Miracle-dz/UniFAR-FTUs huggingface dataset:
PC and monitor night status: Power management enabling and manual turn-off

While office equipment accounts for about 7 percent of commercial building energy use, this reflects considerable energy savings from the use of automatic power management. Most of these savings were gained through the use of low-power modes that meet the criteria of the U.S. EPA's Energy Star program. Despite this success, there are large amounts of additional savings that could be gained if all equipment capable of power management use were enabled and functioning. A considerable portion of equipment is not enabled for power management at all, enabled only partially, or is enabled but prevented from functioning. Additional savings could be gained if more equipment were turned off at night manually. We compiled results from 17 studies from the office equipment literature addressing PCs and monitors. Some factors important for annual energy use, such as power levels, have been documented elsewhere and are not covered. We review methods for estimating office equipment use patterns and energy use, and present findings on night status--power management and manual turn-off rates. In early studies, PC power management was often found to function in 25 percent or less of the Energy Star compliant units (10 percent of all PCs). However, recent assessments have found higher rates, and we estimate that for Energy Star models, 35 percent of PC CPUs and 65 percent of PC monitors are enabled for power management. While the data lack statistical rigor, they can be used to estimate the magnitude of current and potential power management savings, which we did for major types of office equipment. The data also make clear that the topic of enabling rates, and the factors which influence them, deserve greater scrutiny.
Security system throughput modeling

One of the keys to integrating 100% checked bag screening into airports is identifying realistic throughput rates of various explosive detection system (EDS) machines available to Transportation Security Administration (TSA) planners. This study discusses some of the issues associated with the implementation of EDS equipment and performs an analysis on the throughput of the equipment using modeling and discrete event simulation tools. The study determined that there is a significant difference between the scan rate and the effective throughput rate for the InVision CTX5500. This is extremely important for the TSA planners, who are determining the number of EDS machines to buy and deploy at all 429 US commercial airports. The deployments are based on passenger demand at peak hours of the day. Matching this relatively high volume operation against the slow processing rate of stand-alone EDS will generate large passenger queues unless multiple machines are installed to provide and acceptable level of customer (passenger) service. As a result, the effective throughput rate number should be used in determining the number of EDSs to install at a particular airport, not the machine scan rate.
Geometry Discovery with Connectivity Information and Applications in Sensor Networks

Stony Brook University Libraries. Department of Computer Science. Lawrence Martin (Dean of Graduate School), Dr. Joseph S.B. Mitchell - Dissertation Advisor Professor, Computer Science Department and Applied Mathematics and Statistics Department, Dr. Jie Gao - Chairperson of Defense Assistant Professor, Computer Science Department, Dr. Esther M. Arkin - Committee Member Professor, Computer Science Department and Applied Mathematics and Statistics Department, Dr. Jiaqiao Hu - Outside Committee Member Assistant Professor, Applied Mathematics and Statistics Department, Stony Brook University.
Concept Relationships in Knowledge Organization Systems: Elements for Analysis and Common Research Among Fields

ABSTRACT Knowledge organization systems have been studied in several fields and for different and complementary aspects. Among the aspects that concentrate common interests, in this article we highlight those related to the terminological and conceptual relationships among the components of any knowledge organization system. This research aims to contribute to the critical analysis of knowledge organization systems, especially ontologies, thesauri, and classification systems, by the comprehension of its similarities and differences when dealing with concepts and their ways of relating to each other as well as to the conceptual design that is adopted.
Analysis of Postural Adjustment Responses to Perturbation Stimulus by Surface Tilts in the Feet-together Position

Base of support (BOS) influences the postural control of upright standing in humans. Since a narrowed BOS in the upright position makes postural control difficult, it is important to evaluate and practice balance with a narrowed BOS. We performed an experimental study to clarify the postural control mechanism in a feet-together position in response to dynamic perturbations. Subjects were 10 healthy young adults. Using an electrically operated platform, tilting perturbation was applied to subjects standing in feet-together position. Perturbation was in four directions: backward, forward, right, and left. Using a motion analysis system and electromyography (EMG), we simultaneously measured the three-dimensional postural change and EMG of seven muscles of the trunk and legs. We obtained joint angular change from the motion analysis and the integrated values of muscle activity and latency of the lower extremities from the EMG. In terms of pitch tilt, the ankle strategy was observed: this involved simultaneous contraction of the ankle muscles as usual in the upright position. In contrast, in terms of roll tilt, ”counterweighting” was observed. We also observed a change in postural control to respond to the narrowed BOS. We found that, unlike the feet-apart position, the feet-together position, narrowing the BOS in the left-right direction causes a prominent change and makes postural control strategy difficult only in response to roll tilt perturbations. In the future, we need to investigate the effectiveness of balance training including roll tilting in the feet-together position in improving balance ability in the left-right direction.
Effects of a Teacher-Made Multimedia Program on Teaching Driver Education

