nils-herrmann/clean_scirepeval huggingface dataset:
Automated Story Selection for Color Commentary in Sports

Automated sports commentary is a form of automated narrative. Sports commentary exists to keep the viewer informed and entertained. One way to entertain the viewer is by telling brief stories relevant to the game in progress. We present a system called the sports commentary recommendation system (SCoReS) that can automatically suggest stories for commentators to tell during games. Through several user studies, we compared commentary using SCoReS to three other types of commentary and show that SCoReS adds significantly to the broadcast across several enjoyment metrics. We also collected interview data from professional sports commentators who positively evaluated a demonstration of the system. We conclude that SCoReS can be a useful broadcast tool, effective at selecting stories that add to the enjoyment and watchability of sports. SCoReS is a step toward automating sports commentary and, thus, automating narrative.
Principles of Mixed-Initiative User Interfaces

Recent debate has centered on the relative promise of focusinguser-interface research on developing new metaphors and tools thatenhance users abilities to directly manipulate objects versusdirecting effort toward developing interface agents that provideautomation. In this paper, we review principles that show promisefor allowing engineers to enhance human-computer interactionthrough an elegant coupling of automated services with directmanipulation. Key ideas will be highlighted in terms of the Lookoutsystem for scheduling and meeting management.
When Can I Expect an Email Response? A Study of Rhythms in Email Usage

A study of email responsiveness was conducted to understand how the timing of email responses conveys important information. Interviews and observations explored users’ perceptions of how they responded to email and formed expectations of others’ responses to them. We identified ways in which users maintain and cultivate a responsiveness image for projecting expectations about their email response. We also discuss other ways people discover contextual cues for responsiveness, which include using tools such as the calendar and phone, accounting for the amount of work time overlap available, and establishing a pacing between email correspondents. These cues help users develop a sense of when to expect a response and when breakdown has occurred, requiring further action. Anyone who uses email regularly has sent a message and wondered, “When will I get a response to this email?” Or, “How long should I wait for a response to this message before taking further action?” Beyond the content of email messages, the timing of when email is sent, when it is read, and when a response is received are all examples of rhythms of email activity that help users coordinate their email correspondence. Previous work has demonstrated that people have rhythmic temporal patterns of activity in the workplace, and that these rhythms can help coordinate interaction (Begole et al., 2002). We wanted to extend this work by exploring what meaningful temporal patterns occur in the usage of email. Email is clearly a crucial and ubiquitous tool for office workers, and understanding the types of rhythms that govern its use will help guide design, improving the overall effectiveness of email services. Specifically, we explored the following questions about email rhythms: • How do individuals decide when to read and respond to a message? • How do individuals form expectations about how long it will take others to respond to an email? • How do these expectations affect email behaviors?
Extracting Opinions, Opinion Holders, And Topics Expressed In Online News Media Text

This paper presents a method for identifying an opinion with its holder and topic, given a sentence from online news media texts. We introduce an approach of exploiting the semantic structure of a sentence, anchored to an opinion bearing verb or adjective. This method uses semantic role labeling as an intermediate step to label an opinion holder and topic using data from FrameNet. We decompose our task into three phases: identifying an opinion-bearing word, labeling semantic roles related to the word in the sentence, and then finding the holder and the topic of the opinion word among the labeled semantic roles. For a broader coverage, we also employ a clustering technique to predict the most probable frame for a word which is not defined in FrameNet. Our experimental results show that our system performs significantly better than the baseline.
The stochastic CRB for array processing: a textbook derivation

The stochastic Cramer-Rao bound (CRB) for direction estimation in array processing applications was indirectly derived some ten years ago as the (asymptotic) covariance matrix of the maximum likelihood (ML) estimator. Attempts to obtain the stochastic CRB directly via the CRB theory fell short of providing a simple derivation and consequently, no direct derivation of this useful performance bound was available in the open literature. we correct this situation by providing a textbook-like direct derivation of the stochastic CRB.
Convolutional Neural Networks for Speech Recognition

Recently, the hybrid deep neural network (DNN)- hidden Markov model (HMM) has been shown to significantly improve speech recognition performance over the conventional Gaussian mixture model (GMM)-HMM. The performance improvement is partially attributed to the ability of the DNN to model complex correlations in speech features. In this paper, we show that further error rate reduction can be obtained by using convolutional neural networks (CNNs). We first present a concise description of the basic CNN and explain how it can be used for speech recognition. We further propose a limited-weight-sharing scheme that can better model speech features. The special structure such as local connectivity, weight sharing, and pooling in CNNs exhibits some degree of invariance to small shifts of speech features along the frequency axis, which is important to deal with speaker and environment variations. Experimental results show that CNNs reduce the error rate by 6%-10% compared with DNNs on the TIMIT phone recognition and the voice search large vocabulary speech recognition tasks.
Bootstrap estimates for confidence intervals in ASR performance evaluation

