nils-herrmann/clean_scirepeval huggingface dataset:
Sentence Emotion Analysis and Recognition Based on Emotion Words Using

Emotion recognition on text has wide applications. In this study, we make an analysis on sentence emotion based on emotion words using Ren-CECps (a Chinese emotion corpus). Some classification methods (including C4.5 decision tree, SVM, NaiveBayes, ZEROR, and DecisionTable) have been compared. Then a supervised machine learning method (Polynomial kernel method) is proposed to recognize the eight basic emotions (Expect, Joy, Love, Surprise, Anxiety, Sorrow, Angry and Hate). Using Ren-CECps, we get the emotion lexicons for the eight basic emotions. Polynomial kernel (PK) method is used to compute the similarities between sentences and the eight emotion lexicons. Then the experiential knowledge derived from Ren-CECps is used to recognize whether the eight emotion categories are present in a sentence. The experiments showed promising results.
A Convolutional Neural Network for Leaves Recognition Using Data Augmentation

Recently, convolutional neural networks (ConvNets) have achieved marvellous results in different field of recognition, especially in computer vision. In this paper, a seven-layer ConvNet using data augmentation is proposed for leaves recognition. First, we implement multiform transformations (e.g., rotation and translation etc.) to enlarge the dataset without changing their labels. This novel technique recently makes tremendous contribution to the performance of ConvNets as it is able to reduce the over-fitting degree and enhance the generalization ability of the ConvNet. Moreover, in order to get the shapes of leaves, we sharpen all the images with a random parameter. This method is similar to the edge detection, which has been proved useful in the image classification. Then we train a deep convolutional neural network to classify the augmented leaves data with three groups of test set and finally find that the method is quite feasible and effective. The accuracy achieved by our algorithm outperforms other methods for supervised learning on the popular leaf dataset Flavia.
No . 067 April 4 , 2017 Theory of Deep Learning III : Generalization Properties of SGD by

In Theory III we characterize with a mix of theory and experiments the generalization properties of Stochastic Gradient Descent in overparametrized deep convolutional networks. We show that Stochastic Gradient Descent (SGD) selects with high probability solutions that 1) have zero (or small) empirical error, 2) are degenerate as shown in Theory II and 3) have maximum generalization. This work was supported by the Center for Brains, Minds and Machines (CBMM), funded by NSF STC award CCF 123 1216. H.M. is supported in part by ARO Grant W911NF-15-10385.
An Analysis of the Precision and Reliability of the Leap Motion Sensor and Its Suitability for Static and Dynamic Tracking

We present the results of an evaluation of the performance of the Leap Motion Controller with the aid of a professional, high-precision, fast motion tracking system. A set of static and dynamic measurements was performed with different numbers of tracking objects and configurations. For the static measurements, a plastic arm model simulating a human arm was used. A set of 37 reference locations was selected to cover the controller's sensory space. For the dynamic measurements, a special V-shaped tool, consisting of two tracking objects maintaining a constant distance between them, was created to simulate two human fingers. In the static scenario, the standard deviation was less than 0.5 mm. The linear correlation revealed a significant increase in the standard deviation when moving away from the controller. The results of the dynamic scenario revealed the inconsistent performance of the controller, with a significant drop in accuracy for samples taken more than 250 mm above the controller's surface. The Leap Motion Controller undoubtedly represents a revolutionary input device for gesture-based human-computer interaction; however, due to its rather limited sensory space and inconsistent sampling frequency, in its current configuration it cannot currently be used as a professional tracking system.
Two-Step Moving Target Detection Algorithm for Automotive 77 GHz FMCW Radar

Today, 77GHz FMCW (Frequency Modulation Continuous Wave) radar sensors are used for automotive applications. In typical automotive radar, the target of interest is a moving target. Thus, to improve the detection probability and reduce the false alarm rate, an MTD(Moving Target Detection) algorithm should be required. This paper describes the proposed two-step MTD algorithm. The 1st MTD processing consists of a clutter cancellation step and a noise cancellation step. The two steps can cancel almost all clutter including stationary targets. However, clutter still remains among the interest beat frequencies detected during the 1st MTD and CFAR (Constant False Alarm) processing. Thus, in the 2nd MTD step, we remove the rest of the clutter with zero phase variation.
Taking Human out of Learning Applications: A Survey on Automated Machine Learning

