Miracle-dz/UniFAR-FTUs huggingface dataset:
Measuring the client performance and energy consumption in mobile cloud gaming

Mobile cloud gaming allows gamers to play games on resource-constrained mobile devices, and a measurement study to quality the client performance and energy consumption is crucial to attract and retain the gamers. In this paper, we adopt an open source cloud gaming platform to conduct extensive experiments on real mobile clients. Our experiment results show two major findings that are of interests to researchers, developers, and gamers. First, compared to mobile native games, mobile cloud games save energy by up to 30%. Second, the frame rate, bit rate, and resolution all affect the decoders' resource consumption, while frame rate imposes the highest impact. These findings shed some light on the further enhancements of the emerging mobile cloud gaming platforms.
Testing a two-scale focused conservation strategy for reducing phosphorus and sediment loads from agricultural watersheds

This study tested a focused strategy for reducing phosphorus (P) and sediment loads in agricultural streams. The strategy involved selecting small watersheds identified as likely to respond relatively quickly, and then focusing conservation practices on high-contributing fields within those watersheds. Two 5,000 ha (12,360 ac) watersheds in the Driftless Area of south central Wisconsin, previously ranked in the top 6% of similarly sized Wisconsin watersheds for expected responsiveness to conservation efforts to reduce high P and sediment loads, were chosen for the study. The stream outlets from both watersheds were monitored from October of 2006 through September of 2016 for streamflow and concentrations of sediment, total P, and, beginning in October of 2009, total dissolved P. Fields and pastures having the highest potential P delivery to the streams in each watershed were identified using the Wisconsin P Index (Good et al. 2012). After three years of baseline monitoring (2006 to 2009), farmers implemented both field- and farm-based conservation practices in one watershed (treatment) as a means to reduce sediment and P inputs to the stream from the highest contributing areas, whereas there were no out-of-the-ordinary conservation efforts in the second watershed (control). Implementation occurred primarily in 2011 and 2012. In the four years following implementation of conservation practices (2013 through 2016), there was a statistically significant reduction in storm-event suspended sediment loads in the treatment watershed compared to the control watershed when the ground was not frozen (p = 0.047). While there was an apparent reduction in year-round suspended sediment event loads, it was not statistically significant at the 95% confidence level (p = 0.15). Total P loads were significantly reduced for runoff events (p < 0.01) with a median reduction of 50%. Total P and total dissolved P concentrations for low-flow conditions were also significantly reduced (p < 0.01) compared to the control watershed. This study demonstrated that a strategy that first identifies watersheds likely to respond to conservation efforts and then focuses implementation on relatively high-contributing fields within those watersheds can be successful in reducing stream P concentrations and loads.
STDP implementation using memristive nanodevice in CMOS-Nano neuromorphic networks

Implementation of a correlation-based learning rule, Spike-Timing-Dependent-Plasticity (STDP), for asynchronous neuromorphic networks is demonstrated using `memristive' nanodevice. STDP is performed using locally available information at the specific moment of time, for which mapping to crossbar-based CMOS-Nano architectures, such as CMOS-MOLecular (CMOL), is done rather easily. The learning method is dynamic and online in which the synaptic weights are modified based on neural activity. The performance of the proposed method is analyzed for specifically shaped spikes and simulation results are provided for a synapse with STDP properties.
On stochastic control up to a hitting time

We propose a dynamic programming-based solution to a stochastic optimal control problem up to a hitting time for a discrete-time Markov control process. Firstly, we determine an optimal control policy to steer the process toward a compact target set while simultaneously minimizing an expected discounted cost. We then provide a rolling-horizon strategy for approximating the optimal policy, together with quantitative characterization of its sub-optimality with respect to the optimal policy.
Real-Time PCR Assay Using Molecular Beacon for Quantitation of Hepatitis B Virus DNA

ABSTRACT Levels of hepatitis B virus (HBV) DNA in the blood serve as an important marker in monitoring the disease progression and treatment efficacy of chronic HBV infection. Several commercial assays are available for accurate measurement of HBV genomic DNA, but many of them are hampered by relatively low sensitivity and limited dynamic range. The aim of this study was to develop a sensitive and accurate assay for measuring HBV genomic DNA using real-time PCR with a molecular beacon (HBV beacon assay). The performance of this assay was validated by testing serial dilutions of the two EUROHEP HBV DNA standards (ad and ay subtypes) of known concentrations. The assay showed low intra-assay (<7%) and interassay (<5%) variations for both subtypes. Its dynamic range was found to be 101 to 107 copies per reaction (1.0 × 102 to 1.0 × 109 copies ml−1). The assay was further evaluated clinically using serum samples from 175 individuals with chronic hepatitis B. The HBV DNA level measured by this assay showed good correlation with that measured by the commercially available COBAS AMPLICOR HBV Monitor test (r = 0.901; P < 0.001). The higher sensitivity and broader dynamic range of this assay compared to the existing commercial assays will provide an ideal tool for monitoring disease progression and treatment efficacy in HBV-infected patients, in particular for those with low levels of HBV viremia.
Recommender Systems: Kembellec/Recommender Systems

