Miracle-dz/UniFAR-FTUs huggingface dataset:
Detection of Voids on Concrete Surface Using Deep Learning Model

This paper outlines a study on the effectiveness of deep learning techniques at detecting voids given photographs of concrete surfaces. The proposed deep learning model makes use of a convolutional neural network (CNN) and an artificial neural network (ANN). The model was trained on a dataset of 4,032 images of sizes 32x32 and 128x128, and subsequently obtained a training accuracy of 92.08% and a validation accuracy of 89.08%. Furthermore, the trained model was used to develop an automatic detector for detecting and locating voids on the surface of the concrete using the sliding window technique. The performance of the detector was then evaluated on 8 new images that the model had not encountered during training. It was found that a large window size was more effective at detecting large voids but at the expense of detecting smaller voids. Also, a small window size was effective at detecting small voids but struggled at detecting large voids. Finally, the detector was used to determine if there was a relationship between the percentage area occupied by voids and actual porosity. It was found that the relationship between the percentage area covered by voids and actual porosity was unclear.
Resource Allocation Algorithms for Event-Based Enterprise Systems

Resource Allocation Algorithms for Event-Based Enterprise Systems Alex King Yeung Cheung Doctor of Philosophy Graduate Department of Electrical and Computer Engineering University of Toronto 2011 Distributed event processing systems suffer from poor scalability and inefficient resource usage caused by load distributions typical in real-world applications. The results of these shortcomings are availability issues, poor system performance, and high operating costs. This thesis proposes three remedies to solve these limitations in content-based publish/subscribe, which is a practical realization of an event processing system. First, we present a load balancing algorithm that relocates subscribers to distribute load and avoid overloads. Second, we propose publisher relocation algorithms that reduces both the load imposed onto brokers and delivery delay experienced by subscribers. Third, we present “green” resource allocation algorithms that allocate as few brokers as possible while maximizing their resource usage efficiency by reconfiguring the publishers, subscribers, and the broker topology. We implemented and evaluated all of our approaches on an open source content-based publish/subscribe system called PADRES and evaluated them on SciNet, PlanetLab, a cluster testbed, and in simulations to prove the effectiveness of our solutions. Our evaluation findings are summarized as follows. One, the proposed load balancing algorithm is effective in distributing and balancing load originating from a single server to all available servers in the network. Two, our publisher relocation algorithm reduces the average input load of the system by up to 68%, average broker message rate by up to 85%, and average delivery delay by up to 68%. Three, our resource allocation algorithm reduces the average broker message rate even further by up to 92% and the number of allocated brokers by up to 91%.
Tutorial on Hidden Markov Model

Hidden Markov model (HMM) is a powerful mathematical tool for prediction and recognition. Many computer software products implement HMM and hide its complexity, which assist scientists to use HMM for applied researches. However comprehending HMM in order to take advantages of its strong points requires a lot of efforts. This report is a tutorial on HMM with full of mathematical proofs and example, which help researchers to understand it by the fastest way from theory to practice. The report focuses on three common problems of HMM such as evaluation problem, uncovering problem, and learning problem, in which learning problem with support of optimization theory is the main subject.
Tool Supported Analysis of Web Services Protocols

We describe an abstract protocol model suitable for modelling of web services and other protocols communicating via unreliable, asynchronous communication channels. The model is supported by a tool chain where the first step translates tables with state/transition protocol descriptions, often used e.g. in the design of web services protocols, into an intermediate XML format. We further translate this format into a network of communicating state machines directly suitable for verification in the model checking tool UPPAAL. We introduce two types of communication media abstractions in order to ensure the finiteness of the protocol state-spaces while still being able to verify interesting protocol properties. The translations for different kinds of communication media have been implemented and successfully tested, among others, on agreement protocols from WS-Business Activity.
Patient-Centered E-Health Design

This chapter introduces a series of techniques and tools useful for developing patient-centered e-health. As information technology (IT) is revolutionizing health care delivery, a wide range of personal health information management tools have become available to the patients. The variety and quality of information delivered by these tools will determine how useful consumers find them. Equally important is how the information is delivered. To create quality e-health, designers must attend to the needs and wants of users by engaging them in the design and testing processes. User-centered design (UCD) is a formal approach to ensuring that new products address the needs, wants, skills, and preferences of the user throughout the tool’s development. UCD is a design and evaluation process which pays special attention to the intended users, what they will do with the product, where they will use it, and what features they consider essential. This iterative approach ensures that users’ needs and wants are met and ultimately increases the likelihood users will accept the final product. This chapter focuses on UCD methods and techniques, giving examples of how to use them and when.
A deep learning computer vision system for image based cytometry

