nils-herrmann/clean_scirepeval huggingface dataset:
Long-Term Recurrent Convolutional Networks for Visual Recognition and Description

Models based on deep convolutional networks have dominated recent image interpretation tasks; we investigate whether models which are also recurrent are effective for tasks involving sequences, visual and otherwise. We describe a class of recurrent convolutional architectures which is end-to-end trainable and suitable for large-scale visual understanding tasks, and demonstrate the value of these models for activity recognition, image captioning, and video description. In contrast to previous models which assume a fixed visual representation or perform simple temporal averaging for sequential processing, recurrent convolutional models are “doubly deep” in that they learn compositional representations in space and time. Learning long-term dependencies is possible when nonlinearities are incorporated into the network state updates. Differentiable recurrent models are appealing in that they can directly map variable-length inputs (e.g., videos) to variable-length outputs (e.g., natural language text) and can model complex temporal dynamics; yet they can be optimized with backpropagation. Our recurrent sequence models are directly connected to modern visual convolutional network models and can be jointly trained to learn temporal dynamics and convolutional perceptual representations. Our results show that such models have distinct advantages over state-of-the-art models for recognition or generation which are separately defined or optimized.
The treatment of super-refractory status epilepticus: a critical review of available therapies and a clinical treatment protocol.

Super-refractory status epilepticus is defined as status epilepticus that continues or recurs 24 h or more after the onset of anaesthetic therapy, including those cases where status epilepticus recurs on the reduction or withdrawal of anaesthesia. It is an uncommon but important clinical problem with high mortality and morbidity rates. This article reviews the treatment approaches. There are no controlled or randomized studies, and so therapy has to be based on clinical reports and opinion. The published world literature on the following treatments was critically evaluated: anaesthetic agents, anti-epileptic drugs, magnesium infusion, pyridoxine, steroids and immunotherapy, ketogenic diet, hypothermia, emergency resective neurosurgery and multiple subpial transection, transcranial magnetic stimulation, vagal nerve stimulation, deep brain stimulation, electroconvulsive therapy, drainage of the cerebrospinal fluid and other older drug therapies. The importance of treating the identifying cause is stressed. A protocol and flowchart for managing super-refractory status epilepticus is suggested. In view of the small number of published reports, there is an urgent need for the establishment of a database of outcomes of individual therapies.
Towards Citation-Based Summarization of Biomedical Literature

Citation-based summarization is a form of technical summarization that uses citations to an article to form its summary. In biomedical literature, citations by themselves are not reliable to be used for summary as they fail to consider the context of the findings in the referenced article. One way to remedy such problem is to link citations to the related text spans in the reference article. The ultimate goal in TAC1 biomedical summarization track is to generate a citation-based summary, using both the citations and the context information. This paper describes our approach for finding the context information related to each citation and determining their discourse facet (Task 1 of the track). We approach this task as a search task, applying different query reformulation techniques for retrieving the relevant text spans. After finding the relevant spans, we classify each citation to a set of discourse facets to capture the structure of the referenced paper. While our results show 20% improvement over the baseline, the efficiency of the system still leaves much room for improvement.
The Psychology of Radicalization and Deradicalization : How Significance Quest Impacts Violent Extremism

We present a model of radicalization and deradicalization based on the notion that the quest for personal significance constitutes a major motivational force that may push individuals toward violent extremism. Radicalization is defined as the process of supporting or engaging in activities deemed (by others) as in violation of important social norms (e.g., the killing of civilians). In these terms, radicalization (1) is a matter of degree (in which mere attitudinal support for violence reflects a lower degree of radicalization than actual engagement in violence); (2) represents a subjective judgment proffered by those for whom the violated norms seem important but not by those who have devalued or suppressed the norms in question. Our radicalization/deradicalization model contains three crucial components: (1) the motivational component (the quest for personal significance) that defines a goal to which one may be committed, (2) the ideological component that in addition identifies the means of violence as appropriate for this goal’s pursuit, and (3) the social process of networking and group dynamics through which the individual comes to share in the violencejustifying ideology and proceeds to implement it as a means of significance gain. We present empirical evidence consistent with our model’s assumptions and discuss its implications for policies of preventing radicalization and effecting deradicalization.
The Imprecisions of Precision Measures in Process Mining

In process mining, precision measures are used to quantify how much a process model overapproximates the behavior seen in an event log. Although several measures have been proposed throughout the years, no research has been done to validate whether these measures achieve the intended aim of quantifying over-approximation in a consistent way for all models and logs. This paper fills this gap by postulating a number of axioms for quantifying precision consistently for any log and any model. Further, we show through counter-examples that none of the existing measures consistently quantifies precision.
Survey of the State of the Art in Natural Language Generation: Core tasks, applications and evaluation

