Miracle-dz/UniFAR-FTUs huggingface dataset:
Security Mechanisms in Resource-Constrained Computer Systems

In this report, we present a brief description of how the need of security mechanisms will impact and reduce the performance of the different dedicated embedded systems. We provide a brief look to two types of networks -Ad hoc and sensor-, which have challenges in deploying security mechanisms on embedded systems due to the limited resource constraints. We also discuss various design limitations such as processing gap, battery gap, flexibility, tamper resistance, and assurance gap. In addition, we empower our study with the solutions to these limitations, which are represented in various articles.
Learning to Detect Spam: Naive-Euclidean Approach

A method is proposed for learning to classify spam and non-spam emails. It combines the strategy of the Best Stepwise Feature Selection with a classiﬁer of Euclidean nearest-neighbor. Each text email is ﬁrst transformed into a vector of D-dimensional Euclidean space. Emails were divided into training and test sets in the manner of 10-fold cross-validation. Three experiments were performed, and their elapsed CPU times and accuracies reported. The proposed spam detection learner was found to be extremely fast in recognition and with good error rates. It could be used as a baseline learning agent, in terms of CPU time and accuracy, against which other learning agents can be measured.
Nd:Al2O3 as a gain material for integrated devices

Integrated optics, in which optical devices are fabricated on a chip, is a rapidly expanding field. Integrated optics differs from free-space optics in the way that functions that would otherwise need bulky equipment in free-space optics, are combined on a single chip. Light is confined within on-chip lightguides, called 'channel waveguides'. The concept of guiding optical signals in lightguides is known since the early sixties. It was not until the late sixties and early seventies, however, that the importance of integrated optics was realized [1], leading to the first topical meeting on integrated optics in 1972 [2]. Low-cost optical fibers developed in the early 1980's have led to gradual replacement of metallic wires for telecommunication. Meanwhile, improvements in micro- and nanolithography technology led to the introduction of integrated on-chip optical circuits. Integrated optical circuits have many applications in communications and sensing. In particular, Neodymium-doped (Nd) materials have applications in communications because of its emission in the second telecommunication window. Various sensing applications and lab-on-a-chip systems not limited to this specific Nd-ion have been proposed and are being investigated [3{6], and will become common in years to come.
Modeling and Verification of Component-Based Systems with Data Passing Using BIP

Large-scale systems are often modeled and verified in a component-based way. BIP (Behavior, Interaction, Priority) is a flexible component-based framework which supports hierarchical design of heterogeneous systems. BIP components interact via connectors in which data can be passed among multiple components. It also support the modeling of time. Due to its expressiveness and flexibility, many real-time systems can be modeled easily in BIP. Verification, however, is not well supported in the current BIP framework. That is a major disadvantage when it is used in a model-driven design flow. To fill this gap, we propose a translation from slightly restricted BIP models to timed automata. Then model checking can be applied to the latter using Uppaal (which is a sophisticated model checker for timed automata). The correctness of translation is proven formally and the translation is implemented as a tool Bip2Uppaal. Three industrial case studies show that our approach is practical and effective.
Asymptotic Optimality of Running Consensus in Testing Binary Hypotheses

Consensus in sensor networks is a procedure to corroborate the local measurements of the sensors with those of the surrounding nodes, and leads to a final agreement about a common value that, in detection applications, represents the decision statistic. As the amount of collected data increases, the convergence toward the final statistic is ruled by suitable scaling laws, and the question arises if the asymptotic (large sample) properties of a detection statistic are retained when this statistic is approximated via consensus algorithms. We investigate the asymptotic properties of running consensus detectors both under the Neyman-Pearson paradigm (fixed number of data) and in the sequential case. An appropriate asymptotic framework is developed, and exact theoretical results are provided, showing the asymptotic optimality of the running consensus detector. In addition, numerical experiments are performed to address nonasymptotic scenarios.
Dynamics and Control of Non-holonomic Two Wheeled Inverted Pendulum Robot

In most wheeled mobile robots, at least one wheel is the auxiliary wheel aka caster. It must move smoothly without causing the robot to be interrupted if they are to truly do their job. Looking at it practically, however, they don’t work sometimes. That is, the wheels frequently slid or slip when they were dragged, even though they were designed to roll without sliding or slipping. In order to free from this problem, one of the solutions is getting rid of it. Then, the total number of wheels attached on the robot changes, moreover, the mechanical characteristic of the robot having only two driving wheels without caster will be altered to that the robot is supposed to move and balance its body with only two driving wheels. Therefore, for this inverted pendulum type robot, it is necessary to investigate whether it is valid proposal or not and what mechanical characteristics it has. For doing this, dynamics of this kind of robot was governed to provide lots of information that will be helpful for design and control. And experiments with various motions were carried out to show its practical validity.
An improved QOS in the architecture, model and huge traffic of multi-media applications under high speed wireless campus network

