nils-herrmann/clean_scirepeval huggingface dataset:
Authenticated-encryption with associated-data

When a message is transformed into a ciphertext in a way designed to protect both its privacy and authenticity, there may be additional information, such as a packet header, that travels alongside the ciphertext (at least conceptually) and must get authenticated with it. We formalize and investigate this authenticated-encryption with associated-data (AEAD) problem. Though the problem has long been addressed in cryptographic practice, it was never provided a definition or even a name. We do this, and go on to look at efficient solutions for AEAD, both in general and for the authenticated-encryption scheme OCB. For the general setting we study two simple ways to turn an authenticated-encryption scheme that does not support associated-data into one that does: nonce stealing and ciphertext translation. For the case of OCB we construct an AEAD-scheme by combining OCB and the pseudorandom function PMAC, using the same key for both algorithms. We prove that, despite "interaction" between the two schemes when using a common key, the combination is sound. We also consider achieving AEAD by the generic composition of a nonce-based, privacy-only encryption scheme and a pseudorandom function.
Fuzzy supervised classification of remote sensing images

Abstruct-In the conventional remote sensing supervised classification, training information and classification results are represented in a one-pixel-one-class method. Class mixture cannot be taken into consideration in training a classifier and in determining pixels’ membership. The expressive limitation has reduced the classification accuracy level and led to the poor extraction of information. This paper describes a fuzzy supervised classification method in which geographical information is represented as fuzzy sets. The algorithm consists of two major steps: The estimate of fuzzy parameters from fuzzy training data, and afuzzy partition of spectral space. Partial membership of pixels allows component cover classes of mixed pixels to be identified and more accurate statistical parameters to be generated, and, in turn, a higher classification accuracy to be achieved. Results of classifying a Landsat MSS image are presented and their accuracy is analyzed. Remote sensing, fuzzy set theory, image classification, information representation.
Federated Optimization: Distributed Machine Learning for On-Device Intelligence

We introduce a new and increasingly relevant setting for distributed optimization in machine learning, where the data defining the optimization are unevenly distributed over an extremely large number of nodes. The goal is to train a high-quality centralized model. We refer to this setting as Federated Optimization. In this setting, communication efficiency is of the utmost importance and minimizing the number of rounds of communication is the principal goal. A motivating example arises when we keep the training data locally on users’ mobile devices instead of logging it to a data center for training. In federated optimization, the devices are used as compute nodes performing computation on their local data in order to update a global model. We suppose that we have extremely large number of devices in the network — as many as the number of users of a given service, each of which has only a tiny fraction of the total data available. In particular, we expect the number of data points available locally to be much smaller than the number of devices. Additionally, since different users generate data with different patterns, it is reasonable to assume that no device has a representative sample of the overall distribution. We show that existing algorithms are not suitable for this setting, and propose a new algorithm which shows encouraging experimental results for sparse convex problems. This work also sets a path for future research needed in the context of federated optimization.
1D-local binary pattern based feature extraction for classification of epileptic EEG signals

In this paper, an effective approach for the feature extraction of raw Electroencephalogram (EEG) signals by means of one-dimensional local binary pattern (1D-LBP) was presented. For the importance of making the right decision, the proposed method was performed to be able to get better features of the EEG signals. The proposed method was consisted of two stages: feature extraction by 1D-LBP and classification by classifier algorithms with features extracted. On the classification stage, the several machine learning methods were employed to uniform and non-uniform 1D-LBP features. The proposed method was also compared with other existing techniques in the literature to find out benchmark for an epileptic data set. The implementation results showed that the proposed technique could acquire high accuracy in classification of epileptic EEG signals. Also, the present paper is an attempt to develop a general-purpose feature extraction scheme, which can be utilized to extract features from different categories of EEG signals.
Learning Deconvolution Network for Semantic Segmentation

We propose a novel semantic segmentation algorithm by learning a deep deconvolution network. We learn the network on top of the convolutional layers adopted from VGG 16-layer net. The deconvolution network is composed of deconvolution and unpooling layers, which identify pixelwise class labels and predict segmentation masks. We apply the trained network to each proposal in an input image, and construct the final semantic segmentation map by combining the results from all proposals in a simple manner. The proposed algorithm mitigates the limitations of the existing methods based on fully convolutional networks by integrating deep deconvolution network and proposal-wise prediction, our segmentation method typically identifies detailed structures and handles objects in multiple scales naturally. Our network demonstrates outstanding performance in PASCAL VOC 2012 dataset, and we achieve the best accuracy (72.5%) among the methods trained without using Microsoft COCO dataset through ensemble with the fully convolutional network.
Catch the moment: maintaining closed frequent itemsets over a data stream sliding window

