Miracle-dz/UniFAR-FTUs huggingface dataset:
Social Networking in Education

Social networking has become one of the most popular communication tools to have evolved over the past decade, making it a powerful new information sharing resource in society. To date realising the potential of Social Networking Sites (SNSs) beyond their leisure uses has been severely restricted in a number of areas. This paper focuses on the application of SNSs in a learning environment and the impact this could have on academic practices. While undoubtedly, due to the very casual nature of social networking, there are serious concerns over how it could be integrated in a learning environment; the potential positive outcomes are many and varied. As a communication tool, its effectiveness is already manifesting in the millions who use these networks to communicate on a daily basis. So it is conceivable that educators should be able to create a learnscape - an environment for formal and informal learning - that adheres to educational guidelines, but also harnesses the social support system of these on-line communities. This paper examines the risks involved in the creation of this new learning ecology, and explores the challenges faced by both technology experts and teachers in delivering a truly innovative and effective new approach to education.
Growing Up Girl: Psycho-Social Explorations of Gender and Class

Set against a backdrop of deindustrialisation, rising male unemployment and the feminisation and casualisation of the labour market, Growing Up Girl graphically explores the complexities of gender and class during a period of massive social change. It tells the story of today's 'I can have everything' girls who face unprecedented shifts in the organisation of family, education and work, and yet who continue to struggle with the not always visible but always palpable pressures of wealth, poverty, class and ethnicity. Drawing on data spanning nearly twenty years, the authors of this ground-breaking study provide a sobering antidote to commonplace platitudes about 'girl power' and a feminine future. They reveal the hidden price of middle class girls' apparently effortless achievements - obsessive hard work, guilt and devastating feelings of inadequacy - and they trace how the labour market cruelly sets material limits on the disappointed hopes and ambitions of working class girls. Vividly illustrating their arguments with quotations from the research participants, they show how young women's practices of self-invention are regulated both by unconscious processes and real social and economic constraints. Their insistent conclusion is that class is far from dead. Indeed, it is centrally important to our understanding of what it is to be a young woman in today's complex and challenging world, This important and grippingly written book is essential reading for students and scholars alike in sociology, cultural studies, women's studies, education and psychology. It will also be of interest to anyone else struggling to make sense of the position of women in society today.
Effect of Network Traffic on IPS Performance

The importance of network security has grown tremendously and intrusion prevention/detection systems (IPS/IDS) have been widely developed to insure the security of network against suspicious threat. Computer network intrusion detection and prevention system consist of collecting traffic data, analyzing them based on detection rules and generate alerts or dropping them if necessary. However IPS has problems such as accuracy signature, the traffic volume, topology design, monitoring sensors. In this paper, we practically examine the traffic effect on performance of IPS. We first examine the detection of DOS attack on a web server by IPS and then we generate network traffic to see how the behavior of IPS has influenced on detection of DOS attack.
A multiscale approximation scheme for explicit model predictive control with stability, feasibility, and performance guarantees

In this paper, an algorithm is introduced based on classical wavelet multiresolution analysis that returns a low complexity explicit model predictive control law built on a hierarchy of second order interpolating wavelets. It is proven that the resulting interpolation is everywhere feasible. Further, tests to confirm stability and to compute a bound on the performance loss are introduced. Since the controller approximation is built on a gridded hierarchy, the evaluation of the control law in real-time systems is naturally fast and runs in a bounded logarithmic time. A simple example is provided which both illustrates the approach and motivates further research in this direction.
Securing Shareable Life-logs

Sharing life-log information in a social community has many advantages, both for the user and society. But sharing any type of personal information is a threat to privacy. In particular, life-log information requires higher security considerations since it may contains very sensitive information about the user such as biological information, location, communication logs, etc. In this paper, first we discuss the risks of sharing lifelog information in a social community. Then we will introduce a sharing model, which can eliminate the sharing capability of a life-log information object, based on the expiration time. Furthermore, general security approaches which might decrease security related risks for life-log systems will be described.
A self-consistent junction temperature estimation methodology for nanometer scale ICs with implications for performance and thermal management

