Miracle-dz/UniFAR-FTUs huggingface dataset:
1-Benzyl-3-(benzylsulfanyl)-1 H -1,2,4-triazole

In the title compound, C16H15N3S, the phenyl rings make dihedral angles of 73.0 (1) and 87.9 (1)° with the triazole ring. The mol­ecular and crystal structures are stabilized by two intra­molecular hydrogen bonds (C—H⋯S and C—H⋯N) and one inter­molecular C—H⋯N hydrogen bond. The latter links mol­ecules into infinite chains.
Texture segmentation using directional operators

A method for the classification and segmentation of texture in images is presented. The method derives texture features from the image which are independent of mean gray-scale level but dependent on texture orientation. Each pixel in the original image can then be classified using supervised pattern-recognition techniques. A segmentation of the image can be produced by delineating the boundaries within the classified image in horizontal and vertical directions. The algorithm is efficient and can be implemented using general or specialist processors. Results from a sidescan sonar image of a sea bed and a video image containing samples of real texture are given. <<ETX>>
Interference Alignment: A New Look at Signal Dimensions in a Communication Network

This monograph introduces to the reader the idea of interference alignment, traces its origins, reviews a variety of interference alignment schemes, summarizes the diverse settings where the idea of interference alignment is applicable and highlights the common principles that cut across these diverse applications. The focus is on theoretical aspects. Full text available at: http://dx.doi. org/10.1561/0100000047
Architecture toward efficient OS support for distributed multimedia

In designing operating system support for distributed multimedia, we target three areas for improvement: reduced copying, reduced reliance on explicit kernel/user interactions, and provision of rate-based flow control. Towards these goals, we propose an architecture that includes the concept of I/O efficient buffers for reduced copying, the concept of fast system calls for low latency network access, and the concept of kernel threads for flow control. Also included is a concept called direct media streaming which is suitable for applications that require limited user processing of media data. These concepts have been implemented as an extension of SunOS 5.3 (the operating system component of Solaris 2.3). We report some experimental results on the performance of our current system.
Performance-driven layout of CMOS VLSI circuits

As design rules get scaled down to submicron dimensions, physical CAD tools need to be much more performance-driven as opposed to purely geometry-driven. For instance, to help prevent timing bottlenecks, it is necessary to consider timing-critical nets during circuit partitioning, placement, and routing phases. Long parallel lines can cause crosstalk problems; therefore, it is prudent to limit the length of parallel runners, especially in high-speed circuit layout. The issues are even more complex in active circuit layout. For sensitive analog circuits or clock distribution circuits, various symmetry constraints need to be met. Recent developments in performance-driven layout techniques are reviewed, and these newly emerging issues are discussed. <<ETX>>
Adjusted Confidence Intervals for the Expression Change of Proteins Observed in 2-Dimensional Difference Gel Electrophoresis

Differential proteome analyses focus on the detection and quantification of expression changes between samples from different biological groups. While the significance of an expression change is detected by some statistical test, the strength of an expression change is usually quantified by some ratio estimate, e.g. the ‘fold change’. Due to its quantitative character, the fold change is more intuitively for biologists than the decision of a statistical test. However, strong expression changes are often misleading if this change is not significant. For this reason, we propose the employment of confidence intervals, adjusted for multiple hypotheses testing, which naturally comprise both, test decision and quantification. The adjusted confidence intervals can be used for making test decisions under the control of error rates typically considered in multiple hypotheses testing (e.g. the familywise error rate or the false discovery rate). For biologists, test decisions based on adjusted confidence intervals offer a more intuitive method for selecting proteins with a significant expression change between two groups. The length of the intervals can be used for sample size planning of upcoming experiments. Our approach is primarily addressed to protein expression data recorded by two-dimensional Difference Gel Electrophoresis.
Bat Algorithm Inspired Algorithm for Solving Numerical Optimization Problems

Inspired by Bat Algorithm, a novel algorithm, which is called Evolved Bat Algorithm (EBA), for solving the numerical optimization problem is proposed based on the framework of the original bat algorithm. By reanalyzing the behavior of bats and considering the general characteristics of whole species of bat, we redefine the corresponding operation to the bats’ behaviors. EBA is a new method in the branch of swarm intelligence for solving numerical optimization problems. In order to analyze the improvement on the accuracy of finding the near best solution and the reduction in the computational cost, three well-known and commonly used test functions in the field of swarm intelligence for testing the accuracy and the performance of the algorithm, are used in the experiments. The experimental results indicate that our proposed method improves at least 99.42% on the accuracy of finding the near best solution and reduces 6.07% in average, simultaneously, on the computational time than the original bat algorithm.
Recurrent perceptron learning algorithm for completely stable cellular neural networks

