nils-herrmann/clean_scirepeval huggingface dataset:
Face Detection : Histogram of Oriented Gradients and Bag of Feature Method

Face detection has been one of the most studied topics in computer vision literature; so many algorithms have been developed with different approaches to overcome some detection problems such as occlusion, illumination condition, scale, among others. Histograms of Oriented Gradients are an effective descriptor for object recognition and detection. These descriptors are powerful to detect faces with occlusions, pose and illumination changes because they are extracted in a regular grid. We calculate and vector quantizes into different codewords each descriptor and then we construct histograms of this codeword distribution that represent the face image. Finally, a set of experiments are presented to analyze the performance of this method.
The illusion of external agency.

People typically underestimate their capacity to generate satisfaction with future outcomes. When people experience such self-generated satisfaction, they may mistakenly conclude that it was caused by an influential, insightful, and benevolent external agent. In three laboratory experiments, participants who were allowed to generate satisfaction with their outcomes were especially likely to conclude that an external agent had subliminally influenced their choice of partners (Study 1), had insight into their musical preferences (Study 2), and had benevolent intentions when giving them a stuffed animal (Study 3). These results suggest that belief in omniscient, omnipotent, and benevolent external agents, such as God, may derive in part from people's failure to recognize that they have generated their own satisfaction.
A Fixed-Point Model for Pancreas Segmentation in Abdominal CT Scans

Deep neural networks have been widely adopted for automatic organ segmentation from abdominal CT scans. However, the segmentation accuracy of some small organs (e.g., the pancreas) is sometimes below satisfaction, arguably because deep networks are easily disrupted by the complex and variable background regions which occupies a large fraction of the input volume. In this paper, we formulate this problem into a fixed-point model which uses a predicted segmentation mask to shrink the input region. This is motivated by the fact that a smaller input region often leads to more accurate segmentation. In the training process, we use the ground-truth annotation to generate accurate input regions and optimize network weights. On the testing stage, we fix the network parameters and update the segmentation results in an iterative manner. We evaluate our approach on the NIH pancreas segmentation dataset, and outperform the state-of-the-art by more than 4%, measured by the average Dice-Sørensen Coefficient (DSC). In addition, we report 62.43% DSC in the worst case, which guarantees the reliability of our approach in clinical applications.
NISP: Pruning Networks Using Neuron Importance Score Propagation

To reduce the significant redundancy in deep Convolutional Neural Networks (CNNs), most existing methods prune neurons by only considering the statistics of an individual layer or two consecutive layers (e.g., prune one layer to minimize the reconstruction error of the next layer), ignoring the effect of error propagation in deep networks. In contrast, we argue that for a pruned network to retain its predictive power, it is essential to prune neurons in the entire neuron network jointly based on a unified goal: minimizing the reconstruction error of important responses in the "final response layer" (FRL), which is the second-to-last layer before classification. Specifically, we apply feature ranking techniques to measure the importance of each neuron in the FRL, formulate network pruning as a binary integer optimization problem, and derive a closed-form solution to it for pruning neurons in earlier layers. Based on our theoretical analysis, we propose the Neuron Importance Score Propagation (NISP) algorithm to propagate the importance scores of final responses to every neuron in the network. The CNN is pruned by removing neurons with least importance, and it is then fine-tuned to recover its predictive power. NISP is evaluated on several datasets with multiple CNN models and demonstrated to achieve significant acceleration and compression with negligible accuracy loss.
Efficient training of neural nets for nonlinear adaptive filtering using a recursive Levenberg-Marquardt algorithm

The Levenberg—Marquardt algorithm is often superior to other training algorithms in off-line applications. This motivates the proposal of using a recursive version of the algorithm for on-line training of neural nets for nonlinear adaptive filtering. The performance of the suggested algorithm is compared with other alternative recursive algorithms, such as the recursive version of the off-line steepest-descent and Gauss—Newton algorithms. The advantages and disadvantages of the different algorithms are pointed out. The algorithms are tested on some examples, and it is shown that generally, the recursive Levenberg—Marquardt algorithm has better convergence properties than the other algorithms.
On the Benefits of Convolutional Neural Network Combinations in Offline Handwriting Recognition

In this paper, we elaborate the advantages of combining two neural network methodologies, convolutional neural networks (CNN) and long short-term memory (LSTM) recurrent neural networks, with the framework of hybrid hidden Markov models (HMM) for recognizing offline handwriting text. CNNs employ shift-invariant filters to generate discriminative features within neural networks. We show that CNNs are powerful tools to extract general purpose features that even work well for unknown classes. We evaluate our system on a Chinese handwritten text database and provide a GPU-based implementation that can be used to reproduce the experiments. All experiments were conducted with RWTH OCR, an open-source system developed at our institute.
A committee of neural networks for traffic sign classification

