Miracle-dz/UniFAR-FTUs huggingface dataset:
Merging system for multiscale edge detection

We present a multiscale edge detection algorithm whose aim is to detect edges whatever their slope. Our work is based on a gener- alization of the Canny-Deriche filter, characterized by a more realistic edge than the traditional step shape edge. The filter impulse response is used to generate a multiscale edge detection scheme. For the merging of the edge information, we use a geometrical classifier developed in our laboratory. The segmentation system thus set up, after the training phase, does not require any adjustment or depend on any parameter. The main original property of this algorithm is that it leads to a binary edge image without any threshold setting. The quality of the results is inferior to that for classical multiscale merging approaches; nevertheless, this system, studied for real-time functioning, presents satisfactory per- formance for well-contrasted images and excellent performance for noisy
An Ontology-Based Service Composition Approach for Integrating E-Learning Tools

Although Web services technology provides feasibility to integrate different e-learning tools into an e-learning platform, there is not an effective and efficient way to composite different services together. This paper presents an ontology-based service composition approach, which includes an e-learning oriented web service composition model based on ontology, and service composition method that considers both the functional and non-functional requirements of the service, for integrating e-learning software tools. We also compare our method with the other methods by a service composition experiment. The experiment result proves that this approach has much higher performance and efficiency.
Efficient Indexing for Recursive Conditioning Algorithms

In this paper, we consider efficient indexing methods for recursive conditioning algorithms. We compare two wellknown methods for indexing, a top-down method and a bottom-up method, and discuss the redundancy that each of these suffer from. We also present a new method for indexing that is a hybrid of these models. Using this new approach shows an improvement in the amount of indexing operations by 55% or more in our test networks, as well as a reduction in the cumulative time for inference by 33% or more.
Evaluation of static and dynamic task mapping algorithms in NoC-based MPSoCs

Task mapping is an important issue in MPSoC design. Most recent mapping algorithms perform them at design time, an approach known as static mapping. Nonetheless, applications running in MPSoCs may execute a varying number of simultaneous tasks. In some cases, applications may be defined only after system design, enforcing a scenario that requires the use of dynamic task mapping. Static mappings have as main advantage the global view of the system, while dynamic mappings normally provide a local view, which considers only the neighborhood of the mapping task. This work aims to evaluate the pros and cons of static and dynamic mapping solutions. Due to the global system view, it is expected that static mapping algorithms achieve superior performance (w.r.t. latency, congestion, energy consumption). As dynamic scenarios are a trend in present MPSoC designs, the cost of dynamic mapping algorithms must be known, and directions to improve the quality of such algorithms should be provided without increasing execution time. This quantitative comparison between static and dynamic mapping algorithms is the main contribution of this work.
On scheduling optical packet switches with reconfiguration delay

Using optical technology for the design of packet switches/routers offers several advantages such as scalability, high bandwidth, power consumption, and cost. However, reconfiguring the optical fabric of these switches requires significant time under current technology (microelectromechanical system mirrors, tunable elements, bubble switches, etc.). As a result, conventional slot-by-slot scheduling may severely cripple the performance of these optical switches due to the frequent fabric reconfiguration that may entail. A more appropriate way is to use a time slot assignment (TSA) scheduling approach to slow down the scheduling rate. The switch gathers the incoming packets periodically and schedules them in batches, holding each fabric configuration for a period of time. The goal is to minimize the total transmission time, which includes the actual traffic-sending process and the reconfiguration overhead. This optical switch scheduling problem is defined in this paper and proved to be NP-complete. In particular, earlier TSA algorithms normally assume the reconfiguration delay to be either zero or infinity for simplicity. To this end, we propose a practical algorithm, ADJUST, that breaks this limitation and self-adjusts with different reconfiguration delay values. The algorithm runs at O(/spl lambda/N/sup 2/logN) time complexity and guarantees 100% throughput and bounded worst-case delay. In addition, it outperforms existing TSA algorithms across a large spectrum of reconfiguration values.
Serotonergic Innervation of the Salivary Glands and Central Nervous System of Adult Glossina pallidipes Austen (Diptera: Glossinidae), and the Impact of the Salivary Gland Hypertrophy Virus (GpSGHV) on the Host

