nils-herrmann/clean_scirepeval huggingface dataset:
Building a Large-Scale Knowledge Base for Machine Translation

Linguistic Resources Knowledge-based machine translation (KBMT) systems have achieved excellent results in constrained domains, but have not yet scaled up to newspaper text. The reason is that knowledge resources (lexicons, grammar rules, world models) must be painstakingly handcrafted from scratch. One of the hypotheses being tested in the PANGLOSS machine translation project is whether or not these resources can be semi-automatically acquired on a very large scale. This paper focuses on the construction of a large ontology (or knowledge base, or world model) for supporting KBMT. It contains representations for some 70,000 commonly encountered objects, processes, qualities, and relations. The ontology was constructed by merging various online dictionaries, semantic networks, and bilingual resources, through semi-automatic methods. Some of these methods (e.g., conceptual matching of semantic taxonomies) are broadly applicable to problems of importing/exporting knowledge from one KB to another. Other methods (e.g., bilingual matching) allow a knowledge engineer to build up an index to a KB in a second language, such as Spanish or Japanese. USC/Information Sciences Institute 4676 Admiralty Way Marina del Rey, CA 90292 knight,luk
Breath acetone monitoring by portable Si:WO3 gas sensors.

Breath analysis has the potential for early stage detection and monitoring of illnesses to drastically reduce the corresponding medical diagnostic costs and improve the quality of life of patients suffering from chronic illnesses. In particular, the detection of acetone in the human breath is promising for non-invasive diagnosis and painless monitoring of diabetes (no finger pricking). Here, a portable acetone sensor consisting of flame-deposited and in situ annealed, Si-doped epsilon-WO(3) nanostructured films was developed. The chamber volume was miniaturized while reaction-limited and transport-limited gas flow rates were identified and sensing temperatures were optimized resulting in a low detection limit of acetone (∼20ppb) with short response (10-15s) and recovery times (35-70s). Furthermore, the sensor signal (response) was robust against variations of the exhaled breath flow rate facilitating application of these sensors at realistic relative humidities (80-90%) as in the human breath. The acetone content in the breath of test persons was monitored continuously and compared to that of state-of-the-art proton transfer reaction mass spectrometry (PTR-MS). Such portable devices can accurately track breath acetone concentration to become an alternative to more elaborate breath analysis techniques.
Merging and Splitting Eigenspace Models

We present new deterministic methods that given two eigenspace models, each representing a set of n-dimensional observations will: (1) merge the models to yield a representation of the union of the sets; (2) split one model from another to represent the difference between the sets; as this is done, we accurately keep track of the mean. These methods are more efficient than computing new eigenspace models directly from the observations when the eigenmodels are dimensionally small compared to the total number of observations. Such methods are important because they provide a basis for novel techniques in machine learning, using a dynamic split-andmerge paradigm to optimally cluster observations. Here we present a theoretical derivation of the methods, empirical results relating to the efficiency and accuracy of the techniques, and three general applications, including the on-line construction of Gaussian mixture models.
Beyond PASCAL: A benchmark for 3D object detection in the wild

3D object detection and pose estimation methods have become popular in recent years since they can handle ambiguities in 2D images and also provide a richer description for objects compared to 2D object detectors. However, most of the datasets for 3D recognition are limited to a small amount of images per category or are captured in controlled environments. In this paper, we contribute PASCAL3D+ dataset, which is a novel and challenging dataset for 3D object detection and pose estimation. PASCAL3D+ augments 12 rigid categories of the PASCAL VOC 2012  with 3D annotations. Furthermore, more images are added for each category from ImageNet . PASCAL3D+ images exhibit much more variability compared to the existing 3D datasets, and on average there are more than 3,000 object instances per category. We believe this dataset will provide a rich testbed to study 3D detection and pose estimation and will help to significantly push forward research in this area. We provide the results of variations of DPM [6] on our new dataset for object detection and viewpoint estimation in different scenarios, which can be used as baselines for the community. Our benchmark is available online at http://cvgl.stanford.edu/projects/pascal3d.
Stock market prediction exploiting microblog sentiment analysis

This paper proposes a stock market prediction method exploiting sentiment analysis using financial microblogs (Sina Weibo). We analyze the microblog texts to find the financial sentiments, then combine the sentiments and the historical data of the Shanghai Composite Index (SH000001) to predict the stock market movements. Our framework includes three modules: Microblog Filter (MF), Sentiment Analysis (SA), and Stock Prediction (SP). The MF module is based on LDA to get the financial microblogs. The SA module first sets up a financial lexicon, then gets the sentiments of the microblogs obtained from the MF module. The SP module proposes a user-group model which adjusts the importance of different people, and combines it with stock historical data to predict the movement of the SH000001. We use about 6.1 million microblogs to test our method, and the result demonstrates our method is effective.
HEXHOOP: Modular Templates for Converting a Hex-Dominant Mesh to an ALL-Hex Mesh

