Miracle-dz/UniFAR-FTUs huggingface dataset:
Fragment reweighting in ligand-based virtual screening

Based on the molecular similarity principle, functionally similar molecules are sought by searching molecular databases for structurally similar molecules to be used in rational drug design. The conventional 2-dimentional similarity methods are the most used methods to measure similarity of molecules, including fragments that are not related to the biological activity of a molecule. The most common methods among the 2-dimentional similarity methods are the vector space model and the Bayesian networks, which are based on mutual independence between fragments. However, these methods do not consider the importance of fragments. In this thesis, four reweighting approaches are proposed to identify the important fragments. The first approach is based on reweighting the important fragments, where a set of active reference structures are used to reweight the fragments in the reference structure. Secondly, a statistically supervised features selection and minifingerprint to select only the important fragments are applied. In this approach, searching is carried out by using sub-fragments that represent the important ones. Thirdly, a similarity coefficient based on mutually dependent fuzzy correlation coefficient is used. The last approach combined the best two out of the three approaches which are reweighting factors and fragment selection based on statistically supervised features selection. The proposed approaches were tested on the MDL Data Drug Report standard data set. The overall results of this research showed that the proposed fragment reweighting approaches outperformed the conventional industry-standard Tanimoto-based similarity search approach.
Automatic language identification based on Gaussian mixture model and universal background model

When compared with speech technologies in speech processing, automatic language identification is a relatively new yet difficult problem. In this paper, a language identification algorithm is provided and some experiments are conducted using OGI multi-language telephone speech corpus (OGI-TS). Then experiments results are described. It is shown that GMM-UBM is another efficient method to language identification problems.
A multivariate forecasting method for short-term load using chaotic features and RBF neural network

This paper presents a multivariate forecasting method for electric short-term load using chaos theory and radial basis function (RBF) neural networks. To apply the method, the largest Lyapunov exponent and correlation dimension are firstly calculated which show the electric load series is essentially a chaotic time series. Then, a multivariate chaotic prediction method is proposed taking historical load and temperature into account. Phase space reconstruction of a univariate time series is extended to construct a multivariate time series. Delay time and embedding dimension of the historical load series and temperature series are determined by mutual information and minimal forecasting error, respectively. Finally, a three-layer RBF neural network is employed to forecast the load of one day ahead and one week ahead. Real load data of Chongqing Power Grid are tested. Daily and weekly forecasting results show that the proposed multivariate approach improves the accuracy of forecasting significantly comparing with the univariate methods. Discussion of forecasting error and future work are also presented. As an efficient and effective alternative for STLF, the chaos theory based multivariate forecasting is feasible for potential application. Copyright © 2010 John Wiley & Sons, Ltd.
A generic implementation framework for FPGA based stereo matching

This paper investigates the real time implementation requirements for several stereo matching algorithms. The area-based matching techniques sum of absolute differences (SAD), sum of squared differences (SSD), and normalised cross correlation (NCC) are considered as well as the non-parametric census and rank methods. A generic stereo matching framework is presented and algorithm specific implementation requirements compared. The suitability of field programmable gate array (FPGA) based custom computers to the stereo matching problem is discussed and the use of partial reconfigurability and adaptive stereo vision is suggested.
Online Multi-keywords Map-search Algorithm Based on Sub-region Method

Nowadays, the map-search technology is frequently used by people. However, present map-search service can hardlysatisfy people's growing demands. Providing multiplex service support in search service, such as intuitive search results and multi-keywords search, has become a research focus. This paperproposes a novel online multi-keywords map-search algorithm based on sub-region method. This algorithm makes up a graph of separate nodes and transfers the weight of keyword totheir adjacent nodes. When searching for sub-graph of a single keyword, it finds the node with the max weight then tries to accept or rejectits neighbors step by step. Duringthe search ofsub-graphs of multi-keywords, we must find the node with the max harmonic mean weight and union all the sub-graphs of single keyword it belongs to. This algorithm, based on sub-region method, can search multi-keywords in practical map-search experiments, and return several best resultsat the same time.
An Ultra-Wideband Modified Vivaldi Antenna Applied to Ground and Through the Wall Imaging

In this paper, we are interested in the design of a new Ultra-Wideband (UWB) directional Vivaldi antenna with narrow beam, in the frequency range of 1.17 to 4.75 GHz. The simulation of the designed antenna is carried out on Computer Simulation Technology Microwave Studio (CSTMWS). The mutual coupling effect reduction is considered. The designed antenna is tested for Ground Penetrating Radar (GPR) and Through the Wall applications. The emitted waveform is a Stepped Frequency Continuous Wave (SFCW) signal, generated by a Vector Network Analyser (VNA). The acquired raw data are focused by using back projection algorithm.
RASTA-PLP speech analysis technique

