nils-herrmann/clean_scirepeval huggingface dataset:
Gaussian process for long-term time-series forecasting

Gaussian process (GP), as one of the cornerstones of Bayesian non-parametric methods, has received extensive attention in machine learning. GP has intrinsic advantages in data modeling, given its construction in the framework of Bayesian hieratical modeling and no requirement for a priori information of function forms in Bayesian reference. In light of its success in various applications, utilizing GP for time-series forecasting has gained increasing interest in recent years. This paper is concerned with using GP for multiple-step-ahead time-series forecasting, an important type of time-series analysis. Utilizing a large number of real-world time series, this paper evaluates two different GP modeling strategies (direct and recursive) for performing multiple-step-ahead forecasting.
Performance of MIMO spatial multiplexing algorithms using indoor channel measurements and models

Several algorithms have recently been proposed for multiplexing multiple users in multiple input multiple output (MIMO) downlinks. The ability of a transmitter to accomplish this using spatial methods is generally dependent on whether the users’ channels are correlated. Up to this point, most of the multiplexing algorithms have been tested on uncorrelated Gaussian channels, a best-case scenario. In this paper, we examine the performance of multiplexing multiple users under more realistic channel conditions by using indoor channel measurements and a statistical model for test cases. We use a block zero-forcing algorithm to test performance at various user separation distances, optimizing for both maximum throughput under a power constraint and minimum power under a rate constraint. The results show that for the measured indoor environment (rich scattering, non-line-of-sight), a separation of five wavelengths is enough to achieve close to the maximum available performance for two users. Since many spatial multiplexing algorithms require channel state information (CSI) at the transmitter, we also examine the performance loss due to CSI error. The results show that a user can move up to one-half wavelength before the original channel measurement becomes unusable.
Part of Speech Annotation of a Turkish-German Code-Switching Corpus

In this paper we describe our efforts on POS annotation of a code-switching corpus created from Turkish-German tweets. We use Universal Dependencies (UD) POS tags as our tag set. While the German parts of the corpus employ UD specifications, for the Turkish parts we propose annotation guidelines that adopt UD’s language-general rules when it is applicable and adapt its principles to Turkishspecific phenomena when it is not. The resulting corpus has POS annotation of 1029 tweets, which is aligned with existing language identification annotation.
Medieval Islamic Architecture , Quasicrystals , and Penrose and Girih Tiles : Questions from the Classroom

Tiling Theory studies how one might cover the plane with various shapes. Medieval Islamic artisans developed intricate geometric tilings to decorate their mosques, mausoleums, and shrines. Some of these patterns, called girih tilings, first appeared in the 12 Century AD. Recent investigations show these medieval tilings contain symmetries similar to those found in aperiodic Penrose tilings first investigated in the West in the 1970’s. These intriguing discoveries may suggest that the mathematical understanding of these artisans was much deeper than originally thought.
Advances in deep neural network approaches to speaker recognition

The recent application of deep neural networks (DNN) to speaker identification (SID) has resulted in significant improvements over current state-of-the-art on telephone speech. In this work, we report a similar achievement in DNN-based SID performance on microphone speech. We consider two approaches to DNN-based SID: one that uses the DNN to extract features, and another that uses the DNN during feature modeling. Modeling is conducted using the DNN/i-vector framework, in which the traditional universal background model is replaced with a DNN. The recently proposed use of bottleneck features extracted from a DNN is also evaluated. Systems are first compared with a conventional universal background model (UBM) Gaussian mixture model (GMM) i-vector system on the clean conditions of the NIST 2012 speaker recognition evaluation corpus, where a lack of robustness to microphone speech is found. Several methods of DNN feature processing are then applied to bring significantly greater robustness to microphone speech. To direct future research, the DNN-based systems are also evaluated in the context of audio degradations including noise and reverberation.
COBRAS: Fast, Iterative, Active Clustering with Pairwise Constraints

Constraint-based clustering algorithms exploit background knowledge to construct clusterings that are aligned with the interests of a particular user. This background knowledge is often obtained by allowing the clustering system to pose pairwise queries to the user: should these two elements be in the same cluster or not? Active clustering methods aim to minimize the number of queries needed to obtain a good clustering by querying the most informative pairs first. Ideally, a user should be able to answer a couple of these queries, inspect the resulting clustering, and repeat these two steps until a satisfactory result is obtained. We present COBRAS, an approach to active clustering with pairwise constraints that is suited for such an interactive clustering process. A core concept in COBRAS is that of a super-instance: a local region in the data in which all instances are assumed to belong to the same cluster. COBRAS constructs such superinstances in a top-down manner to produce highquality results early on in the clustering process, and keeps refining these super-instances as more pairwise queries are given to get more detailed clusterings later on. We experimentally demonstrate that COBRAS produces good clusterings at fast run times, making it an excellent candidate for the iterative clustering scenario outlined above.
Child-based personas: need, ability and experience

