DEMs) of the study areas. These DEMs are combined with time series of correlation images in an attempt to map surface stability, estimate parameters related to the sediment transport budget and changes in the vegetation cover. This presentation will put focus on results from the Oudalan Province in Burkina Faso, which is the area we are using for method development. We present the available data material and first order products such as DEMs and correlation statistics. We describe corrections of the correlation images for baseline decorrelation, a method for correcting for signal-to-noise-ratio (SNR) effects, and for effects due to terrain slope. The end product is temporal decorrelation images free of other effects. We present estimates of the obtained elevation accuracy. Furthermore we discuss the observed temporal decorrelation for selected surfaces and relate the observations to in-situ and meteorological data.
Customizing the Software Process to Support Avionics Systems Enhancements

system, selecting the process to be adopted must include consideration of the particular encompassing project. This includes the application domain, the size and complexity of the final product, the hosting system characteristics, etc. Simultaneously, it must be driven by the specific organization’s goals, environment, and maturity. By describing and analyzing a real project, this article shows how different approaches and techniques, usually applied in isolation, can be selected, customized and combined to design a software process that better satisfies the organization’s goals and meets its constraints. The project was undertaken to investigate the feasibility of enhancing an aircraft mission system by integrating new capabilities, and eventually to identify a quick, low-cost and low-risk solution. An uncertainty-driven product architectural framework together with an ad-hoc simulation-based supporting environment were used to combine in an effective and controlled fashion the waterfall, the Rapid Application Development, and the Incremental Development models.
Involvement of Fungal Cell Wall Components in Adhesion of Sporothrix schenckii to Human Fibronectin

ABSTRACT Systemic sporotrichosis is an emerging infection potentially fatal for immunocompromised patients. Adhesion to extracellular matrix proteins is thought to play a crucial role in invasive fungal diseases. Here we report studies of the adhesion of Sporothrix schenckii to the extracellular protein fibronectin (Fn). Both yeast cells and conidia of S. schenckii were able to adhere to Fn as detected by enzyme-linked immunosorbent binding assays. Adhesion of yeast cells to Fn is dose dependent and saturable.S. schenckii adheres equally well to 40-kDa and 120-kDa Fn proteolytic fragments. While adhesion to Fn was increased by Ca2+, inhibition assays demonstrated that it was not RGD dependent. A carbohydrate-containing cell wall neutral fraction blocked up to 30% of the observed adherence for the yeast cells. The biochemical nature of this fraction suggests the participation of cell surface glycoconjugates in binding by their carbohydrate or peptide moieties. These results provide new data concerning S. schenckii adhesion mechanisms, which could be important in host-fungus interactions and the establishment of sporotrichosis.
An effective longest prefix matching algorithm for speeding up query routing in skip graphs

The tremendous growth of public interest in Peer-to-Peer (P2P) networks in recent years has initiated a lot of research work on how to design efficient overlay structures for P2P systems. Structured overlay networks that based on various distributed hash tables (DHTs) provide no control over where data is stored and cannot support range queries directly. Ordered overlays such as skip graphs and SkipNet have obviously practical advantages over DHTs, which can support several locality properties and range queries. This paper presents a novel longest prefix matching algorithm for speeding up query routing in skip graphs without consumption of extra memory space. A novel data structure called Triple Linked List (TLL) is designed to store each node's pointers in the network. Experimental results show that the TLL-based longest prefix matching algorithm can speed up both exact-match and range queries in different network sizes.
On the Oscillation Spectra of Ultracompact Stars: an Extensive Survey of Gravitational-Wave Modes

An extensive survey of gravitational-wave modes for uniform density stars is presented. The study covers stars ranging in compactness from R/M=100 to the limit of stability in general relativity: R/M = 9/4. We establish that polar and axial gravitational-wave modes exist for all these stellar models. Moreover, there are two distinct families of both axial and polar modes. We discuss the physics of these modes and argue that one family is primarily associated with the interior of the star, while the second family is mainly associated with the stellar surface. We also show that the problem of axial perturbations has all the essential features of the polar problem as far as gravitational waves are concerned. This means that the axial problem is much more important than has previously been assumed. We also find some surprising features, such as avoided crossings between the polar gravitational-wave modes and the Kelvin f-mode as the star becomes very compact. This seems to suggest that the f-mode should be considered on equal footing with the polar w-modes for ultracompact stars. All modes may have the main character of trapped modes inside the curvature potential barrier for R/M < 3.
Multi-touch gestural interaction in X3D using hidden Markov models

