Miracle-dz/UniFAR-FTUs huggingface dataset:
A presentation and low-level energy usage analysis of two low-power architectural techniques

In recent years, the need to reduce the power and energy requirements of computer microprocessors has increased dramatically. In the past, microprocessor architects succeeded in improving the performance of their designs by increasing clock frequency, and building wider and deeper pipelines. These design choices inevitably lead to increased power and energy usage, and thus increased heat dissipation. With the proliferation of battery-powered embedded and mobile computer systems, it is necessary to reduce energy usage without sacrificing performance. This dissertation analyzes two architectural techniques that are designed to reduce the energy usage required to complete computational tasks, without impacting performance. The first technique is the Tagless-Hit Instruction Cache (TH-IC), which reduces energy used for fetching instructions by disabling several large fetch-related structures when it can be guaranteed they are not needed. The second is Static Pipelining, which reduces power by moving much of the pipeline control from pipeline logic to the compiler. These techniques have previously been studied with high level, architectural models based on the Sim-Wattch simulator. Such models estimate total energy usage by attributing a usage to various architectural events. The energy for each event must be derived from actual physical models of those components, and will be inaccurate if energy usage is dependent on factors that cannot be summarized by a single energy value per event. In addition, issues such as circuit timing and fabrication technology cannot be considered by the model without designing the real circuit it represents. This dissertation presents an analysis of physical models of a traditionally-architected 5-stage OpenRISC processor, an implementation of the TH-IC, and a statically pipelined processor, all of which have been created from scratch using a hardware definition language and computer aided design tools. The OpenRISC processor serves as a baseline for comparison versus the statically pipelined processor. Additionally, the RISC processor is examined both with and without the TH-IC. Accurate estimates of energy usage and timing are derived using these models.
Scene reconstruction with sparse range data and intensity information

The acquisition of a three-dimensional (3-D) model in a real-world environment by scanning only sparsely can save us a great ammount range-sensing time. We address a new method for inferring missing range data based on the given intensity image and sparse range data. It is assumed that the known range data are given on a number of scan lines with 1 pixel width. This assumption is natural for a range sensor to acquire range data in a 3-D real-world environment. Both edge information of the intensity image and linear interpolation of the range data are used. Experiments show that this method gives very good results in inferring missing range data. It outperforms both the previous method and bilinear interpolation when a very small percentage of range data are known.
Bladder fullness detection using multiple electrodes

An investigation has been made of multiple-electrode system to monitor the impedance changes corresponding to volume changes of the urinary bladder, using a finite-element model of the human body. The authors used an adaptive method for determining the optimal currents which would provide maximal impedance changes. They present simulation results and discuss the relationships between impedance change and the number and placement of the electrodes. <<ETX>>
Neuro-Fuzzy Expert Systems: Relevance, Features and Methodologies

Some of the inherent problems of expert system design, and the relevance of fuzzy logic, neural networks and neuro-fuzzy computing in circumventing them are explained. Various existing models of neuro-fuzzy expert systems are described. A comparative study with the traditional and connectionist versions is given in tabular form. The way of generating linguistic rules is explained with an example. The work on recent approaches using knowledge-based networks in this regard has also been highlighted.
FORECASTING SALES THROUGH TIME SERIES CLUSTERING

We study the effect of decomposing time series into multiple components like trend, seasonal and irregular and performing the clustering on those components and generating the forecast values of each component separately. In this project we are working on sales data. Multiple forecast experts are used to forecast each component series. Statistical method ARIMA, Holt winter and exponential smoothing are used to forecast these components. We performed clustering for forecasting and discovered a set of best, good and bad forecasters. Selection of best, good and bad forecasters is performed on the basis of count and rank of expert id’s generated. Since we have thousands of experts, we experiment with combining method to get better forecast. Finally absolute percentage error (APE) is used for comparing forecast.
Horizontal Propagation Through Periodic Vegetation Canopies

Electromagnetic wave propagation through vegeta- tion media has traditionally been modeled using random-media volume scattering approaches, such as the radiative transfer technique. Although such techniques have proven suitable for natural vegetation covers, experimental data suggest that a semideterministic technique is needed to model certain man-made vegetation canopies such. as orchards, plantations, and row crops. A two-dimensional model has been developed to explain wave propagation through such canopies. The model is intended for media containing vertical cylinders, representing the stalks, and randomly oriented disks, representing the leaves. The for- mulation treats the canopy as a one-dimensional array of paral- lel rows, with each row being comprised of an array of parallel stalks and a random distribution of leaves. The quasi-static approximation is used for computing scattering by the leaves which is valid only when the dimensions of the leaves are smaller than the wavelength. The model is a field approach, thereby accounting for all coherent, multiple interactions occur- ring in the canopy. The experimental component of this study includes measurements of the attenuation and phase shift pat- terns for horizontally and vertically polarized waves transmitted through a fully grown canopy of corn plants observed at 1.5 GHz. The model was found to provide excellent agreement with the experimental results.
A Pareto evolutionary artificial neural network approach for remote sensing image classification

