nils-herrmann/clean_scirepeval huggingface dataset:
Graph-based color Gamut Mapping using neighbor metric

Colors are displayed in different ways on various devices, such as cameras, screens and printers. In order to achieve consistent appearance on these devices, color management is usually used, where the core part is Gamut Mapping Algorithm (GMA). However, the widely adopted Point-wise Gamut Mapping Algorithms (PGMAs) have been restricted to compromise between color accuracy and details. In this work, we firstly split color space into small cubes through sampling colors from it. Then, we built a 26-neighbored graph with the sample colors as vertexes and perceptual color differences between adjacent vertexes as weights. Based on the above graph, a new Multi-source Shortest Paths Algorithm (MSS-PA) is proposed to establish color mapping relationships between out-of-gamut colors and colors in gamut boundary. In the MSSPA, distance of shortest path between nonadjacent vertexes are used to replace those calculated directly using CIEDE2000, which is useful to measure large color difference. Experimental results show that our method achieves superior performance on the aspect of keeping accuracy and preserving details compared with HPMinDE and SGCK.
Semantic Web Technologies for Business Intelligence

This chapter describes the convergence of two of the most influential technologies in the last decade, namely business intelligence (BI) and the Semantic Web (SW). Business intelligence is used by almost any enterprise to derive important business-critical knowledge from both internal and (increasingly) external data. When using external data, most often found on the Web, the most important issue is knowing the precise semantics of the data. Without this, the results cannot be trusted. Here, Semantic Web technologies come to the rescue, as they allow semantics ranging from very simple to very complex to be specified for any web-available resource. SW technologies do not only support capturing the “passive” semantics, but also support active inference and reasoning on the data. The chapter first presents a motivating running example, followed by an introduction to the relevant SW foundation concepts. The chapter then goes on to survey the use of SW technologies for data integration, including semantic DOI: 10.4018/978-1-61350-038-5.ch014
Design of bow-tie-type UWB antennas using an ultra-wideband balun

A new UWB antenna in bow-tie-type has been designed and implemented by utilizing the new ultra-wideband balun. In order to reduce antenna size, the balun is bended without sacrificing the balun performance. The fabricated antenna provides the operating frequency ranges from 3.2 to 11.2 GHz covering for the whole UWB frequency. The antenna maintains gains from 2.5 - 4.9 dBi for the operating frequencies with quasi- omni directional radiation patterns.
Specimen Box: A tangible interaction technique for world-fixed virtual reality displays

Object selection and manipulation in world-fixed displays such as CAVE-type systems are typically achieved with tracked input devices, which lack the tangibility of real-world interactions. Conversely, due to the visual blockage of the real world, head-mounted displays allow the use of many types of real world objects that can convey realistic haptic feedback. To bridge this gap, we propose Specimen Box, an interaction technique that allows users to naturally hold a plausible physical object while manipulating virtual content inside it. This virtual content is rendered based on the tracked position of the box in relation to the user's point of view. Specimen Box provides the weight and tactile feel of an actual object and does not occlude rendered objects in the scene. The end result is that the user sees the virtual content as if it exists inside the clear physical box. We hypothesize that the effect of holding a physical box, which is a valid part of the overall scenario, would improve user performance and experience. To verify this hypothesis, we conducted a user study which involved a cognitively loaded inspection task requiring extensive manipulation of the box. We compared Specimen Box to Grab-and-Twirl, a naturalistic bimanual manipulation technique that closely mimics the mechanics of our proposed technique. Results show that in our specific task, performance was significantly faster and rotation rate was significantly lower with Specimen Box. Further, performance of the control technique was positively affected by experience with Specimen Box.
FMEA: a model for reducing medical errors.

Patient safety is a management issue, in view of the fact that clinical risk management has become an important part of hospital management.  Early applications in the health care industry dating back to the 1990s included critical systems in the development and manufacture of drugs and in the prevention of medication errors in hospitals. In 2008, the Technical Committee of the International Organization for Standardization (ISO), licensed a technical specification for medical laboratories suggesting FMEA as a method for prospective risk analysis of high-risk processes. Here we describe the main steps of the FMEA process and review data available on the application of this technique to laboratory medicine. A significant reduction of the risk priority number (RPN) was obtained when applying FMEA to blood cross-matching, to clinical chemistry analytes, as well as to point-of-care testing (POCT).
Effective multi-label active learning for text classification