This paper surveys the current state of the art in Natural Language Generation (nlg), defined as the task of generating text or speech from non-linguistic input. A survey of nlg is timely in view of the changes that the field has undergone over the past two decades, especially in relation to new (usually data-driven) methods, as well as new applications of nlg technology. This survey therefore aims to (a) give an up-to-date synthesis of research on the core tasks in nlg and the architectures adopted in which such tasks are organised; (b) highlight a number of recent research topics that have arisen partly as a result of growing synergies between nlg and other areas of artificial intelligence; (c) draw attention to the challenges in nlg evaluation, relating them to similar challenges faced in other areas of nlp, with an emphasis on different evaluation methods and the relationships between them.
Scalable Construction and Reasoning of Massive Knowledge Bases

In today’s information-based society, there is abundant knowledge out there carried in the form of natural language texts (e.g., news articles, social media posts, scientific publications), which spans across various domains (e.g., corporate documents, advertisements, legal acts, medical reports), and grows at an astonishing rate. How to turn such massive and unstructured text data into structured, actionable knowledge for computational machines, and furthermore, how to teach machines learn to reason and complete the extracted knowledge is a grand challenge to the research community. Traditional IE systems assume abundant human annotations for training high quality machine learning models, which is impractical when trying to deploy IE systems to a broad range of domains, settings and languages. In the first part of the tutorial, we introduce how to extract structured facts (i.e., entities and their relations of different types) from text corpora to construct knowledge bases, with a focus on methods that are minimally-supervised and domain-independent for timely knowledge base construction across various application domains. In the second part, we introduce how to leverage other knowledge, such as the distributional statistics of characters and words, the annotations for other tasks and other domains, and the linguistics and problem structures, to combat the problem of inadequate supervision, and conduct low-resource information extraction. In the third part, we describe recent advances in knowledge base reasoning. We start with the gentle introduction to the literature, focusing on pathbased and embedding based methods. We then describe DeepPath, a recent attempt of using deep reinforcement learning to combine the best of both worlds for knowledge base reasoning.
Weakly-supervised Disentangling with Recurrent Transformations for 3D View Synthesis

An important problem for both graphics and vision is to synthesize novel views of a 3D object from a single image. This is particularly challenging due to the partial observability inherent in projecting a 3D object onto the image space, and the ill-posedness of inferring object shape and pose. However, we can train a neural network to address the problem if we restrict our attention to specific object categories (in our case faces and chairs) for which we can gather ample training data. In this paper, we propose a novel recurrent convolutional encoder-decoder network that is trained end-to-end on the task of rendering rotated objects starting from a single image. The recurrent structure allows our model to capture long-term dependencies along a sequence of transformations. We demonstrate the quality of its predictions for human faces on the Multi-PIE dataset and for a dataset of 3D chair models, and also show its ability to disentangle latent factors of variation (e.g., identity and pose) without using full supervision.
A Multi-Stage Memory Augmented Neural Network for Machine Reading Comprehension

Reading Comprehension (RC) of text is one of the fundamental tasks in natural language processing. In recent years, several end-to-end neural network models have been proposed to solve RC tasks. However, most of these models suffer in reasoning over long documents. In this work, we propose a novel Memory Augmented Machine Comprehension Network (MAMCN) to address long-range dependencies present in machine reading comprehension. We perform extensive experiments to evaluate proposed method with the renowned benchmark datasets such as SQuAD, QUASAR-T, and TriviaQA. We achieve the state of the art performance on both the document-level (QUASAR-T, TriviaQA) and paragraph-level (SQuAD) datasets compared to all the previously published approaches.
Gender swapping and user behaviors in online social games

Modern Massively Multiplayer Online Role-Playing Games (MMORPGs) provide lifelike virtual environments in which players can conduct a variety of activities including combat, trade, and chat with other players. While the game world and the available actions therein are inspired by their offline counterparts, the games' popularity and dedicated fan base are testaments to the allure of novel social interactions granted to people by allowing them an alternative life as a new character and persona. In this paper we investigate the phenomenon of "gender swapping," which refers to players choosing avatars of genders opposite to their natural ones. We report the behavioral patterns observed in players of Fairyland Online, a globally serviced MMORPG, during social interactions when playing as in-game avatars of their own real gender or gender-swapped. We also discuss the effect of gender role and self-image in virtual social situations and the potential of our study for improving MMORPG quality and detecting online identity frauds.
Graph stream algorithms: a survey

