nils-herrmann/clean_scirepeval huggingface dataset:
Iterative Adaptive Approaches to MIMO Radar Imaging

Multiple-input multiple-output (MIMO) radar can achieve superior performance through waveform diversity over conventional phased-array radar systems. When a MIMO radar transmits orthogonal waveforms, the reflected signals from scatterers are linearly independent of each other. Therefore, adaptive receive filters, such as Capon and amplitude and phase estimation (APES) filters, can be directly employed in MIMO radar applications. High levels of noise and strong clutter, however, significantly worsen detection performance of the data-dependent beamformers due to a shortage of snapshots. The iterative adaptive approach (IAA), a nonparametric and user parameter-free weighted least-squares algorithm, was recently shown to offer improved resolution and interference rejection performance in several passive and active sensing applications. In this paper, we show how IAA can be extended to MIMO radar imaging, in both the negligible and nonnegligible intrapulse Doppler cases, and we also establish some theoretical convergence properties of IAA. In addition, we propose a regularized IAA algorithm, referred to as IAA-R, which can perform better than IAA by accounting for unrepresented additive noise terms in the signal model. Numerical examples are presented to demonstrate the superior performance of MIMO radar over single-input multiple-output (SIMO) radar, and further highlight the improved performance achieved with the proposed IAA-R method for target imaging.
Google App Engine

Cloud computing becomes more popular day by day. The “dream” of users to “to get rid” of administrators, huge investments on expensive hardware and human resources becomes true. The goal of this paper is to present a general overview of the new coming service offered by Google company Google App Engine. “What is it for?”, “What can I do with the system?” and “How does it work?” are some of the questions on which this paper will try to give an answer. In addition, brief overview and description of “cloud computing” and comparison of Google App Engine with other similar products will be provided to the reader as well.
CSI-MIMO: Indoor Wi-Fi fingerprinting system

Wi-Fi based fingerprinting systems, mostly utilize the Received Signal Strength Indicator (RSSI), which is known to be unreliable due to environmental and hardware effects. In this paper, we present a novel Wi-Fi fingerprinting system, exploiting the fine-grained information known as Channel State Information (CSI). The frequency diversity of CSI can be effectively utilized to represent a location in both frequency and spatial domain resulting in more accurate indoor localization. We propose a novel location signature CSI-MIMO that incorporates Multiple Input Multiple Output (MIMO) information and use both the magnitude and the phase of CSI of each sub-carrier. We experimentally evaluate the performance of CSI-MIMO fingerprinting using the k-nearest neighbor and the Bayes algorithm. The accuracy of the proposed CSI-MIMO is compared with Finegrained Indoor Fingerprinting System (FIFS) and a simple CSI-based system. The experimental result shows an accuracy improvement of 57% over FIFS with an accuracy of 0.95 meters.
ACME: Adaptive Caching Using Multiple Experts

The gap between CPU speeds and the speed of the technologies p roviding the data is increasing. As a result, latency and bandwidt h to needed data is limited by the performance of the storage devices and the n etworks that connect them to the CPU. Distributed caching techniques are often used to reduce the penalties associated with such caching; however , such techniques need further development to be truly integrated into the net work. This paper describes the preliminary design of an adaptive caching scheme using multiple experts, called ACME. ACME is used to manage the rep lacement policies within distributed caches to further improve the h it rates over static caching techniques. We propose the use of machine learning a lgorithms to rate and select the current best policies or mixtures of poli cies via weight updates based on their recent success, allowing each adapti ve cache node to tune itself based on the workload it observes. Since no cac he databases or synchronization messages are exchanged for adaptivity, he clusters composed of these nodes will be scalable and manageable. We show that static techniques are suboptimal when combined in networks of cach es, providing potential for adaptivity to improve performance.
Polyphase filters - A model for teaching the art of discovery in DSP

By its very nature DSP is a mathematically heavy topic and to fully understand it students need to understand the mathematical developments underlying DSP topics. However, relying solely on mathematical developments often clouds the true nature of the foundation of a result. It is likely that students who master the mathematics may still not truly grasp the key ideas of a topic. Furthermore, teaching DSP topics by merely “going through the mathematics” deprives students of learning the art of discovery that will make them good researchers. This paper uses the topic of polyphase decimation and interpolation to illustrate how it is possible to maintain rigor yet teach using less mathematical approaches that show students how researchers think when developing new ideas.
A New Optimization Layer for Real-Time Bidding Advertising Campaigns

