nils-herrmann/clean_scirepeval huggingface dataset:
Integration of open source platform duckietown and gesture recognition as an interactive interface for the museum robotic guide

In recent years, population aging becomes a serious problem. To decrease the demand for labor when navigating visitors in museums, exhibitions, or libraries, this research designs an automatic museum robotic guide which integrates image and gesture recognition technologies to enhance the guided tour quality of visitors. The robot is a self-propelled vehicle developed by ROS (Robot Operating System), in which we achieve the automatic driving based on the function of lane-following via image recognition. This enables the robot to lead guests to visit artworks following the preplanned route. In conjunction with the vocal service about each artwork, the robot can convey the detailed description of the artwork to the guest. We also design a simple wearable device to perform gesture recognition. As a human machine interface, the guest is allowed to interact with the robot by his or her hand gestures. To improve the accuracy of gesture recognition, we design a two phase hybrid machine learning-based framework. In the first phase (or training phase), k-means algorithm is used to train historical data and filter outlier samples to prevent future interference in the recognition phase. Then, in the second phase (or recognition phase), we apply KNN (k-nearest neighboring) algorithm to recognize the hand gesture of users in real time. Experiments show that our method can work in real time and get better accuracy than other methods.
A collaborative filtering framework based on fuzzy association rules and multiple-level similarity

The rapid development of Internet technologies in recent decades has imposed a heavy information burden on users. This has led to the popularity of recommender systems, which provide advice to users about items they may like to examine. Collaborative Filtering (CF) is the most promising technique in recommender systems, providing personalized recommendations to users based on their previously expressed preferences and those of other similar users. This paper introduces a CF framework based on Fuzzy Association Rules and Multiple-level Similarity (FARAMS).  extended existing techniques by using fuzzy association rule mining, and takes advantage of product similarities in taxonomies to address data sparseness and nontransitive associations. Experimental results show that FARAMS improves prediction quality, as compared to similar approaches.
Design of Two-stage High Gain Operational Amplifier Using Current Buffer Compensation for Low Power Applications

i ii ACKNOWLEDGEMENT I wish to express my sincere appreciation to my thesis advisor, Mr. B. K. Hemant for his timely, informative feedback and support during this effort. It has been a great pleasure to learn from him during the process of expanding and refining my research. He has been a generous mentor. I also like to express my gratefulness to Dr. his perpetual encouragement, generous help and inspiring guidance. for their support and guidance. I am also very grateful for all the help I received from my classmates.
An Integrated Bayesian Approach for Effective Multi-Truth Discovery

Truth-finding is the fundamental technique for corroborating reports from multiple sources in both data integration and collective intelligent applications. Traditional truth-finding methods assume a single true value for each data item and therefore cannot deal will multiple true values (i.e., the multi-truth-finding problem). So far, the existing approaches handle the multi-truth-finding problem in the same way as the single-truth-finding problems. Unfortunately, the multi-truth-finding problem has its unique features, such as the involvement of sets of values in claims, different implications of inter-value mutual exclusion, and larger source profiles. Considering these features could provide new opportunities for obtaining more accurate truth-finding results. Based on this insight, we propose an integrated Bayesian approach to the multi-truth-finding problem, by taking these features into account. To improve the truth-finding efficiency, we reformulate the multi-truth-finding problem model based on the mappings between sources and (sets of) values. New mutual exclusive relations are defined to reflect the possible co-existence of multiple true values. A finer-grained copy detection method is also proposed to deal with sources with large profiles. The experimental results on three real-world datasets show the effectiveness of our approach.
Private and Accurate Data Aggregation against Dishonest Nodes

Privacy-preserving data aggregation in ad hoc networks is a challenging problem, considering the distributed communication and control requirement, dynamic network topology, unreliable communication links, etc. The difficulty is exaggerated when there exist dishonest nodes, and how to ensure privacy, accuracy, and robustness against dishonest nodes remains a n open issue. Different from the widely used cryptographic approaches, in this paper, we address this challenging proble m by exploiting the distributed consensus technique. We first pr opose a secure consensus-based data aggregation (SCDA) algorith m that guarantees an accurate sum aggregation while preservi ng the privacy of sensitive data. Then, to mitigate the polluti on from dishonest nodes, we propose an Enhanced SCDA (E-SCDA) algorithm that allows neighbors to detect dishonest nodes, and derive the error bound when there are undetectable dishones t nodes. We prove the convergence of both SCDA and E-SCDA. We also prove that the proposed algorithms are(ǫ, σ)-dataprivacy, and obtain the mathematical relationship betweenǫ and σ. Extensive simulations have shown that the proposed algori thms have high accuracy and low complexity, and they are robust against network dynamics and dishonest nodes.
Surface Management System Field Trial Results

