nils-herrmann/clean_scirepeval huggingface dataset:
Off-policy learning based on weighted importance sampling with linear computational complexity

Importance sampling is an essential component of model-free off-policy learning algorithms. Weighted importance sampling (WIS) is generally considered superior to ordinary importance sampling but, when combined with function approximation, it has hitherto required computational complexity that is O(n2) or more in the number of features. In this paper we introduce new off-policy learning algorithms that obtain the benefits of WIS with O(n) computational complexity. Our algorithms maintain for each component of the parameter vector a measure of the extent to which that component has been used in previous examples. This measure is used to determine component-wise step sizes, merging the ideas of stochastic gradient descent and sample averages. We present our main WIS-based algorithm first in an intuitive acausal form (the forward view) and then derive a causal algorithm using eligibility traces that is equivalent but more efficient (the backward view). In three small experiments, our algorithms performed significantly better than prior O(n) algorithms for offpolicy policy evaluation. We also show that our adaptive step-size technique alone can improve the performance of on-policy algorithms such as TD( ) and true online TD( ).
DeepTLR: A single deep convolutional network for detection and classification of traffic lights

Reliable real-time detection of traffic lights is a major concern for the task of autonomous driving. As deep convolutional networks have proven to be a powerful tool in visual object detection, we propose DeepTLR, a camera-based system for real-time detection and classification of traffic lights. Detection and state classification are realized using a single deep convolutional network. DeepTLR does not use any prior knowledge about traffic light locations. Also the detection is executed frame by frame without using temporal information. It is able to detect traffic lights on the whole camera image without any presegmentation. This is achieved by classifying each fine-grained pixel region of the input image and performing a bounding box regression on regions of each class. We show that our algorithm is able to run on frame-rates required for real-time applications while reaching notable results.
The Dataflow Model: A Practical Approach to Balancing Correctness, Latency, and Cost in Massive-Scale, Unbounded, Out-of-Order Data Processing

Unbounded, unordered, global-scale datasets are increasingly common in day-to-day business (e.g. Web logs, mobile usage statistics, and sensor networks). At the same time, consumers of these datasets have evolved sophisticated requirements, such as event-time ordering and windowing by features of the data themselves, in addition to an insatiable hunger for faster answers. Meanwhile, practicality dictates that one can never fully optimize along all dimensions of correctness, latency, and cost for these types of input. As a result, data processing practitioners are left with the quandary of how to reconcile the tensions between these seemingly competing propositions, often resulting in disparate implementations and systems. We propose that a fundamental shift of approach is necessary to deal with these evolved requirements in modern data processing. We as a field must stop trying to groom unbounded datasets into finite pools of information that eventually become complete, and instead live and breathe under the assumption that we will never know if or when we have seen all of our data, only that new data will arrive, old data may be retracted, and the only way to make this problem tractable is via principled abstractions that allow the practitioner the choice of appropriate tradeoffs along the axes of interest: correctness, latency, and cost. In this paper, we present one such approach, the Dataflow Model, along with a detailed examination of the semantics it enables, an overview of the core principles that guided its design, and a validation of the model itself via the real-world experiences that led to its development. We use the term “Dataflow Model” to describe the processing model of Google Cloud Dataflow [20], which is based upon technology from FlumeJava [12] and MillWheel .  To view a copy of this license, visit http://creativecommons.org/licenses/by-nc-nd/3.0/. Obtain permission prior to any use beyond those covered by the license. Contact copyright holder by emailing info@vldb.org. Articles from this volume were invited to present their results at the 41st International Conference on Very Large Data Bases, August 31st September 4th 2015, Kohala Coast, Hawaii. Proceedings of the VLDB Endowment, Vol. 8, No. 12
Face Recognition-based Lecture Attendance System

In this paper, we propose a system that takes the attendance of students for classroom lecture. Our system takes the attendance automatically using face recognition. However, it is difficult to estimate the attendance precisely using each result of face recognition independently because the face detection rate is not sufficiently high. In this paper, we propose a method for estimating the attendance precisely using all the results of face recognition obtained by continuous observation. Continuous observation improves the performance for the estimation of the attendance We constructed the lecture attendance system based on face recognition, and applied the system to classroom lecture. This paper first review the related works in the field of attendance management and face recognition. Then, it introduces our system structure and plan. Finally, experiments are implemented to provide as evidence to support our plan. The result shows that continuous observation improved the performance for the estimation of the attendance.
Evaluating usage and quality of technical software documentation: an empirical study