Machine learning techniques have deeply rooted in our everyday life. However, since it is knowledgeand labor-intensive to pursue good learning performance, humans are heavily involved in every aspect of machine learning. To make machine learning techniques easier to apply and reduce the demand for experienced human experts, automated machine learning (AutoML) has emerged as a hot topic with both industrial and academic interest. In this paper, we provide an up to date survey on AutoML. First, we introduce and define the AutoML problem, with inspiration from both realms of automation and machine learning. Then, we propose a general AutoML framework that not only covers most existing approaches to date, but also can guide the design for new methods. Subsequently, we categorize and review the existing works from two aspects, i.e., the problem setup and the employed techniques. The proposed framework and taxonomies provide a detailed analysis of AutoML approaches and explain the reasons underneath their successful applications. We hope this survey can serve as not only an insightful guideline for AutoML beginners but also an inspiration for future research.
Priority-select arbiter: An efficient round-robin arbiter

Round robin arbiter (RRA) is a critical block in nowadays designs. It is widely found in System-on-chips and Network-on-chips. The need of an efficient RRA has increased extensively as it is a limiting performance block. In this paper, we deliver a comparative review between different RRA architectures found in literature. We also propose a novel efficient RRA architecture. The FPGA implementation results of the previous RRA architectures and our proposed one are given, that show the improvements of the proposed RRA.
Event Extraction via Dynamic Multi-Pooling Convolutional Neural Networks

Traditional approaches to the task of ACE event extraction primarily rely on elaborately designed features and complicated natural language processing (NLP) tools. These traditional approaches lack generalization, take a large amount of human effort and are prone to error propagation and data sparsity problems. This paper proposes a novel event-extraction method, which aims to automatically extract lexical-level and sentence-level features without using complicated NLP tools. We introduce a word-representation model to capture meaningful semantic regularities for words and adopt a framework based on a convolutional neural network (CNN) to capture sentence-level clues. However, CNN can only capture the most important information in a sentence and may miss valuable facts when considering multiple-event sentences. We propose a dynamic multi-pooling convolutional neural network (DMCNN), which uses a dynamic multi-pooling layer according to event triggers and arguments, to reserve more crucial information. The experimental results show that our approach significantly outperforms other state-of-the-art methods.
Industrie 4.0: Hit or Hype? [Industry Forum]

In Germany, the term Industrie 4.0 is currently prevalent in almost every industry-related fair, conference, or call for public-funded projects. First used at the Hanover Fair in 2011, the term, raised numerous discussions, and the major question is: is it a hit or hype? Even in politics, this term is used frequently with respect to German industry, and research efforts relating to it are currently supported by €200 million from government-funding bodies?the German Federal Ministry of Education and Research and the German Federal Ministry of Economic Affairs and Energy. The term Industrie 4.0 refers to the fourth industrial revolution and is often understood as the application of the generic concept of cyberphysical systems (CPSs) to industrial production systems (cyberphysical production systems). In North America, similar ideas have been brought up under the name Industrial Internet by General Electric. The technical basis is very similar to Industrie 4.0, but the application is broader than industrial production and also includes, e.g., smart electrical grids. The various definitions have caused confusion rather than increasing transparency. Overambitious marketing reinforced the confusion (Industrie 4.0 is already being done). This obscures the real and sound future visions behind Industrie 4.0. This column is intended to provide easy-to-understand access to the core ideas of Industrie 4.0 and describes the basic industrial requirements that need to be fulfilled for its success.
A robust and enhanced approach for human detection in crowd

In this paper we have presented and enhanced methodology for robust detection of human in videos. Our research covers most of the limitations for detection of human in crowded places like detection of non-human objects and large human shadow as humans. The proposed technique is based on hierarchal structure consisting of three phases. Firstly, segmentation of moving objects is done using Gaussian Mixture model. Secondly, shadow removal technique is applied to avoid detection of large human shadows as human. Finally, human detection is achieved by applying human detection algorithm  on shadowless segmented images. Experiments are performed on different videos having single and multiple humans in indoor and outdoor scenes and videos under different illumination producing large and small shadows. This paper also presents comparative results of our methodology with existing techniques and the results clearly proved that the proposed technique outperforms the existing techniques and this is proved by producing comparative results.
Expected Patch Log Likelihood with a Sparse Prior

Image priors are of great importance in image restoration tasks. These problems can be addressed by decomposing the degraded image into overlapping patches, treating the patches individually and averaging them back together. Recently, the Expected Patch Log Likelihood (EPLL) method has been introduced, arguing that the chosen model should be enforced on the final reconstructed image patches. In the context of a Gaussian Mixture Model (GMM), this idea has been shown to lead to state-of-the-art results in image denoising and debluring. In this paper we combine the EPLL with a sparse-representation prior. Our derivation leads to a close yet extended variant of the popular K-SVD image denoising algorithm, where in order to effectively maximize the EPLL the denoising process should be iterated. This concept lies at the core of the K-SVD formulation, but has not been addressed before due the need to set different denoising thresholds in the successive sparse coding stages. We present a method that intrinsically determines these thresholds in order to improve the image estimate. Our results show a notable improvement over K-SVD in image denoising and inpainting, achieving comparable performance to that of EPLL with GMM in denoising.
A Probabilistic Topic Model for Mashup Tag Recommendation