Using a serotonin antibody and confocal microscopy, this study reports for the first time direct serotonergic innervation of the muscle sheath covering the secretory region of the salivary glands of adult tsetse fly, Glossina pallidipes Austen. Reports to date, however, note that up until this finding, dipteran species previously studied lack a muscle sheath covering of the secretory region of the salivary glands. Direct innervation of the salivary gland muscle sheath of tsetse would facilitate rapid deployment of saliva into the host, thus delaying a host response. Our results also suggest that the neuronal and abnormal pattern seen in viral infected glands by the Glossina pallidipes salivary gland hypertrophy virus (GpSGHV) is due to a compensatory increased branching of the neurons of the salivary glands, which is associated with the increased size of the salivary glands in viral infected flies. This study shows for the first time serotonin in the cell bodies of the brain and thoracico-abdominal ganglion in adult tsetse, G. pallidipes Austen (Diptera: Glossinidae). A hypothesis is proposed as to whether innervation of the muscle sheath covering of the secretory region of the salivary glands is present in brachyceran compared with nematoceran dipterans; and, a plea is made that more research is needed to develop a blood feeding model, similar to that in the blow flies, for elucidating the various mechanisms involved in production and deployment of saliva.
Automatic tracking of face sequences in MPEG video

Human faces are commonly found in video streams and provide useful information for video content analysis. We present a robust face tracking system to extract multiple face sequences from MPEG video without human intervention. Specifically, a view-based DCT-domain face detection algorithm is first applied periodically to capture mostly frontal and slight slanting faces of variable sizes and locations. The face tracker then searches the target faces in local areas across frames in both the forward and backward directions. The tracking combines color histogram matching and skin-color adaptation to provide robust tracking. We focus on developing effective techniques that exploits the features in DCT domain and the characteristics of video compression standards like MPEGs. The effectiveness of the algorithm is demonstrated using a range of videos obtained from multiple sources like the news and movies.
Surface charge based rapid method for detection of microbial contamination in drinking water and food products

— Microbial contamination, most of which are fecal born in drinking water and food industry is a serious threat to humans. Escherichia coli is one of the most common and prevalent among them. We have developed a sensor for rapid and an early detection of contaminants, taking E.coli as a threat indicator organism. The sensor is based on co-polymerizations of aniline and formaldehyde in form of thin film over glass surface using the vacuum deposition technique. The particular doping combination of thin film with Fe-Al and Fe-Cu in different concentrations changes its non conducting properties to p-type semi conductor. This property is exploited to detect the different contaminants, believed to have the different surface charge. It was found through experiments that different microbes at same OD (0.600 at 600 nm) have different conductivity in solution. Also the doping concentration is found to be specific for attracting microbes on the basis of surface charge. This is a simple, cost effective and quick detection method which not only decreases the measurement time but also gives early warnings for highly contaminated samples.
Signal to Noise Issues in Quadrant Detectors

A quadrant detector splits a focused stellar image into four parts or quadrants, with the light in each quadrant being separately detected. The image position is by deenition centered on the detector when these four signals are equal in intensity, while an imbalance implies a centering error. The number of photon events registered in each quadrant is counted, and latched by electronics, and read every cycle. From these the image position can be calculated, and the appropriate signals sent to adaptive mirrors to re-center the image. Quadrant detector behaviour can be used to study the wavefront tilt correction servo a s well as a higher order adaptive optics system. When viewed from the direction of the incoming beam the quadrants are labeled as shown in Figure O.1. We therefore deene the normalized image position as where N A:::D are the weighted number of photon events detected in the four quadrants during the last sample period, and h and v are the normalized horizontal and vertical image positions. If there are no photons detected, the position is deened to be zero in both axes. As this detector system has identical geometry in both the vertical and horizontal axes, we shall only consider the response of the vertical axis. The results for the horizontal follow in an identical manner. After passing through a deening aperture of radius R, the beam is focused onto the quadrant detector. Treating the star as a point source, so Fraunhofer diiraction conditions will apply, results in an Airy disk on the prism surface. Following Born & Wolf 1987, we write the intensity distribution of this Airy disk as
An analysis of z-strip at-cut quartz thickness-shear filters

This paper mainly analyzes the fundamental thickness shear modes of z-strip quartz crystal filter with the scalar equations derived by Tiersten and Smythe. Effects of the mass ratio and the distance between the electrodes on the operation modes and the resonant frequencies are also studied. Exact modes can be obtained and the results show that the operation modes without nodal lines on the electroded area are suitable for filter application. With the mass ration increasing, the vibration frequency decreases. These results are important in the understanding and design of quartz filters.
Improved training algorithms to reduced set vector machine and adaboost cascade classifier for face detection