The field of speech recognition has clearly benefited from precisely defined testing conditions and objective performance measures such as word error rate. In the development and evaluation of new methods, the question arises whether the empirically observed difference in performance is due to a genuine advantage of one system over the other, or just an effect of chance. However, many publications still do not concern themselves with the statistical significance of the results reported. We present a bootstrap method for significance analysis which is, at the same time, intuitive, precise and and easy to use. Unlike some methods, we make no (possibly ill-founded) approximations and the results are immediately interpretable in terms of word error rate.
Motion planning under uncertainty for on-road autonomous driving

We present a motion planning framework for autonomous on-road driving considering both the uncertainty caused by an autonomous vehicle and other traffic participants. The future motion of traffic participants is predicted using a local planner, and the uncertainty along the predicted trajectory is computed based on Gaussian propagation. For the autonomous vehicle, the uncertainty from localization and control is estimated based on a Linear-Quadratic Gaussian (LQG) framework. Compared with other safety assessment methods, our framework allows the planner to avoid unsafe situations more efficiently, thanks to the direct uncertainty information feedback to the planner. We also demonstrate our planner's ability to generate safer trajectories compared to planning only with a LQG framework.
IoT design space challenges: Circuits and systems

The Internet of Things (IoT) is a rapidly emerging application space, poised to become the largest electronics market for the semiconductor industry. IoT devices are focused on sensing and actuating of our physical environment and have a nearly unlimited breadth of uses. In this paper, we explore the IoT application space and then identify two common challenges that exist across this space: ultra-low power operation and system design using modular, composable components. We survey recent low power techniques and discuss a low power bus that enables modular design. Finally, we conclude with three example ultra-low power, millimeter-scale IoT systems.
Spatial Perception in Virtual Environments: Evaluating an Architectural Application

In presenting this thesis in partial fulfillment of the requirements for a Master's degree at the University of Washington, I agree that the Library shall make its copies freely available for inspection. I further agree that extensive copying of this thesis is allowable only for scholarly purposes, consistent with "fair use" as prescribed in the U.S. Copyright Law. Any other reproduction for any purposes or by any means shall not be allowed without my written permission. PREFACE Over the last several years, professionals from many different fields have come to the Human Interface Technology Laboratory (H.I.T.L) to discover and learn about virtual environments. In general, they are impressed by their experiences and express the tremendous potential the tool has in their respective fields. But the potentials are always projected far in the future, and the tool remains just a concept. This is justifiable because the quality of the visual experience is so much less than what people are used to seeing; high definition television, breathtaking special cinematographic effects and photorealistic computer renderings. Instead, the models in virtual environments are very simple looking; they are made of small spaces, filled with simple or abstract looking objects of little color distinctions as seen through displays of noticeably low resolution and at an update rate which leaves much to be desired. Clearly, for most applications, the requirements of precision have not been met yet with virtual interfaces as they exist today. However, there are a few domains where the relatively low level of the technology could be perfectly appropriate. In general, these are applications which require that the information be presented in symbolic or representational form. Having studied architecture, I knew that there are moments during the early part of the design process when conceptual decisions are made which require precisely the simple and representative nature available in existing virtual environments. This was a marvelous discovery for me because I had found a viable use for virtual environments which could be immediately beneficial to architecture, my shared area of interest. It would be further beneficial to architecture in that the virtual interface equipment I would be evaluating at the H.I.T.L. happens to be relatively less expensive and more practical than other configurations such as the "Walkthrough" at the University of North Carolina. The setup at the H.I.T.L. could be easily introduced into architectural firms because it takes up very little physical room (150 …
Traffic classification on the fly

The early detection of applications associated with TCP flows is an essential step for network security and traffic engineering. The classic way to identify flows, i.e. looking at port numbers, is not effective anymore. On the other hand, state-of-the-art techniques cannot determine the application before the end of the TCP flow. In this editorial, we propose a technique that relies on the observation of the first five packets of a TCP connection to identify the application. This result opens a range of new possibilities for online traffic classification.
A Structured Variational Auto-encoder for Learning Deep Hierarchies of Sparse Features