Over the last decade, there has been considerable interest in designing algorithms for processing massive graphs in the data stream model. The original motivation was two-fold: a) in many applications, the dynamic graphs that arise are too large to be stored in the main memory of a single machine and b) considering graph problems yields new insights into the complexity of stream computation. However, the techniques developed in this area are now finding applications in other areas including data structures for dynamic graphs, approximation algorithms, and distributed and parallel computation. We survey the state-of-the-art results; identify general techniques; and highlight some simple algorithms that illustrate basic ideas.
Turkish document classification based on Word2Vec and SVM classifier

In this study, Turkish texts belonging to different categories were classified by using word2vec word vectors. Firstly, vectors of the words in all the texts were extracted then, each text was represented in terms of the mean vectors of the words it contains. Texts were classified by SVM and 0.92 F measurement score was obtained for seven different categories. As a result, it was experimentally shown that word2vec is more successful than tf-idf based classification for Turkish document classification.
Mobile Edge Computing, Fog et al.: A Survey and Analysis of Security Threats and Challenges

For various reasons, the cloud computing paradigm is unable to meet certain requirements (e.g. low latency and jitter, context awareness, mobility support) that are crucial for several applications (e.g. vehicular networks, augmented reality). To fulfil these requirements, various paradigms, such as fog computing, mobile edge computing, and mobile cloud computing, have emerged in recent years. While these edge paradigms share several features, most of the existing research is compartmentalised; no synergies have been explored. This is especially true in the field of security, where most analyses focus only on one edge paradigm, while ignoring the others. The main goal of this study is to holistically analyse the security threats, challenges, and mechanisms inherent in all edge paradigms, while highlighting potential synergies and venues of collaboration. In our results, we will show that all edge paradigms should consider the advances in other paradigms.
Sign Language Recognition and Translation with Kinect

Sign language (SL) recognition, although has been explored for many years, is still a challenging problem for real practice. The complex background and illumination conditions affect the hand tracking and make the SL recognition very difficult. Fortunately, Kinect is able to provide depth and color data simultaneously, based on which the hand and body action can be tracked more accurate and easier. Therefore, 3D motion trajectory of each sign language vocabulary is aligned and matched between probe and gallery to get the recognized result. This demo will show our primary efforts on sign language recognition and translation with Kinect. Keywords-sign language; hand tracking; 3D motion trajectory
Nonprice incentives and energy conservation.

In the electricity sector, energy conservation through technological and behavioral change is estimated to have a savings potential of 123 million metric tons of carbon per year, which represents 20% of US household direct emissions in the United States. In this article, we investigate the effectiveness of nonprice information strategies to motivate conservation behavior. We introduce environment and health-based messaging as a behavioral strategy to reduce energy use in the home and promote energy conservation. In a randomized controlled trial with real-time appliance-level energy metering, we find that environment and health-based information strategies, which communicate the environmental and public health externalities of electricity production, such as pounds of pollutants, childhood asthma, and cancer, outperform monetary savings information to drive behavioral change in the home. Environment and health-based information treatments motivated 8% energy savings versus control and were particularly effective on families with children, who achieved up to 19% energy savings. Our results are based on a panel of 3.4 million hourly appliance-level kilowatt-hour observations for 118 residences over 8 mo. We discuss the relative impacts of both cost-savings information and environmental health messaging strategies with residential consumers.
Integrating community matching and outlier detection for mining evolutionary community outliers

Temporal datasets, in which data evolves continuously, exist in a wide variety of applications, and identifying anomalous or outlying objects from temporal datasets is an important and challenging task. Different from traditional outlier detection, which detects objects that have quite different behavior compared with the other objects, temporal outlier detection tries to identify objects that have different evolutionary behavior compared with other objects. Usually objects form multiple communities, and most of the objects belonging to the same community follow similar patterns of evolution. However, there are some objects which evolve in a very different way relative to other community members, and we define such objects as evolutionary community outliers. This definition represents a novel type of outliers considering both temporal dimension and community patterns. We investigate the problem of identifying evolutionary community outliers given the discovered communities from two snapshots of an evolving dataset. To tackle the challenges of community evolution and outlier detection, we propose an integrated optimization framework which conducts outlier-aware community matching across snapshots and identification of evolutionary outliers in a tightly coupled way. A coordinate descent algorithm is proposed to improve community matching and outlier detection performance iteratively. Experimental results on both synthetic and real datasets show that the proposed approach is highly effective in discovering interesting evolutionary community outliers.
Inverting The Generator Of A Generative Adversarial Network (II)