In this paper we present improved training algorithms to two newly developed classifiers, reduced set vector machines and Adaboost cascade classifier applied in face detection, which are all based on learning from data. Support vector machine (SVM) has been proved to be a powerful tool for solving practical pattern recognition problems based on learning from data. Due to large number of support vectors learnt from huge amount of training data the SVM becomes too computational intensive to many critical problems. Reduced set vector machine (RVM) is a faster approximation of SVM, but calculate a RVM is very difficult. Cascade classifier using Adaboost is a newly proposed method, which is much faster than the SVM and MLP methods and very competitive in performance to the existing systems , but the training is not easy due to feature number option. Our improved training algorithms make training become easier and more reliable and applicable in practice.
Intelligent Gain and Boundary Layer Based Sliding Mode Control for Robotic Systems with Unknown Uncertainties

This paper proposes a intelligent gain and boundary layer based sliding mode control (SMC) method for robotic systems with unknown model uncertainties. For intelligent gain and boundary layer, we employ the self recurrent wavelet neural network (SRWNN) which has the properties such as a simple structure and fast convergence. In our control structure, the SRWNNs are used for estimating the width of boundary layer, uncertainty bound, and nonlinear terms of robotic systems. The adaptation laws for all parameters of SRWNNs and reconstruction error bounds are derived from the Lyapunov stability theorem, which are used for an on- line control of robotic systems with unknown uncertainties. Accordingly, the proposed method can overcome the chattering phenomena in the control efiort and has the robustness regardless of unknown uncertainties. Finally, simulation results for the three-link manipulator, one of the robotic systems, are included to illustrate the efiectiveness of the proposed method.
Rethinking the Design of LR-WPAN IoT Systems with Software-Defined Networking

Wireless Sensor Networks (WSNs) are becoming a key enabling technology for Internet of Things (IoT) by virtue of providing a highly unstructured cloud of wireless devices. Despite these advances, the current Internet architecture is not able to cater to the high volume of new traffic patterns delivered by these smart sensing devices. In this context, Software Defined Networking (SDN) has emerged as an intelligent solution to deliver dramatic improvements in network programmability, agility and flexibility. However, SDN was originally targeted to wired networks deployed in cloud data centers, and does not lend itself well to WSNs due primarily to its higher footprint and lack of WSN programmable interfaces. To address these challenges, this paper describes an approach to realize software-defined wireless sensor networks by introducing a novel SDN-enabled architecture for WSNs that can be used for diverse IoT systems. Specifically, we propose new control plane services for supporting automatic topology discovery, sensor virtualization as well as managing network policies. Additionally, we introduce new customized SDN-enabled flow tables to meet the requirements of sensor network packets. Finally, we introduce a programmable MAC layer to support fine-grained flow processing.
Neural Substrates of Semantic Prospection – Evidence from the Dementias

The ability to envisage personally relevant events at a future time point represents an incredibly sophisticated cognitive endeavor and one that appears to be intimately linked to episodic memory integrity. Far less is known regarding the neurocognitive mechanisms underpinning the capacity to envisage non-personal future occurrences, known as semantic future thinking. Moreover the degree of overlap between the neural substrates supporting episodic and semantic forms of prospection remains unclear. To this end, we sought to investigate the capacity for episodic and semantic future thinking in Alzheimer’s disease (n = 15) and disease-matched behavioral-variant frontotemporal dementia (n = 15), neurodegenerative disorders characterized by significant medial temporal lobe (MTL) and frontal pathology. Participants completed an assessment of past and future thinking across personal (episodic) and non-personal (semantic) domains, as part of a larger neuropsychological battery investigating episodic and semantic processing, and their performance was contrasted with 20 age- and education-matched healthy older Controls. Participants underwent whole-brain T1-weighted structural imaging and voxel-based morphometry analysis was conducted to determine the relationship between gray matter integrity and episodic and semantic future thinking. Relative to Controls, both patient groups displayed marked future thinking impairments, extending across episodic and semantic domains. Analyses of covariance revealed that while episodic future thinking deficits could be explained solely in terms of episodic memory proficiency, semantic prospection deficits reflected the interplay between episodic and semantic processing. Distinct neural correlates emerged for each form of future simulation with differential involvement of prefrontal, lateral temporal, and medial temporal regions. Notably, the hippocampus was implicated irrespective of future thinking domain, with the suggestion of lateralization effects depending on the type of information being simulated. Whereas episodic future thinking related to right hippocampal integrity, semantic future thinking was found to relate to left hippocampal integrity. Our findings support previous observations of significant MTL involvement for semantic forms of prospection and point to distinct neurocognitive mechanisms which must be functional to support future-oriented forms of thought across personal and non-personal contexts.
Fast training of convolutional neural network classifiers through extreme learning machines