NASA Ames Research Center, in cooperation with the FAA, has completed research and development of a proof-ofconcept Surface Management System (SMS). This paper reports on two recent SMS field tests as well as final performance and benefits analyses. Field tests and analysis support the conclusion that substantial portions of SMS technology are ready for transfer to the FAA and deployment throughout the National Airspace System (NAS). Other SMS capabilities were accepted in concept but require additional refinement for inclusion in subsequent development spirals. SMS is a decision support tool that helps operational specialists at Air Traffic Control (ATC) and NAS user facilities to collaboratively manage the movements of aircraft on the surface of busy airports, thereby improving capacity, efficiency, and flexibility. SMS provides accurate predictions of the future demand and how that demand will affect airport resources – information that is not currently available. The resulting shared awareness enables the Air Traffic Control Tower (ATCT), Terminal Radar Approach Control (TRACON), Air Route Traffic Control Center (ARTCC), and air carriers to coordinate traffic management decisions. Furthermore, SMS uses its ability to predict how future demand will play out on the surface to evaluate the effect of various traffic management decisions in advance of implementing them, to plan and advise surface operations. The SMS concept, displays, and algorithms were evaluated through a series of field tests at Memphis International Airport (MEM). An operational trial in September, 2003 evaluated SMS traffic management components, such as runway configuration change planning; shadow testing in January, 2004 tested tactical components (e.g., Approval Request (APREQ) coordination, sequencing for departure, and Expected Departure Clearance Time (EDCT) compliance). Participants in these evaluations rated the SMS concept and many of the traffic management displays very positively. Local and Ground controller displays will require integration with other automation systems. Feedback from FAA and NAS user participants support the conclusion that SMS algorithms currently provide information that has acceptable and beneficial accuracy for traffic management applications. Performance analysis results document the current accuracy of SMS algorithms. Benefits/cost analysis of delay cost reduction due to SMS provides the business case for SMS deployment.
MRJS : A JavaScript MapReduce Framework for Web Browsers

This paper describes MRJS, a JavaScript based MapReduce framework. MRJS runs on the open internet over HTTP rather than in a data center. Instead of using clusters of machines for processing power, it relies on volunteer clients in the form of JavaScript engines in web browsers. Users can specify, execute, and fetch data for arbitrary MapReduce jobs with HTTP requests. Our centralized job server acts as a coordinator. We have implemented MRJS and results show that it performs well for certain types of MapReduce computations.
Evaluating the Performance of a Single Phase PWM Inverter Using 3525A PWM IC

This paper presents the development of control circuit for single phase inverter using a pulse width modulation (PWM) IC. The attractiveness of this configuration is the elimination of a complex circuitry to generate oscillation pulses for transistor switches. The 3525A controller is able to generate the necessary waveforms to control the frequency of the inverter through proper use of switching pulse. The DC to AC inversion was successfully achieved alongside the switching signals; the controller produced inverter output of frequency nearly 50Hz.
Implementation Cost and Efficiency for AI Experience Managers

The study of Artificial Intelligence (AI) experience managers seeks to create software agents that can support compelling, interactive user experiences without needing any online guidance from human experts. Evaluating the utility of such AI managers is important in both academia and industry, both for measuring our progress in the field and for estimating a given manager’s practical viability. While several methods have been studied that evaluate a manager’s effectiveness, relatively few have explored the question of how costly a manager might be to implement in practice. We explore the latter question in this paper, presenting a formal way to estimate the cost of implementing an AI experience manager at scale.
Trellis: Privilege Separation for Multi-user Applications Made Easy

Operating systems provide a wide variety of resource isolation and access control mechanisms, ranging from traditional user-based security models to fine-grained permission systems as found in modern mobile operating systems. However, comparatively little assistance is available for defining and enforcing access control policies within multiuser applications. These applications, often found in enterprise environments, allow multiple users to operate at different privilege levels in terms of exercising application functionality and accessing data. Developers of such applications bear a heavy burden in ensuring that security policies over code and data in this setting are properly expressed and enforced. We present Trellis, an approach for expressing hierarchical access control policies in applications and enforcing these policies during execution. The approach enhances the development toolchain to allow programmers to partially annotate code and data with simple privilege level tags, and uses a static analysis to infer suitable tags for the entire application. At runtime, policies are extracted from the resulting binaries and are enforced by a modified operating system kernel. Our evaluation demonstrates that this approach effectively supports the development of secure multi-user applications with modest runtime performance overhead.
Bootstrap Confidence Intervals