Labeling text data is quite time-consuming but essential for automatic text classification. Especially, manually creating multiple labels for each document may become impractical when a very large amount of data is needed for training multi-label text classifiers. To minimize the human-labeling efforts, we propose a novel multi-label active learning approach which can reduce the required labeled data without sacrificing the classification accuracy. Traditional active learning algorithms can only handle single-label problems, that is, each data is restricted to have one label. Our approach takes into account the multi-label information, and select the unlabeled data which can lead to the largest reduction of the expected model loss. Specifically, the model loss is approximated by the size of version space, and the reduction rate of the size of version space is optimized with Support Vector Machines (SVM). An effective label prediction method is designed to predict possible labels for each unlabeled data point, and the expected loss for multi-label data is approximated by summing up losses on all labels according to the most confident result of label prediction. Experiments on several real-world data sets (all are publicly available) demonstrate that our approach can obtain promising classification result with much fewer labeled data than state-of-the-art methods.
Semantic colorization with internet images

Colorization of a grayscale photograph often requires considerable effort from the user, either by placing numerous color scribbles over the image to initialize a color propagation algorithm, or by looking for a suitable reference image from which color information can be transferred. Even with this user supplied data, colorized images may appear unnatural as a result of limited user skill or inaccurate transfer of colors. To address these problems, we propose a colorization system that leverages the rich image content on the internet. As input, the user needs only to provide a semantic text label and segmentation cues for major foreground objects in the scene. With this information, images are downloaded from photo sharing websites and filtered to obtain suitable reference images that are reliable for color transfer to the given grayscale photo. Different image colorizations are generated from the various reference images, and a graphical user interface is provided to easily select the desired result. Our experiments and user study demonstrate the greater effectiveness of this system in comparison to previous techniques.
Evaluation of a stair-climbing power wheelchair in 25 people with tetraplegia.

To compare the performance of a power wheelchair with stair-climbing capability (TopChair) and a conventional power wheelchair (Storm3).



A single-center, open-label study.



A physical medicine and rehabilitation hospital.



Patients (N=25) who required power wheelchairs because of severe impairments affecting the upper and lower limbs.



Indoor and outdoor driving trials with both devices. Curb-clearing and stair-climbing with TopChair.



Trial duration and Quebec User Evaluation of Satisfaction with Assistive Technology (QUEST) tool; number of failures during driving trials and ability to climb curbs and stairs.



All 25 participants successfully completed the outdoor and indoor trials with both wheelchairs. Although differences in times to trial completion were statistically significant, they were less than 10%. QUEST scores were significantly better with the Storm3 than the TopChair for weight (P=.001), dimension (P=.006), and effectiveness (P=.04). Of the 25 participants, 23 cleared a 20-cm curb without help, and 20 climbed up and down 6 steps. Most participants felt these specific capabilities of the TopChair--for example, curb clearing and stair climbing-were easy to use (22/25 for curb, 21/25 for stairs) and helpful (24/25 and 23/25). A few participants felt insecure (4/25 and 6/25, respectively).



The TopChair is a promising mobility device that enables stair and curb climbing and warrants further study.
Educational Data Mining as a Trend of Data Mining in Educational System

Educational data mining is an emerging trend, concerned with developing methods for exploring the huge data that come from the educational system. This data is used to derive the knowledge which is useful in decision making. EDM methods are useful to measure the performance of students, assessment of students and study students’ behavior etc. In recent years, Educational data mining has proven to be more successful at many of the educational statistics problems due to enormous computing power and data mining algorithms. This paper surveys the history and applications of data mining techniques in the educational field. The objective is to introduce data mining to traditional educational system, web-based educational system, intelligent tutoring system, and e-learning. This paper describes how to apply the main data mining methods such as prediction, classification, relationship mining, clustering, and
High payload secret hiding technology for QR codes

Quick response (QR) code has become one of the more popular two-dimensional barcodes because of its greater data capacity and higher damage resistance. The barcode scanners can easily extract the information hidden in the QR code while scanning the data modules. However, some sensitive data directly stored in QR codes are insecure in real-world QR applications, such as the e-ticket and e-coupon. To protect the sensitive data, this paper explores the characteristics of QR barcodes to design a secret hiding mechanism for the QR barcode with a higher payload compared to the past ones. For a normal scanner, a browser can only reveal the formal information from the marked QR code. The authorized user/scanner can further reveal the sensitive data from the marked QR tag. The experiments demonstrate a satisfactory secret payload and the feasibility of the proposed scheme.
AudioQuilt: 2D Arrangements of Audio Samples using Metric Learning and Kernelized Sorting