This paper presents a new mesh conversion template called HEXHOOP, which fully automates a con-version from a hex-dominant mesh to an all-hex mesh. A HEXHOOP template subdivides a hex/prism/pyramid element into a set of smaller hex elements while main-taining the topological conformity with neighboring elements. A HEXHOOP template is constructed by assembling sub-templates, cores and caps. A dicing template for a hex and a prism is constructed by choosing the appropriate combination of a core and caps. A template that dices a pyramid without losing conformity to the adjacent element is derived from a HEXHOOP template. Some experimental results show that the HEXHOOP templates successfully convert a hex-dominant mesh into an all-hex mesh.
Complete 3D Scene Parsing from an RGBD Image

One major goal of vision is to infer physical models of objects, surfaces, and their layout from sensors. In this paper, we aim to interpret indoor scenes from one RGBD image. Our representation encodes the layout of orthogonal walls and the extent of objects, modeled with CAD-like 3D shapes. We parse both the visible and occluded portions of the scene and all observable objects, producing a complete 3D parse. Such a scene interpretation is useful for robotics and visual reasoning, but difficult to produce due to the well-known challenge of segmentation, the high degree of occlusion, and the diversity of objects in indoor scenes. We take a data-driven approach, generating sets of potential object regions, matching to regions in training images, and transferring and aligning associated 3D models while encouraging fit to observations and spatial consistency. We use support inference to aid interpretation and propose a retrieval scheme that uses convolutional neural networks to classify regions and retrieve objects with similar shapes. We demonstrate the performance of our method on our newly annotated NYUd v2 dataset (Silberman et al.,  Computer vision-ECCV, 2012, pp 746–760, 2012) with detailed 3D shapes.
RelFinder: Revealing Relationships in RDF Knowledge Bases

The Semantic Web has recently seen a rise of large knowledge bases (such as DBpedia) that are freely accessible via SPARQL endpoints. The structured representation of the contained information opens up new possibilities in the way it can be accessed and queried. In this paper, we present an approach that extracts a graph covering relationships between two objects of interest. We show an interactive visualization of this graph that supports the systematic analysis of the found relationships by providing highlighting, previewing, and filtering features.
Monte Carlo methods for security pricing

The Monte Carlo approach has proved to be a valuable and flexible computational tool in modern finance. This paper discusses some of the recent applications of the Monte Carlo method to security pricing problems, with emphasis on improvements in efficiency. We first review some variance reduction methods that have proved useful in finance. Then we describe the use of deterministic low-discrepancy sequences, also known as quasi-Monte Carlo methods, for the valuation of complex derivative securities. We summarize some recent applications of the Monte Carlo method to the estimation of partial derivatives or risk sensitivities and to the valuation of American options. We conclude by mentioning other applications.
Learning Spatiotemporal Features with 3D Convolutional Networks

We propose a simple, yet effective approach for spatiotemporal feature learning using deep 3-dimensional convolutional networks (3D ConvNets) trained on a large scale supervised video dataset. Our findings are three-fold: 1) 3D ConvNets are more suitable for spatiotemporal feature learning compared to 2D ConvNets, 2) A homogeneous architecture with small 3x3x3 convolution kernels in all layers is among the best performing architectures for 3D ConvNets, and 3) Our learned features, namely C3D (Convolutional 3D), with a simple linear classifier outperform state-of-the-art methods on 4 different benchmarks and are comparable with current best methods on the other 2 benchmarks. In addition, the features are compact: achieving 52.8% accuracy on UCF101 dataset with only 10 dimensions and also very efficient to compute due to the fast inference of ConvNets. Finally, they are conceptually very simple and easy to train and use.
Big Five Predictors of Academic Achievement

A large-sample study (N 5 717) is described in which two broad Big Five factor measures were compared with two narrow personality trait measures in the prediction of final grades in an undergraduate psychology course. The Big Five factors evaluated as predictors were Conscientiousness and Openness to Experience (or Intellect). The traits evaluated, which were constituents of the respective factors, were need for Achievement and need for Understanding. In each comparison, the lower level trait measure did better than its higher level factor measure in the prediction of course grades. We conclude that the aggregation of narrow trait measures into broad factor measures can be counterproductive from the points of view of both behavior prediction and behavior explanation.
Lessons from applying the systematic literature review process within the software engineering domain

