nils-herrmann/clean_scirepeval huggingface dataset:
Information Extraction: Methodologies and Applications

This chapter is concerned with the methodologies and applications of information extraction. Information is hidden in the large volume of web pages and thus it is necessary to extract useful information from the web content, called Information Extraction. In information extraction, given a sequence of instances, we identify and pull out a sub-sequence of the input that represents information we are interested in. In the past years, there was a rapid expansion of activities in the information extraction area. Many methods have been proposed for automating the process of extraction. However, due to the heterogeneity and the lack of structure of Web data, automated discovery of targeted or unexpected knowledge information still presents many challenging research problems. In this chapter, we will investigate the problems of information extraction and survey existing methodologies for solving these problems. Several real-world applications of information extraction will be introduced. Emerging challenges will be discussed.
Development of 1-MW Inductive Power Transfer System for a High-Speed Train

Design and fabrication of a 1-MW inductive power transfer (IPT) system that supplies power to the vehicle in real time without any battery charge is proposed for a high-speed train. The IPT system consists of a 1-MW resonant inverter, a 128-m transmitter, four pickups, including rectifiers, and a wireless feedback network to maintain a constant output voltage of the pickups. The operating frequency of the system is 60 kHz to achieve efficient power transfer with a large air gap. The measured efficiency of the IPT system at the 818-kW output power of the pickups for the 5-cm air gap is 82.7%. The electromagnetic field and the induced voltage at the rail are also measured for safety evaluation. The fabricated IPT system was adapted to the high-speed train, and the train successfully accelerates to a speed of 10 km/h according to startup procedures.
Scope+: a stereoscopic video see-through augmented reality microscope

During the usage of conventional stereo microscope, repetitive head movement are often inevitable for the users to retrieve information away from the microscope. Moreover, risks of surgeons loosing focus and increasing fatigue could also arise during microsurgeries. Therefore, Scope+, a stereoscopic video see-through augmented reality system was created not only to solve the problems mentioned above, but also to improve users stereo microscopic experience.
A Hybrid System for Chinese Grammatical Error Diagnosis and Correction

This paper introduces the DM NLP team’s system for NLPTEA 2018 shared task of Chinese Grammatical Error Diagnosis (CGED), which can be used to detect and correct grammatical errors in texts written by Chinese as a Foreign Language (CFL) learners. This task aims at not only detecting four types of grammatical errors including redundant words (R), missing words (M), bad word selection (S) and disordered words (W), but also recommending corrections for errors of M and S types. We proposed a hybrid system including four models for this task with two stages: the detection stage and the correction stage. In the detection stage, we first used a BiLSTM-CRF model to tag potential errors by sequence labeling, along with some handcraft features. Then we designed three Grammatical Error Correction (GEC) models to generate corrections, which could help to tune the detection result. In the correction stage, candidates were generated by the three GEC models and then merged to output the final corrections for M and S types. Our system reached the highest precision in the correction subtask, which was the most challenging part of this shared task, and got top 3 on F1 scores for position detection of errors.
Lateral prefrontal cortex and self-control in intertemporal choice

Disruption of function of left, but not right, lateral prefrontal cortex (LPFC) with low-frequency repetitive transcranial magnetic stimulation (rTMS) increased choices of immediate rewards over larger delayed rewards. rTMS did not change choices involving only delayed rewards or valuation judgments of immediate and delayed rewards, providing causal evidence for a neural lateral-prefrontal cortex–based self-control mechanism in intertemporal choice.
Scenario-Based Performance Analysis of Routing Protocols for Mobile ad-hoc Networks

This study is a comparison of three routing protocols proposed for wireless mobile ad-hoc networks. The protocols are: Destination Sequenced Distance Vector (DSDV), Ad-hoc On demand Distance Vector (AODV) and Dynamic Source Routing (DSR). Extensive simulations are made on a scenario where nodes moves randomly. Results are presented as a function of a novel mobility metric designed to reflect the relative speeds of the nodes in a scenario. Furthermore, three realistic scenarios are introduced to test the protocols in more specialized contexts. In most simulations the reactive protocols (AODV and DSR) performed significantly better than DSDV. At moderate traffic load DSR performed better than AODV for all tested mobility values, while AODV performed better than DSR at higher traffic loads. The latter is caused by the source routes in DSR data packets, which increase the load on the network. routers and hosts, thus a node may forward packets between other nodes as well as run user applications. Mobile ad-hoc networks have been the focus of many recent research and development efforts. Ad-hoc packet radio networks have so far mainly concerned military applications, where a decentralized network configuration is an operative advantage or even a necessity. Networks using ad-hoc configuration concepts can be used in many military applications, ranging from interconnected wireless access points to networks of wireless devices carried by individuals, e.g., digital maps, sensors attached to the body, voice communication, etc. Combinations of wide range and short range ad-hoc networks seek to provide robust, global coverage, even during adverse operating conditions.
Multi-Feature Based Emotion Recognition for Video Clips