A Complete Blood Count (CBC) is one of the most commonly deployed tests for checking the overall medical condition of a human body. It results in a summary of blood cell related measures allowing for a quick preliminary detection of common diseases. Image based cytometry is a modern way of overcoming common efficiency issues related to current gold standard method of Coulter Counter analysis. This thesis describes a computer vision pipeline for measuring main components of CBC based on methods with varied level of human influence in feature design. The low quality input images are acquired from a prototype lensless microscope. Rule-based approaches proved sufficient in cases of red blood cells and platelets analysis and their configuration is explored in more details at the segmentation stage. Leukocyte differentiation is a subproblem of CBC requiring more sophisticated classification approach. The comparison of results based on rule-based, classical and deep learning methods is presented. Support Vector Machines and k-Nearest Neighbours were chosen as representatives of classical approaches. The deep learning scheme has the advantage of finding quick solutions to complex problems involving highly dimensional and hierarchical structure of underlying solutions. It also alleviates the amount of work needed by human experts to design appropriate descriptors. The results discussed here include promising enumeration and feature description of the objects of interest. Their linear dependence with gold standard clinical results proved their usability for point of care blood analysis. Classification results showed superiority of convolutional neural networks in several important aspects. Further analysis explored the learned parameters and analyzed wrongly classified cases in order to understand better what kind of cytological objects are most fragile for particular classifier and why.
Gravity waves trapped under the leading anvil of an MCS during BAMEX

Gravity waves are common features in and around deep moist convection. These gravity waves are dispersive and act in response to vertical fluctuations in heating and momentum caused by the convection. Two general types of waves can be generated by these fluctuations: Low frequency modes and high frequency modes (Mapes 1993; Fovell et al. 2006, hereafter referred to as F06). The low frequency waves result from the slow, consistent release of latent heat associated with the mesoscale structure of the convection. Sporadic variations in this heating within the convective cells result in the high frequency waves. The low frequency waves are hydrostatic; therefore, their energy cannot propagate vertically. The high frequency waves are not hydrostatic so their energy can propagate vertically. In many situations, the energy from these high frequency waves simply passes through the troposphere and into the stratosphere. However, if the vertical profiles of stability and wind shear are favorable, trapping or ducting of the waves can occur. Fovell (2002) and others showed that the low frequency waves and other mesoscale circulations can aid in producing favorable profiles for wave trapping in the pre-squall line environment. This pre-line trapping is very similar to the trapping of gravity waves by the trailing anvil described by Yang and Houze (1995). Trapping of these high
Application of structural EM algorithm to learning Bayesian networks for small sample

Existing data sets of cases can significantly reduce the knowledge engineering effort required to learning Bayesian networks.When a data set is small,many conditioning cases are represented by too few or no data records and they do not offer sufficient basis for learning Bayesian networks.It is proposed a method that combines data revising and the Bayesian Structural EM algorithm.Experimental results show that this method is effective in learning Bayesian networks from small data set.
UNED at CLEF RepLab 2014: Author Proling

This paper describes a learning system developed for the RepLab 2014 author proling task at UNED. The system uses a vot- ing model, which employs a small set of features based mainly on the tweet text information such as POS tags, number of hashtags or number of links. In the unocial run, the feature set was increased with Twit- ter metadata such as number of followers or retweet speed. The system achieved good results in author categorisation, although its performance in author ranking was low.
Extracting clauses in dialogue corpora: Applications to spoken language understanding

In this paper, we discuss a method for identifying and extracting clauses in utterances of human-human and human-machine dialog corpora. We illustrate the utility of these clauses in the context of robust spoken language understanding. Robust spoken language understanding in large-scale conversational dialog applications is usually performed by classification of the user utterances into one or many semantic classes. The features used for classification are sensitive to variations that are natural in spoken language, such as edits, repairs and other dysfluencies. Furthermore, the performance of these classifiers typically degrades when the user's utterance contains multiple clauses resulting in multiple semantic classes. We present a semantic classification technique that first automatically removes dysfluencies and segments the user's utterance into clauses and then classifies the utterance based on the classification of the clauses. We show that this preprocessing improves the semantic classification accuracy for utterances and significantly decreases the amount of training data needed for a given level of classification accuracy.
A GPU Accelerated Discontinuous Galerkin Conservative Level Set Method for Simulating Atomization