This paper presents a Pareto evolutionary artificial neural network (Pareto-EANN) approach based on the evolutionary algorithms for multiobjective optimization augmented with local search for the classification of remote sensing image. Its novelty lies in the use of a multiobjective genetic algorithm where single hidden layers Multilayer Perceptrons (MLP) are employed to indicate the accuracy/complexity trade-off. Some advantages of this approach include the ability to accommodate multiple criteria such as accuracy of the classifier and number of hidden units. We compared Pareto-EANN classifiers results of the classification of remote sensing image against standard backpropagation neural network classifiers and EANN classifiers; we show experimentally the efficiency of the proposed methodology.
Using Decision Trees and Soft Labeling to Filter Mislabeled Data

In this paper we present a new noise filtering method, called soft decision tree noise filter (SDTNF), to identify and remove mislabeled data items in a data set. In this method, a sequence of decision trees are built from a data set, in which each data item is assigned a soft class label (in the form of a class probability vector). A modified decision tree algorithm is applied to adjust the soft class labeling during the tree building process. After each decision tree is built, the soft class label of each item in the data set is adjusted using the decision tree’s predictions as the learning targets. In the next iteration, a new decision tree is built from a data set with the updated soft class labels. This tree building process repeats iteratively until the data set labeling converges. This procedure provides a mechanism to gradually modify and correct mislabeled items. It is applied in SDTNF as a filtering method by identifying data items whose classes have been relabeled by decision trees as mislabeled data. The performance of SDTNF is evaluated using 16 data sets drawn from the UCI data repository. The results show that it is capable of identifying a substantial amount of noise for most of the tested data sets and significantly improving performance of nearest neighbor classifiers at a wide range of noise levels. We also compare SDTNF to the consensus and majority voting methods proposed by Brodley and Friedl [1996, 1999] for noise filtering. The results show SDTNF has a more efficient and balanced filtering capability than these two methods in terms of filtering mislabeled data and keeping non-mislabeled data. The results also show that the filtering capability of SDTNF can significantly improve the performance of nearest neighbor classifiers, especially at high noise levels. At a noise level of 40%, the improvement on the accuracy of nearest neighbor classifiers is 13.1% by the consensus voting method and 18.7% by the majority voting method, while SDTNF is able to achieve an improvement by 31.3%.
Girls in Computer Science: a Female Only Introduction Class in High School

GIRLS IN COMPUTER SCIENCE: A FEMALE ONLY INTRODUCTION CLASS IN HIGH SCHOOL Ann W. Drobnis, Ph.D. George Mason University, 2010 Dissertation Director: Dr. Priscilla Norton This study examined the impact of an all girls‟ classroom environment in a high school introductory computer science class on the student‟s attitudes towards computer science and their thoughts on future involvement with computer science. It was determined that an all girls‟ introductory class could impact the declining female enrollment and female students‟ efficacy towards computer science. This research was conducted in a summer school program through a regional magnet school for science and technology which these students attend during the school year. Three different groupings of students were examined for the research: female students in an all girls‟ class, female students in mixed gender classes and male students in mixed gender classes. A survey, Attitudes about Computers and Computer Science (ACCS), was designed to obtain an understanding of the students‟ thoughts, preconceptions, attitude, knowledge of computer science, and future intentions around computer science, both in education and career. Students in all three groups were administered the ACCS prior to taking the class and upon completion of the class. In addition, students in the all girls‟ class wrote in a journal throughout the course, and some of those students were also interviewed upon completion of the course. The data was analyzed using quantitative and qualitative techniques. While there were no major differences found in the quantitative data, it was determined that girls in the all girls‟ class were truly excited by what they had learned and were more open to the idea of computer science being a part of their future.
Fast enhanced face-based adaptive skin color model