On the transdisciplinary approach, engines and recommender systems brings together contributions linking information science and communications, marketing, sociology, mathematics and computing. It deals with the understanding of the underlying models for recommender systems and describes their historical perspective. It also analyzes their development in the content offerings and assesses their impact on user behavior.
Implementing an Innovation Cluster in Educational Settings In Order to Develop Constructivist-Based Learning Environments

This forum will initiate with a description of the implementation of a constructivist-based reform effort in k-12 classrooms in the US. The reform effort was an online collaboration among four teachers in 4 th and 5 th grades classrooms to implement a design problem-based unit meant to develop higher-order thinking responses in students. The unit design principles are correlated to current research in the cognitive sciences and the teachers’ efforts at implementing reform are correlated to current research in theories of innovation. The initial description will also briefly characterize aspects of the implementation process that led to productive or less productive reform results. The questions that will be developed in this forum include: 1. How can teachers’ professional development programs to increase the effectiveness of innovation in education?
Variability and predictability of bush fire in Guinea on inter-annual and multi-year timescales based on NDVI-MODIS datasets analysis

This study investigates the variability and the predictability of bush fire on inter-annual and multi-year timescales in Guinea (latitudes 7° 05 and 12° 51 N and longitudes 7° 30 and 15° 10 W). Using Moderate Resolution Imaging Spectroradiometer (MODIS) with a spatial resolution of 231 m × 231 m and 16 days composite temporal resolution between 2001 and 2016, two brush fire hazard indices are calculated based on the NDVI variability. Results show that both indices could be considered as good indicators of NDVI deficiency corresponding to the drought of vegetation. Multiple linear regression model using these risk indices as predictors and burned areas as predictands has shown a non-significant model skill of 0.33 (lower than the significant threshold equals to 0.42), at the inter-annual scale, while at the multi-year timescale (>5 years), the model’s skill rise up to 0.89. These indices can therefore be used as predictors of Guinea burned areas on multi-year timescale. This novel finding improved our understanding on the forecasting of burned area in Guinea, and could therefore help for successful adaptation strategies. Key words: Bush fire, Guinea, index of risk, NDVI-MODIS, variability and predictability.
Triggering Mechanism for the Filament Eruption on 2005 September 13 in NOAA Active Region 10808

On 2005 September 13 a filament eruption accompanied by a halo coronal mass ejection (CME) occurred in the most flare-productive active region, NOAA 10808, in solar cycle 23. Using multiwavelength observations before the filament eruption on September 13, we investigate the processes leading to the catastrophic eruption. We find that the filament slowly ascended at a speed of 0.1 km s-1 over 2 days before the eruption. During slow ascension, many small flares were observed close to the footpoints of the filament, where new magnetic elements were emerging. On the basis of the observational facts, we discuss the triggering mechanism leading to the filament eruption. We suggest that the process toward the eruption is as follows. First, a series of small flares played a role in changing the topology of the loops overlying the filament. Second, the small flares gradually changed the equilibrium state of the filament and caused the filament to ascend slowly over 2 days. Finally, a C2.9 flare that occurred when the filament was close to the critical point for loss of equilibrium directly led to the catastrophic filament eruption right after it.
Long-term trends in the total columns of ozone and its precursor gases derived from satellite measurements during 2004–2015 over three different regions in South Asia: Indo-Gangetic Plain, Himalayas and Tibetan Plateau

ABSTRACT Long-term (2004–2015) satellite data over three adjacent yet contrasting regions: Indo-Gangetic Plain, Himalayas and Tibetan Plateau (TP) were used to study the spatiotemporal distribution of total ozone column (TOC) and its precursor gases (such as nitrogen dioxide (NO2), methane (CH4) and carbon monoxide (CO)). The ozone precursor emission data and forest fire points were used to explore the findings. Trace gases showed increasing trend probably due to increasing emission from South Asia as supported by the Emission Database for Global Atmospheric Research emission data. Strong seasonal variation in trace gases was observed with the highest value during the pre-monsoon season, over three regions, possibly due to the biomass burning, pollution build-up and also long-range transport of pollution. TOC exhibited the similar seasonal variation as shown by the earlier ground-based studies over the region. The total column of precursor gases (except methane) exhibited strong seasonality with the highest column during the pre-monsoon season. Patterns in the variations of TOC and related precursors over the Himalayas were similar with that of the TP. Seasonal climatological trends also exhibited increasing pattern except for CO. This work provides an overview on the long-term TOC and its precursor gases which are necessary to understand the regional climate variability especially over the Himalayas and Tibetan Plateau region.
Mortality from Alzheimer's, motor neuron and Parkinson's disease in relation to magnetic field exposure: findings from the study of UK electricity generation and transmission workers, 1973–2004