Most speech parameter estimation techniques are easily influenced by the frequency response of the communication channel. The authors have developed a technique that is more robust to such steady-state spectral factors in speech. The approach is conceptually simple and computationally efficient. The new method is described, and experimental results are proposed that show significant advantages for the proposed method. <<ETX>>
Line Detection by Centre and Width Estimation

Lines, thin regions of approximately-uniform width and colour, are important in the visual interpretation of information. Lines are a primary feature of many objects, and are used to convey semantic information in the human world, e.g. road markings. In order to interpret such information, an efficient method is desired for the detection of lines under this basic definition, independent of the colour and shape of the lines to be detected. A method of obtaining the centre-line and width of arbitrarilycoloured and -curving salient lines is described in this paper. However, this method alone is shown to be inadequate for its originally-intended purpose, the detection of wires in the disassembly of end-oflife electronics, due to complications such as twisted wires and the large number of other lines present in images taken during assembly. Examples are also provided of the application of the described method to other contexts.
Using an expert system for design diagnosis and design synthesis

This paper describes the concepts which allow an expert system to be used for both design diagnosis and design synthesis. An example of the implementation of these concepts is presented in the domain of preliminary design of domestic kitchens in the expert system PREDIKT. PREDIKT carries out both design diagnosis and design synthesis using the same knowledge base and utilises an existing expert system shell which has forward- and backward-chaining capabilities. The significance of graphical interaction with expert systems in design domains is demonstrated.
Fast Analysis of Power System Stability by Artificial Neural Network

Power system small signal stability concerns the ability of the power system to maintain stability subject to small disturbances. The analysis of small signal stability often has to deal with high-order system matrix due to the large number of generating units so that it is not easy to calculate and analyze the original system matrix and the whole set of eigenvalues. In this paper a new approach is proposed to take advantage of the specific feature of the parallel structure of artificial neural network for calculating the most critical eigenvalue or all eigenvalues of the unstable oscillation mode. The developed algorithm is tested on a sample power system to validate the feasibility of the proposed method for the calculation of the critical eigenvalue.
Simulating observations from the MODIS instrument with variable surface and atmospheric conditions

This paper describes the theoretical model for simulating Moderate-Resolution Imaging Spectroradiometer (MODIS) observations under varying environmental conditions. The model includes mathematical descriptions of the orbit of the Earth Observing System (EOS) AM platform, imaging process of the optical sensor, spectral response of the detectors, and realistic illumination and observation geometry for the scene being viewed. It includes a data base that supplies the model with variable surface and atmospheric conditions. Land surface elements are characterized with biome type, and reflectance is described using a time-dependent model bidirectional reflectance distribution function (BRDF). Ocean whitecap and Dun glint are dependent on National Meteorological Center (NMC) analysis wind speed and direction, and water leaving radiance is determined from chlorophyll concentration obtained from the Coastal Zone Color Scanner (CZCS). Cloud cover comes from the International Satellite Cloud Climatology Project (ISCCP) climatology data set. The mathematical descriptions and databases are implemented in a software package that produces data sets of calibrated MODIS data in the exact format that operational processing of real data will provide. The results are used in the test and development of MODIS data processing software.
24.4: WITHDRAWN 24.5L: Late‐News Paper: A Novel User Interface for Flexible AMOLED

To increase the convenience and interest in manipulating flexible AMOLED devices, a novel user interface making use of their bendable and impact‐proof features was developed, where sensors and interaction were respectively integrated and designed into a flexible 6″ AMOLED. The performance of the sensor‐integrated AMOLED validated the developed user interface.
Classifier combination for wafer segmentation

In the last decade, the accessibility of inexpensive and powerful computers has allowed true digital holography to be used for industrial inspection. This technique allows capturing a complex image of a scene (i.e. containing magnitude and phase), and reconstructing the phase and magnitude information. Digital holograms give a new dimension to texture analysis since the topology information can be used as an additional way to extract features. This new technique can be used to extend previous work on image segmentation of patterned wafers for defect detection. This paper presents a combination of features obtained from Gabor filters on different complex images. The combination enables to cope with the intensity variations occurring during the holography and provides final results which are independent from the selected training samples.
Analysis of Crosstalk, a work by Michael Vaughan

This article presents a full analysis of Crosstalk – a piece for tape by Michael Vaughan (1989). The methodological approach employed here develops its concepts and techniques from those of Music Semiology and Spectromorphology. The former provides the concept and method of semiological neutral inference – as applied by Nattiez (1982, 1990). The latter – developed by Smalley (1986) – offers tools and a terminology for the characterisation of timbre and temporal functionality within the musical work.
Measurement of the inductance of resistive magnets : two case studies