A supervised learning algorithm for obtaining the template coefficients in completely stable cellular neural networks (CNNs) is presented. The proposed algorithm resembles the well-known perceptron learning algorithm and hence is called as recurrent perceptron learning algorithm (RPLA) as applied to a dynamical network, CNN. The RPLA can be described as the following set of rules: (i) increase each feedback template coefficient which defines the connection to a mismatching cell from its neighbor whose steady-state output is same with the mismatching cell's desired output. On the contrary, decrease each feedback template coefficient which defines the connection to a mismatching cell from its neighbor whose steady-state is different from the mismatching cell's desired output. (ii) Change the input template coefficients according to the rule stated in (i) by only replacing the word of "neighbor" with "input". (iii) Retain the template coefficients unchanged if the actual outputs match the desired outputs. The proposed algorithm RPLA has been applied for training CNNs to perform several image processing tasks such as edge detection, hole filling and corner detection. The performance of the templates obtained for the chosen input-(desired)output training pairs has been tested on a set of images which are different from the input images used in the training phase. <<ETX>>
Optimal placement of power supply pads and pins

Power-distribution networks of very large-scale integrated (VLSI) chips should be designed carefully to ensure reliable performance. A sound power network requires an adequate number of power-supply input connections (pads and pins). Placing them at the best vantage locations helps to reduce the number of supply connections necessary for obtaining quality power distribution. This paper addresses the problem of finding an optimum set of pads, pins, and on-chip voltage regulators, and their placement in a given power-supply network, subject to constraints on the voltage drops in the network and maximum currents through the pads, pins, and regulators. The problem is modeled as a mixed-integer linear program (MILP) with the help of macromodeling techniques. Two new heuristics, in addition to the commonly used branch-and-bound technique, are proposed to make the problem tractable. The effectiveness of the proposed technique is demonstrated on several real chips and memories used in low-power and high-performance applications.
A new accurate pill recognition system using imprint information

Great achievements in modern medicine benefit human beings. Also, it has brought about an explosive growth of pharmaceuticals that current in the market. In daily life, pharmaceuticals sometimes confuse people when they are found unlabeled. In this paper, we propose an automatic pill recognition technique to solve this problem. It functions mainly based on the imprint feature of the pills, which is extracted by proposed MSWT (modified stroke width transform) and described by WSC (weighted shape context). Experiments show that our proposed pill recognition method can reach an accurate rate up to 92.03% within top 5 ranks when trying to classify more than 10 thousand query pill images into around 2000 categories.
Trends, features, and tests of common and recently introduced global optimization methods

Global optimization techniques have been used extensively due to their capability in handling complex engineering problems. In addition to a number of well known global optimization techniques, many new methods have been introduced recently for various optimal design applications. In this work, a number of representative, well known and recently introduced global optimization techniques are closely examined and compared. The historical development, special features and trends on the development of global optimization algorithms are reviewed. Special attention is devoted to the recent developments of multidisciplinary design optimization algorithms based on effective metamodelling techniques. Commonly used benchmark optimization problems are used as test examples to reveal the pros and cons of these global optimization methods. A new meta-model based global optimization search method, introduced and improved recently by the authors, is also included in the tests and comparison.
Research on Building a Chinese Sentiment Lexicon Based on SO-PMI

Considering user behavior, this paper has built a Chinese sentiment lexicon based on improved SO-PMI algorithm. Sematic lexicons were used to classify the sentiment of the collected Chinese hotel reviews. The experiment has compared the feature extraction between CHI and sentiment lexicons to find out different classification performances. The results indicate that feature extraction based on sentiment lexicon gains higher F1. The performance of classification method “Basic Semantic Lexicon + BOOL + NB” gains 92.40% of F1. Based on different sentiment lexicons, the experimental results shows that (SO-A) and (SO-P) is slightly better than NB classifier. Therefore, it would be effective to use ((SO-A) and (SO-P) as text sentiment classifiers. The experiment also finds out the method “Hotel Reviews Semantic Lexicon using improved SO-PMI algorithm +((SO-A)” gains the highest F1 which is 92.84%. The results reveal that improved SO-PMI does more effective on weight calculation and sentiment lexicon building.
Built-in test engine and fault simulation for memory

In this paper an on-chip method for testing high performance memory devices will be presented. This new technique occupies minimal area and retains the full flexibility of existing methods for the dynamic introduction of new test patterns. This is achieved through microcode test instructions and the associated on-chip state machine. The proposed methodology will enable at-speed testing of memory devices, reducing the overall test time. The relevancy of this work is placed in context with an introduction to memory testing and the techniques and algorithms generally used today. Additionally, we examine the use of fault simulation in methodology evaluation for memory test. Finally we present a prototype design for the implementation of this methodology that incurs minimal test latency and provides a programmable interface to enable varying fault coverage and location patterns.
Image Background Removal by Contour Feature

