nils-herrmann/clean_scirepeval huggingface dataset:
Denitrification in aquaculture systems : an example of a fuzzy logic control problem

Nitrification in commercial aquaculture systems has been accomplished using many different technologies (e.g. trickling filters, fluidized beds and rotating biological contactors) but commercial aquaculture systems have been slow to adopt denitrification. Denitrification (conversion of nitrate, NO3 − to nitrogen gas, N2) is essential to the development of commercial, closed, recirculating aquaculture systems (B1 water turnover 100 day). The problems associated with manually operated denitrification systems have been incomplete denitrification (oxidation–reduction potential, ORP\−200 mV) with the production of nitrite (NO2 ), nitric oxide (NO) and nitrous oxide (N2O) or over-reduction (ORPB−400 mV), resulting in the production of hydrogen sulfide (H2S). The need for an anoxic or anaerobic environment for the denitrifying bacteria can also result in lowered dissolved oxygen (DO) concentrations in the rearing tanks. These problems have now been overcome by the development of a computer automated denitrifying bioreactor specifically designed for aquaculture. The prototype bioreactor (process control version) has been in operation for 4 years and commercial versions of the bioreactor are now in continuous use; these bioreactors can be operated in either batch or continuous on-line modes, maintaining NO3 − concentrations below 5 ppm. The bioreactor monitors DO, ORP, pH and water flow rate and controls water pump rate and carbon feed rate. A fuzzy logic-based expert system replaced the classical process control system for operation of the bioreactor, continuing to optimize denitrification rates and eliminate discharge of toxic by-products (i.e. NO2 , NO, N2O or www.elsevier.nl/locate/aqua-online * Corresponding author.  E-mail address: pglee@utmb.edu (P.G. Lee) 0144-8609/00/$ see front matter  S0144 -8609 (00 )00046 -7 38 P.G. Lee et al. / Aquacultural Engineering 23 (2000) 37–59 H2S). The fuzzy logic rule base was composed of \40 fuzzy rules; it took into account the slow response time of the system. The fuzzy logic-based expert system maintained nitrate-nitrogen concentration B5 ppm while avoiding any increase in NO2 or H2S concentrations.
ON THE VALUE SET OF FERMAT QUOTIENTS

We also define qp(kp) = 0, k ∈ Z. Fermat quotients appear and play a major role in various questions of computational and algebraic number theory and thus their distribution modulo p has been studied in a number of works; see, for example, [1, 5, 6, 7, 8, 9, 10, 11, 13, 15, 16, 17, 18] and the references therein. In particular, the image set Ip(U) = {qp(u) : 1 ≤ u ≤ U} has been investigated in some of these works. Let Ip(U) = #Ip(U) be the cardinality of Ip(U). It is well known (see, for example, [6, Section 2]) that
Revisit Long Short-Term Memory : An Optimization Perspective

Long Short-Term Memory (LSTM) is a deep recurrent neural network architecture with high computational complexity. Contrary to the standard practice to train LSTM online with stochastic gradient descent (SGD) methods, we propose a matrix-based batch learning method for LSTM with full Backpropagation Through Time (BPTT). We further solve the state drifting issues as well as improving the overall performance for LSTM using revised activation functions for gates. With these changes, advanced optimization algorithms are applied to LSTM with long time dependency for the first time and show great advantages over SGD methods. We further demonstrate that large-scale LSTM training can be greatly accelerated with parallel computation architectures like CUDA and MapReduce.
Channel-Level Acceleration of Deep Face Representations

A major challenge in biometrics is performing the test at the client side, where hardware resources are often limited. Deep learning approaches pose a unique challenge: while such architectures dominate the field of face recognition with regard to accuracy, they require elaborate, multi-stage computations. Recently, there has been some work on compressing networks for the purpose of reducing run time and network size. However, it is not clear that these compression methods would work in deep face nets, which are, generally speaking, less redundant than the object recognition networks, i.e., they are already relatively lean. We propose two novel methods for compression: one based on eliminating lowly active channels and the other on coupling pruning with repeated use of already computed elements. Pruning of entire channels is an appealing idea, since it leads to direct saving in run time in almost every reasonable architecture.
Lexical normalization for social media text

Twitter provides access to large volumes of data in real time, but is notoriously noisy, hampering its utility for NLP. In this article, we target out-of-vocabulary words in short text messages and propose a method for identifying and normalizing lexical variants. Our method uses a classifier to detect lexical variants, and generates correction candidates based on morphophonemic similarity. Both word similarity and context are then exploited to select the most probable correction candidate for the word. The proposed method doesn't require any annotations, and achieves state-of-the-art performance over an SMS corpus and a novel dataset based on Twitter.
Learning a Warping Distance from Unlabeled Time Series Using Sequence Autoencoders

