nils-herrmann/clean_scirepeval huggingface dataset:
Theory of Outlier Ensembles

Outlier detection is an unsupervised problem, in which labels are not available with data records . As a result, it is generally more challenging to design ensemble analysis algorithms for outlier detection. In particular, methods that require the use of labels in intermediate steps of the algorithm cannot be generalized to outlier detection. For example, in the case of boosting, the classifier algorithm needs to be evaluated in the intermediate steps of the algorithm with the use of training-data labels. Such methods are generally not possible in the case of outlier analysis. As discussed in , there are unique reasons why ensemble analysis is generally more difficult in the case of outlier analysis as compared to classification. In spite of the unsupervised nature of outlier ensemble analysis, we show that the theoretical foundations of outlier analysis and classification are surprisingly similar. A number of useful discussions on the theory of classification ensembles may be found in [27, 29, 33]. Further explanations on the use of the bias-variance decomposition in different types of classifiers such as neural networks, support vector machines, and rule-based classifiers are discussed in [17, 30, 31]. It is noteworthy that the biasvariance decomposition is often used in customized ways for different types of base classifiers and combination methods; this general principle is also true in outlier detection. Several arguments have recently been proposed on the theory explaining the accuracy improvements of outlier ensembles. In some cases, incorrect new arguments (such as those in [32]) are proposed to justify experimental results that can be explained by well-known ideas, and an artificial distinction is made between the theory of classification ensembles and outlier ensembles. A recent paper
15 Planar Antenna Technology for mm-Wave Automotive Radar , Sensing

Planar antennas are common components in sensing applications due to their low cost, low profile and simple integration with systems. They can be used commonly at frequencies as high as 77 GHz and above, for example in automotive sensing. Also, new ultra-wideband communication systems operating around 60 GHz will heavily rely on planar antennas due to their unique properties. One special advantage of planar elements is that they can easily form array structures combining very simple elements, like microstrip patches. Also phased and conformal arrays can be built. Due to all these characteristics, planar antennas are very good candidates for building front ends for mm-wave applications in radar, sensing or communications. For some applications, which are either established today or which will become commercial in a few years, general requirements can be given. In addition to automotive radar, this mainly applies to 60 GHz ultra-broadband wireless indoor communications. Microwave imaging at 94 GHz and above is still much under research, as well as other sensing applications in the millimeter-wave region above 100 GHz. Prominent frequency bands are in the 122 and 140 GHz range1. Typical antenna requirements are multi-beam or scanning capability, high gain up to 30..36 dBi and moderate to low sidelobes. In monopulse radar systems, the sidelobes may be included in the angle determination scheme, then the sidelobe level requirements are rather relaxed. Loss is generally an important issue. Comparing a planar approach to a commercially available dielectric lens, the planar antenna exhibits significantly higher losses, especially if the beamforming device is included. The antenna efficiency of 77 GHz planar array columns is roughly 50%. These losses are caused by dielectric losses and conductor losses. To attain reliable predictions of the losses in full-wave simulations, care has to be taken in the
Spotlight SAR data focusing based on a two-step processing approach

We present a new spotlight SAR data-focusing algorithm based on a two-step processing strategy that combines the advantages of two commonly adopted processing approaches: the efficiency of SPECAN algorithms and the precision of stripmap focusing techniques. The first step of the proposed algorithm implements a linear and space-invariant azimuth filtering that is carried out via a deramping-based technique representing a simplified version of the SPECAN approach. This operation allows us to perform a bulk azimuth raw data compression and to achieve a pixel spacing smaller than (or equal to) the expected azimuth resolution of the fully focused image. Thus, the azimuth spectral folding phenomenon, typically affecting the spotlight data, is overcome, and the space-variant characteristics of the stripmap system transfer function are preserved. Accordingly, the residual and precise focusing of the SAR data is achieved by applying a conventional stripmap processing procedure requiring a minor modification and implemented in the frequency domain. The extension of the proposed technique to the case of high bandwidth transmitted chirp signals is also discussed. Experiments carried out on real and simulated data confirm the validity of the presented approach, which is mainly focused on spaceborne systems.
Robust, Real-Time 3D Tracking of Multiple Objects with Similar Appearances

