Miracle-dz/UniFAR-FTUs huggingface dataset:
The application of the Critical Power construct to endurance exercise

Critical power (CP) is a theoretical construct reflecting the highest work rate that can be maintained by continuous aerobic energy resynthesis for an infinite period of time. In practice, however, the CP estimate derived from mathematical modelling usually leads to exhaustion within 1 h. While previous research has used traditional measures of aerobic fitness to validate CP, there is disagreement in the literature as to whether CP reflects a physiological steady or a non-steady state. Furthermore, the practical applications of the CP construct have received limited research attention. Therefore, the purpose of this thesis was to clarify the applicability of CP to endurance exercise. Following experimentation for measurement reliability and equipment validation, two experimental studies formed the core of the thesis work. In the first of these two studies, participants were grouped by their peak aerobic power (VO2peak) as either low (LOW: 26.8 – 40.6 mL∙kg-1∙min-1, n = 9), moderate (MOD: 43.6 – 49.6 mL∙kg-1∙min-1, n = 8) or high (HIGH: 57.8 – 69.0 mL∙kg-1∙min-1, n = 8) fitness. The relationships between CP and traditional measures of aerobic fitness (e.g., lactate threshold, VO2peak and maximal minute power) were found to be similar for all fitness groups. Furthermore, VO2, blood lactate concentration and heart rate continued to rise over time during exercise at CP for all groups. In the second main study, recreationally active participants were randomly assigned to groups that trained for six weeks either below CP (<CP, n = 14), at CP (CP, n = 15) or intermittently around CP (CPINT, n = 14). Total work was matched between groups and training time was significantly shorter for the CP group compared with the <CP and CPINT groups. While all training interventions resulted in significant increases in CP and other measures of aerobic fitness (e.g., lactate threshold, economy, VO2peak and muscle enzyme content), there were no interaction effects between groups. In summary, CP was shown to reflect an unstable physiological state, irrespective of fitness status, that is responsive to continuous and intermittent training and may be used as a time-efficient training intensity improving aerobic fitness.
Spotting the Malicious Moment: Characterizing Malware Behavior Using Dynamic Features

While mobile devices have become more pervasive every day, the interest in them from attackers has also been increasing, making effective malware detection tools of ultimate importance for malware investigation and user protection. Most informative malware identification techniques are the ones that are able to identify where the malicious behavior is located in applications. In this way, better understanding of malware can be achieved and effective tools for its detection can be written. However, due to complexity of such a task, most of the current approaches just classify applications as malicious or benign, without giving any further insights. In this work, we propose a technique for automatic analysis of mobile applications which allows its users to automatically identify the sub-sequences of execution traces where malicious activity happens, hence making further manual analysis and understanding of malware easier. Our technique is based on dynamic features concerning resources usage and system calls, which are jointly collected while the application is executed. An execution trace is then split in shorter chunks that are analyzed with machine learning techniques to detect local malicious behaviors. Obtained results on the analysis of 3,232 Android applications show that collected features contain enough information to identify suspicious execution traces that should be further analysed and investigated.
Semantic categorization of outdoor scenes with uncertainty estimates using multi-class gaussian process classification

This paper presents a novel semantic categorization method for 3D point cloud data using supervised, multiclass Gaussian Process (GP) classification. In contrast to other approaches, and particularly Support Vector Machines, which probably are the most used method for this task to date, GPs have the major advantage of providing informative uncertainty estimates about the resulting class labels. As we show in experiments, these uncertainty estimates can either be used to improve the classification by neglecting uncertain class labels or - more importantly - they can serve as an indication of the under-representation of certain classes in the training data. This means that GP classifiers are much better suited in a lifelong learning framework, where not all classes are represented initially, but instead new training data arrives during the operation of the robot.
A Formalism and Environment for the Development of a Large Grammar of English

Natural language grammars with large coverage are typically the result of many person-years of effort, working with clumsy formalisms and sub-optimal software support for grammar development. This paper describes our approach to the task of writing a substantial grammar, as part of a collaboration to produce a general purpose morphological and syntactic analyser for English. The grammatical formalism we have developed for the task is a metagrammatical notation which is a more expressive and computationally tractable variant of Generalized Phrase Structure Grammar. We have also implemented a software system which provides a highly integrated and very powerful set of tools for developing and managing a large grammar based on this notation. The system provides a grammarian with an environment which we have found to be essential for rapid but successful production of a substantial grammar.
VLT-VIMOS Integral Field Spectroscopy of Luminous and Ultraluminous Infrared Galaxies I. The Sample and First Results

