nils-herrmann/clean_scirepeval huggingface dataset:
Learning to Schedule Control Fragments for Physics-Based Characters Using Deep Q-Learning

Given a robust control system, physical simulation offers the potential for interactive human characters that move in realistic and responsive ways. In this article, we describe how to learn a scheduling scheme that reorders short control fragments as necessary at runtime to create a control system that can respond to disturbances and allows steering and other user interactions. These schedulers provide robust control of a wide range of highly dynamic behaviors, including walking on a ball, balancing on a bongo board, skateboarding, running, push-recovery, and breakdancing. We show that moderate-sized Q-networks can model the schedulers for these control tasks effectively and that those schedulers can be efficiently learned by the deep Q-learning algorithm.
Leaf image based cucumber disease recognition using sparse representation classification

Most existing image-based crop disease recognition algorithms rely on extracting various kinds of features from leaf images of diseased plants. They have a common limitation as the features selected for discriminating leaf images are usually treated as equally important in the classification process. We propose a novel cucumber disease recognition approach which consists of three pipelined procedures: segmenting diseased leaf images by K-means clustering, extracting shape and color features from lesion information, and classifying diseased leaf images using sparse representation (SR). A major advantage of this approach is that the classification in the SR space is able to effectively reduce the computation cost and improve the recognition performance. We perform a comparison with four other feature extraction based methods using a leaf image dataset on cucumber diseases. The proposed approach is shown to be effective in recognizing seven major cucumber diseases with an overall recognition rate of 85.7%, higher than those of the other methods.
Paragraph text segmentation into lines with Recurrent Neural Networks

The detection of text lines, as a first processing step, is critical in all text recognition systems. State-of-the-art methods to locate lines of text are based on handcrafted heuristics fine-tuned by the image processing community's experience. They succeed under certain constraints; for instance the background has to be roughly uniform. We propose to use more “agnostic” Machine Learning-based approaches to address text line location. The main motivation is to be able to process either damaged documents, or flows of documents with a high variety of layouts and other characteristics. A new method is presented in this work, inspired by the latest generation of optical models used for text recognition, namely Recurrent Neural Networks. As these models are sequential, a column of text lines in our application plays here the same role as a line of characters in more traditional text recognition settings. A key advantage of the proposed method over other data-driven approaches is that compiling a training dataset does not require labeling line boundaries: only the number of lines are required for each paragraph. Experimental results show that our approach gives similar or better results than traditional handcrafted approaches, with little engineering efforts and less hyper-parameter tuning.
SIFT, SURF & seasons: Appearance-based long-term localization in outdoor environments

In this paper, we address the problem of outdoor, appearance-based topological localization, particularly over long periods of time where seasonal changes alter the appearance of the environment. We investigate a straight-forward method that relies on local image features to compare single image pairs. We first look into which of the dominating image feature algorithms, SIFT or the more recent SURF, that is most suitable for this task. We then fine-tune our localization algorithm in terms of accuracy, and also introduce the epipolar constraint to further improve the result. The final localization algorithm is applied on multiple data sets, each consisting of a large number of panoramic images, which have been acquired over a period of nine months with large seasonal changes. The final localization rate in the single-image matching, cross-seasonal case is between 80 to 95%.
Multiracial Facial Golden Ratio and Evaluation of Facial Appearance

This study aimed to investigate the association of facial proportion and its relation to the golden ratio with the evaluation of facial appearance among Malaysian population. This was a cross-sectional study with 286 randomly selected from Universiti Sains Malaysia (USM) Health Campus students (150 females and 136 males; 100 Malaysian Chinese, 100 Malaysian Malay and 86 Malaysian Indian), with the mean age of 21.54 ± 1.56 (Age range, 18-25). Facial indices obtained from direct facial measurements were used for the classification of facial shape into short, ideal and long. A validated structured questionnaire was used to assess subjects' evaluation of their own facial appearance. The mean facial indices of Malaysian Indian (MI), Malaysian Chinese (MC) and Malaysian Malay (MM) were 1.59 ± 0.19, 1.57 ± 0.25 and 1.54 ± 0.23 respectively. Only MC showed significant sexual dimorphism in facial index (P = 0.047; P<0.05) but no significant difference was found between races. Out of the 286 subjects, 49 (17.1%) were of ideal facial shape, 156 (54.5%) short and 81 (28.3%) long. The facial evaluation questionnaire showed that MC had the lowest satisfaction with mean score of 2.18 ± 0.97 for overall impression and 2.15 ± 1.04 for facial parts, compared to MM and MI, with mean score of 1.80 ± 0.97 and 1.64 ± 0.74 respectively for overall impression; 1.75 ± 0.95 and 1.70 ± 0.83 respectively for facial parts.



