nils-herrmann/clean_scirepeval huggingface dataset:
'It's just like you talk to a friend' relational agents for older adults

Relational agents—computational artifacts designed to build and maintain longterm social-emotional relationships with users—may provide an effective interface modality for older adults. This is especially true when the agents use simulated face-toface conversation as the primary communication medium, and for applications in which repeated interactions over long time periods are required, such as in health behavior change. In this article we discuss the design of a relational agent for older adults that plays the role of an exercise advisor, and report on the results of a longitudinal study involving 21 adults aged 62 to 84, half of whom interacted with the agent daily for two months in their homes and half who served as a standard-of-care control. Results indicate the agent was accepted and liked, and was significantly more efficacious at increasing physical activity (daily steps walked) than the control.
Faster Algorithms for Max-Product Message-Passing

Maximum A Posteriori inference in graphical models is often solved via message-passing algorithms, such as the junction-tree algorithm, or loopy belief-propagation. The exact solution to this problem is well known to be exponential in the size of the model’s maximal cliques after it is triangulated, while approximate inference is typically exponential in the size of the model’s factors. In this paper, we take advantage of the fact that many models have maximal cliques that are larger than their constituent factors, and also of the fact that many factors consist entirely of latent variables (i.e., they do not depend on an observation). This is a common case in a wide variety of applications, including grids, trees, and ringstructured models. In such cases, we are able to decrease the exponent of complexity for message-passing by 0.5 for both exact and approximate inference.
IT Governance : How Top Performers Manage IT Decision Rights for Superior Results

Successful electronic government requires the successful implementation of technology. This book lays out a framework for understanding a system of decision processes that have been shown to be associated with the successful use of technology. Peter Weill and Jeanne Ross are based at the Center for Information Systems Research at MIT’s Sloan School of Management, which has been doing research on the management of information technology since 1974. Understanding how to make decisions about information technology has been a primary focus of the Center for decades. Weill and Ross’ book is based on two primary studies and a number of related projects. The more recent study is a survey of 256 organizations from the Americas, Europe, and Asia Pacific that was led by Peter Weill between 2001 and 2003. This work also included 32 case studies. The second study is a set of 40 case studies developed by Jeanne Ross between 1999 and 2003 that focused on the relationship between information technology (IT) architecture and business strategy. This work identified governance issues associated with IT and organizational change efforts. Three other projects undertaken by Weill, Ross, and others between 1998 and 2001 also contributed to the material described in the book. Most of this work is available through the CISR Web site,  Taken together, these studies represent a substantial body of work on which to base the development of a frameBOOK REVIEW
MODIFIED WAVENUMBER DOMAIN ALGORITHM FOR THREE-DIMENSIONAL MILLIMETER-WAVE IMAGING

Millimeter-wave (MMW) imaging techniques have been used for the detection of concealed weapons and contraband carried on personnel at airports and other secure locations. The combination of frequency-modulated continuous-wave (FMCW) technology and MMW imaging techniques should lead to compact, light-weight, and low-cost systems which are especially suitable for security and detection application. However, the long signal duration time leads to the failure of the conventional stop-and-go approximation of the pulsed system. Therefore, the motion within the signal duration time needs to be taken into account. Analytical threedimensional (3-D) backscattered signal model, without using the stop-and-go approximation, is developed in this paper. Then, a wavenumber domain algorithm, with motion compensation, is presented. In addition, conventional wavenumber domain methods use Stolt interpolation to obtain uniform wavenumber samples and compute the fast Fourier transform (FFT). This paper uses the 3D nonuniform fast Fourier transform (NUFFT) instead of the Stolt interpolation and FFT. The NUFFT-based method is much faster than the Stolt interpolation-based method. Finally, point target simulations are performed to verify the algorithm.
Tone Reproduction and Physically Based Spectral Rendering

The ultimate aim of realistic graphics is the creation of images that provoke the same responses that a viewer would have to a real scene. This STAR addresses two related key problem areas in this effort which are located at opposite ends of the rendering pipeline, namely the data structures used to describe light during the actual rendering process, and the issue of displaying such radiant intensities in a meaningful way. The interest in the first of these subproblems stems from the fact that it is common industry practice to use RGB colour values to describe light intensity and surface reflectancy. While viable in the context of methods that do not strive to achieve true realism, this approach has to be replaced by more physically accurate techniques if a prediction of nature is intended. The second subproblem is that while research into ways of rendering images provides us with better and faster methods, we do not necessarily see their full effect due to limitations of the display hardware. The low dynamic range of a standard computer monitor requires some form of mapping to produce images that are perceptually accurate. Tone reproduction operators attempt to replicate the effect of real-world luminance intensities. This STAR report will review the work to date on spectral rendering and tone reproduction techniques. It will include an investigation into the need for spectral imagery synthesis methods and accurate tone reproduction, and a discussion of major approaches to physically correct rendering and key tone mapping algorithms. The future of both spectral rendering and tone reproduction techniques will be considered, together with the implications of advances in display hardware.
Text recognition in scene image and video frame using Color Channel selection