Interactive technologies are becoming ubiquitous in many children’s lives. From school to home, technologies are changing the way children live. However, current methods of designing these technologies do not adequately consider children’s needs and developmental abilities. This paper describes and illustrates a new approach for creating user abstractions of children called the child-persona technique. Child-personas integrate theoretical concepts, empirically generated data and experiential goals. An analysis of the utility of this technique provides insights about how this technique can benefit designers by generating realistic child-user abstractions through a process which supports designers in child-centric design.
Design and thermal analysis on high torque low speed fractional-slot concentrated windings in-wheel traction motor

This paper investigates a fractional-slot concentrated windings surface-mounted permanent magnet synchronous machine for low speed high torque traction application. This motor is designed with the combination of equivalent magnetic circuit method and finite-element analysis for the consideration of calculation time and accuracy. Attention is focused on the electromagnetic, thermal analysis and optimization design of this machine, which is crucial for the high torque low speed application. Parameter sensitivity analysis of liner thickness, cooling water volume flow rate and interface of stator laminations and housing on heat dissipation is covered in this paper. Performance of the machine are calculated with equivalent magnetic circuit and finite element analysis method and design techniques are described for improving the machine efficiency especially at high torque operating point.
Optimal Dynamic Assortment Planning with Demand Learning

We study a family of stylized assortment planning problems, where arriving customers make purchase decisions among offered products based on maximizing their utility. Given limited display capacity and no a priori information on consumers’ utility, the retailer must select which subset of products to offer. By offering different assortments and observing the resulting purchase behavior, the retailer learns about consumer preferences, but this experimentation should be balanced with the goal of maximizing revenues. We develop a family of dynamic policies that judiciously balance the aforementioned tradeoff between exploration and exploitation, and prove that their performance cannot be improved upon in a precise mathematical sense. One salient feature of these policies is that they “quickly” recognize, and hence limit experimentation on, strictly suboptimal products.
Emergence of simple-cell receptive field properties by learning a sparse code for natural images

THE receptive fields of simple cells in mammalian primary visual cortex can be characterized as being spatially localized, oriented1–4 and bandpass (selective to structure at different spatial scales), comparable to the basis functions of wavelet transforms5,6. One approach to understanding such response properties of visual neurons has been to consider their relationship to the statistical structure of natural images in terms of efficient coding7–12. Along these lines, a number of studies have attempted to train unsupervised learning algorithms on natural images in the hope of developing receptive fields with similar properties13–18, but none has succeeded in producing a full set that spans the image space and contains all three of the above properties. Here we investigate the proposal8,12 that a coding strategy that maximizes sparseness is sufficient to account for these properties. We show that a learning algorithm that attempts to find sparse linear codes for natural scenes will develop a complete family of localized, oriented, bandpass receptive fields, similar to those found in the primary visual cortex. The resulting sparse image code provides a more efficient representation for later stages of processing because it possesses a higher degree of statistical independence among its outputs.
Equity price direction prediction for day trading: Ensemble classification using technical analysis indicators with interaction effects

We investigate the performance of complex trading rules in equity price direction prediction, over and above continuous-valued indicators and simple technical trading rules. Ten of the most popular technical analysis indicators are included in this research. We use Random Forest ensemble classifiers using minute-by-minute stock market data. Results show that our models have predictive power and yield better returns than the buy-and-hold strategy when disregarding transaction costs both in terms of number of stocks with profitable trades as well as overall returns. Moreover, our findings show that two-way and three-way combinations, i.e., complex trading rules, are important to “beat” the buy-and-hold strategy.
ShoeSoleSense: proof of concept for a wearable foot interface for virtual and real environments

ShoeSoleSense is a proof of concept, novel body worn interface - an insole that enables location independent hands-free interaction through the feet. Forgoing hand or finger interaction is especially beneficial when the user is engaged in real world tasks. In virtual environments as moving through safety training applications is often conducted via finger input, which is not very suitable. To enable a more intuitive interaction, alternative control concepts utilize gesture control, which is usually tracked by statically installed cameras in CAVE-like-installations. Since tracking coverage is limited, problems may also occur. The introduced prototype provides a novel control concept for virtual reality as well as real life applications. Demonstrated functions include movement control in a virtual reality installation such as moving straight, turning and jumping. Furthermore the prototype provides additional feedback by heating up the feet and vibrating in dedicated areas on the surface of the insole.
Learning from Clinical Judgments: Semi-Markov-Modulated Marked Hawkes Processes for Risk Prognosis