In this paper, we present our latest progress in Emotion Recognition techniques, which combines acoustic features and facial features in both non-temporal and temporal mode. This paper presents the details of our techniques used in the Audio-Video Emotion Recognition subtask in the 2018 Emotion Recognition in the Wild (EmotiW) Challenge. After the multimodal results fusion, our final accuracy in Acted Facial Expression in Wild (AFEW) test dataset achieves 61.87%, which is 1.53% higher than the best results last year. Such improvements prove the effectiveness of our methods.
Collocated photo sharing, story-telling, and the performance of self

This article reports empirical findings from four inter-related studies, with an emphasis on collocated sharing. Collocated sharing remains important, using both traditional and emerging image-related technologies. Co-present viewing is a dynamic, improvisational construction of a contingent, situated interaction between story-teller and audience. The concept of performance, as articulated differently by Erving Goffman and Judith Butler, is useful understand the enduring importance of co-present sharing of photos and the importance of oral narratives around images in enacting identity and relationships. Finally, we suggest some implications for both HCI research and the design of image-related technologies.
Big data analytics in healthcare: promise and potential

To describe the promise and potential of big data analytics in healthcare.



The paper describes the nascent field of big data analytics in healthcare, discusses the benefits, outlines an architectural framework and methodology, describes examples reported in the literature, briefly discusses the challenges, and offers conclusions.



The paper provides a broad overview of big data analytics for healthcare researchers and practitioners.



Big data analytics in healthcare is evolving into a promising field for providing insight from very large data sets and improving outcomes while reducing costs. Its potential is great; however there remain challenges to overcome.
Parental delay or refusal of vaccine doses, childhood vaccination coverage at 24 months of age, and the Health Belief Model.

We evaluated the association between parents' beliefs about vaccines, their decision to delay or refuse vaccines for their children, and vaccination coverage of children at aged 24 months.



We used data from 11,206 parents of children aged 24-35 months at the time of the 2009 National Immunization Survey interview and determined their vaccination status at aged 24 months. Data included parents' reports of delay and/or refusal of vaccine doses, psychosocial factors suggested by the Health Belief Model, and provider-reported up-to-date vaccination status.



In 2009, approximately 60.2% of parents with children aged 24-35 months neither delayed nor refused vaccines, 25.8% only delayed, 8.2% only refused, and 5.8% both delayed and refused vaccines. Compared with parents who neither delayed nor refused vaccines, parents who delayed and refused vaccines were significantly less likely to believe that vaccines are necessary to protect the health of children (70.1% vs. 96.2%), that their child might get a disease if they aren't vaccinated (71.0% vs. 90.0%), and that vaccines are safe (50.4% vs. 84.9%). Children of parents who delayed and refused also had significantly lower vaccination coverage for nine of the 10 recommended childhood vaccines including diphtheria-tetanus-acellular pertussis (65.3% vs. 85.2%), polio (76.9% vs. 93.8%), and measles-mumps-rubella (68.4% vs. 92.5%). After adjusting for sociodemographic differences, we found that parents who were less likely to agree that vaccines are necessary to protect the health of children, to believe that their child might get a disease if they aren't vaccinated, or to believe that vaccines are safe had significantly lower coverage for all 10 childhood vaccines.



Parents who delayed and refused vaccine doses were more likely to have vaccine safety concerns and perceive fewer benefits associated with vaccines. Guidelines published by the American Academy of Pediatrics may assist providers in responding to parents who may delay or refuse vaccines.
A survey of remote optical photoplethysmographic imaging methods

In recent years researchers have presented a number of new methods for recovering physiological parameters using just low-cost digital cameras and image processing. The ubiquity of digital cameras presents the possibility for many new, low-cost applications of vital sign monitoring. In this paper we present a review of the work on remote photoplethysmographic (PPG) imaging using digital cameras. This review specifically focuses on the state-of-the-art in PPG imaging where: 1) measures beyond pulse rate are evaluated, 2) non-ideal conditions (e.g., the presence of motion artifacts) are explored, and 3) use cases in relevant environments are demonstrated. We discuss gaps within the literature and future challenges for the research community. To aid in the continuing advancement of PPG imaging research, we are making available a website with the references collected for this review as well as information on available code and datasets of interest. It is our hope that this website will become a valuable resource for the PPG imaging community. The site can be found at: http://web.mit.edu/~djmcduff/www/ remote-physiology.html.
Natural Language Access to Data : It Takes Common Sense !