This paper presents a fast algorithmic method to train convolutional neural network (CNN) classifiers through extreme learning which has been verified on popular datasets on classification and pedestrian detection. CNN has been one of the best classifiers for images and object recognition. However, the Backpropagation (BP) algorithm, mostly used for training CNN, suffers from slow learning, local minimum, and poor generalization. To solve these problems, a novel architecture called CNN-ELM has been proposed here. Its core architecture is based on a local image (local receptive field) version of the ELM (Extreme Learning Machine) adopting random feature learning. Using MATLAB 2015a, classification experiments using the raw image data as input, show a comparable or mostly better classification performance compared to the BP trained CNN, with its training speed up to 200 times faster on MNIST, NORB, and CIFAR-10 datasets. Pedestrian detection experiments using INRIA datasets also exhibits much faster training than the BP trained CNN without sacrificing detection performance.
Critical Variable Recomputation for Transient Error Detection

This paper presents a technique to derive and implement error detectors to protect an application from data errors. The error detectors are derived automatically using compiler-based static analysis from the backward program slice of critical variables in the program. Critical variables are defined as those that are highly sensitive to errors, and deriving error detectors for these variables provides high coverage for errors in any data value used in the program. The error detectors take the form of checking expressions and are optimized for each control flow path followed at runtime. The derived detectors are implemented using a combination of hardware and software.
VAIT: A Visual Analytics System for Metropolitan Transportation

With the increasing availability of metropolitan transportation data, such as those from vehicle Global Positioning Systems (GPSs) and road-side sensors, it has become viable for authorities, operators, and individuals to analyze the data for better understanding of the transportation system and, possibly, improved utilization and planning of the system. We report our experience in building the Visual Analytics for Intelligent Transportation (VAIT) system, which is the first system on real-life large-scale data sets for intelligent transportation. Our key observation is that metropolitan transportation data are inherently visual as they are spatio-temporal around road networks. Therefore, we visualize and manage traffic data, together with digital maps, and support analytical queries through this interactive visual interface. As a case study, we demonstrate VAIT on real-world taxi GPS and meter data sets from 15 000 taxis running for two months in a Chinese city of over 10 million people. We discuss the technical challenges in data calibration, storage, visualization, and query processing and offer first-hand lessons learned from developing the system. Based on our extensive empirical experiment results, VAIT beats state-of-the-art methods and systems in terms of scalability, efficiency, and effectiveness and offers us an easy-to-use, efficient, and scalable platform to shed more light on intelligent transportation research.
Real-time welfare-maximizing regulation allocation in aggregator-EVs systems

The concept of vehicle-to-grid (V2G) has gained recent interest as more and more electric vehicles (EVs) are put to use. In this paper, we consider a dynamic aggregator-EVs system, where an aggregator centrally coordinates a large number of EVs to perform regulation service. We propose a Welfare-Maximizing Regulation Allocation (WMRA) algorithm for the aggregator to fairly allocate the regulation amount among the EVs. The algorithm operates in real time and does not require any prior knowledge on the statistical information of the system. Compared with previous works, WMRA accommodates a wide spectrum of vital system characteristics, including limited EV battery size, EV self charging/discharging, EV battery degradation cost, and the cost of using external energy sources. Furthermore, our simulation results indicate that WMRA can substantially outperform a suboptimal greedy algorithm.
Development, Characterization, and Pharmacodynamic Evaluation of Hydrochlorothiazide Loaded Self-Nanoemulsifying Drug Delivery Systems

The objective of the current work was to develop optimized self-nanoemulsifying drug delivery systems (SNEDDS) and evaluate their in vitro and in vivo performance. The research comprised various studies which includes solubility studies in various vehicles, pseudoternary phase diagram construction, and preparation and characterization of SNEDDS along with in vitro dissolution and in vivo pharmacodynamic profiling. Based on dissolution profile, a remarkable increase in rate of dissolution was observed in comparison with plain drug and marketed formulation. Optimized SNEDDS formulation was composed of Capmul MCM (19.17% w/w), Tween 80 (57.5% w/w), Transcutol P (12.7% w/w), and HCT (4.17% w/w). In vivo pharmacodynamic evaluation in Wistar rats showed considerable increase in pharmacological effect of HCT by SNEDDS formulation as compared with plain HCT.