Background: There are a number of reports linking magnetic field exposure to increased risks of Alzheimer's disease and motor neuron disease. Methods: The mortality experienced by a cohort of 83 997 employees of the former Central Electricity Generating Board of England and Wales was investigated for the period 1973–2004. All employees were employed for at least six months with some employment in the period 1973–82. Computerised work histories were available for 79 972 study subjects for the period 1971–93. Information on job and facility (location) were used to estimate exposures to magnetic fields. Two analytical approaches were used to evaluate risks, indirect standardisation (n = 83 997) and Poisson regression (n = 79 972). Results: Based on serial mortality rates for England and Wales, deaths from Alzheimer's disease and motor neuron disease were unexceptional. There was an excess of deaths from Parkinson's disease of borderline significance. No statistically significant trends were shown for risks of any of these diseases to increase with lifetime cumulative exposure to magnetic fields (RR per 10 μT-y: Alzheimer's disease 1.10 (95% CI 0.90 to 1.33); motor neuron disease 1.06 (95% CI 0.86 to 1.32); Parkinson's disease 0.88 (95% CI 0.74 to 1.05)) Conclusions: There is no convincing evidence that UK electricity generation and transmission workers have suffered increased risks from neurodegenerative diseases as a consequence of exposure to magnetic fields.
Acoustic Channel Modeling and Simulation for Underwater Acoustic Wireless Sensing Networks

Underwater acoustic channels are band-limited and reverberant, posing many obstacles to reliable, phase-coherent acoustic communications. In terrestrial wireless sensor networks, the nodes use radio frequency (RF) to build up the communication. In underwater environments, due to water absorption, radio wave does not work well. Compared to radio waves, sound has superior propagation characteristics in water, making it the preferred technology for underwater communications. While many high frequency communication experiments have been conducted in shallow water, few have carried out systematic studies on the channel properties at a time scale relevant for communications.
Neural Network Approach for Software Defect Prediction Based on Quantitative and Qualitative Factors

These Quality of a software component can be expressed in terms of level of number of faults present in data. Quality estimations are made using fault data available from previously developed similar type of projects and the training data consisting of software measurements. In this paper, an attempt is made to use Batch Gradient Descent (BGD), Batch Gradient Descent with momentum (BGDWM), Variable Learning Rate (VLR), Variable Learning Rate training with momentum (VLRM) and Resilient Backpropagation (RB) based neural network approach to identify the relation between the various qualitative as well as quantitative factor of the modules with the number of faults present in the module that will be helpful for prediction of the level of number of faults present in the modules. The dataset used is elicited from 31 completed software projects in the consumer electronics industry. The data were gathered using a questionnaire distributed to managers of recent projects. The performance of the algorithms is recorded in terms of MAE, RMSE and Accuracy percentage values.
Classifier Combining: Analytical Results and Implications

Several researchers have experimentally shown that substantial improvements can be obtained in diicult pattern recognition problems by combining or integrating the outputs of multiple classiiers. This paper summarizes our recent theoretical results that quantify the improvements due to multiple classiier combining. Furthermore, we present an extension of this theory that leads to an estimate of the Bayes error rate. Practical aspects such as expressing the con-dences in decisions and determining the best data partition/classiier selection are also discussed.
Usability Analysis of a Novel Biometric Authentication Approach for Android-Based Mobile Devices

Mobile devices are widely replacing the standard personal computers thanks to their small size and userfriendly use. As a consequence, the amount of information, often confidential, exchanged through these devices is raising. This makes them potential targets of malicious network hackers. The use of simple passwords or PIN are not sufficient to provide a suitable security level for those applications requiring high protection levels on data and services. In this paper a biometric authentication system, as a running Android application, has been developed and implemented on a real mobile device. A system test on real users has been also carried out in order to evaluate the human-machine interaction quality, the recognition accuracy of the proposed technique, and the scheduling latency of the operating system and its degree of acceptance. Several measures, such as system usability, users satisfaction, and tolerable speed for identification, have been carried out in order to evaluate the performance of the proposed approach. Keywords—fingerprints authentication, mobile device security, operating system, usability.
New insights into atmospheric sources and sinks of isocyanic acid, HNCO, from recent urban and regional observations

