nils-herrmann/clean_scirepeval huggingface dataset:
Real-time Rumor Debunking on Twitter

In this paper, we propose the first real time rumor debunking algorithm for Twitter. We use cues from 'wisdom of the crowds', that is, the aggregate 'common sense' and investigative journalism of Twitter users. We concentrate on identification of a rumor as an event that may comprise of one or more conflicting microblogs. We continue monitoring the rumor event and generate real time updates dynamically based on any additional information received. We show using real streaming data that it is possible, using our approach, to debunk rumors accurately and efficiently, often much faster than manual verification by professionals.
PointFlowNet: Learning Representations for 3D Scene Flow Estimation from Point Clouds

Despite significant progress in image-based 3D scene flow estimation, the performance of such approaches has not yet reached the fidelity required by many applications. Simultaneously, these applications are often not restricted to image-based estimation: laser scanners provide a popular alternative to traditional cameras, for example in the context of self-driving cars, as they directly yield a 3D point cloud. In this paper, we propose to estimate 3D scene flow from such unstructured point clouds using a deep neural network. In a single forward pass, our model jointly predicts 3D scene flow as well as the 3D bounding box and rigid body motion of objects in the scene. While the prospect of estimating 3D scene flow from unstructured point clouds is promising, it is also a challenging task. We show that the traditional global representation of rigid body motion prohibits inference by CNNs, and propose a translation equivariant representation to circumvent this problem. For training our deep network, a large dataset is required. Because of this, we augment real scans from KITTI with virtual objects, realistically modeling occlusions and simulating sensor noise. A thorough comparison with classic and learning-based techniques highlights the robustness of the proposed approach.
Comfort Theory: a unifying framework to enhance the practice environment.

The application of theory to practice is multifaceted. It requires a nursing theory that is compatible with an institution's values and mission and that is easily understood and simple enough to guide practice. Comfort Theory was chosen because of its universality. The authors describe how Kolcaba's Comfort Theory was used by a not-for-profit New England hospital to provide a coherent and consistent pattern for enhancing care and promoting professional practice, as well as to serve as a unifying framework for applying for Magnet Recognition Status.
Learning Text Representations for 500K Classification Tasks on Named Entity Disambiguation

Named Entity Disambiguation algorithms typically learn a single model for all target entities. In this paper we present a word expert model and train separate deep learning models for each target entity string, yielding 500K classification tasks. This gives us the opportunity to benchmark popular text representation alternatives on this massive dataset. In order to face scarce training data we propose a simple data-augmentation technique and transfer-learning. We show that bagof-word-embeddings are better than LSTMs for tasks with scarce training data, while the situation is reversed when having larger amounts. Transferring an LSTM which is learned on all datasets is the most effective context representation option for the word experts in all frequency bands. The experiments show that our system trained on out-ofdomain Wikipedia data surpasses comparable NED systems which have been trained on indomain training data.
Turning programs against each other: high coverage fuzz-testing using binary-code mutation and dynamic slicing

Mutation-based fuzzing is a popular and widely employed black-box testing technique for finding security and robustness bugs in software. It owes much of its success to its simplicity; a well-formed seed input is mutated, e.g. through random bit-flipping, to produce test inputs. While reducing the need for human effort, and enabling security testing even of closed-source programs with undocumented input formats, the simplicity of mutation-based fuzzing comes at the cost of poor code coverage. Often millions of iterations are needed, and the results are highly dependent on configuration parameters and the choice of seed inputs. In this paper we propose a novel method for automated generation of high-coverage test cases for robustness testing. Our method is based on the observation that, even for closed-source programs with proprietary input formats, an implementation that can generate well-formed inputs to the program is typically available. By systematically mutating the program code of such generating programs, we leverage information about the input format encoded in the generating program to produce high-coverage test inputs, capable of reaching deep states in the program under test. Our method works entirely at the machine-code level, enabling use-cases similar to traditional black-box fuzzing. We have implemented the method in our tool MutaGen, and evaluated it on 7 popular Linux programs. We found that, for most programs, our method improves code coverage by one order of magnitude or more, compared to two well-known mutation-based fuzzers. We also found a total of 8 unique bugs.
Investigating the cross-linguistic potential of VerbNet-style classification

