nils-herrmann/clean_scirepeval huggingface dataset:
Using noise signature to optimize spike-sorting and to assess neuronal classification quality

We have developed a simple and expandable procedure for classification and validation of extracellular data based on a probabilistic model of data generation. This approach relies on an empirical characterization of the recording noise. We first use this noise characterization to optimize the clustering of recorded events into putative neurons. As a second step, we use the noise model again to assess the quality of each cluster by comparing the within-cluster variability to that of the noise. This second step can be performed independently of the clustering algorithm used, and it provides the user with quantitative as well as visual tests of the quality of the classification.
Survey of semi-regular multiresolution models for interactive terrain rendering

Rendering high quality digital terrains at interactive rates requires carefully crafted algorithms and data structures able to balance the competing requirements of realism and frame rates, while taking into account the memory and speed limitations of the underlying graphics platform. In this survey, we analyze multiresolution approaches that exploit a certain semi-regularity of the data. These approaches have produced some of the most efficient systems to date. After providing a short background and motivation for the methods, we focus on illustrating models based on tiled blocks and nested regular grids, quadtrees and triangle bin-trees triangulations, as well as cluster-based approaches. We then discuss LOD error metrics and system-level data management aspects of interactive terrain visualization, including dynamic scene management, out-of-core data organization and compression, as well as numerical accuracy.
Extending the Understanding of End User Information Systems Satisfaction Formation: An Equitable Needs Fulfillment Model Approach

End user satisfaction (EUS) is critical to successful information systems implementation. Many EUS studies in the past have attempted to identify the antecedents of EUS, yet most of Bernard Tan was the accepting senior editor for this paper. Guy Paré was the associate editor. Anne-Marie Croteau, William DeLone, and Ronald Thompson served as reviewers. the relationships found have been criticized for their lack of a strong theoretical underpinning. Today it is generally understood that IS failure is due to psychological and organizational issues rather than technological issues, hence individual differences must be addressed. This study proposes a new model with an objective to extend our understanding of the antecedents of EUS by incorporating three well-founded theories of motivation, namely expectation theory, needs theory, and equity theory. The uniqueness of the model not only recognizes the three different needs (i.e., work performance, relatedness, and self-development) that users may have with IS use, but also the corresponding inputs required from each individual to achieve those needs fulfillments, which have been ignored in most previous studies. This input/needs fulfillment ratio, referred to as equitable needs fulfillment, is likely to vary from one individual to another and satisfaction will only result in a user if the needs being fulfilled are perceived as “worthy” to obtain. The partial least squares (PLS) method of structural equation modeling was used to analyze 922 survey returns collected form the hotel and airline sectors. The results of the study show that IS end users do have different needs. Equitable work performance fulfillment and equitable relatedness fulfillment play a significant role in affecting the satisfaction of end users. The results also indicate that the impact of perceived IS performance expectations on EUS is not as significant as most previous studies have suggested. The conclusion is that merely focusing on the technical soundness of the IS and the way in which it benefits employees may not Au et al./Understanding EUS Formation 44 MIS Quarterly Vol. 32 No. 1/March 2008 be sufficient. Rather, the input requirements of users for achieving the corresponding needs fulfillments also need to be examined.
Coupled-slotline-hybrid sampling mixer integrated with step-recovery-diode pulse generator for UWB applications

A new compact low-cost sampling mixer with internal local strobe-pulse generator for (carrierless) ultra-wideband (UWB) applications, especially for sub-sampling of UWB video pulse signals, is presented along with detailed design information. The sampler employs a double-sided planar structure to facilitate interface with the strobe-pulse generator and the baseband circuit without using wire connections or air bridges, enabling hybrid microwave integrated-circuit integration with significant reduction in assembly effort. A novel coupling structure for both coupling and termination of the RF and local-oscillator signals to the Schottky-diode sampling bridge was designed to suppress ringing in the strobe pulse signal, resulting in harmonic-distortion reduction in the baseband output signal. Development of strobe-pulse generators using step recovery diodes for good output power efficiency is also presented along with design information for relatively low clock frequencies. The developed sampler achieves an unprecedented conversion loss of 4.5-7.5 dB (without a baseband amplifier) and conversion gain from 6.5 to 9.5 dB (with an amplifier) over a 5.5-GHz RF bandwidth, a dynamic range of over 50 dB, and low harmonic distortion in the baseband output. A new intrinsic conversion-loss equation that allows the sampler's conversion loss and operating RF bandwidth to be predicted accurately was also derived.
Optimal sizing of solar photovoltaic – Wind hybrid system