This note concerns the main definitions of self-inductance (i.e. differential, secant and energybased) that are relevant for the electromagnetic modelling of accelerator magnets. In particular, we consider the case of iron-dominated resistive magnets. The different definitions, which coincide in the linear limit at low field, tend to diverge dramatically as the iron becomes saturated. We describe the measurement technique and the results for two case studies, i.e. the PS MTE octupole and a main SPS dipole of MBB type. We show that the differential inductance may drop dramatically at high current, up to about a factor two, even for magnets where the field non linearity is within a few percent only.
Real-Time Video Stabilization Based on Smoothing Feature Trajectories

In this paper, a real-time video stabilization algorithm based on smoothing feature trajectories is proposed. For each input frame, our approach generates multiple feature trajectories by performing inter-frame template match and optical flow. A Kalman filter is then performed to smooth these feature trajectories. Finally, at the stage of image composition, the motion consistency of the feature trajectory is considered for achieving a visually plausible stabilized video. The proposed method can offer real-time video stabilization and its removed the delays for caching coming images. Experiments show that our approach can offer real-time stabilizing for videos with various complicated scenes.
Human and machine recognition of faces: a survey

The goal of this paper is to present a critical survey of existing literature on human and machine recognition of faces. Machine recognition of faces has several applications, ranging from static matching of controlled photographs as in mug shots matching and credit card verification to surveillance video images. Such applications have different constraints in terms of complexity of processing requirements and thus present a wide range of different technical challenges. Over the last 20 years researchers in psychophysics, neural sciences and engineering, image processing analysis and computer vision have investigated a number of issues related to face recognition by humans and machines. Ongoing research activities have been given a renewed emphasis over the last five years. Existing techniques and systems have been tested on different sets of images of varying complexities. But very little synergism exists between studies in psychophysics and the engineering literature. Most importantly, there exists no evaluation or benchmarking studies using large databases with the image quality that arises in commercial and law enforcement applications In this paper, we first present different applications of face recognition in commercial and law enforcement sectors. This is followed by a brief overview of the literature on face recognition in the psychophysics community. We then present a detailed overview of move than 20 years of research done in the engineering community. Techniques for segmentation/location of the face, feature extraction and recognition are reviewed. Global transform and feature based methods using statistical, structural and neural classifiers are summarized. >
Wi-fi backscatter

RF-powered computers are small devices that compute and communicate using only the power that they harvest from RF signals. While existing technologies have harvested power from ambient RF sources (e.g., TV broadcasts), they require a dedicated gateway (like an RFID reader) for Internet connectivity. We present Wi-Fi Backscatter, a novel communication system that bridges RF-powered devices with the Internet. Specifically, we show that it is possible to reuse existing Wi-Fi infrastructure to provide Internet connectivity to RF-powered devices. To show Wi-Fi Backscatter's feasibility, we build a hardware prototype and demonstrate the first communication link between an RF-powered device and commodity Wi-Fi devices. We use off-the-shelf Wi-Fi devices including Intel Wi-Fi cards, Linksys Routers, and our organization's Wi-Fi infrastructure, and achieve communication rates of up to 1 kbps and ranges of up to 2.1 meters. We believe that this new capability can pave the way for the rapid deployment and adoption of RF-powered devices and achieve ubiquitous connectivity via nearby mobile devices that are Wi-Fi enabled.
Ontological engineering versus metaphysics

It has been recognized that ontologies are a semantic version of world wide web and can be found in knowledge-based systems. A recent time survey of this field also suggest that practical artificial intelligence systems may be motivated by this research. Especially strong artificial intelligence as well as concept of homo computer can also benefit from their use. The main objective of this contribution is to present and review already created ontologies and identify the main advantages which derive such approach for knowledge management systems. We would like to present what ontological engineering borrows from metaphysics and what a feedback it can provide to natural language processing, simulations and modelling. The potential topics of further development from philosophical point of view is also underlined.
Perspectives on Time: performance practice, mapping strategies, & composition with MIGSI

This paper presents four years of development in performance and compositional practice on an electronically augmented trumpet called MIGSI. Discussion is focused on conceptual and technical approaches to data mapping, sonic interaction, and composition that are inspired by philosophical questions of time: what is now? Is time linear or multi-directional? Can we operate in multiple modes of temporal perception simultaneously? A number of mapping strategies are presented which explore these ideas through the manipulation of temporal separation between user input and sonic output. In addition to presenting technical progress, this paper will introduce a body of original repertoire composed for MIGSI, in order to illustrate how these tools and approaches have been utilized in live performance and how they may find use in other creative applications.
On vocabulary-independent speech modeling