Software documentation is an integral part of any software development process. However, software practitioners are often concerned about the lack of usage and quality of documentation in practice. Unfortunately, in many projects, practitioners find that software documentation artifacts are outdated, incomplete and sometimes not beneficial.  Motivated by the needs of NovAtel Inc. (NovAtel), a world-leading company of GPS software systems, we propose in this paper an approach to analyze the usage and quality of software documentation in development and maintenance phases.  The approach incorporates inputs from automated analysis (e.g., mining of project's data) and also experts' opinion extracted from survey-based questionnaire. The approach has been designed based on the "action-research" approach and in close collaboration between industry and academia.  To evaluate the feasibility and usefulness of the proposed approach, we have applied it in an industrial setting and results are presented in this paper. One of the results is that, in the context of our case-study, usage of documentation for an implementation purpose is higher than the usage for maintenance purposes.  It is concluded that the usage of documentation differs for various purposes and it depends on the type of the information needs as well as the task to be completed (e.g. development and maintenance). In addition, we identify the most important and relevant quality attributes which are critical to improving documentation quality.
Machine Learning in Genomic Medicine: A Review of Computational Problems and Data Sets

In this paper, we provide an introduction to machine learning tasks that address important problems in genomic medicine. One of the goals of genomic medicine is to determine how variations in the DNA of individuals can affect the risk of different diseases, and to find causal explanations so that targeted therapies can be designed. Here we focus on how machine learning can help to model the relationship between DNA and the quantities of key molecules in the cell, with the premise that these quantities, which we refer to as cell variables, may be associated with disease risks. Modern biology allows high-throughput measurement of many such cell variables, including gene expression, splicing, and proteins binding to nucleic acids, which can all be treated as training targets for predictive models. With the growing availability of large-scale data sets and advanced computational techniques such as deep learning, researchers can help to usher in a new era of effective genomic medicine.
Semi-Global Matching: A Principled Derivation in Terms of Message Passing

Semi-global matching, originally introduced in the context of dense stereo, is a very successful heuristic to minimize the energy of a pairwise multi-label Markov Random Field defined on a grid. We offer the first principled explanation of this empirically successful algorithm, and clarify its exact relation to belief propagation and tree-reweighted message passing. One outcome of this new connection is an uncertainty measure for the MAP label of a variable in a Markov Random Field.
Multilingual Models for Compositional Distributed Semantics

We present a novel technique for learning semantic representations, which extends the distributional hypothesis to multilingual data and joint-space embeddings. Our models leverage parallel data and learn to strongly align the embeddings of semantically equivalent sentences, while maintaining sufficient distance between those of dissimilar sentences. The models do not rely on word alignments or any syntactic information and are successfully applied to a number of diverse languages. We extend our approach to learn semantic representations at the document level, too. We evaluate these models on two cross-lingual document classification tasks, outperforming the prior state of the art. Through qualitative analysis and the study of pivoting effects we demonstrate that our representations are semantically plausible and can capture semantic relationships across languages without parallel data.
Emotion regulation through execution, observation, and imagery of emotional movements

According to Damasio's somatic marker hypothesis, emotions are generated by conveying the current state of the body to the brain through interoceptive and proprioceptive afferent input. The resulting brain activation patterns represent unconscious emotions and correlate with subjective feelings. This proposition implies a corollary that the deliberate control of motor behavior could regulate feelings. We tested this possibility, hypothesizing that engaging in movements associated with a certain emotion would enhance that emotion and/or the corresponding valence. Furthermore, because motor imagery and observation are thought to activate the same mirror-neuron network engaged during motor execution, they might also activate the same emotional processing circuits, leading to similar emotional effects. Therefore, we measured the effects of motor execution, motor imagery and observation of whole-body dynamic expressions of emotions (happiness, sadness, fear) on affective state. All three tasks enhanced the corresponding affective state, indicating their potential to regulate emotions.
Open-access MIMIC-II database for intensive care research

The critical state of intensive care unit (ICU) patients demands close monitoring, and as a result a large volume of multi-parameter data is collected continuously. This represents a unique opportunity for researchers interested in clinical data mining. We sought to foster a more transparent and efficient intensive care research community by building a publicly available ICU database, namely Multiparameter Intelligent Monitoring in Intensive Care II (MIMIC-II). The data harnessed in MIMIC-II were collected from the ICUs of Beth Israel Deaconess Medical Center from 2001 to 2008 and represent 26,870 adult hospital admissions (version 2.6). II consists of two major components: clinical data and physiological waveforms. The clinical data, which include patient demographics, intravenous medication drip rates, and laboratory test results, were organized into a relational database. The physiological waveforms, including 125 Hz signals recorded at bedside and corresponding vital signs, were stored in an open-source format. II data were also deidentified in order to remove protected health information. Any interested researcher can gain access to MIMIC-II free of charge after signing a data use agreement and completing human subjects training. II can support a wide variety of research studies, ranging from the development of clinical decision support algorithms to retrospective clinical studies. We anticipate that MIMIC-II will be an invaluable resource for intensive care research by stimulating fair comparisons among different studies.
Comparison of SQL, NoSQL and NewSQL databases for internet of things