1) Only 17.1% of Malaysian facial proportion conformed to the golden ratio, with majority of the population having short face (54.5%); 2) Facial index did not depend significantly on races; 3) Significant sexual dimorphism was shown among Malaysian Chinese; 4) All three races are generally satisfied with their own facial appearance; 5) No significant association was found between golden ratio and facial evaluation score among Malaysian population.
Variable selection by association rules for customer churn prediction of multimedia on demand

Multimedia on demand (MOD) is an interactive system that provides a number of value-added services in addition to traditional TV services, such as video on demand and interactive online learning. This opens a new marketing and managerial problem for the telecommunication industry to retain valuable MOD customers. Data mining techniques have been widely applied to develop customer churn prediction models, such as neural networks and decision trees in the domain of mobile telecommunication. However, much related work focuses on developing the prediction models per se. Few studies consider the pre-processing step during data mining whose aim is to filter out unrepresentative data or information. This paper presents the important processes of developing MOD customer churn prediction models by data mining techniques. They contain the pre-processing stage for selecting important variables by association rules, which have not been applied before, the model construction stage by neural networks (NN) and decision trees (DT), which are widely adapted in the literature, and four evaluation measures including prediction accuracy, precision, recall, and F-measure, all of which have not been considered to examine the model performance. The source data are based on one telecommunication company providing the MOD services in Taiwan, and the experimental results show that using association rules allows the DT and NN models to provide better prediction performances over a chosen validation dataset. In particular, the DT model performs better than the NN model. Moreover, some useful and important rules in the DT model, which show the factors affecting a high proportion of customer churn, are also discussed for the marketing and managerial purpose.
A Stereo and Color-based Method for Face Pose Estimation and Facial Feature Extraction

This paper describes a method to perform face pose estimation and high resolution facial feature extraction on the basis of stereoscopic color images. Unlike other approaches no light projection is required at running time. In our method face detection is based on color driven clustering of 3D points derived from stereo. A mesh model is registered with the post-processed face cluster using a variant of the iterative closest point algorithm (ICP). Pose is derived from correspondence. Then, pose and model information is used for face normalization and facial feature localization. Results show, stereo and color are powerful cues for finding the face and its pose under a wide range of poses, illuminations and expressions (PIE). Head orientation may vary in out of plane rotations up to
Design of a Rule-based Stemmer for Natural Language Text in Bengali

This paper presents a rule-based approach for finding out the stems from text in Bengali, a resource-poor language. It starts by introducing the concept of orthographic syllable, the basic orthographic unit of Bengali. Then it discusses the morphological structure of the tokens for different parts of speech, formalizes the inflection rule constructs and formulates a quantitative ranking measure for potential candidate stems of a token. These concepts are applied in the design and implementation of an extensible architecture of a stemmer system for Bengali text. The accuracy of the system is calculated to be ~89% and above.
Image denoising with multi-layer perceptrons, part 1: comparison with existing algorithms and with bounds

Image denoising can be described as the problem of mapping from a noisy image to a noisefree image. The best currently available denoising methods approximate this mapping with cleverly engineered algorithms. In this work we attempt to learn this mapping directly with plain multi layer perceptrons (MLP) applied to image patches. We will show that by training on large image databases we are able to outperform the current state-of-the-art image denoising methods. In addition, our method achieves results that are superior to one type of theoretical bound and goes a large way toward closing the gap with a second type of theoretical bound. Our approach is easily adapted to less extensively studied types of noise, such as mixed Poisson-Gaussian noise, JPEG artifacts, salt-and-pepper noise and noise resembling stripes, for which we achieve excellent results as well. We will show that combining a block-matching procedure with MLPs can further improve the results on certain images. In a second paper (Burger et al., 2012b), we detail the training trade-offs and the inner mechanisms of our MLPs.
Personalized digital TV content recommendation with integration of user behavior profiling and multimodal content rating

This paper presents the novel development of an embedded system that aims at digital TV content recommendation based on descriptive metadata collected from versatile sources. The described system comprises a user profiling subsystem identifying user preferences and a user agent subsystem performing content rating. TV content items are ranked using a combined multimodal approach integrating classification-based and keyword-based similarity predictions so that a user is presented with a limited subset of relevant content. Observable user behaviors are discussed as instrumental in user profiling and a formula is provided for implicitly estimating the degree of user appreciation of content. A new relation-based similarity measure is suggested to improve categorized content rating precision. Experimental results show that our system can recommend desired content to users with significant amount of accuracy.
A Compact Multiband Monopole Antenna With a Defected Ground Plane

