Miracle-dz/UniFAR-FTUs huggingface dataset:
A NOVEL DATA GATHERING SCHEME FOR MONITORING-ORIENTED WIRELESS SENSOR NETWORKS

Wireless sensor networks have great potential as a means of realizing a wide range of applications, such as natural environmental monitoring, environmental control in residential spaces or plants, and object tracking. As a frame to actualize these sensor applications, this study assumes a monitoring-oriented wireless sensor network, which periodically gathers their sensing data from all sensors placed in a large scale area. This paper proposes a novel autonomous decentralized control scheme with data aggregation technique to prolong the lifetime of monitoring-oriented wireless sensor networks. This is a novel scheme devised by considering the application environment of a wireless sensor network as a typical example of a complex system where the adaptive adjustment of the entire system is realized from the local interactions of components of the system. We evaluate our scheme using simulation experiments, and also discuss its development potential.
Infrared and visible image fusion based on object extraction and adaptive pulse coupled neural network via non-subsampled Shearlet transform

Taking into account the imaging characteristics of the infrared and visual images and the insufficient information content of the fused images, combined with the benefits of non-subsampled Shearlet transform and adaptive pulse coupled neural network, a kind of infrared and visual image fusion algorithm based on object extraction and adaptive pulse coupled neural network via non-subsampled Shearlet transform is proposed, which fuses the object region and background respectively. First, the object regions from infrared image are segmented by region growing method. Then, non-subsampled Shearlet transform is utilized for multiscale geometric decomposition of the source images, the object regions and background region are fused by different rules. The high frequency sub-band coefficients of background region are selected by using the global coupling and pulse synchronization of the adaptive pulse coupled neural network, and finally the fused image is reconstructed by the inverse non-subsampled Shearlet transform. Experimental results demonstrate that the proposed fusion algorithm outperforms typical wavelet-based, Contourlet-based and non-subsampled Contourlet-based fusion algorithms in terms of qualitative and quantitative evaluations.
Comparing Two Operating Configurations in a Full-Scale Arsenic Removal Plant. Case Study: Guatemala

The present study was conducted in Naranjo County located in the municipality of Mixco, Guatemala. The water supply source comes from two wells with a maximum flow of 25.24 and 33.44 L·s−1. The main problem with this source is the high arsenic concentration—between 0.1341 and 0.1671 mg·L−1. The aim of this study was to conduct laboratory tests, basic engineering and supervision of the construction and evaluation of an operations plant using two configurations, A (low-rate sedimentation and ceramic filter) and B (high-rate sedimentation and clinoptilolite filter), to remove arsenic present in water for human use and consumption. This plant supplies water to Naranjo County in Mixco, Guatemala (5000 inhabitants). First, a laboratory Jar Test was performed to evaluate arsenic removal efficiency. And second, a conventional clarification plant was then built (design flow: 25.24 L·s−1). The best results were achieved with configuration B, with the following reagents and dosage as defined by the laboratory tests: 10 mg L−1 ferric chloride as coagulant; 1.8 mg·L−1 CH-polyfocal as flocculant and 0.4 mg L−1 MIT03 as color removal; 1 mg L−1 sodium hypochlorite as oxidant and adjusting pH to ≈7.0 with sodium hydroxide. Once the plant began operating, the efficiency of the treatment process was evaluated. The maximum elimination efficiencies were obtained 100% for turbidity (0 UTN), 89.54% (3.66 UPt-Co) for color and 96.80% (0.005 mg L−1) for arsenic, values that comply with Guatemalan standards. For this case, the relation between Fe(III) dosage/mg and As(V) removal was 1:46.
Education Research: Cognitive performance is preserved in sleep-deprived neurology residents

