nils-herrmann/clean_scirepeval huggingface dataset:
Estimation of probabilities from sparse data for the language model component of a speech recognizer

The description of a novel type of rn-gram language model is given. The model offers, via a nonlinear recursive procedure, a computation and space efficient solution to the problem of estimating probabilities from sparse data. This solution compares favorably to other proposed methods. While the method has been developed for and successfully implemented in the IBM Real Time Speech Recognizers, its generality makes it applicable in other areas where the problem of estimating probabilities from sparse data arises. Sparseness of data is an inherent property of any real text, and it is a problem that one always encounters while collecting frequency statistics on words and word sequences (m-grams) from a text of finite size. This means that even for a very large data collection, the maximum likelihood estimation method does not allow us to adequately estimate probabilities of rare but nevertheless possible word sequences-many sequences occur only once (“singletons”); many more do not occur at all. Inadequacy of the maximum likelihood estimator and the necessity to estimate the probabilities of m-grams which did not occur in the text constitute the essence of the problem. The main idea of the proposed solution to the problem is to reduce unreliable probability estimates given by the observed frequencies and redistribute the “freed” probability “mass” among m-grams which never occurred in the text. The reduction is achieved by replacing maximum likelihood estimates for m-grams having low counts with renormalized Turing’s estimates [l], and the redistribution is done via the recursive utilization of lower level conditional distributions. We found Turing’s method attractive because of its simplicity and its characterization as the optimal empirical Bayes’ estimator of a multinomial probability. Robbins in  introduces the empirical Bayes’ methodology and Nadas in  gives various derivations of the Turing’s formula. Let N be a sample text size and let n, be the number of words (m-grams) which occurred in the text exactly r times, so that N = C rn,. (1) Turing’s estimate PT for a probability of a word (m-gram) which occurred in the sample r times is r
Emotion Detection from Speech to Enrich Multimedia Content

The paper describes an experimental study on the detection of emotion from speech. As computer based characters such as avatars and virtual chat faces become more common, the use of emotion to drive the expression of the virtual characters become more important. The study utilizes a corpus containing emotional speech with 721 short utterances expressing four emotions: anger, happiness, sadness, and the neutral (unemotional) state, which were captured manually from movies and teleplays. We introduce a new concept to evaluate emotions in speech. Emotions are so complex that most speech sentences cannot be precisely assigned into a particular emotion category; however, most emotional states nevertheless can be described as a mixture of multiple emotions. Based on this concept we have trained SVMs (support vector machines) to recognize utterances within these four categories and developed an agent that can recognize and express emotions.
Partially supervised clustering for image segmentation

All clustering algorithms process unlabeled data and, consequently, suffer from two problems: (P1) choosing and validating the correct number of clusters and (P2) insuring that algorithmic labels correspond to meaningful physical labels. Clustering algorithms such as hard and fuzzy c-means, based on optimizing sums of squared errors objective functions, suffer from a third problem: (P3) a tendency to recommend solutions that equalize cluster populations. The semi-supervised c-means algorithms introduced in this paper attempt to overcome these three problems for problem domains where a few data from each class can be labeled. Segmentation of magnetic resonance images is a problem of this type and we use it to illustrate the new algorithm. Our examples show that the semi-supervised approach provides MRI segmentations that are superior to ordinary fuzzy c-means and to the crisp k-nearest neighbor rule and further, that the new method ameliorates (P1)-(P3). Cluster analysis Fuzzy c-means Partial supervision Image segmentation Magnetic resonance images
An Exact Method for the Determination of Differential Leakage Factors in Electrical Machines With Non-Symmetrical Windings

An exact and simple method for the determination of differential leakage factors in polyphase ac electrical machines with non-symmetrical windings is presented in this paper. The method relies on the properties of Görges polygons that are used to transform an infinite series expressing the differential leakage factor into a finite sum in order to significantly simplify the calculations. Some examples are shown and discussed in order to practically demonstrate the effectiveness of the proposed method.
Use Generalized Representations, But Do Not Forget Surface Features

Only a year ago, all state-of-the-art coreference resolvers were using an extensive amount of surface features. Recently, there was a paradigm shift towards using word embeddings and deep neural networks, where the use of surface features is very limited. In this paper, we show that a simple SVM model with surface features outperforms more complex neural models for detecting anaphoric mentions. Our analysis suggests that using generalized representations and surface features have different strength that should be both taken into account for improving coreference resolution.
Human Motion Analysis with Deep Metric Learning

