Miracle-dz/UniFAR-FTUs huggingface dataset:
Skip game: an autonomic approach for QoS and energy management in IEEE 802.15.4 WSN

In some Wireless Sensor Network applications the sensor nodes share the same sensing activity, which means that for a considerable number of applications, not all nodes are required to perform sensing tasks during the network lifetime. Sleep-scheduling approaches can be applied in this scenario, enabling that some nodes turn off their radios, saving energy and bandwidth, as long as there are enough nodes to ensure the required Quality of Service (QoS) of the network. This paper presents a new adaptive approach for QoS and energy management in IEEE 802.15. 4 networks, entitled Skip Game. This approach targets a trade-off between increasing the network lifetime and maintaining the QoS of the network, aiming a greater number of nodes to participate in the monitoring application. In order to evaluate the proposed approach, we performed some experiments using the OMNeT++ simulator tool under the MiXiM framework. The results show that the Skip Game outperforms both the traditional Gur Game and Gureen Game approaches in terms of QoS provision and network lifetime.
Improving on the Kalman Swarm Extracting Its Essential Characteristics

The Kalman Swarm (KSwarm) is a new approach to particle motion in PSO that reduces the number of iterations required to reach good solutions [1]. Unfortunately, it has much higher computational complexity than basic PSO. This paper addresses the runtime of KSwarm in a new algorithm called \Linear Kalman Swarm" (LinkSwarm) which has linear complexity and performs even better than KSwarm. Some possible reasons for the success of KSwarm are also explored.
On Propagation of Phenomena in Interdependent Networks

When multiple networks are interconnected because of mutual service interdependence, propagation of phenomena across the networks is likely to occur. Depending on the type of networks and phenomenon, the propagation may be a desired effect, such as the spread of information or consensus in a social network, or an unwanted one, such as the propagation of a virus or a cascade of failures in a communication or service network. In this paper, we propose a general analytic model that captures multiple types of dependency and of interaction among nodes of interdependent networks, that may cause the propagation of phenomena. The above model is used to evaluate the effects of different diffusion models in a wide range of network topologies, including different models of random graphs and real networks. We propose a new centrality metric and compare it to more traditional approaches to assess the impact of individual network nodes in the propagation. We propose guidelines to design networks in which the diffusion is either a desired phenomenon or an unwanted one, and consequently must be fostered or prevented, respectively. We performed extensive simulations to extend our study to large networks and to show the benefits of the proposed design solutions.
Content publishing framework for interactive paper documents

Paper persists as an important medium for documents and this has motivated the development of new technologies for interactive paper that enable actions on paper to be linked to digital actions. A major issue that remains is how to integrate these technologies into the document life cycle and, specifically, how to facilitate the authoring of links between printed documents and digital documents and services. We describe how we have extended a general web publishing framework to support the production of interactive paper documents, thereby integrating paper as a new web channel in a platform for multi-channel access.
Fluoxetine during Development Reverses the Effects of Prenatal Stress on Depressive-Like Behavior and Hippocampal Neurogenesis in Adolescence

Depression during pregnancy and the postpartum period is a growing health problem, which affects up to 20% of women. Currently, selective serotonin reuptake inhibitor (SSRIs) medications are commonly used for treatment of maternal depression. Unfortunately, there is very little research on the long-term effect of maternal depression and perinatal SSRI exposure on offspring development. Therefore, the aim of this study was to determine the role of exposure to fluoxetine during development on affective-like behaviors and hippocampal neurogenesis in adolescent offspring in a rodent model of maternal depression. To do this, gestationally stressed and non-stressed Sprague-Dawley rat dams were treated with either fluoxetine (5 mg/kg/day) or vehicle beginning on postnatal day 1 (P1). Adolescent male and female offspring were divided into 4 groups: 1) prenatal stress+fluoxetine exposure, 2) prenatal stress+vehicle, 3) fluoxetine exposure alone, and 4) vehicle alone. Adolescent offspring were assessed for anxiety-like behavior using the Open Field Test and depressive-like behavior using the Forced Swim Test. Brains were analyzed for endogenous markers of hippocampal neurogenesis via immunohistochemistry. Results demonstrate that maternal fluoxetine exposure reverses the reduction in immobility evident in prenatally stressed adolescent offspring. In addition, maternal fluoxetine exposure reverses the decrease in hippocampal cell proliferation and neurogenesis in maternally stressed adolescent offspring. This research provides important evidence on the long-term effect of fluoxetine exposure during development in a model of maternal adversity.
A novel real-time edge-preserving smoothing filter An approach to color based prefiltering for efficient real-time image segmentation

