Miracle-dz/UniFAR-FTUs huggingface dataset:
The burn-in test of three-phase UPS by energy feedback method

In the burn-in test of three-phase online uninterruptible power supply (UPS) system, RLC is usually used as a load in the conventional method. For saving energy and decreasing the test cost of UPS, the energy feedback method is proposed to apply the test of the online UPS. In this method, the utility system is used instead of a load. A voltage regulator is used in parallel operation to turn back the energy, which was dissipated in conventional method, to the utility system. The values of real power and reactive power in the output of UPS can be regulated in the proposed method to imitate various loads. From the experimental results, the efficiency of saving energy is more than 85% near half-load and will be higher while the load increases. <<ETX>>
Decay Properties of 266 Bh and 262 Db Produced in the 248 Cm + 23 Na Reaction

Decay properties of an isotope 266 Bh and its daughter nucleus 262 Db produced by the 248 Cm( 23 Na,5 n ) reaction were studied by using a gas-filled recoil separator coupled with a position-sensitive semiconductor detector. 266 Bh was clearly identified from the correlation of the known nuclide, 262 Db. The obtained decay properties of 266 Bh and 262 Db are consistent with those observed in the 278 113 chain, which provided further confirmation of the discovery of 278 113.
Parallel Algorithm Engineering

Algorithmics is the subdiscipline of computer science that studies the systematic development of efficient algorithms. Algorithm Engineering (AE) is a methodology for algorithmic research that views design, analysis, implementation, and experimental evaluation of algorithms as a cycle driving algorithmic research. Further components are realistic models, algorithm libraries and a multitude of interrelations to applications. Figure 1 gives an overview. A more detailed definition can be found in [6]. This article is concerned with particular issues that arise in engineering parallel algorithms.
Detection of SSDF Attack Using SVDD Algorithm in Cognitive Radio Networks

In this paper, a new robust algorithm is proposed for spectrum sensing in cognitive radio networks. The goal of spectrum sensing is to identify holes. Malicious nodes are degraded the performance of spectrum sensing. To mitigate spectrum sensing data falsification attack, we employ support vector data description in sensing procedure. The SVDD algorithm distinguishes malicious nodes from trusted ones and omits them from decision phase. In other words, the proposed algorithm omits outliers from decision phase. It tries to construct the boundary around the target data by enclosing the target data within a minimum hyper-sphere. Inspired by the support vector machine the SVDD decision boundary is described by a few target objects, known as support vectors. Then the algorithm votes between trusted nodes to decide whether the spectrum is empty. The performance of the proposed algorithm is evaluated by computer simulations.
Denoising for liver CT image based on texture analysis

The liver CT play an important role in the clinical diagnosis and treatment of liver disease. In this paper, we present a improved approach which introduce more information of texture image in the original NL-means algorithm. The improved algorithm keeps the excellent denioising function and preserving with edge, detail, especially the texture. The artificial artifact was also restrained effectively. The availability of the improved algorithm was validated for liver CT images by the experiment result.
An Architecture for Engagement in Collaborative Conversations between a Robot and Humans

This paper reports on our research on developing the ability for robots to engage with humans in a collaborative conversation. Engagement is the process by which two (or more) participants establish, maintain and end their perceived connection during interactions they jointly undertake. The paper makes two contributions: an architecture for human-robot collaborative conversation with engagement, and a set of rules and associated algorithm for the engagement process. This work may not be copied or reproduced in whole or in part for any commercial purpose. Permission to copy in whole or in part without payment of fee is granted for nonprofit educational and research purposes provided that all such whole or partial copies include the following: a notice that such copying is by permission of Mitsubishi Electric Research Laboratories, Inc.; an acknowledgment of the authors and individual contributions to the work; and all applicable portions of the copyright notice. Copying, reproduction, or republishing for any other purpose shall require a license with payment of fee to Mitsubishi Electric Research Laboratories, Inc. All rights reserved. Copyright c ©Mitsubishi Electric Research Laboratories, Inc., 2003 201 Broadway, Cambridge, Massachusetts 02139 An Architecture for Engagement in Collaborative Conversations between a Robot and Humans Candace L. Sidner Christopher Lee Mitsubishi Electric Research Laboratories 201 Broadway Cambridge, MA 02139 {Sidner, Lee}@merl.com Abstract This paper reports on our research on developing the ability for robots to engage with humans in a collaborative conversation. Engagement is the process by which two (or more) participants establish, maintain and end their perceived connection during interactions they jointly undertake. The paper makes two contributions: an architecture for human-robot collaborative conversation with engagement, and a set of rules and associated algorithm for the engagement process.This paper reports on our research on developing the ability for robots to engage with humans in a collaborative conversation. Engagement is the process by which two (or more) participants establish, maintain and end their perceived connection during interactions they jointly undertake. The paper makes two contributions: an architecture for human-robot collaborative conversation with engagement, and a set of rules and associated algorithm for the engagement process.
An Adaptive Traffic Sampling Method for Anomaly Detection