We describe the approach that won the preliminary phase of the German traffic sign recognition benchmark with a better-than-human recognition rate of 98.98%.We obtain an even better recognition rate of 99.15% by further training the nets. Our fast, fully parameterizable GPU implementation of a Convolutional Neural Network does not require careful design of pre-wired feature extractors, which are rather learned in a supervised way. A CNN/MLP committee further boosts recognition performance.
Workflows and e-Science: An overview of workflow system features and capabilities

Scientific workflow systems have become a necessary tool for many applications, enabling the composition and execution of complex analysis on distributed resources. Today there are many workflow systems, often with overlapping functionality. A key issue for potential users of workflow systems is the need to be able to compare the capabilities of the various available tools. There can be confusion about system functionality and the tools are often selected without a proper functional analysis. In this paper we extract a taxonomy of features from the way scientists make use of existing workflow systems and we illustrate this feature set by providing some examples taken from existing workflow systems. The taxonomy provides end users with a mechanism by which they can assess the suitability of workflow in general and how they might use these features to make an informed choice about which workflow system would be a good choice for their particular application.
A Decade of System Justification Theory : Accumulated Evidence of Conscious and Unconscious Bolstering of the Status Quo

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Ultrasound image segmentation: a survey

This paper reviews ultrasound segmentation methods, in a broad sense, focusing on techniques developed for medical B-mode ultrasound images. First, we present a review of articles by clinical application to highlight the approaches that have been investigated and degree of validation that has been done in different clinical domains. Then, we present a classification of methodology in terms of use of prior information. We conclude by selecting ten papers which have presented original ideas that have demonstrated particular clinical usefulness or potential specific to the ultrasound segmentation problem
Frequency Domain Enhancement Algorithm Based on Log-Gabor Filter in FFT Domain By Mrs

Minutiae extraction is one of the most important steps for an Automatic Identification and Authentication Systems. Minutiae are the local fingerprint patterns mostly in the form of terminations and bifurcations. True minutiae are needed for further process. Those true minutiae are extracted only from a good quality and better enhanced image. To achieve this, we propose a frequency domain enhancement algorithm based on the Log-Gabor Filtering Technique on the Fast Fourier‟s Frequency domain. The performance of the algorithm is measured in terms of Peak Signal to Noise Ratio and Mean Square Error and Standard Deviations.
Curating and contextualizing Twitter stories to assist with social newsgathering

While journalism is evolving toward a rather open-minded participatory paradigm, social media presents overwhelming streams of data that make it difficult to identify the information of a journalist's interest. Given the increasing interest of journalists in broadening and democratizing news by incorporating social media sources, we have developed TweetGathering, a prototype tool that provides curated and contextualized access to news stories on Twitter. This tool was built with the aim of assisting journalists both with gathering and with researching news stories as users comment on them. Five journalism professionals who tested the tool found helpful characteristics that could assist them with gathering additional facts on breaking news, as well as facilitating discovery of potential information sources such as witnesses in the geographical locations of news.
Soft Sampling for Robust Object Detection

We study the robustness of object detection under the presence of missing annotations. In this setting, the unlabeled object instances will be treated as background, which will generate an incorrect training signal for the detector. Interestingly, we observe that after dropping 30% of the annotations (and labeling them as background), the performance of CNN-based object detectors like Faster-RCNN only drops by 5% on the PASCAL VOC dataset. We provide a detailed explanation for this result. To further bridge the performance gap, we propose a simple yet effective solution, called Soft Sampling. Soft Sampling re-weights the gradients of RoIs as a function of overlap with positive instances. This ensures that the uncertain background regions are given a smaller weight compared to the hardnegatives. Extensive experiments on curated PASCAL VOC datasets demonstrate the effectiveness of the proposed Soft Sampling method at different annotation drop rates. Finally, we show that on OpenImagesV3, which is a real-world dataset with missing annotations, Soft Sampling outperforms standard detection baselines by over 3%.
Visual Sentiment Analysis for Social Images Using Transfer Learning Approach