The use of vocabulary-independent (VI) models to improve the usability of speech recognizers is described. Initial results using generalized triphones as VI models show that with more training data and more detailed modeling, the error rate of VI models can be reduced substantially. For example, the error rates for VI models with 5000, 10000, and 15000 training sentences, are 23.9%, 15.2%, and 13.3%, respectively. Moreover, if task-specific training data are available, one can interpolate them with VI models. This task adaptation can reduce the error rate by 18% over task-specifying models. <<ETX>>
SF-SCTP: a new transport protocol to support QoS for FCS applications

The communication of Future Combat Systems (FCS), with rigid timing and reliability requirements, has posed a great challenge for the existing popular transport layer protocols such as TCP and UDP. The Stream Control Transmission Protocol (SCTP), first designed to transmit telephony signaling messages over Internet, is a promising transport layer candidate for FCS networks. The new SCTP features such as multi-homing, multi-streaming, and enhanced security can significantly improve the performance of FCS applications. In this paper, we propose modifications to the congestion control and multi-streaming parts of current SCTP specifications to allow the support of QoS for FCS applications. Multiple streams in an SCTP association provide an aggregation mechanism to accommodate heterogeneous objects, which belong to the same application but may require different types of QoS from the network. However, the current SCTP specification lacks an internal mechanism to support the preferential treatment among its streams. Our work introduces the concept of grouping SCTP streams into subflows based on their required QoS. We propose to modify the current SCTP to implement subflows (named SF-SCTP), each with its own flow and congestion mechanism to prevent the so-called false sharing problem. To improve the fairness of SF-SCTP towards the original SCTP, we integrate Fractional Congestion Control into the design. The throughput performance evaluation of SF-SCTP is studied through ns-2 experiments in a simplified Diff-Serv network. The simulation results prove the SF-SCTP's capability to support QoS among its streams, confirm the accuracy of the analytic models, and justify our effects to integrate FCC into SF-SCTP since it improves the fairness between SF-SCTP and the original SCTP.
SensorShoe: Mobile Gait Analysis for Parkinson's Disease Patients

We present the design and initial evaluation of a mobile gait analysis system, SensorShoe. The target user group is represented by Parkinson's Disease patients, which need continuous assistance with the physical therapy in their home environment. SensorShoe analyses the gait by using a low-power sensor node equipped with movement sensors. In addition, SensorShoe gives real-time feedback and therapy assistance to the patient, and provides the caregivers an effective remote monitoring and control tool.
Diagnostic tools for hierarchical linear models

Hierarchical structures are omnipresent in today's society—this is reflected in the data that we collect on all aspects of this society. Hierarchical linear models allow a representation of structural levels in a statistical modeling framework. Diagnostic tools are used to assess the quality of model estimation and explore features of the data not well described by the model. Residual and influence diagnostics are familiar tools for the classical regression model with independent observations. For hierarchical linear models, these diagnostic tools must be adjusted to reflect the dependence introduced by the nested data structure. Residual analysis now includes the assessment of distributional assumptions at each level of the model. This requires the use of level‐dependent residual quantities. Similarly, the parameter estimates may be influenced at each level of the model, requiring influence diagnostics that can pinpoint specific levels of the model, as well as specific aspects of the model. We present an overview of the diagnostic tools available for hierarchical linear models that are familiar from linear models. Additionally, we discuss the utility of the lineup protocol for residual analysis with complex models. WIREs Comput Stat 2013, 5:48–61. doi: 10.1002/wics.1238
Making Instance-based Learning Theory Usable, Transparent, and Understandable

Although cognitive models of human behavior enjoy a rich history in cognitive psychology, they lack a widespread impact, partly due to the complexities of the modeling process including the need to know software programming. We will demonstrate a modeling tool, called IBLTool, which represents Instance-based Learning Theory (IBLT). IBLT is a theory of decisions from experience in interactive, dynamic environments. IBLTool addresses the complexity and programming challenges in cognitive modeling and helps in the development of cognitive models for a particular task. The IBLTool makes IBLT usable, transparent, and understandable for the cognitive modeling community. The tool uses graphical user interfaces to represent the modeling process and knowledge representations from the IBLT in a step-by-step manner. To derive predictions of human behavior in the IBLTool for a decision task, a task implementation, independent from the IBLTool connects and interacts with the tool interactively as a human would. Data about human behavior is collected as part of the task, while the IBLTool collects data about the execution of the model and its parameters. We will introduce IBLT and provide a step-by-step demonstration of building cognitive models with the IBLTool.