Context. (Ultra)Luminous Infrared Galaxies [(U)LIRGs] are much more numerous at cosmological distances than locally, and are likely the precursors of elliptical galaxies. Studies of the physical structure and kinematics of representative samples of these galaxies at low redshift are needed in order to understand the interrelated physical processes involved. Integral field spectroscopy (IFS) offers the possibility of performing such a detailed analysis. Aims. Our goal is to carry out IFS of 42 southern systems which are part of a representative sample of about 70 low redshift (z ≤ 0.26) (U)LIRGs, covering different morphologies from spirals to mergers over the entire infrared luminosity range (LIR = 10 11 −10 12.6 L� ). Methods. The present study is based on optical IFS obtained with the VIMOS instrument on the VLT. Results. The first results of the survey are presented here with the study of two galaxies representative of the two major morphological types observed in (U)LIRGs, interacting pairs and morphologically- regular, weakly-interacting spirals, respectively. We have found that IRAS F06076−2139 consists of two low-intermediate mass (0.15 and 0.4 m∗) galaxies with relative velocities of ∼550 km s −1 and, therefore, it is unlikely that they will ever merge. The VIMOS IFS has also discovered the presence of a rapidly expanding and rotating ring of gas in the southern galaxy (Gs). This ring is interpreted as the result of a nearly central head-on passage of an intruder about 140 million years ago. The mass, location and relative velocity of the northern galaxy (Gn) rules out this galaxy as the intruder. IRASF 12115−4656 is a spiral for which we have found a mass of 1.2 m∗. The ionized gas shows all the kinematic characteristics of a massive, fast rotating disk. The neutral gas traced by the NaI doublet shows distinct features not completely compatible with pure rotation. The neutral and ionized gas components are spatially and kinematically decoupled. The analysis presented here illustrates the full potential of IFS in two important aspects: (i) the study of the kinematics and ionization structure of complex interacting/merging systems, and (ii) the study of the kinematics of the different gas phases, neutral (cool) and ionized (warm), traced by the NaD and Hα lines, respectively.
Correlating power consumption and network traffic for improving data centers resiliency

The deployment of business critical applications and information infrastructures are moving to the cloud. This means they are hosted in large scale data centers with other business applications and infrastructures with less (or none) mission critical constraints. This mixed and complex environment makes very challenging the process of monitoring critical applications and handling (detecting and recovering) possible failures of servers' data center that could affect responsiveness and/or reliability of mission critical applications. Monitoring mechanisms used in data center are usually intrusive in the sense that they need to install agents on each single server. This has considerable drawbacks: huge usage of human resources to install and patch the system and interference with the critical application because agents share application resources. In order to detect (and possibly predict) failures in data centers the paper does a first attempt in showing the correlation between network traffic and servers' power consumption. This is an important step in deriving non-intrusive monitoring systems, as both network traffic and power consumption can be captured without installing any software at the servers. This will improve in its turn the overall resiliency of the data center and its self-managing capacity.
Research and Improvement of AODV Protocol in Ad Hoc Network

The running process of AODV protocol mainly includes two processes, routing find and routing maintenance. AODV protocol uses the method of hop-by-hop routing to transmit packets. Wormhole attack is a special attack method aimed at Ad hoc network. Based on the analysis of AODV protocol and the attack conditions of wormhole attack, the process and algorithm aimed at wormhole attack are researched. At last, a method to improve AODV protocol is put forward.
Fluid needs for training and competition in athletics

Abstract The diverse nature of the athletic events, together with the varied training programmes and individuality of athletes taking part, inevitably means that fluid needs are highly variable – between athletes, perhaps between training and competition, and with differing environmental conditions and degree of training and heat acclimatization. There are limited data from athletics on all aspects of fluid balance, but wherever possible we have focused on this information to draw conclusions. When appropriate, euhydration will best be ensured by consuming 6 – 8 ml · kg body mass−1 of a sodium-containing fluid, or sodium-free fluid together with food, about 2 h before exercise. The individual sweat responses are so variable that athletes should assess their own individual sweat losses to determine if these are likely to be a cause for concern. The volume of drink that is consumed should never be so much that an athlete gains mass over an event, unless perhaps there is evidence that they began in a hypohydrated state. This may be a particular concern in the field events and multi-event disciplines when competition can be spread over a number of hours and when there are significant rest periods between activities.
Clustering analysis for large scale data sets