Visual sentiment analysis framework can predict the sentiment of an image by analyzing the image contents. Nowadays, people are uploading millions of images in social networks such as Twitter, Facebook, Google Plus, and Flickr. These images play a crucial part in expressing emotions of users in online social networks. As a result, image sentiment analysis has become important in the area of online multimedia big data research. Several research works are focusing on analyzing the sentiment of the textual contents. However, little investigation has been done to develop models that can predict sentiment of visual content. In this paper, we propose a novel visual sentiment analysis framework using transfer learning approach to predict sentiment. We use hyper-parameters learned from a very deep convolutional neural network to initialize our network model to prevent overfitting. We conduct extensive experiments on a Twitter image dataset and prove that our model achieves better performance than the current state-of-the-art.
An Investigation Into Machine Learning Regression Techniques for the Leaf Rust Disease Detection Using Hyperspectral Measurement

The complex impacts of disease stages and disease symptoms on spectral characteristics of the plants lead to limitation in disease severity detection using the spectral vegetation indices (SVIs). Although machine learning techniques have been utilized for vegetation parameters estimation and disease detection, the effects of disease symptoms on their performances have been less considered. Hence, this paper investigated on 1) using partial least square regression (PLSR), v support vector regression (v-SVR), and Gaussian process regression (GPR) methods for wheat leaf rust disease detection, 2) evaluating the impact of training sample size on the results, 3) the influence of disease symptoms effects on the predictions performances of the above-mentioned methods, and 4) comparisons between the performances of SVIs and machine learning techniques. In this study, the spectra of the infected and non infected leaves in different disease symptoms were measured using a non imaging spectroradiometer in the electromagnetic region of 350 to 2500 nm. In order to produce a ground truth dataset, we employed photos of a digital camera to compute the disease severity and disease symptoms fractions. Then, different sample sizes of collected datasets were utilized to train each method. PLSR showed coefficient of determination (R2) values of 0.98 (root mean square error (RMSE) = 0.6) and 0.92 (RMSE = 0.11) at leaf and canopy, respectively. SVR showed R2 and RMSE close to PLSR at leaf (R2 = 0.98, RMSE = 0.05) and canopy (R2 = 0.95, RMSE = 0.12) scales. GPR showed R2 values of 0.98 (RMSE = 0.03) and 0.97 (RMSE = 0.11) at leaf and canopy scale, respectively. Moreover, GPR represents better performances than others using small training sample size. The results represent that the machine learning techniques in contrast to SVIs are not sensitive to different disease symptoms and their results are reliable.
Toward a Coherent Theory of Environmentally Significant Behavior

This article develops a conceptual framework for advancing theories of environmentally significant individual behavior and reports on the attempts of the author’s research group and others to develop such a theory. It discusses definitions of environmentally significant behavior; classifies the behaviors and their causes; assesses theories of environmentalism, focusing especially on value-belief-norm theory; evaluates the relationship between environmental concern and behavior; and summarizes evidence on the factors that determine environmentally significant behaviors and that can effectively alter them. The article concludes by presenting some major propositions supported by available research and some principles for guiding future research and informing the design of behavioral programs for environmental protection.
Computational principles of mobile robotics

As known, to finish this book, you may not need to get it at once in a day. Doing the activities along the day may make you feel so bored. If you try to force reading, you may prefer to do other entertaining activities. But, one of concepts we want you to have this book is that it will not make you feel bored. Feeling bored when reading will be only unless you don't like the book. computational principles of mobile robotics really offers what everybody wants.
Bayesian Quadratic Discriminant Analysis

Quadratic discriminant analysis is a common tool for classification, but estimation of the Gaussian parameters can be ill-posed. This paper contains theoretical and algorithmic contributions to Bayesian estimation for quadratic discriminant analysis. A distribution-based Bayesian classifier is derived using information geometry. Using a calculus of variations approach to define a functional Bregman divergence for distributions, it is shown that the Bayesian distribution-based classifier that minimizes the expected Bregman divergence of each class conditional distribution also minimizes the expected misclassification cost. A series approximation is used to relate regularized discriminant analysis to Bayesian discriminant analysis. A new Bayesian quadratic discriminant analysis classifier is proposed where the prior is defined using a coarse estimate of the covariance based on the training data; this classifier is termed BDA7. Results on benchmark data sets and simulations show that BDA7 performance is competitive with, and in some cases significantly better than, regularized quadratic discriminant analysis and the cross-validated Bayesian quadratic discriminant analysis classifier Quadratic Bayes.
Solving Square Jigsaw Puzzles Using Dynamic Programming and the Hungarian Procedure