A compact multiband antenna is proposed that consists of a printed circular disc monopole antenna with an L-shaped slot cut out of the ground, forming a defected ground plane. Analysis of the current distribution on the antenna reveals that at low frequencies the addition of the slot creates two orthogonal current paths, which are responsible for two additional resonances in the response of the antenna. By virtue of the orthogonality of these modes the antenna exhibits orthogonal pattern diversity, while enabling the adjacent resonances to be merged, forming a wideband low-frequency response and maintaining the inherent wideband high-frequency response of the monopole. The antenna exhibits a measured -10 dB S 11 bandwidth of 600 MHz from 2.68 to 3.28 GHz, and a bandwidth of 4.84 GHz from 4.74 to 9.58 GHz, while the total size of the antenna is only 24 times 28.3 mm. The efficiency is measured using a modified Wheeler cap method and is verified using the gain comparison method to be approximately 90% at both 2.7 and 5.5 GHz.
ConFirm: Detecting firmware modifications in embedded systems using Hardware Performance Counters

Critical infrastructure components nowadays use microprocessor-based embedded control systems. It is often infeasible, however, to employ the same level of security measures used in general purpose computing systems, due to the stringent performance and resource constraints of embedded control systems. Furthermore, as software sits atop and relies on the firmware for proper operation, software-level techniques cannot detect malicious behavior of the firmware. In this work, we propose ConFirm, a low-cost technique to detect malicious modifications in the firmware of embedded control systems by measuring the number of low-level hardware events that occur during the execution of the firmware. In order to count these events, ConFirm leverages the Hardware Performance Counters (HPCs), which readily exist in many embedded processors. We evaluate the detection capability and performance overhead of the proposed technique on various types of firmware running on ARM- and PowerPC-based embedded processors. Experimental results demonstrate that ConFirm can detect all the tested modifications with low performance overhead.
Summary in context: Searching versus browsing

The use of text summaries in information-seeking research has focused on query-based summaries. Extracting content that resembles the query alone, however, ignores the greater context of the document. Such context may be central to the purpose and meaning of the document. We developed a generic, a query-based, and a hybrid summarizer, each with differing amounts of document context. The generic summarizer used a blend of discourse information and information obtained through traditional surface-level analysis. The query-based summarizer used only query-term information, and the hybrid summarizer used some discourse information along with query-term information. The validity of the generic summarizer was shown through an intrinsic evaluation using a well-established corpus of human-generated summaries. All three summarizers were then compared in an information-seeking experiment involving 297 subjects. Results from the information-seeking experiment showed that the generic summaries outperformed all others in the browse tasks, while the query-based and hybrid summaries outperformed the generic summary in the search tasks. Thus, the document context of generic summaries helped users browse, while such context was not helpful in search tasks. Such results are interesting given that generic summaries have not been studied in search tasks and the that majority of Internet search engines rely solely on query-based summaries.
Health Care Fraud Detection with Community Detection Algorithms

Fraud detection is interesting research topic and it not only needs data mining techniques but also needs a lot of inputs from domain experts. In health care claims, relationships between physicians and patients form complex communities structures and these communities could lead to potential fraud discoveries. Traditionally, researchers have focused on clustering physicians and patients and tried to find the suspicious communities. In this paper, we studied and discussed different types of relationships and focus on small but exclusive relationships that are suspicious and may indicate potential health care frauds. We developed two algorithms to detect these small and exclusive communities. These algorithms can be applied to larger dataset and are highly scalable. We tested these algorithms with a set of synthesized datasets. These synthesized datasets were created to resemble the real health care claims datasets and used to test the fraud detection algorithms. The test results show the these algorithms are very efficient and can evaluate the communities structures of 50,000 providers in about 1 minute.
Navigation System of the Autonomous Agricultural Robot “ BoniRob ” *

—In the context of the public funded project BoniRob we have developed an autonomous agricultural robot that can autonomously perform repeating phenotyping tasks for individual plants on different days. These tasks require a robust and reliable navigation and perception system. In this paper, we summarize our navigation approach and give details about the employed sensors, algorithms and the system integration. The system has successfully been demonstrated at various public occasions and we are now, with our partners, investigating opportunities for a limited-lot production for research and evaluation purposes. An overview of the complete project is given in . Furthermore, we are currently developing the next version of the robot that will allow in addition mechanical weed control.
An Identity-Authentication System Using Fingerprints