Isocyanic acid (HNCO) has only recently been measured in the ambient atmosphere, and many aspects of its atmospheric chemistry are still uncertain. HNCO was measured during three diverse field campaigns: California Nexus—Research at the Nexus of Air Quality and Climate Change (CalNex 2010) at the Pasadena ground site, Nitrogen, Aerosol Composition, and Halogens on a Tall Tower (NACHTT 2011) at the Boulder Atmospheric Observatory (BAO) in Weld County, CO, and Biofuel Crops emission of Ozone precursors intensive (BioCORN 2011), in a cornfield NW of Fort Collins, CO. Mixing ratios varied from below detection limit (~0.003 ppbv) to over 1.2 ppbv during a period when agricultural burning impacted the BAO Tower site. Urban areas, such as the CalNex 2010 Pasadena site, appear to have both primary (combustion) and secondary (photochemical) sources of HNCO, 50 ± 9%, and 33 ± 12%, respectively, while primary sources were responsible for the large mixing ratios of HNCO observed during the wintertime NACHTT study in suburban Colorado. Isocyanic acid during the BioCORN study in rural NE Colorado was closely correlated to ozone and therefore likely photochemically produced as a secondary product from amines or formamide. The removal of HNCO from the lower atmosphere is thought to be due to deposition, as common gas phase loss processes of photolysis and reactions with hydroxyl radicals, are slow. These ambient measurements are consistent with some HNCO deposition, which was evident at night at these surface sites.
Bioavailability enhancement, Caco-2 cells uptake and intestinal transport of orally administered lopinavir-loaded PLGA nanoparticles

Abstract Nanoparticles (NPs) can be absorbed via M cells of Peyer’s patches after oral delivery leading to passive lymphatic targeting followed by systemic drug delivery. Hence, the study was aimed to formulate PLGA NPs of lopinavir. The NPs were prepared by nanoprecipitation, optimized by 33 factorial design and characterized by TEM, DSC, FTIR studies and safety was assessed by MTT assay. In vivo pharmacokinetic studies were performed in rats. The NPs were discrete spherical structures having particle size of 142.1 ± 2.13 nm and entrapment of 93.03 ± 1.27%. There was absence of drug-polymer interaction. Confocal images revealed the penetration and absorption of coumarin-loaded NPs in Caco-2 cells and intestine after oral delivery. There was 3.04 folds permeability and 13.9 folds bioavailability enhancement from NPs. The NPs can be promising delivery system for antiretroviral drug by delivering the drug to lymph (major HIV reservoir site) via direct absorption through intestine before reaching systemic circulation.
Foundational Data Services for Water Resources Intelligence within the NWS Office of Water Prediction

The National Weather Service (NWS) Office of Water Prediction (OWP) is building an extensible web services framework for discovery and access of select hydrologic and meteorologic data, metadata, and geospatial information. The services framework will be implemented using community data exchange and format standards to provide an interoperable platform that will support an ecosystem of application development both within OWP and by external partners in the public, private, and academic sectors. The initial implementation of the services framework will encompass three essential classes of dissemination for hydrologic data: gridded data services, time-series services, and geospatial services. Development priorities are based on the data services needed in evaluation, modeling, and dissemination supporting the interoperability goals of Integrated Water Resources Science and Services (IWRSS) consortium partners and the development of the National Water Model (NWM). The services framework will integrate currently fragmented water information into a connected hydroinformatic system for water data that leverages existing applications, infrastructure and capabilities for the support of national water resources objectives.
Design and analysis of a multi-dimensional data sampling service for large scale data analysis applications

Sampling is a widely used technique to increase efficiency in database and data mining applications operating on large dataset. In this paper, we present a scalable sampling implementation that supports efficient, multi-dimensional spatio-temporal sample generation on dynamic, large scale datasets stored on a storage cluster The proposed algorithm leverages Hilbert space-filling curves in order to provide an approximate linear order of multidimensional data while maintaining spatial locality. This new implementation is then bootstrapped on top of our previous implementation, which efficiently samples large datasets along a single dimension (e.g., time), thereby realizing a service for spatio-temporal sampling. We evaluate the performance of our approach comparing it to the popular R-tree based technique. The experimental results show that our approach achieves up to an order of magnitude higher efficiency and scalability
Hierarchical Bayesian Models for Collaborative Tagging Systems

Collaborative tagging systems with user generated content have become a fundamental element of websites such as Delicious, Flickr or CiteULike. By sharing common knowledge, massively linked semantic data sets are generated that provide new challenges for data mining. In this paper, we reduce the data complexity in these systems by finding meaningful topics that serve to group similar users and serve to recommend tags or resources to users. We propose a well-founded probabilistic approach that can model every aspect of a collaborative tagging system. By integrating both user information and tag information into the well-known Latent Dirichlet Allocation framework, the developed models can be used to solve a number of important information extraction and retrieval tasks.