In recent years, recognition of text from natural scene image and video frame has got increased attention among the researchers due to its various complexities and challenges. Because of low resolution, blurring effect, complex background, different fonts, color and variant alignment of text within images and video frames, etc., text recognition in such scenario is difficult. Most of the current approaches usually apply a binarization algorithm to convert them into binary images and next OCR is applied to get the recognition result. In this paper, we present a novel approach based on color channel selection for text recognition from scene images and video frames. In the approach, at first, a color channel is automatically selected and then selected color channel is considered for text recognition. Our text recognition framework is based on Hidden Markov Model (HMM) which uses Pyramidal Histogram of Oriented Gradient features extracted from selected color channel. From each sliding window of a color channel our color-channel selection approach analyzes the image properties from the sliding window and then a multi-label Support Vector Machine (SVM) classifier is applied to select the color channel that will provide the best recognition results in the sliding window. This color channel selection for each sliding window has been found to be more fruitful than considering a single color channel for the whole word image. Five different features have been analyzed for multi-label SVM based color channel selection where wavelet transform based feature outperforms others. Our framework of color channel selection is script-independent. It has been tested in English (Roman) and Devanagari (Indic) scripts. We have tested our approach on English datasets (ICDAR 2003, ICDAR 2013, MSRA-TD500, IIIT5K, SVT, YVT) publicly available for both video and scene images. For Devanagari script, we collected our own dataset. The performances obtained from experimental results are encouraging and show the advantage of the proposed method.
Integrated online trajectory planning and optimization in distinctive topologies

This paper presents a novel integrated approach for efficient optimization based online trajectory planning of topologically distinctive mobile robot trajectories. Online trajectory optimization deforms an initial coarse path generated by a global planner by minimizing objectives such as path length, transition time or control effort. Kinodynamic motion properties of mobile robots and clearance from obstacles impose additional equality and inequality constraints on the trajectory optimization. Local planners account for efficiency by restricting the search space to locally optimal solutions only. However, the objective function is usually non-convex as the presence of obstacles generates multiple distinctive local optima. The proposed method maintains and simultaneously optimizes a subset of admissible candidate trajectories of distinctive topologies and thus seeking the overall best candidate among the set of alternative local solutions. Time-optimal trajectories for differential-drive and carlike robots are obtained efficiently by adopting the Timed-Elastic-Band approach for the underlying trajectory optimization problem. The investigation of various example scenarios and a comparative analysis with conventional local planners confirm the advantages of integrated exploration, maintenance and optimization of topologically distinctive trajectories. ∗Corresponding author Email address: christoph.roesmann@tu-dortmund.de (Christoph Rösmann)  2016
Scene Text Detection via Holistic, Multi-Channel Prediction

Recently, scene text detection has become an active research topic in computer vision and document analysis, because of its great importance and significant challenge. However, vast majority of the existing methods detect text within local regions, typically through extracting character, word or line level candidates followed by candidate aggregation and false positive elimination, which potentially exclude the effect of wide-scope and long-range contextual cues in the scene. To take full advantage of the rich information available in the whole natural image, we propose to localize text in a holistic manner, by casting scene text detection as a semantic segmentation problem. The proposed algorithm directly runs on full images and produces global, pixel-wise prediction maps, in which detections are subsequently formed. To better make use of the properties of text, three types of information regarding text region, individual characters and their relationship are estimated, with a single Fully Convolutional Network (FCN) model. With such predictions of text properties, the proposed algorithm can simultaneously handle horizontal, multi-oriented and curved text in real-world natural images. The experiments on standard benchmarks, including ICDAR 2013, ICDAR 2015 and MSRA-TD500, demonstrate that the proposed algorithm substantially outperforms previous state-ofthe-art approaches. Moreover, we report the first baseline result on the recently-released, large-scale dataset COCO-Text. Scene text detection, fully convolutional network, holistic prediction, natural images.
Automatic seeded region growing for color image segmentation

In this paper, we present an automatic seeded region growing algorithm for color image segmentation. First, the input RGB color image is transformed into YCbCr color space. Second, the initial seeds are automatically selected. Third, the color image is segmented into regions where each region corresponds to a seed. Finally, region-merging is used to merge similar or small regions. Experimental results show that our algorithm can produce good results as favorably compared to some existing algorithms. q 2005 Elsevier B.V. All rights reserved.
Cognition. The manifold ways of perception.