Accurate estimation of the silicon junction (or die) temperature in high-end microprocessors is crucial for various performance analyses and also for chip-level thermal management. This work introduces for the first time, the notion of self-consistency in estimating the die temperature for sub-100 nm CMOS technologies by taking into account various electrothermal couplings between supply voltage, operating frequency, power dissipation and die temperature. It also comprehends chip-level reliability constraints and the impact of employing various packaging and cooling solutions in an integrated manner. The self-consistent solutions of die temperature are shown to have significant implications for evaluating various power-performance-reliability-cooling cost tradeoffs and can be used to optimize the performance of nanoscale ICs.
Considering both Environmental Impact and Economic Costs in the Optimization of the Tactical Planning for the Fast Moving Consumer Goods Industry

In this paper, the environmental impact of operating a supply chain of a Fast Moving Consumer Goods (FMCG) company over a one year horizon is considered. The environmental impact is evaluated using the Ecoindicator 99. In the optimization of the tactical planning decisions both this environmental objective and the total costs are considered using the e-constraint method for identifying a set of Pareto-optimal solutions. For a case study containing 10 Stock-Keeping Units (SKUs), which was optimized with a 1% optimality tolerance, the environmental impact could be reduced by 2.9% without increasing the total costs. A further reduction of environmental impact of up to 6.3% was possible at an increase in total costs of 5.2%. An SKU decomposition algorithm was applied to optimize a larger case study containing 100 SKUs.
The development of virtual reality technologies for people on the autism spectrum

The role of virtual reality technologies to help people with autism has been well documented and is an area of research that continues to develop. While the evidence base is somewhat limited, there are many studies that have started to explore the potential of virtual reality technologies for people with autism. Work conducted by Strickland et al. (1996), Murray (1997), Charitos et al. (2000), Parsons and Mitchell (2002), Parsons et al. (2006, 2007), Cobb (2007), Fabri and Moore (2005), and Fabri et al. (2004) have all added to this positive picture of virtual reality technologies to support people on the autism spectrum, specifically in terms of social interaction and social skills development. This chapter uncovers the evidence base and work of others in relation to virtual reality technologies used by people with autism. This chapter concludes with a view as to what future work might pursue in this field.
Tilt testing in neurocardiogenic syncope: isosorbide versus isoproterenol.

OBJECTIVE To compare the diagnostic value of pharmacological stimulation with sublingual isosorbide dinitrate and intravenous isoproterenol during tilt testing in patients with neurocardiogenic syncope and with a negative tilt test without pharmacological provocation. METHODS AND RESULTS One hundred and twenty patients with a history of neurocardiogenic syncope (aged 15 to 77 years) and 50 healthy volunteers (aged 25 to 70 years) were prospectively submitted to head-up tilt (HUT). Those who did not develop syncope or presyncope during passive HUT for 30 minutes underwent repeated HUT with isoproterenol infusion at 4 microg/min (ISOP HUT), for 10 minutes, and, subsequently, were tilted after sublingual administration of 5 mg of isosorbide dinitrate (ISDN HUT) for another 12 minutes. ISDN HUT was always performed after ISOP HUT. Sensitivity and specificity of passive HUT were 41% (95% C.I. 32.9% to 51.0%) and 100%, respectively. Sensitivity of ISOP HUT was 51.4% (95% C.I. 39.2% to 63.6%) and specificity 70% (95% C.I. 55.4% to 82.1%) and for ISDN HUT were 70% (95% C.I. 57.9% to 80.4%) and 88% (95% C.I. 75.7% to 95.5%), respectively. The accuracy of ISDN HUT was significantly higher than the accuracy of ISOP HUT 77.5% (95% C.I. 68.9% to 84.6%). There were fewer side effects during ISDN HUT. CONCLUSION Sublingual isosorbide dinitrate is at least as sensitive as isoproterenol to assess patients with suspected neurocardiogenic syncope and with a negative tilt test without provocation. The low rate of side effects and the higher accuracy of ISDN HUT, along with the simplicity of this challenge compared to ISOP HUT, suggest that sublingual isosorbide dinitrate should be preferred as a provocative agent to evaluate neurocardiogenic syncope after a negative passive tilt test.
Spinal Cord Injury Therapy through Active Learning

