nils-herrmann/clean_scirepeval huggingface dataset:
Deep Sentiment Representation Based on CNN and LSTM

Traditional machine learning techniques, including support vector machine (SVM), random walk, and so on, have been applied in various tasks of text sentiment analysis, which makes poor generalization ability in terms of complex classification problem. In recent years, deep learning has made a breakthrough in the research of Natural Language Processing. Convolutional neural network (CNN) and recurrent neural networks (RNNs) are two mainstream methods of deep learning in document and sentence modeling. In this paper, a model of capturing deep sentiment representation based on CNN and long short-term memory recurrent neural network (LSTM) is proposed. The model uses the pre-trained word vectors as input and employs CNN to gain significant local features of the text, then features are fed to two-layer LSTMs, which can extract context-dependent features and generate sentence representation for sentiment classification. We evaluate the proposed model by conducting a series of experiments on dataset. The experimental results show that the model we designed outperforms existing CNN, LSTM, CNN-LSTM (our implement of one-layer LSTM directly stacked on one-layer CNN) and SVM (support vector machine).
Efficient elasticity for character skinning with contact and collisions

We present a new algorithm for near-interactive simulation of skeleton driven, high resolution elasticity models. Our methodology is used for soft tissue deformation in character animation. The algorithm is based on a novel discretization of corotational elasticity over a hexahedral lattice. Within this framework we enforce positive definiteness of the stiffness matrix to allow efficient quasistatics and dynamics. In addition, we present a multigrid method that converges with very high efficiency. Our design targets performance through parallelism using a fully vectorized and branch-free SVD algorithm as well as a stable one-point quadrature scheme. Since body collisions, self collisions and soft-constraints are necessary for real-world examples, we present a simple framework for enforcing them. The whole approach is demonstrated in an end-to-end production-level character skinning system.
A Maximum Entropy Approach to Biomedical Named Entity Recognition

Machine learning approaches are frequently used to solve name entity (NE) recognition (NER). In this paper we propose a hybrid method that uses maximum entropy (ME) as the underlying machine learning method incorporated with dictionary-based and rule-based methods for post-processing. Simply using ME for NER, inaccurate boundary detection of NEs and misclassification may occur. Some NEs are partially recognized by ME. In the post-processing stage, we use dictionary-based and rule-based methods to extend boundary of partially recognized NEs and to adjust classification. We use GENIA corpus 3.01 to conduct 10-fold crossverification experiments. To evaluate the performance, we consider the longest NE annotations. We evaluate our approach using standard precision (P), recall (R), and F-score, where F-score is defined as 2PR/(P+R). The precision, recall and F-score ([P, R, F]) of our ME module for overall 23 categories is [0.512, 0.538, 0.525], and after the postprocessing the performance becomes [0.729, 0.711, 0.72] for [P, R, F]. For protein, DNA and RNA classes, our method achieves [P, R, F] of [0.77, 0.80, 0.785], [0.653, 0.748, 0.7], and [0.716, 0.788, 0.752], respectively. The post-processing stage significantly improves the performance of our MEbased NER module.
Imagining predictions: mental imagery as mental emulation.

We argue that the primary function of mental imagery is to allow us to generate specific predictions based upon past experience. All imagery allows us to answer 'what if' questions by making explicit and accessible the likely consequences of being in a specific situation or performing a specific action. Imagery is also characterized by its reliance on perceptual representations and activation of perceptual brain systems. We use this conception of imagery to argue that all imagery is simulation-more specifically, it is a specific type of simulation in which the mental processes that 'run' the simulation emulate those that would actually operate in the simulated scenario. This type of simulation, which we label emulation, has benefits over other types of simulations that merely mimic the content of the simulated scenario.
Fast image stitching and editing for panorama painting on mobile phones

We propose a fast image stitching and editing approach for panorama painting and have implemented it on a mobile phone. A seam search using dynamic programming finds a good boundary along which to merge the source images, and a transition smoothing process using instant image cloning removes merging artifacts, yielding highquality panoramic images. A sequential image stitching procedure requires only keeping the current source image in memory during image stitching, which saves memory and is more suitable to mobile phone implementations.
Exploring trajectory-driven local geographic topics in foursquare

