Miracle-dz/UniFAR-FTUs huggingface dataset:
International Conference on Ambient Systems , Networks and Technologies ( ANT 2013 ) Energy-E ffi cient Optimization Layer for Event-Based Communications on Wi-Fi Things

The Web-of-Things or WoT offers a way to standardize the access to services embedded on everyday objects, leveraging on well accepted standards of the Web such as HTTP and REST services. The WoT offers new ways to build mashups of object services, notably in smart buildings composed of sensors and actuators. Many things are now taking advantage of the progresses of embedded systems relying on the ubiquity of Wi-Fi networks following the 802.11 standards. Such things are often battery powered and the question of energy efficiency is therefore critical. In our research, we believe that several optimizations can be applied in the application layer to optimize the energy consumption of things. More specifically in this paper, we propose an hybrid layer automatically selecting the most appropriate communication protocol between current standards of WoT. Our results show that indeed not all protocols are equivalent in terms of energy consumption, and that some noticeable energy saves can be achieved by using our hybrid layer. c © 2011 Published by Elsevier Ltd. Selection and/or peer-review under responsibility of [name organizer]
Nu : Towards an Aspect-Oriented Invocation Mechanism

The contribution of this work is the design, implementation and evaluation of a new aspect-oriented invocation mechanism for preserving design modularity in object code. We call our mechanism Bind. We make three basic claims. First, it is feasible to realize a programming model that supports Bind to preserve design modularity in object code. Second, the new invocation mechanism further improves the conceptual integrity of the aspect-oriented programming models by allowing advising and runtime properties of aspect-like constructs to be modeled as simple combinations of invocation primitives as opposed to new language constructs. Third, it brings new possibility for structuring aspect-oriented systems, removing the commitment to a single aspect-language model, and expanding the program design space to include arbitrary combinations of language models and advising structures. To support these claims, we present the design and implementation of Nu, a programming model based on the . NET Framework that supports Bind as an invocation mechanism. We show that Nu supports aspect-oriented program designs where multiple aspect-language models can be emulated using Bind, and used in arbitrary combinations without compromising the design modularity in the object code.
Cassandra: Proactive conflict minimization through optimized task scheduling

Software conflicts arising because of conflicting changes are a regular occurrence and delay projects. The main precept of workspace awareness tools has been to identify potential conflicts early, while changes are still small and easier to resolve. However, in this approach conflicts still occur and require developer time and effort to resolve. We present a novel conflict minimization technique that proactively identifies potential conflicts, encodes them as constraints, and solves the constraint space to recommend a set of conflict-minimal development paths for the team. Here we present a study of four open source projects to characterize the distribution of conflicts and their resolution efforts. We then explain our conflict minimization technique and the design and implementation of this technique in our prototype, Cassandra. We show that Cassandra would have successfully avoided a majority of conflicts in the four open source test subjects. We demonstrate the efficiency of our approach by applying the technique to a simulated set of scenarios with higher than normal incidence of conflicts.
A Flexible Parallel Hardware Architecture for AdaBoost-Based Real-Time Object Detection

Real-time object detection is becoming necessary for a wide number of applications related to computer vision and image processing, security, bioinformatics, and several other areas. Existing software implementations of object detection algorithms are constrained in small-sized images and rely on favorable conditions in the image frame to achieve real-time detection frame rates. Efforts to design hardware architectures have yielded encouraging results, yet are mostly directed towards a single application, targeting specific operating environments. Consequently, there is a need for hardware architectures capable of detecting several objects in large image frames, and which can be used under several object detection scenarios. In this work, we present a generic, flexible parallel architecture, which is suitable for all ranges of object detection applications and image sizes. The architecture implements the AdaBoost-based detection algorithm, which is considered one of the most efficient object detection algorithms. Through both field-programmable gate array emulation and large-scale implementation, and register transfer level synthesis and simulation, we illustrate that the architecture can detect objects in large images (up to 1024 × 768 pixels) with frame rates that can vary between 64-139 fps for various applications and input image frame sizes.
Learning analysis for correlation of fuzzy rules in applying RLS for neural fuzzy systems