The segmentation of textured and noisy areas in images is a very challenging task due to the large variety of objects and materials in natural environments, which cannot be solved by a single similarity measure. In this paper, we address this problem by proposing a novel edge-preserving texture filter, which smudges the color values inside uniformly textured areas, thus making the processed image more workable for color-based image segmentation. Due to the highly parallel structure of the method, the implementation on a GPU runs in real-time, allowing us to process standard images within tens of milliseconds. By preprocessing images with this novel filter before applying a recent real-time color-based image segmentation method, we obtain significant improvements in performance for images from the Berkeley dataset, outperforming an alternative version using a standard bilateral filter for preprocessing. We further show that our combined approach leads to better segmentations in terms of a standard performance measure than graph-based and mean-shift segmentation for the Berkeley image dataset.
A New Approach to Model Verification, Falsification and Selection

This paper shows that a qualitative analysis, i.e. , an assessment of the consistency of a hypothesized sign pattern for structural arrays with the sign pattern of the estimated reduced form, can always provide decisive insight into a model’s validity both in general and compared to other models. Qualitative analysis can show that it is impossible for some models to have generated the data used to estimate the reduced form, even though standard specification tests might show the model to be adequate. A partially specified structural hypothesis can be falsified by estimating as few as one reduced form equation. Zero restrictions in the structure can themselves be falsified. It is further shown how the information content of the hypothesized structural sign patterns can be measured using a commonly applied concept of statistical entropy. The lower the hypothesized structural sign pattern’s entropy, the more a priori information it proposes about the sign pattern of the estimated reduced form. As an hypothesized structural sign pattern has a lower entropy, it is more subject to type 1 error and less subject to type 2 error. Three cases illustrate the approach taken here.
Carnosine Is Neuroprotective Against Permanent Focal Cerebral Ischemia in Mice

Background and Purpose— Carnosine is a naturally occurring dipeptide with multiple neuroprotective properties. In addition, it is well tolerated in high doses with minimal side effects. The purposes of this study were to determine whether carnosine is neuroprotective in permanent focal cerebral ischemia and to determine potential mechanisms of neuroprotection. Methods— We investigated the efficacy of carnosine in a mouse model of permanent focal cerebral ischemia. The effects of carnosine were investigated with respect to neuronal damage and infarct formation, endogenous antioxidant status, and matrix metalloproteinase activity. Results— Carnosine significantly decreased infarct size and neuronal damage when administered at time points both before and after the induction of ischemia. Carnosine also decreased reactive oxygen species levels in the ischemic brain, preserved normal glutathione levels, and decreased matrix metalloproteinase protein levels and activity. Conclusions— Carnosine is neuroprotective in focal cerebral ischemia and appears to influence deleterious pathological processes that are activated after the onset of ischemia.
Les effets de la multiplicité des normes et des référentiels de bonnes pratiques : le cas de la Direction des Systèmes d’Information