Commonsense reasoning proves to be an essential tool for natural-language access to data. In a deductive approach to this problem, language processing technology translates English queries into a first-order logical form, which is regarded as a conjecture to be established by a theorem prover. Subject domain knowledge is encoded in an axiomatic theory equipped with links to appropriate databases. Commonsense reasoning is necessary to disambiguate the query, to connect the query with relevant tables in the databases, to deal with logical relationships in the query, and to achieve interoperability between disparate databases. This is illustrated with examples from a proof-of-concept system called Quest, which deals with queries over business enterprise data for an industrial QA system.
Revisiting Complex Moments for 2-D Shape Representation and Image Normalization

When comparing 2-D shapes, a key issue is their normalization. Translation and scale are easily taken care of by removing the mean and normalizing the energy. However, defining and computing the orientation of a 2-D shape is not so simple. In fact, although for elongated shapes the principal axis can be used to define one of two possible orientations, there is no such tool for general shapes. As we show in the paper, previous approaches fail to compute the orientation of even noiseless observations of simple shapes. We address this problem and show how to uniquely define the orientation of an arbitrary 2-D shape, in terms of what we call its Principal Moments. We start by showing that a small subset of these moments suffices to describe the underlying 2-D shape, i.e., that they form a compact representation, which is particularly relevant when dealing with large databases. Then, we propose a new method to efficiently compute the shape orientation: Principal Moment Analysis. Finally, we discuss how this method can further be applied to normalize gray-level images. Besides the theoretical proof of correctness, we describe experiments demonstrating robustness to noise and illustrating with real images.
β-VAE : L EARNING B ASIC

Learning an interpretable factorised representation of the independent data generative factors of the world without supervision is an important precursor for the development of artificial intelligence that is able to learn and reason in the same way that humans do. We introduce β-VAE, a new state-of-the-art framework for automated discovery of interpretable factorised latent representations from raw image data in a completely unsupervised manner. Our approach is a modification of the variational autoencoder (VAE) framework. We introduce an adjustable hyperparameter β that balances latent channel capacity and independence constraints with reconstruction accuracy. We demonstrate that β-VAE with appropriately tuned β > 1 qualitatively outperforms VAE (β = 1), as well as state of the art unsupervised (InfoGAN) and semi-supervised (DC-IGN) approaches to disentangled factor learning on a variety of datasets (celebA, faces and chairs). Furthermore, we devise a protocol to quantitatively compare the degree of disentanglement learnt by different models, and show that our approach also significantly outperforms all baselines quantitatively. Unlike InfoGAN, β-VAE is stable to train, makes few assumptions about the data and relies on tuning a single hyperparameter β, which can be directly optimised through a hyperparameter search using weakly labelled data or through heuristic visual inspection for purely unsupervised data.
Methods for extracting and classifying pairs of cognates and false friends

The identification of cognates has attracted the attention of researchers working in the area of Natural Language Processing, but the identification of false friends is still an under-researched area. This paper proposes novel methods for the automatic identification of both cognates and false friends from comparable bilingual corpora. The methods are not dependent on the existence of parallel texts, and make use of only monolingual corpora and a bilingual dictionary necessary for the mapping of co-occurrence data across languages. In addition, the methods do not require that the newly discovered cognates or false friends are present in the dictionary and hence are capable of operating on out-of-vocabulary expressions. These methods are evaluated on English, French, German and Spanish corpora in order to identify English–French, English–German, English–Spanish and French–Spanish pairs of cognates or false friends. The experiments were performed in two settings: (i) assuming ‘ideal’ extraction of cognates and false friends from plain-text corpora, i.e. when the evaluation data contains only cognates and false friends, and (ii) a real-world extraction scenario where cognates and false friends have to first be identified among words found in two comparable corpora in different languages. The evaluation results show that the developed methods identify cognates and false friends with very satisfactory results for both recall and precision, with methods that incorporate background semantic knowledge, in addition to co-occurrence data obtained from the corpora, delivering the best results.
A standard compliant security framework for IEEE 802.15.4 networks