Objective: To test the hypotheses that sleep deprivation in neurology residents is associated with performance deficits and that vigilance and cognitive performance is more compromised after overnight on-call duty compared to night shift. Methods: Thirty-eight neurology residents of a university teaching hospital participated in a prospective single-blind comparison study. Residents were recruited according to their working schedule and divided into 3 groups: 24 hours overnight on-call duty, night shift, and regular day shift (controls). All participants underwent serial measurements of sleepiness and cognitive performance in the morning directly after or before their shift. Pupillary sleepiness test and Paced Auditory Serial Addition Test were applied. Perceived sleepiness was assessed by a questionnaire. Results: Sleepiness was increased in residents after night shift and overnight call compared to controls while the type of night duty was not associated with the extent of sleepiness. Sleep-deprived residents did not show any performance deficits on the Paced Auditory Serial Addition Test. Cognitive performance was not associated with sleepiness measures. Conclusions: Night shift and overnight call duty have a similar impact on alertness in neurology residents. Sleep-deprived neurology residents may be able to overcome sleep loss–related performance difficulties for short periods.
Monty Hall Particle filter: A new method to tackle predictive model uncertainties

This paper proposes a simple adaptive weight computing method for particle filters that utilizes knowledge about predictive model uncertainty. In each time step the particles are assigned into subsets based on the corresponding uncertainty estimates. The weights are then updated based on accumulated subset-inclusion and likelihood information using a discrete set of measurement likelihood functions. By controlling the aggressiveness of the weight computing, the method strives to achieve faster convergence without losing robustness to model errors. Two localization experiments are conducted to verify that the method has a clear advantage over particle filters with single likelihood function. In the first experiment we use synthetic Gaussian Process data. In the second experiment real indoor magnetic field data with very coarse interpolation and uncertainty approximation is used to verify the method's effectiveness in real-world scenarios. One of the main advantages of the proposed method is that despite its flexibility, it adds only little implementational or computational overhead to conventional particle filters.
Mapping home-network appliances to TCP/IP sockets using a three-tiered home gateway architecture

A three-tier gateway architecture for internetworking between home automation networks and a TCP/IP based wide area network, such as the Internet, is described. The architecture abstracts the functionality of any home network into a driver layer (tier one), and provides a common access layer (tier two) from any TCP/IP network application (tier three) to a local home automation network. Clients and application programs may transparently access services and resources on the home network and appliances connected to the home network may access resources and services on the TCP/IP network.
Context-based specification of Web service policies using WSPL

The adoption of WSPL as a language for expressing Web service policies has shown its adequacy. The expressivity and simplicity of that language were the key factors behind this success. A desired feature in specifying Web services policies is the introduction of the context concept. In fact, very often Web service designers look for expressing rules that depend on the context in which a Web service is operational. We propose in this paper a specification and an implementation of context-based Web services policies using the WSPL language. We show the expressivity of the proposed context specification and discuss its applicability. We also provide a prototype that illustrates the specification and implementation of context-based Web services policies.
Super Resolution Blind Reconstruction of Low Resolution Images Using Framelets Based Fusion

In this paper, we propose a fusion technique based on framelets to obtain super resolution image from sub-pixel shifted, noisy, blurred low resolution images. This method has high advantages over all existing methods. A Tight frame filter bank provides symmetry and has a redundancy that allows for approximate shift invariance which leads to clear edges, high spatial information with effective denoising which was lacked in critically sampled discrete wavelet transform. They are also shorter and results in smoother scaling and wavelet functions. The reconstructed super resolution image obtained by this technique has high peak signal to noise ratio (PSNR) and low mean square error (MSE) than that obtained by wavelet based fusion method, which is evident through the experimental results.
Assessing Conflict Tactics Scale Validity by Examining Intimate Partner Violence Overreporting