Multi-touch interaction on tabletop displays is a very active field of todays HCI research. However, most publications still focus on tracking techniques or develop gesture configurations for a specific application setup using a small set of simple gestures. In this work we present a new approach to easily set up the recognition of even complex gestures for multi-touch applications. Our gesture recognition module is based on Hidden Markov Models, which offer a robust recognition of multiple gestures in real-time. An X3D interface of the recognition module is provided to qualify designers and other non-programmers to apply gesture recognition functionalities to multi-touch applications in an easy and straightforward manner.
Managing Web Content Using Linked Data Principles - Combining Semantic Structure with Dynamic Content Syndication

Despite the success of the emerging Linked Data Web, offering content in a machine-processable way and -- at the same time -- as a traditional Web site is still not a trivial task. In this paper, we present the OntoWiki-CMS -- an extension to the collaborative knowledge engineering toolkit OntoWiki for managing semantically enriched Web content. OntoWiki-CMS is based on OntoWiki for the collaborative authoring of semantically enriched Web content, vocabularies and taxonomies for the semantic structuring of the Web content and the OntoWiki Site Extension, a template and dynamic syndication system for representing the semantically enriched content as a Web site and the dynamic integration of supplementary content. OntoWiki-CMS facilitates and integrates existing content-specific content management strategies (such as blogs, bibliographic repositories or social networks). OntoWiki-CMS helps to balance between the creation of rich, stable semantic structures and the participatory involvement of a potentially large editor and contributor community. As a result semantic structuring of the Web content facilitates better search, browsing and exploration as we demonstrate with a use case.
The action workflow approach to workflow management technology

Abstract This paper describes the ActionWorkflow™ approach to workflow management technology: a design methodology and associated computer software for the support of work in organizations. The approach is based on theories of communicative activity as language/action, developed in a series of systems for coordination among users of networked computers. This article describes the approach, gives an example of its application, and shows the architecture of a workflow management system based on it.
Vehicle Make Recognition Based on Convolutional Neural Network

Vehicle analysis has been investigated for decades, which involves license plate recognition, intelligent traffic. Among these applications, vehicle make recognition is a challenging task due to the close appearance between car models. In this paper, we propose an architecture to recognize vehicle make based on convolutional neural network (CNN). The moving car is first localized by frame difference, the resultant binary image is used to detect the frontal view of a car by a symmetry filter. The detected frontal view is used to train and test the CNN. Experimental results show that our proposed framework achieves favorable recognition accuracy.
Typhoid Fever and Invasive Nontyphoid Salmonellosis, Malawi and South Africa

To determine the prevalence of invasive nontyphoid salmonellosis and typhoid fever in Malawi and South Africa, we compared case frequency and patient age distribution. Invasive nontyphoid salmonellosis showed a clear bimodal age distribution; the infection developed in women at a younger age than in men. Case frequency for typhoid fever was lower than for salmonellosis.
Potential role of ATM in hepatocyte endocytosis of ApoE-deficient, ApoB48-containing lipoprotein in ApoE-deficient mice