Increase in energy demand has made the renewable resources more attractive. Additionally, use of renewable energy sources reduces combustion of fossil fuels and the consequent CO2 emission which is the principal cause of global warming. The concept of photovoltaic-Wind hybrid system is well known and currently thousands of PV-Wind based power systems are being deployed worldwide, for providing power to small, remote, grid-independent applications. This paper shows the way to design the aspects of a hybrid power system that will target remote users. It emphasizes the renewable hybrid power system to obtain a reliable autonomous system with the optimization of the components size and the improvement of the cost. The system can provide electricity for a remote located village. The main power of the hybrid system comes from the photovoltaic panels and wind generators, while the batteries are used as backup units. The optimization software used for this paper is HOMER. HOMER is a design model that determines the optimal architecture and control strategy of the hybrid system. The simulation results indicate that the proposed hybrid system would be a feasible solution for distributed generation of electric power for stand-alone applications at remote locations
Camera Models and Optical Systems Used in Computer Graphics: Part I, Object-Based Techniques

Images rendered with traditional computer graphics techniques, such as scanline rendering and ray tracing, appear focused at all depths. However, there are advantages to having blur, such as adding realism to a scene or drawing attention to a particular place in a scene. In this paper we describe the optics underlying camera models that have been used in computer graphics, and present object space techniques for rendering with those models. In our companion paper , we survey image space techniques to simulate these models. These techniques vary in both speed and accuracy.
Rule extraction from linear support vector machines

We describe an algorithm for converting linear support vector machines and any other arbitrary hyperplane-based linear classifiers into a set of non-overlapping rules that, unlike the original classifier, can be easily interpreted by humans. Each iteration of the rule extraction algorithm is formulated as a constrained optimization problem that is computationally inexpensive to solve. We discuss various properties of the algorithm and provide proof of convergence for two different optimization criteria We demonstrate the performance and the speed of the algorithm on linear classifiers learned from real-world datasets, including a medical dataset on detection of lung cancer from medical images. The ability to convert SVM's and other "black-box" classifiers into a set of human-understandable rules, is critical not only for physician acceptance, but also to reducing the regulatory barrier for medical-decision support systems based on such classifiers.
Zero-Suppressed BDDs for Set Manipulation in Combinatorial Problems

In this paper, we propose Zero-Suppressed BDDs (0-Sup-BDDs), which are BDDs based on a new reduction rule. This data structure brings unique and compact representation of sets which appear in many combinatorial problems. Using 0-Sup-BDDs, we can manipulate such sets more simply and efficiently than using original BDDs. We show the properties of 0-Sup-BDDs, their manipulation algorithms, and good applications for LSI CAD systems.
Heart rate variability biofeedback as a method for assessing baroreflex function: a preliminary study of resonance in the cardiovascular system.

This study describes the use of a biofeedback method for the noninvasive study of baroreflex mechanisms. Five previously untrained healthy male participants learned to control oscillations in heart rate using biofeedback training to modify their heart rate variability at specific frequencies. They were instructed to match computer-generated sinusoidal oscillations with oscillations in heart rate at seven frequencies within the range of 0.01-0.14 Hz. All participants successfully produced high-amplitude target-frequency oscillations in both heart rate and blood pressure. Stable and predictable transfer functions between heart rate and blood pressure were obtained in all participants. The highest oscillation amplitudes were produced in the range of 0.055-0.11 Hz for heart rate and 0.02-0.055 Hz for blood pressure. Transfer functions were calculated among sinusoidal oscillations in the target stimuli, heart rate, blood pressure, and respiration for frequencies at which subjects received training. High and low target-frequency oscillation amplitudes at specific frequencies could be explained by resonance among various oscillatory processes in the cardiovascular system. The exact resonant frequencies differed among individuals. Changes in heart rate oscillations could not be completely explained by changes in breathing. The biofeedback method also allowed us to quantity characteristics of inertia, delay, and speed sensitivity in baroreflex system. We discuss the implications of these findings for using heart rate variability biofeedback as an aid in diagnosing various autonomic and cardiovascular system disorders and as a method for treating these disorders.
Fuzzy hierarchical TOPSIS for supplier selection