The Internet of Things (IoT) in all its essentiality is a collection of sensors bridged together tightly. The present day development in the technology industry has thus elevated the emphasis of large amounts of data. IoT is expected to generate and collect an enormous amount of data from varied locations very quickly. The concerns for features like storage and performance factors are coercing the databases to outperform themselves. Traditional relational databases have time and again proved to be efficient. NoSQL and more recently NewSQL databases having gained an impetus are asserted to perform better than the classic SQL counterpart. This paper compares the performance of three databases each from the said technologies; SQL (MySQL), NoSQL (MongoDB) and NewSQL (VoltDB) respectively for sensor readings. The sensor data handled ranges in vast (MB to GB) size and tested against: single write, single read, single delete and multi write operations.
Integrating Models of Personality and Emotions into Lifelike Characters

A growing number of research projects in academia and industry have recently started to develop lifelike agents as a new metaphor for highly personalised human-machine communication. A strong argument in favour of using such characters in the interface is the fact that they make humancomputer interaction more enjoyable and allow for communication styles common in human-human dialogue. In this paper we discuss three ongoing projects that use personality and emotions to address different aspects of the affective agent-user interface: (a) Puppet uses affect to teach children how the different emotional states can change or modify a character’s behaviour, and how physical and verbal actions in social interactions can induce emotions in others; (b) the Inhabited Market Place uses affect to tailor the roles of actors in a virtual market place; and (c) Presence uses affect to enhance the believability of a virtual character, and produce a more natural conversational manner.
Factors A ff ecting the Adoption of Gamified Smart Tourism Applications : An Integrative Approach

Considering that the core concerns in sustainability are threats to the survival of humankind and the ecosystems that humans depend on, changing the consumption and production behaviors of individuals and society is inevitable. However, people are reluctant to change their own behavior in support of sustainability goals. This tendency seems to be especially strong in tourism because the main value of tourism is hedonic utility. Thus, the tourism industry is now introducing gamification and smart tourism to shift tourist behavior toward sustainability, but most of studies and practices only focus on the performance and application of gamification without considering customer adoption patterns and perceptions during the process. This study empirically investigated what factors affect the adoption of smart tourism applications that incorporate game elements, using the Google Maps tourist guide program. As an initial approach, we incorporated diverse theoretical approaches: perceived usefulness; perceived ease of use; perceived enjoyment from technology acceptance model; information and interaction motivations from the uses and gratifications theory; the network effect; distributive justice; flow as responses to the game characteristics of smart tourism applications; and information privacy concerns as a negative factor for diffusion. The result showed that hedonic characteristics of the gamified smart tourism application (GSTA) are strong in adoption. Perceived enjoyment had a significant influence on the intention to use, but information quality, related to cognitive experience, did not. The flow and perceived distributive justice associated with the game content were not significant, but the interaction motivation was significant in the research model. The results of this study show that individuals regard a GSTA as a low-level game tool. Also, it is important to preoccupy the smart tourism application market in terms of marketing strategy because the network effect is relevant to both perceived usefulness and perceived enjoyment. We also found that the need to provide personal information would negatively affect the adoption of a gamified smart tourism application.
Frustratingly Easy Domain Adaptation

We describe an approach to domain adaptation that is appropriate exactly in the case when one has enough “target” data to do slightly better than just using only “source” data. Our approach is incredibly simple, easy to implement as a preprocessing step (10 lines of Perl!) and outperforms stateof-the-art approaches on a range of datasets. Moreover, it is trivially extended to a multidomain adaptation problem, where one has data from a variety of different domains.
N-gram-based Machine Translation

This article describes in detail an n-gram approach to statistical machine translation. This approach consists of a log-linear combination of a translation model based on n-grams of bilingual units, which are referred to as tuples, along with four specific feature functions. Translation performance, which happens to be in the state of the art, is demonstrated with Spanish-to-English and English-to-Spanish translations of the European Parliament Plenary Sessions (EPPS).
Semi-supervised time series classification