Mashups are prevalent Service-Oriented Architecture (SOA) based applications consisting of multiple Web Application Programming Interfaces (APIs) and content. Tags have been extensively used to organize and index mashup services. However, people favor manual tags creation in the past. This approach demands user intervention, which is extremely time-consuming and probes to errors. In this paper we propose a novel Mashup-API-Tag model for automatic mashup tag recommendation. The model simultaneously incorporates the composition relationships between mashups and APIs as well as the annotation relationships between APIs and tags to discover the latent topics. Then the semantic similarity between Web APIs and mashups can be acquired. Subsequently, tags of chosen APIs are recommended to a mashup where the mashup and the APIs are most similar. In addition, we develop a tag filtering algorithm to select the most relevant tags for recommendation. The experimental results on a real world dataset prove that our approach outperforms other methods, including frequency-based methods and the methods that only consider the composition relationships and the annotation relationships separately.
PID Controller Design for a Flexible-Link Manipulator

This paper investigates the application of the H∞proportional-integral-derivative (PID) control synthesis method to tip position control of a flexible-link manipulator. To achieve high performance of PID control, this particular control design problem is cast into the H∞framework. Based on the recently proposed H∞PID control synthesis method, a set of admissible controllers is then obtained to be robust against uncertainty introduced by neglecting the higher-order modes of the link and to achieve the desired time-response specifications. The most important feature of the H∞PID control synthesis method is the ability to provide the knowledge of the entire admissible PID controller gain space which can facilitate controller fine tuning. Finally, experimental results are given to demonstrate the effectiveness of H∞PID control.
Improving Distributed Word Representation and Topic Model by Word-Topic Mixture Model

We propose a Word-Topic Mixture(WTM) model to improve word representation and topic model simultaneously. Firstly, it introduces the initial external word embeddings into the Topical Word Embeddings(TWE) model based on Latent Dirichlet Allocation(LDA) model to learn word embeddings and topic vectors. Then the results learned from TWE are integrated in the LDA by defining the probability distribution of topic vectors-word embeddings according to the idea of latent feature model with LDA (LFLDA), meanwhile minimizing the KL divergence of the new topic-word distribution function and the original one. The experimental results prove that the WTM model performs better on word representation and topic detection compared with some state-of-the-art models.
Sequence modelling for sentence classification in a legal summarisation system

We describe a set of experiments using a wide range of machine learning techniques for the task of predicting the rhetorical status of sentences. The research is part of a text summarisation project for the legal domain for which we use a new corpus of judgments of the UK House of Lords. We present experimental results for classification according to a rhetorical scheme indicating a sentence's contribution to the overall argumentative structure of the legal judgments using four learning algorithms from the Weka package (C4.5, naïve Bayes, Winnow and SVMs). We also report results using maximum entropy models both in a standard classification framework and in a sequence labelling framework. The SVM classifier and the maximum entropy sequence tagger yield the most promising results.
ALIZE 3.0 - open source toolkit for state-of-the-art speaker recognition

ALIZE is an open-source platform for speaker recognition. The ALIZE library implements a low-level statistical engine based on the well-known Gaussian mixture modelling. The toolkit includes a set of high level tools dedicated to speaker recognition based on the latest developments in speaker recognition such as Joint Factor Analysis, Support Vector Machine, i-vector modelling and Probabilistic Linear Discriminant Analysis. Since 2005, the performance of ALIZE has been demonstrated in series of Speaker Recognition Evaluations (SREs) conducted by NIST and has been used by many participants in the last NISTSRE 2012. This paper presents the latest version of the corpus and performance on the NIST-SRE 2010 extended task.
Human grasp choice and robotic grasp analysis

In studying grasping and manipulation we find two very different approaches to the subject: knowledge-based approaches based primarily on empirical studies of human grasping and manipulation, and analytical approaches based primarily on physical models of the manipulation process. This chapter begins with a review of studies of human grasping, in particular our development of a grasp taxonomy and an expert system for predicting human grasp choice. These studies show how object geometry and task requirements (as well as hand capabilities and tactile sensing) combine to dictate grasp choice. We then consider analytic models of grasping and manipulation with robotic hands. To keep the mathematics tractable, these models require numerous simplifications which restrict their generality. Despite their differences, the two approaches can be correlated. This provides insight into why people grasp and manipulate objects as they do, and suggests different approaches for robotic grasp and manipulation planning. The results also bear upon such issues such as object representation
A 3.6cm2 wirelessly-powered UWB SoC with −30.7dBm rectifier sensitivity and sub-10cm range resolution