Effectively measuring the similarity between two human motions is necessary for several computer vision tasks such as gait analysis, person identification and action retrieval. Nevertheless, we believe that traditional approaches such as L2 distance or Dynamic Time Warping based on hand-crafted local pose metrics fail to appropriately capture the semantic relationship across motions and, as such, are not suitable for being employed as metrics within these tasks. This work addresses this limitation by means of a triplet-based deep metric learning specifically tailored to deal with human motion data, in particular with the problem of varying input size and computationally expensive hard negative mining due to motion pair alignment. Specifically, we propose (1) a novel metric learning objective based on a triplet architecture and Maximum Mean Discrepancy; as well as, (2) a novel deep architecture based on attentive recurrent neural networks. One benefit of our objective function is that it enforces a better separation within the learned embedding space of the different motion categories by means of the associated distribution moments. At the same time, our attentive recurrent neural network allows processing varying input sizes to a fixed size of embedding while learning to focus on those motion parts that are semantically distinctive. Our experiments on two different datasets demonstrate significant improvements over conventional human motion metrics.
An Ultra-Low Power 1.7-2.7 GHz Fractional-N Sub-Sampling Digital Frequency Synthesizer and Modulator for IoT Applications in 40 nm CMOS

This paper introduces an ultra-low power 1.7-2.7-GHz fractional-N sub-sampling digital PLL (SS-DPLL) for Internet-of-Things (IoT) applications targeting compliance with Bluetooth Low Energy (BLE) and IEEE802.15.4 standards. A snapshot time-to-digital converter (TDC) acts as a digital sub-sampler featuring an increased out-of-range gain and without any assistance from the traditional counting of DCO edges, thus further reducing power consumption. With a proposed DCO-divider phase rotation in the feedback path, the impact of the digital-to-time converter’s (DTC’s) non-linearity on the PLL is reduced and improves fractional spurs by at least 8 dB across BLE channels. Moreover, a “variable-preconditioned LMS” calibration algorithm is introduced to dynamically correct the DTC gain error with fractional frequency control word (FCW) down to 1/16384. Fabricated in 40 nm CMOS, the SS-DPLL achieves phase noise performance of −109 dBc/Hz at 1 MHz offset, while consuming a record-low power of 1.19 mW.
Efficient and fast multi-modal foreground-background segmentation using RGBD data

This paper addresses the problem of foreground and background segmentation. Multi-modal data specifically RGBD data has gain many tasks in computer vision recently. However, techniques for background subtraction based only on single-modality still report state-of-the-art results in many benchmarks. Succeeding the fusion of depth and color data for this task requires a robust formalization allowing at the same time higher precision and faster processing. To this end, we propose to make use of kernel density estimation technique to adapt multi-modal data. To speed up kernel density estimation, we explore the fast Gauss transform which allows the summation of a mixture of M kernel at N evaluation points in ) time as opposed to O(MN) time for a direct evaluation. Extensive experiments have been carried out on four publicly available RGBD foreground/background datasets. Results demonstrate that our proposal outperforms state-of-the-art methods for almost all of the sequences acquired in challenging indoor and outdoor contexts with a fast and non-parametric operation.
Latent Regression Forest: Structured Estimation of 3D Articulated Hand Posture

In this paper we present the Latent Regression Forest (LRF), a novel framework for real-time, 3D hand pose estimation from a single depth image. In contrast to prior forest-based methods, which take dense pixels as input, classify them independently and then estimate joint positions afterwards, our method can be considered as a structured coarse-to-fine search, starting from the centre of mass of a point cloud until locating all the skeletal joints. The searching process is guided by a learnt Latent Tree Model which reflects the hierarchical topology of the hand. Our main contributions can be summarised as follows: (i) Learning the topology of the hand in an unsupervised, data-driven manner. (ii) A new forest-based, discriminative framework for structured search in images, as well as an error regression step to avoid error accumulation. (iii) A new multi-view hand pose dataset containing 180K annotated images from 10 different subjects. Our experiments show that the LRF out-performs state-of-the-art methods in both accuracy and efficiency.
A structural model of health behavior: a pragmatic approach to explain and influence health behaviors at the population level.

Behavior is influenced by individual-level attributes as well as by the conditions under which people live. Altering policies, practices, and the conditions of life can directly and indirectly influence individual behavior. This paper builds on existing ecological theories of health behavior by specifying structural mechanisms by which population-level factors effect change in individual health behaviors.



This paper moves ecological theory from model building to a pragmatic characterization of structural interventions. We examined social and environmental factors beyond individual control and mechanisms as to how they influence behavior.



Four categories of structural factors are identified: (1) availability of protective or harmful consumer products, (2) physical structures (or physical characteristics of products), (3) social structures and policies, and (4) media and cultural messages. The first three can directly influence individuals through facilitating or constraining behavior. The fourth, media, operates by changing individual-level attitudes, beliefs, and cognitions, as well as group norms.