Man machine interface by video analysis becomes popular recently. The most typical body gesture utilized for computer interaction is hand gesture. Therefore, it is a very important topic to accurately extract hand regions from a sequence of images in real time. In this paper, we propose an adaptive skin color model which is based on detected face color. Skin colors are sampled from extracted face region where non-skin color pixels like eyebrow or glasses are excluded. Gaussian distributions of normalized RGB are then used to define the skin color model for the detected person. To demonstrate the robustness of proposed model, experiments under diversified lighting and background are tested. Traditional methods based on RGB, Normalized RGB, and YCbCr are all implemented for comparison. From experimental results, skin color pixels could be detected for each person. The accuracy rate is 95.73% on average and is superior to previously mentioned methods.
Revised4 report on the algorithmic language scheme

The report gives a defining description of the programming language Scheme. Scheme is a statically scoped and properly tail-recursive dialect of the Lisp programming language invented by Guy Lewis Steele Jr. and Gerald Jay Sussman. It was designed to have an exceptionally clear and simple semantics and few different ways to form expressions. A wide variety of programming paradigms, including imperative, functional, and message passing styles, find convenient expression in Scheme.
Low-complexity channel-adapted space-time coding schemes for frequency-selective wireless MIMO channels

By exploiting the wireless MIMO (multiple input multiple output) channel structure, a brand new space-time code design criterion is derived. Based on the new criterion, we propose two low-complexity channel-adapted space-time (CAST) coding schemes, where trade-offs among codeword error rate, data throughput and computational complexity are very flexible. Simulation results confirm that, in frequency-selective MIMO channels, the CAST coding schemes can perform significantly better than existing space-time codes, e.g., Alamouti space-time orthogonal code.
Hierarchical path planning with hexagonal decomposition

An algorithm based on an artificial potential field and hierarchical cell decomposition technique is developed to solve the find-path problem for a mobile robot. The basic cell structure used for decomposition is a hexagon. The artificial potential field is based on an attractive force from the goal position and repelling forces from the obstacles. Computer simulations of the algorithm for various obstacle scenarios are also presented. <<ETX>>
Multimodality in Safaricom Advertisement Communication in the Kenyan Daily Nation Newspaper

Advertising is a form of communication which has one main function: to make the audience buy the advertised product or subscribe to the services displayed. Consumer advertisements employ more than one mode of communication in designing messages that target their audiences. This often causes interpretational difficulties to the target audiences. This study examined how language co-occurs with the other semiotic forms as used in Safaricom advertisements. The study examined how the visual texts in Safaricom newspaper advertisements impact on the audience's purchasing decisions, analyzed how visual semiotics and written language were used in the Safaricom advertisements to aid meaning interpretation and determined the impact of using the visual images and written language on the audience's ability to interpret the messages. The data analysis was based on Multimodal Discourse Analysis (MDA) a theoretical framework by Kress and Van Leeuwen. DOI: 10.5901/ajis.2014. v3n6p403
The Electronic Text Corpus of Sumerian Literature

Abstract With invaluable help from and in close co-operation with colleagues from around the world, the Electronic Text Corpus of Sumerian Literature project at the University of Oxford has compiled, lemmatised and made publicly available a large body of Sumerian literature. Building a corpus of literary compositions originally written on clay tablets in the cuneiform script, and dating back nearly four thousand years, poses special challenges, not least with regard to mark-up and automatic processing of data. Some of these challenges are discussed in this paper together with issues relating to the fact that Sumerian is a language isolate and lacks resources we take for granted when working with other languages, modern or extinct, such as a standardised sign list and a comprehensive dictionary.
The Design and Realization of the Single-Block Hash Function for the Short Message

The paper puts forward a message digest algorithm for processing the short message, the Single-Block Hash function, SBH-256. The message digest algorithm designed is processed by 32-step computation only for the characters whose length is less than 256 bits. The algorithm designed solves the shortages and blemish of these traditional message digests effectively, improves the operating efficiency and the length of its output value is 128 bits at the same time, it guarantees the same security with MD5. The experimental results show that the new message digest algorithm guarantees the security and improves the efficiency of short messages processed greatly and overcomes the shortage of traditional message digest algorithms.
What do the Public Search for on the Catalogue of the State Library of Victoria

This study examines what the public search for in the catalogue of the State Library of Victoria (SLV). As well as indicating the type of content being accessed, this gives an indication of what catalogue users expect of the State Library collection. A content analysis was undertaken of a random, stratified sample of 4,000 search queries typed in to the SLV catalogue by the public between 2005 and 2008. This study goes some way to providing empirical evidence that the library catalogue is used to research a different range of topics to those searched via the internet.
Introducing New Resource Management Policies Using a Virtual Machine Monitor