Therapy employing epidural electrostimulation holds great potential for improving therapy for patients with spinal cord injury (SCI) (Harkema et al., 2011). Further promising results from combined therapies using electrostimulation have also been recently obtained (e.g., van den Brand et al., 2012). The devices being developed to deliver the stimulation are highly flexible, capable of delivering any individual stimulus among a combinatorially large set of stimuli (Gad et al., 2013). While this extreme flexibility is very useful for ensuring that the device can deliver an appropriate stimulus, the challenge of choosing good stimuli is quite substantial, even for expert human experimenters. To develop a fully implantable, autonomous device which can provide useful therapy, it is necessary to design an algorithmic method for choosing the stimulus parameters. Such a method can be used in a clinical setting, by caregivers who are not experts in the neurostimulator's use, and to allow the system to adapt autonomously between visits to the clinic. To create such an algorithm, this dissertation pursues the general class of active learning algorithms that includes Gaussian Process Upper Confidence Bound (GP-UCB, Srinivas et al., 2010), developing the Gaussian Process Batch Upper Confidence Bound (GP-BUCB, Desautels et al., 2012) and Gaussian Process Adaptive Upper Confidence Bound (GP-AUCB) algorithms. This dissertation develops new theoretical bounds for the performance of these and similar algorithms, empirically assesses these algorithms against a number of competitors in simulation, and applies a variant of the GP-BUCB algorithm in closed-loop to control SCI therapy via epidural electrostimulation in four live rats. The algorithm was tasked with maximizing the amplitude of evoked potentials in the rats' left tibialis anterior muscle. These experiments show that the algorithm is capable of directing these experiments sensibly, finding effective stimuli in all four animals. Further, in direct competition with an expert human experimenter, the algorithm produced superior performance in terms of average reward and comparable or superior performance in terms of maximum reward. These results indicate that variants of GP-BUCB may be suitable for autonomously directing SCI therapy.
Sozio-ökonomische Schätzungen Olympischer Medaillengewinne: Analyse, Prognose und Benchmarkmöglichkeiten

Die sozio-okonomischen Einflussfaktoren der Olympischen Medaillengewinne von 1960 bis 2004 werden mithilfe einer Tobit-Analyse geschatzt. Fruhere Arbeiten werden bestatigt, wonach das Bruttoinlandproduktes pro Kopf, die Bevolkerungsgrose, der Heimvorteil, das Vorliegen eines sozialistischen Systems sowie der Erfolg bei vorangegangenen Olympischen Spielen einen positiven Einfluss zeigen. Beim Test von weiteren Variablen ergab sich, dass auch die zukunftige Austragung Olympischer Spiele in einem Land und ein gemasigtes Klima einen signifikanten Einfluss haben. Andere Variablen wie das age dependency ratio, die Verbreitung von Massenmedien, die Existenz eines extrem trockenen Klimas, eine foderale Staatsstruktur sowie eine Proxy fur die nationale Innovationsfahigkeit erwiesen sich nicht als signifikant. Es wird gezeigt, dass derartige Schatzungen unter Parameterinstabilitat leiden konnen. Erst seit den Olympischen Spielen 1996 ist eine Parameterstabilisierung festzustellen. Out-of-sample Prognosen zeigen, dass der Erfolg wichtiger Teilnehmerlander durch das Modell unterschatzt wird. Dies offnet den Raum fur eine grundsatzlich andere Interpretationsmoglichkeit sozio-okonomischer Medaillenschatzungen: Sie liefern letztlich eine Berechnung, wie viele Medaillen eine Nation angesichts seiner Ressourcenausstattung bei weltdurchschnittlicher Effizienz seines Hochleistungssportsystems gewinnen sollte. Lander, die mehr (weniger) Medaillen gewinnen als prognostiziert, weisen ein uberdurchschnittlich (unterdurchschnittlich) effizientes Hochleistungssportsystem auf.
Integrating Novel Data Streams to Support Biosurveillance in Commercial Livestock Production Systems in Developed Countries: Challenges and Opportunities

