Miracle-dz/UniFAR-FTUs huggingface dataset:
Banks of automatic capacitors in electrical distribution systems: a hibrid algorithm of control

A microgenetic algorithm (MGA) in conjunction with fuzzy logic (FL) is proposed for solving the capacitor placement problem. The objective function includes economic savings obtained by energy loss reduction and peak power loss reduction in contrast with acquisition and installation costs of fixed and switched capacitors. Voltage constraints are considered. A simple and efficient method for load flow solution is used, with acceptable CPU time, even for very extensive distribution systems under full load. An approximate load-duration curve divided in different load levels is used to compute energy losses. A 104-bus test system is presented and the results are compared to the solution given by another search technique. This comparison confirms the efficiency of the proposed method which makes it promising to solve complex problems of capacitor placement in distribution feeders.
ISPRS benchmark for multi - platform photogrammetry

Airborne high resolution oblique imagery systems and RPAS/UAVs are very promising technologies that will keep on influencing the development of geomatics in the future years closing the gap between terrestrial and classical aerial acquisitions. These two platforms are also a promising solution for National Mapping and Cartographic Agencies (NMCA) as they allow deriving complementary mapping information. Although the interest for the registration and integration of aerial and terrestrial data is constantly increasing, only limited work has been truly performed on this topic. Several investigations still need to be undertaken concerning algorithms ability for automatic co-registration, accurate point cloud generation and feature extraction from multiplatform image data. One of the biggest obstacles is the non-availability of reliable and free datasets to test and compare new algorithms and procedures. The Scientific Initiative “ISPRS benchmark for multi-platform photogrammetry”, run in collaboration with EuroSDR, aims at collecting and sharing state-of-the-art multi-sensor data (oblique airborne, UAV-based and terrestrial images) over an urban area. These datasets are used to assess different algorithms and methodologies for image orientation and dense matching. As ground truth, Terrestrial Laser Scanning (TLS), Aerial Laser Scanning (ALS) as well as topographic networks and GNSS points were acquired to compare 3D coordinates on check points (CPs) and evaluate cross sections and residuals on generated point cloud surfaces. In this paper, the acquired data, the pre-processing steps, the evaluation procedures as well as some preliminary results achieved with commercial software will be presented.
Embedded Memory Hierarchy Exploration Based on Magnetic Random Access Memory

Static random access memory (SRAM) is the most commonly employed semiconductor in the design of on-chip processor memory. However, it is unlikely that the SRAM technology will have a cell size that will continue to scale below 45 nm, due to the leakage current that is caused by the quantum tunneling effect. Magnetic random access memory (MRAM) is a candidate technology to replace SRAM, assuming appropriate dimensioning given an operating threshold voltage. The write current of spin transfer torque (STT)-MRAM is a known limitation; however, this has been recently mitigated by leveraging perpendicular magnetic tunneling junctions. In this article, we present a comprehensive comparison of spin transfer torque-MRAM (STT-MRAM) and SRAM cache set banks. The non-volatility of STT-MRAM allows the definition of new instant on/off policies and leakage current optimizations. Through our experiments, we demonstrate that STT-MRAM is a candidate for the memory hierarchy of embedded systems, due to the higher densities and reduced leakage of MRAM.We demonstrate that adopting STT-MRAM in L1 and L2 caches mitigates the impact of higher write latencies and increased current draw due to the use of MRAM. With the correct system-on-chip (SoC) design, we believe that STT-MRAM is a viable alternative to SRAM, which minimizes leakage current and the total power consumed by the SoC.
Use of Noun Phrases in Interactive Search Refinement

The paper presents an approach to interactively refining user search formulations and its evaluation in the new High Accuracy Retrieval from Documents (HARD) track of TREC-12. The method consists of showing to the user a list of noun phrases, extracted from the initial document set, and then expanding the query with the terms taken from the phrases selected by the user. The results show that the method yielded significant gains in retrieval performance. The paper also discusses post-TREC experiments conducted to explore the use of Pointwise Mutual Information measure in selecting multiword units for query expansion and the use of n-grams in the search process.
A Novel Approach for Image Retrieval System Combining Color, Shape & Texture Features

In many areas of commerce, government, academia, and hospitals, large collections of digital images are being created. Many of these collections are the product of digitizing existing collections of analogue photographs, diagrams, drawings, paintings, and prints. Usually, the only way of searching these collections was by keyword indexing, or simply by browsing. Digital images databases however, open the way to content based searching. In this paper a review on the current state of the art in content based image retrieval (CBIR), a technique for retrieving images on the basis of automatically derived features such as color, texture and shape is presented. Our findings are based both on a review of the relevant literature and on discussions with researchers and practitioners in the field. Here we discuss a new content based image retrieval approach based on three well known algorithms Mean Shift Segmentation, Wavelet Transform and PCA. The evaluation of the proposed approach is carried out using the standard precision and recall measures. Experimental results for the Caltech and Corel databases showed the effectiveness of our proposed method to be very promising and performance to be better than other existing approaches.
Better extractors for better codes?