This paper proposes a novel method for tracking multiple moving objects and recovering their three-dimensional (3D) models separately using multiple calibrated cameras. For robustly tracking objects with similar appearances, the proposed method uses geometric information regarding 3D scene structure rather than appearance. A major limitation of previous techniques is foreground confusion, in which the shapes of objects and/or ghosting artifacts are ignored and are hence not appropriately specified in foreground regions. To overcome this limitation, our method classifies foreground voxels into targets (objects and artifacts) in each frame using a novel, probabilistic two-stage framework. This is accomplished by step-wise application of a track graph describing how targets interact and the maximum a posteriori expectation-maximization algorithm for the estimation of target parameters. We introduce mixture models with semiparametric component distributions regarding 3D target shapes. In order to not confuse artifacts with objects of interest, we automatically detect and track artifacts based on a closed-world assumption. Experimental results show that our method outperforms state-of-the-art trackers on seven public sequences while achieving real-time performance.
What can quantum theory bring to information retrieval

The probabilistic formalism of quantum physics is said to provide a sound basis for building a principled information retrieval framework. Such a framework can be based on the notion of information need vector spaces where events, such as document relevance or observed user interactions, correspond to subspaces. As in quantum theory, a probability distribution over these subspaces is defined through weighted sets of state vectors (density operators), and used to represent the current view of the retrieval system on the user information need. Tensor spaces can be used to capture different aspects of information needs. Our evaluation shows that the framework can lead to acceptable performance in an ad-hoc retrieval task. Going beyond this, we discuss the potential of the framework for three active challenges in information retrieval, namely, interaction, novelty and diversity.
3D scattered data approximation with adaptive compactly supported radial basis functions

We develop an adaptive RBF fitting procedure for a high quality approximation of a set of points scattered over a piecewise smooth surface. We use compactly supported RBFs whose centers are randomly chosen from the points. The randomness is controlled by the point density and surface geometry. For each RBF, its support size is chosen adoptively according to surface geometry at a vicinity of the RBF center. All these lead to a noise-robust high quality approximation of the set. We also adapt our basic technique for shape reconstruction from registered range scans by taking into account measurement confidences. Finally, an interesting link between our RBF fitting procedure and partition of unity approximations is established and discussed.
LSTM-Based ECG Classification for Continuous Monitoring on Personal Wearable Devices

A novel ECG classification algorithm is proposed for continuous cardiac monitoring on wearable devices with limited processing capacity. The proposed solution employs a novel architecture consisting of wavelet transform and multiple LSTM recurrent neural networks . Experimental evaluations show superior ECG classification performance compared to previous works. Measurements on different hardware platforms show the proposed algorithm meets timing requirements for continuous and real-time execution on wearable devices. In contrast to many compute-intensive deep-learning based approaches, the proposed algorithm is lightweight, and therefore, brings continuous monitoring with accurate LSTM-based ECG classification to wearable devices. The source code is available online .
An information theoretic approach to improve semantic similarity assessments across multiple ontologies

Semantic similarity has become, in recent years, the backbone of numerous knowledge-based applications dealing with textual data. From the different methods and paradigms proposed to assess semantic similarity, ontology-based measures and, more specifically, those based on quantifying the Information Content (IC) of concepts are the most widespread solutions due to their high accuracy. However, these measures were designed to exploit a single ontology. They thus cannot be leveraged in many contexts in which multiple knowledge bases are considered. In this paper, we propose a new approach to achieve accurate IC-based similarity assessments for concept pairs spread throughout several ontologies. Based on Information Theory, our method defines a strategy to accurately measure the degree of commonality between concepts belonging to different ontologies—this is the cornerstone for estimating their semantic similarity. Our approach therefore enables classic IC-based measures to be directly applied in a multiple ontology setting. An empirical evaluation, based on well-established benchmarks and ontologies related to the bio-medical domain, illustrates the accuracy of our approach, and demonstrates that similarity estimations provided by our approach are significantly more correlated with human ratings of similarity than those obtained via related works. Semantic similarity is a pillar of text understanding since it quantifies the degree of resemblance between the meanings of textual terms. In recent years, due to the marked increase in electronically available textual data, considerable effort has been focused on defining semantic measures, which have been extensively applied in various contexts such as information retrieval [54], information extraction [56], word sense disambiguation [32], data clustering [4,27], data privacy [6,45–47] and biomedicine (e.g. protein classification and interaction , chemical entity identification [18], identification of the communalities between brain data and linguistic data , etc.) to cite a few. Different knowledge sources have been used to facilitate the semantic similarity calculus, including: measures relying solely on textual corpora to estimate similarity from the degree of co-occurrence of terms [11], and measures involving structured knowledge bases such as ontologies [36,37,44], whereby the similarity calculus is based on the analysis of semantic relationships modelled between concepts. Compared to corpora-based approaches, ontology-based measures have a dual benefit: concept meanings can be
Statistics of Patch Offsets for Image Completion

