Miracle-dz/UniFAR-FTUs huggingface dataset:
Hyper-spectral impulse denoising: A row-sparse Blind Compressed Sensing formulation

This paper addresses the problem of impulse denoising from hyper-spectral images. Impulse noise is sparse; removing impulse noise requires minimizing an l1-norm data fidelity term. Prior studies have exploited the intra-band spatial correlation (leading to sparsity in transform domain) and inter-band spectral-correlation (joint-sparsity) of hyper-spectral images for Gaussian denoising. In this work, we propose to learn the joint-sparsity promoting dictionary adaptively from the data for impulse denoising problems. Unlike dictionary learning techniques, the sparsifying dictionary is not learnt in an offline training phase. We follow the Blind Compressed Sensing (BCS) framework - dictionary learning and denoising proceeds simultaneously. The optimization problem that arises out of our formulation is solved using the Split Bregman approach. The proposed algorithm, when compared against prior techniques (on real hyper-spectral datasets) shows more than 5dB improvement in PSNR on average.
Object tracking using SIFT features in a particle filter

This paper adds sift matching features into the particle filter tracking framework based on color histogram feature, and proposes a dual character tracking algorithm, in which the particle weights are calculated considering both the sift matching features and the color histogram feature. Experimental results show that the algorithm can effectively solve the problem of accumulating errors when inappropriately update the reference template in particle filter simply based on color histogram, especially in cases that the illumination changes or the color feature differences between the background and the target or the targets are relatively small, which enhances the robustness of the system.
A Novel Agent-Based Simulation Framework for Sensing in Complex Adaptive Environments

In this paper, we present a novel formal agent-based simulation framework (FABS). FABS uses formal specification as a means of clear description of wireless sensor networks (WSNs) sensing a complex adaptive environment. This specification model is then used to develop an agent-based model of both the WSN as well as the environment. As proof of concept, we demonstrate the application of FABS to a boids model of self-organized flocking of animals monitored by a random deployment of proximity sensors.
A method for detecting watermarks in print using smart phone: finding no mark

A method has been developed for detecting watermarks using a smartphone. A smartphone with a reader application based on this method and held over a watermarked printed image can detect a watermark in the image and use it to obtain corresponding product information. To reduce the computational complexity of a full search for each possible geometrical correction of the image, the locations where watermarks are not embedded are determined in advance. Image quality is maintained by not embedding watermarks for calibration purposes. System testing demonstrated that the developed method is practical to some degree under the constraint that the camera must be positioned within a restricted search range with respect to the printed image.
A Modified Evolving Graph for Reliable Bi-Directional Routing on Highways in VANET

Wireless ad hoc network is a distributed type of network where it forms and deforms the network spontaneously and automatically. One of the types of ad hoc network is a Vehicular ad-hoc networks (VANETs), a promising technology enable communication among the vehicles and between vehicle and road side units. Vehicles are travelling with a high speed so, the communication links are highly vulnerable to disconnection. This paper has focused the reliable routing on highways scenario. Link reliability model is developed to calculate the reliability between the vehicles and estimate the most reliable path from source to destination. VANET have the problem of frequent disconnection of communication links and recurrent changes in topology. For an efficient routing assumed that vehicles are moving in same directional. This paper proposes a red rover algorithm for vehicles are moving in bi-directional. Simulation results shows that proposed protocol outperforms the existing protocols.
Connectivity-based error evaluation for ellipse fitting

We propose a new method for fitting an ellipse to a point sequence extracted from an image. This method can fit an ellipse if a point sequence consists of elliptic arcs and non-elliptic arcs such as line segments. Assuming that input points are spatially connected, we iteratively select inlier points and fit an ellipse to them by computing curvatures of the residual graph. By using simulated data and real images, we compare the performance of our method with existing methods and show that the accuracy and computation time of the proposed method is superior to existing methods.
A Decision Model of Adaptive Interaction Selection for a Robot Companion

This paper is motivated by two ideas. First, establishing a system for a robot companion that is capable to switch between different types of interaction according to the human’s needs. Second, representing the system with a model that outperforms Partially Observable Markov decision processes (POMDPs) for large-scale problems. For this end, we propose a unified model of Human-Robot Multi-Type Interaction (HRMTI). The objective is to observe the human’s behavior and try to predict/estimate the human’s intention/need and therefore react appropriately (assist, cooperate, collaborate, . . . ). POMDPs are largely used by the community of collaborative and assistive Human-Robot Interaction (HRI). However, solving an HRMTI problem for large-scale applications by employing POMDPs is intractable. We present an approach to overcome this limitation by dividing the problem of HRMTI to three levels: first, estimate the human intention; second, select the appropriate type of interaction; last, choose one of the pre-calculated policies with respect to the human intention and the needed type of interaction. We present the unified model in detail, it includes: a belief update model concerning the intention of the human using multiple MDP models defined by empathy for each specific human task; an MDP model that supports the robot’s decision making towards the human needs by selecting the appropriate interaction type; finally, an algorithm that first defines the task to accomplish by the robot, then chooses the pre-calculated policy to achieve it with respect to the type of interaction. We present some interesting performance and scalability analysis and, with a representative scenario inspired from robocup-at-work, we present an implementation on a real robot showing the feasibility of the approach.
Optimum parameters for MPEG-4 data over wireless sensor network