Measuring similarities between unlabeled time series trajectories is an important problem in domains as diverse as medicine, astronomy, finance, and computer vision. It is often unclear what is the appropriate metric to use because of the complex nature of noise in the trajectories (e.g. different sampling rates or outliers). Domain experts typically hand-craft or manually select a specific metric, such as dynamic time warping (DTW), to apply on their data. In this paper, we propose Autowarp, an end-to-end algorithm that optimizes and learns a good metric given unlabeled trajectories. We define a flexible and differentiable family of warping metrics, which encompasses common metrics such as DTW, Euclidean, and edit distance. Autowarp then leverages the representation power of sequence autoencoders to optimize for a member of this warping distance family. The output is a metric which is easy to interpret and can be robustly learned from relatively few trajectories. In systematic experiments across different domains, we show that Autowarp often outperforms hand-crafted trajectory similarity metrics.
Siamese Capsule Networks

Capsule Networks have shown encouraging results on defacto benchmark computer vision datasets such as MNIST, CIFAR and smallNORB. Although, they are yet to be tested on tasks where (1) the entities detected inherently have more complex internal representations and (2) there are very few instances per class to learn from and (3) where point-wise classification is not suitable. Hence, this paper carries out experiments on face verification in both controlled and uncontrolled settings that together address these points. In doing so we introduce Siamese Capsule Networks, a new variant that can be used for pairwise learning tasks. The model is trained using contrastive loss with `2-normalized capsule encoded pose features. We find that Siamese Capsule Networks perform well against strong baselines on both pairwise learning datasets, yielding best results in the few-shot learning setting where image pairs in the test set contain unseen subjects.
SoK: Fraud in Telephony Networks

Telephone networks first appeared more than a hundred years ago, long beforetransistors were invented. They, therefore, form the oldest large scale networkthat has grown to touch over 7 billion people. Telephony is now merging manycomplex technologies and because numerous services enabled by these technologiescan be monetized, telephony attracts a lot of fraud. In 2015, a telecom fraudassociation study estimated that the loss of revenue due to global telecom fraudwas worth 38 billion US dollars per year. Because of the convergence oftelephony with the Internet, fraud in telephony networks can also have anegative impact on security of online services. However, there is littleacademic work on this topic, in part because of the complexity of such networksand their closed nature. This paper aims to systematically explorefraud in telephony networks. Our taxonomy differentiates the root causes, thevulnerabilities, the exploitation techniques, the fraud types and finally theway fraud benefits fraudsters. We present an overview of eachof these and use CAller NAMe (CNAM) revenue share fraud as aconcrete example to illustrate how our taxonomy helps in better understandingthis fraud and to mitigate it.
Learning to Disambiguate by Asking Discriminative Questions

The ability to ask questions is a powerful tool to gather information in order to learn about the world and resolve ambiguities. In this paper, we explore a novel problem of generating discriminative questions to help disambiguate visual instances. Our work can be seen as a complement and new extension to the rich research studies on image captioning and question answering. We introduce the first large-scale dataset with over 10,000 carefully annotated images-question tuples to facilitate benchmarking. In particular, each tuple consists of a pair of images and 4.6 discriminative questions (as positive samples) and 5.9 non-discriminative questions (as negative samples) on average. In addition, we present an effective method for visual discriminative question generation. The method can be trained in a weakly supervised manner without discriminative images-question tuples but just existing visual question answering datasets. Promising results are shown against representative baselines through quantitative evaluations and user studies.
A Fast Search Algorithm for a Large Fuzzy Database

In this paper, we propose a fast search algorithm for a large fuzzy database that stores iris codes or data with a similar binary structure. The fuzzy nature of iris codes and their high dimensionality render many modern search algorithms, mainly relying on sorting and hashing, inadequate. The algorithm that is used in all current public deployments of iris recognition is based on a brute force exhaustive search through a database of iris codes, looking for a match that is close enough. Our new technique, Beacon Guided Search (BGS), tackles this problem by dispersing a multitude of ldquobeaconsrdquo in the search space. Despite random bit errors, iris codes from the same eye are more likely to collide with the same beacons than those from different eyes. By counting the number of collisions, BGS shrinks the search range dramatically with a negligible loss of precision. We evaluate this technique using 632,500 iris codes enrolled in the United Arab Emirates (UAE) border control system, showing a substantial improvement in search speed with a negligible loss of accuracy. In addition, we demonstrate that the empirical results match theoretical predictions.
Co-occurrence matrix and its statistical features as a new approach for face recognition