Through a case study, the authors share a story of a high school special education teacher who developed a multimedia program to teach driver education (i.e., speed limits and road signs). The high school students (n=24) had mild disabilities, and were enrolled in a driver education class in a rural high school. The teacher developed the multimedia program using Microsoft PowerPoint. The computer program involved picture and video segments that were captured in the students’ familiar environments. As a result of the lessons, the students significantly improved their scores on posttests. In addition, the multimedia lesson was shown to be more effective than a non-multimedia lesson.
The Impact of Supplemental Reading on Vocabulary Acquisition and Retention with EFL Learners in Taiwan

Research has shown that incidental vocabulary learning takes place when learners read extensively. However, not many EFL students can engage in large amounts of reading in Taiwan. In an attempt to incorporate larger amounts of reading into EFL classroom instruction and to compare the effect of this practice with that of vocabulary exercises, the researchers conducted a classroom-based study over a 5-week period. The participants were 50 male students, at an intermediate level of English proficiency, in their third year of study in a senior high school. Half of them received the reading-plus-vocabulary- enhancement (RV) instructional treatment: they read selected texts and also practiced various vocabulary exercises. The other half received the narrow reading (NR) treatment: they read thematically-related supplemental materials on the same topics presented in the selected texts. A Chinese version of the Vocabulary Knowledge Scale (VKS) was then employed to assess students' knowledge of 50 vocabulary items. The RV group significantly outperformed the NR group on both acquisition and retention tests, and demonstrated significantly more productive knowledge on the acquisition test and more receptive knowledge on both tests.
Preparing Middle School Students for the Transition to High School Mathematics: Assessing Latinas/os' Mathematical Understanding, Academic Language and English Proficiency

Latina/o English Language Learners (ELLs) in mathematics classes have the dual responsibility of understanding mathematics content while simultaneously acquiring disciplinary language in English. The Authentic Math and Writing Assessment System was a formative assessment developed to offer middle school mathematics teachers’ instructional guidance for addressing the language and mathematics learning needs of ELLs. Mixed methods analysis of student writing samples focused on mathematical content and revealed possibilities for informing teachers about practices that address both language and content learning goals.
A review of modeling methods for predicting in-hospital mortality of patients in intensive care unit

Severity scoring models, which can be used to predict the probability of death for patients in intensive care units (ICUs) have been developed for nearly 30 years and are clearly the recognized risk stratified tools and ICU quality metrics available today. However, the accuracy of these scoring models remains far from perfect when it was applied in a new ICU setting. Therefore, implementing locally customized prognostic models that show better predictive performance rather than adopting standard severity scoring models seem to be more worthwhile in the future as the electronic medical records getting popular. In this review, we aim to outline the essential procedures and concepts for the development of reliable models in the field of in-hospital mortality prediction of ICU patients. First, we present the common variables or predictors necessary to construct mortality prediction models. Then, we elaborate the following modeling methodologies: logistic regression (LR), artificial neural network (ANN), decision tree (DT), and support vector machine (SVM). LR underlies most of the illness scoring models, while the others are extensions of traditional statistical approaches and show promise as more useful tools in the analysis of large, and heterogeneous data. Finally, we describe the common approaches and measures used to evaluate model performance.
Tutorial: Items to be included in a report on a near infrared spectroscopy project

There are nearly 40 items that should ideally be reported when an NIR (near infrared) spectroscopy project is completed, either as a report or as a scientific paper. However, in our reading of the extensive literature, many of the papers presented or published report no more than 6–10 of these. The purpose of this tutorial is to indicate all of the items and the reasons for reporting them. Most of the items that need to be reported are important for anyone who seeks to duplicate the type of application and methods reported in a peer-reviewed journal article for their own work. Practically, all of the items are significant to any worker if the eventual objective of their work is to extend it to the level of industrial application. The tutorial will summarize these items, and give some explanation for their inclusion. The tutorial should be useful to potential authors, as well as to reviewers.
Image registration: algorithms and applications