The random packet sampling method is the simplest methodology for reducing the amount of packets that the network monitoring system has to process. However, the accuracy of anomaly detection is affected by the fact that this method biases a large IP flow. In order to reduce the impact of sampled traffic on network anomaly detecting, an adaptive traffic sampling method is proposed. This method is developed based on time stratification. Our adaptive method lies in an innovative scheme. It divides time into strata and then samples an incoming packet with a probalility, which is a decreasing function f of the predicted size of the flow the packet belongs to. Instead of data streaming algorithms, we use packet samples and a sampling probability to estimate flow size, thus to save resources. A force sampling is also employed to increase the accuracy of estimation of smaller flows. Experiments results show that our scheme is more accurate than traditional random packet sampling for estimating anomalous traffic, thus the performance of anomalous detecting is improved.
Service-Mode Observations for Ground-Based Solar Physics

There are significant advantages in combining Hinode observations with ground-based instruments that can observe additional spectral diagnostics at higher data rates and with greater flexibility. However, ground-based observations, because of the random effects of weather and seeing as well as the complexities data analysis due to changing instrumental configurations, have traditionally been less efficient than satellite observations in producing useful datasets. Future large ground-based telescopes will need to find new ways to optimize both their operational efficiency and scientific output. We have begun experimenting with service-mode or queue-mode observations at the Dunn Solar Telescope using the Interferometric Bidimensional Spectrometer (IBIS) as part of joint Hinode campaigns. We describe our experiences and the advantages of such an observing mode for solar physics.
Overview of the IWSLT 2007 evaluation campaign

In this paper we give an overview of the 2007 evaluation campaign for the International Workshop on Spoken Language Translation (IWSLT) 1 . As with previous evaluation campaigns, the primary focus of the workshop was the translation of spoken language in the travel domain. This year there were four language pairs; the translation of Chinese, Italian, Arabic, and Japanese into English. The input data consisted of the output of ASR systems for read speech and clean text. The exceptions were the challenge task of the Italian English language pair which used spontaneous speech ASR outputs and transcriptions and the Chinese English task which used only clean text. A new characteristic of this year’s evaluation campaign was an increased focus on the sharing of resources. Participants were requested to submit the data and supplementary resources used in building their systems so that the other participants might be able to take advantage of the same resources. A second new characteristic this year was the focus on the human evaluation of systems. Each primary run was judged in the human evaluation for every task using a straightforward ranking of systems. This year's workshop saw an increased participation over last year's workshop. This year 24 groups submitted runs to one or more of the tasks, compared to the 19 groups that submitted runs last year [1]. Automatic and human evaluation were carried out to measure MT performance under each condition, ASR system outputs for read speech, spontaneous travel dialogues, and clean text.
Mobile Application Menu Design for Elderly in Indonesia with Cognitive Consideration

Higher life expectancy resulted in the growing number of elderlies in Indonesia. Unlike elderly in the develop country, many elderlies in Indonesia are digital immigrants with no prior experience using technology such as computer, telephone, and other information technology. Nowadays, technology has become pervasive and penetrate all aspect of modern life. However, elderly in Indonesia may not fully taking advantage of technology because they are not technological savvy. To encourage elder people making use of technology, can be done by designing a user-friendly interface. This study is part of several studies conducted in investigating various elements of user interface, more specifically this study looked at various menu layout (namely horizontal, list, and grid). Using task to measure user performance (completion time and number of steps), it was found that grid menu was the most efficient layout for elderly. This study also investigates the influence of cognitive ability in the successfulness of interacting with different menu layouts.
Multi-processor system for real-time deconvolution and flow estimation in medical ultrasound