In this paper, a new face recognition technique is introduced based on the gray-level co-occurrence matrix (GLCM). GLCM represents the distributions of the intensities and the information about relative positions of neighboring pixels of an image. We proposed two methods to extract feature vectors using GLCM for face classification. The first method extracts the well-known Haralick features from the GLCM, and the second method directly uses GLCM by converting the matrix into a vector that can be used in the classification process. The results demonstrate that the second method, which uses GLCM directly, is superior to the first method that uses the feature vector containing the statistical Haralick features in both nearest neighbor and neural networks classifiers. The proposed GLCM based face recognition system not only outperforms well-known techniques such as principal component analysis and linear discriminant analysis, but also has comparable performance with local binary patterns and Gabor wavelets.
CASSL: Curriculum Accelerated Self-Supervised Learning

Recent self-supervised learning approaches focus on using a few thousand data points to learn policies for high-level, low-dimensional action spaces. However, scaling this framework for higher-dimensional control requires either scaling up the data collection efforts or using a clever sampling strategy for training. We present a novel approach - Curriculum Accelerated Self-Supervised Learning (CASSL) - to train policies that map visual information to high-level, higher-dimensional action spaces. CASSL orders the sampling of training data based on control dimensions: the learning and sampling are focused on few control parameters before other parameters. The right curriculum for learning is suggested by variance-based global sensitivity analysis of the control space. We apply our CASSL framework to learning how to grasp using an adaptive, underactuated multi-fingered gripper, a challenging system to control. Our experimental results indicate that CASSL provides significant improvement and generalization compared to baseline methods such as staged curriculum learning (8% increase) and complete end-to-end learning with random exploration (14% improvement) tested on a set of novel objects.
Hybrid Beamforming via the Kronecker Decomposition for the Millimeter-Wave Massive MIMO Systems

Millimeter-wave (mmWave) massive multiple-input multiple-output (MIMO) seamlessly integrates two wireless technologies, mmWave communications and massive MIMO, which provides spectrums with tens of GHz of total bandwidth and supports aggressive space division multiple access using large-scale arrays. Though it is a promising solution for next-generation systems, the realization of mmWave massive MIMO faces several practical challenges. In particular, implementing massive MIMO in the digital domain requires hundreds to thousands of radio frequency chains and analog-to-digital converters matching the number of antennas. Furthermore, designing these components to operate at the mmWave frequencies is challenging and costly. These motivated the recent development of the hybrid-beamforming architecture, where MIMO signal processing is divided for separate implementation in the analog and digital domains, called the analog and digital beamforming, respectively. Analog beamforming using a phase array introduces uni-modulus constraints on the beamforming coefficients. They render the conventional MIMO techniques unsuitable and call for new designs. In this paper, we present a systematic design framework for hybrid beamforming for multi-cell multiuser massive MIMO systems over mmWave channels characterized by sparse propagation paths. The framework relies on the decomposition of analog beamforming vectors and path observation vectors into Kronecker products of factors being uni-modulus vectors. Exploiting properties of Kronecker mixed products, different factors of the analog beamformer are designed for either nulling interference paths or coherently combining data paths. Furthermore, a channel estimation scheme is designed for enabling the proposed hybrid beamforming. The scheme estimates the angles-of-arrival (AoA) of data and interference paths by analog beam scanning and data-path gains by analog beam steering. The performance of the channel estimation scheme is analyzed. In particular, the AoA spectrum resulting from beam scanning, which displays the magnitude distribution of paths over the AoA range, is derived in closed form. It is shown that the inter-cell interference level diminishes inversely with the array size, the square root of pilot sequence length, and the spatial separation between paths, suggesting different ways of tackling pilot contamination.
Identifying sunni extremist propaganda with deep learning

Sunni extremism poses a significant danger to society, yet it is relatively easy for these extremist organizations to spread jihadist propaganda and recruit new members via the Internet, Darknet, and social media. The sheer volume of these sites make them very difficult to police. This paper discusses an approach that can assist with this problem, by automatically identifying a subset of web pages and social media content (or any text) that contains extremist content. The approach utilizes machine learning, specifically neural networks and deep learning, to classify text as containing “extremist” or “benign” (i.e., not extremist) content. This method is robust and can effectively learn to classify extremist multilingual text of varying length. This study also involved the construction of a high quality dataset for training and testing, put together by a team of 40 people (some with fluency in Arabic) who expended 9,500 hours of combined effort. This dataset should facilitate future research on this topic.
Servicescapes : The Impact of Physical Surroundings on Customers and Employees