In the task of the image processing and analysis, the background noise removal is a important step. In the image background noise removal, there are many methods which is popular for the researchers. For example, the gray threshold methods are commonly taken to remove the noises which have large contrast to the interest objects. However, there are many noises with no variance with the interest objects in the gray level. For these noises, the gray level based noise removal method is totally futile, while the contour feature has its super performance for reducing this sort of noise. For the contour feature based image background removal method, the contour model is the key. This paper proposes a novel method for modeling the contour feature of the interest objects. With this method, a novel background noise which has the same gray level to the background noise is totally removed.
INVERSE DYNAMICS MODEL OF A PARALLEL ORIENTING DEVICE

Abstract Parallel orienting mechanisms have usually higher potential benefits with respect to serial devices but their actual performances can be strongly limited by the characteristic anisotropy of their workspace: for this reason, model-based control schemes should be better developed, aiming at improving the closed-loop dynamic manipulability of the whole mechatronic system. The paper presents the inverse dynamics model of a spherical wrist in-parallel actuated by 3 linear motors: open loop dynamics is studied by means of computer simulations and some hints are worked out in view of the on-going realization of a prototype device.
A dynamic scaling FFT processor for DVB-T applications

This paper presents an 8192-point FFT processor for DVB-T systems, in which a three-step radix-8 FFT algorithm, a new dynamic scaling approach, and a novel matrix prefetch buffer are exploited. About 64 K bit memory space can be saved in the 8 K point FFT by the proposed dynamic scaling approach. Moreover, with data scheduling and pre-fetched buffering, single-port memory can be adopted without degrading throughput rate. A test chip for 8 K mode DVB-T system has been designed and fabricated using 0.18-/spl mu/m single-poly six-metal CMOS process with core area of 4.84 mm/sup 2/. Power dissipation is about 25.2 mW at 20 MHz.
Educational simulation in practice: a teaching experience using a flight simulator

The use of appropriate Educational Simulation systems (software and hardware for learning purposes) may contribute to the application of the “Learning by Doing” (LbD) paradigm in classroom, thus helping the students to assimilate the theoretical concepts of a subject and acquire certain pre-defined competencies in a more didactical way. The main objective of this work is to conduct a teaching experience using a flight simulation environment so that the students of Aeronautical Management degree can assume the role of an aircraft pilot, in order to allow the students understanding the basic processes of the air navigation and observe how the new technologies can transform and improve these processes. This is especially helpful in classroom to teach the contents of the Single European Sky ATM Research (SESAR) programme, an European project that introduces a new Air Traffic Management (ATM) paradigm based on several relevant technological and procedural changes that will affect the entire air transportation system in the short and medium term. After the execution of several activities with a flight simulator in the classroom a short test and a satisfaction survey have been requested to the students in order to assess the teaching experience.
Neuroutin': a novel high-speed adaptive-routing scheme using a neural network as a communications network simulator

The author discusses the hardware implementation of Neuroutin' and problems that arise with applying this scheme to large-scale communications networks. In every node of a communications network, neural networks are installed to simulate the traffic in the communications network and to determine the optimum output link. A neural unit in the simulator has delay circuits that correspond to the buffer memories in a communications network node. Since the simulator was implemented using analog hardware and operated in a parallel processing manner, this scheme is suitable for high-speed routing. <<ETX>>
Building Global Learning Communities.

Within the background where education is increasingly driven by the economies of scale and research funding, we propose an alternative online open and connected framework (OOC) for building global learning communities using mobile social media. We critique a three year action research case study involving building collaborative global learning communities around a community of practice of learning researchers and practitioners. The results include the development of a framework for utilising mobile social media to support collaborative curriculum development across international boundaries. We conclude that this framework is potentially transferrable to a range of educational contexts where the focus is upon student-generated mobile social media projects. Keywords: community of practice; mlearning; global collaboration framework (Published: 6 September 2013) Citation: Research in Learning Technology 2013, 21 : 21955 - http://dx.doi. org/10.3402/rlt.v21i0. 21955
Action fuzzy rule based classifier for analysis of dermatology databases

In healthcare industries, the classification systems are applied to localise primary tumours, prognosis and recurrence of breast cancer, diagnosis of thyroid diseases, and rheumatology. The major requirements for such a system are prediction accuracy and comprehensibility of the learned knowledge by human experts. Success of a classification learning algorithm, in terms of these criteria, is directly related to the scheme used for representing the learned classification knowledge. An Adaptive Neuro Fuzzy Inference System (ANFIS) mechanism is proposed for erythemato-squamous disease analysis and to manage the knowledge acquired and found from the system. In addition, this system utilises Action Rule based Diagnostic System (ARDS) action and fuzzy rule based classifiers in accordance with the severity levels of the diseases. Empirical results endorse that this system is an authentic and reliable tool to reduce the human errors and improve the quality of medical care provided to the public.
Image Processing Software For Imaging Spectrometry