This dissertation describes a process for interface capturing via an arbitrary-order, nearly quadrature free, discontinuous Galerkin (DG) scheme for the conservative level set method (Olsson et al., 2005, 2008). The DG numerical method is utilized to solve both advection and reinitialization, and executed on a refined level set grid (Herrmann, 2008) for effective use of processing power. Computation is executed in parallel utilizing both CPU and GPU architectures to make the method feasible at high order. Finally, a sparse data structure is implemented to take full advantage of parallelism on the GPU, where performance relies on well-managed memory operations. With solution variables projected into a kth order polynomial basis, a k + 1 order convergence rate is found for both advection and reinitialization tests using the method of manufactured solutions. Other standard test cases, such as Zalesak’s disk and deformation of columns and spheres in periodic vortices are also performed, showing several orders of magnitude improvement over traditional WENO level set methods. These tests also show the impact of reinitialization, which often increases shape and volume errors as a result of level set scalar trapping by normal vectors calculated from the local level set field. Accelerating advection via GPU hardware is found to provide a 30x speedup factor comparing a 2.0GHz Intel Xeon E5-2620 CPU in serial vs. a Nvidia Tesla K20 GPU, with speedup factors increasing with polynomial degree until shared memory is filled. A similar algorithm is implemented for reinitialization, which relies on heavier use of shared and global memory and as a result fills them more quickly and produces smaller speedups of 18x.
Determination of radon concentrations near mobile towers in selected cities of Babylon governorate, Iraq

Determination of radon concentration in a dwelling is preferred method for monitoring the internal exposure of radiological contaminants. Measurement the concentration of radon and their daughter products in indoor and outdoor of dwelling for Al-Medhatyah and Al-Hashimiyah cities, Babylon governorate near mobile stations were carried out by nuclear track detectors (CR-39) in a bare mode. The results show that the concentrations of indoor radon were found to vary from 85.51 Bq/m3 to 157.48 Bq/m3 with an average value of 131.43 ± 20.71 Bq/m3.This values was lower than the ICRP reco mmended values of (200 – 300 Bq/m3) and thus is within safe limits. The outdoor radon concentrations was vary fro m 100.75 Bq/m3 to 110.06 Bq/m3 with average 105.26 ± 3.8 Bq/m3, the outdoor radon concentration is usually low and less than average indoor levels. The equilibriu m-equivalent radon concentration (EECRN), potential alpha energy concentration (PAEC), annual exposure to potential alpha energy (EP), annual effective dose (AED) and the lung cancer cases per year per million people (CPPP) were calculated.
On the Principal Component Based Fingerprint Classification Using Directional Images

This study presents a method for fingerprint recognition based on principal component analysis (PCA) and point patterns (minutae) obtained from the directional histograms of a fingerprint. We first employ Principal Component Analysis (PCA) method to compress fingerprint data. The compressed data are then used for directional image representation. After the compressed data are obtained, the process continues with directional image formation, directional image block representation, and fingerprint matching, respectively. Our method determines the direction of each pixel, process the images in blocks and uses directional histograms thus removes the need for thinning. The method gives the same performance as that of the uncompressed data, but reduces computation. Furthermore, the parts of the system that successfully use artificial neural networks (ANN) are mentioned.
Body-fixed SLAM with Local Submaps for Planetary Rover

In traditional Simultaneous Localisation and Mapping (SLAM) algorithms based on Extended Kalman Filtering (EKF-SLAM), the uncertainty of state estimation will increase rapidly with the development of the exploration process and the increase of map area. Likewise, the computational complexity of the EKF-SLAM is proportional to the square of the number of feature points contained in the state variables in a single filtering process. A new SLAM algorithm combining the local submaps and the body-fixed coordinates of the rover is presented in this paper. The algorithm can reduce the computational complexity and enhance computational speed in consideration of the processing capability of the onboard computer. Due to the introduction of local submaps, the algorithm represented in this paper is able to reduce the number of feature points contained in the state variables in each single filtering process. Therefore, the algorithm could reduce the computational complexity and improve the computational speed. In addition, rover body-fixed SLAM could improve the navigation accuracy of a rover and decrease the cumulative linearization error by coordinates transformation during the update process, which is shown in the simulation results.
A proposal of a non-intrusive, global movement analysis of hemiparesis treatment

