Miracle-dz/UniFAR-FTUs huggingface dataset:
Conditional random fields for noisy text normalisation

Conditional Random Fields for Noisy Text Normalisation Dirko Coetsee Department of Electrical and Electronic Engineering, University of Stellenbosch, Private Bag X1, Matieland 7602, South Africa. Thesis: MScEng (E & E) December 2014 The increasing popularity of microblogging services such as Twitter means that more and more unstructured data is available for analysis. The informal language usage in these media presents a problem for traditional text mining and natural language processing tools. We develop a pre-processor to normalise this noisy text so that useful information can be extracted with standard tools. A system consisting of a tokeniser, out-of-vocabulary token identifier, correct candidate generator, and N-gram language model is proposed. We compare the performance of generative and discriminative probabilistic models for these different modules. The effect of normalising the training and testing data on the performance of a tweet sentiment classifier is investigated. A linear-chain conditional random field, which is a discriminative model, is found to work better than its generative counterpart for the tokenisation module, achieving a 0.76% character error rate compared to 1.41% for the finite state automaton. For the candidate generation module, however, the generative weighted finite state transducer works better, getting the correct clean version of a word right 36% of the time on the first guess, while the discriminatively trained hidden alignment conditional random field only achieves 6%. The use of a normaliser as a pre-processing step does not significantly affect the performance of the sentiment classifier. ii Stellenbosch University http://scholar.sun.ac.za
Aspects of the compilation of nested parallel imperative languages

We report on our experiences with the implementation of the imperative nested parallel language V. We give an overview of the compiler and a description of its building blocks and their interplay. We show how functional and imperative constructs such as control structures and pointers are handled by transformation rules. We justify additional restrictions that had to be placed on side effects and imperative constructs and were not initially thought to be necessary.
Observations on solving discrete-event control problems: patterns and strategies

An observational study of solving discrete-event supervisory control problems is described. Five graduate students with knowledge of discrete-event system control were asked to think aloud while solving two supervisory control problems. The problem-solving sessions were recorded using audio and video equipment, and subsequently analysed using protocol analysis. Patterns and strategies in solving discreteevent supervisory control problems are identified and described, such as the role of control specifications and supervisors, the selection of events and the significance of controllability, the representation of discreteevent system elements and relations in the interface of control software, the flexibility necessary for different styles of problem solving, and the techniques for verification of solutions. The implications for teaching discrete-event systems and for designing discrete-event software interfaces are discussed.
TYPE OF PROPOSAL: poster TITLE: User Centred interactive search: A study of humanities researchers in a digital library environment KEYWORDS: Digital library, humanities users, usability

User Centred Interactive Search: A study of humanities researchers in a digital library environment. This poster proposal describes research on humanities users of a digital library (DL). It seeks to understand their needs and behaviour both in digital and more traditional information environments, in order to develop and refine a digital library system, the better to support use in the humanities. This study of humanities users forms part of the larger User Centred Interactive Search (UCIS) project.
An approach to machine learning of Chinese Pinyin-to-character conversion for small-memory application

Chinese Pinyin-to-character conversion is used in Chinese character input through keyboard and Chinese speech recognition. The key of this kind of system is machine learning that fits system for specific user. In this paper, an effective approach of machine learning of Chinese Pinyin-to-character conversion for small-memory application is presented. The approach is based on iterative new word identification and word frequency increasing that results in more accurate segmentation of Chinese character gradually and satisfy the need of user finally. Applying proposed machine learning to Chinese character input system through keyboard improves accuracy of Pinyin-to-character conversion from 90% up to 98%. Such a system can run in very small memory (limited in 120 K) and satisfy the need of small-memory platform. With rapid development of digital appliances such as PDA, mobile telephone, intelligent refrigerator and etc., and with development of embedded operating system, Pinyin-to-character conversion presented in this paper has found its new application.
TCP Proxy with Data Redundancy Elimination and Compressing Technology

TCP protocol is a connection-oriented transmission protocol with mechanisims such as sliding window,data acknowledgement,slow start and congestion avoidance to ensure reliability[1]. But these machanisims bring low efficiency because of delay and loss.Thus,we need to design an improved TCP protocol to achieve the purpose of WAN acceleration.This paper mainly introduces two kinds of WAN accelebration technology of TCP flow optimization and data cache technology.TCP flow optimization uses TCP transparent proxy technology based on an improved TCP protocol to solve the problem of large delay and high packet loss rate.The improved TCP protocol includes slow start improved,large receive buff, the improvement algorithm of congestion avoidance,etc.Besides,data redundancy elimination and compression technology[2] are also used in TCP data interation to reduce link load and improve transmission efficiency.
Accurate performance analysis of indoor distributed antenna systems for W-CDMA