This study simplifies the complicated metric distance method [L.S. Chen, C.H. Cheng, Selecting IS personnel using ranking fuzzy number by metric distance method,  2005 803–820], and proposes an algorithm to modify Chen’s Fuzzy TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) [C.T. Chen, Extensions of the TOPSIS for group decision-making under fuzzy environment, Fuzzy Sets Syst., 114 (2000) 1–9]. From experimental verification, Chen directly assigned the fuzzy numbers 1̃ and 0̃ as fuzzy positive ideal solution (PIS) and negative ideal solution (NIS). Chen’s method sometimes violates the basic concepts of traditional TOPSIS. This study thus proposes fuzzy hierarchical TOPSIS, which not only is well suited for evaluating fuzziness and uncertainty problems, but also can provide more objective and accurate criterion weights, while simultaneously avoiding the problem of Chen’s Fuzzy TOPSIS. For application and verification, this study presents a numerical example and build a practical supplier selection problem to verify our proposed method and compare it with other methods.
Posture Recognition Based on Fuzzy Logic for Home Monitoring of the Elderly

We propose in this paper a computer vision-based posture recognition method for home monitoring of the elderly. The proposed system performs human detection prior to the posture analysis; posture recognition is performed only on a human silhouette. The human detection approach has been designed to be robust to different environmental stimuli. Thus, posture is analyzed with simple and efficient features that are not designed to manage constraints related to the environment but only designed to describe human silhouettes. The posture recognition method, based on fuzzy logic, identifies four static postures and is robust to variation in the distance between the camera and the person, and to the person's morphology. With an accuracy of 74.29% of satisfactory posture recognition, this approach can detect emergency situations such as a fall within a health smart home.
Topic model allocation of conversational dialogue records by Latent Dirichlet Allocation

The topic information of conversational content is important for continuation with communication, so topic detection and tracking is one of important research. Due to there are many topic transform occurring frequently in long time communication, and the conversation maybe have many topics, so it's important to detect different topics in conversational content. This paper detects topic information by using agglomerative clustering of utterances and Dynamic Latent Dirichlet Allocation topic model, uses proportion of verb and noun to analyze similarity between utterances and cluster all utterances in conversational content by agglomerative clustering algorithm. The topic structure of conversational content is friability, so we use speech act information and gets the hypernym information by E-HowNet that obtains robustness of word categories. Latent Dirichlet Allocation topic model is used to detect topic in file units, it just can detect only one topic if uses it in conversational content, because of there are many topics in conversational content frequently, and also uses speech act information and hypernym information to train the latent Dirichlet allocation models, then uses trained models to detect different topic information in conversational content. For evaluating the proposed method, support vector machine is developed for comparison. According to the experimental results, we can find the proposed method outperforms the approach based on support vector machine in topic detection and tracking in spoken dialogue.
Towards printable robotics: Origami-inspired planar fabrication of three-dimensional mechanisms

This work presents a technique which allows the application of 2-D fabrication methods to build 3-D robotic systems. The ability to print robots introduces a fast and low-cost fabrication method to modern, real-world robotic applications. To this end, we employ laser-engraved origami patterns to build a new class of robotic systems for mobility and manipulation. Origami is suitable for printable robotics as it uses only a flat sheet as the base structure for building complicated functional shapes, which can be utilized as robot bodies. An arbitrarily complex folding pattern can be used to yield an array of functionalities, in the form of actuated hinges or active spring elements. For actuation, we use compact NiTi coil actuators placed on the body to move parts of the structure on-demand. We demonstrate, as a proof-of-concept case study, the end-to-end fabrication and assembly of a simple mobile robot that can undergo worm-like peristaltic locomotion.
A tutorial on task-parameterized movement learning and retrieval

Task-parameterized models of movements aims at automatically adapting movements to new situations encountered by a robot. The task parameters can for example take the form of positions of objects in the environment, or landmark points that the robot should pass through. This tutorial aims at reviewing existing approaches for task-adaptive motion encoding. It then narrows down the scope to the special case of task parameters that take the form of frames of reference, coordinate systems, or basis functions, which are most commonly encountered in service robotics. Each section of the paper is accompanied with source codes designed as simple didactic examples implemented in Matlab with a full compatibility with GNU Octave, closely following the notation and equations of the article. It also presents ongoing work and further challenges that remain to be addressed, with examples provided in simulation and on a real robot (transfer of manipulation behaviors to the Baxter bimanual robot). The repository for the accompanying source codes is available at
Design Science Research in Information Systems