The real-world big data can be clustered along desired dimensions but it is limited in its applicability to large-scale problems due to its high computational complexity, user's desire, number of dimensions etc. Recently, many approaches have been proposed to accelerate the large scale data clustering. Unfortunately, these methods usually sacrifice quite a lot of information of the original data; incompetent to produce multiple clustering etc and don't consider the geometrical, psychological and physiological interpretation of naturally occurring data whose representation may be parts-based in human brain. In this paper seven clustering algorithms are analyzed which is based on large scale data of seven different environment and dimensions to find out a universal framework for the representation and processing of knowledge. Our empirical study shows the encouraging results of the LSC-K algorithm in comparisons to state-of-the-art algorithms.
Institutional Environments and Diversification Strategy of Emerging Market Firms

This Paper develops an institutionalist analytical framework to explain diversification strategies of East Asian firms, using the concepts of market uncertainty and industry maturity. The highly diversified structure of East Asian firms is primarily due to demand shift caused by rapid income growth, the appropriation and adaptation of mature technology imported from developed markets, and government intervention to lower the level of market uncertainty. Diversified business groups in emerging markets are seen as a response to the lack of market institutions. Their dynamic competitive advantage is derived from transaction costs savings from the formation of a quasi-internal capital market, efficient internal deployment of resources and reputation linkage effect. However, intensifying global competition and the need for technological innovation will force them to adopt a more focused strategy, partly as a consequence of the financial crisis and corporate restructuring.
A Monte-Carlo approach for the endgame of Ms. Pac-Man

Ms. Pac-Man is a challenging video game which provides an interesting platform for artificial intelligence and computational intelligence research. This paper introduces the novel concept of path testing and reports an effective Monte-Carlo approach to develop an endgame module of an intelligent agent that plays the game. Our experimental results show that the proposed method often helps Ms. Pac-Man to eat pills effectively in the endgame. It enables the agent to advance to higher stages and earn more scores. Our agent with the endgame module has achieved a 20% increase in average score over the same agent without the module.
Harnessing the Intelligence of Physarum Polycephalum for Unconventional Computing-Aided Musical Composition

This paper introduces Die Lebensfreude, a pioneering piece of music composed with the aid of an amoeba-like plasmodial slime mould called Physarum polycephalum. The composition is for an ensemble of five instruments (flute, clarinet, violin, cello and piano) and six channels of electronically synthesises sounds. The instrumental part and the synthesised sounds are musifications and sonifications, respectively, of a multi-agent based simulation of Physarum foraging for food. The slime mould, its simulation, and musifications and sonification methods are introduced in this paper. The rational for using Physarum in music is also discussed.
Towards Understanding Text with a Very Large Vocabulary

In order to meet the information processing demands of the next decade, natural language systems must have the capability of processing very large amounts of text, commonly called "messages", from highly diverse sources written in any of a few dozen languages. One of the key issues in building systems with this scale of competence is handling large numbers of different words and word senses. Natural language understanding systems today are typically limited to vocabularies of less than 10,000 words; tomorrow's systems will need vocabularies at least 5 times that to effectively handle the volume and diversity of messages needing to be processed.
LockDoll: Providing Ambient Feedback of Smartphone Usage within Social Interaction

In group activities, members often could hurt others' feelings by using smartphones more than expected due to the lack of awareness on smartphone usage. Other members can remind them of their usage by asking not to use their smartphones, but our survey results show that explicit asking could bring about negative experiences in a group. To overcome this, we designed LockDoll, a tangible artifact that provides ambient feedback of smartphone usage to help members manage their usage. By following an ambient design pattern of symbolic sculptural display, we expect LockDoll to softly and yet, effectively intervene with smartphone usage in the group context. In this paper, we present the preliminary working prototype and discuss further design issues through a pilot study.
Prognostic Classification of Early Ovarian Cancer Based on very Low Dimensionality Adaptive Texture Feature Vectors from Cell Nuclei from Monolayers and Histological Sections