Multi-media applications are in high demand by many users. Many telecommunication companies are into the business of providing these applications such as the Voice over Internet Protocol (VOIP) and video conferencing. Users often become unsatisfied with the services they receive from their service providers. This is as a result of lack of guaranteed QoS in today’s high speed wireless campus network (WCN). The internet is the main platform on which these applications run on and therefore all credits and blame goes to the designers of the internet. Several researchers have worked towards improving the QoS of these applications and they have contributed immensely to it. However, this work reviewed previous works and comes up with a new methodology that ensures QoS of multi-media applications in the high speed Wireless Local Area Network (WLAN). The design and simulation shows a great improvement in the overall network performance. This is measured in terms of three network parameters, viz: average end-to-end delay, average jitter and throughput. We introduce a mapping technique with which we assign the highest of all priorities to the multi-media traffic. Firstly there is a mapping between the Quality of Service Class Identifier (QCI) to the Differentiated Service Code Points (DSCP), where each of it is a QoS giver. Then, the QCI/DSCP is mapped to our multi-media traffics. Our design has already excelled the default design by achieving lower end-to-end delay, regular jitter and higher throughput. We then incorporate a huge traffic in our scenario and measured the performance again; our new mechanism achieves the best result compared to all the similar scenarios with huge traffic. The overall performance of the network is improved with no less than 50%.
Heat Waves and Cause-specific Mortality at all Ages

Background: Mortality has been shown to increase with extremely hot ambient temperatures. Details on the specific cause of mortality can be useful for improving preventive policies. Infants are often identified as a population that is vulnerable to extreme heat conditions; however, information on heat and infant mortality is scarce, with no studies reporting on cause-specific mortality. Methods: The study includes all deaths in the Catalonia region of Spain during the warm seasons of 1983–2006 (503,389 deaths). We used the case-crossover design to evaluate the association between the occurrence of extremely hot days (days with maximum temperature above the 95th percentile) and mortality. Total mortality and infant mortality were stratified into 66 and 8 causes of death, respectively. Results: Three consecutive hot days increased total daily mortality by 19%. We calculated that 1.6% of all deaths were attributable to heat. About 40% of attributable deaths did not occur during heat-wave periods. The causes of death that were increased included cardiovascular and respiratory diseases, mental and nervous system disorders, infectious and digestive system diseases, diabetes, and some external causes such as suicide. In infants, the effect of heat was observed on the same day and was detected only for conditions originating in the perinatal period (relative risk = 1.53 [95% confidence interval = 1.16–2.02]). Within the perinatal causes, cardiovascular, respiratory, digestive system, and hemorrhagic and hematologic disorders were the causes of death with stronger effects. Conclusions: Heat contributes to an increase in mortality from several causes. In infants, the first week of life is the most critical window of vulnerability.
Intelligent Web interface using flexible conversational agent with semantic Bayesian networks

As online information services have become widespread in the Web nowadays, conversational agents appear as an effective and familiar user interface. Since conversational agents analyze the user's query based on a static process, it cannot manage complex expressions. In this paper, we propose semantic Bayesian networks that infer the user's intention based on Bayesian networks and their semantic information representing the relationship among nodes. Since conversation often contains ambiguous expressions, managing the context or the uncertainty should be necessary to support flexible conversational agents. The proposed method drives the mixed initiative interaction (MII) that prompts for missing concepts and clarifies for spurious concepts to understand the user's intention correctly. Actually implementing a Web information guide with the proposed method, we have confirmed the usefulness of the proposed method.
Discovering Knowledge from Relational Data Extracted from Business News

Thousands of business news stories (including press releases, earningsreports, general business news, etc.) are released each day. Recently, informationtechnology advances have partially automated the processing ofdocuments, reducing the amount of text that must be read. Current techniques(e.g., text classification and information extraction) for full-text analysis for themost part are limited to discovering information that can be found in singledocuments. Often, however, important information does not reside in a singledocument, but in the relationships between information distributed over multipledocuments. This paper reports on an investigation into whether knowledgecan be discovered automatically from relational data extracted from large corporaof business news stories. We use a combination of information extraction,network analysis, and statistical techniques. We show that relationally interlinkedpatterns distributed over multiple documents can indeed be extracted,and (specifically) that knowledge about companiesAÂAÂAÂAÂ¢AÂAÂAÂAÂAÂAÂAÂAÂ interrelationships can bediscovered. We evaluate the extracted relationships in several ways: we give abroad visualization of related companies, showing intuitive industry clusters;we use network analysis to ask who are the central players, and finally, weshow that the extracted interrelationships can be used for important tasks, suchas for classifying companies by industry membership.
Toward error-bounded algorithms for infinite-horizon DEC-POMDPs