Verb classes which integrate a wide range of linguistic properties (Levin, 1993) have proved useful for natural language processing (NLP) applications. However, the real-world use of these classes has been limited because for most languages, no resources similar to VerbNet (KipperSchuler, 2005) are available. We apply a verb clustering approach developed for English to French – a language for which no such experiment has been conducted yet. Our investigation shows that not only the general methodology but also the best performing features are transferable between the languages, making it possible to learn useful VerbNet style classes for French automatically, and without the need for language-specific tuning.
OF LOGISTIC REGRESSION CLASSIFIERS USED FOR VERY LARGE SCALE

This thesis is dedicated to my mother, who taught me that success is not the key to happiness. Happiness is the key to success. If we love what we are doing, we will be successful. This thesis is dedicated to my father, who taught me that luck is not something that is given to us at random and should be waited for. Luck is the sense to recognize an opportunity and the ability to take advantage of it. iii ACKNOWLEDGEMENTS I would like to thank my thesis committee –
Improving Communication between Physicians and Their Patients through Mindfulness and Compassion-Based Strategies: A Narrative Review

Communication between physicians and patients is a key pillar of psychosocial support for enhancing the healing process of patients and for increasing their well-being and quality of life. Physicians and other health professionals might benefit from interventions that increase their self-care, awareness, compassion, and other-focused concern, and reduce the chances of distress and burnout. There is substantial evidence for the contribution of different management strategies to achieve these aims. The goal of this article is to review the potential effect of mindfulness and compassion-based strategies for the improvement of physician-patient interactions. The acquisition of the necessary skills by physicians requires continuous education. Future research will be useful for identifying more evidence on the cost-effectiveness of this type of intervention.
On the Dangers of Pulling a Fast One : Advertisement Disclaimer Speed , Brand Trust , and Purchase Intention

Two experiments demonstrated that fast (vs. normal-paced) end-of-advertisement disclaimers undermine consumers’ purchase intention toward untrusted brands (both trust-unknown and not-trusted brands), but that disclaimer speed has no effect on consumers’ purchase intention toward trusted brands. The differential effects of disclaimer speed for untrusted versus trusted brands were not due to differences in consumers’ familiarity with the brands (experiment 1). Consistent with the hypothesis that fast disclaimers adversely affect purchase intention via heuristic rather than elaborative processes, the disclaimer speed # brand trust interaction effect remained robust even when the disclaimer presented positive information about the advertised product (experiment 2).
LCNN: Lookup-Based Convolutional Neural Network

Porting state of the art deep learning algorithms to resource constrained compute platforms (e.g. VR, AR, wearables) is extremely challenging. We propose a fast, compact, and accurate model for convolutional neural networks that enables efficient learning and inference. We introduce LCNN, a lookup-based convolutional neural network that encodes convolutions by few lookups to a dictionary that is trained to cover the space of weights in CNNs. Training LCNN involves jointly learning a dictionary and a small set of linear combinations. The size of the dictionary naturally traces a spectrum of trade-offs between efficiency and accuracy. Our experimental results on ImageNet challenge show that LCNN can offer 3.2x speedup while achieving 55.1% top-1 accuracy using AlexNet architecture. Our fastest LCNN offers 37.6x speed up over AlexNet while maintaining 44.3% top-1 accuracy. LCNN not only offers dramatic speed ups at inference, but it also enables efficient training. In this paper, we show the benefits of LCNN in few-shot learning and few-iteration learning, two crucial aspects of on-device training of deep learning models.
Context-Sensitive Twitter Sentiment Classification Using Neural Network

Sentiment classification on Twitter has attracted increasing research in recent years. Most existing work focuses on feature engineering according to the tweet content itself. In this paper, we propose a contextbased neural network model for Twitter sentiment analysis, incorporating contextualized features from relevant Tweets into the model in the form of word embedding vectors. Experiments on both balanced and unbalanced datasets show that our proposed models outperform the current state-of-the-art.
Crime news analysis: Location and story detection

It is always a good choice to consider multiple sources when analyzing crime. Newspaper can be a good source of crime analysis as it always contains the massive amount of data related to any particular subject but the fact is they are not structured enough to make a definite decision. In this paper, we represent a system to analyze crime news from online newspaper using different data mining techniques. These techniques help to extract useful pieces of information about crime from unstructured crime news. The analysis will provide the definite information about a news whether it is a crime related news or not, crime location, similarity and grouping crime news as stories based on a crime news.
Neural Variational Inference for Text Processing