This article surveys bootstrap methods for producing good approximate confidence intervals. The goal is to improve by an order of magnitude upon the accuracy of the standard intervals θ̂ ± zασ̂ , in a way that allows routine application even to very complicated problems. Both theory and examples are used to show how this is done. The first seven sections provide a heuristic overview of four bootstrap confidence interval procedures: BCa, bootstrap-t, ABC and calibration. Sections 8 and 9 describe the theory behind these methods, and their close connection with the likelihood-based confidence interval theory developed by Barndorff-Nielsen, Cox and Reid and others.
Two dimensional model of a permanent magnet spur gear

This paper extends an analysis method developed for a radial magnetized spur gear. The extension describes how a parallel magnetized spur gear can be modeled analytically. The analytical method for a parallel magnetized gear is verified with the finite element method, which showed good agreements. The results with the parallel magnetized are also compared with results from the previous developed analytical method for radial magnetized gear, and the parallel magnetized version turned out to give a better performance. A test model was also built to verify the theoretical calculations.
Stability of matrix factorization for collaborative filtering

We study the stability vis a vis adversarial noise of matrix factorization algorithm for matrix completion. In particular, our results include: (I) we bound the gap between the solution matrix of the factorization method and the ground truth in terms of root mean square error; (II) we treat the matrix factorization as a subspace fitting problem and analyze the difference between the solution subspace and the ground truth; (III) we analyze the prediction error of individual users based on the subspace stability. We apply these results to the problem of collaborative filtering under manipulator attack, which leads to useful insights and guidelines for collaborative filtering system design.
Random projection-based multiplicative data perturbation for privacy preserving distributed data mining

This paper explores the possibility of using multiplicative random projection matrices for privacy preserving distributed data mining. It specifically considers the problem of computing statistical aggregates like the inner product matrix, correlation coefficient matrix, and Euclidean distance matrix from distributed privacy sensitive data possibly owned by multiple parties. This class of problems is directly related to many other data-mining problems such as clustering, principal component analysis, and classification. This paper makes primary contributions on two different grounds. First, it explores independent component analysis as a possible tool for breaching privacy in deterministic multiplicative perturbation-based models such as random orthogonal transformation and random rotation. Then, it proposes an approximate random projection-based technique to improve the level of privacy protection while still preserving certain statistical characteristics of the data. The paper presents extensive theoretical analysis and experimental results. Experiments demonstrate that the proposed technique is effective and can be successfully used for different types of privacy-preserving data mining applications.
Differentially Private Distributed Constrained Optimization

Many resource allocation problems can be formulated as an optimization problem whose constraints contain sensitive information about participating users. This paper concerns a class of resource allocation problems whose objective function depends on the aggregate allocation (i.e., the sum of individual allocations); in particular, we investigate distributed algorithmic solutions that preserve the privacy of participating users. Without privacy considerations, existing distributed algorithms normally consist of a central entity computing and broadcasting certain public coordination signals to participating users. However, the coordination signals often depend on user information, so that an adversary who has access to the coordination signals can potentially decode information on individual users and put user privacy at risk. We present a distributed optimization algorithm that preserves differential privacy, which is a strong notion that guarantees user privacy regardless of any auxiliary information an adversary may have. The algorithm achieves privacy by perturbing the public signals with additive noise, whose magnitude is determined by the sensitivity of the projection operation onto user-specified constraints. By viewing the differentially private algorithm as an implementation of stochastic gradient descent, we are able to derive a bound for the suboptimality of the algorithm. We illustrate the implementation of our algorithm via a case study of electric vehicle charging. Specifically, we derive the sensitivity and present numerical simulations for the algorithm. Through numerical simulations, we are able to investigate various aspects of the algorithm when being used in practice, including the choice of step size, number of iterations, and the trade-off between privacy level and suboptimality.
Multi-type attributes driven multi-camera person re-identification

One of the major challenges in person Re-Identification (ReID) is the inconsistent visual appearance of a person. Current works on visual feature and distance metric learning have achieved significant achievements, but still suffer from the limited robustness to pose variations, viewpoint changes, etc., and the high computational complexity. This makes person ReID among multiple cameras still challenging. This work is motivated to learn mid-level human attributes which are robust to visual appearance variations and could be used as efficient features for person matching. We propose a weakly supervised multi-type attribute learning framework which considers the contextual cues among attributes and progressively boosts the accuracy of attributes only using a limited number of labeled data. Specifically, this framework involves a threestage training. A deep Convolutional Neural Network (dCNN) is first trained on an independent dataset labeled with attributes. Then it is fine-tuned on another dataset only labeled with person IDs using our defined triplet loss. Finally, the updated dCNN predicts attribute labels for the target dataset, which is combined with the independent dataset for the final round of fine-tuning. The predicted attributes, namely deep attributes exhibit promising generalization ability across different datasets. By directly using the deep attributes with simple Cosine distance, we have obtained competitive accuracy on four person ReID datasets. Experiments also show that a simple distance metric learning modular further boosts our method, making it outperform many recent ∗Corresponding author Email address: slzhang.jdl@pku.edu.cn (Shiliang Zhang)  to Pattern Recognition September 11, 2017
Backward Stochastic Differential Equations and Applications

