nils-herrmann/clean_scirepeval huggingface dataset:
Action Recognition with Joint Attention on Multi-Level Deep Features

We propose a novel deep supervised neural network for the task of action recognition in videos, which implicitly takes advantage of visual tracking and shares the robustness of both deep Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN). In our method, a multi-branch model is proposed to suppress noise from background jitters. Specifically, we firstly extract multi-level deep features from deep CNNs and feed them into 3dconvolutional network. After that we feed those feature cubes into our novel joint LSTM module to predict labels and to generate attention regularization. We evaluate our model on two challenging datasets: UCF101 and HMDB51. The results show that our model achieves the state-of-art by only using convolutional features.
Stacked Attention Networks for Image Question Answering

This paper presents stacked attention networks (SANs) that learn to answer natural language questions from images. SANs use semantic representation of a question as query to search for the regions in an image that are related to the answer. We argue that image question answering (QA) often requires multiple steps of reasoning. Thus, we develop a multiple-layer SAN in which we query an image multiple times to infer the answer progressively. Experiments conducted on four image QA data sets demonstrate that the proposed SANs significantly outperform previous state-of-the-art approaches. The visualization of the attention layers illustrates the progress that the SAN locates the relevant visual clues that lead to the answer of the question layer-by-layer.
The Impact of Observational Learning and Electronic Word of Mouth on Consumer Purchase Decisions: The Moderating Role of Consumer Expertise and Consumer Involvement

The social media revolution has created a dynamic shift in the digital marketing landscape. The voice of influence is moving from traditional marketers towards consumers through online social interactions. In this study, we focus on two types of online social interactions, namely, electronic word of mouth (eWOM) and observational learning (OL), and explore how they influence consumer purchase decisions. We also examine how receiver characteristics, consumer expertise and consumer involvement, moderate consumer purchase decision process. Analyzing panel data collected from a popular online beauty forum, we found that consumer purchase decisions are influenced by their online social interactions with others and that action-based OL information is more influential than opinion-based eWOM. Further, our results show that both consumer expertise and consumer involvement play an important moderating role, albeit in opposite direction: Whereas consumer expertise exerts a negative moderating effect, consumer involvement is found to have a positive moderating effect. The study makes important contributions to research and practice.
Ideal ratio mask estimation using deep neural networks for robust speech recognition

We propose a feature enhancement algorithm to improve robust automatic speech recognition (ASR). The algorithm estimates a smoothed ideal ratio mask (IRM) in the Mel frequency domain using deep neural networks and a set of time-frequency unit level features that has previously been used to estimate the ideal binary mask. The estimated IRM is used to filter out noise from a noisy Mel spectrogram before performing cepstral feature extraction for ASR. On the noisy subset of the Aurora-4 robust ASR corpus, the proposed enhancement obtains a relative improvement of over 38% in terms of word error rates using ASR models trained in clean conditions, and an improvement of over 14% when the models are trained using the multi-condition training data. In terms of instantaneous SNR estimation performance, the proposed system obtains a mean absolute error of less than 4 dB in most frequency channels.
Visual-Inertial-Aided Navigation for High-Dynamic Motion in Built Environments Without Initial Conditions

In this paper, we present a novel method to fuse observations from an inertial measurement unit (IMU) and visual sensors, such that initial conditions of the inertial integration, including gravity estimation, can be recovered quickly and in a linear manner, thus removing any need for special initialization procedures. The algorithm is implemented using a graphical simultaneous localization and mapping like approach that guarantees constant time output. This paper discusses the technical aspects of the work, including observability and the ability for the system to estimate scale in real time. Results are presented of the system, estimating the platforms position, velocity, and attitude, as well as gravity vector and sensor alignment and calibration on-line in a built environment. This paper discusses the system setup, describing the real-time integration of the IMU data with either stereo or monocular vision data. We focus on human motion for the purposes of emulating high-dynamic motion, as well as to provide a localization system for future human-robot interaction.
Visual road following using intrinsic images

We present a real-time visual-based road following method for mobile robots in outdoor environments. The approach combines an image processing method, that allows to retrieve illumination invariant images, with an efficient path following algorithm. The method allows a mobile robot to autonomously navigate along pathways of different types in adverse lighting conditions using monocular vision. To validate the proposed method, we have evaluated its ability to correctly determine boundaries of pathways in a challenging outdoor dataset. Moreover, the method's performance was tested on a mobile robotic platform that autonomously navigated long paths in urban parks. The experiments demonstrated that the mobile robot was able to identify outdoor pathways of different types and navigate through them despite the presence of shadows that significantly influenced the paths' appearance.
Methodologies for system-level remaining useful life prediction