A consequence of the growing number of empirical studies in software engineering is the need to adopt systematic approaches to assessing and aggregating research outcomes in order to provide a balanced and objective summary of research evidence for a particular topic. The paper reports experiences with applying one such approach, the practice of systematic literature review, to the published studies relevant to topics within the software engineering domain. The systematic literature review process is summarised, a number of reviews being undertaken by the authors and others are described and some lessons about the applicability of this practice to software engineering are extracted. The basic systematic literature review process seems appropriate to software engineering and the preparation and validation of a review protocol in advance of a review activity is especially valuable. The paper highlights areas where some adaptation of the process to accommodate the domain-specific characteristics of software engineering is needed as well as areas where improvements to current software engineering infrastructure and practices would enhance its applicability. In particular, infrastructure support provided by software engineering indexing databases is inadequate. Also, the quality of abstracts is poor; it is usually not possible to judge the relevance of a study from a review of the abstract alone.
Regularized linear and kernel redundancy analysis

Redundancy analysis (RA) is a versatile technique used to predict multivariate criterion variables from multivariate predictor variables. The reduced-rank feature of RA captures redundant information in the criterion variables in a most parsimonious way. A ridge type of regularization was introduced in RA to deal with the multicollinearity problem among the predictor variables. The regularized linear RA was extended to nonlinear RA using a kernel method to enhance the predictability. The usefulness of the proposed procedures was demonstrated by a Monte Carlo study and through the analysis of two real data sets.
Business Models for Internet based E-Procurement Systems and B2B Electronic Markets: An Exploratory Assessment

Information technology (IT) has long been applied to support the exchange of goods, services and information between organizations. It is with the advent of Internet-based e-procurement systems and businessto-business (B2B) electronic markets that the real opportunities for online transactions have opened up across space and over time. In this paper, we draw on IS and economics theory to investigate the motivation for the various online business models, and the adoption requirements of purchasing firms, through the examination of a set of mini-cases. Our exploratory study finds that private aggregating and negotiating mechanisms are being adopted for large quantity business supply purchases, while public market mechanisms are more often adopted when firms face uncertain and high variance demand. Moreover, market facilitation, expertise sharing and collaboration are gradually attracting more attention, and call for future investigation.
The age of multilevel converters arrives

This work is devoted to review and analyze the most relevant characteristics of multilevel converters, to motivate possible solutions, and to show that we are in a decisive instant in which energy companies have to bet on these converters as a good solution compared with classic two-level converters. This article presents a brief overview of the actual applications of multilevel converters and provides an introduction of the modeling techniques and the most common modulation strategies. It also addresses the operational and technological issues.
Span: An energy-efficient coordination algorithm for topology maintenance in Ad Hoc wireless networks

This paper presents Span, a power saving technique for multi-hop ad hoc wireless networks that reduces energy consumption without significantly diminishing the capacity or connectivity of the network. Span builds on the observation that when a region of a shared-channel wireless network bag a sufficient density of nodes, only a small number of them need be on at any time to forward traffic for active connections.
Span is a distributed, randomized algorithm where nodes make local decisions on whether to sleep, or to join a forwarding backbone as a coordinator. Each node bases its decision on an estimate of how many of its neighbors will benefit from it being awake, and the amount of energy available to it. We give a randomized algorithm where coordinators rotate with time, demonstrating how localized node decisions lead to a connected, capacity-preserving global topology.
Improvement in system lifetime due to Span increases as the ratio of idle-to-sleep energy consumption increases, and increases as the density of the network increases. For example, our simulations show that with a practical energy model, system lifetime of an 802.11 network in power saving mode with Span is a factor of two better than without. Span integrates nicely with 802.11—when run in conjunction with the 802.11 power saving mode, Span improves communication latency, capacity, and system lifetime.
A hybrid edge-cloud architecture for reducing on-demand gaming latency