The modern musician enjoys access to a staggering number of audio samples. Composition software can ship with many gigabytes of data, and there are many more to be found online. However, conventional methods for navigating these libraries are still quite rudimentary, and often involve scrolling through alphabetical lists. We present AudioQuilt, a system for sample exploration that allows audio clips to be sorted according to user taste, and arranged in any desired 2D formation such that similar samples are located near each other. Our method relies on two advances in machine learning. First, metric learning allows the user to shape the audio feature space to match their own preferences. Second, kernelized sorting finds an optimal arrangement for the samples in 2D. We demonstrate our system with three new interfaces for exploring audio samples, and evaluate the technology qualitatively and quantitatively via a pair of user studies.
Automatic Speech Recognition System for Isolated & Connected Words of Hindi Language By Using Hidden Markov Model Toolkit ( HTK )

Speech recognition is the process of converting an acoustic waveform into the text similar to the information being conveyed by the speaker. In this paper implementation of isolated words and connected words Automatic Speech Recognition system (ASR) for the words of Hindi language will be discussed. The HTK (hidden markov model toolkit) based on Hidden Markov Model (HMM), a statistical approach, is used to develop the system. Initially the system is trained for 100 distinct Hindi words .This paper also describes the working of HTK tool, which is used in various phases of ASR system, by presenting a detailed architecture of an ASR system developed using various HTK library modules and tools. The recognition results will show that the overall system accuracy for isolated words is 95% and for connected words is 90%.
OpenChirp: A Low-Power Wide-Area Networking architecture

Infrastructure monitoring applications currently lack a cost-effective and reliable solution for supporting the last communication hop for low-power devices. The use of cellular infrastructure requires contracts and complex radios that are often too power hungry and cost prohibitive for sensing applications that require just a few bits of data each day. New low-power, sub-GHz, long-range radios are an ideal technology to help fill this communication void by providing access points that are able to cover multiple kilometers of urban space with thousands of end-point devices. These new Low-Power Wide-Area Networking (LPWAN) platforms provide a cost-effective and highly deployable option that could piggyback off of existing public and private wireless networks (WiFi, Cellular, etc). In this paper, we present OpenChirp, a prototype end-to-end LPWAN architecture built using LoRa Wide-Area Network (LoRaWAN) with the goal of simplifying the design and deployment of Internet-of-Things (IoT) devices across wide areas like campuses and cities. We present a software architecture that exposes an application layer allowing users to register devices, describe transducer properties, transfer data and retrieve historical values. We define a service model on top of LoRaWAN that acts as a session layer to provide basic encoding and syntax to raw data streams. At the device-level, we introduce and benchmark an open-source hardware platform that uses Bluetooth Low-Energy (BLE) to help provision LoRa clients that can be extended with custom transducers. We evaluate the system in terms of end-node energy consumption, radio penetration into buildings as well as coverage provided by a network currently deployed at Carnegie Mellon University.
Unitary space-time modulation for multiple-antenna communications in Rayleigh flat fading

Motivated by information-theoretic considerations, we pr opose a signalling scheme, unitary spacetime modulation, for multiple-antenna communication links. This modulati on s ideally suited for Rayleigh fast-fading environments, since it does not require the rec iv r to know or learn the propagation coefficients. Unitary space-time modulation uses constellations of T M space-time signals f `; ` = 1; : : : ; Lg, whereT represents the coherence interval during which the fading i s approximately constant, and M < T is the number of transmitter antennas. The columns of each ` are orthonormal. When the receiver does not know the propagation coefficients, which between pa irs of transmitter and receiver antennas are modeled as statistically independent, this modulation per forms very well either when the SNR is high or whenT M . We design some multiple-antenna signal constellations and simulate their effectiveness as measured by bit error probability with maximum likelihood decoding. We demonstrate that two antennas have a 6 dB diversity gain over one antenna at 15 dB SNR. Index Terms —Multi-element antenna arrays, wireless communications, channel coding, fading channels, transmitter and receiver diversity, space-time modu lation
Efficient Eye Blink Detection Method for disabled- helping domain

In this paper, we present a real time method based on some video and image processing algorithms for eye blink detection. The motivation of this research is the need of disabling who cannot control the calls with human mobile interaction directly without the need of hands. A Haar Cascade Classifier is applied for face and eye detection for getting eye and facial axis information. In addition, the same classifier is used based on Haarlike features to find out the relationship between the eyes and the facial axis for positioning the eyes. An efficient eye tracking method is proposed which uses the position of detected face. Finally, an eye blinking detection based on eyelids state (close or open) is used for controlling android mobile phones. The method is used with and without smoothing filter to show the improvement of detection accuracy. The application is used in real time for studying the effect of light and distance between the eyes and the mobile device in order to evaluate the accuracy detection and overall accuracy of the system. Test results show that our proposed method provides a 98% overall accuracy and 100% detection accuracy for a distance of 35 cm and an artificial light. eye detection; eye tracking; eye blinking; smoothing filter; detection accuracy
3D object pose estimation using stereo vision for object manipulation system