The problem of time series classification has attracted great interest in the last decade. However current research assumes the existence of large amounts of labeled training data. In reality, such data may be very difficult or expensive to obtain. For example, it may require the time and expertise of cardiologists, space launch technicians, or other domain specialists. As in many other domains, there are often copious amounts of unlabeled data available. For example, the PhysioBank archive contains gigabytes of ECG data. In this work we propose a semi-supervised technique for building time series classifiers. While such algorithms are well known in text domains, we will show that special considerations must be made to make them both efficient and effective for the time series domain. We evaluate our work with a comprehensive set of experiments on diverse data sources including electrocardiograms, handwritten documents, and video datasets. The experimental results demonstrate that our approach requires only a handful of labeled examples to construct accurate classifiers.
Literature Fingerprinting: A New Method for Visual Literary Analysis

In computer-based literary analysis different types of features are used to characterize a text. Usually, only a single feature value or vector is calculated for the whole text. In this paper, we combine automatic literature analysis methods with an effective visualization technique to analyze the behavior of the feature values across the text. For an interactive visual analysis, we calculate a sequence of feature values per text and present them to the user as a characteristic fingerprint. The feature values may be calculated on different hierarchy levels, allowing the analysis to be done on different resolution levels. A case study shows several successful applications of our new method to known literature problems and demonstrates the advantage of our new visual literature fingerprinting.
A Flexible Architecture for Ray Tracing Terrain Heightfields

High-quality interactive rendering of terrain surfaces is a challenging task, which requires compromises between rendering quality, rendering time and available resources. However, current solutions typically provide optimized strategies tailored to particular constraints. In this paper we propose a more scalable approach based on functional programming and introduce a flexible ray tracer for rendering terrain heightfields. This permits the dynamic composition of complex and recursive shaders. In order to exploit the concurrency of the GPU for a large number of dynamically created tasks with inter-dependencies, the functional model is represented as a token stream and is iteratively rewritten via pattern matching on multiple shader cores in parallel. A first prototype demonstrates the feasibility of our approach.
Designing a Tag-Based Statistical Math Word Problem Solver with Reasoning and Explanation

Big Data mainly aims to explore the correlation between surface features but not their underlying causality relationship, the Big Mechanism 2 program has been proposed by DARPA to find out “why” behind the “Big Data”. However, the pre-requisite for it is that the machine can read each document and learn its associated knowledge, which is the task of Machine Reading (MR). Since a domain-independent MR system is complicated and difficult to build, the math word problem (MWP)  is frequently chosen as the first test case to study MR (as it usually uses less complicated syntax and requires less amount of domain knowledge). According to the framework for making the decision while there are several candidates, previous MWP algebra solvers can be classified into: (1) Rule-based approaches with logic inference [2-7], which apply rules to get the answer (via identifying entities, quantities, operations, etc.) with a logic inference engine. (2) Rule-based approaches without logic inference [8-13], which apply rules to get the answer without a logic inference engine. (3) Statistics-based approaches [14, 15], which use statistical models to identify entities, quantities, operations, and get the answer. To our knowledge, all the statistics-based approaches do not adopt logic inference. The main problem of the rule-based approaches mentioned above is that the coverage rate problem is serious, as rules with wide coverage are difficult and expensive to construct. Also, since they adopt Go/No-Go approach (unlike statistical approaches which can adopt a large Top-N to have high including rates), the error accumulation problem would be severe. On the other hand, the main problem of those approaches without adopting logic inference is that they usually need to implement a new handling procedure for each new type of problems (as the general logic inference mechanism is not adopted). Also, as there is no inference engine to generate the reasoning chain [16], additional effort would be required for
A Hybrid Epidemic Model: Combining The Advantages Of Agent-Based And Equation-Based Approaches

Agent-based models (ABMs) are powerful in describing structured epidemiological processes involving human behavior and local interaction. The joint behavior of the agents can be very complex and tracking the behavior requires a disciplined approach. At the same time, equation-based models (EBMs) can be more tractable and allow for at least partial analytical insight. However, inadequate representation of the detailed population structure can lead to spurious results, especially when the epidemic process is beginning and individual variation is critical. In this paper, we demonstrate an approach that combines the two modeling paradigms and introduces a hybrid model that starts as agent-based and switches to equation-based after the number of infected individuals is large enough to support a population-averaged approach. This hybrid model can dramatically save computational times and, more fundamentally, allows for the mathematical analysis of emerging structures generated by the ABM.