Fingerprint verification is an important biometric technique for personal identification. In this paper, we describe the design and implementation of a prototype automatic identity-authentication system that uses fingerprints to authenticate the identity of an individual. We have developed an improved minutiae-extraction al-gorithm that is faster and more accurate than our earlier algorithm [58]. An alignment-based minutiae-matching algorithm has been proposed. This algorithm is capable of finding the correspondences between input minutiae and the stored template without resorting to exhaustive search and has the ability to compensate adaptively for the nonlinear deformations and inexact transformations between an input and a template. To establish an objective assessment of our system, both the Michigan State University and the National Institute of Standards and Technology NIST 9 fingerprint data bases have been used to estimate the performance numbers. The experimental results reveal that our system can achieve a good performance on these data bases. We also have demonstrated that our system satisfies the response-time requirement. A complete authentication procedure, on average, takes about 1.4 seconds on a Sun ULTRA 1 workstation (it is expected to run as fast or faster on a 200 HMz Pentium ).
Rapid Prototyping for Wearables: Concept Design and Development for head- and wrist-mounted Wearables (Smart Watches and Google Glass)

This half-day hands-on studio will teach how to design and develop effective interfaces for head mounted and wrist worn wearable computers through the application of user-centered design principles. Attendees will learn gain the knowledge and tools needed to rapidly develop prototype applications, and also complete a hands-on design task. They will also learn good design guidelines for wearable systems and how to apply those guidelines. A variety of tools will be used that do not require any hardware or software experience, many of which are free and/or open source. Attendees will also be provided with material that they can use to continue their learning after the studio is over.
Broadband Compact SIW Phase Shifter Using Omega Particles

In this letter, a broadband and compact phase shifter using omega particles is designed. Bandwidth of the 90 <sup>°</sup> and 45 <sup>°</sup> versions of the designed phase shifter are around 55% with the accuracy of 3 <sup>°</sup> and 60% with the accuracy of 2.5 <sup>°</sup>, respectively. The proposed phase shifter has compact size compared with previously published SIW based phase shifter designs. A prototype of the proposed 90 <sup>°</sup> phase shifter is fabricated and comparison of the measured and simulated results is provided.
Overview and Evaluation of Bluetooth Low Energy : An Emerging Low-Power Wireless Technology

-Bluetooth Low Energy (BLE) is an emerging low-power wireless technology developed for short-range control and monitoring applications that is expected to be incorporated into billions of devices in the next few years. This paper describes the main features of BLE, explores its potential applications, and investigates the impact of various critical parameters on its performance. BLE represents a trade-off between energy consumption, latency, piconet size, and throughput that mainly depends on parameters such as connInterval and connSlaveLatency. According to theoretical results, the lifetime of a BLE device powered by a coin cell battery ranges between 2.0 days and 14.1 years. The number of simultaneous slaves per master ranges between 2 and 5,917. The minimum latency for a master to obtain a sensor reading is 676 μs, although simulation results show that, under high bit error rate, average latency increases by up to three orders of magnitude. The paper provides experimental results that complement the theoretical and simulation findings, and indicates implementation constraints that may reduce BLE performance.
Simultaneous feature selection and feature weighting using Hybrid Tabu Search/K-nearest neighbor classifier

Feature selection and feature weighting are useful techniques for improving the classification accuracy of K-nearest-neighbor (K-NN) rule. The term feature selection refers to algorithms that select the best subset of the input feature set. In feature weighting, each feature is multiplied by a weight value proportional to the ability of the feature to distinguish pattern classes. In this paper, a novel hybrid approach is proposed for simultaneous feature selection and feature weighting of K-NN rule based on Tabu Search (TS) heuristic. The proposed TS heuristic in combination with K-NN classifier is compared with several classifiers on various available data sets. The results have indicated a significant improvement in the performance in classification accuracy. The proposed TS heuristic is also compared with various feature selection algorithms. Experiments performed revealed that the proposed hybrid TS heuristic is superior to both simple TS and sequential search algorithms. We also present results for the classification of prostate cancer using multispectral images, an important problem in biomedicine.
Design of integrated transformer and inductor for high frequency dual active bridge GaN Charger for PHEV

This paper discusses the design of high power density transformer and inductor for the high frequency dual active bridge (DAB) GaN charger. Because the charger operates at 500 kHz, the inductance needed to achieve ZVS for the DAB converter is reduced to as low as 3μH. As a result, it is possible to utilize the leakage inductor as the series inductor of DAB converter. To create such amount of leakage inductance, certain space between primary and secondary winding is allocated to store the leakage flux energy. The designed transformer is above 99.2% efficiency while delivering 3.3kW. The power density of the designed transformer is 6.3 times of the lumped transformer and inductor in 50 kHz Si Charger. The detailed design procedure and loss analysis are discussed.