Critically ill patients in regular wards are vulnerable to unanticipated adverse events which require prompt transfer to the intensive care unit (ICU). To allow for accurate prognosis of deteriorating patients, we develop a novel continuoustime probabilistic model for a monitored patient’s temporal sequence of physiological data. Our model captures “informatively sampled” patient episodes: the clinicians’ decisions on when to observe a hospitalized patient’s vital signs and lab tests over time are represented by a marked Hawkes process, with intensity parameters that are modulated by the patient’s latent clinical states, and with observable physiological data (mark process) modeled as a switching multi-task Gaussian process. In addition, our model captures “informatively censored” patient episodes by representing the patient’s latent clinical states as an absorbing semi-Markov jump process. The model parameters are learned from offline patient episodes in the electronic health records via an EM-based algorithm. Experiments conducted on a cohort of patients admitted to a major medical center over a 3-year period show that risk prognosis based on our model significantly outperforms the currently deployed medical risk scores and other baseline machine learning algorithms.
Gamification-based incentive mechanism for participatory sensing

Since participatory sensing relies on users' active participation, several monetary incentive mechanisms for attracting users' participation have been proposed. However, to make users participate in “heavy” sensing tasks (either physically or mentally), stronger incentive is required. Thus, the total amount of rewards paid by the client will quickly rise. In this paper, we propose a novel incentive mechanism based on gamification for participatory sensing to reduce the total amount of rewards paid by the client. The proposed incentive mechanism incorporates a status level scheme depending on earned reward points like airline's mileage services, so that users with higher status can earn more reward points. We also introduce a ranking scheme among users and a badge scheme based on gamification so that users are attracted by getting not only monetary reward points but also sense of accomplishment. We formulate the problem of sensing given PoI (points of interest) with minimal reward points and devise a heuristic algorithm for deriving the set of users to which requests are sent and appropriate reward points for each request. The algorithm requires the participation probability distribution for each tuple of a user, reward points, and the burden of a sensing task. To obtain the probability distribution, we implemented a prototype of a participatory sensing system with the proposed incentive mechanism and conducted an experiment with 18 users for 30 days. As a result, we confirmed that the gamification mechanism increased participation probability from 53% (without gamification) to 73%.
Rotation Invariant Multi-View Color Face Detection Based on Skin Color and Adaboost Algorithm

As the training of Adaboost is complicated or does not work well in the multi-view face detection with large plane-rotated angle, this paper proposes a rotation invariant multi-view color face detection method combining skin color segmentation and multi-view Adaboost algorithm. First the possible face region is fast detected by skin color table, and the skin-color background adhesion of face is separated by color clustering. After region merging, the candidate face region is received. Then the face direction in plane is calculated by K-L transform, and finally the candidate face region corrected by rotating is scanned by multi-view Adaboost classifier to locate the face accurately. The experiments show that the method can effectively detect the plane large-angle multi-view face image which the conventional Adaboost can not do. It can be effectively applied to the cases of multi-view and multi-face image with complex background.
Assessing heterogeneity in meta-analysis: Q statistic or I2 index?

In meta-analysis, the usual way of assessing whether a set of single studies is homogeneous is by means of the Q test. However, the Q test only informs meta-analysts about the presence versus the absence of heterogeneity, but it does not report on the extent of such heterogeneity. Recently, the I(2) index has been proposed to quantify the degree of heterogeneity in a meta-analysis. In this article, the performances of the Q test and the confidence interval around the I(2) index are compared by means of a Monte Carlo simulation. The results show the utility of the I(2) index as a complement to the Q test, although it has the same problems of power with a small number of studies.
Data Representation Based on Interval-Sets for Anomaly Detection in Time Series

Anomaly detection in time series is a popular topic focusing on a variety of applications, which achieves a wealth of results. However, there are many cases of missing anomaly and increased false alarm in most of the existing works. Inspired by the concept of interval-sets, this paper proposes an anomaly detection algorithm called a fuzzy interval-set and tries to detect the potential value of the time series from a new perspective. In the proposed algorithm, a time series will be divided into several subsequences. Each subsequence is regarded as an interval-set depending on its value space and boundary of the subsequence. The similarity measurements between interval sets adopt interval operations and point probability distributions of the interval bounds. In addition, an anomaly score is defined based on similarity results. The experimental results on synthetic and real data sets indicate that the proposed algorithm has better discriminative performance than the piecewise aggregate approximation method and reduces the false alarm rate significantly.
Neuronal Specificity of Acupuncture in Alzheimer's Disease and Mild Cognitive Impairment Patients: A Functional MRI Study