Recent advances in neural variational inference have spawned a renaissance in deep latent variable models. In this paper we introduce a generic variational inference framework for generative and conditional models of text. While traditional variational methods derive an analytic approximation for the intractable distributions over latent variables, here we construct an inference network conditioned on the discrete text input to provide the variational distribution. We validate this framework on two very different text modelling applications, generative document modelling and supervised question answering. Our neural variational document model combines a continuous stochastic document representation with a bagof-words generative model and achieves the lowest reported perplexities on two standard test corpora. The neural answer selection model employs a stochastic representation layer within an attention mechanism to extract the semantics between a question and answer pair. On two question answering benchmarks this model exceeds all previous published benchmarks.
Automatic Heap Layout Manipulation for Exploitation

Heap layout manipulation is integral to exploiting heapbased memory corruption vulnerabilities. In this paper we present the first automatic approach to the problem, based on pseudo-random black-box search. Our approach searches for the inputs required to place the source of a heap-based buffer overflow or underflow next to heap-allocated objects that an exploit developer, or automatic exploit generation system, wishes to read or corrupt. We present a framework for benchmarking heap layout manipulation algorithms, and use it to evaluate our approach on several real-world allocators, showing that pseudo-random black box search can be highly effective. We then present SHRIKE, a novel system that can perform automatic heap layout manipulation on the PHP interpreter and can be used in the construction of controlflow hijacking exploits. Starting from PHP’s regression tests, SHRIKE discovers fragments of PHP code that interact with the interpreter’s heap in useful ways, such as making allocations and deallocations of particular sizes, or allocating objects containing sensitive data, such as pointers. SHRIKE then uses our search algorithm to piece together these fragments into programs, searching for one that achieves a desired heap layout. SHRIKE allows an exploit developer to focus on the higher level concepts in an exploit, and to defer the resolution of heap layout constraints to SHRIKE. We demonstrate this by using SHRIKE in the construction of a control-flow hijacking exploit for the PHP interpreter.
DLS$@$CU: Sentence Similarity from Word Alignment

We present an algorithm for computing the semantic similarity between two sentences. It adopts the hypothesis that semantic similarity is a monotonically increasing function of the degree to which (1) the two sentences contain similar semantic units, and (2) such units occur in similar semantic contexts. With a simplistic operationalization of the notion of semantic units with individual words, we experimentally show that this hypothesis can lead to state-of-the-art results for sentencelevel semantic similarity. At the SemEval 2014 STS task (task 10), our system demonstrated the best performance (measured by correlation with human annotations) among 38 system runs.
Ontology-based concept map for planning personalized learning path

Developing personalized Web-based learning systems has been an important research issue in the e-learning field because no fixed learning pathway will be appropriate for all learners. However, the current most Web-based learning platforms with personalized curriculum sequencing tend to emphasize the learnerspsila preferences and interests for the personalized learning services, but they fail to consider difficulty levels of course materials, learning order of prior and posterior knowledge, and learnerspsila abilities while constructing a personalized learning path. As a result, these ignored factors easily lead to generating poor quality learning paths. Generally, learners could generate cognitive overload or fall into cognitive disorientation due to inappropriate curriculum sequencing during learning processes, thus reducing learning effect. With advancement of the artificial intelligence technologies, ontology technologies enable a linguistic infrastructure to represent concept relationships between courseware. Ontology can be served as a structured knowledge representation scheme, which can assist the construction of personalized learning path. Therefore, this study proposes a novel genetic-based curriculum sequencing scheme based on a generated ontology-based concept map, which can be automatically constructed by a large amount of learnerspsila pre-test results, to plan appropriate learning paths for individual learners. The experimental results indicated that the proposed approach is indeed capable of creating learning paths with high quality for individual learners. This will be helpful to learners to learn more effectively and to likely reduce learnerspsila cognitive overloads during learning processes.
Active Object Localization with Deep Reinforcement Learning

We present an active detection model for localizing objects in scenes. The model is class-specific and allows an agent to focus attention on candidate regions for identifying the correct location of a target object. This agent learns to deform a bounding box using simple transformation actions, with the goal of determining the most specific location of target objects following top-down reasoning. The proposed localization agent is trained using deep reinforcement learning, and evaluated on the Pascal VOC 2007 dataset. We show that agents guided by the proposed model are able to localize a single instance of an object after analyzing only between 11 and 25 regions in an image, and obtain the best detection results among systems that do not use object proposals for object localization.
Deep CNN ensembles and suggestive annotations for infant brain MRI segmentation