Image registration is the task of determining positions of corresponding points in two images. A fast and accurate image registration plays an important role in many computer vision and graphics problems. This thesis presents fast and robust pixel and feature-based image registration algorithms and explores new applications in computer vision and graphics. It is divided into two parts. In the first part, we propose a fast and accurate pixel-based image registration algorithm that registers a pair of stereo images to extract the three-dimensional information of the scene. The algorithm finds an appropriate match for each reference image pixel in the target image to compute dense depth maps and works very well in case of repetitive patterns, object boundaries, as well as in occluded and non-textured image regions without increasing computational overhead significantly. The threedimensional information obtained using pixel-based registration is then used to recognize complex human activities. A sequence of depth maps is represented as a sequence of codewords that are learnt to discriminate between activities. These discriminative sub-sequences are then used to classify and localize an activity in a given test video. To demonstrate the efficiency of these algorithms, we provide experimental results on standard datasets which are publicly available. In the second part, we present a feature-based registration method that registers different image features to find most similar images from very large-scale image datasets. The proposed system retrieves and ranks database images based on their similarity to any given input image. By registering different image features, the retrieval and indexing of the relevant images is improved. We use these similar images to solve an ill-posed computer graphics problem, viz., colorization of grayscale images. The algorithm uses similar color images as the reference images and transfers color information to a given input grayscale image using a graph-based optimization method. The algorithm works at the resolution of superpixels and uses local properties for proper color transfer. Our use of superpixels reduces the complexity of our algorithm significantly and it also empowers the colorizations to exhibit a much higher extent of spatial consistency in the colorization as compared to those using independent pixels. We evaluate these approaches on a wide variety of images. Both qualitative and quantitative analyses have been used to demonstrate the greater effectiveness of the proposed approaches.
Multi-level acoustic segmentation of continuous speech

As part of the goal to better understand the relationship between the speech signal and the underlying phonemic representation, the authors have developed a procedure that describes the acoustic structure of the signal. Acoustic events are embedded in a multi-level structure in which information ranging from coarse to fine is represented in an organized fashion. An analysis of the acoustic structure, using 500 utterances from 100 different talkers, show that it captures over 96% of the acoustic-phonetic events of interest with an insertion rate of less than 5%. The signal representation, and the algorithms for determining the acoustic segments and the multi-level structure are described. Performance results and a comparison with scale-space filtering is also included. Possible use of this segmental description for automatic speech recognition is discussed. <<ETX>>
Computing on Encrypted Data into the Cloud Though Fully Homomorphic Encryption

Securing Data in the cloud based on Fully Homomorphic Encryption (FHE) is a new and potential form of security that allows computing on encrypted data without decrypted it first. However, a practical FHE solution is not available for implementation today. In this work, we propose a platform based on open source solutions to perform data computations (addition and multiplication) on encrypted form. In addition, taking account of efficiently and the security component, the most popular partially homomorphic encryption algorithms (RSA, Paillier and ElGamal) are studied to analyze the process times of encryption, decryption and computation of each algorithm. Furthermore, to compromise between performance and security, we need to study different key sizes and different data sizes as parameters.
Theoretical and applied mechanics letters

A concise and factual abstract is required. The abstract should state briefly the purpose of the research, the principal results and major conclusions. An abstract is often presented separately from the article, so it must be able to stand alone. For this reason, References should be avoided, but if essential, then cite the author(s) and year(s). Also, non-standard or uncommon abbreviations should be avoided, but if essential they must be defined at their first mention in the abstract itself. Graphical abstract Although a graphical abstract is optional, its use is encouraged as it draws more attention to the online article. The graphical abstract should summarize the contents of the article in a concise, pictorial form designed to capture the attention of a wide readership. Graphical abstracts should be submitted as a separate file in the online submission system. Image size: Please provide an image with a minimum of 531 × 1328 pixels (h × w) or proportionally more. The image should be readable at a size of 5 × 13 cm using a regular screen resolution of 96 dpi. Preferred file types: TIFF, EPS, PDF or MS Office files. You can view Example Graphical Abstracts on our information site. Authors can make use of Elsevier's Illustration and Enhancement service to ensure the best presentation of their images and in accordance with all technical requirements: Illustration Service. Highlights Highlights are a short collection of bullet points that convey the core findings of the article. Highlights are optional and should be submitted in a separate editable file in the online submission system. Please use 'Highlights' in the file name and include 3 to 5 bullet points (maximum 85 characters, including spaces, per bullet point). You can view example Highlights on our information site.
DU VIEILLISSEMENT POSITIF AU VIEILLISSEMENT CRÉATIF