In this note we present a generative model of natural images c on isting of a deep hierarchy of layers of latent random variables, each of whic h follows a new type of distribution that we call rectified Gaussian . These rectified Gaussian units allow spike-and-slab type sparsity, while retaining the diff erentiability necessary for efficient stochastic gradient variational inference. To le arn the parameters of the new model, we approximate the posterior of the latent variab les with a variational auto-encoder. Rather than making the usual mean-field assum ption however, the encoder parameterizes a new type of structured variational approximation that retains the prior dependencies of the generative model. Using this structured posterior approximation, we are able to perform joint training of deep models with many layers of latent random variables, without having to re so t to stacking or other layerwise training procedures. 1 A structured variational auto-encoder model We propose a directed generative model consisting of a hiera rchy of layers of latent features z , z, . . . , z, where the features in each layer are generated independent ly co ditional on the features in the layer above, i.e. z i ∼ p(z j i |z ). For the conditional distribution p() we propose what we call therectified Gaussian distribution RG(μji , σ j i ). We can define this distribution by describing how we can sample from it: Definition (Rectified Gaussian distribution ). If ǫ ∼ N(0, 1), andz i = maximum(μ j i + σ j i ǫ, 0) thenz j i ∼ RG(μ j i , σ j i ). The rectified Gaussian distribution is thus a mixture of a poi nt mass at zero, and a truncated Gaussian distribution with support on the positive real line. Bo th the mass at zero and the shape of the truncated Gaussian component are determined by the same par amete s. Because of this property, the random drawz i is differentiable in(μ j i , σ j i ) for fixed ǫ, a property we will exploit later to perform efficient stochastic gradient variational inference. For the top layer of latent features z, we defineμ to be a learnable parameter vector. The standard deviationsσ i of the top layer are fixed at 1. After that, the parameters of ea ch l yer are recursively set to be μ = bμ +W j μ · z , σ = exp ( b j σ +W j σ · z j−1 ) , wherebμ andb j σ are (column) parameter vectors, W j μ andW j σ are parameter matrices, and · efines the matrix-vector dot product. The exponential function exp() is applied elementwise. After generating the last layer of latent features z, we generate the observed data x from an appropriate conditional distributionp(x|z). For example, for binary data we use independent Bernoulli distributions, where the probabilities are given by applyi ng the logistic sigmoid to another linear transformation of the latent features z. For continuous data we could use independent Gaussian
Multi-objective reasoning with constrained goal models

Goal models have been widely used in computer science to represent software requirements, business objectives, and design qualities. Existing goal modelling techniques, however, have shown limitations of expressiveness and/or tractability in coping with complex real-world problems. In this work, we exploit advances in automated reasoning technologies, notably satisfiability and optimization modulo theories (SMT/OMT), and we propose and formalize: (1) an extended modelling language for goals, namely the constrained goal model (CGM), which makes explicit the notion of goal refinement and of domain assumption, allows for expressing preferences between goals and refinements and allows for associating numerical attributes to goals and refinements for defining constraints and optimization goals over multiple objective functions, refinements, and their numerical attributes; (2) a novel set of automated reasoning functionalities over CGMs, allowing for automatically generating suitable refinements of input CGMs, under user-specified assumptions and constraints, that also maximize preferences and optimize given objective functions. We have implemented these modelling and reasoning functionalities in a tool, named CGM-Tool, using the OMT solver OptiMathSAT as automated reasoning backend. Moreover, we have conducted an experimental evaluation on large CGMs to support the claim that our proposal scales well for goal models with 1000s of elements.
Feature Selection for High-Dimensional Data: A Fast Correlation-Based Filter Solution

Feature selection, as a preprocessing step to machine learning, is effective in reducing dimensionality, removing irrelevant data, increasing learning accuracy, and improving result comprehensibility. However, the recent increase of dimensionality of data poses a severe challenge to many existing feature selection methods with respect to efficiency and effectiveness. In this work, we introduce a novel concept, predominant correlation, and propose a fast filter method which can identify relevant features as well as redundancy among relevant features without pairwise correlation analysis. The efficiency and effectiveness of our method is demonstrated through extensive comparisons with other methods using real-world data of high dimensionality.
Five years after the Deep Learning revolution of computer vision : State of the art methods for online image and video analysis

The purpose of this document is to re ect on novel and upcoming methods for computer vision that might have relevance for application in robot vision and video analytics. The document covers many di erent subelds of computer vision, most of which have been addressed by our research activity at the computer vision laboratory. The report has been written based on a request of, and supported by, FOI.
Erasure-coding based routing for opportunistic networks

