Miracle-dz/UniFAR-FTUs huggingface dataset:
New directions in gaussian mixture learning and semi-supervised learning

High dimensional data analysis involves, among many other tasks, modeling the unknown underlying data generating process and predicting one of the few possible sources that might have generated the data. If data have some underlying pattern or structure, it is conceivable that such a hidden pattern or structure might provide important cues toward predicting the possible data generating source. On the other hand, for modeling purposes, it is important to understand the computational aspects of the problem of learning model parameters, especially the dependence on dimension. This thesis addresses the computational aspects of such a modeling problem especially in high dimension and also describes a novel predictive framework that exploits the underlying structure/pattern present in data. The first part of this thesis addresses the problem of Gaussian mixture learning. Gaussian mixture model is a fundamental model in applied statistics and is a very popular choice for many scientific/engineering modeling tasks. However, computational aspects of learning the parameters of this model, especially in high dimension, is not well understood. The question of polynomial learnability of probability distributions, particularly Gaussian mixture distributions, has recently received significant attention in theoretical computer science and learning theory communities. However, despite major progress, the general question of polynomial learnability of Gaussian mixture distributions still remained open. The result presented in this thesis resolves the question of polynomial learnability for Gaussian mixtures in high dimension with an arbitrary but fixed number of components. The second part of this thesis describes studies on semi-supervised learning for multi-modal data, especially when data have natural clusters. Many real world datasets in scientific/engineering applications have clusters, e.g., hand written digit dataset or breast cancer dataset. To deal with such multi-modal data, this thesis proposes a novel framework which establishes a natural connection between “cluster assumption” in semi-supervised learning and sparse approximation. Specifically, when the number of labeled data is limited, the proposed method can learn a classifier which has a sparse representation in an appropriate set of bases and whose performance is comparable to the state of the art semi-supervised learning algorithms on a number of real world datasets.
PHP 5 Fast & Easy Web Development

"PHP 5 Fast & Easy Web Development" is an update to the highly successful edition covering version 4 (193184187X). Following the same format, this book includes easy-to-follow instructions and a multitude of actual screen shots. It also includes complete coverage of version 5 updates, including enhanced internal scripting engine, improved OO framework, new Object model, the SQLite embedded database engine, and XML support. It includes coverage of the final version of PHP 5 and is not based on the beta. This comprehensive book also includes coverage of Apache and MySQL, a basic PHP language reference, and MySQL language primer. Readers will gain a thorough understanding of PHP 5 using step-by-step instructions for creating a simple database and sample table.
Improved Synthesis of 28-Norbrassinolide

An improved procedure for the preparation of 28-norbrassinolide is described. The key step of the whole process was the construction of the side-chain carbon skeleton, which was achieved by successive addition of C23–C24 and C25–C27 fragments to 22C-steroidal aldehyde via the Normant reaction and Claisen rearrangement. The method affords the title compound in 16% overall yield from stigmasterol and represents a significant improvement over literature procedures.
Thermal-controlled design flow for the three-dimensional dual-mode forward error correction architecture

Abstract Forward error correction (FEC) is often designed with multiple decoding modes to support different communication standards. Three-dimensional dual-mode forward error correction (3D DFEC) supports two decoding modes and applies the features of the 3D IC to improve the transmission bandwidth. In this work, the design flow is proposed to explore the block-based memory architecture which can support two decoding modes in 3D DFEC. Based on the thermal difference in two decoding modes, the thermal-aware mappings are proposed. The thermal-aware mappings consider the constraints of the thermal limitation, latency, and hardware overhead. Thus, the thermal-aware mappings can solve thermal problems at reasonable overhead cost of hardware and latency. Using the proposed thermal-aware mappings, the peak temperature could be reduced by 5.4–62.9 °C in our experiments.
Automatically adapting the context of an intranet query

In this paper we propose the use of an interactive interface to allow user exploration of the context of an intranet query. The underlying domain model is that of a Formal Concept Analysis (FCA) lattice. Understanding the difficulty of achieving optimum document descriptors, essential for a browsable lattice, we propose harnessing implicit user feedback in learning document/term associations.
Structural classification and similarity measurement of malware

This paper proposes a new lightweight method that utilizes the growing hierarchical self‐organizing map (GHSOM) for malware detection and structural classification. It also shows a new method for measuring the structural similarity between classes. A dynamic link library (DLL) file is an executable file used in the Windows operating system that allows applications to share codes and other resources to perform particular tasks. In this paper, we classify different malware by the data mining of the DLL files used by the malware. Since the malware families are evolving quickly, they present many new problems, such as how to link them to other existing malware families. The experiment shows that our GHSOM‐based structural classification can solve these issues and generate a malware classification tree according to the similarity of malware families. © 2014 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.
A Parallel Front Propagation Method : Simulating geological folds on parallel architectures