Objective: Although many scholars have questioned, on a logical basis, the validity of the Conflict Tactics Scale (CTS) to adequately measure intimate partner violence (IPV), relatively few critiques have used extensive empirical data gathered specifically for this purpose. This research analyzed these types of data to investigate an important mechanism of potential validity problems, IPV misreporting, by adding context questions to determine whether participants endorsed (as if actual IPV) accidents or other acts that neither party took seriously. The objective was to determine not only the extent to which this form of overreporting occurs but also how males and females differed in misreporting patterns. Method: Students from 1 U.S. and 1 Australian university (Total N = 1,758) completed a computer-administered survey. Multilevel logistic regression subsequently assessed the degree to which several factors predicted whether participants overreported CTS items. Results: Of the 1,174 event endorsements, 22.1% were classified as overreports. Whether males or females were more prone to overreporting, however, differed across event type, sample, age, relationship status, perpetration versus victimization, and current versus former partnerships. There were statistically significant interactions between gender and many of these factors. Among the most important of the findings was that males were more likely to overreport victimizations by female partners, whereas females were more likely to overreport perpetrations against male partners. Conclusions: The magnitude and intricate gendered nature of the overreporting problem imply that overreporting is a substantial problem, having the potential to negatively affect scale validity and thus the testing of partner-violence theories.
Treatment Effects of Electroconvulsive Therapy ( ECT ) on the Hippocampus in Major Depressive Disorder

Major Depressive Disorder S. H. Joshi1, R. Espinoza2, T. Pirnia1, J. Shi4, Y. Wang4, Brandon Ayers3, A. Leaver1, R. P. Woods1, 2, and K. L. Narr 1, 2 1Ahmanson-Lovelace Brain Mapping Center, Department of Neurology, University of California at Los Angeles, Los Angeles, CA; 2Department of Psychiatry and Biobehavioral Sciences, University of California at Los Angeles, Los Angeles, CA; 3Department of Computational and Systems Biology, University of California at Los Angeles, Los Angeles, CA; 4School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, AZ
Evolving stylized images using a user-interactive genetic algorithm

The application of evolutionary computation to art has produced very interesting results with respect to the generation of images [5]. Systems that rely on automatic image generation, where the computer is relied upon for the production of creative images, often fall short when results are presented to a user for feedback, as results in the field of visual arts are highly subjective. Many systems incorporate user-guided evolution to remedy this situation, requiring the user to supervise the evolutionary process at every step, though this task is largely repetitive. This paper introduces JNetic, a highly user-interactive image evolution system that does not rely on user feedback or supervision. It can be run in two modes, automatic and interactive, using color distance between a source image and generated image as a fitness function in automatic mode. JNetic is a tool for artists, created with user intervention and interaction in mind, but ultimately focusing on the automatic evolution of images.
Violence, Dramaturgical Repertoires and Neoliberal Imaginaries in Cairo

This article reflects upon the monopoly and repertoires of violence in the city of Cairo perpetrated in counter-revolutionary moments by the successive military and Islamist regimes, which lack alternative visions and imaginaries. It counters the myth that the Egyptian revolution was non-violent. It also reflects upon some of the debates about the Arab revolutions, the question of militarization, and the return of ‘order’ with the re-emergence of the army in public life. It also reflects upon the multiplication of segregating walls, first as buffer zones to isolate protesters, then as the walls of the gated communities and compounds of the rich, examining the extent to which the supposed revitalization of downtown Cairo actually represents the flight of wealth and capital to isolated areas far from the city center.
Spatial sensor data processing and analysis for mobile media applications

Currently, an increasing number of user-generated videos (UGVs) are collected and uploaded to the Web -- a trend that is driven by the ubiquitous availability of smartphones and the advances in their camera technology. Additionally, with these sensor-equipped mobile devices, various spatial sensor data (e.g., data from GPS, digital compass etc.) can be continuously acquired in conjunction with the captured video stream without any difficulty. Thus, it has become easy to record and fuse various contextual metadata with UGVs, such as the location and orientation of a camera. This has led to the emergence of large repositories of media contents that are automatically geo-tagged at the fine granularity of frames. Moreover, the collected spatial sensor information becomes a useful and powerful contextual feature to facilitate multimedia/GIS analysis and management in diverse mobile applications. Most sensor information collected from mobile devices, however, is not highly accurate due to two main reasons: (a) the varying surrounding environmental conditions during data acquisition, and (b) the use of low-cost, consumer-grade sensors in current mobile devices. To obtain the best performance from systems that utilize sensor data as important contextual information, highly accurate sensor data input is desirable and efficient sensor data correction algorithms and systems would be extremely useful. Therefore, in this research we aim to enhance the accuracy of such noisy spatial sensor data generated by smartphones during video recording, and utilize this emerging contextual information in mobile applications.
Face detection using statistical and multi-resolution texture features Manian and Ross 1 Face Detection Using Statistical and Multi-Resolution Texture Features