It is well-known that Self-constructing neural fuzzy inference network (SONFIN) is a nice fuzzy inference system that has been equipped with structure learning capability. For the learning mechanisms, SONFIN can either be employed with the adaptive learning algorithm from neural network, which is often called backpropagation (BP) learning algorithm or use the Recursive Least Squares (RLS) algorithm in finding the parameters in the consequence part. In this paper, we reported the analysis on the use of RLS algorithm for neural fuzzy systems under the structure of SONFIN. Such a RLS algorithm is originally proposed to learn parameters in the consequence part only for TSK fuzzy systems. RLS has been demonstrated to be capable of providing great learning performance to neural fuzzy systems. From our previous work, it can be observed that the advantages of using RLS over BP and various issues, such as forgetting factor or reset operation are also investigated. All the above studies are based on the use of the full covariance matrixin the RLS algorithm. However, such an approach may result in heavy computational burden especially when the rule number is large. An alternative approach is to neglect the correlation among rules. In this study, we will report our analyses on the effects of correlation among rules. In order to have a clear demonstration on those effects, some special designs for the system are considered. From our study, it is clearly evidence that such neglect may results in large errors.
Analysis of Human Blood Plasma Proteome from Ten Healthy Volunteers from Indian Population

Analysis of any mammalian plasma proteome is a challenge, particularly by mass spectrometry, due to the presence of albumin and other abundant proteins which can mask the detection of low abundant proteins. As detection of human plasma proteins is valuable in diagnostics, exploring various workflows with minimal fractionation prior to mass spectral analysis, is required in order to study population diversity involving analysis in a large cohort of samples. Here, we used ‘reference plasma sample’, a pool of plasma from 10 healthy individuals from Indian population in the age group of 25–60 yrs including 5 males and 5 females. The 14 abundant proteins were immunodepleted from plasma and then evaluated by three different workflows for proteome analysis using a nanoflow reverse phase liquid chromatography system coupled to a LTQ Orbitrap Velos mass spectrometer. The analysis of reference plasma sample a) without prefractionation, b) after prefractionation at peptide level by strong cation exchange chromatography and c) after prefractionation at protein level by sodium dodecyl sulfate polyacrylamide gel electrophoresis, led to the identification of 194, 251 and 342 proteins respectively. Together, a comprehensive dataset of 517 unique proteins was achieved from all the three workflows, including 271 proteins with high confidence identified by≥2 unique peptides in any of the workflows or identified by single peptide in any of the two workflows. A total of 70 proteins were common in all the three workflows. Some of the proteins were unique to our study and could be specific to Indian population. The high-confidence dataset obtained from our study may be useful for studying the population diversity, in discovery and validation process for biomarker identification.
The prospective, 24-week assessment of cost-efficacy of and compliance to antidepressant medications in a rural setting (PACECAR) study

Background: Anxiety and depression are common mental health disorders that are responsible for considerable societal burden. There are no data on cost-efficacy and medication compliance related to the treatment of these disorders in rural India. Materials and Methods: All consenting adults (n = 455) diagnosed with generalized anxiety or (unipolar) depressive disorders in Suttur village, Karnataka, were treated with open-label fluoxetine (20–60 mg/day), sertraline (50–150 mg/day), escitalopram (10–20 mg/day), desvenlafaxine (50–150 mg/day), duloxetine (30–90 mg/day), amitriptyline (75–150 mg/day), or clomipramine (75–150 mg/day) in a structured, monotherapy dosing plan. The study was nonrandomized and otherwise naturalistic. Patients were followed up every 4 weeks for 24 weeks. Study discontinuation was defined as medication noncompliance for 3 or more days or withdrawal due to treatment nonresponse. Results: There was substantial discontinuation (34.5%) in the first 4 weeks; 55.4% had discontinued by 12 weeks. Subsequently, only 11.2% discontinued treatment. Only 33.4% of the subjects tolerated the treatment, responded to it, and remained compliant for 24 weeks. Such successful completion was highest for escitalopram and desvenlafaxine (46%–47%) and lowest for clomipramine and amitriptyline (10%–14%). Adverse events were the most common reason for noncompliance with clomipramine and amitriptyline (45%–46%); the experience of sufficient improvement was the most common reason for noncompliance with the remaining drugs (28%–49%). Whereas the average cost of efficacious treatment for a continuous period of 24 weeks was lowest for fluoxetine, an examination of the cost-efficacy tradeoff suggested maximum advantage for escitalopram, sertraline, and desvenlafaxine. The cost-efficacy profile for amitriptyline and clomipramine was poor. Conclusions: Reasons for noncompliance vary by drug class and need to be considered when prescribing antidepressant drugs. Escitalopram, sertraline, and desvenlafaxine perhaps have the most favorable 24-week cost-efficacy profile; tricyclics are poorly tolerated. Rural subjects need to be educated that treatment must be continued even after improvement is established.
Ego-centric and allo-centric abstraction in self-organized hierarchical neural networks

The computational systems supporting the cognitive capacity of artificial agents are often structured hierarchically, with sensory-motor details placed in the lower levels, and abstracted conceptual items in the upper levels. Such an architecture mimics the structural properties of the animal and human nervous system. To operate efficiently in varying circumstances, artificial agents are necessary to consider both ego-centric (i.e. self-centered) and allo-centric (i.e. other-centered) information, which are further combined to address given tasks. The present work investigates effective assemblies for simultaneously placing ego-centric and allo-centric processes in the cognitive hierarchy, by evolving self-organized neural network controllers. The systematic study of the internal network mechanisms has showed that effective neural assemblies are developed by placing allo-centric information in the upper levels of the cognitive hierarchy, followed by ego-centric abstracted representations in the middle and finally sensory-motor details in the lower level. We present and discuss the obtained results considering how they are related with known assumptions about human brain functionality.
Mechanism of the beneficial effects of pentoxifylline on hepatocellular function after trauma hemorrhage and resuscitation.