The IEEE 802.15.4 standard is widely recognized as one of the most successful enabling technologies for short range low rate wireless communications. It covers all the details related to the MAC and PHY layers of the protocol stack. In addition, it supports the possibility to protect MAC packets by using symmetric-key cryptography techniques and it offers several security options. But, at the same time, the standard relies on upper layers to orchestrate the usage of the plethora of security profiles and configuration settings it makes available, as well as to handle the creation and the exchange of encryption keys. In support of this functionality, this work describes a standard compliant security framework aimed at proposing: (i) different kind of security architectures, (ii) an efficient mechanism for initializing a secure IEEE 802.15.4 domain, and (iii) a lightweight mechanism to negotiate link keys among devices.
Why Johnny Can't Encrypt: A Usability Evaluation of PGP 5.0

User errors cause or contribute to most computer security failures, yet user interfaces for security still tend to be clumsy, confusing, or near-nonexistent. Is this simply due to a failure to apply standard user interface design techniques to security? We argue that, on the contrary, effective security requires a different usability standard, and that it will not be achieved through the user interface design techniques appropriate to other types of consumer software. To test this hypothesis, we performed a case study of a security program which does have a good user interface by general standards: PGP 5.0. Our case study used a cognitive walkthrough analysis together with a laboratory user test to evaluate whether PGP 5.0 can be successfully used by cryptography novices to achieve effective electronic mail security. The analysis found a number of user interface design flaws that may contribute to security failures, and the user test demonstrated that when our test participants were given 90 minutes in which to sign and encrypt a message using PGP 5.0, the majority of them were unable to do so successfully. We conclude that PGP 5.0 is not usable enough to provide effective security for most computer users, despite its attractive graphical user interface, supporting our hypothesis that user interface design for effective security remains an open problem. We close with a brief description of our continuing work on the development and application of user interface design principles and techniques for security.
IMGPU: GPU-Accelerated Influence Maximization in Large-Scale Social Networks

Influence Maximization aims to find the top-$(K)$ influential individuals to maximize the influence spread within a social network, which remains an important yet challenging problem. Proven to be NP-hard, the influence maximization problem attracts tremendous studies. Though there exist basic greedy algorithms which may provide good approximation to optimal result, they mainly suffer from low computational efficiency and excessively long execution time, limiting the application to large-scale social networks. In this paper, we present IMGPU, a novel framework to accelerate the influence maximization by leveraging the parallel processing capability of graphics processing unit (GPU). We first improve the existing greedy algorithms and design a bottom-up traversal algorithm with GPU implementation, which contains inherent parallelism. To best fit the proposed influence maximization algorithm with the GPU architecture, we further develop an adaptive K-level combination method to maximize the parallelism and reorganize the influence graph to minimize the potential divergence. We carry out comprehensive experiments with both real-world and sythetic social network traces and demonstrate that with IMGPU framework, we are able to outperform the state-of-the-art influence maximization algorithm up to a factor of 60, and show potential to scale up to extraordinarily large-scale networks.
Semantic full-text search with broccoli

We combine search in triple stores with full-text search into what we call \emph{semantic full-text search}. We provide a fully functional web application that allows the incremental construction of complex queries on the English Wikipedia combined with the facts from Freebase. The user is guided by context-sensitive suggestions of matching words, instances, classes, and relations after each keystroke. We also provide a powerful API, which may be used for research tasks or as a back end, e.g., for a question answering system. Our web application and public API are available under \url{http://broccoli.cs.uni-freiburg.de}.
Computational design of actuated deformable characters

We present a method for fabrication-oriented design of actuated deformable characters that allows a user to automatically create physical replicas of digitally designed characters using rapid manufacturing technologies. Given a deformable character and a set of target poses as input, our method computes a small set of actuators along with their locations on the surface and optimizes the internal material distribution such that the resulting character exhibits the desired deformation behavior. We approach this problem with a dedicated algorithm that combines finite-element analysis, sparse regularization, and constrained optimization. We validate our pipeline on a set of two- and three-dimensional example characters and present results in simulation and physically-fabricated prototypes.
Teaching and working with robots as a collaboration

New applications for autonomous robots bring them into the human environment where they are to serve as helpful assistants to untrained users in the home or office, or work as capable members of human-robot teams for security, military, and space efforts. These applications require robots to be able to quickly learn how to perform new tasks from natural human instruction, and to perform tasks collaboratively with human teammates. Using joint intention theory as our theoretical framework, our approach integrates learning and collaboration through a goal based task structure. Specifically, we use collaborative discourse with accompanying gestures and social cues to teach a humanoid robot a structurally complex task. Having learned the representation for the task, the robot then performs it shoulder-to-shoulder with a human partner, using social communication acts to dynamically mesh its plans with those of its partner, according to the relative capabilities of the human and the robot.