nils-herrmann/clean_scirepeval huggingface dataset:
Equation Parsing : Mapping Sentences to Grounded Equations

Identifying mathematical relations expressed in text is essential to understanding a broad range of natural language text from election reports, to financial news, to sport commentaries to mathematical word problems. This paper focuses on identifying and understanding mathematical relations described within a single sentence. We introduce the problem of Equation Parsing – given a sentence, identify noun phrases which represent variables, and generate the mathematical equation expressing the relation described in the sentence. We introduce the notion of projective equation parsing and provide an efficient algorithm to parse text to projective equations. Our system makes use of a high precision lexicon of mathematical expressions and a pipeline of structured predictors, and generates correct equations in 70% of the cases. In 60% of the time, it also identifies the correct noun phrase → variables mapping, significantly outperforming baselines. We also release a new annotated dataset for task evaluation.
Web scale NLP: a case study on url word breaking

This paper uses the URL word breaking task as an example to elaborate what we identify as crucial in designing statistical natural language processing (NLP) algorithms for Web scale applications: (1) rudimentary multilingual capabilities to cope with the global nature of the Web, (2) multi-style modeling to handle diverse language styles seen in the Web contents, (3) fast adaptation to keep pace with the dynamic changes of the Web, (4) minimal heuristic assumptions for generalizability and robustness, and (5) possibilities of efficient implementations and minimal manual efforts for processing massive amount of data at a reasonable cost. We first show that the state-of-the-art word breaking techniques can be unified and generalized under the Bayesian minimum risk (BMR) framework that, using a Web scale N-gram, can meet the first three requirements. We discuss how the existing techniques can be viewed as introducing additional assumptions to the basic BMR framework, and describe a generic yet efficient implementation called word synchronous beam search. Testing the framework and its implementation on a series of large scale experiments reveals the following. First, the language style used to build the model plays a critical role in the word breaking task, and the most suitable for the URL word breaking task appears to be that of the document title where the best performance is obtained. Models created from other language styles, such as from document body, anchor text, and even queries, exhibit varying degrees of mismatch. Although all styles benefit from increasing modeling power which, in our experiments, corresponds to the use of a higher order N-gram, the gain is most recognizable for the title model. The heuristics proposed by the prior arts do contribute to the word breaking performance for mismatched or less powerful models, but are less effective and, in many cases, lead to poorer performance than the matched model with minimal assumptions. For the matched model based on document titles, an accuracy rate of 97.18% can already be achieved using simple trigram without any heuristics.
Coupled Multi-Layer Attentions for Co-Extraction of Aspect and Opinion Terms

The task of aspect and opinion terms co-extraction aims to explicitly extract aspect terms describing features of an entity and opinion terms expressing emotions from user-generated texts. To achieve this task, one effective approach is to exploit relations between aspect terms and opinion terms by parsing syntactic structure for each sentence. However, this approach requires expensive effort for parsing and highly depends on the quality of the parsing results. In this paper, we offer a novel deep learning model, named coupled multi-layer attentions. The proposed model provides an end-to-end solution and does not require any parsers or other linguistic resources for preprocessing. Specifically, the proposed model is a multilayer attention network, where each layer consists of a couple of attentions with tensor operators. One attention is for extracting aspect terms, while the other is for extracting opinion terms. They are learned interactively to dually propagate information between aspect terms and opinion terms. Through multiple layers, the model can further exploit indirect relations between terms for more precise information extraction. Experimental results on three benchmark datasets in SemEval Challenge 2014 and 2015 show that our model achieves stateof-the-art performances compared with several baselines.
Shoulder mechanism design of an exoskeleton robot for stroke patient rehabilitation

Shoulder girdle movement is critical for stabilizing and orientating the arm during daily activities. During robotic arm rehabilitation with stroke patients, the robot must assist movements of the shoulder girdle. Shoulder girdle movement is characterized by a highly nonlinear function of the humeral orientation, which is different for each person. Hence it is improper to use pre-calculated shoulder girdle movement. If an exoskeleton robot cannot mimic the patient's shoulder girdle movement well, the robot axes will not coincide with the patient's, which brings reduced range of motion (ROM) and discomfort to the patients. A number of exoskeleton robots have been developed to assist shoulder girdle movement. The shoulder mechanism of these robots, along with the advantages and disadvantages, are introduced. In this paper, a novel shoulder mechanism design of exoskeleton robot is proposed, which can fully mimic the patient's shoulder girdle movement in real time.
Study of a T-Shaped Slot With a Capacitor for High Isolation Between MIMO Antennas

In this letter, an isolation improvement method between two closely packed planar inverted-F antennas (PIFAs) is proposed via a miniaturized ground slot with a chip capacitor. The proposed T-shaped ground slot acts as a notch filter, and the capacitor is utilized to reduce the slot length. The equivalent circuit model of the proposed slot with the capacitor is derived. The measured isolation between two PIFAs is down to below -20 dB at the whole WLAN band of 2.4 GHz.
Framework fabrication with rib cartilage in partial and total nasal reconstruction.