This paper considers the problem of mining closed frequent itemsets over a data stream sliding window using limited memory space. We design a synopsis data structure to monitor transactions in the sliding window so that we can output the current closed frequent itemsets at any time. Due to time and memory constraints, the synopsis data structure cannot monitor all possible itemsets. However, monitoring only frequent itemsets will make it impossible to detect new itemsets when they become frequent. In this paper, we introduce a compact data structure, the closed enumeration tree (CET), to maintain a dynamically selected set of itemsets over a sliding window. The selected itemsets contain a boundary between closed frequent itemsets and the rest of the itemsets. Concept drifts in a data stream are reflected by boundary movements in the CET. In other words, a status change of any itemset (e.g., from non-frequent to frequent) must occur through the boundary. Because the boundary is relatively stable, the cost of mining closed frequent itemsets over a sliding window is dramatically reduced to that of mining transactions that can possibly cause boundary movements in the CET. Our experiments show that our algorithm performs much better than representative algorithms for the sate-of-the-art approaches.
Go with the flow : optimal AUV path planning in coastal environments

This paper describes a novel optimum path planning strategy for long duration AUV operations in environments with time-varying ocean currents. These currents can exceed the maximum achievable speed of the AUV, as well as temporally expose obstacles. In contrast to most other path planning strategies, paths have to be defined in time as well as space. The solution described here exploits ocean currents to achieve mission goals with minimal energy expenditure, or a tradeoff between mission time and required energy. The proposed algorithm uses a parallel swarm search as a means to reduce the susceptibility to large local minima on the complex cost surface. The performance of the optimisation algorithms is evaluated in simulation and experimentally with the Starbug AUV using a validated ocean model of Brisbane’s Moreton Bay.
A neural network approach for attenuation relationships : An application using strong ground motion data from Turkey

This paper presents an application of neural network approach for the prediction of peak ground acceleration (PGA) using the strong motion data from Turkey, as a soft computing technique to remove uncertainties in attenuation equations. A training algorithm based on the Fletcher–Reeves conjugate gradient back-propagation was developed and employed for three sample sets of strong ground motion. The input variables in the constructed artificial neural network (ANN) model were the magnitude, the source-to-site distance and the site conditions, and the output was the PGA. The generalization capability of ANN algorithms was tested with the same training data. To demonstrate the authenticity of this approach, the network predictions were compared with the ones from regressions for the corresponding attenuation equations. The results indicated that the fitting between the predicted PGA values by the networks and the observed ones yielded high correlation coefficients (R). In addition, comparisons of the correlations by the ANN and the regression method showed that the ANN approach performed better than the regression. Even though the developed ANN models suffered from optimal configuration about the generalization capability, they can be conservatively used to well understand the influence of input parameters for the PGA predictions.
Using automatic keyword extraction to detect off-topic posts in online discussion boards

Online discussions boards represent a rich repository of knowledge organized in a collection of user generated content. These conversational cyberspaces allow users to express opinions, ideas and pose questions and answers without imposing strict limitations about the content. This freedom, in turn, creates an environment in which discussions are not bounded and often stray from the initial topic being discussed. In this paper we focus on approaches to assess the relevance of posts to a thread and detecting when discussions have been steered off-topic. A set of metrics estimating the level of novelty in online discussion posts are presented. These metrics are based on topical estimation and contextual similarity between posts within a given thread. The metrics are aggregated to rank posts based on the degree of relevance they maintain. The aggregation scheme is data-dependent and is normalized relative to the post length.
Combining control-flow integrity and static analysis for efficient and validated data sandboxing

In many software attacks, inducing an illegal control-flow transfer in the target system is one common step. Control-Flow Integrity (CFI) protects a software system by enforcing a pre-determined control-flow graph. In addition to providing strong security, CFI enables static analysis on low-level code. This paper evaluates whether CFI-enabled static analysis can help build efficient and validated data sandboxing. Previous systems generally sandbox memory writes for integrity, but avoid protecting confidentiality due to the high overhead of sandboxing memory reads. To reduce overhead, we have implemented a series of optimizations that remove sandboxing instructions if they are proven unnecessary by static analysis. On top of CFI, our system adds only 2.7% runtime overhead on SPECint2000 for sandboxing memory writes and adds modest 19% for sandboxing both reads and writes. We have also built a principled data-sandboxing verifier based on range analysis. The verifier checks the safety of the results of the optimizer, which removes the need to trust the rewriter and optimizer. Our results show that the combination of CFI and static analysis has the potential of bringing down the cost of general inlined reference monitors, while maintaining strong security.
Variants of Jump Flooding Algorithm for Computing Discrete Voronoi Diagrams