Object pose estimation is one of the crucial parts in vision-based object manipulation system using standard industrial robot manipulator, particularly in pose estimation of the end effector of the robot arm to grasp the object targeted. This paper presents the utilization of stereo vision system to estimate the 3D (3 dimensional) object position and orientation to pick up and place the object targeted in an arbitrary location within the workspace. In order to accomplish this task, a calibrated stereo camera in the eye to hand configuration is used to capture the images of the object on the left and right camera. Then, the specific object feature is extracted and the 3D position and orientation of the object are calculated using image processing algorithm. Finally, the end effector of robot arm equipped with gripper will pick up the object targeted according to the object pose estimation output, and then place it to the desired location. The experimental results using 6 DOF robot arm are demonstrated and show the effectiveness of the proposed approach with good performance.
Top-Down Dysregulation—From ADHD to Emotional Instability

Deficient cognitive top-down executive control has long been hypothesized to underlie inattention and impulsivity in attention-deficit/hyperactivity disorder (ADHD). However, top-down cognitive dysfunction explains a modest proportion of the ADHD phenotype whereas the salience of emotional dysregulation is being noted increasingly. Together, these two types of dysfunction have the potential to account for more of the phenotypic variance in patients diagnosed with ADHD. We develop this idea and suggest that top-down dysregulation constitutes a gradient extending from mostly non-emotional top-down control processes (i.e., "cool" executive functions) to mainly emotional regulatory processes (including "hot" executive functions). While ADHD has been classically linked primarily to the former, conditions involving emotional instability such as borderline and antisocial personality disorder are closer to the other. In this model, emotional subtypes of ADHD are located at intermediate levels of this gradient. Neuroanatomically, gradations in "cool" processing appear to be related to prefrontal dysfunction involving dorsolateral prefrontal cortex (dlPFC) and caudal anterior cingulate cortex (cACC), while "hot" processing entails orbitofrontal cortex and rostral anterior cingulate cortex (rACC). A similar distinction between systems related to non-emotional and emotional processing appears to hold for the basal ganglia (BG) and the neuromodulatory effects of the dopamine system. Overall we suggest that these two systems could be divided according to whether they process non-emotional information related to the exteroceptive environment (associated with "cool" regulatory circuits) or emotional information related to the interoceptive environment (associated with "hot" regulatory circuits). We propose that this framework can integrate ADHD, emotional traits in ADHD, borderline and antisocial personality disorder into a related cluster of mental conditions.
Curved and straight crop row detection by accumulation of green pixels from images in maize fields

This study proposes a new method for detecting curved and straight crop rows in images captured in maize fields during the initial growth stages of crop and weed plants. The images were obtained under perspective projection with a camera installed onboard and conveniently arranged at the front of a tractor. The final goal was the identification of the crop rows which are crucial for precise autonomous guidance and site-specific treatments, including weed removal based on the identification of plants outside the crop rows. Image quality is affected by uncontrolled lighting conditions in outdoor agricultural environments and by gaps in the crop rows (due to lack of germination or defects during planting). Also, different plants heights and volumes occur due to different growth stages affecting the crop row detection process. The proposed method was designed with the required robustness to cope with the above undesirable situations and it consists of three sequentially linked phases: (i) image segmentation, (ii) identification of starting points and (iii) crop row detection. The main contribution is the ability of the method to detect curved crop rows as well as straights rows even with irregular inter-row spaces. The method performance has been tested in terms of accuracy and time processing.
Momentary Pleasure or Lasting Meaning?: Distinguishing Eudaimonic and Hedonic User Experiences

User experience (UX) research has expanded our notion of what makes interactive technology good, often putting hedonic aspects of use such as fun, affect, and stimulation at the center. Outside of UX, the hedonic is often contrasted to the eudaimonic, the notion of striving towards one's personal best. It remains unclear, however, what this distinction offers to UX research conceptually and empirically. We investigate a possible role for eudaimonia in UX research by empirically examining 266 reports of positive experiences with technology and analyzing its relation to established UX concepts. Compared to hedonic experiences, eudaimonic experiences were about striving towards and accomplishing personal goals through technology use. They were also characterized by increased need fulfillment, positive affect, meaning, and long-term importance. Taken together, our findings suggest that while hedonic UX is about momentary pleasures directly derived from technology use, eudaimonic UX is about meaning from need fulfilment.