The framework reconstruction of the nose is a significant and complex component of its partial or total reconstruction. On the one hand, the design of the individual framework parts is based on the anatomic nature of available rib or ear cartilage, which must on the other hand be adapted to the anatomic characteristics of the defect. The framework parts must be anchored not only to each other but also stably to the facial skeleton. The symmetry of the framework reconstruction is an essential component of the aesthetics of the reconstructed nose. If these points are already considered in planning, the reconstruction of the nasal framework can be standardized insofar as the same principles for the basic design of the individual parts as well as stable solutions for the anchoring points can be chosen. With reproducible techniques, functionally and aesthetically good to very good results can be achieved, including in the long term. The surgeon must possess special skills in the field of nasal reconstruction to correctly choose, apply, and combine the various techniques of nasal framework reconstruction.
OctNetFusion: Learning Depth Fusion from Data

In this paper, we present a learning based approach to depth fusion, i.e., dense 3D reconstruction from multiple depth images. The most common approach to depth fusion is based on averaging truncated signed distance functions, which was originally proposed by Curless and Levoy in 1996. While this method is simple and provides great results, it is not able to reconstruct (partially) occluded surfaces and requires a large number frames to filter out sensor noise and outliers. Motivated by the availability of large 3D model repositories and recent advances in deep learning, we present a novel 3D CNN architecture that learns to predict an implicit surface representation from the input depth maps. Our learning based method significantly outperforms the traditional volumetric fusion approach in terms of noise reduction and outlier suppression. By learning the structure of real world 3D objects and scenes, our approach is further able to reconstruct occluded regions and to fill in gaps in the reconstruction. We demonstrate that our learning based approach outperforms both vanilla TSDF fusion as well as TV-L1 fusion on the task of volumetric fusion. Further, we demonstrate state-of-the-art 3D shape completion results.
Improving Storage Availability in Cloud-of-Clouds with Hybrid Redundant Data Distribution

With the increasing utilization and popularity of the cloud infrastructure, more and more data are moved to the cloud storage systems. This makes the availability of cloud storage services critically important, particularly given the fact that outages of cloud storage services have indeed happened from time to time. Thus, solely depending on a single cloud storage provider for storage services can risk violating the service-level agreement (SLA) due to the weakening of service availability. This has led to the notion of Cloud-of-Clouds, where data redundancy is introduced to distribute data among multiple independent cloud storage providers, to address the problem. The key in the effectiveness of the Cloud-of-Clouds approaches lies in how the data redundancy is incorporated and distributed among the clouds. However, the existing Cloud-of-Clouds approaches utilize either replication or erasure codes to redundantly distribute data across multiple clouds, thus incurring either high space or high performance overheads. In this paper, we propose a hybrid redundant data distribution approach, called HyRD, to improve the cloud storage availability in Cloud-of-Clouds by exploiting the workload characteristics and the diversity of cloud providers. In HyRD, large files are distributed in multiple cost-efficient cloud storage providers with erasure-coded data redundancy while small files and file system metadata are replicated on multiple high-performance cloud storage providers. The experiments conducted on our lightweight prototype implementation of HyRD show that HyRD improves the cost efficiency by 33.4% and 20.4%, and reduces the access latency by 58.7% and 34.8% than the DuraCloud and RACS schemes, respectively.
Customer churn prediction using improved FCM algorithm

Data mining can provide support for bank managers to effectively analyze and predict customer churn in the era of big data. After analyzing the reasons for the bank customer churn and the defects of FCM algorithm as a data mining algorithm, a new method of calculating the effectiveness function to improve the FCM algorithm was raised. At the same time, it has been applied to predict bank customer churn. Through data mining experiments of customer information conducted on a commercial bank, it's found out the clients have been lost and will be lost. Contrast of confusion matrixes shows that the improved FCM algorithm has high accuracy, which can provide new ideas and new methods for the analysis and prediction of bank customer churn.
An Open, Secure and Flexible Platform Based on Internet of Things and Cloud Computing for Ambient Aiding Living and Telemedicine