Reducing the burden of emerging and endemic infectious diseases on commercial livestock production systems will require the development of innovative technology platforms that enable information from diverse animal health resources to be collected, analyzed, and communicated in near real-time. In this paper, we review recent initiatives to leverage data routinely observed by farmers, production managers, veterinary practitioners, diagnostic laboratories, regulatory officials, and slaughterhouse inspectors for disease surveillance purposes. The most commonly identified challenges were (1) the lack of standardized systems for recording essential data elements within and between surveillance data streams, (2) the additional time required to collect data elements that are not routinely recorded by participants, (3) the concern over the sharing and use of business sensitive information with regulatory authorities and other data analysts, (4) the difficulty in developing sustainable incentives to maintain long-term program participation, and (5) the limitations in current methods for analyzing and reporting animal health information in a manner that facilitates actionable response. With the significant recent advances in information science, there are many opportunities to develop more sophisticated systems that meet national disease surveillance objectives, while still providing participants with valuable tools and feedback to manage routine animal health concerns.
Ideal Free Distribution in Agents with Evolved Neural Architectures

We investigate the matching of agents to resources in a computational ecology configured to present heterogeneous resource patches to evolving, neurally controlled agents. We repeatedly find a nearly optimal, ideal free distribution (IFD) of agents to resources. Deviations from IFD are shown to be consistent with models of human foraging behaviors, and possibly driven by spatial constraints and maximum foraging rates. The lack of any model parameters addressing agent foraging or clustering behaviors and the biological verisimilitude of our agent control systems differentiates these results from simpler models and suggests the possibility of exploring the underlying mechanisms by which optimal foraging emerges.
Psychodynamic Diagnostic Manual

The Psychodynamic Diagnostic Manual (PDM; PDM Task Force, 2006) is the first psychological diagnostic classification system that considers the whole person in various stages of development. A task force of five major psychoanalytic organizations and leading researchers, under the guidance of Stanley I. Greenspan, Nancy McWilliams, and Robert Wallerstein, came together to develop the PDM. The resulting nosology goes from the deep structural foundation of personality to the surface symptoms that include the integration of behavioral, emotional, cognitive, and social functioning. Keywords: psychodynamic; psychoanalytic; personality; mental health; nosology; diagnostic; assessment
In Harm's Way: Essays in Honor of Joel Feinberg

Preface Contributors 1. Liberalism and group rights Allen Buchanan 2. The argument from liberty Shelly Kagan 3. Autonomy and preference formation Richard J. Arneson 4. Critical analysis and constructive interpretation David Lyons 5. Liberalism, free speech and justice for minorities David A. J. Richards 6. Liberalism and the new skeptics Thomas Morawetz 7. Tort liability and the limits of corrective justice Jules L. Coleman 8. Liberalism, retribution and criminality Jean Hampton 9. Responsibility for consequences John Martin Fischer and Mark Ravizza 10. Some ruminations on women, violence and the criminal law Jeffrie G. Murphy 11. Force, consent and the reasonable women Joan McGregor 12. Self-defense Robert F. Schopp 13. Letting patients die: legal and moral reflections Sanford H. Kadish 14. Fetal-maternal conflicts Holly M. Smith 15. Benign and malign morality Hyman Gross Publications by Joel Feinberg.
The Pro-inflammatory Effects of Glucocorticoids in the Brain