One of the great puzzles of visual perception is how an image that is in perpetual flux can still be seen by the observer as the same object. In an informative Perspective, Seung and Lee explain the mathematical intricacies of two new algorithms for modeling the variability of perceptual stimuli and other types of high-dimensional data (Tenenbaum et al., and Roweis and Saul).
Emotion Classification Using Massive Examples Extracted from the Web

In this paper, we propose a data-oriented method for inferring the emotion of a speaker conversing with a dialog system from the semantic content of an utterance. We first fully automatically obtain a huge collection of emotion-provoking event instances from the Web. With Japanese chosen as a target language, about 1.3 million emotion provoking event instances are extracted using an emotion lexicon and lexical patterns. We then decompose the emotion classification task into two sub-steps: sentiment polarity classification (coarsegrained emotion classification), and emotion classification (fine-grained emotion classification). For each subtask, the collection of emotion-proviking event instances is used as labelled examples to train a classifier. The results of our experiments indicate that our method significantly outperforms the baseline method. We also find that compared with the singlestep model, which applies the emotion classifier directly to inputs, our two-step model significantly reduces sentiment polarity errors, which are considered fatal errors in real dialog applications.
Hoax detection system on Indonesian news sites based on text classification using SVM and SGD

A deliberate falsehood intentionally fabricated to appear as the truth, or often called as hoax (hocus to trick) has been increasing at an alarming rate. This situation may cause restlessness/anxiety and panic in society. Even though hoaxes have no effect on threats, however, new perceptions can be spread that they can affect both the social and political conditions. Imagery blown from hoaxes can bring negative effects and intervene state policies that may decrease the economy. An early detection on hoaxes helps the Government to reduce and even eliminate the spread. There are some system that filter hoaxes based on title and also from voting processes from searching processes in a search engine. This research develops Indonesian hoax filter based on text vector representation based on Term Frequency and Document Frequency as well as classification techniques. There are several classification techniques and for this research, Support Vector Machine and Stochastic Gradient Descent are chosen. Support Vector Machine divides a word vector using linear function and Stochastic Gradient Descent divides a word vector using nonlinear function. SVM and SGD are chosen because the characteristic of text classification includes multidimensional matrixes. Each word in news articles can be modeled as feature and with Linear SVC and SGD, the feature of word vector can be reduced into two dimensions and can be separated using linear and non-linear lines. The highest accuracy obtained from SGD classifier using modifled-huber is 86% over 100 hoax and 100 nonhoax websites which are randomly chosen outside dataset which are used in the training process.
Toward a Unified Color Space for Perception-Based Image Processing

Image processing methods that utilize characteristics of the human visual system require color spaces with certain properties to operate effectively. After analyzing different types of perception-based image processing problems, we present a list of properties that a unified color space should have. Due to contradictory perceptual phenomena and geometric issues, a color space cannot incorporate all these properties. We therefore identify the most important properties and focus on creating opponent color spaces without cross contamination between color attributes (i.e., lightness, chroma, and hue) and with maximum perceptual uniformity induced by color-difference formulas. Color lookup tables define simple transformations from an initial color space to the new spaces. We calculate such tables using multigrid optimization considering the Hung and Berns data of constant perceived hue and the CMC, CIE94, and CIEDE2000 color-difference formulas. The resulting color spaces exhibit low cross contamination between color attributes and are only slightly less perceptually uniform than spaces optimized exclusively for perceptual uniformity. We compare the CIEDE2000-based space with commonly used color spaces in two examples of perception-based image processing. In both cases, standard methods show improved results if the new space is used. All color-space transformations and examples are provided as MATLAB codes on our website.
An effective hybrid learning system for telecommunication churn prediction