Performance analyses of in building distributed antenna (DA) systems for W-CDMA are presented. Uplink and downlink outage probabilities are related to the transmitted power, the shadowing statistics and the loading of the cell and it is observed that by deploying DA in a multi-floor environment the advantages in terms of both coverage and capacity are significant. However, capacity limitations are present due to the noise rise of home cell power controlled interferers, which give rise over thermal noise. In order to overcome these limitations two more flexible architectures, namely zoning and switching, are presented and compared.
A Survey on Nearest Keyword Set Search in Multi-dimensional Datasets using Index Hashing

Catchphrase predicated seek in content prosperous multi-dimensional datasets encourages numerous novel applications and executes. In this paper, we consider objects that are labeled with catchphrases and are inserted in a vector space. For these datasets, we ponder inquiries that request the most impenetrable gatherings of focuses slaking a given arrangement of watchwords. We propose a novel technique called ProMiSH (Projection and Multi Scale Hashing) that uses self-assertive projection and hash-predicated list structures, and accomplishes high adaptability and speedup. We introduce a correct and an inexact variant of the calculation. Our exploratory outcomes on credible and manufactured datasets demonstrate that ProMiSH has up to 60 times of speedup over cutting edge tree-predicated systems.
Reducing Energy Consumption in Manet Using A Layer Three Routing Protocol

Mobile ad-hoc network (MANET) is an autonomous system based on wireless links in which information is exchanged without having any centralized administration. In this paper, one of its main challenge, the limited battery power is treated. Based on the network layer information an energy reduction algorithm was proposed and the influence of reduced energy consumption over other metrics was studied. The paper pays a special attention to the effects of the hidden terminal problem in mobile wireless network.
Background Subraction in a Video System Using Morphology Technique

------------------------------------------------------ABSTRACT---------------------------------------------------The capability of extracting moving objects from a video sequence captured using a static camera is a typical first step in visual surveillance. The idea of background subtraction is to subtract or difference the current image from a reference background model. This paper proposes a new method to detect moving object based on background subtraction. First of all, we establish a reliable background updating model based on statistical and use a dynamic optimization threshold method to obtain a more complete moving object. Here we have written the core processor Microblaze is designed in VHDL (VHSIC hardware description language), implemented using XILINX ISE 8.1 Design suite the algorithm is written in system C Language and tested in SPARTAN-3 FPGA kit by interfacing a test circuit with the PC using the RS232 cable. The test results are seen to be satisfactory. The area taken and the speed of the algorithm are also evaluated.
Dictionary based coding for ECG data compression

ECG data compression is usually performed in two steps: (a) modelling of the signal and (b) coding of the data that result from the model in (a). A dictionary based coding scheme is proposed that can efficiently handle the arbitrary valued data that result from the modelling phase. The majority of the compression schemes described in the literature employ Huffman and more rarely arithmetic encoders, adopted from text compression, that were not designed to handle data exceeding the range covered by an 8-bit quantity. The coding scheme proposed can handle 16-bit symbols and furthermore is faster and exhibits better compression ratio than 0-order Huffman encoders. <<ETX>>
Towards Internet of Things Interaction Framework Using Geometric Annotated Multimedia Objects

The prevalent visions of ambient intelligence leverage natural interactions between users and available services in a smart space. In recent years, we have seen a huge interest from industry and academia in using handheld devices to interact with things, places and people in the real world. To facilitate such interactions, things are usually annotated with RFID tags or visual markers. These tags or markers are read by a handheld device equipped with an integrated RFID reader or a camera, in order to fetch related information and initiate further actions. Interacting with the Internet of Things (IoT) in a real environment has become increasingly desirable and feasible. This thesis contributes to the domain of physical interactions with IoT; however, we use a spatial-geometric approach instead of RFID or marker based solutions. Using this approach, for example, a user can point his/her handheld device to an annotated thing, from a distance, for the purpose of interaction. The pointing direction and location is determined based on the fusion of the mobile position and of the accelerometer data of the handheld device. To annotate things, their geometric coordinates are specified and related information or services are associated to them. In this thesis, we present a comprehensive and extensible framework to integrate various physical interactions with IoT into multimedia applications. The framework supports the implementations of pointMe, touchMe, and context-aware based interactions with geometrically annotated IoT. We define specific methods and practices that can be incorporated in order to build the interactions. We realize smart home, atlas learning, presentation interaction, smart haptic interaction, and learning based video interaction game prototypes in order to perform experiments and demonstrate the applicability and potential of the proposed geometric based annotation approach. In the analysis of the interaction techniques of the prototypes, we present the advantages and disadvantages of the geometric based annotation of IoT as seen by potential users, in comparison to RFID tags or visual markers based approaches.
AR Tamagotchi : Animate Everything Around Us