Jump flooding algorithm (JFA) is an interesting way to utilize the graphics processing unit to efficiently compute Voronoi diagrams and distance transforms in 2D discrete space. This paper presents three novel variants of JFA. They focus on different aspects of JFA: the first variant can further reduce the errors of JFA; the second variant can greatly increase the speed of JFA; and the third variant enables JFA to compute Voronoi diagrams in 3D space in a slice-by-slice manner, without a high end graphics processing unit. These variants are orthogonal to each other. In other words, it is possible to combine any two or all of them together.
Internet Gambling: Issues, Concerns, and Recommendations

The influence of technology in the field of gambling innovation continues to grow at a rapid pace. After a brief overview of gambling technologies and deregulation issues, this review examines the impact of technology on gambling by highlighting salient factors in the rise of Internet gambling (i.e., accessibility, affordability, anonymity, convenience, escape immersion/dissociation, disinhibition, event frequency, asociability, interactivity, and simulation). The paper also examines other factors in relation to Internet gambling including the relationship between Internet addiction and Internet gambling addiction. The paper ends by overviewing some of the social issues surrounding Internet gambling (i.e., protection of the vulnerable, Internet gambling in the workplace, electronic cash, and unscrupulous operators). Recommendations for Internet gambling operators are also provided.
Decoding IEEE 802.11a/g/p OFDM in software using GNU radio

We just released an Open Source receiver that is able to decode IEEE 802.11a/g/p Orthogonal Frequency Division Multiplexing (OFDM) frames in software. This is the first Software Defined Radio (SDR) based OFDM receiver supporting channel bandwidths up to 20MHz that is not relying on additional FPGA code. Our receiver comprises all layers from the physical up to decoding the MAC packet and extracting the payload of IEEE 802.11a/g/p frames. In our demonstration, visitors can interact live with the receiver while it is decoding frames that are sent over the air. The impact of moving the antennas and changing the settings are displayed live in time and frequency domain. Furthermore, the decoded frames are fed to Wireshark where the WiFi traffic can be further investigated. It is possible to access and visualize the data in every decoding step from the raw samples, the autocorrelation used for frame detection, the subcarriers before and after equalization, up to the decoded MAC packets. The receiver is completely Open Source and represents one step towards experimental research with SDR.
Scanning micromirrors : An overview

An overview of the current state of the art in scanning micromirror technology for switching, imaging, and beam steering applications is presented. The requirements that drive the design and fabrication technology are covered. Electrostatic, electromagnetic, and magnetic actuation techniques are discussed as well as the motivation toward combdrive configurations from parallel plate configurations for large diameter (mm range) scanners. Suitability of surface micromachining, bulk micromachining, and silicon on insulator (SOI) micromachining technology is presented in the context of the length scale and performance for given scanner applications.
A Tensor-Based Approach for Big Data Representation and Dimensionality Reduction

Variety and veracity are two distinct characteristics of large-scale and heterogeneous data. It has been a great challenge to efficiently represent and process big data with a unified scheme. In this paper, a unified tensor model is proposed to represent the unstructured, semistructured, and structured data. With tensor extension operator, various types of data are represented as subtensors and then are merged to a unified tensor. In order to extract the core tensor which is small but contains valuable information, an incremental high order singular value decomposition (IHOSVD) method is presented. By recursively applying the incremental matrix decomposition algorithm, IHOSVD is able to update the orthogonal bases and compute the new core tensor. Analyzes in terms of time complexity, memory usage, and approximation accuracy of the proposed method are provided in this paper. A case study illustrates that approximate data reconstructed from the core set containing 18% elements can guarantee 93% accuracy in general. Theoretical analyzes and experimental results demonstrate that the proposed unified tensor model and IHOSVD method are efficient for big data representation and dimensionality reduction.
Large-scale sparse logistic regression

Logistic Regression is a well-known classification method that has been used widely in many applications of data mining, machine learning, computer vision, and bioinformatics. Sparse logistic regression embeds feature selection in the classification framework using the l1-norm regularization, and is attractive in many applications involving high-dimensional data. In this paper, we propose Lassplore for solving large-scale sparse logistic regression. Specifically, we formulate the problem as the l1-ball constrained smooth convex optimization, and propose to solve the problem using the Nesterov's method, an optimal first-order black-box method for smooth convex optimization. One of the critical issues in the use of the Nesterov's method is the estimation of the step size at each of the optimization iterations. Previous approaches either applies the constant step size which assumes that the Lipschitz gradient is known in advance, or requires a sequence of decreasing step size which leads to slow convergence in practice. In this paper, we propose an adaptive line search scheme which allows to tune the step size adaptively and meanwhile guarantees the optimal convergence rate. Empirical comparisons with several state-of-the-art algorithms demonstrate the efficiency of the proposed Lassplore algorithm for large-scale problems.
A comparison of the noise sensitivity of nine QRS detection algorithms