Generative adversarial networks (GANs) learn a deep generative model that is able to synthesize novel, high-dimensional data samples. New data samples are synthesized by passing latent samples, drawn from a chosen prior distribution, through the generative model. Once trained, the latent space exhibits interesting properties that may be useful for downstream tasks such as classification or retrieval. Unfortunately, GANs do not offer an ``inverse model,'' a mapping from data space back to latent space, making it difficult to infer a latent representation for a given data sample. In this paper, we introduce a technique, inversion, to project data samples, specifically images, to the latent space using a pretrained GAN. Using our proposed inversion technique, we are able to identify which attributes of a data set a trained GAN is able to model and quantify GAN performance, based on a reconstruction loss. We demonstrate how our proposed inversion technique may be used to quantitatively compare the performance of various GAN models trained on three image data sets. We provide codes for all of our experiments in the website (https://github.com/ToniCreswell/InvertingGAN).
Surfacing contextual hate speech words within social media

Social media platforms have recently seen an increase in the occurrence of hate speech discourse which has led to calls for improved detection methods. Most of these rely on annotated data, keywords, and a classification technique. While this approach provides good coverage, it can fall short when dealing with new terms produced by online extremist communities which act as original sources of words which have alternate hate speech meanings. These code words (which can be both created and adopted words) are designed to evade automatic detection and often have benign meanings in regular discourse. As an example, “skypes, googles, yahoos” are all instances of words which have an alternate meaning that can be used for hate speech. This overlap introduces additional challenges when relying on keywords for both the collection of data that is specific to hate speech, and downstream classification. In this work, we develop a community detection approach for finding extremist hate speech communities and collecting data from their members. We also develop a word embedding model that learns the alternate hate speech meaning of words and demonstrate the candidacy of our code words with several annotation experiments, designed to determine if it is possible to recognize a word as being used for hate speech without knowing its alternate meaning. We report an interannotator agreement rate of K = 0.871, and K = 0.676 for data drawn from our extremist community and the keyword approach respectively, supporting our claim that hate speech detection is a contextual task and does not depend on a fixed list of keywords. Our goal is to advance the domain by providing a high quality hate speech dataset in addition to learned code words that can be fed into existing classification approaches, thus improving the accuracy of automated detection.
Wireless Body Area Networks: Challenges, Trends and Emerging Technologies

We provide a comprehensive review of the challenges and emerging technologies for WBANs: Wireless Body Area Networks. We describe leading applications and the tiers of the WBAN architectures, itemizing their major design areas. A succinct review of solutions, progress and possible future research in the areas of sensors, power and radio hardware components, the network stack, localization and mobility techniques, security and privacy, and of certification and standardization follows. We finally make the connection between WBANs and the fast growing research field of Cognitive Radios, which help establish efficient wireless transmissions in an increasingly crowded radio spectrum.
Semantics and complexity of SPARQL

SPARQL is the standard language for querying RDF data. In this article, we address systematically the formal study of the database aspects of SPARQL, concentrating in its graph pattern matching facility. We provide a compositional semantics for the core part of SPARQL, and study the complexity of the evaluation of several fragments of the language. Among other complexity results, we show that the evaluation of general SPARQL patterns is PSPACE-complete. We identify a large class of SPARQL patterns, defined by imposing a simple and natural syntactic restriction, where the query evaluation problem can be solved more efficiently. This restriction gives rise to the class of well-designed patterns. We show that the evaluation problem is coNP-complete for well-designed patterns. Moreover, we provide several rewriting rules for well-designed patterns whose application may have a considerable impact in the cost of evaluating SPARQL queries.
Learning Precise Timing with LSTM Recurrent Networks

The temporal distance between events conveys information essential for numerous sequential tasks such as motor control and rhythm detection. While Hidden Markov Models tend to ignore this information, recurrent neural networks (RNNs) can in principle learn to make use of it. We focus on Long Short-Term Memory (LSTM) because it has been shown to outperform other RNNs on tasks involving long time lags. We find that LSTM augmented by “peephole connections” from its internal cells to its multiplicative gates can learn the fine distinction between sequences of spikes spaced either 50 or 49 time steps apart without the help of any short training exemplars. Without external resets or teacher forcing, our LSTM variant also learns to generate stable streams of precisely timed spikes and other highly nonlinear periodic patterns. This makes LSTM a promising approach for tasks that require the accurate measurement or generation of time intervals.