While it is relatively easy to start an online advertising campaign, obtaining a high Key Performance Indicator (KPI) can be challenging. A large body of work on this subject has already been performed and platforms known as DSPs are available on the market that deal with such an optimization. From the advertiser’s point of view, each DSP is a different black box, with its pros and cons, that needs to be configured. In order to take advantage of the pros of every DSP, advertisers are well-advised to use a combination of them when setting up their campaigns. In this paper, we propose an algorithm for advertisers to add an optimization layer on top of DSPs. The algorithm we introduce, called skott, maximizes the chosen KPI by optimally configuring the DSPs and putting them in competition with each other. skott is a highly specialized iterative algorithm loosely based on gradient descent that is made up of three independent sub-routines, each dealing with a different problem: partitioning the budget, setting the desired average bid, and preventing under-delivery. In particular, one of the novelties of our approach lies in our taking the perspective of the advertisers rather than the DSPs. Synthetic market data is used to evaluate the efficiency of skott against other state-of-the-art approaches adapted from similar problems. The results illustrate the benefits of our proposals, which greatly outperforms the other methods.
Analyzing Neighborhoods of Falsifying Traces in Cyber-Physical Systems

We study the problem of analyzing falsifying traces of cyber-physical systems. Specifically, given a system model and an input which is a counterexample to a property of interest, we wish to understand which parts of the inputs are "responsible" for the counterexample as a whole. Whereas this problem is well known to be hard to solve precisely, we provide an approach based on learning from repeated simulations of the system under test.
 Our approach generalizes the classic concept of "one-at-a-time" sensitivity analysis used in the risk and decision analysis community to understand how inputs to a system influence a property in question. Specifically, we pose the problem as one of finding a neighborhood of inputs that contains the falsifying counterexample in question, such that each point in this neighborhood corresponds to a falsifying input with a high probability. We use ideas from statistical hypothesis testing to infer and validate such neighborhoods from repeated simulations of the system under test. This approach not only helps to understand the sensitivity of these counterexamples to various parts of the inputs, but also generalizes or widens the given counterexample by returning a neighborhood of counterexamples around it.
 We demonstrate our approach on a series of increasingly complex examples from automotive and closed loop medical device domains. We also compare our approach against related techniques based on regression and machine learning.
Air-light estimation using haze-lines

Outdoor images taken in bad weather conditions, such as haze and fog, look faded and have reduced contrast. Recently there has been great success in single image dehazing, i.e., improving the visibility and restoring the colors from a single image. A crucial step in these methods is the calculation of the air-light color, the color of an area of the image with no objects in line-of-sight. We propose a new method for calculating the air-light. The method relies on the haze-lines prior that was recently introduced. This prior is based on the observation that the pixel values of a hazy image can be modeled as lines in RGB space that intersect at the air-light. We use Hough transform in RGB space to vote for the location of the air-light. We evaluate the proposed method on an existing dataset of real world images, as well as some synthetic and other real images. Our method performs on-par with current state-of-the-art techniques and is more computationally efficient.
Additive Manufacturing of Metals: A Review

Over the past 20 years, additive manufacturing (AM) has evolved from 3D printers used for rapid prototyping to sophisticated rapid manufacturing that can create parts directly without the use of tooling. AM technologies build near net shape components layer by layer using 3D model data. AM could revolutionize many sectors of manufacturing by reducing component lead time, material waste, energy usage, and carbon footprint. Furthermore, AM has the potential to enable novel product designs that could not be fabricated using conventional processes. This proceedings is a review that assesses available AM technologies for direct metal fabrication. Included is an outline of the definition of AM, a review of the commercially available and under development technologies for direct metal fabrication, possibilities, and an overall assessment of the state of the art. Perspective on future research opportunities is also be presented.
Types of minority class examples and their influence on learning classifiers from imbalanced data

Many real-world applications reveal difficulties in learning classifiers from imbalanced data. Although several methods for improving classifiers have been introduced, the identification of conditions for the efficient use of the particular method is still an open research problem. It is also worth to study the nature of imbalanced data, characteristics of the minority class distribution and their influence on classification performance. However, current studies on imbalanced data difficulty factors have been mainly done with artificial datasets and their conclusions are not easily applicable to the real-world problems, also because the methods for their identification are not sufficiently developed. In our paper, we capture difficulties of class distribution in real datasets by considering four types of minority class examples: safe, borderline, rare and outliers. First, we confirm their occurrence in real data by exploring multidimensional visualizations of selected datasets. Then, we introduce a method for an identification of these types of examples, which is based on analyzing a class distribution in a local neighbourhood of the considered example. Two ways of modeling this neighbourhood are presented: with k-nearest examples and with kernel functions. Experiments with artificial datasets show that these methods are able to re-discover simulated types of examples. Next contributions of this paper include carrying out a comprehensive experimental study with 26 real world imbalanced datasets, where (1) we identify new data characteristics basing on the analysis of types of minority examples; (2) we demonstrate that considering the results of this analysis allow to differentiate classification performance of popular classifiers and pre-processing methods and to evaluate their areas of competence. Finally, we highlight directions of exploiting the results of our analysis for developing new algorithms for learning classifiers and pre-processing methods.
A Proposal to Measure Success Factors for Location-Based Mobile Cardiac Telemedicine System ( LMCTS )