The noise sensitivities of nine different QRS detection algorithms were measured for a normal, single-channel, lead-II, synthesized ECG corrupted with five different types of synthesized noise: electromyographic interference, 60-Hz power line interference, baseline drift due to respiration, abrupt baseline shift, and a composite noise constructed from all of the other noise types. The percentage of QRS complexes detected, the number of false positives, and the detection delay were measured. None of the algorithms were able to detect all QRS complexes without any false positives for all of the noise types at the highest noise level. Algorithms based on amplitude and slope had the highest performance for EMG-corrupted ECG. An algorithm using a digital filter had the best performance for the composite-noise-corrupted data.<<ETX>>
Mindfulness : A Proposed Operational Definition

Scott R. Bishop, University of Toronto Mark Lau, University of Toronto Shauna Shapiro, VA Palo Alto Health Care System Linda Carlson, University of Calgary Nicole D. Anderson, University of Toronto James Carmody, University of Massachusetts Medical School Zindel V. Segal, University of Toronto Susan Abbey, University of Toronto Michael Speca, University of Calgary Drew Velting, Columbia University Gerald Devins, University of Toronto
Analysis of preprocessing methods on classification of Turkish texts

Preprocessing is an important task and critical step in information retrieval and text mining. The objective of this study is to analyze the effect of preprocessing methods in text classification on Turkish texts. We compiled two large datasets from Turkish newspapers using a crawler. On these compiled data sets and using two additional datasets, we perform a detailed analysis of preprocessing methods such as stemming, stopword filtering and word weighting for Turkish text classification on several different Turkish datasets. We report the results of extensive experiments.
Human Pose Estimation from Monocular Images: A Comprehensive Survey

Human pose estimation refers to the estimation of the location of body parts and how they are connected in an image. Human pose estimation from monocular images has wide applications (e.g., image indexing). Several surveys on human pose estimation can be found in the literature, but they focus on a certain category; for example, model-based approaches or human motion analysis, etc. As far as we know, an overall review of this problem domain has yet to be provided. Furthermore, recent advancements based on deep learning have brought novel algorithms for this problem. In this paper, a comprehensive survey of human pose estimation from monocular images is carried out including milestone works and recent advancements. Based on one standard pipeline for the solution of computer vision problems, this survey splits the problem into several modules: feature extraction and description, human body models, and modeling methods. Problem modeling methods are approached based on two means of categorization in this survey. One way to categorize includes top-down and bottom-up methods, and another way includes generative and discriminative methods. Considering the fact that one direct application of human pose estimation is to provide initialization for automatic video surveillance, there are additional sections for motion-related methods in all modules: motion features, motion models, and motion-based methods. Finally, the paper also collects 26 publicly available data sets for validation and provides error measurement methods that are frequently used.
"Why Should I Trust You?": Explaining the Predictions of Any Classifier

Despite widespread adoption, machine learning models remain mostly black boxes. Understanding the reasons behind predictions is, however, quite important in assessing trust, which is fundamental if one plans to take action based on a prediction, or when choosing whether to deploy a new model. Such understanding also provides insights into the model, which can be used to transform an untrustworthy model or prediction into a trustworthy one.
 In this work, we propose LIME, a novel explanation technique that explains the predictions of any classifier in an interpretable and faithful manner, by learning an interpretable model locally varound the prediction. We also propose a method to explain models by presenting representative individual predictions and their explanations in a non-redundant way, framing the task as a submodular optimization problem. We demonstrate the flexibility of these methods by explaining different models for text (e.g. random forests) and image classification (e.g. neural networks). We show the utility of explanations via novel experiments, both simulated and with human subjects, on various scenarios that require trust: deciding if one should trust a prediction, choosing between models, improving an untrustworthy classifier, and identifying why a classifier should not be trusted.
A novel microstrip line backward directional coupler with high directivity

A uni-planar backward directional couplers is analyzed and designed. Microstrip parallel coupled lines and asymmetrical delay lines make multi-sectioned coupler which enhances the directivity. In this paper, 20 dB multi-sectioned couplers with single and double delay lines are designed and measured to show the validation of analysis. The coupler with two delay lines has the directivity over 30 dB and the fractional bandwidth of 30% at the center frequency of 1.8 GHz.