Routing in Delay Tolerant Networks (DTN) with unpredictable node mobility is a challenging problem because disconnections are prevalent and lack of knowledge about network dynamics hinders good decision making. Current approaches are primarily based on redundant transmissions. They have either high overhead due to excessive transmissions or long delays due to the possibility of making wrong choices when forwarding a few redundant copies. In this paper, we propose a novel forwarding algorithm based on the idea of erasure codes. Erasure coding allows use of a large number of relays while maintaining a constant overhead, which results in fewer cases of long delays.We use simulation to compare the routing performance of using erasure codes in DTN with four other categories of forwarding algorithms proposed in the literature. Our simulations are based on a real-world mobility trace collected in a large outdoor wild-life environment. The results show that the erasure-coding based algorithm provides the best worst-case delay performance with a fixed amount of overhead. We also present a simple analytical model to capture the delay characteristics of erasure-coding based forwarding, which provides insights on the potential of our approach.
Direct torque control of brushless DC motor drives with improved reliability

Currently, road vehicles are more and more equipped by PM synchronous motors. Of particular interest are brushless DC (BLDC) motors with their low resolution encoder make it feasible their integration in a large scale industry such as the automotive one. Within this trend, the paper deals with the synthesis and implementation of a new DTC strategy dedicated to the control of BLDC motor drives. Compared to the most recent and high-performance DTC strategy, the proposed one offers an improved reliability thanks to the achievement of a balanced switching frequencies of the inverter upper and lower IGBTs, on one hand, and the reduction of the average value of the motor common mode voltage, on the other hand. Furthermore, the torque ripple is significantly damped during sequence-to-sequence commutations using a three-level hysteresis torque controller. An experimentally-based comparative study between the most recent and high-performance DTC strategy and the introduced one clearly highlights the potentialities exhibited by the latter.
Divide and conquer: Partitioning OSPF networks with SDN

Software Defined Networking (SDN) is an emerging network control paradigm focused on logical centralization and programmability. At the same time, distributed routing protocols, most notably OSPF and IS-IS, are still prevalent in IP networks, as they provide shortest path routing, fast topological convergence after network failures, and, perhaps most importantly, the confidence based on decades of reliable operation. Therefore, a hybrid SDN/OSPF operation remains a desirable proposition. In this paper, we propose a new method of hybrid SDN/OSPF operation. Our method is different from other hybrid approaches, as it uses SDN nodes to partition an OSPF domain into sub-domains thereby achieving the traffic engineering capabilities comparable to full SDN operation. We place SDN-enabled routers as subdomain border nodes, while the operation of the OSPF protocol continues unaffected. In this way, the SDN controller can tune routing protocol updates for traffic engineering purposes before they are flooded into sub-domains. While local routing inside sub-domains remains stable at all times, inter-sub-domain routes can be optimized by determining the routes in each traversed sub-domain. As the majority of traffic in non-trivial topologies has to traverse multiple subdomains, our simulation results confirm that a few SDN nodes allow traffic engineering up to a degree that renders full SDN deployment unnecessary.
Emotional Dialogue Generation using Image-Grounded Language Models

Computer-based conversational agents are becoming ubiquitous. However, for these systems to be engaging and valuable to the user, they must be able to express emotion, in addition to providing informative responses. Humans rely on much more than language during conversations; visual information is key to providing context. We present the first example of an image-grounded conversational agent using visual sentiment, facial expression and scene features. We show that key qualities of the generated dialogue can be manipulated by the features used for training the agent. We evaluate our model on a large and very challenging real-world dataset of conversations from social media (Twitter). The image-grounding leads to significantly more informative, emotional and specific responses, and the exact qualities can be tuned depending on the image features used. Furthermore, our model improves the objective quality of dialogue responses when evaluated on standard natural language metrics.
From e3-value to REA: Modeling Multi-party E-business Collaborations

In order to open-up enterprise applications to e-businessand make them profitable for a communication with otherenterprise applications, a business model is needed showingthe business essentials of the e-commerce business caseto be developed. Currently there are two major businessmodeling techniques - e3-value and REA (Resource-Event-Agent). Whereas e3-value was designed for modeling valueexchanges within an e-business network of multiple businesspartners, the REA ontology assumes that, in the presence ofmoney and available prices, all multi-party collaborationsmay be decomposed into a set of corresponding binarycollaborations. This paper is a preliminary attempt to viewe3-value and REA used side-by-side to see where they cancomplement each other in coordinated use in the context ofmultiple-partner collaboration. A real life scenario from theprint media domain has been taken to proof our approach.