The cloud was originally designed to provide general-purpose computing using commodity hardware and its focus was on increasing resource consolidation as a means to lower cost. Hence, it was not particularly adapted to the requirements of multimedia applications that are highly latency sensitive and require specialized hardware, such as graphical processing units. Existing cloud infrastructure is dimensioned to serve general-purpose workloads and to meet end-user requirements by providing high throughput. In this paper, we investigate the effectiveness of using this general-purpose infrastructure for serving latency-sensitive multimedia applications. In particular, we examine on-demand gaming, also known as cloud gaming, which has the potential to change the video game industry. We demonstrate through a large-scale measurement study that the existing cloud infrastructure is unable to meet the strict latency requirements necessary for acceptable on-demand game play. Furthermore, we investigate the effectiveness of incorporating edge servers, which are servers located near end-users (e.g., CDN servers), to improve end-user coverage. Specifically, we examine an edge-server-only infrastructure and a hybrid infrastructure that consists of using edge servers in addition to the cloud. We find that a hybrid infrastructure significantly improves the number of end-users served. However, the number of satisfied end-users in a hybrid deployment largely depends on the various deployment parameters. Therefore, we evaluate various strategies that determine two such parameters, namely, the location of on-demand gaming servers and the games that are placed on these servers. We find that, through both a careful selection of on-demand gaming servers and the games to place on these servers, we significantly increase the number of end-users served over the basic random selection and placement strategies.
Keyword extraction based on tf/idf for Chinese news document

Keyword extraction is an important research topic of information retrieval. This paper gave the specification of keywords in Chinese news documents based on analyzing linguistic characteristics of news documents and then proposed a new keyword extraction method based on tf/idf with multi-strategies. The approach selected candidate keywords of uni-, bi-and tri- grams, and then defines the features according to their morphological characters and context information. Moreover, the paper proposed several strategies to amend the incomplete words gotten from the word segmentation and found unknown potential keywords in news documents. Experimental results show that our proposed method can significantly outperform the baseline method. We also applied it to retrospective event detection. Experimental results show that the accuracy and efficiency of news retrospective event detection can be significantly improved.
Towards Biologically Plausible Deep Learning

Neuroscientists have long criticised deep learning algorithms as incompatible with current knowledge of neurobiology. We explore more biologically plausible versions of deep representation learning, focusing here mostly on unsupervised learning but developing a learning mechanism that could account for supervised, unsupervised and reinforcement learning. The starting point is that the basic learning rule believed to govern synaptic weight updates (Spike-TimingDependent Plasticity) can be interpreted as gradient descent on some objective function so long as the neuronal dynamics push firing rates towards better values of the objective function (be it supervised, unsupervised, or reward-driven). The second main idea is that this corresponds to a form of the variational EM algorithm, i.e., with approximate rather than exact posteriors, implemented by neural dynamics. Another contribution of this paper is that the gradients required for updating the hidden states in the above variational interpretation can be estimated using an approximation that only requires propagating activations forward and backward, with pairs of layers learning to form a denoising auto-encoder. Finally, we extend the theory about the probabilistic interpretation of auto-encoders to justify improved sampling schemes based on the generative interpretation of denoising auto-encoders, and we validate all these ideas on generative learning tasks.
3D printing of robotic soft actuators with programmable bioinspired architectures

Soft actuation allows robots to interact safely with humans, other machines, and their surroundings. Full exploitation of the potential of soft actuators has, however, been hindered by the lack of simple manufacturing routes to generate multimaterial parts with intricate shapes and architectures. Here, we report a 3D printing platform for the seamless digital fabrication of pneumatic silicone actuators exhibiting programmable bioinspired architectures and motions. The actuators comprise an elastomeric body whose surface is decorated with reinforcing stripes at a well-defined lead angle. Similar to the fibrous architectures found in muscular hydrostats, the lead angle can be altered to achieve elongation, contraction, or twisting motions. Using a quantitative model based on lamination theory, we establish design principles for the digital fabrication of silicone-based soft actuators whose functional response is programmed within the material's properties and architecture. Exploring such programmability enables 3D printing of a broad range of soft morphing structures. 3D-printed soft actuators have limited motion and are far from reaching the level of complexity found in biological systems. Here the authors present a multimaterial 3D printing platform for the fabrication of soft actuators displaying a wide range of motions that are programmable.
Controlling data in the cloud: outsourcing computation without outsourcing control

Cloud computing is clearly one of today's most enticing technology areas due, at least in part, to its cost-efficiency and flexibility. However, despite the surge in activity and interest, there are significant, persistent concerns about cloud computing that are impeding momentum and will eventually compromise the vision of cloud computing as a new IT procurement model. In this paper, we characterize the problems and their impact on adoption. In addition, and equally importantly, we describe how the combination of existing research thrusts has the potential to alleviate many of the concerns impeding adoption. In particular, we argue that with continued research advances in trusted computing and computation-supporting encryption, life in the cloud can be advantageous from a business intelligence standpoint over the isolated alternative that is more common today.