Over the past few years, attempts to scale up infinite-horizon DECPOMDPs are mainly due to approximate algorithms, but without the theoretical guarantees of their exact counterparts. In contrast, e-optimal methods have only theoretical significance but are not efficient in practice. In this paper, we introduce an algorithmic frame-work (β-pi) that exploits the scalability of the former while preserving the theoretical properties of the latter. We build upon β-pi a family of approximate algorithms that can find (provably) errorbounded solutions in reasonable time. Among this family, h-pi uses a branch-and-bound search method that computes a near-optimal solution over distributions over histories experienced by the agents. These distributions often lie near structured, low-dimensional subspace embedded in the high-dimensional sufficient statistic. By planning only on this subspace, h-pi successfully solves all tested benchmarks, outperforming standard algorithms, both in solution time and policy quality.
A Neural Network Approach for Forestal Fire Risk Estimation

This paper describes an intelligent system for the prediction of forest fire risk in Galicia, a region in north-west Spain. The system has been designed to calculate a risk fire index for each of the 360 squares of 10x10 kms into which the area map has been divided digitally. In our research, the problem was approached using a feedforward neural network. The information used to train the network was gathered at five meteorological stations on a daily basis from 1985 to 1999, and consisted of basically meteorological data, namely temperature, humidity and rainfall, in conjunction with previous fire records for the areas represented by squares. Network topologies were tested using 125,156 training data and validated over 13,906 test samples, and that achieving the best performance was the 6-9-1 topology. Finally, our results indicate that the system performs satisfactorily, with a sensitivity of 0.857 and a specificity of 0.768.
Robo1 and Robo2 Cooperate to Control the Guidance of Major Axonal Tracts in the Mammalian Forebrain

The function of the nervous system depends on the precision of axon wiring during development. Previous studies have demonstrated that Slits, a family of secreted chemorepellent proteins, are crucial for the proper development of several major forebrain tracts. Mice deficient in Slit2 or, even more so, in both Slit1 and Slit2 have defects in multiple axonal pathways, including corticofugal, thalamocortical, and callosal connections. In the spinal cord, members of the Robo family of proteins help mediate the function of Slits, but the relative contribution of these receptors to the guidance of forebrain projections remains to be determined. In the present study, we addressed the function of Robo1 and Robo2 in the guidance of forebrain projections by analyzing Robo1-, Robo2-, and Robo1;Robo2-deficient mice. Mice deficient in Robo2 and, more dramatically, in both Robo1 and Robo2, display prominent axon guidance errors in the development of corticofugal, thalamocortical, and corticocortical callosal connections. Our results demonstrate that Robo1 and Robo2 mostly cooperate to mediate the function of Slit proteins in guiding the major forebrain projections.
A preliminary characterization of a whole body vibration platform prototype for medical and rehabilitation application

Whole-body vibration (WBV) is receiving increasing interest as an exercise intervention in physiology and rehabilitation. Although there are many commercial and professional WBV platforms to provide controlled vibrations, very few have actually been tested in terms of amplitude (mm), frequency spectra (Hz) and shape of the vibratory motion wave. In this regard a prototype of a novel WBV platform where the vibration amplitude can be set to three values in the frequency range 20-60 Hz is here proposed. The device has been preliminarily characterized by processing the measurement signal from a piezoelectric monoaxial accelerometer mounted in the center of the WBV plate. In particular the accelerometer signal has been processed to evaluate frequency spectrum, waveform shape and displacement. Moreover to evaluate the performance of the prototype, same tests were carried out on an equivalent professional WBV platform, so that data coming from the two platforms have been compared and commented: test results have shown that the vibrations produced by the two WBV plates are very similar, nevertheless it has been found that the prototype works with a maximum error of about 5 % in frequency, that is less than in the other devices, furthermore the greater distortion of the signal is always at the twice of the operating frequency (second harmonic). Further investigations are needed to complete the characterization of the prototype and assess the vibration amplitude is not load dependent.
DTI-based visualization strategies for the pyramidal tract

With the introduction of diffusion tensor imaging a method became available which is capable to detect major white matter tracts in-vivo. For the visualization of the data several techniques have been developed which, however, show various drawbacks for a comprehensive medical and technical analysis. Although fractional anisotropy maps and streamlines, typically denoted as fiber tracking, are widely used they are suboptimal in several situations of preand intraoperative application. Going beyond these standard approaches, several new and more advanced visualization techniques, namely directional volume growing, hulls and hardware-accelerated glyphs are introduced for an improved exploration of the pyramidal tract. The approaches have been evaluated with respect to diagnosis and therapy planning in neurosurgery. Overall, it is shown that the presented strategies for the visualization of diffusion tensor imaging data are capable to significantly support neurosurgical planning and intervention.
Strategy Execution: Translating Strategy into Action in Complex Organizations