Integrating new resource management policies into operating systems (OSes) is an ongoing process. Despite innovative policy proposals being developed, it is unrealistic to widely deploy a new one because it is a difficult, costly and often an impractical endeavor to modify an existing operating system to integrate a new policy. To address this problem, we explore the possibility of using virtual machine technology to incorporate a new policy into an existing OS without the need to make any changes to it. This paper describes FoxyTechnique, which virtualizes physical devices differently from real ones and tricks a guest OS into producing behavior similar to a desired policy. FoxyTechnique has three advantages. First, it allows us to implement a new policy without the need to make any changes to OS kernels. Second, Foxy-based policies are expected to be portable across different operating systems because they are isolated from guest OSes by stable virtual hardware interfaces. Finally, Foxy-based policies sometimes outperform guest OS policies because they can measure performance indicators more accurately than guest OSes. To demonstrate the usefulness of FoxyTechnique, we conducted two case studies, FoxyVegas and FoxyIdle, on the Xen virtual machine monitor. FoxyVegas and FoxyIdle tricked the original Linux and successfully mimicked TCP Vegas and Idletime scheduling, respectively.
Foreground detection for content-based image retrieval

Content-based image retrieval is an important task in many practical applications. All the existing algorithms can be divided into two categories, the global methods and the local methods. These methods both have their own drawbacks. Every kind of image property equally contributes to image retrieval applications in global methods, and only some selected ones are used in local methods. Generally, the global methods can not fully represent the specific properties of user interest, and many image properties are lost in local methods. In this paper, a novel method has been proposed, in which image properties are assigned with different weights, denoting the importance of the property in image retrieval application. The represented method simulates human perception of image foreground, and can make a trade-off between global methods and local methods. Experiments show that this method can improve image retrieval performance, without the lost of simplicity and compactness.
Assessing machine-learning algorithms and image- and lidar-derived variables for GEOBIA classification of mining and mine reclamation

This study investigates machine-learning algorithms and measures derived from RapidEye satellite imagery and light detection and ranging (lidar) data for geographic object-based image analysis classification of mining and mine reclamation. Support vector machines, random forests, and boosted classification and regression trees classification algorithms were assessed and compared with the k-nearest neighbour (k-NN) classifier. For geographic object-based image analysis classification of mine landscapes, the use of disparate data (i.e. lidar data) improved overall accuracy, whereas the use of complex, object-oriented variables such as object geometry measures, first-order texture, and second-order texture from the grey-level co-occurrence matrix decreased or did not improve the classification accuracy. Support vector machines generally outperformed k-NN and the ensemble tree classifiers when only using the band means. With the incorporation of lidar-descriptive statistics, all four algorithms provided statistically comparable accuracies. K-NN suffered reduced classification accuracy with high-dimensional feature spaces, suggesting that a more complex machine-learning algorithm may be more appropriate when a large number of predictor variables are used.
OxLM: A Neural Language Modelling Framework for Machine Translation

Abstract This paper presents an open source implementation1 of a neural language model for machine translation. Neural language models deal with the problem of data sparsity by learning distributed representations for words in a continuous vector space. The language modelling probabilities are estimated by projecting a word's context in the same space as the word representations and by assigning probabilities proportional to the distance between the words and the context's projection. Neural language models are notoriously slow to train and test. Our framework is designed with scalability in mind and provides two optional techniques for reducing the computational cost: the so-called class decomposition trick and a training algorithm based on noise contrastive estimation. Our models may be extended to incorporate direct n-gram features to learn weights for every n-gram in the training data. Our framework comes with wrappers for the cdec and Moses translation toolkits, allowing our language models to be incorporated as normalized features in their decoders (inside the beam search).
Well I’m not sure I think… The use of well by non-native speakers

Pragmatic markers are an important part of the grammar of conversation and not simply markers of disfluency. They have a number of functions that help the speaker to organise the conversation and to express feelings and attitudes. Advanced EFL learners use frequent pragmatic markers such as well. However their use of well diverges from the native speaker norm. The present study uses data from the Swedish component of the LINDSEI corpus and its native speaker counterpart (LOCNEC) to examine similarities and differences between native and non-native speakers. The overall picture is that Swedish learners overuse well, although there are considerable individual differences. Thus learners use well above all as a fluency device to cope with speech management problems but underuse it for attitudinal purposes. Pragmatic markers cannot be taught in the same way as other lexical items but it is important to discuss how and where they are used.