En montrant que depuis les premieres theories en gerontologie le concept de creativite a ete associe au vieillissement positif, l'auteur detaille la polysemie du terme, les orientations theoriques des etudes psychologiques et la singularite de la production creative lors de l'avancee en âge debouchant sur la creation a Geneve de l'Association pour le Vieillissement creatif.
A Two-Detector Signal Detection Analysis of Learning to Use Alarms

The learning to use (or disregard) alarms was analyzed in an experiment in which participants had to decide whether a simulated system was intact or malfunctioning, based on the reading of a gauge-like display. In two of the three experimental conditions participants received a warning when the system, that functioned as a signal detector with the same sensitivity as the participant, detected a value that was beyond a preset response criterion (which was either lnβw = −1 or lnβw = .5). Results showed that participants' responses were always less cautious than the optimal setting. Already in the first 100 trial block (out of 5) existed clear differences between the warning conditions, as well as differences between the no-warning condition and a warning condition for which the optimal β were the same. These results indicate that the learning process has two components—first a rapid adjustment of the response to the condition, and than a slow, and in some conditions non-existing, convergence towards the optimum.
Type of Automation Failure: The Effects on Trust and Reliance in Automation

Past automation research has focused primarily on machine-related factors (e.g., automation reliability) and human-related factors (e.g., accountability). Other machine-related factors such as type of automation errors, misses or false alarms, have been noticeably overlooked. These two automation errors correspond to potential operator errors, omission (misses) and commission (false alarms), which have proven to directly affect operators' trust in automation. This proposed research will examine how automation-error-type affects operator trust and begin to develop baseline trust measures as they relate to error type and participant age. It is expected that participants presented with more automation false alarms than misses will experience a larger degradation of subjective trust than those presented with equal numbers of false alarms and misses or more automation misses than false alarms.
Teaching advanced Visual Basic .NET in a distributed classroom

A trend of declining enrollment in Computer Science (CS) is being felt at the 2-year University of Wisconsin (UW) Colleges, and it therefore does not seem an opportune time to expand the CS curriculum. Nevertheless, in 2003 the UW Colleges successfully introduced a new CS course in Advanced Visual Basic . NET, a course offered via distance education to the thirteen UW Colleges, which are geographically spread over the entire state of Wisconsin. Two issues bear examination: first, the success of this course, which is believed to be due to the distributed classroom model adopted for the course, and second, the interest of students in a course which is neither part of the standard CS curriculum, nor easily transferable to the 4-year UW Colleges.
Using autotuning for accelerating tensor contraction on graphics processing units (GPUs)

Tensors are mathematical representations of physical entities that have magnitude with multiple directions. Tensor contraction is a form of creating these objects using the Einstein summation equation. It is commonly used in physics and chemistry for solving problems like spectral elements and coupled cluster computation. Mathematically, tensor contraction operations can be reduced to expressions similar to matrix multiplications. However, linear algebra libraries (e.g., BLAS and LAPACK) perform poorly on the small matrix sizes that commonly arise in certain tensor contraction computations. Another challenge seen in the computation of tensor contraction is the difference between the mathematical representation and an efficient implementation. This thesis proposes a framework that allows users to express a tensor contraction problem in a high-level mathematical representation and transform it into a linear algebra expression that is mapped to a high-performance implementation. The framework produces code that takes advantage of the parallelism that graphics processing units (GPUs) provide. It relies on autotuning to find the preferred implementation that achieves high performance on the available device. Performance results from the benchmarks tested, nekbone and NWChem, show that the output of the framework achieves a speedup of 8.56x and 14.25x, respectively, on an NVIDIA Tesla C2050 GPU against the sequential version; while using an NVIDIA Tesla K20c GPU it achieved speedups of 8.87x and 17.62x. The parallel decompositions found by the tool were also tested with an OpenACC implementation and achieved a speedup of 8.87x and 10.42x for nekbone, while NWChem obtained a speedup of 7.25x and 10.34x compared to the choices made by default in the OpenACC compiler. The contributions of this work are: (1) a simplified interface that allows the user to express tensor contraction using a high-level representation and transform it into high-performance code; (2) a decision algorithm that explores a set of optimization strategies for achieving performance; and, (3) a demonstration that this approach can achieve better performance than OpenACC and can be used to accelerate OpenACC. This work is dedicated to my wife, Zuleika Almanzar, our soon to be born baby and my parents, Isander Rivera and Ruth Rodŕıguez, for their unconditional support during this journey. They are my source of motivation to accomplish the goals in my life.