This page is dedicated to design science research in Information Systems (IS). Design science research is yet another "lens" or set of synthetic and analytical techniques and perspectives (complementing the Positivist and Interpretive perspectives) for performing research in IS. Design science research involves the creation of new knowledge through design of novel or innovative artifacts (things or processes that have or can have material existence) and analysis of the use and/or performance of such artifacts along with reflection and abstraction—to improve and understand the behavior of aspects of Information Systems. Such artifacts include—but certainly are not limited to—algorithms (e.g. for information retrieval), human/computer interfaces, and system design methodologies or languages. Design science researchers can be found in many disciplines and fields, notably Engineering and Computer Science; they use a variety of approaches, methods and techniques. In Information Systems, following a number of years of a general shift in IS research away from technological to managerial and organizational issues, an increasing number of observers are calling for a return to an exploration of the "IT" that underlies all IS research (Orlikowski and Iacono, 2001) thus underlining the need for IS design science research.
Time Series Prediction Using Support Vector Machines: A Survey

Time series prediction techniques have been used in many real-world applications such as financial market prediction, electric utility load forecasting , weather and environmental state prediction, and reliability forecasting. The underlying system models and time series data generating processes are generally complex for these applications and the models for these systems are usually not known a priori. Accurate and unbiased estimation of the time series data produced by these systems cannot always be achieved using well known linear techniques, and thus the estimation process requires more advanced time series prediction algorithms. This paper provides a survey of time series prediction applications using a novel machine learning approach: support vector machines (SVM). The underlying motivation for using SVMs is the ability of this methodology to accurately forecast time series data when the underlying system processes are typically nonlinear, non-stationary and not defined a-priori. SVMs have also been proven to outperform other non-linear techniques including neural-network based non-linear prediction techniques such as multi-layer perceptrons.The ultimate goal is to provide the reader with insight into the applications using SVM for time series prediction, to give a brief tutorial on SVMs for time series prediction, to outline some of the advantages and challenges in using SVMs for time series prediction, and to provide a source for the reader to locate books, technical journals, and other online SVM research resources.
Golden Eggs and Hyperbolic Discounting

Hyperbolic discount functions induce dynamically inconsistent preferences, implying a motive for consumers to constrain their own future choices. This paper analyzes the decisions of a hyperbolic consumer who has access to an imperfect commitment technology: an illiquid asset whose sale must be initiated one period before the sale proceeds are received. The model predicts that consumption tracks income, and the model explains why consumers have asset-specic marginal propensities to consume. The model suggests that nancial innovation may have caused the ongoing decline in U. S. savings rates, since nancial innovation increases liquidity, eliminating commitment opportunities. Finally, the model implies that nancial market innovation may reduce welfare by providing “too much” liquidity.
Simple English Wikipedia: A New Text Simplification Task

In this paper we examine the task of sentence simplification which aims to reduce the reading complexity of a sentence by incorporating more accessible vocabulary and sentence structure. We introduce a new data set that pairs English Wikipedia with Simple English Wikipedia and is orders of magnitude larger than any previously examined for sentence simplification. The data contains the full range of simplification operations including rewording, reordering, insertion and deletion. We provide an analysis of this corpus as well as preliminary results using a phrase-based translation approach for simplification.
CheckInside: a fine-grained indoor location-based social network

Existing location-based social networks (LBSNs), e.g. Foursquare, depend mainly on GPS or network-based localization to infer users' locations. However, GPS is unavailable indoors and network-based localization provides coarse-grained accuracy. This limits the accuracy of current LBSNs in indoor environments, where people spend 89% of their time. This in turn affects the user experience, in terms of the accuracy of the ranked list of venues, especially for the small-screens of mobile devices; misses business opportunities; and leads to reduced venues coverage.
 In this paper, we present CheckInside: a system that can provide a fine-grained indoor location-based social network. CheckInside leverages the crowd-sensed data collected from users' mobile devices during the check-in operation and knowledge extracted from current LBSNs to associate a place with its name and semantic fingerprint. This semantic fingerprint is used to obtain a more accurate list of nearby places as well as automatically detect new places with similar signatures. A novel algorithm for handling incorrect check-ins and inferring a semantically-enriched floorplan is proposed as well as an algorithm for enhancing the system performance based on the user implicit feedback.
 Evaluation of CheckInside in four malls over the course of six weeks with 20 participants shows that it can provide the actual user location within the top five venues 99% of the time. This is compared to 17% only in the case of current LBSNs. In addition, it can increase the coverage of current LBSNs by more than 25%.