We present an explicit construction of codes that can be list decoded from a fraction (1-ε) of errors in sub-exponential time and which have rate ε/logO(1)(1/ε). This comes close to the optimal rate of Ω(ε), and is the first sub-exponential complexity construction to beat the rate of ε2 achieved by Reed-Solomon or algebraic-geometric codes. Our construction is based on recent extractor constructions with very good seed length [17]. While the "standard" way of viewing extractors as codes (as in [16]) cannot beat the O(ε2) rate barrier due to the 2 log (1/ε) lower bound on seed length for extractors, we use such extractor codes as a component in a well-known expander-based construction scheme to get our result. The O(ε2) rate barrier also arises if one argues about list decoding using the minimum distance (via the so-called Johnson bound) --- so this also gives the first explicit construction that "beats the Johnson bound" for list decoding from errors.The main message from our work is perhaps conceptual, namely that good strong extractors for low min-entropies will yield near-optimal list decodable codes. Given all the progress that has been made on extractors, we view this as an optimistic avenue to look for better list decodable codes, both by looking for better explicit extractor constructions, as well as by importing non-trivial techniques from the extractor world in reasoning about and constructing codes.
Design and Characterization of CMOS On-Chip Antennas for 60 GHz Communications

In this paper, we present the design and the measurement of two antennas realized on a 130nm CMOS process. They both radiate in the 60 GHz band and are dedicated to Wireless Personal Area Network (WPAN) applications. The antennas are manufactured within the frame of a multi-wafer project with several surrounding microelectronic circuits. The first antenna is an Inverted-F antenna (IFA). It has a maximum gain of -8 dBi and a -10 dB matching bandwidth of 20%. The second radiator is a meandered dipole. It has a maximum gain of -14 dBi and a -10 dB matching bandwidth of 10%. The challenging measurement of their reflection coefficient and their gain radiation pattern are presented. Simulated versus measured curves are analyzed. We especially demonstrate the necessity to take into account the closest microelectronic circuits of the antennas for accurate modeling of the radiating performance of 60 GHz on-chip dies
Towards fully-automated micrograsping for microassembly

Fully automated microassembly procedure has become an essential subject of interest among microsystem researchers due to the promising potential of the establishment of fully functional Micro Electro Mechanical System (MEMS) devices and significant cost reduction on large number of consumer based products once the system is realized. In the heart of microassembly, the development of high precision micrograsping technique for dexterous manipulation of minute parts becomes the key factor which determines the success of microassembly process. This paper presents the development of a microgripper for the purpose of manipulating micro sized objects in precision environment. The gripper is realized based on compliant mechanism concept that uses flexure hinge to replace the traditional rigid hinge due to its favorable advantages of eliminating the backlash and coulomb friction problems. Modeling of this gripper was performed utilizing a combination of Pseudo Rigid Body Model (PRBM) and Finite Element Analysis (FEA) which effectively accelerates the prototyping procedure. The gripper was cut out of aluminum plate using electro-discharge machining (EDM) technique and experimental studies were conducted to investigate the performance as well as for model verification. The result between experiment and computational analysis has shown high degree of agreement. High displacement amplification was successfully recorded and maximum stroke of 100 mum can be achieved.
Predictors of lexical retrieval in Hindi–English bilingual speakers

Abstract When bilinguals name pictures while in ‘monolingual mode’, we expect that under conditions of language-constraint and no cognate facilitation, factors influencing lexical retrieval in monolinguals ought to exert similar effects on bilinguals. To this end, we carried out a L1-only naming task on early Hindi–English bilinguals. Results of linear mixed effects analysis reveal AoA, Familiarity, Image Agreement and Codability (availability of alternate names) to be the most significant predictors of lexical retrieval speed for early bilinguals, confirming our expectations. However, we report, for the first time, a by-subject variation in Codability for bilinguals. Implications of the results are discussed in the context of current theories of bilingual lexical access and competition. In preparation for this study, Hindi norms from bilinguals for items in the Snodgrass and Vanderwart set have been established, which will be of use for stimuli selection in experimental studies involving bilinguals.
Testing the Impact of Biased Jurors on the Efficiency of the Story Model of Jury Deliberation Process