Nowadays, video streaming application is widely used in wired and wireless environment. Extending this application into Wireless sensor network (WSN) applications featuring low data rate transmission, low energy consumption, ease of deployment and low cost has attracted lots of attention in the research community. However, video transmission over such network is more challenging because of the large amount of bandwidth required. To cater this problem, video compression is of utmost importance to decrease the amount of bandwidth required over WSN. MPEG-4 video codec is one of the compression scheme that was identified to be suitable for WSN environment. In this paper, a simulation study for MPEG-4 video encoding scheme based on an experimental model was carried out to determine conformance with IEEE 802.15. 4 requirements. The results obtained from this paper would be used as a benchmark for the configuration of the video encoding scheme for WSN applications. There are three parameters that we are concerned with in this experiment, which are quantization scale, group of picture (GOP) and frame rate (fps). The results from this simulation study shows that an optimal selection of the parameters value that enhances the video transmission over WSN.
Multi-target device-free tracking using radio frequency tomography

This paper examines device-free tracking of multiple targets based on radio-frequency received signal strength (RSS) measurements recorded by a sensor network. We report experimental results that validate, for relatively uncluttered outdoor environments, a recently proposed model in which targets cause additive attenuation. Using this model, we examine the performance of three multi-target tracking algorithms using a experimental sensor network testbed consisting of 24 nodes that conducts surveillance of an outdoor area of size 50m2. The experiments are restricted to the case of a fixed number of targets (up to four). For four targets, all algorithms are able to track with average error less than 1m (as measured using the second-order OMAT metric); for two targets the error is close to 0.2m.
A Cloud Climatology of the Southern Great Plains ARM CART

Abstract Cloud amount statistics from three different sources were processed and compared. Surface observations from a National Centers for Environmental Prediction dataset were used. The data (Edited Cloud Report; ECR) consist of synoptic weather reports that have been edited to facilitate cloud analysis. Two stations near the Southern Great Plains (SGP) Cloud and Radiation Test Bed (CART) in north-central Oklahoma (Oklahoma City, Oklahoma and Wichita, Kansas) were selected. The ECR data span a 10-yr period from December 1981 to November 1991. The International Satellite Cloud Climatology Project (ISCCP) provided cloud amounts over the SGP CART for an 8-yr period (1983–91). Cloud amounts were also obtained from Micro Pulse Lidar (MPL) and Belfort Ceilometer (BLC) cloud-base height measurements made at the SGP CART over a 1-yr period. The annual and diurnal cycles of cloud amount as a function of cloud height and type were analyzed. The three datasets closely agree for total cloud amount. Good agreement w.
Presentation Trainer, your Public Speaking Multimodal Coach

The Presentation Trainer is a multimodal tool designed to support the practice of public speaking skills, by giving the user real-time feedback about different aspects of her nonverbal communication. It tracks the user's voice and body to interpret her current performance. Based on this performance the Presentation Trainer selects the type of intervention that will be presented as feedback to the user. This feedback mechanism has been designed taking in consideration the results from previous studies that show how difficult it is for learners to perceive and correctly interpret real-time feedback while practicing their speeches. In this paper we present the user experience evaluation of participants who used the Presentation Trainer to practice for an elevator pitch, showing that the feedback provided by the Presentation Trainer has a significant influence on learning.
Concurrent object oriented programming with Asynchronous

In the very next future, applications will have to be highly concurrent to get some speedup from the new trend of chip-multithreading processor architecture. But writing concurrent applications is provably more difficult than developing sequential ones. We define three objectives a concurrent programming model should target to be massively adopted by the developer community. We propose such a model for imperative object oriented languages based on an extension of the standard synchronous reference called Asynchronous Reference that naturally provides the Asynchronous Method Invocation paradigm. Our model, though simple, has many interesting side effect issues such as interand intra-object concurrency degree, asynchronous policy mapping, asynchronous policy sharing, non-determinism proportion and exception handling. Our proposition can be seen as a base brick on top of which many different concurrent frameworks can be built upon. The open-source Mandala framework includes a Java implementation of our proposition.
Balance and Filtering in Structured Satisfiable Problems