Currently Internet of Things (IoT) and multimedia technologies have entered the healthcare field through ambient aiding living and telemedicine. However there are still several obstacles blocking in the way, the toughest ones among which are IoT interoperability, system security, streaming Quality of Service (QoS) and dynamic increasing storage. The major contribution of this paper is proposing an open, secure and flexible platform based on IoT and Cloud computing, on which several mainstream short distant ambient communication protocols for medical purpose are discussed to address interoperability; Secure Sockets Layer (SSL), authentication and auditing are taken into consideration to solve the security issue; an adaptive streaming QoS model is utilized to improve streaming quality in dynamic environment; and an open Cloud computing infrastructure is adopted to support elastic Electronic Health Record (EHR) archiving in the backend. Finally an integrated reference implementation is introduced to demonstrate feasibility.
CYCLOPS: A micro service based approach for dynamic rating, charging & billing for cloud

In the present days of rapid adoption of cloud services and varying cost models it is imperative to have a system which converts the cloud resource usage data into a measurable and payable cost. The system should be able to collect the cloud resource usage data, process it against a dynamically determined rate to get a charge for which the customer could be billed upon. These operations need to be done in a secured manner with appropriate authentication and authorization checks. As the cloud services are still evolving, the rating, charging and billing components for cloud are at a nascent stage of its maturity. In this paper we are describing a novel modular and micro services based design & architecture for developing a dynamic rating, charging and billing for cloud service providers with emphasis being given to the validation of the architecture through mathematical modeling and the implementation experience of the micro services.
English Multiword Expression-aware Dependency Parsing Including Named Entities

Because syntactic structures and spans of multiword expressions (MWEs) are independently annotated in many English syntactic corpora, they are generally inconsistent with respect to one another, which is harmful to the implementation of an aggregate system. In this work, we construct a corpus that ensures consistency between dependency structures and MWEs, including named entities. Further, we explore models that predict both MWEspans and an MWE-aware dependency structure. Experimental results show that our joint model using additional MWEspan features achieves an MWE recognition improvement of 1.35 points over a pipeline model.
Solving mazes with memristors: a massively-parallel approach

Solving mazes is not just a fun pastime: They are prototype models in several areas of science and technology. However, when maze complexity increases, their solution becomes cumbersome and very time consuming. Here, we show that a network of memristors--resistors with memory--can solve such a nontrivial problem quite easily. In particular, maze solving by the network of memristors occurs in a massively parallel fashion since all memristors in the network participate simultaneously in the calculation. The result of the calculation is then recorded into the memristors' states and can be used and/or recovered at a later time. Furthermore, the network of memristors finds all possible solutions in multiple-solution mazes and sorts out the solution paths according to their length. Our results demonstrate not only the application of memristive networks to the field of massively parallel computing, but also an algorithm to solve mazes, which could find applications in different fields.
Human-robot collaborative manipulation planning using early prediction of human motion

In this paper we present a framework that allows a human and a robot to perform simultaneous manipulation tasks safely in close proximity. The proposed framework is based on early prediction of the human's motion. The prediction system, which builds on previous work in the area of gesture recognition, generates a prediction of human workspace occupancy by computing the swept volume of learned human motion trajectories. The motion planner then plans robot trajectories that minimize a penetration cost in the human workspace occupancy while interleaving planning and execution. Multiple plans are computed in parallel, one for each robot task available at the current time, and the trajectory with the least cost is selected for execution. We test our framework in simulation using recorded human motions and a simulated PR2 robot. Our results show that our framework enables the robot to avoid the human while still accomplishing the robot's task, even in cases where the initial prediction of the human's motion is incorrect. We also show that taking into account the predicted human workspace occupancy in the robot's motion planner leads to safer and more efficient interactions between the user and the robot than only considering the human's current configuration.
Mesh networks: commodity multihop ad hoc networks

In spite of the massive efforts in researching and developing mobile ad hoc networks in the last decade, this type of network has not yet witnessed mass market deployment. The low commercial penetration of products based on ad hoc networking technology could be explained by noting that the ongoing research is mainly focused on implementing military or specialized civilian applications. On the other hand, users are interested in general-purpose applications where high bandwidth and open access to the Internet are consolidated and cheap commodities. To turn mobile ad hoc networks into a commodity, we should move to more pragmatic "opportunistic ad hoc networking" in which multihop ad hoc networks are not isolated self-configured networks, but rather emerge as a flexible and low-cost extension of wired infrastructure networks coexisting with them. Indeed, a new class of networks is emerging from this view: mesh networks. This article provides an overview of mesh networking technology. In particular, starting from commercial case studies we describe the core building blocks and distinct features on which wireless mesh networks should be based. We provide a survey of the current state of the art in off-the-shelf and proprietary solutions to build wireless mesh networks. Finally, we address the challenges of designing a high-performance, scalable, and cost-effective wireless mesh network.
Folding and crumpling adaptive sheets