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Domain transform for edge-aware image and video processing

We present a new approach for performing high-quality edge-preserving filtering of images and videos in real time. Our solution is based on a transform that defines an isometry between curves on the 2D image manifold in 5D and the real line. This transform preserves the geodesic distance between points on these curves, adaptively warping the input signal so that 1D edge-preserving filtering can be efficiently performed in linear time. We demonstrate three realizations of 1D edge-preserving filters, show how to produce high-quality 2D edge-preserving filters by iterating 1D-filtering operations, and empirically analyze the convergence of this process. Our approach has several desirable features: the use of 1D operations leads to considerable speedups over existing techniques and potential memory savings; its computational cost is not affected by the choice of the filter parameters; and it is the first edge-preserving filter to work on color images at arbitrary scales in real time, without resorting to subsampling or quantization. We demonstrate the versatility of our domain transform and edge-preserving filters on several real-time image and video processing tasks including edge-preserving filtering, depth-of-field effects, stylization, recoloring, colorization, detail enhancement, and tone mapping.
The impact of corporate reputation on brand attitude and purchase intention

Researchers have agreed that a favorable corporate reputation is one of the most important intangible assets driving company performance (Chun 2005; Fisher-Buttinger and Vallaster 2011; Gibson et al. 2006). Not to be confused with brand identity and image, corporate reputation is often defined as consumers’ accumulated opinions, perceptions, and attitudes towards the company (Fombrun et al. 2000; Fombrun and Shanley 1990; Hatch and Schultz 2001; Weigelt and Camerer 1988). In addition, corporate reputation is established by individuals’ relative perspective; thus, corporate reputation is closely linked to the consumers’ subjective evaluation about the company (Fombrun and Shanley 1990; Weigelt and Camerer 1988). The effect of corporate reputation on corporate performance has been supported in many articles. Earlier studies have reported that a positive reputation has a significant Abstract
A system failure framework for innovation policy design

This article sets out a policy framework for implementing ‘system of innovation’ (SI)-based strategies. On the basis of a literature review on system failures, the study designs an SI-policy framework that can provide policy makers with practical leads how to design, analyse and evaluate policy measures in the field of innovation. The functioning of the framework is illustrated on the basis of an evaluation of Dutch cluster policy. From this illustration, it can be concluded that the SI-framework provides helpful leads for policy design and evaluation and renders more specific policy recommendations than the generally used market failure approach.
Combining modality specific deep neural networks for emotion recognition in video

In this paper we present the techniques used for the University of Montréal's team submissions to the 2013 Emotion Recognition in the Wild Challenge. The challenge is to classify the emotions expressed by the primary human subject in short video clips extracted from feature length movies. This involves the analysis of video clips of acted scenes lasting approximately one-two seconds, including the audio track which may contain human voices as well as background music. Our approach combines multiple deep neural networks for different data modalities, including: (1) a deep convolutional neural network for the analysis of facial expressions within video frames; (2) a deep belief net to capture audio information; (3) a deep autoencoder to model the spatio-temporal information produced by the human actions depicted within the entire scene; and (4) a shallow network architecture focused on extracted features of the mouth of the primary human subject in the scene. We discuss each of these techniques, their performance characteristics and different strategies to aggregate their predictions. Our best single model was a convolutional neural network trained to predict emotions from static frames using two large data sets, the Toronto Face Database and our own set of faces images harvested from Google image search, followed by a per frame aggregation strategy that used the challenge training data. This yielded a test set accuracy of 35.58%. Using our best strategy for aggregating our top performing models into a single predictor we were able to produce an accuracy of 41.03% on the challenge test set. These compare favorably to the challenge baseline test set accuracy of 27.56%.
MADE: Masked Autoencoder for Distribution Estimation

There has been a lot of recent interest in designing neural network models to estimate a distribution from a set of examples. We introduce a simple modification for autoencoder neural networks that yields powerful generative models. Our method masks the autoencoder’s parameters to respect autoregressive constraints: each input is reconstructed only from previous inputs in a given ordering. Constrained this way, the autoencoder outputs can be interpreted as a set of conditional probabilities, and their product, the full joint probability. We can also train a single network that can decompose the joint probability in multiple different orderings. Our simple framework can be applied to multiple architectures, including deep ones. Vectorized implementations, such as on GPUs, are simple and fast. Experiments demonstrate that this approach is competitive with stateof-the-art tractable distribution estimators. At test time, the method is significantly faster and scales better than other autoregressive estimators.