We present a texture-based technique for detecting faces in static gray-scale images under diverse conditions. The proposed method uses a moving window to examine the texture of an image at multiple scales in order to detect facial patterns. The features considered in this work is a set of statistical and multi-resolution texture features including a 3-level wavelet transform that uses an orthonormal Daubechies filter of length 8. These features are augmented with the pixel values corresponding to an 8x8 rendition of the image resulting in a 73-dimensional vector. The algorithm first utilizes the feature vectors extracted from a set of training images containing facial patterns to compute a pair of thresholds (based on the Euclidean distance metric) which is later used to eliminate non-facial window patterns from an image. The training vectors are next subjected to a clustering procedure that emits a pre-determined number of clusters each characterized by a centroid, a radius and a covariance structure. When a test image is presented to the system, the non-face window patterns are eliminated by utilizing the threshold values computed during the training phase. The feature vectors associated with the remaining candidate face windows are then compared with the centroid of individual clusters via the Mahalanobis distance in order to determine facial patterns. The algorithm uses fewer training samples than current learning-based techniques; does not require non-facial training patterns; can detect multiple faces in a scene; and is observed to perform well on images containing frontal, sideprofile and slightly tilted faces under diverse lighting and background conditions. 1 Key words— face detection; statistical features; multiresolution features; texture; one-class problem. Vidya Manian is with the University of Puerto Rico. (Tel: +1-787832-2825 ; Fax: +1-787-832-2485; Email: manian@ece.uprm.edu) Arun Ross is with West Virginia University. (Tel: +1-304-293-0405; Fax: +1-304-293-8602; Email: arun.ross@mail.wvu.edu)
A Novel Overactuated Quadrotor UAV: Modeling, Control and Experimental Validation

—Standard quadrotor UAVs possess a limited mobility because of their inherent underactuation, i.e., availability of 4 independent control inputs (the 4 propeller spinning velocities) vs. the 6 dofs parameterizing the quadrotor position/orientation in space. As a consequence, the quadrotor pose cannot track arbitrary trajectories in space (e.g., it can hover on the spot only when horizontal). Since UAVs are more and more employed as service robots for interaction with the environment, this loss of mobility due to their underactuation can constitute a limiting factor. In this paper we present a novel design for a quadrotor UAV with tilting propellers which is able to overcome these limitations. Indeed, the additional set of 4 control inputs actuating the propeller tilting angles is shown to yield full actuation to the quadrotor position/orientation in space, thus allowing it to behave as a fully-actuated flying vehicle. We then develop a comprehensive modeling and control framework for the proposed quadrotor, and subsequently illustrate the hardware/software specifications of an experimental prototype. Finally, the results of several simulations and real experiments are reported to illustrate the capabilities of the proposed novel UAV design.
A New Effective Information Decomposition Approach for Missing Data Recovery