Although acupuncture is considered to be effective and safe for Alzheimer's disease (AD) and mild cognitive impairment (MCI), the mechanism underlying its therapeutic effect is still unknown. Most studies clarifying the neuronal pathway produced by acupuncture were still applied to healthy subjects with limited single acupuncture point stimulation, which was inconsistency with clinical practice. Thus, in our present study, we investigate the differences between brain activity changes in AD and MCI patients caused by multi-acupuncture point Siguan (four gates), in order to provide visualized evidence for neuronal specificity of clinical acupuncture. Forty-nine subjects were recruited, including 21 AD patients, 14 MCI patients, and 14 healthy controls (HC). AD and MCI patients were randomly divided into two groups, respectively: real acupuncture point group (14 AD and 8 MCI) and sham acupuncture point group (7 AD and 6 MCI). We adopted a 16-minute, single-block, experimental design for acquiring functional MRI images. We found, in AD and MCI patients, Siguan (four gates) elicited extensive activations and deactivations in cognitive-related areas, visual-related areas, the sensorimotor-related area, basal ganglia, and cerebellum. Compared with HC, AD and MCI patients showed similar activations in cognitive-related brain areas (inferior frontal gyrus, supramarginal gyrus, and rolandic operculum) as well as deactivations in cognitive-related areas, visual-related areas, basal ganglia, and cerebellum, which were not found in HC. Compared with sham acupuncture points, real acupuncture points produced more specific brain changes with both activated and deactivated brain activities in AD and MCI. The preliminary results in our study verified the objective evidence for neuronal specificity of acupuncture in AD and MCI patients.
A review of eye-tracking applications as tools for training

Substantial literature exists regarding how eye-tracking systems can be used to measure cognitive load and how these measurements can be useful for adapting training in real time. Much of the published literature discusses the applications and limitations of the research and typically provides recommendations for improvement. This review assesses these articles collectively to provide a clearer solution for implementing eye-tracking systems into a training environment. Although limitations exist for using eye tracking as an interface tool, gaze and pupillary response have been successfully used to reflect changes in cognitive load and are starting to be incorporated into adaptive training systems, although issues are still present with differentiating pupil responses from simultaneous psychological effects. Additionally, current eye-tracking systems and data analysis software have proven accurate enough for general use, but issues including system cost and software integration prevent this technology from becoming commercialized for use in common instructional settings.
Real time Sign Language Recognition using PCA

The Sign Language is a method of communication for deaf-dumb people. This paper presents the Sign Language Recognition system capable of recognizing 26 gestures from the Indian Sign Language by using MATLAB. The proposed system having four modules such as: pre-processing and hand segmentation, feature extraction, sign recognition and sign to text and voice conversion. Segmentation is done by using image processing. Different features are extracted such as Eigen values and Eigen vectors which are used in recognition. The Principle Component Analysis (PCA) algorithm was used for gesture recognition and recognized gesture is converted into text and voice format. The proposed system helps to minimize communication barrier between deaf-dumb people and normal people.
Power Budgets for CubeSat Radios to Support Ground Communications and Inter-Satellite Links

CubeSats are a class of pico-satellites that have emerged over the past decade as a cost-effective alternative to the traditional large satellites to provide space experimentation capabilities to universities and other types of small enterprises, which otherwise would be unable to carry them out due to cost constraints. An important consideration when planning CubeSat missions is the power budget required by the radio communication subsystem, which enables a CubeSat to exchange information with ground stations and/or other CubeSats in orbit. The power that a CubeSat can dedicate to the communication subsystem is limited by the hard constraints on the total power available, which are due to its small size and light weight that limit the dimensions of the CubeSat power supply elements (batteries and solar panels). To date, no formal studies of the communications power budget for CubeSats are available in the literature, and this paper presents a detailed power budget analysis that includes communications with ground stations as well as with other CubeSats. For ground station communications, we outline how the orbital parameters of the CubeSat trajectory determine the distance of the ground station link and present power budgets for both uplink and downlink that include achievable data rates and link margins. For inter-satellite communications, we study how the slant range determines power requirements and affects the achievable data rates and link margins.
Algorithmics of Matching Under Preferences

• Page 17, line 13: “constrast” −→ “contrast”. (Due to Mechthild Opperud.) • Page 21, line 17: “attributes” −→ “attributed”. • Page 21, lines -4 to -3: change “pairs in which either (i) ri is unassigned if she is unassigned in both M and M ′, or (ii)” to “pairs obtained as follows: for each resident ri, ri is unassigned if she is unassigned in both M and M ′, otherwise”. • Page 21, line -1: “join” −→ “meet”. (Due to Didac Busquets.) • Page 22, line 1: “meet” −→ “join”. (Due to Didac Busquets.) • Page 23, line 4: add “In an sm instance, any matching is automatically assumed to have size n.”