BACKGROUND The purpose of this study was to determine whether pentoxifylline administration restores the depressed hepatocellular function after trauma hemorrhage and crystalloid resuscitation and, if so, whether this is the result of the down-regulation of inflammatory cytokines, tumor necrosis factor (TNF) and interleukin-6 (IL-6). METHODS After laparotomy rats were bled to and maintained at a mean arterial pressure of 40 mm Hg until 40% of maximum shed blood volume was returned in the form of Ringer's lactate. They were then resuscitated with Ringer's lactate to four times the shed blood volume. Pentoxifylline (50 mg/kg body weight) or saline solution was infused intravenously for 95 minutes during and after resuscitation. One and a half hours and 4 hours after resuscitation, hepatocellular function (maximal velocity [Vmax] and the efficiency of the active transport [Km] of indocyanine green clearance) and plasma. TNF and IL-6 levels were determined with in vivo hemoreflectometer and cellular assays, respectively. RESULTS Circulating TNF and IL-6 levels increased significantly after hemorrhage and resuscitation. Pentoxifylline treatment, however, markedly decreased the levels of these cytokines, and the values were similar to those of sham rats. The decreased Vmax and Km values were also restored by pentoxifylline treatment. Moreover, there was a significant correlation between Vmax and TNF or IL-6 levels. CONCLUSIONS The down-regulation of inflammatory cytokines by pentoxifylline may be the mechanism by which this agent restores the depressed hepatocellular function after trauma hemorrhage and resuscitation.
Research Note - A Multilevel Analysis of the Effect of Group Appropriation on Collaborative Technologies Use and Performance

This study develops a comprehensive model to predict and explain the use of collaborative technologies (CT) and the task performance of individual users as a result of using CT. The integrated model attempts to capture how the individual user's extent of use of CT is a function of both the technical features and the structures embedded within or created by the interactions among the technology, group, and organization. The model developed is tested using data collected from a national bank with 279 members working in 40 different workgroups. A hierarchical linear model (HLM) is used to test the hypotheses generated from the model. Results show that our integrated model provides a more complete explanation of the use of CT and task performance beyond those of the individual-level factors. The study is an early effort to develop an integrated theory to provide comprehensive insight into individual use of CT in a group or organizational context.
Discoid Lupus Erythematosus and Lupus Profundus in Childhood: A Report of Two Cases

Abstract: Discoid lupus erythematosus (DLE) is a rare disorder in childhood, with 22 cases reported in the English‐language literature. Less than 2% of patients with DLE have an onset before 10 years of age. We describe two children with DLE and lupus profundus with an onset of the disease at the ages of 11 and 15 years and focus on likely histopathologic differences between DLE in children and in adults. Histopathologic characteristics for childhood DLE might be an intense periadnexal and perivascular infiltrate extending into the interstitium and subcutaneous tissue consisting of lymphocytes, histiocytes, eosinophilic granulocytes, and plasma cells and lacking epidermal atrophy. The diagnosis of DLE in our two patients was established by clinicopathologic correlation based on clinical presentation, histologic and immunofluorescent findings in skin biopsy specimens, and the absence of clinical and laboratory evidence of systemic involvement. Therapy with antimalarials resulted in reduction of the skin lesions, but in one patient severe lipoatrophy occurred.
Measuring Depth Perception for Stereoscopic Images Using a Phase-shift Model

We present an effective depth perception assessment index for stereoscopic images using a phase-shift model. To be more specific, we use Gabor filter to compute the responses of left and right images respectively, and proposed a phase-shift model for computing disparity maps based on phase gradient and phase difference information. Then, quality score for depth perception is obtained by measuring the similarity between the estimated disparities of the original and distorted stereoscopic images. Experimental results on two publicly 3D image quality assessment databases demonstrate that, in comparison with the most related existing methods, the devised algorithm achieves high consistency alignment with subjective assessment.
Sensitivity of the diurnal warm layer to meteorological fluctuations. Part 2: A new parameterisation for diurnal warming