While most prognostics approaches focus on accurate computation of the degradation rate and the Remaining Useful Life (RUL) of individual components, it is the rate at which the performance of subsystems and systems degrade that is of greater interest to the operators and maintenance personnel of these systems. Accurate and reliable predictions make it possible to plan the future operations of the system, optimize maintenance scheduling activities, and maximize system life. In system-level prognostics, we are interested in determining when the performance of a system will fall below pre-defined levels of acceptable performance. Our focus in this paper is on developing a comprehensive methodology for system-level prognostics under uncertainty that combines the use of an estimation scheme that tracks system state and degradation parameters, along with a prediction scheme that computes the RUL as a stochastic distribution over the life of the system. Two parallel methods have been developed for prediction: (1) methods based on stochastic simulation and (2) optimization methods, such as first order reliability method (FORM). We compare the computational complexity and the accuracy of the two prediction approaches using a case study of a system with several degrading components.
Social signals and algorithmic trading of Bitcoin

The availability of data on digital traces is growing to unprecedented sizes, but inferring actionable knowledge from large-scale data is far from being trivial. This is especially important for computational finance, where digital traces of human behaviour offer a great potential to drive trading strategies. We contribute to this by providing a consistent approach that integrates various datasources in the design of algorithmic traders. This allows us to derive insights into the principles behind the profitability of our trading strategies. We illustrate our approach through the analysis of Bitcoin, a cryptocurrency known for its large price fluctuations. In our analysis, we include economic signals of volume and price of exchange for USD, adoption of the Bitcoin technology and transaction volume of Bitcoin. We add social signals related to information search, word of mouth volume, emotional valence and opinion polarization as expressed in tweets related to Bitcoin for more than 3 years. Our analysis reveals that increases in opinion polarization and exchange volume precede rising Bitcoin prices, and that emotional valence precedes opinion polarization and rising exchange volumes. We apply these insights to design algorithmic trading strategies for Bitcoin, reaching very high profits in less than a year. We verify this high profitability with robust statistical methods that take into account risk and trading costs, confirming the long-standing hypothesis that trading-based social media sentiment has the potential to yield positive returns on investment.
Concrete Problems in AI Safety

Rapid progress in machine learning and artificial intelligence (AI) has brought increasing attention to the potential impacts of AI technologies on society. In this paper we discuss one such potential impact: the problem of accidents in machine learning systems, defined as unintended and harmful behavior that may emerge from poor design of real-world AI systems. We present a list of five practical research problems related to accident risk, categorized according to whether the problem originates from having the wrong objective function (“avoiding side effects” and “avoiding reward hacking”), an objective function that is too expensive to evaluate frequently (“scalable supervision”), or undesirable behavior during the learning process (“safe exploration” and “distributional shift”). We review previous work in these areas as well as suggesting research directions with a focus on relevance to cutting-edge AI systems. Finally, we consider the high-level question of how to think most productively about the safety of forward-looking
PurdueNLP at SemEval-2017 Task 1: Predicting Semantic Textual Similarity with Paraphrase and Event Embeddings

This paper describes our proposed solution for SemEval 2017  Semantic Textual Similarity (Daniel Cer and Specia, 2017). The task aims at measuring the degree of equivalence between sentences given in English. Performance is evaluated by computing Pearson Correlation scores between the predicted scores and human judgements. Our proposed system consists of two subsystems and one regression model for predicting STS scores. The two subsystems are designed to learn Paraphrase and Event Embeddings that can take the consideration of paraphrasing characteristics and sentence structures into our system. The regression model associates these embeddings to make the final predictions. The experimental result shows that our system acquires 0.8 of Pearson Correlation Scores in this task.
GEOMETRICAL TOLERANCE STACK UP TECHNIQUES

Tolerance design has become a very sensitive and important issue in product and process development because of increasing demand for quality products and the growing requirements for automation in manufacturing. This chapter presents tolerance stack up analysis of dimensional and geometrical tolerances. The stack up of tolerances is important for functionality of the mechanical assembly as well as optimizing the cost of the system. Many industries are aware of the importance of geometrical dimensioning & Tolerancing (GDT) of their product design. Conventional methods of tolerance stack up analysis are tedious and time consuming. Stack up of geometrical tolerances is usually difficult as it involves application of numerous rules & conditions. This chapter introduces the various approaches viz. Generic Capsule, Quickie and Catena methods, used towards tolerance stack up analysis for geometrical tolerances. Automation of stack up of geometrical tolerances can be used for tolerance allocation on the components as well as their assemblies considering the functionality of the system. Stack of geometrical tolerances has been performed for individual components as well as assembly of these components.
Structure of NB-IoT NodeB system