Recent advances in remote sensing have enabled scientists to collect image data in literally hundreds of spectral channels simul-taneously, from the near ultraviolet through the short-wavelength infrared, using imaging spectrometers. In many cases this data is of sufficient resolution to provide a direct surface materials identification. Yet the volume and complexity of the data produced requires new algorithms and approaches beyond those traditionally used for multispectral image analysis, including algorithms for fast image segmentation, spectral identification and mixture analysis. This paper describes a software system specifically designed to provide the scientist with the tools necessary for exploratory analysis of imaging spectrometer data using only modest computa-tional resources.
A Design Scheme of Operating System to Run Windows' Application Programs on Linux Platform

The realization of interoperability between Linux and Windows is beneficial to the development of operating system technology in depth and breadth. From such aspects as architecture, kernel module, user module,system call,device driver module and so on, this paper elaborates a design scheme of operating system to run Windows' application programs on Linux platform.
Umiejętności Polaków – wyniki Międzynarodowego Badania Kompetencji Osób Dorosłych (PIAAC)

Raport przygotowany w ramach projektu systemowego Badanie jakości i efektywności edukacji oraz instytucjonalizacja zaplecza badawczego, wspolfinansowanego przez Unie Europejską ze środkow Europejskiego Funduszu Spolecznego, realizowanego przez Instytut Badan Edukacyjnych.
Ubiquitous Computing: Extending Access To Mobile Data

iii DEDICATIONS The most important dedication: to Nicole, my wife-to-be, for sticking by me and being so understanding when I was frustrated and grumpy. I'd really like to thank my advisor, Gregory Abowd, for giving such a wonderful opportunity (and a chance to play with cool toys) even though I was only a Masters student. Gregory never once laughed at my ideas, and stuck up for me when I needed it. Of course, if it were up to him, this project would have been called " Newton's AGo Go. " We'll try to ignore that. I'd also like to the entire Future Computing Environments group (I can't name you all) for providing such a fertile playground in which to develop such new and fascinating ideas. My work would have been useless without applications to existing projects. I'm glad I could do my part in furthering their research in ubiquitous computing.
A blind calibration method for an adaptive array antenna in DS-CDMA systems using an MMSE algorithm

A new blind calibration method is proposed to compensate for the amplitude and phase errors in the antenna branches of DS-CDMA base stations with adaptive array antennas. The proposed method calibrates these errors using the MMSE (minimum mean square error) algorithm, requiring only the received (or transmitted) signals at the array antenna and not requiring any additional reference signal to be inserted for calibration. The proposed method and structure can be applied to both the downlink and uplink. The fast convergence property of this method ensures a tracking capability against amplitude-phase variances. In an uplink, using the proposed calibrator, a beam steering adaptive array antenna based on a directional finding algorithm of a desired signal has a BER performance that improves by about 2 dB at the BER of 10/sup -3/ when a non-linear RF amplifier is used. We have also evaluated the downlink performance of this method when a non-linear RF amplifier is used and have confirmed its robustness against that of the amplifier.
Genetic Clustering with Semantic Vector Expansion

This paper proposes a new document clustering system using fuzzy logic-based genetic algorithm (GA) and semantic vector expansion technology. It has been known in many GA papers that the success depends on two factors, the diversity of the population and the capability to convergence. We use the fuzzy logic-based operators to adaptively adjust the influence between these two factors. In traditional document clustering, the most popular and straightforward approach to represent the document is vector space model (VSM). However, this approach not only leads to a high dimensional feature space, but also ignores the semantic relationships between some important words, which would affect the accuracy of clustering. In this paper we use latent semantic analysis (LSA)to expand the documents to corresponding semantic vectors conceptually, rather than the individual terms. Meanwhile, the sizes of the vectors can be reduced drastically. We test our clustering algorithm on 20 news groups and Reuter collection data sets. The results show that our method outperforms the conventional GA in various document representation environments.
A correlation study of phase plane and step response measures of upper extremity performance for Parkinsonian patients

Random-step-tracking test data from 15 Parkinson's disease patients are analyzed using step-response and phase-plane analysis. Correlation analysis is applied to investigate the relationship between three step-response measures and two phase-plane measures. Results indicate that reasonable estimates of the two of the step-response measures can be obtained from the phase-plane measures, but the third measure should be computed directly from the data. <<ETX>>