The location based social networking services (LBSNSs) are becoming very popular today. In LBSNSs, such as Foursquare, users can explore their places of interests around their current locations, check in at these places to share their locations with their friends, etc. These check-ins contain rich information and imply human mobility patterns; thus, they can greatly facilitate mining and analysis of local geographic topics driven by users' trajectories. The local geographic topics indicate the potential and intrinsic relations among the locations in accordance with users' trajectories. These relations are useful for users in both location and friend recommendations. In this paper, we focus on exploring the local geographic topics through check-ins in Pittsburgh area in Foursquare. We use the Latent Dirichlet Allocation (LDA) model to discover the local geographic topics from the checkins. We also compare the local geographic topics on weekdays with those at weekends. Our results show that LDA works well in finding the related places of interests.
SummaRuNNer: A Recurrent Neural Network based Sequence Model for Extractive Summarization of Documents

We present SummaRuNNer, a Recurrent Neural Network (RNN) based sequence model for extractive summarization of documents and show that it achieves performance better than or comparable to state-of-the-art. Our model has the additional advantage of being very interpretable, since it allows visualization of its predictions broken up by abstract features such as information content, salience and novelty. Another novel contribution of our work is abstractive training of our extractive model that can train on human generated reference summaries alone, eliminating the need for sentence-level extractive labels.
A Mathematical Formalization of Hierarchical Temporal Memory’s Spatial Pooler

Hierarchical temporal memory (HTM) is an emerging machine learning algorithm, with the potential to provide a means to perform predictions on spatiotemporal data. The algorithm, inspired by the neocortex, consists of two primary components, namely the spatial pooler (SP) and the temporal memory (TM). The SP is utilized to map similar inputs into generalized sparse distributed representations (SDRs). Those SDRs are then utilized by the TM, which performs sequence learning and prediction. One challenge with HTM is ensuring that proper SDRs are generated from the SP. If the SDRs are not generalizable, the TM will not be able to make proper predictions. This work focuses on the SP and its corresponding output SDRs. A single unifying mathematical framework was created for the SP. The primary learning mechanism was explored, where a maximum likelihood estimator for determining the degree of permanence update was proposed. The boosting mechanisms were studied and found to only be relevant during the initial few iterations of the network. Observations were made relating HTM to well-known algorithms such as competitive learning and attribute bagging. Methods were provided for using the SP for classification as well as dimensionality reduction. Empirical evidence verified that given the proper parameterizations, the SP may be used for feature learning. Similarity metrics were created for scoring the SDRs produced by the SP. The overlap metric proved that the SP is extremely robust to noise. The SP was able to produce similar outputs for a given input, provided the noise did not cause the input to change classes. This overlap metric was further utilized to create a classifier for novelty detection. The SP proved to be able to withstand more noise than the well-known support vector machine (SVM).
The influence of diversity and experience on the effects of crowd size

One advantage of crowds over traditional teams is that crowds enable the assembling of a large number of individuals to address problems. The literature is unclear, however, about the relationship between the size of crowds and its impact on outcomes. To better understand the effects of crowd size we conducted a study on the retention and performance based on 4,317 articles in the WikiProject Film community. Our results suggest that crowds benefit from their size when they are diverse, experienced, and have low retention rates.
High-fidelity simulation for evaluating robotic vision performance

Robotic vision, unlike computer vision, typically involves processing a stream of images from a camera with time varying pose operating in an environment with time varying lighting conditions and moving objects. Repeating robotic vision experiments under identical conditions is often impossible, making it difficult to compare different algorithms. For machine learning applications a critical bottleneck is the limited amount of real world image data that can be captured and labelled for both training and testing purposes. In this paper we investigate the use of a photo-realistic simulation tool to address these challenges, in three specific domains: robust place recognition, visual SLAM and object recognition. For the first two problems we generate images from a complex 3D environment with systematically varying camera paths, camera viewpoints and lighting conditions. For the first time we are able to systematically characterise the performance of these algorithms as paths and lighting conditions change. In particular, we are able to systematically generate varying camera viewpoint datasets that would be difficult or impossible to generate in the real world. We also compare algorithm results for a camera in a real environment and a simulated camera in a simulation model of that real environment. Finally, for the object recognition domain, we generate labelled image data and characterise the viewpoint dependency of a current convolution neural network in performing object recognition. Together these results provide a multi-domain demonstration of the beneficial properties of using simulation to characterise and analyse a wide range of robotic vision algorithms.
Towards a Trust Management System for Cloud Computing