NB-IoT system focues on indoor coverage, low cost, long battery life, and enabling a large number of connected devices. The NB-IoT system in the inband mode should share the antenna with the LTE system and support mult-PRB to cover many terminals. Also, the number of used antennas should be minimized for price competitiveness. In this paper, the structure and implementation of the NB-IoT base station system will be describe.
Digital Phased Arrays: Challenges and Opportunities

Digital beamforming (DBF) has long been heralded as the next frontier in phased array technology, and not without reason. The digitization of transmit and receive signals at the element level opens the door to new processing and beamforming schemes and promises to deliver maximum flexibility and unprecedented dynamic range in large systems. However, it is not without inherent technological risks and practical challenges associated with the amount of data to process and the use of less sophisticated transceivers. This paper provides broad overviews of several interrelated aspects of the resulting DBF trade spaces for these systems. In particular, emerging concepts are highlighted for the roles and interconnection of distributed beamforming/processing for fixed and adaptive beamforming. These are then related to digital array calibration mechanisms that can potentially reduce the need for data- and processing-intensive beamforming algorithms.
Collaborative Filtering: Weighted Nonnegative Matrix Factorization Incorporating User and Item Graphs

Collaborative filtering is an important topic in data mining and has been widely used in recommendation system. In this paper, we proposed a unified model for collaborative filtering based on graph regularized weighted nonnegative matrix factorization. In our model, two graphs are constructed on users and items, which exploit the internal information (e.g. neighborhood information in the user-item rating matrix) and external information (e.g. content information such as user’s occupation and item’s genre, or other kind of knowledge such as social trust network). The proposed method not only inherits the advantages of model-based method, but also owns the merits of memory-based method which considers the neighborhood information. Moreover, it has the ability to make use of content information and any additional information regarding user-user such as social trust network. Due to the use of these internal and external information, the proposed method is able to find more interpretable lowdimensional representations for users and items, which is helpful for improving the recommendation accuracy. Experimental results on benchmark collaborative filtering data sets demonstrate that the proposed methods outperform the state of the art collaborative filtering methods a lot.
Large Scale GAN Training for High Fidelity Natural Image Synthesis

Despite recent progress in generative image modeling, successfully generating high-resolution, diverse samples from complex datasets such as ImageNet remains an elusive goal. To this end, we train Generative Adversarial Networks at the largest scale yet attempted, and study the instabilities specific to such scale. We find that applying orthogonal regularization to the generator renders it amenable to a simple “truncation trick,” allowing fine control over the trade-off between sample fidelity and variety by truncating the latent space. Our modifications lead to models which set the new state of the art in class-conditional image synthesis. When trained on ImageNet at 128×128 resolution, our models (BigGANs) achieve an Inception Score (IS) of 166.3 and Fréchet Inception Distance (FID) of 9.6, improving over the previous best IS of 52.52 and FID of 18.65.
Additive Manufacturing : A Framework for Implementation

As mass production has migrated to developing countries, European and US companies are forced to rapidly switch towards low volume production of more innovative, customised and sustainable products with high added value. To compete in this turbulent environment, manufacturers have sought new fabrication techniques to provide the necessary tools to support the need for increased flexibility and enable economic low volume production. One such emerging technique is Additive Manufacturing (AM). AM is a method of manufacture which involves the joining of materials, usually layer-upon-layer, to create objects from 3D model data. The benefits of this methodology include new design freedom, removal of tooling requirements, and economic low volumes. AM consists various technologies to process versatile materials, and for many years its dominant application has been the manufacture of prototypes, or Rapid Prototyping. However, the recent growth in applications for direct part manufacture, or Rapid Manufacturing, has resulted in much research effort focusing on development of new processes and materials. This study focuses on the implementation process of AM and is motivated by the lack of socio-technical studies in this area. It addresses the need for existing and potential future AM project managers to have an implementation framework to guide their efforts in adopting this new and potentially disruptive technology class to produce high value products and generate new business opportunities. Based on a review of prior works and through qualitative case study analysis, we construct and test a normative structural model of implementation factors related to AM technology, supply chain, organisation, operations and strategy.
Non-edible vegetable oils : A critical evaluation of oil extraction , fatty acid compositions , biodiesel production , characteristics , engine performance and emissions production