As image processing and computer vision(CV) technology become available in real time, Augmented reality(AR) technology enables us to more interact with Real world through the augmented data. As this paradigm, we can interact with everyday object through augmented data such as facial expression. This work describes use of CV and AR to interact with a everyday object as making them animated. For smart device application purpose to interact with everyday object, we developed touch event based object tracking algorithm using the camshift , grabcut and particle filter hybrid system. The Poisson image edit was used for facial expression blending to make look like the object had a facial expression inherent. To make the system fun to use, we adoptted the Tamagotchi storytelling concept, which is a virtual pet simulator. For multiplatform game application purpose and computer vision programming, the AR programming environment was built using the cocos2d-x game engine and OpenCV. In experiments, users felt that AR made them feel that the everyday object was animated, had emotions and could interact with them.
Structured Analysis And System Specification

Follow up what we will offer in this article about structured analysis and system specification. You know really that this book is coming as the best seller book today. So, when you are really a good reader or you're fans of the author, it does will be funny if you don't have this book. It means that you have to get this book. For you who are starting to learn about something new and feel curious about this book, it's easy then. Just get this book and feel how this book will give you more exciting lessons.
Dynamic Bug Triage System with Data Classification Techniques

— Bug triage system is the process of fixing bug, which aims to correctly assign a developer to a new bug. Programming organizations spend more than 45 percent of expense in managing programming bugs. We have proposed an approach to diminish the time cost in manual work, content classification procedures connected to lead programmed bug triage. In this paper, we address the issue of information diminishment for bug triage, i.e. How to lessen the scale and enhance the nature of bug information. In proposed approach we are performing data reduction on bug data set which will reduce the scale of the data as well as increase the quality of the data and then apply k-NN and Support Vector Machine classification or combinational approach of both on the reduced dataset. We are using instance selection and feature selection simultaneously with historical bug data. In this paper we have presented a new module which will describe the status of the bug like whether it assigned to any developer or not and whether the developer has rectified it or not. We consolidate case determination with highlight choice to at the same time lessen information scale on the bug measurement and the word measurement. The goal of bug triaging is to assign potentially experienced developers to new-coming bug reports. We balance the load between developers based on their experience and past history. We conduct experiments on four real data sets. The outcomes demonstrate that our data lessening can successfully decrease the information scale and enhance the exactness of bug triage. Our work gives a way to deal with utilizing systems on information preparing to shape lessened and superb bug information in programming advancement and maintenance.
Infrared and Visible Images Fusion Method Based On Discrete Wavelet Transform

For infrared images, the performance of texture details in the target scene is not good, and for visible images, the performance is constrained by illumination and environment. In this paper, an image fusion method based on Discrete Wavelet Transform (DWT) is proposed. First, the infrared and visible images are decomposed by DWT, the low frequency and high frequency components will be obtained. For the low frequency component, the fusion method is based on the regional energy. For the high frequency components, the fusion method is based on the weighted sum of the difference between the neighboring coefficients. Finally, the fusion image is obtained by the inverse DWT. The results prove that, compared with the similar research methods, the performance of the proposed method is better.
A Lexicon Verification Strategy in a BLSTM Cascade Framework

Handwriting recognition always has been a difficult problem, with image related problems on the one hand and language processing on the other hand. Significant improvements have been made in handwriting recognition thanks to new recurrent neural networks based on LSTM cells. The high character recognition performances of these networks are almost systematically combined with linguistic knowledge, that is to say lexicon driven decoding method, to correct character misrecognitions. However with such high performance, we wonder on the possibility to use them without lexical decoding for word recognition. In this article, we explore this idea by proposing a lexicon verification strategy that provides a very low error rate, while conceding a consequent amount of rejects. Therefore, this verification approach perfectly fits in a cascade framework, where the rejects of a classifier are processed by the next cascade's classifier. The resulting system is nearly insensitive to the lexicon size, while providing a much faster decoding process than a standard lexicon driven decoding. Furthermore, when processing the final rejects of the cascade by a basic lexical decoding, our approach reach state of the art performance for isolated word recognition.
Optimal prioritization of handovers in mobile cellular networks