Static non-linear Hamilton-Jacobi equations are often used to describe a propagating front. Advanced numerical algorithms are needed to efficiently compute solutions to these non-linear equations. In geological modelling, layers of rocks can be described as the position of a propagating front at different times. A fast simulation of such layers is a key component in exploration software developed by Kalkulo AS for Statoil AS. Developing fast algorithms and solvers is essential in this application, since faster solvers enables users to test more geological scenarios, leading to a better understanding of the inner earth. Front propagation is also used in other applications, such as reservoir simulation, seismic processing and medical imaging, making a fast algorithm highly versatile. The recent years rise of parallel architectures has made substantial computational resources available. One way to originate faster solvers is therefore to develop algorithms that are able to exploit the increasing parallelism that these architectures offer. In this thesis, a novel threedimensional anisotropic front propagation algorithm for simulation of geological folds on parallel architecture is presented. The algorithm’s abundant parallelism is demonstrated on multi-core CPUs and GPU architectures. Implementation on multi-core architectures is achieved by using the OpenMP API, while the Mint programming model is used to facilitate with the GPU programming. We demonstrate 7x to 2x speedups running on the Nvidia GeForce GTX 590 GPU, compared with a multi-threaded implementation on a NUMAmachine using two interconnected 12 core AMD Opteron processors. These results point to enormous potential performance advances of our algorithm on parallel architectures.
Enviropreneurial orientation in SME supply chains: construct measurement development

Purpose The purpose of this paper is to propose a new model that is inclusive and practical because of the deficiencies in models for construct measurement. Further, the authors demonstrate the value of the proposed model by describing its application to the development and validation of a multi-dimensional construct, enviropreneurial orientation. Although used in the literature, enviropreneurial orientation had not been developed nor tested as a construct. The paper provides detailed explanation of development and validation processes exemplified by experiences of research into the factors that motivate individual enviropreneurial orientation among owner/managers in supply chains. Design/methodology/approach The authors review models of construct development and propose an eight step model to overcome the deficiencies in the existing models. The eight steps are: defining the construct; identifying its dimensions; generating measurement items for each dimension; pretesting the measurement items; collecting data; constructing scales; analysing reliability; and evaluating the relationships. Each step is explained through examples based on the authors’ experience in using the model to develop the new construct – enviropreneurial orientation. Findings All correlation results were positive and significant as were the multiple regression results with one exception, competitive aggressiveness. Originality/value The authors provide a practical model to guide new construct measurement development which can be used by researchers and research students for multi-dimensional constructs, especially they are constrained by time and financial resources. By using an example to demonstrate the applicability of the model, the authors go beyond the usual description of construct development models to make the proposed model more comprehensible and thus useful.
Introducing Pragmatics of Fiction: Approaches, trends and developments

In this introduction to the handbook on the Pragmatics of Fiction we introduce the aims of the collection and position it within the field of pragmatics. Considering fiction as texts that deserve to be studied in their own right, we argue that a pragmatic perspective opens interesting avenues of investigating both the techniques of fiction and how they pattern as well as the unique communication situation into which readers/viewers enter when engaging with fictional texts. We review the changing attitudes towards fictional data in the linguists’ community and then outline the themes of the collection.
Introducing Complexity Curtailing Techniques for the Tour Construction Heuristics for the Travelling Salesperson Problem

In this paper, complexity curtailing techniques are introduced to create faster version of insertion heuristics, that is, cheapest insertion heuristic (CIH) and largest insertion heuristic (LIH), effectively reducing their complexities from to with no significant effect on quality of solution. This paper also examines relatively not very known heuristic concept of max difference and shows that it can be culminated into a full-fledged max difference insertion heuristic (MDIH) by defining its missing steps. Further to this the paper extends the complexity curtailing techniques to MDIH to create its faster version. The resultant heuristic, that is, fast max difference insertion heuristic (FMDIH), outperforms the “farthest insertion” heuristic (FIH) across a wide spectrum of popular datasets with statistical significance, even though both the heuristics have the same worst case complexity of . It should be noted that FIH is considered best among lowest order complexity heuristics. The complexity curtailing techniques presented here open up the new area of research for their possible extension to other heuristics.
Design and Implementation of the Bridgeless AC-DC Adapter for DC Power Applications