Ce travail de recherche se donne pour ambition d'explorer les effets de la multiplicite des normes et des referentiels de bonnes pratiques en systemes d'information au niveau de la direction des systemes d'information. L'enjeu principal de cette these est de mettre en evidence une question qui n'a pas ete traitee auparavant dans la recherche academique. Les carences de litterature sur le sujet ainsi que le manque de lisibilite revele dans la litterature praticienne soutiennent la singularite et le bien-fonde de notre question.Notre strategie de recherche a ete elaboree dans le but de contribuer a la connaissance d'un objet meconnu dans la recherche academique. La question de recherche a ete mise a l'epreuve de la realite professionnelle a l'aide d'un dispositif empirique qui s'articule autour de trois elements. Tout d'abord, trois entretiens exploratoires nous ont permis d'affiner notre question de recherche et d'affirmer son interet vis-a-vis de la communaute professionnelle. Ensuite, dix entretiens semi-directifs aupres de cinq entreprises francaises ont eu pour but d'approfondir la comprehension des effets de la multiplicite des normes et des referentiels pour la direction systemes d'information. Enfin, une etude de cas unique basee sur deux vagues d'entretiens approfondis avec le DSI d'une societe francaise cotee a Wall Street, a eu pour ambition de prolonger la reflexion en se focalisant sur un aspect particulier de notre objet de recherche.Ces elements ont ete complementaires et pertinents pour apporter un eclairage sur la multiplicite des normes et des referentiels en systemes d'information. S'agissant d'une thematique non spontanement maitrisee par les repondants, nous avons suggere une mise en perspective de nos resultats a travers des cles de comprehension theoriques. En tenant compte de la nature exploratoire de notre recherche, les resultats de cette these constituent principalement des propositions plausibles et acceptables dont le but de suggerer des pistes de reflexion pour des projets de recherche ulterieurs.
A N A PPLICATION OF S YMBOLIC L EARNING TO I NTRUSION D ETECTION : P RELIMINARY R ESULTS FROM THE LUS M ETHODOLOGY

This paper describes briefly a method for applying AQ symbolic learning to problems of computer user modeling and intrusion detection. The method, called LUS (Learning User Signatures), learns models of users’ interaction in the form of sets of rules in attributional calculus, and signals a possible intrusion when a user interaction with a computer violates the model. An important characteristic of LUS is that the generated user signatures are easy to interpret and understand. We describe briefly the LUS method, the machine learning and inference tools developed to support it, and selected initial experimental results from its application to real-world data.
Novel Therapeutic Approaches for the Treatment of Depression and Cognitive Deficits in a Rodent Model of Gulf War Veterans' Illness

Abstract : Approximately 1/3rd of the returning veterans from the 1991 Persian Gulf War exhibit chronic multi-symptom illnesses that includes neurologic morbidities such as depression, anxiety and cognitive impairments. Amongst a host of causative factors, exposure to low-levels of the nerve gas Sarin is strongly implicated for expression of Gulf War Illness (GWI). Nerve agents similar to pesticides are organophosphate (OP) compounds. Here, we used various exposures (repeated low-dose to single high-dose) to OP diisopropyl fluorophosphate (DFP) over a 1 to 10-day period to approximate levels of Sarin exposure during the First Gulf War. We then tested the rats at 3-months post DFP exposure to reflect the current status of GW veterans. Using a battery of rodent behavioral assays, we observed the presence of symptoms of chronic depression, anxiety, and memory problems as characterized by increased immobility in the Forced Swim Test, anhedonia in the Sucrose Preference Test, anxiety in the Elevated Plus Maze, and spatial and recognition memory impairments in the Object Location/ Recognition Test. These rats also demonstrated neuronal damage in hippocampus, piriform cortex, amygdala, and thalamus. This animal model will help decipher molecular mechanisms for the expression of GWI neurological symptoms and screen effective treatment of GWIs.
Maximum power tracking for PV generating system using novel optimized fractional order open circuit voltage- FOINC method