Investigates channel allocation policies that choose among incoming handover requests to a particular cell. Within a class of non-reserving policies that efficiently utilize the scarce channel resources, the authors identify a most critical first policy that is optimal with respect to handover failure probability. The proof of optimality is accompanied by the description of two techniques to realize the policy in practice. Simulation results that demonstrate its improvement over previous policies are presented as well.
IMAGE STEGANOGRAPHY USING MODIFIED KEKRE ALGORITHM

The main goal of steganography is to hide information in the other cover media so that other person will not notice the presence of the information. This is a major distinction between this method and the other methods of covert exchange of information because, for example, in cryptography, the individuals notice the information by seeing the coded information but they will not be able to comprehend the information. This paper presents a secured steganography method capable of embedding high volume of information in digital cover image without incurring and perceptual distortion. This method is based compression and encryption. In this method the compression is done by Lempel-Ziv-Welch technique. Encryption and hiding of data is done by kekre algorithm.
Algorithm for Route Planning via Facilities with Time Dependent

In today’s century , people are looking for faster and efficient communication for travelling from source to destination via facilities. As the human tendency always try to go from source to cover or attend some facilities and then reach to destination within a required time. This paper focus on algorithm for optimal route planning via facility ( i.e petrol, grocery etc) with time dependent shortest path. The algorithm proposed here to minimize the travel time and provide a optimal path from source to destination with the facilities come in our path. We present here a algorithm on route planning which minimize the distance as well as time from source to destination.
A Feature Construction and Classication Approach to Pixel Annotation

In this paper, we present a Machine Learning approach to an important problem in Computer Vision: the pixel annotation task. This task is concerned with assigning one of a small set of predened classes to every pixel in an image. The classes can be any concept dened by the user, but in our case typically will represent particular surface-types. A logical example that we will be considering is the classication of road-surface. We will demonstrate how likely areas of tarmac can be identied using a trained classier, and demonstrate how this low-level information may be used for navigation purposes. An important characteristic of the selected approach is the importance of feature construction. We dene a large collection of features based on dierent aspects of a pixel and its environment. These include concepts such as colour, edges, textures and so on. Additionally, we will include a range of features derived from the spatial information available from stereo images. The information about surface location and orientation obtained through stereo vision may hold important clues about the pixel class that cannot be obtained from single images.
Applying ant colony optimization for load balancing on grid

Grid computing is comprised of many distributed nodes in order to compute and analyze a set of distributed data. To improve the processing performance, an appropriate load-balancing algorithm is required to equally distribute loads among the grid’s nodes. In this article, an algorithm based on ant colony optimization is proposed to deal with load-balancing problems. In this approach, when an ant reaches a node, the ant’s table and the node’s table exchange their information and update each other. In order to move to the most appropriate node, the ant selects the next node from the current node’s table according to the nodes’ loads and their CPU rates. This process is continued until the ant passes the predefined steps. The experimental results show that while implementing the proposed algorithm to the grid environment, increasing the number of jobs and their length has insignificant impact on the system response time.
Rule-based filtering algorithm for textual document

Textual document is usually in unstructured form and high dimensional data.The exploration of hidden information from the unstructured text is useful to find interesting patterns and valuable knowledge.However, not all terms in the text are relevant and can lead to misclassification. Improper filtration might cause terms that have similar meaning to be removed.Thus, to reduce the high-dimensionality of text, this study proposed a filtering algorithm that is able to filter the important terms from the pre-processed text and applied term weighting scheme to solve synonym problem which will help the selection of relevant term.The proposed filtering algorithm utilizes a keyword library that contained special terms which is developed to ensure that important terms are not eliminated during filtration process.The performance of the proposed filtering algorithm is compared with rough set attribute reduction (RSAR) and information retrieval (IR) approaches.From the experiment, the proposed filtering algorithm has outperformed both RSAR and IR in terms of extracted relevant terms.
How Users View Web Pages: An Exploration of Cognitive and Perceptual Mechanisms

This chapter presents the basic cognitive and perceptual attentional mechanisms that affect how users view Web pages and the methods used to measure this attention. It describes the groundbreaking work of Faraday (2000), who proposed a visual scanning model of Web pages based on salient visual elements, and summarizes data from eye-tracking techniques that reveal the strengths and weaknesses of the Faraday model. The primary goal of the chapter is to help the reader gain an understanding of what visual elements on a Web page draw a user’s attention, how that knowledge can be collected, and how it can be applied to the design of useful and usable Web sites.