In order to study the prognostic value of quantifying the chromatin structure of cell nuclei from patients with early ovarian cancer, low dimensionality adaptive fractal and Gray Level Cooccurrence Matrix texture feature vectors were extracted from nuclei images of monolayers and histological sections. Each light microscopy nucleus image was divided into a peripheral and a central part, representing 30% and 70% of the total area of the nucleus, respectively. Textural features were then extracted from the peripheral and central parts of the nuclei images. The adaptive feature extraction was based on Class Difference Matrices and Class Distance Matrices. These matrices were useful to illustrate the difference in chromatin texture between the good and bad prognosis classes of ovarian samples. Class Difference and Distance Matrices also clearly illustrated the difference in texture between the peripheral and central parts of cell nuclei. Both when working with nuclei images from monolayers and from histological sections it seems useful to extract separate features from the peripheral and central parts of the nuclei images.
Plasma pharmacokinetics and cerebrospinal fluid concentrations of idarubicin and idarubicinol in pediatric leukemia patients: a Childrens Cancer Study Group report.

Idarubicin (4-demethoxydaunomycin) is an anthracycline analogue with striking in vitro and in vivo activity against murine leukemias. Based on activity in adults with acute lymphoblastic leukemia, the Childrens Cancer Study Group initiated studies to evaluate idarubicin in children with leukemia in second or subsequent relapses. As part of those studies, we have characterized the plasma pharmacokinetics of idarubicin and the major circulating metabolite idarubicinol in 21 patients. Idarubicin plasma elimination was described by a three-compartment open model following i.v. infusion (10-15 mg/m2) on a schedule of weekly for 3 weeks and on a schedule of daily for 3 days every 3 weeks (total dose, 30-45 mg/m2). There was substantial variability in idarubicin elimination among patients, but no indication of dose-dependent or of schedule-dependent changes in pharmacokinetic parameters. The mean terminal half-life, total body clearance, and steady state volume of distribution were 17.6 h, 679 ml/min/m2, and 562 l/m2, respectively. Idarubicinol elimination was prolonged compared to that of the parent drug with a terminal half-life of 56.8 h. This metabolite clearly accumulated in plasma during the 3 days of treatment on the schedule of daily for 3 days. Urinary recoveries (48 h) of idarubicin and idarubicinol after a single dose of idarubicin were 2.4 and 10.1%, respectively. Idarubicin was detected in 2 of 21 cerebrospinal fluid samples obtained 18-30 h after administration. In marked contrast, idarubicinol was detected in 20 of those 21 samples. Concentrations in the 20 samples varied from 0.22-1.05 ng/ml with a mean value of 0.51 ng/ml.
Bilateral kernel parameter optimization by risk minimization

A statistical approach is presented for determining the optimal parameters of a bilateral filter for suppressing noise in an image. A closed-form solution to obtaining Stein's unbiased estimate of the bilateral filter-based image estimation risk is derived. The values of the parameters are then determined by minimization of the estimated risk, which is equivalent to maximization of the estimated signal to noise ratio. Experimental results show that the method provides estimates of noise intensity that are close to the actual, and the performance of the bilateral filter, in terms of providing good tradeoff between noise suppression and edge preservation, is improved significantly by using the optimal parameters estimate.
A model to evaluate C-RAN architectures deployment impact in urban scenarios

This work presents a study concerning the impact of Cloud Radio Access Network and virtualisation techniques in an operator's network, namely in terms of the necessary number of storage and processing nodes, and the links in between, taking increasingly network constraints into account (e.g., latency) as well as deployment ones (e.g., service area, deployment strategy, and expected proliferation of Remote Radio Heads). A model was implemented, which takes the positioning of cell sites available in an urban scenario as input, and computes the number and a possible placement of processing nodes for different constraints - an estimate of the number of required blade servers in each node is also computed. Results show that between 2 and 62 Baseband Processing Unit pools are required to cover the whole scenario of Lisbon, depending on the fronthaul delay restriction. An inverse proportionality relation between the number of blade servers and their corresponding capacity has been ascertained.
On Expressive Number Restrictions in Description Logics

We consider expressive Description Logics (ALCN) allowing for number restrictions on complex roles built with combinations of role construc-tors. In particular, we are mainly interested in Logics (called ALC N) allowing for the same kind of complex roles both in number and in value restrictions, which represent very expressive description languages and can be shown very useful for applications. We investigate the computational properties of various ALCN extensions and slightly improve the (un)decidability results following from the study published by Baader and Sattler in 1999. In particular, we will show by reduction of a domino problem that ALC N(+ ; t) and ALC N(+ ; u) are undecidable.