In this paper, power factor correction (PFC) is one of the most active process in most of the ac-dc converters, generally semi bridgeless rectifier followed by a boost converter has been the most commonly used PFC circuit. However, a boost PFC frontend exhibits lower efficiency, in that cases a SEPIC converter performance would be greater , hence A bridgeless SEPIC rectifier is proposed which substantially improves efficiency and power factor. The operation and performance of the proposed circuit was verified on PLECS.
Crossing Borders within the ABC Automation, Biomedical Engineering and Computer Science

Within the ﬁeld of mobile robotics, mapping is a well known problem to be solved to enable a mobile system to drive autonomously. In this work we present an improved approach on dynamic map adaptation with a previously given, static map, built with a laser scanner. We are focusing on modeling changes within this map at different time scales, so old states of the environment are not getting lost instantaneously. We discuss this problem under the consideration of a) keeping the map consistent while including changes and b) avoiding the conﬂict on fast adaptation vs. forgetting important environment structures. To model these properties we built a stack of maps considering different time levels. In each stack observation data are included with different insertion rate, and statistics of laser beam pairs are built to guarantee consistency of the estimated map. This allows us to construct a view of the change of the environment over time, which classical SLAM approaches cannot do.
Multivariate Statistical Analysis of Protein Variation

ZHAO, JIEPING. Multivariate statistical analysis of protein variation. (Under the direction of Dr. William R. Atchley) The purpose of this research is to study the protein sequence metric problem solution and apply it to explore the structural, functional and evolutionary aspects of basic helix-loophelix (bHLH) protein family. Sequence metric problem is caused by the alphabetic coding of the amino acids and has long been a hindrance to efficient protein sequence analysis. This dissertation started with revisiting sequence metric problem solution initiated by Atchley et al (2005) [PNAS102(18):6401-6]. Some of the unsolved issues, such as information loss, model robustness, and concordance between factor analysis and principal component analysis were studied. Further, classification of 20 amino acids was explored in the numerical factor space resolved by Atchley et al (2005) Next two parts of the dissertation were focused on computational and statistical studies of the bHLH protein family. All the protein sequence data were transformed into numerical vectors by using the amino acid factor scores from the sequence metric solution. In the second part of the dissertation, protein sequence variability in the level of statistically supported lineages (=clades) was studied using the stepwise discriminant analysis. Some of the important sites for the clades discrimination were selected and hierarchical classification strategies for the clades were proposed. In the third part of the dissertation, 147 Arabidopsis bHLH proteins were studied by a series of multivariate analyses. Results showed that there were significant sequence differences between plant (e.g. Arabidopsis) and animal bHLH proteins, and some of the contributing discriminant sites were selected and discussed. Binding property of each of the Arabidopsis bHLH proteins was assigned by using the classification rules derived from animal bHLH proteins. Multivariate Statistical Analysis of Protein Variation by
Cognitive Sensors Based on Ridge Phase-Smoothing Localization and Multiregional Histograms of Oriented Gradients

This study presents a smart cognitive sensor “iRecorder” that can spontaneously locate speakers among attendees at a boardroom using ubiquitous arrays of audiovisual sensors. The proposed system “iRecorder” consists of two major components—Sound localization and mouth tracking. For acoustic processing, this work proposes ridge phase-smoothing direction-of-arrival (DOA) estimation, which refines the distorted phase of a signal and robustly determines acoustic directions. During visual detection, this study develops novel Multiregional Histograms of Oriented Gradients (MHOGs) to model an uttering mouth. Unlike HOGs, the proposed feature is no longer limited to fixed-sized windows or blocks. It relies on facial regions. Finally, the system uses a fusion mechanism that integrates both clues from audiovisual sensors based on majority voting to target an actual speaker. The experimental result of DOA estimation showed that the directional errors were successfully improved by 6.6 degree on average. Concerning detection of talking faces, the accuracy reached as high as a rate of 85.19 percent. The fusion test results also supported the effectiveness of the system. Such findings reveal that the proposed system is superior to the other approaches and establishes its feasibility.
Promoting Discarded Packing Waste into Value-Added 2D Porous Carbon Flakes for Multifunctional Applications