This Harnessing energy from the abundant and free sunlight is now a days a hot-topic among the research community. The availability of cheap solar modules has made it possible to harvest solar energy with better efficiency. The nature of solar modules is nonlinear and therefore the process of impedance matching is essential. The proper impedance matching ensures extraction of the maximum power from solar PV module. Several algorithms are reported in literatures that are used to operate the DC-DC converter around MPP. Maximum power point tracking (MPPT) algorithm acting an significant part in photovoltaic (PV) power generating system because it exploit the output power from a PV generating set for a various climatically conditions. This paper suggested a new MPPT work for PV energy gathering system by using the intelligent novel fractional order open circuit voltage (FOCV) connected fractional order incremental conductance (FOINC) method. In the proposed technique deals with intelligent about measurement of voltage and current. In this paper MATLAB simulation process shows with MPPT controller, a zero voltage and zero current dc to dc buck-boost Luo converter feeding a battery load is achieved. The results demonstrate that the suggested FOCV-FOINC MPPT in the PV generating system is valid.
Linear Network Coding Construction for Multi-Source Multicast Network

The multi-source network coding problem, in which multiple multicast sessions with independent data share a network, is an open challenge. This paper proposes an approach to implement multi-source multicast sessions with linear network coding. We divide the original network into several sub-graphs, and each sub-graph contains a source node and its corresponding sink nodes. Links belonging to different sub-graphs do not overlap each other. In order to make the whole network throughput as large as possible, we transform partitioning sub-graphs into a combinatorial optimization problem and use genetic algorithm to solve it. Moreover, we give a pretreatment to narrow the search space. Then, it can be easy to construct coding scheme for each sub-graph. Furthermore, we prove that with the proposed approach, the throughput of the whole network is not less than the one with routing. Simulation results show that the proposed approach is feasible.
Conscientious withdrawal from religious education in Scotland: anachronism or necessary right?

Abstract This paper considers the right of parental withdrawal from the statutory subject Religious Education (RE) in Scottish primary and secondary schools. The background, history and current legislative situation relating to this right are considered, as well as current discussions and debates about this issue. The intentions are to establish how often and for what reasons parents opt to withdraw their children, as well as to investigate how schools and local authorities respond to requests for withdrawal. This research is informed by a survey of primary and secondary schools within two local authorities in particular. It emerges that there are relatively few cases of withdrawal. A majority of participating schools argued for the abolition of this right.
Dust emission size distribution impact on aerosol budget and radiative forcing over the Mediterranean region: a regional climate model approach

Abstract. The present study investigates the dust emission and load over the Mediterranean basin using the coupled chemistry–aerosol–regional climate model RegCM-4. The first step of this work focuses on dust particle emission size distribution modeling. We compare a parameterization in which the emission is based on the individual kinetic energy of the aggregates striking the surface to a recent parameterization based on an analogy with the fragmentation of brittle materials. The main difference between the two dust schemes concerns the mass proportion of fine aerosol that is reduced in the case of the new dust parameterization, with consequences for optical properties. At the episodic scale, comparisons between RegCM-4 simulations, satellite and ground-based data show a clear improvement using the new dust distribution in terms of aerosol optical depth (AOD) values and geographic gradients. These results are confirmed at the seasonal scale for the investigated year 2008. This change of dust distribution has sensitive impacts on the simulated regional dust budget, notably dry dust deposition and the regional direct aerosol radiative forcing over the Mediterranean basin. In particular, we find that the new size distribution produces a higher dust deposition flux, and smaller top of atmosphere (TOA) dust radiative cooling. A multi-annual simulation is finally carried out using the new dust distribution over the period 2000–2009. The average SW radiative forcing over the Mediterranean Sea reaches −13.6 W m−2 at the surface, and −5.5 W m−2 at TOA. The LW radiative forcing is positive over the basin: 1.7 W m−2 on average over the Mediterranean Sea at the surface, and 0.6 W m−2 at TOA.
Unsupervised Representation Learning of Dynamic Retinal Image Changes by Predicting the Follow-up Image