The story model of juror deliberation describes the deliberative process as a 3-step cognitive process in which jurors attempt to assimilate and evaluate case-relevant information using predetermined verdict decision rules. However, observational research has shown that not all juries engage in deliberations that resemble the deliberation style described by the story model. An important question is whether these two observed styles of deliberation, evidence driven and verdict driven, can be incorporated into the theoretical structure of the story model. A computer simulation is proposed that will allow researchers to develop some testable hypotheses regarding differences in jury deliberation styles. Introduction Because of laws protecting the secrecy of jury deliberations, and the mathematical elegance of the deliberation process, computer simulations can be very useful in developing hypotheses in jury research. These jury simulations have typically taken one of two forms, models of information processing and models of influence. However, the forces that come to play during the jury deliberation process include both content related processing and group level influence effects. This dual process model of decision making has well researched correlated in basic attitude research. Attitude change has been shown to be affected by both the content of persuasive messages as well as more superficial qualities associated with the purveyor of the persuasive message (Petty & Cacciopo, 1986). Also, studies on the relationship between attitude and behaviors show that norms and group level influence are as important as one's attitude in determining behavior (Fishbein & Ajzen, 1980). To date, no computer simulations have been run to attempt an understanding of the dual processes involved in the jury deliberation process.
On Ways to Improve Adaptive Filter Performance

(ABSTRACT) Adaptive filtering techniques are used in a wide range of applications, including echo cancellation, adaptive equalization, adaptive noise cancellation, and adaptive beamforming. The performance of an adaptive filtering algorithm is evaluated based on its convergence rate, misadjustment, computational requirements, and numerical robustness. We attempt to improve the performance by developing new adaptation algorithms and by using " unconventional " structures for adaptive filters. Part I of this dissertation presents a new adaptation algorithm, which we have termed the Normalized LMS algorithm with Orthogonal Correction Factors (NLMS-OCF). The NLMS-OCF algorithm updates the adaptive filter coefficients (weights) on the basis of multiple input signal vectors, while NLMS updates the weights on the basis of a single input vector. The well-known Affine Projection Algorithm (APA) is a special case of our NLMS-OCF algorithm. We derive convergence and tracking properties of NLMS-OCF using a simple model for the input vector. Our analysis shows that the convergence rate of NLMS-OCF (and also APA) is exponential and that it improves with an increase in the number of input signal vectors used for adaptation. While we show that, in theory, the misadjustment of the APA class is independent of the number of vectors used for adaptation, simulation results show a weak dependence. For white input the mean squared error drops by 20 dB in about () 1 / 5 + M N iterations, where N is the number of taps in the adaptive filter and () 1 + M is the number of vectors used for adaptation. The dependence of the steady-state error and of the tracking properties on the three user-selectable parameters, namely step size µ , number of vectors used for adaptation () 1 + M , and input vector delay D used for adaptation, is discussed. While the lag error depends on all of the above parameters, the fluctuation error depends only on µ. Increasing D results in a linear
Statistical Learning, Letter Reversals, and Reading

Reversal errors play a prominent role in theories of reading disability. We examined reversal errors in the writing of letters by 5- to 6-year-old children. Of the 130 children, 92 had a history of difficulty in producing speech sounds, a risk factor for reading problems. Children were more likely to reverse letter forms that face left, such as and , than forms that face right, such as and . We propose that this asymmetry reflects statistical learning: Children implicitly learn that the right-facing pattern is more typical of Latin letters. The degree of asymmetry that a child showed was not related to the child’s reading skill at Time 2, 2¾ years later. Although children who went on to become poorer readers made more errors in the letter writing task than children who went on to become better readers, they were no more likely to make reversal errors.
A Parallelized Data Stream Processing System Using Dynamic Time Warping Distance

Due to the recent development of sensing technologies, sensor stream processing systems have been getting great attention. Sensor stream processing systems enable us to recognize the state of objects. For example, attach an acceleration sensor to a man and the system beforehand stores sample sensor data for the man’s walking and standing states. By comparing the current data stream with the stored sample data, the system can recognize whether he is walking or standing. Recognizable states increases in proportion to the number of the sample data. However, the processing time lengthens as the number of sample data increases. Long processing time is impractical for data stream processing systems. In this paper, we design and implement a data stream processing system using Dynamic Time Warping (DTW) distance. DTW is robust for changes in time and often used for streaming data analysis. In our implemented system, we reduce the processing time by parallelizing the calculation of the DTW distance.
Predicting e-commerce adoption using data about product search and supplier search behavior

In this paper we use a semi-supervised learning model to predict whether a person thinks buying a specific product online is appropriate. As input, information is used about the channels one deems appropriate to find product information or to find suppliers. Both online and offline channel preferences are found to be valuable to predict e-commerce adoption. The practical consequence of the work is that (binary) data about a user’s preferred channel for information retrieval can be helpful to estimate the probability the person is interested to buy a specific product online so that publicity for an online shop is only shown to people who actually believe buying that product online is appropriate. The predictive performance of our approach is considerably better than that reported in earlier research. Our results also show that semi-supervised learning has advantages in terms of predictive performance compared to supervised learning.
Subject-Dependent Co-Occurence and Word Sense Disambiguation

