nils-herrmann/clean_scirepeval huggingface dataset:
Building a Japanese-Chinese Dictionary Using Kanji/Hanzi Conversion

A new bilingual dictionary can be built using two existing bilingual dictionaries, such as Japanese-English and English-Chinese to build Japanese-Chinese dictionary. However, Japanese and Chinese are nearer languages than English, there should be a more direct way of doing this. Since a lot of Japanese words are composed of kanji, which are similar to hanzi in Chinese, we attempt to build a dictionary for kanji words by simple conversion from kanji to hanzi. Our survey shows that around 2/3 of the nouns and verbal nouns in Japanese are kanji words, and more than 1/3 of them can be translated into Chinese directly. The accuracy of conversion is 97%. Besides, we obtain translation candidates for 24% of the Japanese words using English as a pivot language with 77% accuracy. By adding the kanji/hanzi conversion method, we increase the candidates by 9%, to 33%, with better quality candidates.
Function-Based Question Classification for General QA

In contrast with the booming increase of internet data, state-of-art QA (question answering) systems, otherwise, concerned data from specific domains or resources such as search engine snippets, online forums and Wikipedia in a somewhat isolated way. Users may welcome a more general QA system for its capability to answer questions of various sources, integrated from existed specialized sub-QA engines. In this framework, question classification is the primary task. However, the current paradigms of question classification were focused on some specified type of questions, i.e. factoid questions, which are inappropriate for the general QA. In this paper, we propose a new question classification paradigm, which includes a question taxonomy suitable to the general QA and a question classifier based on MLN (Markov logic network), where rule-based methods and statistical methods are unified into a single framework in a fuzzy discriminative learning approach. Experiments show that our method outperforms traditional question classification approaches.
Challenges in clinical natural language processing for automated disorder normalization

Identifying key variables such as disorders within the clinical narratives in electronic health records has wide-ranging applications within clinical practice and biomedical research. Previous research has demonstrated reduced performance of disorder named entity recognition (NER) and normalization (or grounding) in clinical narratives than in biomedical publications. In this work, we aim to identify the cause for this performance difference and introduce general solutions.



We use closure properties to compare the richness of the vocabulary in clinical narrative text to biomedical publications. We approach both disorder NER and normalization using machine learning methodologies. Our NER methodology is based on linear-chain conditional random fields with a rich feature approach, and we introduce several improvements to enhance the lexical knowledge of the NER system. Our normalization method - never previously applied to clinical data - uses pairwise learning to rank to automatically learn term variation directly from the training data.



We find that while the size of the overall vocabulary is similar between clinical narrative and biomedical publications, clinical narrative uses a richer terminology to describe disorders than publications. We apply our system, DNorm-C, to locate disorder mentions and in the clinical narratives from the recent ShARe/CLEF eHealth Task. For NER (strict span-only), our system achieves precision=0.797, recall=0.713, f-score=0.753. For the normalization task (strict span+concept) it achieves precision=0.712, recall=0.637, f-score=0.672. The improvements described in this article increase the NER f-score by 0.039 and the normalization f-score by 0.036. We also describe a high recall version of the NER, which increases the normalization recall to as high as 0.744, albeit with reduced precision.



We perform an error analysis, demonstrating that NER errors outnumber normalization errors by more than 4-to-1. Abbreviations and acronyms are found to be frequent causes of error, in addition to the mentions the annotators were not able to identify within the scope of the controlled vocabulary.



Disorder mentions in text from clinical narratives use a rich vocabulary that results in high term variation, which we believe to be one of the primary causes of reduced performance in clinical narrative. We show that pairwise learning to rank offers high performance in this context, and introduce several lexical enhancements - generalizable to other clinical NER tasks - that improve the ability of the NER system to handle this variation. C is a high performing, open source system for disorders in clinical text, and a promising step toward NER and normalization methods that are trainable to a wide variety of domains and entities. (DNorm-C is open source software, and is available with a trained model at the DNorm demonstration website: http://www.ncbi.nlm.nih.gov/CBBresearch/Lu/Demo/tmTools/#DNorm.).
Technologies for Printing Sensors and Electronics Over Large Flexible Substrates: A Review