Hemiparesis is a loss of muscle strength on one side of the body, and the most disabling condition after a stroke. Hemiparetic individuals suffer from a loss of muscle strength on one side of the body, resulting in a decreased capacity of performing movements. To assess the quality of Physiotherapy treatment, rating scales are commonly used but with the shortcoming of being subjective. With the aim of developing a system that objectively outcomes how a hemiparetic individual is responding to a Physiotherapy treatment, this paper proposes a method to analyze human functional movement by means of an apparatus comprised of multiple low-cost RGB-D cameras. After extrinsically calibrating the cameras, the setup system should be able to compose a composite skeleton of the target patient, to globally analyze his/her movement according to a reachable workspace and specific energy. These latter both are proposed to be carried out by tracking the hand movements of the patient, and the movement volume produced. Here we present the concept of the proposed system, as well as, the idea of its parts.
Tangram The Ancient Chinese Shapes Game

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Recognition of Chinese Loan Words in Uyghur Based on String Similarity

There are many Out-Of-Vocabulary words in Uyghur-Chinese machine translation,a large part of them are loan words(including person names,place names,et.al). This paper presents a novel method that recognition the Chinese loan words in Uyghur according to the feature that one loan word pronounce similar with its original word.This method training the existing corpus first,and getting the Uyghur Latin rules that use to recognize Chinese loan word in Uyghur;this paper Latin the Uyghur words according to the rules,Romanization of Chinese words,these transform the sounds similarity to strings similarity which is easy to quantification;proposed three models:Positionrelated Minimum Edit Distance model,Weighted Common Subsequence model and the fusion model that fused above two with parameters.The experimental results show that the fusion model considering strings' global similarity and local similarity,so it gets the best recognition results.
Fuzzy Computational Semantics ∗

Other than clear-cut symbolic representational formats employed sofar in natural language processing by machine, it is argued here, that fuzzy distributional representations correspond directly to the way word meanings are constituted and understood by (natural and artiﬁcial) information processing systems. Based upon such systems’ theoretical performance in general and the pragmatics of communicative interaction by real language users in particular, the notions of situation and language game as introduced by Barwise/Perry and Wittgenstein respectively are combined to allow for a numerical reconstruction of processes that simulate the constitution of meaning and the interpretation of signs. This is achieved by modelling the linear or syn-tagmatic and selective or paradigmatic constraints which natural language structure imposes on the formation of (strings of) linguistic entities. A formalism with related algorithms and test results of their implementation are produced in order to sub-stantiate the claim for a model of a semiotic cognitive information processing system (SCIPS) that operates in a linguistic environment as some meaning acquisition and understanding device.
Coastal ecosystem monitoring using long-term satellite data records: A case study of Chilika Lake, Odisha

Changing trend in coastal ecosystem can be quantified by performing time series analysis. Time series analysis performed using long term remote sensing data will help us to identify the dynamic changes happening in the coastal ecosystem and its surrounding regions. In the present study we performed time series analysis on northern sector of Chilika Lake and its nearby regions of Odisha, which is situated in the east coast of India using three decades of freely available Landsat archive data. In order to detect dynamic changes trend parameters were calculated by using available data sets from Landsat Thematic Mapper (TM) and Operational Land Imager and Thermal Infrared Sensor (OLI/TIRS) for the observation period from 1988 to 2017. Two multi-spectral indices i.e. NDVI and EVI were generated from the available data sets and the trend analyses were performed using Theil-Sen (T-S) regression method by identifying robust trend parameters (slope and pvalue). The average mean values for NDVI and EVI were recorded at 0.4 and 0.2. Significant positive trend was observed in both vegetation indices (NDVI and EVI) with a mean slope value of 0.004 and 0.003 and pvalue of 0.03 and 0.02 respectively.
Review of step-pool hydrodynamics in mountain streams

Step-pools are one of the major types of bed morphology prevalent in mountain streams. They have a unique flow structure as compared to low-gradient streams, in terms of large boundary elements and alternating super-critical and sub-critical flow conditions, which result in a non-uniform flow regime. Step-pools may also be constructed artificially to restore mountain incisions, and for creating close-to-nature fish passes. For hydraulic model development and various design considerations, the accurate prediction of flow phenomenon is required. This necessitates a detailed study of the turbulence phenomenon in natural step-pool reaches and its effect on the total flow. However, the influence of aerated conditions in step-pool hydrodynamics has not yet been adequately addressed. This paper presents a review of the mechanism of flow resistance and energy dissipation in step-pool mountain streams. Also, the significance of incorporating air entrainment in flow analysis, limitations and the way forward in modeling air–water flow in laboratory studies are discussed.