It is well recognized that missing data could cause severe problem in data mining. Due to its importance lots of work has been done in the past. Several algorithms [5-8] are proposed for missing data recovery. This paper presents a new 1-dimensional linear information decomposition (1DLID) approach which is easier for use in missing data recovery. In this article, we study one particular problem, in which 1-dimensional data set is given and certain percentage of data are missing without any other additional information. Then the proposed 1-DLID method is used for creating the complete data set from both the generated data set and realworld data set. Comparatively, our experiments showed that the proposed method is reliable and can be used for the recovery of data set with missing values. The advantages of the proposed method are: 1) Will not change the distribution of the data set. 2) Easy to use for 1-dimensional dataset. 3) Have a higher accuracy, especially there is 10%~30% data missing. 4) No need to provide the historical data set.
FUNCTIONAL INVOLVEMENT OF RAT ORGANIC ANION TRANSPORTER 2 (SLC22A7) IN THE HEPATIC UPTAKE OF THE NONSTEROIDAL ANTI-INFLAMMATORY DRUG KETOPROFEN

Rat organic anion transporter 2 (rOat2, Slc22a7) is a sinusoidal multispecific organic anion transporter in the liver. The role of rOat2 in the hepatic uptake of drugs has not been thoroughly investigated yet. rOat2 substrates include nonsteroidal anti-inflammatory drugs, such as ketoprofen, indomethacin, and salicylate. In the present study, the uptake of ketoprofen, indomethacin, and salicylate by freshly isolated rat hepatocytes was characterized. The uptake of ketoprofen, indomethacin, and salicylate by hepatocytes was sodium-independent, and the rank order of their uptake activities was indomethacin > ketoprofen > salicylate. Kinetic analysis based on Akaike's Information Criterion suggested that the uptake of ketoprofen and indomethacin by hepatocytes consists of two saturable components and one nonsaturable one. The Km and Vmax values for the high- and low-affinity components for ketoprofen uptake were 0.84 and 97 μM and 35 and 1800 pmol/min/mg protein, respectively, whereas those for indomethacin were 1.1 and 140 μM and 130 and 16,000 pmol/min/mg protein, respectively. The Km values of the high-affinity component were similar to those for rOat2 (3.3 and 0.37 μM for ketoprofen and indomethacin, respectively). The uptake of ketoprofen by hepatocytes was significantly inhibited by probenecid and rOat2 inhibitors (indocyanine green, indomethacin, glibenclamide, and salicylate). Other inhibitors of rOatps (taurocholate and pravastatin) and rOat3 (pravastatin and p-aminohippurate) had a slight effect, but digoxin had no effect. These results suggest that rOat2 accounts partly for the hepatic uptake of ketoprofen and, presumably, indomethacin as a high-affinity site and that other transporters, such as rOatps, but not rOatp2, and rOat3, are also involved.
Mining for Building a Biomedical Knowledge Base on Diseases , Risk Factors , and Symptoms

In view of today’s information avalanche, well structured knowledge bases play an important role in simplifying the access to knowledge and its further processing. In the biomedical domain, research results holding important information are hidden in publications or online forums in the form of unstructured free texts. Determining and storing relational information into machine-readable data is therefore crucial to advance the scientific research. In this work we introduce a system providing convenient access to knowledge about environmental and behavioral factors involved in human diseases, as well as body parts and symptoms that are affected and caused by diseases. The system is capable of automatically extracting relations between these entities from textual Web sources. Our knowledge base is bootstrapped by integrating entities from hand-crafted and well organized sources like MeSH, OMIM and UMLS. As these are short on relationships between different types of biomedical entities, this system employs flexible and robust pattern learning and constraint-based reasoning methods to automatically extract new relational facts from textual sources, which are then added to the knowledge base. The result is a semantic graph of typed entities and relations between diseases, their symptoms, affected body parts, and determining factors, with emphasis on behavioral and environmental factors, including molecular determinants. The facts stored in our knowledge base are provided to the user in a Web-browser interface. We validated our approach on the basis of four data sets on diseases and their factors gained from different sources. With our approach, we were able to achieve a precision of >80%, a recall of >75%, and thus F1-score of >77%. Affirmation in Lieu of an Oath I, Min Ye, hereby confirm that I have written this thesis on my own and that I have not used any other media or materials than the ones referred to in this thesis. The work presented in this thesis has not been presented in this or any similar way to any other examination procedure until now. Saarbrücken, March 31, 2011, .