Glucocorticoids are a class of steroid hormones derived from cholesterol. Their actions are mediated by the glucocorticoid and mineralocorticoid receptors, members of the superfamily of nuclear receptors, which, once bound to their ligands, act as transcription factors that can directly modulate gene expression. Through protein–protein interactions with other transcription factors, they can also regulate the activity of many genes in a composite or tethering way. Rapid non-genomic signaling was also demonstrated since glucocorticoids can act through membrane receptors and activate signal transduction pathways, such as protein kinases cascades, to modulate other transcriptions factors and activate or repress various target genes. By all these different mechanisms, glucocorticoids regulate numerous important functions in a large variety of cells, not only in the peripheral organs but also in the central nervous system during development and adulthood. In general, glucocorticoids are considered anti-inflammatory and protective agents due to their ability to inhibit gene expression of pro-inflammatory mediators and other possible damaging molecules. Nonetheless, recent studies have uncovered situations in which these hormones can act as pro-inflammatory agents depending on the dose, chronicity of exposure, and the structure/organ analyzed. In this review, we will provide an overview of the conditions under which these phenomena occur, a discussion that will serve as a basis for exploring the mechanistic foundation of glucocorticoids pro-inflammatory gene regulation in the brain.
Power-source-aware backbone routing in wireless sensor networks

Due to the limited energy-source and mostly unattended nature of the wireless sensor networks, efficient use of energy has a critical importance on the lifetime of the applications accomplished by such networks. Although in most of the cases sensor nodes are battery-powered, there are application scenarios in which battery- and mains-powered nodes coexist. In this paper, we present an approach and algorithms based on this approach that increase the lifetime of wireless sensor networks in such heterogeneous deployment cases. In the proposed approach, a backbone, which is composed of mains-powered nodes, sink, and battery-powered nodes if required, is constructed to relay the data packets. Simulation results show that, the proposed approach is able to increase the network lifetime up to more than a factor of two, compared to the case in which battery- and mains-powered nodes are not distinguished.
Deep scattering transform applied to note onset detection and instrument recognition

Automatic Music Transcription (AMT) is one of the oldest and most well-studied problems in the field of music information retrieval. Within this challenging research field, onset detection and instrument recognition take important places in transcription systems, as they respectively help to determine exact onset times of notes and to recognize the corresponding instrument sources. The aim of this study is to explore the usefulness of multiscale scattering operators for these two tasks on plucked string instrument and piano music. After resuming the theoretical background and illustrating the key features of this sound representation method, we evaluate its performances comparatively to other classical sound representations. Using both MIDI-driven datasets with real instrument samples and real musical pieces, scattering is proved to outperform other sound representations for these AMT subtasks, putting forward its richer sound representation and invariance properties.
Analyzing the Low Power Wireless Links for Wireless Sensor Networks

There is now an increased understanding of the need for realistic link layer models in the wireless sensor networks. In this paper, we have used mathematical techniques from communication theory to model and analyze low power wireless links. Our work provides theoretical models for the link layer showing how Packet Reception Rate vary with Signal to Noise Ratio and distance for different modulation schemes and a comparison between MICA2 and TinyNode in terms of PRR.
L'importance de la note initiale et du type de changement dans la mesure de l'impact de la notation financière (rating) sur le marché actions

This article brings new lines of inquiry relating to the surplus informational announcements actually issued by the rating agencies for financial different players in the stock market. Thus, it offers a different and complementary perpspective to the previous studies focused primarily on a global analysis of events in a dual operating segmentation by type of downgrading by notes of the issuer prior to the announcement. The study is based on all the negative changes made by the three major agencies worldwide for 440 large european capitalization components major stock indexes national and sectoral over the period 1998-2006. Ultimately, 723 studies of events of 212 different companies were performed and the results and analysis have been validated by a dozen interviews. In addition to the affirmation of abnormal returns negative averages for the period [+1,-1] trading days around the date of the announcement, the research shows the slightest market anticipation for declines in perspective than for decreases notes. In addition, the class is more discriminating that the note itself and the lower the credit rating is less than A- the higher is the impact on the stock market. It is, with the last note of the investment grade BBB-, the two key thresholds of the rating scale. Finally, the magnitude of the reaction of creditwatch negative appears totally independent of the initial rating.