Cloud computing provides cost-efficient opportunities for enterprises by offering a variety of dynamic, scalable, and shared services. Usually, cloud providers provide assurances by specifying technical and functional descriptions in Service Level Agreements (SLAs) for the services they offer. The descriptions in SLAs are not consistent among the cloud providers even though they offer services with similar functionality. Therefore, customers are not sure whether they can identify a trustworthy cloud provider only based on its SLA. To support the customers in reliably identifying trustworthy cloud providers, we propose a multi-faceted Trust Management (TM) system architecture for a cloud computing marketplace. This system provides means to identify the trustworthy cloud providers in terms of different attributes (e.g., security, performance, compliance) assessed by multiple sources and roots of trust information.
Safe Exploration of State and Action Spaces in Reinforcement Learning

In this paper, we consider the important problem of safe exploration in reinforcement learning. While reinforcement learning is well-suited to domains with complex transition dynamics and high-dimensional state-action spaces, an additional challenge is posed by the need for safe and efficient exploration. Traditional exploration techniques are not particularly useful for solving dangerous tasks, where the trial and error process may lead to the selection of actions whose execution in some states may result in damage to the learning system (or any other system). Consequently, when an agent begins an interaction with a dangerous and high-dimensional state-action space, an important question arises; namely, that of how to avoid (or at least minimize) damage caused by the exploration of the state-action space. We introduce the PI-SRL algorithm which safely improves suboptimal albeit robust behaviors for continuous state and action control tasks and which efficiently learns from the experience gained from the environment. We evaluate the proposed method in four complex tasks: automatic car parking, pole-balancing, helicopter hovering, and business management.
A bioinspired soft manipulator for minimally invasive surgery.

This paper introduces a novel, bioinspired manipulator for minimally invasive surgery (MIS). The manipulator is entirely composed of soft materials, and it has been designed to provide similar motion capabilities as the octopus's arm in order to reach the surgical target while exploiting its whole length to actively interact with the biological structures. The manipulator is composed of two identical modules (each of them can be controlled independently) with multi-directional bending and stiffening capabilities, like an octopus arm. In the authors' previous works, the design of the single module has been addressed. Here a two-module manipulator is presented, with the final aim of demonstrating the enhanced capabilities that such a structure can have in comparison with rigid surgical tools currently employed in MIS. The performances in terms of workspace, stiffening capabilities, and generated forces are characterized through experimental tests. The combination of stiffening capabilities and manipulation tasks is also addressed to confirm the manipulator potential employment in a real surgical scenario.
Offset-corrected 5GHz CMOS dynamic comparator using bulk voltage trimming: Design and analysis

This paper presents an improved two-stage dynamic comparator using a bulk voltage trimming technique for offset calibration. The comparator requires only a one-phase clock while exerting no extra load on the first stage, leading to higher operating speed. The calibration does not require any extra power supply and does not consume any quiescent current, while increasing the offset calibrating range by a factor of 2 over previous techniques. Detailed analysis of the method of calibrating both stages of the dynamic comparator is provided. Simulation results in a 65nm digital CMOS process show that the comparator is capable of working at a speed of 5GHz with 90uW of power consumption from a 1V power supply, achieving an input-referred offset calibrating range of ±35mV at ∼±2.3mV/step at the typical-typical process corner.
SARL: A General-Purpose Agent-Oriented Programming Language

Complex software systems development require appropriate high-level features to better and easily tackle the new requirements in terms of interactions, concurrency and distribution. This requires a paradigm change in software engineering and corresponding programming languages. We are convinced that agent-oriented programming may be the support for this change by focusing on a small corpus of commonly accepted concepts and the corresponding programming language in line with the current developers' programming practices. This papers introduces SARL, a new general-purpose agent-oriented programming language undertaking this challenge. SARL comes with its full support in the Eclipse IDE for compilation and debugging, and a new version 2.0 of the Janus platform for execution purposes. The main perspective that guided the creation of SARL is the establishment of an open and easily extensible language. Our expectation is to provide the community with a common forum in terms of a first working test bed to study and compare various programming alternatives and associated metamodels.
Toward Scalable Verification for Safety-Critical Deep Networks

The increasing use of deep neural networks for safety-critical applications, such as autonomous driving and flight control, raises concerns about their safety and reliability. Formal verification can address these concerns by guaranteeing that a deep learning system operates as intended, but the state-of-the-art is limited to small systems. In this work-in-progress report we give an overview of our work on mitigating this difficulty, by pursuing two complementary directions: devising scalable verification techniques, and identifying design choices that result in deep learning systems that are more amenable to verification.  Lindsey Kuper, Guy Katz, Justin Gottschlich, Kyle Julian, Clark Barrett, and Mykel J. Kochenderfer. 2018. Toward Scalable Verification for SafetyCritical Deep Networks. In Proceedings of SysML Conference (SysML). ACM, New York, NY, USA, 3 pages.
Trust and TAM in Online Shopping: An Integrated Model