New methods to generate hard random problem instances have driven progress on algorithms for deduction and constraint satisfaction. Recently Achlioptas et al. (AAAI 2000) introduced a new generator based on Latin squares that creates only satisfiable problems, and so can be used to accurately test incomplete (one sided) solvers. We investigate how this and other generators are biased away from the uniform distribution of satisfiable problems and show how they can be improved by imposing a balance condition. More generally, we show that the generator is one member of a family of related models that generate distributions ranging from ones that are everywhere tractable to ones that exhibit a sharp hardness threshold. We also discuss the critical role of the problem encoding in the performance of both systematic and local search solvers.
Application of Modified Gravitational Search Algorithm to Solve the Problem of Teaching Hidden Markov Model

Hidden Markov Model is a finite series of states that is continues with a probability distribution in a special state, an output can be obtained by continuous probability distribution. Since states are hidden from outside, this model is called Hidden Markov Model. In ordinary Markov Model, the position is directly visible to observer so probabilities transference state will be the only parameters. In Hidden Markov Model, the position is not visible directly but the affected variants by the position are visible. Each state taken for a possible output will have a probability distribution. Therefore, the sequence of taken states created by HMM would provide some information about the sequence state. Hidden Markov Models will be distinguished for their instruction in identifying the temporary patterns such as speech, handwriting, identifying hint and pointing, bioinformatics and so on. In this paper, a new method based on Modified Gravitational Search Algorithm (MGSA) has been used to improve the teaching of Hidden Markov Model (HMM). The teaching of HMM is based on Baum-Welch algorithm (BW). One of the problems of HMM teaching is the absence of any assurance about reaching of this algorithm to global optimum and the convergence of this method is often towards a local optimum. In this paper, the Modified Gravitational Search Algorithm has been used to exit Baum-Welch from local optimum and search for other optimal points. Furthermore, we have compared the proposed algorithm with two algorithms, PSO and Ant Colony, which have been used finally in Speech Recognition.
Frailty and Its Impact on Health-Related Quality of Life: A Cross-Sectional Study on Elder Community-Dwelling Preventive Health Service Users

Background The purpose of this study was to identify the incidence of frailty and to investigate the relationship between frailty status and health-related quality of life (HRQoL) in the community-dwelling elderly population who utilize preventive health services. Methods People aged 65 years and older who visited a medical center in Taipei City from March to August in 2011 for an annual routine check-up provided by the National Health Insurance were eligible. A total of 374 eligible elderly adults without cognitive impairment had a mean age of 74.6±6.3 years. Frailty status was determined according to the Fried frailty criteria. HRQoL was measured with Short Form-36 (SF-36). Multiple regression analyses examined the relationship between frailty status and the two summary scales of SF-36. Models were adjusted for the participants' sociodemographic and health status. Results After adjusting for sociodemographic and health-related covariables, frailty was found to be more significantly associated (p<0.001) with lower scores on both physical and mental health-related quality of life summary scales compared with robustness. For the frailty phenotypes, slowness represented the major contributing factor in the physical component scale of SF-36, and exhaustion was the primary contributing factor in the mental component scale. Conclusion The status of frailty is closely associated with HRQoL in elderly Taiwanese preventive health service users. The impacts of frailty phenotypes on physical and mental aspects of HRQoL differ.
An Energy-Efficient Unified Register File for Mobile GPUs

The programmability of mobile GPUs have raised in recently years, where the shaders inside are instructed by shading programs for realistic 3D effects. The register files for a conventional high throughput multithreaded shader consumes 10% to 20% energy of it. However the register usages of shading program are quite low. In order to reduce the dynamic energy for register file in a multithreaded mobile shader, this paper proposed an unified register file design to reduce both dynamic and leakage energy of it. The result shows that proposed design reduces 85% of dynamic energy in a multithreaded register file. Furthermore, the proposed design reduces 59% of leakage energy and 25% of area with negligible performance degradation. Also, the energy savings in proposed designs are at least 75% more than related work.
Smart Mesoporous Nanomaterials for Antitumor Therapy