Longitudinal imaging allows to capture both, the static anatomical structures and the dynamic changes of the morphology due to aging or disease progression. However, common supervised or unsupervised methods for medical imaging do not consider dynamic aspects and process longitudinal data as individual data points. For natural images, algorithms already exist that learn a representation from videos [Walker et al., 2016]. In retinal imaging, however, the temporal sampling resulting from follow-up to disease progression is much lower than in videos. Predictions are therefore more ambiguous and prone to noise
Knowledge discovery in a dairy cattle database: Automated knowledge acquisition

Knowledge Discovery in Databases (also known as Data Mining) is a powerful paradigm for automated knowledge acquisition. In this paper, we apply two data mining (DM) techniques, C5 and RULEX, on the Australian Dairy Herd Improvement Service (ADHIS) database for the Australian State of Victoria. The objective is to obtain accurate and comprehensible rule sets for reasoning about the expected milk yield of daughters arising from a potential mating. The rules sets are to be incorporated into the knowledge base of an intelligent decision support system (IDSS) for the dairy breeding industry in Australia. The data is pre-processed and categorised in two different ways, using Autoclass (a Bayesian classifier) and a domain expert’s heuristic. We present a comparison study and find that rule sets derived from Autoclass categorisation and C5 decision tree induction give best results.
Semi-Markov Conditional Random Fields for Information Extraction

We describe semi-Markov conditional random fields (semi-CRFs), a conditionally trained version of semi-Markov chains. Intuitively, a semi-CRF on an input sequence x outputs a "segmentation" of x, in which labels are assigned to segments (i.e., subsequences) of x rather than to individual elements xi of x. Importantly, features for semi-CRFs can measure properties of segments, and transitions within a segment can be non-Markovian. In spite of this additional power, exact learning and inference algorithms for semi-CRFs are polynomial-time—often only a small constant factor slower than conventional CRFs. In experiments on five named entity recognition problems, semi-CRFs generally outperform conventional CRFs.
Turbo equalization

Turbo equalization is an iterative equalization and decoding technique that can achieve equally impressive performance gains for communication systems that send digital data over channels that require equalization, i.e., those that suffer from intersymbol interference (ISI). In this article, we discuss the turbo equalization approach to coded data transmission over ISI channels, with emphasis on the basic ideas and some of the practical details. The original system introduced by Douillard et al. can be viewed as an extension of the turbo decoding algorithm by considering the effect of the ISI channel as another form of error protection, i.e., as a rate-1 convolutional code.
Unexpected Smoking-Linked High MMP-9 in Induced Sputum of Hazardous Dust-Exposed Workers

We investigated whether metalloproteinase-9 (MMP-9) values in induced sputum (IS) and pulmonary function tests can serve as a marker of damage and intensity of exposure to hazardous dust. Thirty-nine factory employees (28 workers exposed to metal particles and 11 supposedly nonexposed office workers) underwent IS induction. Samples were processed by conventional methods within 2 hours. The proportion of particles with diameters of 0–2 μ in IS samples was significantly higher in nonexposed than exposed workers and in smoker compared to nonsmoker workers. MMP-9 and TIMP-1 levels were similar for both groups. A linear regression model for MMP-9 based on exposure, smoking habits, and proportion of particles < 5 μ revealed a positive correlation between each of the explanatory variables and MMP-9 values. MMP-9 may serve as a marker for pulmonary injury.
Verifier-Local Revocation Group Signature Schemes with Backward Unlinkability from Bilinear Maps

An approach of membership revocation in group signatures is verifier-local revocation (VLR for short). In this approach, only verifiers are involved in the revocation mechanism, while signers have no involvement. Thus, since signers have no load, this approach is suitable for mobile environments. Although Boneh and Shacham recently proposed a VLR group signature scheme from bilinear maps, this scheme does not satisfy the backward unlikability. The backward unlikability means that even after a member is revoked, signatures produced by the member before the revocation remain anonymous. In this paper, we propose VLR group signature schemes with the backward unlinkability from bilinear maps.