This paper discusses the design of a battery-less wirelessly-powered UWB system-on-a-chip (SoC) tag for area-constrained localization applications. An antenna-rectifier co-design methodology optimizes sensitivity and increases range under tag area constraints. A low-voltage (0.8-V) UWB TX enables high rectifier sensitivity by reducing required rectifier output voltage. The 2.4GHz rectifier, power-management unit and 8GHz UWB TX are integrated in 65nm CMOS and the rectifier demonstrates state-of-the-art -30.7dBm sensitivity for 1V output with only 1.3cm2 antenna area, representing a 2.3× improvement in sensitivity over previously published work, at 2.6× higher frequency with 9× smaller antenna area. Measurements in an office corridor demonstrate 20m range with 36dBm TX EIRP. The 0.8-V 8GHz UWB TX consumes 64pJ/pulse at 28MHz pulse repetition rate and achieves 2.4GHz -10dB bandwidth. Wireless measurements demonstrate sub-10cm range resolution at range > 10m.
Automatic Exposure Correction of Consumer Photographs

We study the problem of automatically correcting the exposure of an input image. Generic auto-exposure correction methods usually fail in individual over-/under-exposed regions. Interactive corrections may fix this issue, but adjusting every photograph requires skill and time. This paper will automate the interactive correction technique by estimating the image specific S-shaped nonlinear tone curve that best fits the input image. Our first contribution is a new Zone-based region-level optimal exposure evaluation, which would consider both the visibility of individual regions and relative contrast between regions. Then a detail-preserving S-curve adjustment is applied based on the optimal exposure to obtain the final output. We show that our approach enables better corrections comparing with popular image editing tools and other automatic methods.
Web media semantic concept retrieval via tag removal and model fusion

Multimedia data on social websites contain rich semantics and are often accompanied with user-defined tags. To enhance Web media semantic concept retrieval, the fusion of tag-based and content-based models can be used, though it is very challenging. In this article, a novel semantic concept retrieval framework that incorporates tag removal and model fusion is proposed to tackle such a challenge. Tags with useful information can facilitate media search, but they are often imprecise, which makes it important to apply noisy tag removal (by deleting uncorrelated tags) to improve the performance of semantic concept retrieval. Therefore, a multiple correspondence analysis (MCA)-based tag removal algorithm is proposed, which utilizes MCA's ability to capture the relationships among nominal features and identify representative and discriminative tags holding strong correlations with the target semantic concepts. To further improve the retrieval performance, a novel model fusion method is also proposed to combine ranking scores from both tag-based and content-based models, where the adjustment of ranking scores, the reliability of models, and the correlations between the intervals divided on the ranking scores and the semantic concepts are all considered. Comparative results with extensive experiments on the NUS-WIDE-LITE as well as the NUS-WIDE-270K benchmark datasets with 81 semantic concepts show that the proposed framework outperforms baseline results and the other comparison methods with each component being evaluated separately.
Generative Temporal Models with Memory

We consider the general problem of modeling temporal data with long-range dependencies, wherein new observations are fully or partially predictable based on temporally-distant, past observations. A sufficiently powerful temporal model should separate predictable elements of the sequence from unpredictable elements, express uncertainty about those unpredictable elements, and rapidly identify novel elements that may help to predict the future. To create such models, we introduce Generative Temporal Models augmented with external memory systems. They are developed within the variational inference framework, which provides both a practical training methodology and methods to gain insight into the models’ operation. We show, on a range of problems with sparse, long-term temporal dependencies, that these models store information from early in a sequence, and reuse this stored information efficiently. This allows them to perform substantially better than existing models based on well-known recurrent neural networks, like LSTMs.
Widespread distribution of microplastics in subsurface seawater in the NE Pacific Ocean.

We document the abundance, composition and distribution of microplastics in sub-surface seawaters of the northeastern Pacific Ocean and coastal British Columbia. Samples were acid-digested and plastics were characterized using light microscopy by type (fibres or fragments) and size (<100, 100-500, 500-100 and >1000 μm). Microplastics concentrations ranged from 8 to 9200 particles/m(3); lowest concentrations were in offshore Pacific waters, and increased 6, 12 and 27-fold in west coast Vancouver Island, Strait of Georgia, and Queen Charlotte Sound, respectively. Fibres accounted for ∼ 75% of particles on average, although nearshore samples had more fibre content than offshore (p<0.05). While elevated microplastic concentrations near urban areas are consistent with land-based sources, the high levels in Queen Charlotte Sound appeared to be the result of oceanographic conditions that trap and concentrate debris. This assessment of microplastics in the NE Pacific is of interest in light of the on-coming debris from the 2011 Tohoku Tsunami.