We present a technique for simulating plastic deformation in sheets of thin materials, such as crumpled paper, dented metal, and wrinkled cloth. Our simulation uses a framework of adaptive mesh refinement to dynamically align mesh edges with folds and creases. This framework allows efficient modeling of sharp features and avoids bend locking that would be otherwise caused by stiff in-plane behavior. By using an explicit plastic embedding space we prevent remeshing from causing shape diffusion. We include several examples demonstrating that the resulting method realistically simulates the behavior of thin sheets as they fold and crumple.
Texture descriptors based on co-occurrence matrices

This paper focuses on the problem of texture classification using statistical descriptors based on the co-occurrence matrices. A major part of the paper is dedicated to the derivation of a general model for analysis and interpretation of experimental results in texture analysis when individual and groups of classifiers are being used, and a technique for evaluating their performance. Using six representative classifiers; that is, second angular moment fl, contrast f2, inverse difference moment f5, entropy f9, and information measures of correlation I and II, f12 and f13, we give a systematic study of the discrimination power of all 63 combinations of these classifiers on 13 samples of Brodatz textures. The conclusion that can be drawn from our study is that it is useful to combine classifiers up to a certain order. Here it turned out that groups of four classifiers are optimal. 9 1990 Academic Press, Inc.
A Broadband Dual-Polarized Omnidirectional Antenna for Base Stations

A broadband vertically/horizontally dual-polarized omnidirectional antenna is proposed for mobile communications. The dual-polarized antenna is a combination of a modified low-profile monopole for vertical polarization (VP) and a circular planar loop for horizontal polarization (HP). The modified low-profile monopole is a circular folded patch shorted by four tubes while the circular loop consists of four half-wavelength arc dipoles. The dual-polarized omnidirectional antenna achieves a bandwidth of ~ 25% (1.7-2.2 GHz) with an isolation of around 40 dB. The gain variations in the horizontal plane are less than 2.5 dB for VP and 1.5 dB for HP. An eight-element dual-polarized antenna array is developed for base station applications. The antenna gains of the array for both VP and HP are ~ 8 dBi with a difference of less than 1 dB. The beamwidths in the vertical plane are 9±1° for VP and 9±1.5° for HP. The cross-polarization levels in the horizontal plane for both VP and HP are lower than -20 dB.
Rice crop yield prediction in India using support vector machines

Food production in India is largely dependent on cereal crops including rice, wheat and various pulses. The sustainability and productivity of rice growing areas is dependent on suitable climatic conditions. Variability in seasonal climate conditions can have detrimental effect, with incidents of drought reducing production. Developing better techniques to predict crop productivity in different climatic conditions can assist farmer and other stakeholders in better decision making in terms of agronomy and crop choice. Machine learning techniques can be used to improve prediction of crop yield under different climatic scenarios. This paper presents the review on use of such machine learning technique for Indian rice cropping areas. This paper discusses the experimental results obtained by applying SMO classifier using the WEKA tool on the dataset of 27 districts of Maharashtra state, India. The dataset considered for the rice crop yield prediction was sourced from publicly available Indian Government records. The parameters considered for the study were precipitation, minimum temperature, average temperature, maximum temperature and reference crop evapotranspiration, area, production and yield for the Kharif season (June to November) for the years 1998 to 2002. For the present study the mean absolute error (MAE), root mean squared error (RMSE), relative absolute error (RAE) and root relative squared error (RRSE) were calculated. The experimental results showed that the performance of other techniques on the same dataset was much better compared to SMO.
Multi-Level Graph Layout on the GPU

This paper presents a new algorithm for force directed graph layout on the GPU. The algorithm, whose goal is to compute layouts accurately and quickly, has two contributions. The first contribution is proposing a general multi-level scheme, which is based on spectral partitioning. The second contribution is computing the layout on the GPU. Since the GPU requires a data parallel programming model, the challenge is devising a mapping of a naturally unstructured graph into a well-partitioned structured one. This is done by computing a balanced partitioning of a general graph. This algorithm provides a general multi-level scheme, which has the potential to be used not only for computation on the GPU, but also on emerging multi-core architectures. The algorithm manages to compute high quality layouts of large graphs in a fraction of the time required by existing algorithms of similar quality. An application for visualization of the topologies of ISP (Internet service provider) networks is presented.
Bridging Physical and Virtual Worlds with Electronic Tags

The role of computers in the modern office has divided ouractivities between virtual interactions in the realm of thecomputer and physical interactions with real objects within thetraditional office infrastructure. This paper extends previous workthat has attempted to bridge this gap, to connect physical objectswith virtual representations or computational functionality, viavarious types of tags. We discuss a variety of scenarios we haveimplemented using a novel combination of inexpensive, unobtrusiveand easy to use RFID tags, tag readers, portable computers andwireless networking. This novel combination demonstrates theutility of invisibly, seamlessly and portably linking physicalobjects to networked electronic services and actions that arenaturally associated with their form.