A new diurnal warming parameterisation has been developed which allows the shape of the diurnal sea surface temperature (SST) signal to change in response to meteorological fluctuations during the day based on an extensive examination of over 300 diurnal cycles. The parameterisation estimates the diurnal variation in temperature at the surface and at 1 m over a local day based only on wind and insolation measurements averaged over several different periods of the day. The parameterisation is validated against independent data and is compared against other existing parameterisations. Results show that diurnal warming estimates are more accurate when daily fluctuations in wind and insolation are taken into consideration and the new parameterisation captures some of the variability of the shape of the diurnal SST cycle which other parameterisations are unable to achieve. The parameterisation is currently being trialled in the European Space Agency MEDSPIRATION project (a contribution to the Global Ocean Data.
Detecting Spam on Social Networking Sites : Related Work

1. RELATED WORK The rise of social media has made Social Networking Services (SNSs) more attractive targets for spam and fraud, leading to increasingly sophisticated attacks. This trend is reflected in recent research, as papers have focused on identifying and classifying the various types of social media spam. Many of these studies employ techniques previously used to combat conventional email and web spam. SNSs also provide opportunities to take advantage of user reputation and other social graph-dependent features to improve classification. Nevertheless, most research has been carried out on publicly-available data from SNSs, making it difficult up until now to measure the effect of private user data on algorithms for detecting site misuse.
Hydrological modelling and water balance of the Negro River basin: evaluation based on in situ and spatial altimetry data

This study intends to bridge the gap between global‐ and local‐scale hydrological studies in the Amazon basin by assessing the hydrological response of the Negro basin. The large basin model from the Institute of Hydraulic Research (MGB‐IPH hydrological model) has been used to simulate the hydrological processes over the basin during the period 1997–2006. The model was forced with a precipitation field for the Amazon basin derived from the Hybam observatory and climatological data provided by NCEP. Calibration and validation procedures were developed based on daily discharge datasets from 22 gauge stations. In addition, this paper focusses on ENVISAT spatial altimetry data from 27 virtual stations (VS). The spatial altimetry datasets were used as a complement to the model validation. For each computational cell, water heights could be derived from a robust discharge × height relationship generalized to most parts of the basin. The modelled discharges and water heights feature adequate performance levels at gauge and VS along the main river and tributaries with drainage areas varying from 5900 to 712 500 km2. Nash–Sutcliffe coefficients of modelled discharges varied from 0·47 to 0·94 and correlation coefficients of water heights averaged 0·86 for the entire basin. According to the water balance provided by the model, the amount of precipitation received over the basin is 7·2 mm day−1 during the study period with an evapotranspiration (ET) rate of 3·2 mm day−1. The model could also capture the different ET rates over the basin, as presented in previous studies. Results suggest relatively low ET values in the Northern basin, in the Roraima State, of about 3·1 mm day−1, and higher values near Manaus (3·8 mm day−1). Copyright © 2010 John Wiley & Sons, Ltd.
Improvements in the estimation of daily minimum air temperature in peninsular Spain using MODIS land surface temperature

Air temperature (Ta) is a key variable in many environmental risk models and plays a very important role in climate change research. In previous studies we developed models for estimating the daily maximum (Tmax), mean (Tmean), and minimum air temperature (Tmin) in peninsular Spain over cloud-free land areas using Moderate Resolution Imaging Spectroradiometer (MODIS) data. Those models were obtained empirically through linear regressions between daily Ta and daytime Terra-MODIS land surface temperature (LST), and then optimized by including spatio-temporal variables. The best Tmean and Tmax models were satisfactory (coefficient of determination (R2) of 0.91–0.93; and residual standard error (RSE) of 1.88–2.25 K), but not the Tmin models (R2 = 0.80–0.81 and RSE = 2.83–3.00 K). In this article Tmin models are improved using night-time Aqua LST instead of daytime Terra LST, and then refined including total precipitable water (W) retrieved from daytime Terra-MODIS data and the spatio-temporal variables curvature (c), longitude (λ), Julian day of the year (JD) and elevation (h). The best Tmin models are based on the National Aeronautics and Space Administration (NASA) standard product MYD11 LST; and on the direct broadcast version of this product, the International MODIS/AIRS Processing Package (IMAPP) LST product. Models based on Sobrino’s LST1 algorithm were also tested, with worse results. The improved Tmin models yield R2 = 0.91–0.92 and RSE = 1.75 K and model validations obtain similar R2 and RSE values, root mean square error of the differences (RMSD) of 1.87–1.88 K and bias = 0.11 K. The main advantage of the Tmin models based on the IMAPP LST product is that they can be generated in nearly real-time using the MODIS direct broadcast system at the University of Oviedo.