Cardiac telemedicine systems facilitate treatment of patients by sending blood pressure and cardiac performance information to hospitals and medical emergency care units. Location-based services can be used in mobile cardiac telemedicine systems to improve efficiency, robustness and accuracy. This paper proposes a combination of mobile telemedicine systems and location-based services that will enable hospitals and emergency departments to do continuous monitoring of a patient’s heart activity; such a system would decrease the probability of critical condition patients in emergency cases and increase the chance of saving lives. In order to evaluate whether this kind of health care system might work, we explore the success factors for such a system by adapting the DeLone & McLean IS success model to the context of location-based mobile system in cardiac healthcare. After reviewing previous works, we identify fourteen factors which can affect the success of a location-based mobile cardiac telemedicine system: Realization of user expectations, Portability, Accessibility, Extent of data processing, Time response, Urgency, Currency, Sufficiency, Understandability, Reliability, Extent of Mobility (spatial, temporal, contextual), System support, Responsiveness, and Assurance. We apply these factors to propose a success model for our location-based mobile cardiac telemedicine system.
As-rigid-as-possible shape interpolation

We present an object-space morphing technique that blends the interiors of given two- or three-dimensional shapes rather than their boundaries. The morph is rigid in the sense that local volumes are least-distorting as they vary from their source to target configurations. Given a boundary vertex correspondence, the source and target shapes are decomposed into isomorphic simplicial complexes. For the simplicial complexes, we find a closed-form expression allocating the paths of both boundary and interior vertices from source to target locations as a function of time. Key points are the identification of the optimal simplex morphing and the appropriate definition of an error functional whose minimization defines the paths of the vertices. Each pair of corresponding simplices defines an affine transformation, which is factored into a rotation and a stretching transformation. These local transformations are naturally interpolated over time and serve as the basis for composing a global coherent least-distorting transformation.
THE CONTROL OF THE FALSE DISCOVERY RATE IN MULTIPLE TESTING UNDER DEPENDENCY By

Benjamini and Hochberg suggest that the false discovery rate may be the appropriate error rate to control in many applied multiple testing problems. A simple procedure was given there as an FDR controlling procedure for independent test statistics and was shown to be much more powerful than comparable procedures which control the traditional familywise error rate. We prove that this same procedure also controls the false discovery rate when the test statistics have positive regression dependency on each of the test statistics corresponding to the true null hypotheses. This condition for positive dependency is general enough to cover many problems of practical interest, including the comparisons of many treatments with a single control, multivariate normal test statistics with positive correlation matrix and multivariate t. Furthermore, the test statistics may be discrete, and the tested hypotheses composite without posing special difficulties. For all other forms of dependency, a simple conservative modification of the procedure controls the false discovery rate. Thus the range of problems for which a procedure with proven FDR control can be offered is greatly increased.
Multilabel Text Classification for Automated Tag Suggestion

The increased popularity of tagging during the last few years can be mainly attributed to its embracing by most of the recently thriving user-centric content publishing and management Web 2.0 applications. However, tagging systems have some limitations that have led researchers to develop methods that assist users in the tagging process, by automatically suggesting an appropriate set of tags. We have tried to model the automated tag suggestion problem as a multilabel text classification task in order to participate in the ECML/PKDD 2008 Discovery Challenge.
Full-Duplex Aided User Virtualization for Mobile Edge Computing in 5G Networks

Driven by the increasing demand of intensive computing services and the resource limitation of mobile devices at the edge of mobile networks, mobile edge computing (MEC) is concerned with being an emerging paradigm towards the 5G communications. A main issue of MEC is the coordination of communication, computation, and storage. In this paper, we propose a novel MEC framework with a user virtualization scheme in the software-defined network virtualization cellular network, in which radio resources are virtualized along with computation and storage resources to cooperatively finish MEC services. Besides, we introduce the user virtualization assisted by the full-duplex communication which can extend the radio resource of wireless networks and provide the potential to increase system performance. Moreover, enabled by user virtualization, users can offload the edge computation tasks directly to the MEC server implemented at infrastructure providers or via the virtualized mobile device attributed to different mobile virtual network operators (MVNOs). Under this MEC framework, we formulate the virtual resource allocation as a joint optimization problem. A distributed resource allocation algorithm based on an alternating direction method of multipliers is proposed which can reduce computational complexity and signaling overhead. We evaluate the proposed algorithm through extensive simulations, and the results show that the total utility of MVNOs can be improved significantly which benefited from user virtualization.
A recursive approach to low complexity codes