The use of nanomaterials for the treatment of solid tumours is receiving increasing attention by the scientific community. Among them, mesoporous silica nanoparticles (MSNs) exhibit unique features that make them suitable nanocarriers to host, transport and protect drug molecules until the target is reached. It is possible to incorporate different targeting ligands to the outermost surface of MSNs to selectively drive the drugs to the tumour tissues. To prevent the premature release of the cargo entrapped in the mesopores, it is feasible to cap the pore entrances using stimuli-responsive nanogates. Therefore, upon exposure to internal (pH, enzymes, glutathione, etc.) or external (temperature, light, magnetic field, etc.) stimuli, the pore opening takes place and the release of the entrapped cargo occurs. These smart MSNs are capable of selectively reaching and accumulating at the target tissue and releasing the entrapped drug in a specific and controlled fashion, constituting a promising alternative to conventional chemotherapy, which is typically associated with undesired side effects. In this review, we overview the recent advances reported by the scientific community in developing MSNs for antitumor therapy. We highlight the possibility to design multifunctional nanosystems using different therapeutic approaches aimed at increasing the efficacy of the antitumor treatment.
Improving colorization-based coding by using local correlation between luminance and chrominancein texture component

Recently, a novel approach to color image compression based on colorization has been presented. Although the conventional method of colorization-based coding outperforms JPEG in terms of subjective quality, the decoded chrominance components lose the local oscillation that the original images had. A large number of color assignations is required to restore these oscillations. We focus on the local correlation that exists between luminance and chrominance in separated texture components, and we present a new colorization-based coding method. Experimental results showed that our coding method can restore the oscillation and improve the coding efficiency compared with the conventional method.
Scalability of 2-D wavelet transform algorithms: analytical and experimental results on coarse-grained parallel computers

We present analytical and experimental results for the scalability of 2-D discrete wavelet transform algorithms on coarse-grained parallel architectures. The principal operation in the 2-D DWT is the filtering operation used to implement the filter banks of the 2-D subband decomposition. We derive analytical results comparing time domain and frequency domain parallel algorithms for realizing the filter banks. Experiments on the Intel Paragon validate the analytical results. We demonstrate that there exist combinations of the machine size, image size, and wavelet size for which the time-domain algorithms outperform the frequency domain algorithms, and vice-versa.
The neglected universals: Learnability constraints and discourse cues

Abstract Converging findings from English, Mandarin, and other languages suggest that observed “universals” may be algorithmic. First, computational principles behind recently developed algorithms that acquire productive constructions from raw texts or transcribed child-directed speech impose family resemblance on learnable languages. Second, child-directed speech is particularly rich in statistical (and social) cues that facilitate learning of certain types of structures.
Streamline : Efficient OS Communication Through Versatile Streams

Streamline is a stream-based communication subsystem that spans from embedded hardware to userspace processes. It improves performance of memory and I/O bound applications by constructing tailor-made datapaths for each application at runtime. Datapath optimisation removes unnecessary copying, context switching and cache replacement. It simplifies integration of embedded and distributed hardware. Streamline automates datapath optimisation and only presents users a clear, concise request language. Streamline exports the BSD sockets, POSIX file & pipe and PCAP interfaces. Observed performance improvements compared to Linux are up to an order of magnitude.
Pedagogical characteristics of classroom learning tasks: a three-dimensional methodological framework

Abstract In recent years considerable advances in both theoretical formulation and in methodologies have appeared in the Educational effectiveness research field. Yet there is a significant lack of studies that are focused on the effectiveness of learning tasks. Structured content analysis of learning tasks can provide information about learning opportunities and therefore, it has the potential to refine this field. The goal of this study was to describe and demonstrate a methodological framework for evaluating the quality of learning tasks based on innovative developments in the field of education. The methodological framework includes three dimensions: educational principles and practices, high-order thinking skills, and psychological approaches to learning. The methodology was tested in four learning contexts: inter-disciplinary inquiry lessons; mathematics; sciences; and extensive narrative subject lessons. Pedagogical differences were characterised. The tool makes several theoretical and practical contributions: It can serve researchers in pedagogical characterisation processes; it can advance teachers’ professional development with a focus on the development of the ‘new teacher’ as a designer of effective learning environments; and it can be used to mediate between theory and practice.
Low { Bandwidth Access : An Evaluation ofApplication Level Protocol

Wide area wireless networks will have limited bandwidth. One standard approach for increasing the eeective throughput on such links is to employ data compression. In conjunction with any data compression supplied by applications, low{bandwidth systems may also use hardware data compression provided by the data communications device. Unfortunately, compression at such a low level can cause problems with both traac analysis and cost estimation on limited bandwidth links. This investigation identiies some of the issues that arise from employing hardware compression and of monitoring low{bandwidth systems. It describes a compression investigation using CPU based link level compression rather than hardware compression. The results of this investigation, in conjunction with a previous study identifying the traac characteristics of low{bandwidth links, detail the compressibility statistics for many of the application level protocols typically owing over current low{bandwidth networks. This protocol compressibility information quantiies the general performance of packet compression on low{bandwidth traac and gives insight as to how compression schemes may be improved for a wireless low{bandwidth environment.