World energy demand is expected to increase due to the expanding urbanization, better living standards and increasing population. At a time when society is becoming increasingly aware of the declining reserves of fossil fuels beside the environmental concerns, it has become apparent that biodiesel is destined to make a substantial contribution to the future energy demands of the domestic and industrial economies. There are different potential feedstocks for biodiesel production. Non-edible vegetable oils which are known as the second generation feedstocks can be considered as promising substitutions for traditional edible food crops for the production of biodiesel. The use of non-edible plant oils is very significant because of the tremendous demand for edible oils as food source. Moreover, edible oils’ feedstock costs are far expensive to be used as fuel. Therefore, production of biodiesel from non-edible oils is an effective way to overcome all the associated problems with edible oils. However, the potential of converting non-edible oil into biodiesel must be well examined. This is because physical and chemical properties of biodiesel produced from any feedstock must comply with the limits of ASTM and DIN EN specifications for biodiesel fuels. This paper introduces non-edible vegetable oils to be used as biodiesel feedstocks. Several aspects related to these feedstocks have been reviewed from various recent publications. These aspects include overview of non-edible oil resources, advantages of non-edible oils, problems in exploitation of non-edible oils, fatty acid composition profiles (FAC) of various non-edible oils, oil extraction techniques, technologies of biodiesel production from non-edible oils, biodiesel standards and characterization, properties and characteristic of non-edible biodiesel and engine performance and emission production. As a conclusion, it has been found that there is a huge chance to produce biodiesel from non-edible oil sources and therefore it can boost the future production of biodiesel. & 2012 Elsevier Ltd. All rights reserved.
Uniform random number generation

This chapter covers the basic design principles and methods for uniform random number generators used in simulation. We also briefly mention the connections between these methods and those used to construct highly-uniform point sets for quasi-Monte Carlo integration. The emphasis is on the methods based on linear recurrences modulo a large integer, or modulo 2. This reflects the fact that their mathematical structure is much better understood than other types of generators, and that most generators used in simulation have that form. We discuss the main requirements for a good generator, theoretical figures of merit for certain classes of linear-type generators, implementation issues, nonlinear generators, and statistical testing.
Ankle injuries and ankle strength, flexibility, and proprioception in college basketball players.

To determine if ankle muscular strength, flexibility and proprioception can predict ankle injury in college basketball players and to compare ankle injury rates in female and male players.



In this prospective, correlational study, subjects were tested at the start of the competitive season for ankle joint muscle strength, flexibility, and proprioception. The first ankle injury for each subject was recorded on an injury report form, and the data were analyzed to determine if any of these preseason measurements predicted future injury. The setting was a competitive 9-week season for four women's and four men's college basketball teams.



A convenience sample of 31 female and 11 male college basketball players.



Subjects were tested for ankle dorsiflexion range of motion, various measures of ankle proprioception, and isokinetic peak torque of ankle dorsiflexion-plantar flexion and eversion-inversion at 30 degrees /sec and 180 degrees /sec before the start of the conference basketball seasons. Data were analyzed using a series of multiple regression equations to determine the variance in ankle injury attributed to each variable.



Various measures of proprioception predicted left ankle injury in all subjects (p < .05), while ankle strength and flexibility measures failed to account for additional variance. There was no statistically significant difference in ankle injury rate between women and men.



Ankle joint proprioceptive deficits can be used to predict ankle injury, but further research is needed to identify other sources of variance. In our study, ankle injury rate was similar in female and male college basketball players.
Influence of the speaking style and the noise spectral tilt on the lombard reflex and automatic speech recognition

To study the Lombard reflex, more realistic databases representing real world conditions need to be recorded and analyzed. In this paper we 1) propose a procedure to record Lombard data which provides a good approximation of realistic conditions and 2) present a comparison between two sets of experiments where subjects are in communication with a device while listening to noise through open-ear headphones and where subjects are reading a list. By studying acoustic correlates of the Lombard reflex and performing off-line speakerindependent recognition experiments it is shown that the communication factor affects the Lombard reflex . We also show evidence that several types of noise differing mainly by their spectral tilt induce different acoustic changes . This result reinforces the notion that it is difficult to separate the speaker from the environment stressor (in this case the noise) when studying the Lombard reflex.