We describe a method for obtaining subject-dependent word sets relative to some (subject) domain. Using the subject classifications given in the machine-redable version of Longman's Dictionary of Contemporary English, we established subject-dependent co-occurrence links between words of the defining vocabulary to construct these "neighborhoods". Here, we describe the application of these neighborhoods to information retrieval, and present a method of word sense disambiguation based on these co-occurrences, an extension of previous work.
Moving object detection of dynamic scenes using spatio-temporal context and background modeling

Within the field of automated video analysis, detection of moving objects remains a challenging task due to the presence of dynamic background and camera motion. Dynamic scenes contain some moving objects such as trees jiggling slightly and water flowing irregularly. In this paper, we present an algorithm to address the problem of dynamic background, which employs spatio-temporal context and background modeling according to Bayes theorem. Spatial context refers to connections of pixels exist almost everywhere while keeping interrupted at boundaries between foreground and background. We use spatial context to eliminate noise points and obtain continuous foreground region. Temporal context interacts with mixture background model, which alleviates spurious detection of dynamic scenes. Object detection is finally carried out by minimizing the energy function of formulation in Markov Random Field. Employing spatio-temporal context helps to sustain high levels of detection accuracy. The efficiency of our algorithm is demonstrated by experiments performed on a variety of challenging video sequences.
An Attempt to Incorporate Moodle into Putonghua Teaching-A Case Study on Intermediate Putonghua Course in HKIEd

Moodle is an open source eLearning software platform that was originally developed by Martin Dougiamas which enables teachers to create online courses supporting interactions between teachers and their students. In recent years, Moodle has drawn the attention of teachers in Hong Kong. This is because undergraduate degree courses provided by universities in Hong Kong have been changed from three-year degrees to more generalized four-year courses in academic year 2012-2013. Due to double cohort, big classes are formed in Hong Kong Institute of Education (HKIEd); this has made it very difficult for Putonghua teachers to cater every student's needs in class and caused many other problems. In light of this, universities in Hong Kong are exploring the potentials of Moodle in enhancing teaching and learning. In this paper, with reference to Intermediate Putonghua course in HKIEd, we will deal with the following two problems: 1) How various activities and resources have been used in the Moodle; 2) How Moodle course can improve students' learning experience.
Forecasting uncertain hotel room demand

Forecasting of uncertain economic variables is an instrumental activity in any organization. This paper takes the hotel industry as a practical application of forecasting using the Holt-Winters method. The problem is to forecast the uncertain demand for rooms at a hotel for each arrival day. Forecasting is part of hotel revenue management system whose objective is to maximize the revenue by making decisions regarding when to make rooms available for customers and at what price. The forecast approach discussed in this paper is based on quantitative models and does not incorporate management expertise. Actual data from a hotel are used to illustrate the forecasting mechanism.
Young Super Star Clusters in the Starburst of M82: The Catalog

Recent results from the Hubble Space Telescope (HST) have resolved starbursts as collections of compact young stellar clusters. Here we present a photometric catalog of the young stellar clusters in the nuclear starburst of M82, observed with the HST WFPC2 in Hα (F656N) and in four optical broadband filters. We identify 197 young super stellar clusters. The compactness and high density of the sources led us to develop specific techniques to measure their sizes. Strong extinction lanes divide the starburst into five different zones, and we provide a catalog of young super star clusters for each of these. In the catalog we include relative coordinates, radii, fluxes, luminosities, masses, equivalent widths, extinctions, and other parameters. Extinction values have been derived from the broadband images. The radii range between 3 and 9 pc, with a mean value of 5.7 ± 1.4 pc, and a stellar mass between 104 and 106 M☉. The inferred masses and mean separation, comparable to the size of the super star clusters, together with their high volume density, provide strong evidence for the key ingredients postulated by Tenorio-Tagle and coworkers as required for the development of a supergalactic wind.
On storing and changing shape information

A data structure for modelling engineering components and assemblies is described. It is shown how the same structure, slightly extended, can also be used to represent a single component defined as boolean combinations of more primitive shapes. This leads to a unified method of handling both the concise boolean description and the longer explicit description in terms of faces, edges and vertices. The technique permits close control of evaluation from boolean to explicit description, and facilitates saving of partly evaluated results and back tracking. The data structure is built up and evaluation is controlled through a geometrical language embedded in Algol 68. The language is intended for the construction of computer-aided design systems, for simulating engineering production processes, or for any application in which shapes and changes to shapes must be recorded and manipulated.