To design, implement, and test an algorithm for so lving the square jigsaw puzzle problem, which has many applications in image processing, pattern recognition, and computer vision such as restoration of archeologica l artifacts and image descrambling.  The algorithm used the gray level profiles of border pi xels for local matching of the puzzle pieces, which was performed using dynamic programming to facilita te non-rigid alignment of pixels of two gray level profiles. Unlike the classical best-first sea rch, the algorithm simultaneously located the neigh bors of a puzzle piece during the search using the wellknown Hungarian procedure, which is an optimal assignment procedure. To improve the search for a g lobal solution, every puzzle piece was considered as starting piece at various starting locations.  Experiments using four well-known images demonstrated the effectiveness of the proposed appr o ch over the classical piece-by-piece matching approach. The performance evaluation was based on a new precision performance measure. For all four test images, the proposed algorithm achieved 1 00% precision rate for puzzles up to 8×8.  The proposed search mechanism based on simultaneou s all cation of puzzle pieces using the Hungarian procedure provided better performance than piece-by-piece used in classical methods.
Competitive Pricing for Spectrum Sharing in Cognitive Radio Networks: Dynamic Game, Inefficiency of Nash Equilibrium, and Collusion

We address the problem of spectrum pricing in a cognitive radio network where multiple primary service providers compete with each other to offer spectrum access opportunities to the secondary users. By using an equilibrium pricing scheme, each of the primary service providers aims to maximize its profit under quality of service (QoS) constraint for primary users. We formulate this situation as an oligopoly market consisting of a few firms and a consumer. The QoS degradation of the primary services is considered as the cost in offering spectrum access to the secondary users. For the secondary users, we adopt a utility function to obtain the demand function. With a Bertrand game model, we analyze the impacts of several system parameters such as spectrum substitutability and channel quality on the Nash equilibrium (i.e., equilibrium pricing adopted by the primary services). We present distributed algorithms to obtain the solution for this dynamic game. The stability of the proposed dynamic game algorithms in terms of convergence to the Nash equilibrium is studied. However, the Nash equilibrium is not efficient in the sense that the total profit of the primary service providers is not maximized. An optimal solution to gain the highest total profit can be obtained. A collusion can be established among the primary services so that they gain higher profit than that for the Nash equilibrium. However, since one or more of the primary service providers may deviate from the optimal solution, a punishment mechanism may be applied to the deviating primary service provider. A repeated game among primary service providers is formulated to show that the collusion can be maintained if all of the primary service providers are aware of this punishment mechanism, and therefore, properly weight their profits to be obtained in the future.
Log Clustering Based Problem Identification for Online Service Systems

Logs play an important role in the maintenance of large-scale online service systems. When an online service fails, engineers need to examine recorded logs to gain insights into the failure and identify the potential problems. Traditionally, engineers perform simple keyword search (such as "error" and "exception") of logs that may be associated with the failures. Such an approach is often time consuming and error prone. Through our collaboration with Microsoft service product teams, we propose LogCluster, an approach that clusters the logs to ease log-based problem identification. LogCluster also utilizes a knowledge base to check if the log sequences occurred before. Engineers only need to examine a small number of previously unseen, representative log sequences extracted from the clusters to identify a problem, thus significantly reducing the number of logs that should be examined, meanwhile improving the identification accuracy. Through experiments on two Hadoop-based applications and two large-scale Microsoft online service systems, we show that our approach is effective and outperforms the state-of-the-art work proposed by Shang et al. in ICSE 2013. We have successfully applied LogCluster to the maintenance of many actual Microsoft online service systems. In this paper, we also share our success stories and lessons learned.
CQI table design to support 256 QAM in small cell environment

The mobile data traffic is expected to grow beyond 1000 times by 2020 compared with it in 2010. In order to support 1000 times of capacity increase, improving spectrum efficiency is one of the important approaches. Meanwhile, in Long Term Evolution (LTE)-Advanced, small cell and hotspot are important scenarios for future network deployment to increase the capacity from the network density domain. Under such environment, the probability of high Signal to Interference plus Noise Ratio (SINR) region becomes larger which brings the possibility of introducing higher order modulation, i.e., 256 Quadrature Amplitude Modulation(QAM) to improve the spectrum efficiency. Channel quality indicator (CQI) table design is a key issue to support 256 QAM. In this paper, we investigate the feasibility of 256 QAM by SINR geometry and propose two methods on CQI table design to support the 256 QAM transmission. Simulation results show proposed methods can improve average user equipment (UE) throughput and cell center UE throughput with almost no loss on cell edge UE throughput.