A bstruciA method is described for constructing long error-correcting codes from one or more shorter error-correcting codes, referred to as subcodes, and a bipartite graph. A graph is shown which specifies carefully chosen subsets of the digits of the new codes that must be codewords in one of the shorter subcodes. Lower bounds to the rate and the minimum distance of the new code are derived in terms of the parameters of the graph and the subcodes. Both the encoders and decoders proposed are shown to take advantage of the code’s explicit decomposition into subcodes to decompose and simplify the associated computational processes. Bounds on the performance of two specific decoding algorithms are established, and the asymptotic growth of the complexity of decoding for two types of codes and decoders is analyzed. The proposed decoders are able to mahe effective use of probabilistic information supplied by the channel receiver, e.g., reliability information, without greatly increasing the number of computations required. It is shown that choosing a transmission order for the digits that is appropriate for the graph and the subcodes can give the code excellent burst-error correction abilities. The construction principles are illustrated by several examples.
Road Extraction From a High Spatial Resolution Remote Sensing Image Based on Richer Convolutional Features

The extraction and vectorization of roads from high spatial resolution remote sensing (HSRRS) images are of great significance to city planning and development. However, significant as they are, it is usually an arduous task to put them into practice because the HSRRS images are often filled with complex ground information. Furthermore, extracted roads may suffer from netsplit or brokenness. This paper thus proposes Richer convolutional features (RCFs)-based road extraction (Road-RCF) as a method which targets these issues. A modified roads sample set and RCF network are applied to generate road probabilities in order to extract initial road information. After the road centerlines extraction by the refinement algorithm, vectorized roads are ultimately extracted. The compared experiment results show that the Road-RCF method produce better road extraction results than the other four state-of-the-art methods, in both quantitative road extraction accuracy metrics and the qualitative visual evaluation. The benefits of this model are threefold. First, the image-to-image network structure of side-output realizes multi-scale and multi-level road feature fusion in order to make a full use of the information from a low level to a high level. Second, according to the deep supervision of the side-output, it guides the learning of the correct road information. Third, after the detection of the road, the road centerlines are vectorized to facilitate the attribute information management and electronic map production. In a word, the proposed Road-RCF method is both practical and meaningful toward updating the geo-information system database.
Extensor mechanism of the fingers: MR imaging-anatomic correlation.

Extension of the fingers is a complex function carried out by simultaneous action of extrinsic and intrinsic muscles, as well as retinacular structures in the dorsum of the wrist, hand, and fingers that support and coordinate the action of the muscles. The extensor mechanism of the fingers is divided into topographic zones, which extend from the forearm to the distal phalanx. Magnetic resonance (MR) imaging shows in detail the musculotendinous and retinacular structures of the extensor apparatus. In the different extensor zones, MR imaging findings are similar to those seen macroscopically in anatomic sections. Understanding of and familiarity with the extensor anatomy of the hand and fingers by the radiologist is crucial for better assessment of pathologic conditions with MR imaging and optimization of this modality as a diagnostic tool. Extensor tendon injuries and tenosynovitis represent clinical situations in which knowledge of this anatomy is useful for the clinical radiologist.
Evaluation of the Spiritual Well-Being Scale in a Sample of Korean Adults

This study explored the psychometric qualities and construct validity of the Spiritual Well-Being Scale (SWBS; Ellison in J Psychol Theol 11:330–340, 1983) using a sample of 470 Korean adults. Two factor analyses, exploratory factor analysis and confirmatory factor analysis, were conducted in order to test the validity of the SWBS. The results of the factor analyses supported the original two-dimensional structure of the SWBS—religious well-being (RWB) and existential well-being (EWB) with method effects associated with negatively worded items. By controlling for method effects, the evaluation of the two-factor structure of SWBS is confirmed with clarity. Further, the differential pattern and magnitude of correlations between the SWB subscales and the religious and psychological variables suggested that two factors of the SWBS were valid for Protestant, Catholic, and religiously unaffiliated groups except Buddhists. The Protestant group scored higher in RWB compared to the Buddhist, Catholic, and unaffiliated groups. The Protestant group scored higher in EWB compared to the unaffiliated groups. Future studies may need to include more Buddhist samples to gain solid evidence for validity of the SWBS on a non-Western religious tradition.