Precise 3D segmentation of infant brain tissues is an essential step towards comprehensive volumetric studies and quantitative analysis of early brain developement. However, computing such segmentations is very challenging, especially for 6-month infant brain, due to the poor image quality, among other difficulties inherent to infant brain MRI, e.g., the isointense contrast between white and gray matter and the severe partial volume effect due to small brain sizes. This study investigates the problem with an ensemble of semi-dense fully convolutional neural networks (CNNs), which employs T1-weighted and T2-weighted MR images as input. We demonstrate that the ensemble agreement is highly correlated with the segmentation errors. Therefore, our method provides measures that can guide local user corrections. To the best of our knowledge, this work is the first ensemble of 3D CNNs for suggesting annotations within images. Furthermore, inspired by the very recent success of dense networks , we propose a novel architecture, SemiDenseNet, which connects all convolutional layers directly to the end of the network. Our architecture allows the efficient propagation of gradients during training, while limiting the number of parameters, requiring one order of magnitude less parameters than popular medical image segmentation networks such as 3D U-Net . Another contribution of our work is the study of the impact that early or late fusions of multiple image modalities might have on the performances of deep architectures. We report evaluations of our method on the public data of the MICCAI iSEG-2017 Challenge on 6-month infant brain MRI segmentation, and show very competitive results among 21 teams, ranking first or second in most metrics.
Thread Scheduling for Multiprogrammed Multiprocessors

We present a user-level thread scheduler for shared-memory multiprocessors, and we analyze its performance under multiprogramming. We model multiprogramming with two scheduling levels: our scheduler runs at user-level and schedules threads onto a fixed collection of processes, while below this level, the operating system kernel schedules processes onto a fixed collection of processors. We consider the kernel to be an adversary, and our goal is to schedule threads onto processes such that we make efficient use of whatever processor resources are provided by the kernel. Our thread scheduler is a non-blocking implementation of the work-stealing algorithm. For any multithreaded computation with work T 1 and critical-path length T ∈ fty , and for any number P of processes, our scheduler executes the computation in expected time O(T 1 /P A + T ∈ fty P/P A ) , where P A is the average number of processors allocated to the computation by the kernel. This time bound is optimal to within a constant factor, and achieves linear speedup whenever P is small relative to the parallelism T 1 /T ∈ fty .
Cognitive Robotics Using the Soar Cognitive Architecture

Our long-term goal is to develop autonomous robotic systems that have the cognitive abilities of humans, including communication, coordination, adapting to novel situations, and learning through experience. Our approach rests on the integration of the Soar cognitive architecture with both virtual and physical robotic systems. Soar has been used to develop a wide variety of knowledge-rich agents for complex virtual environments, including distributed training environments and interactive computer games. For development and testing in robotic virtual environments, Soar interfaces to a variety of robotic simulators and a simple mobile robot. We have recently made significant extensions to Soar that add new memories and new non-symbolic reasoning to Soar’s original symbolic processing, which improves Soar abilities for control of robots. These extensions include mental imagery, episodic and semantic memory, reinforcement learning, and continuous model learning. This paper presents research in mobile robotics, relational and continuous model learning, and learning by situated, interactive instruction.
Deep Hidden Physics Models: Deep Learning of Nonlinear Partial Differential Equations

A long-standing problem at the interface of artificial intelligence and applied mathematics is to devise an algorithm capable of achieving human level or even superhuman proficiency in transforming observed data into predictive mathematical models of the physical world. In the current era of abundance of data and advanced machine learning capabilities, the natural question arises: How can we automatically uncover the underlying laws of physics from high-dimensional data generated from experiments? In this work, we put forth a deep learning approach for discovering nonlinear partial differential equations from scattered and potentially noisy observations in space and time. Specifically, we approximate the unknown solution as well as the nonlinear dynamics by two deep neural networks. The first network acts as a prior on the unknown solution and essentially enables us to avoid numerical differentiations which are inherently ill-conditioned and unstable. The second network represents the nonlinear dynamics and helps us distill the mechanisms that govern the evolution of a given spatiotemporal data-set. We test the effectiveness of our approach for several benchmark problems spanning a number of scientific domains and demonstrate how the proposed framework can help us accurately learn the underlying dynamics and forecast future states of the system. In particular, we study the Burgers’, Kortewegde Vries (KdV), Kuramoto-Sivashinsky, nonlinear Schrödinger, and NavierStokes equations.