Customer churn has emerged as a critical issue for Customer Relationship Management and customer retention in the telecommunications industry, thus churn prediction is necessary and valuable to retain the customers and reduce the losses. Moreover, high predictive accuracy and good interpretability of the results are two key measures of a classification model. More studies have shown that single model-based classification methods may not be good enough to achieve a satisfactory result. To obtain more accurate predictive results, we present a novel hybrid model-based learning system, which integrates the supervised and unsupervised techniques for predicting customer behaviour. The system combines a modified k-means clustering algorithm and a classic rule inductive technique (FOIL). Three sets of experiments were carried out on telecom datasets. One set of the experiments is for verifying that the weighted k-means clustering can lead to a better data partitioning results; the second set of experiments is for evaluating the classification results, and comparing it to other well-known modelling techniques; the last set of experiment compares the proposed hybrid-model system with several other recently proposed hybrid classification approaches. We also performed a comparative study on a set of benchmarks obtained from the UCI repository. All the results show that the hybrid model-based learning system is very promising and outperform the existing models. With recent evolution in the Information and Communication Technology (ICT) sector, numerous new and attractive services have been introduced, and they put huge pressure on traditional services. Customer churn has emerged as one of the major issues in Customer Relationship Management (CRM) in telecommunica-tion services around the world, for both wireless providers and long-distance carriers. For instance, in the U.S., telecom providers of long-distance and international services have been bearing the churn rates from 45% to 70% percent for some years (Mattison, 2001). Under the fierce competitive environment, it becomes very important for the telecom operators to retain their existing customers as acquiring new customers is much more expensive. Consequently , predicting which customers are likely to stop their subscription and switch to competitors (churn) is critical. Predicting the potential churners and successfully retain them, especially the valuable ones, can substantially increase the profitability of a company. In the telecommunications industry, operators usually capture the transactional data, which reflects the service usage, and some static data such as subscriber's personal information and contract details. Data mining (DM) methods have emerged as a good alternative to study the …
Analytical Solution for Cogging Torque in Surface Permanent-Magnet Motors Using Conformal Mapping

We present an analytical method for the calculation of cogging torque in surface permanent-magnet (PM) motors. The cogging torque is calculated by integrating the Maxwell stress tensor inside the air gap. The principle of complex relative air-gap permeance derived from conformal transformation of the slot geometry is used to take into account the effect of slotting and to calculate the radial and tangential components of the air-gap flux density required for integration of the tangential component of the Maxwell stress tensor. We implemented the proposed analytical solution on a 7-kW four-pole surface PM motor and compared the results with finite-element solutions. We present an example of finding the optimal magnet angular span to yield minimum cogging torque as an example of the effectiveness of the method.
Substring Matching for Clone Detection and Change Tracking

Legacy systems pose problems to muintainers that can be solved partially with effective tools. A prototype tool for determining collections offiles sharing a large amount of text has been developed and applied to a 40 megabyte source tree containing two releases of the gcc compiler. Similarities in source code and documentation corresponding to software cloning, movement and inertia between releases, as well as the effects of preprocessing easily stand out in a way that immediately conveys nonobvious structural information to a maintainer taking responsibility for such a system.
Photovoltaic-Battery-Powered DC Bus System for Common Portable Electronic Devices

Renewable energy sources based on photovoltaic (PV) along with battery-based energy storage necessitate power conditioning to meet load requirements and/or be connected to the electrical grid. The power conditioning is achieved via a dc-dc converter and a DC-AC inverter stages to produce the desired AC source. This is also the case even when the load is of dc type, such as the typical portable electronic devices that require AC adaptors to be powered from the AC mains. The letter presents a hybrid PV-battery-powered dc bus system that eliminates the DC-AC conversion stage, resulting in lower cost and improved overall energy conversion efficiency. It is also shown experimentally that the switching ac adaptors associated with the various commonly used portable electronic devices can be reused with the proposed dc bus system. A novel high-gain hybrid boost-flyback converter is also introduced with several times higher voltage conversion ratio than the conventional boost converter topology. This arrangement results in higher DC bus levels and lower cable conduction losses. Moreover, the voltage stress on the hybrid boost-flyback converter power switch is within half the output voltage. Experimental results taken from a laboratory prototype are presented to confirm the effectiveness of the proposed converter/system.
Monolithically integrated high-power broad-band RF switch based on III-N insulated gate transistors

We report on the high-performance monolithically integrated RF switch based on metal-oxide-semiconductor III-N heterostructure field-effect transistors (MOSHFETs). The radio frequency (RF) switch microwave monolithic integrated circuit (MMIC) consists of three submicron-gate MOSHFETs connected into /spl pi/-type configuration. In the 0-10 GHz frequency range, the insertion loss is less than 1dB and the isolation is better than 20 dB. The switching powers well exceed 20 W per 1mm of the active element width. The high performance parameters of the switch are achieved due to unique properties of III-nitride MOSHFET, which combines a low channel resistance and high breakdown voltage features of AlGaN/GaN HFETs and extremely low gate leakage currents, large gate voltage swing and low gate capacitance specific to insulated gate design. The combination of these parameters makes MOSHFETs excellent candidates for high-power switching. The experimental data obtained from the RF switch are in close agreement with the results of simulations.