More and more advanced algorithms are being introduced for performing signal and image processing on medical ultrasound signals. The algorithms often use the RF ultrasound signal and perform adaptive signal processing. Two examples are the cross-correlation estimator for blood velocity estimation and adaptive blind deconvolution. The first algorithm uses the RF signal from a number of pulse emissions and correlates segments within different pulse-echo lines to obtain a velocity estimate. Real-time processing makes it necessary to perform around 600 million multiplications and additions per second for this algorithm. This has until now only been possible by using the sign of the signals, and such an implementation does not give optimal performance. The second algorithm also uses the RF data, and first performs an estimation of the one-dimensional pulse in the tissue as a function of depth. Then a Kalman filter is used with a second time-reversed recursive estimation step. Here it is necessary to perform about 70 arithmetic operations per RF sample or about 1 billion operations per second for real-time deconvolution. Furthermore, these have to be floating point operations due to the adaptive nature of the algorithms. Many of the algorithms can only be properly evaluated in a clinical setting with real-time processing, which generally cannot be done with conventional equipment. This paper therefore presents a multi-processor system capable of performing 1.2 billion floating point operations per second on RF ultrasound signals. It consists of 16 ADSP 21060 processors each capable of 80 Mflops. Four processors are placed on one board with 24 MBytes external storage and an internal storage of 0.5 MBytes per processor. All processors can access all storage on its physical board, and are further connected through parallel interface channels. Each channel can transmit 40 MBytes a second without slowing the processor down, and each processor has 6 of these channels. Four of these are accessible through front panel connectors, so that an almost arbitrary network of the 16 processors can be made. The system has been interfaced to our previously-developed real-time sampling system that can acquire RF data at a rate of 20 MHz and simultaneously transmit the data at 20 MHz to the processing system via several parallel channels. These two systems can, thus, perform real-time processing of ultrasound data. The advantage of the system is its generous input/output bandwidth, that makes it easy to balance the computational load between the processors and prevents data starvation. Due to the use of floating point calculations it is possible to simulate all types of signal processing in modem ultrasound scanners, and this system is, thus, a complete software scanner. The system has been connected to a B and K Medical type 3535 ultrasound scanner. Data is received by the PC through an Analog Devices EZ-LAB card with a ADSP21062 processor.
Tailoring Use Cases for Product Line Modeling

Use cases are used for single system requirements engineering to capture requirements from an external point of view. When utilizing use cases for product line modeling they cannot be used as is but they have to be extended with a variability mechanism. Stereotypes can be used as this variability mechanism for use case diagrams and tags can be used for textual use cases. In this paper we describe how to tailor use cases for product line modeling, describe in which situations the approach can be applied and illustrate the use case approach by an example.
Optical afterglow luminosities in the Swift epoch: confirming clustering and bimodality

We show that Gamma Ray Bursts (GRBs) of known redshift and rest frame optical extinction detected by the Swift satellite fully confirm earlier results concerning the dist ribution of the optical afterglow luminosity at 12 hours after trigge r (rest frame time). This distribution is bimodal and relatively narrow, especially for the high lu minosity branch. This is intriguing, given that Swift GRBs have, on average, a redshift larger than pre‐Swift ones, and is unexpected in the common scenario explaining the GRB afterglow. We investigate if the observed distribution can be the result of selection effects affecti ng a unimodal parent luminosity distribution, and find that either the distribution is intrinsi cally bimodal, or most (60 per cent) of the bursts are absorbed by a substantial amount of grey dust. In both cases we suggest that most dark bursts should belong to the underluminous optical family.
Motion Detection Algorithm

Motion detection is the first essential process in the extraction of information regarding moving objects and makes use of stabilization in functional areas, such as tracking, classification, recognition, and so on. The frequently-used algorithms for motion detection are studied, including frame difference method and background subtraction method, and an algorithm composing of those methods for motion detection is proposed. This paper presents a new algorithm to detect moving objects within a scene acquired by stationary camera. In this paper Difference of successive frames and background were calculated by taking mean of n consecutive frames and then compare it with current frame with the help sub block matching-based scheme. It increases the sensitivity of human motion detection.
Species Richness and Evenness in Australian Birds

Species richness and evenness are the two major components of biodiversity, but the way in which they are interrelated is a subject of contention. We found a negative relationship between the two variables for bird communities at 92 woodland sites across Australia and sought an explanation. Actual evapotranspiration (AET) was by far the best predictor of species richness. When AET was controlled for, the relationship between richness and evenness became nonsignificant. Richness is greater at sites with higher AET because such sites support a greater number of individuals. However, such sites have a greater number of rare species, resulting in lower evenness. A complicating factor is that evenness is best predicted by degree of vegetation cover, with sparsely vegetated sites having significantly lower evenness. We conclude that there are two competing ecological processes, related to energy and water availability, that determine richness and evenness. The first drives total abundance (leading to high richness, low evenness), while the second drives productivity and niche availability (leading to high richness, high evenness). The relative strength of these two processes and the observed relationship between richness and evenness are likely to depend on the scale of the analysis and the species and range of habitats studied.
A Renormalisation Group approach to Stochastic Loewner Evolutions and the Doob h-transform