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Adaptive regularization of weight vectors

We present AROW, an online learning algorithm for binary and multiclass problems that combines large margin training, confidence weighting, and the capacity to handle non-separable data. AROW performs adaptive regularization of the prediction function upon seeing each new instance, allowing it to perform especially well in the presence of label noise. We derive mistake bounds for the binary and multiclass settings that are similar in form to the second order perceptron bound. Our bounds do not assume separability. We also relate our algorithm to recent confidence-weighted online learning techniques. Empirical evaluations show that AROW achieves state-of-the-art performance on a wide range of binary and multiclass tasks, as well as robustness in the face of non-separable data.
Safe Policy Search for Lifelong Reinforcement Learning with Sublinear Regret

Lifelong reinforcement learning provides a promising framework for developing versatile agents that can accumulate knowledge over a lifetime of experience and rapidly learn new tasks by building upon prior knowledge. However, current lifelong learning methods exhibit non-vanishing regret as the amount of experience increases, and include limitations that can lead to suboptimal or unsafe control policies. To address these issues, we develop a lifelong policy gradient learner that operates in an adversarial setting to learn multiple tasks online while enforcing safety constraints on the learned policies. We demonstrate, for the first time, sublinear regret for lifelong policy search, and validate our algorithm on several benchmark dynamical systems and an application to quadrotor control.
BUILDING GENERALIZABLE AGENTS

Towards bridging the gap between machine and human intelligence, it is of utmost importance to introduce environments that are visually realistic and rich in content. In such environments, one can evaluate and improve a crucial property of practical intelligent systems, namely generalization. In this work, we build House3D, a rich, extensible and efficient environment that contains 45,622 human-designed 3D scenes of houses, ranging from single-room studios to multistoreyed houses, equipped with a diverse set of fully labeled 3D objects, textures and scene layouts, based on the SUNCG dataset (Song et al., 2017). With an emphasis on semantic-level generalization, we study the task of concept-driven navigation, RoomNav, using a subset of houses in House3D. In RoomNav, an agent navigates towards a target specified by a semantic concept. To succeed, the agent learns to comprehend the scene it lives in by developing perception, understand the concept by mapping it to the correct semantics, and navigate to the target by obeying the underlying physical rules. We train RL agents with both continuous and discrete action spaces and show their ability to generalize in new unseen environments. In particular, we observe that (1) training is substantially harder on large house sets but results in better generalization, (2) using semantic signals (e.g. segmentation mask) boosts the generalization performance, and (3) gated networks on semantic input signal lead to improved training performance and generalization. We hope House3D1, including the analysis of the RoomNav task, serves as a building block towards designing practical intelligent systems and we wish it to be broadly adopted by the community.
A probabilistic framework for stereo-vision based 3D object search with 6D pose estimation

This paper presents a method whereby an autonomous mobile robot can search for a 3-dimensional (3D) object using an on-board stereo camera sensor mounted on a pan-tilt head. Search efficiency is realized by the combination of a coarse-scale global search coupled with a fine-scale local search. A grid-based probability map is initially generated using the coarse search, which is based on the color histogram of the desired object. Peaks in the probability map are visited in sequence, where a local (refined) search method based on 3D SIFT features is applied to establish or reject the existence of the desired object, and to update the probability map using Bayesian recursion methods. Once found, the 6D object pose is also estimated. Obstacle avoidance during search can be naturally integrated into the method. Experimental results obtained from the use of this method on a mobile robot are presented to illustrate and validate the approach, confirming that the search strategy can be carried out with modest computation.
Fast Computation of Graph Kernels

Using extensions of linear algebra concepts to Reproducing Kernel Hilbert Spaces (RKHS), we define a unifying framework for random walk kernels on graphs. Reduction to a Sylvester equation allows us to compute many of these kernels in O(n) worst-case time. This includes kernels whose previous worst-case time complexity was O(n), such as the geometric kernels of Gärtner et al.  and the marginal graph kernels of Kashima et al. . Our algebra in RKHS allow us to exploit sparsity in directed and undirected graphs more effectively than previous methods, yielding sub-cubic computational complexity when combined with conjugate gradient solvers or fixed-point iterations. Experiments on graphs from bioinformatics and other application domains show that our algorithms are often more than 1000 times faster than existing approaches.