Interventions that target the four identified structural factors are a means to provide conditions that not only reduce high-risk behavior but also prevent the adoption of high-risk behaviors. Structural interventions are important and underutilized approaches for improving our nation's health.
Cellulose: fascinating biopolymer and sustainable raw material.

As the most important skeletal component in plants, the polysaccharide cellulose is an almost inexhaustible polymeric raw material with fascinating structure and properties. Formed by the repeated connection of D-glucose building blocks, the highly functionalized, linear stiff-chain homopolymer is characterized by its hydrophilicity, chirality, biodegradability, broad chemical modifying capacity, and its formation of versatile semicrystalline fiber morphologies. In view of the considerable increase in interdisciplinary cellulose research and product development over the past decade worldwide, this paper assembles the current knowledge in the structure and chemistry of cellulose, and in the development of innovative cellulose esters and ethers for coatings, films, membranes, building materials, drilling techniques, pharmaceuticals, and foodstuffs. New frontiers, including environmentally friendly cellulose fiber technologies, bacterial cellulose biomaterials, and in-vitro syntheses of cellulose are highlighted together with future aims, strategies, and perspectives of cellulose research and its applications.
Real-Time Systems - Design Principles for Distributed Embedded Applications

Now, we come to offer you the right catalogues of book to open. real time systems design principles for distributed embedded applications is one of the literary work in this world in suitable to be reading material. That's not only this book gives reference, but also it will show you the amazing benefits of reading a book. Developing your countless minds is needed; moreover you are kind of people with great curiosity. So, the book is very appropriate for you.
MOBA: a New Arena for Game AI

Games have always been popular testbeds for Artificial Intelligence (AI). In the last decade, we have seen the rise of the Multiple Online Battle Arena (MOBA) games, which are the most played games nowadays. In spite of this, there are few works that explore MOBA as a testbed for AI Research. In this paper we present and discuss the main features and opportunities offered by MOBA games to Game AI Research. We describe the various challenges faced along the game and also propose a discrete model that can be used to better understand and explore the game. With this, we aim to encourage the use of MOBA as a novel research platform for Game AI.
Representation Learning of Knowledge Graphs with Entity Descriptions

Representation learning (RL) of knowledge graphs aims to project both entities and relations into a continuous lowdimensional space. Most methods concentrate on learning representations with knowledge triples indicating relations between entities. In fact, in most knowledge graphs there are usually concise descriptions for entities, which cannot be well utilized by existing methods. In this paper, we propose a novel RL method for knowledge graphs taking advantages of entity descriptions. More specifically, we explore two encoders, including continuous bag-of-words and deep convolutional neural models to encode semantics of entity descriptions. We further learn knowledge representations with both triples and descriptions. We evaluate our method on two tasks, including knowledge graph completion and entity classification. Experimental results on real-world datasets show that, our method outperforms other baselines on the two tasks, especially under the zero-shot setting, which indicates that our method is capable of building representations for novel entities according to their descriptions. The source code of this paper can be obtained from https://github.com/xrb92/DKRL.
Digitalism: The New Realism?

Today’s society is increasingly digitalised, with mobile smartphones being routinely carried and used by a significant percentage of the population. This provides an augmented experience for the individual that does not depend on their geographical separation with respect to their community of friends and other contacts. This changes the nature of relationships between people. Individuals may live in a “digital bubble”, close to others physically, but far away from them in their digital world. More specifically, digital images can be generated and shared with ever greater ease. Sometimes the digital image takes on an important part of the individual’s experience of reality. This paper explores examples of the phenomenon, within the context of the arts in particular and culture in general. We also consider the assortment of terms used in a variety of ways by researchers in different fields with regard to our ever more digital society, such as digitalism, digitality, digitalisation, digital culture, digital philosophy, etc. We survey these terms, exploring them from alternative viewpoints, including sociological and philosophical aspects, and attempt to pinpoint some of these terms more precisely, especially in a cultural and artistic context.
Closing loops without places

This paper proposes a new topo-metric representation of the world based on co-visibility that simplifies data association and improves the performance of appearance-based recognition. We introduce the concept of dynamic bagof-words, which is a novel form of query expansion based on finding cliques in the landmark co-visibility graph. The proposed approach avoids the - often arbitrary - discretisation of space from the robot's trajectory that is common to most image-based loop closure algorithms. Instead we show that reasoning on sets of co-visible landmarks leads to a simple model that out-performs pose-based or view-based approaches. Using real and simulated imagery, we demonstrate that dynamic bag-of-words query expansion can improve precision and recall for appearance-based localisation.
Block-Matching Convolutional Neural Network for Image Denoising