In this notes we shall describe the relation of a certain class of simple random curves arising in 2D statistical mechanics models in the scaling limit, which can be described dynamically by Stochastic L{\oe}wner Evolutions (SLE), and the equivalent Renormalisation Group (RG) theoretic interpretation in Conformal Field Theory, as a fixed point of the RG flow. Further, we shall recall the relation of this random curves with String Theory, and how one can derive a general measure on such random paths, by using weighted regularised determinants, which come from sections of twisted line bundles. Importantly, the null vector at level two in the Verma module for the highest-weight representation of the Virasoro algebra corresponds to a generalised Doob-Getoor h-transform.
Can We Talk About Race? Exploring Online Comments about Race-Related Shootings

Abstract A quantitative content analysis was conducted of online comments (N = 1,840) posted on news stories about 4 shooting deaths involving Black Americans and police officers to assess how Americans talk about race in the digital space. Findings showed that uncivil attributes, such as profanity or name-calling, were more frequent than deliberative attributes, such as using evidence to support one’s point. Findings suggested there are scant but meaningful deliberative discussions about race in the comments we studied, but incivility mires that conversation. Results are discussed in terms of critical race theory and deliberative democracy.
A longitudinal study exploring pain control, treatment and service provision for individuals with end-stage lower limb osteoarthritis.

OBJECTIVE To investigate the level of pain control, treatment and service provision amongst individuals with end-stage lower limb osteoarthritis who were on the waiting list for hip or knee joint replacement. METHODS A total of 105 patients on a waiting list for primary knee or hip replacement from a regional orthopaedic centre in the UK were recruited. The study was longitudinal and based on direct interviews at baseline and 6 months, with a postal questionnaire at 3 months. Data were collected on pain by a visual analogue scale (VAS) and the Western Ontario and McMaster Universities (WOMAC) osteoarthritis index. In addition, medication and the use of services and treatments were recorded. RESULTS Participants experienced high levels of pain as measured by VAS [mean 7.0; 95% confidence interval (CI) 6.6-7.5] and WOMAC pain (mean 11.2; 95% CI 10.6-11.9). The majority of participants (78, 74%) was taking analgesics more than once a day. Primary care utilization was variable. Of the 74 (70%) participants who had consulted their GP in the previous 3 months, 41 (55%) had not discussed their pain or osteoarthritis during these consultations. Just below one-third of participants (31, 30%) reported to have received information on osteoarthritis. CONCLUSIONS Pain appears to be difficult to manage in individuals with end-stage lower limb osteoarthritis. Individuals appeared not to be taking appropriate levels of analgesia in relation to levels of pain. Improvements are required in the provision of information on pain and osteoarthritis. Patients would benefit from more proactive management by health professionals (especially by GPs).
The Heeger & Bergen Pyramid Based Texture Synthesis Algorithm

This contribution deals with the Heeger-Bergen pyramid-based texture analysis/synthesis algorithm. It brings a detailed explanation of the original algorithm tested on many characteristic examples. Our analysis reproduces the original results, but also brings a minor improvement concerning non-periodic textures. Inspired by visual perception theories, Heeger and Bergen proposed to characterize a texture by its first-order statistics of both its color and its responses to multiscale and multi-orientation filters, namely the steerable pyramid. The Heeger-Bergen algorithm consists in the following procedure: starting from a white noise image, histogram matchings are performed to the noise alternatively in both the image domain and steerable pyramid domain, so that the corresponding histograms match the ones of the input texture.
Optimal Time-Series Motifs

Motifs are the most repetitive/frequent patterns of a time-series. The discovery of motifs is crucial for practitioners in order to understand and interpret the phenomena occurring in sequential data. Currently, motifs are searched among series sub-sequences, aiming at selecting the most frequently occurring ones. Search-based methods, which try out series sub-sequence as motif candidates, are currently believed to be the best methods in finding the most frequent patterns. However, this paper proposes an entirely new perspective in finding motifs. We demonstrate that searching is non-optimal since the domain of motifs is restricted, and instead we propose a principled optimization approach able to find optimal motifs. We treat the occurrence frequency as a function and time-series motifs as its parameters, therefore we \textit{learn} the optimal motifs that maximize the frequency function. In contrast to searching, our method is able to discover the most repetitive patterns (hence optimal), even in cases where they do not explicitly occur as sub-sequences. Experiments on several real-life time-series datasets show that the motifs found by our method are highly more frequent than the ones found through searching, for exactly the same distance threshold.