Individuals carrying mutations at both ataxia telangiectasia mutated (ATM) gene alleles reportedly have increased plasma cholesterol and triglyceride levels. Previous studies have demonstrated that defective ATM function promotes atherosclerosis. We previously demonstrated that ATM facilitates the clearance of plasma apolipoprotein (Apo)E-deficient, ApoB48-containing (E−/B48) lipoproteins in ApoE-deficient mice (ApoE−/− mice). However, to date there is no exact explanation available as to the mechanism(s) through which ATM is involved in the removal of E−/B48 lipoprotein in ApoE−/− mice. In this study, to our knowledge, we demonstrate for the first time that heterozygous ATM mutation reduces the hepatocyte uptake of E−/B48 lipoproteins in ApoE−/− mice; however, heterozygous ATM mutation did not affect hepatocyte binding to E−/B48 lipoproteins. Moreover, our results revealed that ATM proteins were localized in the nucleus, early endosomes and late endosomes, but not in the plasma membrane in the hepatocytes of ApoE−/− mice. In addition, following treatment with the ATM activator, chloroquine, and E−/B48 lipoproteins, ATM interacted with class III phosphatidylinositol-3-kinases (PI3Ks) and the activated ATM protein enhanced class III PI3K activity. Furthermore, treatment with a class III PI3K inhibitor (LY290042 and 3-MA) attenuated the intracellular total cholesterol accumulation induced by ATM activation. These results provide insight into the mechanisms behind the involvment of ATM in the process of endocytosis of E−/B48 lipoprotein in ApoE−/− mice, demonstrating the role of class III PI3K protein.
The deployer's problem: configuring application servers for performance and reliability

Frameworks such as J2EE are designed to simplify the process of developing enterprise applications by handling much of the complexity of concurrency, transaction, and persistence management. An application server that supports such a framework implements these concerns, freeing the application developer to focus on the task of implementing the business logic aspect of the application. In such frameworks, the deployer, the individual(s) who configures the application server to manage concurrency, transaction and persistence correctly and efficiently, plays a central role. A deployer has few tools to assist with performing this complicated task. Incorrect configuration can lead to application failure or severe underperformance. We outline the problems facing the deployer of applications, present a methodology that can assist the programmer with the task of configuring application servers, and present two case studies that validate the usefulness of our methodology.
Latent Space Learning for Enhanced Short Text Classification

There has been recently a growing interest in short text classification and analysis. However, conventional machine learning and text mining algorithms are not suitable for analyzing short texts due to their shortness and sparsity. In this paper, we put forward a novel representation model for short text classification. Our proposed model basically learns a compact latent space for modeling short text based on semantic similarity among words in training corpus. We first capture semantic relationship between words by using Word2Vec. Sparse autoencoder will be then applied to learn a compact latent space for representation of short texts. With the learned space, reliable features can be estimated based on least square technique. We conduct experiments on the two classification tasks: sentiment text classification and news title classification to evaluate the proposed method. Experimental results on two real-world datasets demonstrate that our proposed method produces more stable features that enhance short-text classification than state-of-the-art latent feature representations.
Survey of commercial parallel machines

We have presented in this paper the survey of the parallel machines that are marketed today. The survey includes the latest machines available from Kendell Square Research, Thinking Machines Corporation, MasPar Computer Corporation, NCUBE Corporation, Sequent Computer Systems and Parsytec. We have provided the topology, architecture, cache coherence, synchronization and performance in MFLOPs for each of the machines subject to the availability of information.
Search Space Allocation Schemes Review for Control Channels in LTE and LTE-advanced

The search space allocation scheme is a very important operation done to schedule users in the resources of the control channels PDCCH and enhanced PDCCH. However, the standard scheme has many drawbacks as it allocates each user a contiguous number of candidates to blindly search for its control information. This paper reviews some of the proposed designs in literature for the search space allocation scheme and compares them to provide a thorough performance analysis. Simulation results showed some comparisons between the proposed schemes in literature and the standard scheme using three different metrics. Finally, the scattered design was proved to be of the best overall performance although some schemes have comparable performance in the blocking probability metric.
Gaussian states minimize the output entropy of one-mode quantum Gaussian channels

We prove the long-standing conjecture stating that Gaussian thermal input states minimize the output von Neumann entropy of one-mode phase-covariant quantum Gaussian channels among all the input states with a given entropy. Phase-covariant quantum Gaussian channels model the attenuation and the noise that affect any electromagnetic signal in the quantum regime. Our result is crucial to prove the converse theorems for both the triple trade-off region and the capacity region for broadcast communication of the Gaussian quantum-limited amplifier. Our result extends to the quantum regime the entropy power inequality that plays a key role in classical information theory. Our proof exploits a completely new technique based on the recent determination of the p→q norms of the quantum-limited amplifier [De Palma et al., arXiv:1610.09967]. This technique can be applied to any quantum channel.