In this work, a prolific reuse of waste foam peanuts into eco-benign and cost-effective two-dimensional (2D) activated carbon nanoflakes (CFs) was presented for use in CO2 capture and supercapacitor applications. The 2D nanoporous carbon sheets-like structures were derived by a two step process including simple carbonization followed by KOH activation (C:KOH = 1:1). With the varied activation temperature, the surface area and porosity have been controlled, which demonstrate the narrow pore size distribution in the range of <1 nm and mesopore in the range of ∼40 nm. Among the samples derived, CF-800 shows excellent CO2 sorption capacity corresponding to 3.3–3.45 mmol/g with the highest surface area corresponding to 1041 m2/g and outstanding cyclic stability over 21 cycles. Meanwhile, the prepared 2D activated CFs demonstrated superior energy storage properties including high specific capacitance of 250 F/g at 1 A/g with exceptional cyclic stability. Moreover, the symmetric supercapacitor has been fabricate.
Review on Load Balancing in Mobile Ad-hoc Networks

The dynamic nature of network topology, high mobility, limited bandwidth, limited battery power makes routing a challenging task. Routing protocols play a vital role in the functioning of adhoc networks. An ideal routing protocol should fairly distribute the routing tasks among mobile nodes. Most current routing protocols consider only the shortest path with minimum hop count as optimal route neglecting node's traffic. This can have an adverse effect on network performance causing issues like congestion, power depletion and queuing delay. Therefore, it is important to investigate the load balancing in routing protocols for efficient data transmission in MANETs.
Unpacking models, approaches and materialisations of the design PhD

Doctoral education in design has been expanding considerably since the mid 1990s and is now globally a lively part of many design schools. In this article we unpack what it is that constitutes, and is specific to, a doctorate in design. We offer a mapping of current international approaches, models and formats of the PhD in design. The design PhD is a site of considerable material, cultural and discursive innovation in terms of its genres, concern with design materials and communicability to interdisciplinary and professional audiences. This article presents one of few such analyses of the doctorate in design by addressing matters of its socio-cultural and economic location and interplays between practice and critical analysis. We argue for an open, unfolding mode of understanding the emergent character, discursive innovation and multimodal qualities of a wider knowledge materialisation of the PhD in design.
Design of WSN in Real Time Application of Health Monitoring System

Health monitoring is emerging topic in recent era for safety and healthy public life in remote place. In health monitoring system, sensor devices have major role to collect data, communication and analysis the data for real time applications, such as automation of old-age home, industry, ICU, etc. It can measure different parameters of the body through different physiological body sensors. The body sensors can be used to sense the data from the body and send to the remote system for analysis. The condition of the health of a body can be analyzed and monitor remotely by using concept of body sensors in health monitoring system through different communication media, such as WiFi, ZigBee, etc. The parameter values of body can be transmitted to remote data centre with reliability, simplicity, low power, low bandwidth and low cost, in lightweight wireless networks. This may be used in real time application like; emotion and stress analysis, psychological study, physiological study, health condition, etc.
Tap Here: A Diff-powered Programming Tutorial Engine

We discuss an interactive tutorial engine implemented in TouchDevelop [2, 3], a browser-hosted touch-based development environment for creating mobile+cloud applications. The tutorials are written as programs with directions embedded in comments, á la literate programming, and then automatically turned into direct and adaptive guidance to the user. The engine was deployed during the Hour of Code [1] event in December 2013 and used by more than 250,000 people, completing over 3,000,000 steps.
An Introduction To Electrospinning And Nanofibers

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Joint Training of a Neural Network and a Structured Model for Computer Vision

Identifying objects and telling where they are in real world images is one of the most important problems in Artificial Intelligence. The problem is challenging due to: occluded objects, varying object viewpoints and object deformations. This makes the vision problem extremely difficult and cannot be efficiently solved without learning. This thesis explores hybrid systems that combine a neural network as a trainable feature extractor and structured models that capture high level information such as object parts. The resulting models combine the strengths of the two approaches: a deep neural network which provides a powerful non-linear feature transformation and a high level structured model which integrates domain-specific knowledge. We develop discriminative training algorithms to jointly optimize these entire models end-to-end. First, we proposed a unified model which combines a deep neural network with a latent topic model for image classification. The hybrid model is shown to outperform models based solely on neural networks or topic model alone. Next, we investigate techniques for training a neural network system, introducing an effective way of regularizing the network called DropConnect. DropConnect allows us to train large models while avoiding over-fitting. This yields state-of-the-art results on a variety of standard benchmarks for image classification. Third, we worked on object detection for PASCAL challenge. We improved the deformable parts model and proposed a new non-maximal suppression algorithm. This system was the joint winner of the 2011 challenge. Finally, we develop a new hybrid model which integrates a deep network, deformable parts model and nonmaximal suppression. Joint training of our hybrid model shows clear advantage over train each component individually, and achieving competitive result on standard benchmarks.