A new type of stochastic differential equation, called the backward stochastic differentil equation (BSDE), where the value of the solution is prescribed at the final (rather than the initial) point of the time interval, but the solution is nevertheless required to be at each time a function of the past of the underlying Brownian motion, has been introduced recently, independently by Peng and the author in , and by Dufne and Epstein in . This class of equations is a natural nonlinear extension of linear equations that appear both as the equation for the adjoint process in the maximum principle for optimal stochastic control (see ), and as a basic model for asset pricing in financial mathematics. It was soon after discovered (see [22], ) that those BSDEs provide probabilistic formulas for solutions of certain semilinear partial differential equations (PDEs), which generalize the well-known Feynmann-Kac formula for second order linear PDEs. This provides a new additional tool for analyzing solutions of certain PDEs, for instance reaction-diffusion equations.
Low-complexity Bayer-pattern video compression using distributed video coding

Most consumer digital color cameras capture video using a single chip. Single chip cameras do not capture RGB triples for every pixel, but a subsampled version with only one color component per pixel (e.g. Bayer pattern). Conventionally, a full resolution video is constructed from the Bayer pattern by demosaicing before being converted to YUV domain for compression. In order to lower the encoding complexity, we propose in this work a novel color space conversion in the pre-processing step. Compared to the conventional method, the proposed scheme reduces the encoding complexity almost by half. Moreover, it improves the reconstructed video quality by up to 1.5 dB in CPSNR, when H.264/AVC is used for compression. To further lower the encoding complexity, we additionally use our Wyner-Ziv video coder for compression. Again, we observe in our experiments a similar gain of the proposed scheme over the conventional one.
Understanding Mutual Information and its Use in InfoGAN

Interpretable variables are useful in generative models. Generative Adversarial Networks (GANs) are generative models that are flexible in their input. The Information Maximizing GAN (InfoGAN) ties the output of the generator to a component of its input called the latent codes. By forcing the output to be tied to this input component, we can control some properties of the output representation. It is notoriously difficult to find the Nash equilibrium when jointly training the discriminator and generator in a GAN. We uncover some successful and unsuccessful configurations for generating images using InfoGAN.
An LTE Base-Station Magnetoelectric Dipole Antenna with Anti-Interference Characteristics and Its MIMO System Application

In this research, a wideband long-term evolution (LTE) base-station antenna with flexible band-notch characteristics is introduced by two window-type slots etched on radiation patches of the magnetoelectric dipole. The presented antenna can provide a band-notch range with an impedance bandwidth of 2.39-2.52 GHz in the band of 1.825-2.925 GHz, which well applies the wideband antenna for LTE with the good anti-interference from wireless local area network narrow communication band. The typical parameters are investigated to show that the antenna with anti-interference characteristics exhibits large scope band-notch adjustability in the whole wideband region. Based on above element, an LTE base station antenna multiple-input, multiple-output system with anti-interference characteristics is investigated and measured to show its good performances: anti-interference, high isolation, high gain, unidirectional radiation, low back radiation, and low cross-polarization.
Combining deep learning and level set for the automated segmentation of the left ventricle of the heart from cardiac cine magnetic resonance

We introduce a new methodology that combines deep learning and level set for the automated segmentation of the left ventricle of the heart from cardiac cine magnetic resonance (MR) data. This combination is relevant for segmentation problems, where the visual object of interest presents large shape and appearance variations, but the annotated training set is small, which is the case for various medical image analysis applications, including the one considered in this paper. In particular, level set methods are based on shape and appearance terms that use small training sets, but present limitations for modelling the visual object variations. Deep learning methods can model such variations using relatively small amounts of annotated training, but they often need to be regularised to produce good generalisation. Therefore, the combination of these methods brings together the advantages of both approaches, producing a methodology that needs small training sets and produces accurate segmentation results. We test our methodology on the MICCAI 2009 left ventricle segmentation challenge database (containing 15 sequences for training, 15 for validation and 15 for testing), where our approach achieves the most accurate results in the semi-automated problem and state-of-the-art results for the fully automated challenge.