Image completion involves filling missing parts in images. In this paper we address this problem through the statistics of patch offsets. We observe that if we match similar patches in the image and obtain their offsets (relative positions), the statistics of these offsets are sparsely distributed. We further observe that a few dominant offsets provide reliable information for completing the image. With these offsets we fill the missing region by combining a stack of shifted images via optimization. A variety of experiments show that our method yields generally better results and is faster than existing state-of-the-art methods.
Multi-Task Active Learning for Linguistic Annotations

We extend the classical single-task active learning (AL) approach. In the multi-task active learning (MTAL) paradigm, we select examples for several annotation tasks rather than for a single one as usually done in the context of AL. We introduce two MTAL metaprotocols, alternating selection and rank combination, and propose a method to implement them in practice. We experiment with a twotask annotation scenario that includes named entity and syntactic parse tree annotations on three different corpora. MTAL outperforms random selection and a stronger baseline, onesided example selection, in which one task is pursued using AL and the selected examples are provided also to the other task.
Deformable Convolutional Networks

Convolutional neural networks (CNNs) are inherently limited to model geometric transformations due to the fixed geometric structures in their building modules. In this work, we introduce two new modules to enhance the transformation modeling capability of CNNs, namely, deformable convolution and deformable RoI pooling. Both are based on the idea of augmenting the spatial sampling locations in the modules with additional offsets and learning the offsets from the target tasks, without additional supervision. The new modules can readily replace their plain counterparts in existing CNNs and can be easily trained end-to-end by standard back-propagation, giving rise to deformable convolutional networks. Extensive experiments validate the performance of our approach. For the first time, we show that learning dense spatial transformation in deep CNNs is effective for sophisticated vision tasks such as object detection and semantic segmentation. The code is released at https://github.com/msracver/Deformable-ConvNets.
Augmented reality in education: a meta-review and cross-media analysis

Augmented reality (AR) is an educational medium increasingly accessible to young users such as elementary school and high school students. Although previous research has shown that AR systems have the potential to improve student learning, the educational community remains unclear regarding the educational usefulness of AR and regarding contexts in which this technology is more effective than other educational mediums. This paper addresses these topics by analyzing 26 publications that have previously compared student learning in AR versus non-AR applications. It identifies a list of positive and negative impacts of AR experiences on student learning and highlights factors that are potentially underlying these effects. This set of factors is argued to cause differences in educational effectiveness between AR and other media. Furthermore, based on the analysis, the paper presents a heuristic questionnaire generated for judging the educational potential of AR experiences.
Deep Edge-Aware Saliency Detection

There has been profound progress in visual saliency thanks to the deep learning architectures, however, there still exist three major challenges that hinder the detection performance for scenes with complex compositions, multiple salient objects, and salient objects of diverse scales. In particular, output maps of the existing methods remain low in spatial resolution causing blurred edges due to the stride and pooling operations, networks often neglect descriptive statistical and handcrafted priors that have potential to complement saliency detection results, and deep features at different layers stay mainly desolate waiting to be effectively fused to handle multi-scale salient objects. In this paper, we tackle these issues by a new fully convolutional neural network that jointly learns salient edges and saliency labels in an end-to-end fashion. Our framework first employs convolutional layers that reformulate the detection task as a dense labeling problem, then integrates handcrafted saliency features in a hierarchical manner into lower and higher levels of the deep network to leverage available information for multi-scale response, and finally refines the saliency map through dilated convolutions by imposing context. In this way, the salient edge priors are efficiently incorporated and the output resolution is significantly improved while keeping the memory requirements low, leading to cleaner and sharper object boundaries. Extensive experimental analyses on ten benchmarks demonstrate that our framework achieves consistently superior performance and attains robustness for complex scenes in comparison to the very recent state-of-the-art approaches.
Efficient Learning for Undirected Topic Models