There are two main streams in up-to-date image denoising algorithms: non-local self similarity (NSS) prior based methods and convolutional neural network (CNN) based methods. The NSS based methods are favorable on images with regular and repetitive patterns while the CNN based methods perform better on irregular structures. In this paper, we propose a blockmatching convolutional neural network (BMCNN) method that combines NSS prior and CNN. Initially, similar local patches in the input image are integrated into a 3D block. In order to prevent the noise from messing up the block matching, we first apply an existing denoising algorithm on the noisy image. The denoised image is employed as a pilot signal for the block matching, and then denoising function for the block is learned by a CNN structure. Experimental results show that the proposed BMCNN algorithm achieves state-of-the-art performance. In detail, BMCNN can restore both repetitive and irregular structures.
Lifetime improvement of NAND flash-based storage systems using dynamic program and erase scaling

The cost-per-bit of NAND flash memory has been continuously improved by semiconductor process scaling and multi-leveling technologies (e.g., a 10 nm-node TLC device). However, the decreasing lifetime of NAND flash memory as a side effect of recent advanced technologies is regarded as a main barrier for a wide adoption of NAND flash-based storage systems. In this paper, we propose a new system-level approach, called dynamic program and erase scaling (DPES), for improving the lifetime (particularly, endurance) of NAND flash memory. The DPES approach is based on our key observation that changing the erase voltage as well as the erase time significantly affects the NAND endurance. By slowly erasing a NAND block with a lower erase voltage, we can improve the NAND endurance very effectively. By modifying NAND chips to support multiple write and erase modes with different operation voltages and times, DPES enables a flash software to exploit the new tradeoff relationships between the NAND endurance and erase voltage/speed under dynamic program and erase scaling. We have implemented the first DPES-aware FTL, called autoFTL, which improves the NAND endurance with a negligible degradation in the overall write throughput. Our experimental results using various I/O traces show that autoFTL can improve the maximum number of P/E cycles by 61.2% over an existing DPES-unaware FTL with less than 2.2% decrease in the overall write throughput.
Convolutional Neural Network for Brain MR Imaging Extraction Using Silver-Standards Masks

Convolutional neural networks (CNN) for medical imaging are constrained by the number of annotated data required for the training stage. Usually, manual annotation is considered to be the “gold-standard’. However, medical imaging datasets with expert manual segmentation are scarce as this step is time-consuming and expensive. Moreover, the single-rater manual annotation is often used in data-driven approaches making the algorithm optimal for one guideline only. In this work, we propose a convolutional neural network (CNN) for brain magnetic resonance (MR) imaging extraction, fully trained with what we refer to as silver-standard masks. To the best of our knowledge, our approach is the first deep learning approach that is fully trained using silver-standards masks, having an optimal generalization. Furthermore, regarding the Dice coefficient, we outperform the current skull-stripping (SS) state-of-the-art method in three datasets. Our method consists of 1) a dataset with silver-standard masks as input, 2) a tri-planar method using parallel 2D U-Net-based CNNs, which we refer to as CONSNet, and 3) an auto-context implementation of CONSNet. CONSNet refers to our integrated approach, i.e., training with silver-standard masks and us∗Corresponding author Email address: oeslle@dca.fee.unicamp.br (Oeslle Lucena) URL: miclab.fee.unicamp.br (Oeslle Lucena)  2018 ar X iv :1 80 4. 04 98 8v 1 [ cs .C V ] 1 3 A pr 2 01 8 ing a CNN architecture. Masks are generated by forming a consensus from a set of publicly available automatic segmentation methods using the simultaneous truth and performance level estimation (STAPLE) algorithm. The CNN architecture is robust, with dropout and batch normalization as regularizers. It was inspired by the 2D U-Net model published by the RECOD group. We conducted our analysis using three publicly available datasets: the Calgary-Campinas-359 (CC-359 ), the LONI Probabilistic Brain Atlas (LPBA40), and the Open Access Series of Imaging Studies (OASIS). Five performance metrics were used in our experiments: Dice coefficient, sensitivity, specificity, Hausdorff distance, and symmetric surface-to-surface mean distance. Our results also demonstrate that we have comparable overall performance versus current methods, including recently proposed deep learningbased strategies that were trained using manually annotated masks. Our usage of silver-standard masks, however, reduced the cost of manual annotation, diminished inter-/intra-rater variability, and avoided CNN segmentation super-specialization towards one specific SS method found when mask generation is done through an agreement among many automatic methods. Moreover, usage of silver-standard masks enlarges the volume of input annotated data since we can generate annotation from unlabelled data. Also, our method has the advantage that, once trained, it takes only a few seconds to process a typical brain image volume using a modern graphics processing unit (GPU). In contrast, many of the other competitive consensus-building methods have processing times on the order of minutes.