1. Introduction 2. What is Strategy Execution? 3. The Strategy Execution Challenge 4. Diagnosing Strategy Execution Problems 5. A Framework for Strategy Execution 6. Translating Strategy into Action 7. Aligning Action with Strategy 8. Aligning Organizational Designs and Systems 9. Conclusion
A Semi-automatic Methodology for Facial Landmark Annotation

Developing powerful deformable face models requires massive, annotated face databases on which techniques can be trained, validated and tested. Manual annotation of each facial image in terms of landmarks requires a trained expert and the workload is usually enormous. Fatigue is one of the reasons that in some cases annotations are inaccurate. This is why, the majority of existing facial databases provide annotations for a relatively small subset of the training images. Furthermore, there is hardly any correspondence between the annotated land-marks across different databases. These problems make cross-database experiments almost infeasible. To overcome these difficulties, we propose a semi-automatic annotation methodology for annotating massive face datasets. This is the first attempt to create a tool suitable for annotating massive facial databases. We employed our tool for creating annotations for MultiPIE, XM2VTS, AR, and FRGC Ver. 2 databases. The annotations will be made publicly available from http://ibug.doc. ic.ac. uk/ resources/facial-point-annotations/. Finally, we present experiments which verify the accuracy of produced annotations.
Explorer Towards Merging Plat and PGIP

The PGIP protocol is a standard, abstract interface protocol to connect theorem provers with user interfaces. Interaction in PGIP is based on ASCII-text input and a single focus point-ofcontrol, which indicates a linear position in the input that has been checked thus far. This fits many interactive theorem provers whose interaction model stems from command-line interpreters. PlatΩ, on the other hand, is a system with a new protocol tailored to transparently integrate theorem provers into text editors like TEXmacs that support semi-structured XML input files and multiple foci of attention. In this paper we extend the PGIP protocol and middleware broker to support the functionalities provided by PlatΩ and beyond. More specifically, we extend PGIP (i) to support multiple foci in provers; (ii) to display semi-structured documents; (iii) to combine prover updates with user edits; (iv) to support context-sensitive service menus, and (v) to allow multiple displays. As well as supporting TEXmacs, the extended PGIP protocol in principle can support other editors such as OpenOffice, Word 2007 and graph viewers; we hope it will also provide guidance for extending provers to handle multiple foci.
Solutions to Instability Problems with Sequential Wrapper-based Approaches to Feature Selection

It is generally accepted that Wrapper approaches will outperform Filter-based approaches to feature selection, particularly in situations where an adequate amount of data is available. What is often overlooked is that Wrapper approaches can be unstable. For instance, different partitionings of the training data can result in different routes through the search space and thus in different feature subsets being selected. In this paper we illustrate examples of this problem and a solution based on the aggregation of several runs of a sequential search is suggested. This is essentially an ensemble solution to instability in feature subset selection and it does seem to stabilise the process.
A hybrid iterative algorithm for Nonnegative Matrix Factorization

The aim of Non-negative Matrix Factorization (NMF) is to decompose a non-negative matrix into a product of two (or multiple) non-negative matrices with reduced ranks. Several iterative methods have been developed for this purpose, e.g. the Alternating Least Squares (ALS) or Lee-Seung (LS) multiplicative methods. Despite its fast convergence, the ALS algorithm suffers from its instability, and may diverge in practice. The LS method, although reasonably stable, is known to converge slowly. In this paper, we develop a hybrid algorithm using mixed iterations based on these two methods. We show theoretically that the hybrid algorithm outperforms both methods by achieving a better tradeoff between the convergence speed and stability without increasing computational complexity. We also provide numerical examples in which we compare our hybrid algorithm with the LS and ALS algorithms.
Essential, Mandatory, and Shared Parts in Conceptual Data Models

This chapter focuses on formally representing life cycle semantics of part-whole relations in conceptual data models by utilizing the temporal modality. We approach this by resorting to the temporal conceptual data modeling language ERV T and extend it with the novel notion of status relations. This enables a precise axiomatization of the constraints for essential parts and wholes compared to mandatory parts and wholes, as well as introduction of temporally suspended partwhole relations. To facilitate usage in the conceptual stage, a set of closed questions and decision diagram are proposed. The long-term objectives are to ascertain which type of shareability and which lifetime aspects are possible for part-whole relations, investigate the formal semantics for sharability, and how to model these kind of differences in conceptual data models.