Replicated Softmax model, a well-known undirected topic model, is powerful in extracting semantic representations of documents. Traditional learning strategies such as Contrastive Divergence are very inefficient. This paper provides a novel estimator to speed up the learning based on Noise Contrastive Estimate, extended for documents of variant lengths and weighted inputs. Experiments on two benchmarks show that the new estimator achieves great learning efficiency and high accuracy on document retrieval and classification.
Benchmarking of Document Image Analysis Tasks for Palm Leaf Manuscripts from Southeast Asia

This paper presents a comprehensive test of the principal tasks in document image analysis (DIA), starting with binarization, text line segmentation, and isolated character/glyph recognition, and continuing on to word recognition and transliteration for a new and challenging collection of palm leaf manuscripts from Southeast Asia. This research presents and is performed on a complete dataset collection of Southeast Asian palm leaf manuscripts. It contains three different scripts: Khmer script from Cambodia, and Balinese script and Sundanese script from Indonesia. The binarization task is evaluated on many methods up to the latest in some binarization competitions. The seam carving method is evaluated for the text line segmentation task, compared to a recently new text line segmentation method for palm leaf manuscripts. For the isolated character/glyph recognition task, the evaluation is reported from the handcrafted feature extraction method, the neural network with unsupervised learning feature, and the Convolutional Neural Network (CNN) based method. Finally, the Recurrent Neural Network-Long Short-Term Memory (RNN-LSTM) based method is used to analyze the word recognition and transliteration task for the palm leaf manuscripts. The results from all experiments provide the latest findings and a quantitative benchmark for palm leaf manuscripts analysis for researchers in the DIA community.
Image-based phenotyping of plant disease symptoms

Plant diseases cause significant reductions in agricultural productivity worldwide. Disease symptoms have deleterious effects on the growth and development of crop plants, limiting yields and making agricultural products unfit for consumption. For many plant-pathogen systems, we lack knowledge of the physiological mechanisms that link pathogen infection and the production of disease symptoms in the host. A variety of quantitative high-throughput image-based methods for phenotyping plant growth and development are currently being developed. These methods range from detailed analysis of a single plant over time to broad assessment of the crop canopy for thousands of plants in a field and employ a wide variety of imaging technologies. Application of these methods to the study of plant disease offers the ability to study quantitatively how host physiology is altered by pathogen infection. These approaches have the potential to provide insight into the physiological mechanisms underlying disease symptom development. Furthermore, imaging techniques that detect the electromagnetic spectrum outside of visible light allow us to quantify disease symptoms that are not visible by eye, increasing the range of symptoms we can observe and potentially allowing for earlier and more thorough symptom detection. In this review, we summarize current progress in plant disease phenotyping and suggest future directions that will accelerate the development of resistant crop varieties.
Variational Inference for the Nested Chinese Restaurant Process

The nested Chinese restaurant process (nCRP) is a powerful nonparametric Bayesian model for learning tree-based hierarchies from data. Since its posterior distribution is intractable, current inference methods have all relied on MCMC sampling. In this paper, we develop an alternative inference technique based on variational methods. To employ variational methods, we derive a tree-based stick-breaking construction of the nCRP mixture model, and a novel variational algorithm that efficiently explores a posterior over a large set of combinatorial structures. We demonstrate the use of this approach for text and hand written digits modeling, where we show we can adapt the nCRP to continuous data as well.
Getting to know your Card: Reverse-Engineering the Smart-Card Application Protocol Data Unit

Smart-cards are considered to be one of the most secure, tamper-resistant, and trusted devices for implementing confidential operations, such as authentication, key management, encryption and decryption for financial, communication, security and data management purposes. The commonly used RSA PKCS#11 standard defines the Application Programming Interface for cryptographic devices such as smart-cards. Though there has been work on formally verifying the correctness of the implementation of PKCS#11 in the API level, little attention has been paid to the low-level cryptographic protocols that implement it.
 We present REPROVE, the first automated system that reverse-engineers the low-level communication between a smart-card and a reader, deduces the card's functionality and translates PKCS#11 cryptographic functions into communication steps. REPROVE analyzes both standard-conforming and proprietary implementations, and does not require access to the card. To the best of our knowledge, REPROVE is the first system to address proprietary implementations and the only system that maps cryptographic functions to communication steps and on-card operations. We have evaluated REPROVE on five commercially available smart-cards and we show how essential functions to gain access to the card's private objects and perform cryptographic functions can be compromised through reverse-engineering traces of the low-level communication.