Printing sensors and electronics over flexible substrates are an area of significant interest due to low-cost fabrication and possibility of obtaining multifunctional electronics over large areas. Over the years, a number of printing technologies have been developed to pattern a wide range of electronic materials on diverse substrates. As further expansion of printed technologies is expected in future for sensors and electronics, it is opportune to review the common features, the complementarities, and the challenges associated with various printing technologies. This paper presents a comprehensive review of various printing technologies, commonly used substrates and electronic materials. Various solution/dry printing and contact/noncontact printing technologies have been assessed on the basis of technological, materials, and process-related developments in the field. Critical challenges in various printing techniques and potential research directions have been highlighted. Possibilities of merging various printing methodologies have been explored to extend the lab developed standalone systems to high-speed roll-to-roll production lines for system level integration.
Turn Waste into Wealth: On Simultaneous Clustering and Cleaning over Dirty Data

Dirty data commonly exist. Simply discarding a large number of inaccurate points (as noises) could greatly affect clustering results. We argue that dirty data can be repaired and utilized as strong supports in clustering. To this end, we study a novel problem of clustering and repairing over dirty data at the same time. Referring to the minimum change principle in data repairing, the objective is to find a minimum modification of inaccurate points such that the large amount of dirty data can enhance the clustering. We show that the problem can be formulated as an integer linear programming (ILP) problem. Efficient approximation is then devised by a linear programming (LP) relaxation. In particular, we illustrate that an optimal solution of the LP problem can be directly obtained without calling a solver. A quadratic time approximation algorithm is developed based on the aforesaid LP solution. We further advance the algorithm to linear time cost, where a trade-off between effectiveness and efficiency is enabled. Empirical results demonstrate that both the clustering and cleaning accuracies can be improved by our approach of repairing and utilizing the dirty data in clustering.
Designing Learning by Teaching Agents: The Betty's Brain System

The idea that teaching others is a powerful way to learn is intuitively compelling and supported in the research literature. We have developed computer-based, domain-independent Teachable Agents that students can teach using a visual representation. The students query their agent to monitor their learning and problem solving behavior. This motivates the students to learn more so they can teach their agent to perform better. This paper presents a teachable agent called Betty’s Brain that combines learning by teaching with self-regulated learning feedback to promote deep learning and understanding in science domains. A study conducted in a 5 grade science classroom compared three versions of the system: a version where the students were taught by an agent, a baseline learning by teaching version, and a learning by teaching version where students received feedback on self-regulated learning strategies and some domain content. In the other two systems, students received feedback primarily on domain content. Our results indicate that all three groups showed learning gains during a main study where students learnt about river ecosystems, but the two learning by teaching groups performed better than the group that was taught. These differences persisted in the transfer study, but the gap between the baseline learning by teaching and self-regulated learning group decreased. However, there are indications that self-regulated learning feedback better prepared students to learn in new domains, even when they no longer had access to the self-regulation environment.
51 . 1 : Novel Simultaneous Emission Driving Scheme for Crosstalk-free 3 D AMOLED TV

A novel driving scheme based on simultaneous emission is reported for 2D/3D AMOLED TVs. The new method reduces leftright crosstalk without sacrificing luminance. The new scheme greatly simplifies the pixel circuit as the number of transistors for Vth compensation is reduced from 6 to 3. The capacitive load of scan lines is reduced by 48%, enabling very high refresh rate (240 Hz).
XBRL and open data for global financial ecosystems: A linked data approach

Received in revised form 9 February 2012 Accepted 15 February 2012 Information professionals performing business activity related investigative analysis must routinely associate data from a diverse range of Web based general-interest business and financial information sources. XBRL has become an integral part of the financial data landscape. At the same time, Open Data initiatives have contributed relevant financial, economic, and business data to the pool of publicly available information on the Web but the use of XBRL in combination with Open Data remains at an early state of realisation. In this paper we argue that Linked Data technology, created for Web scale information integration, can accommodate XBRL data and make it easier to combine it with open datasets. This can provide the foundations for a global data ecosystem of interlinked and interoperable financial and business information with the potential to leverage XBRL beyond its current regulatory and disclosure role. We outline the uses of Linked Data technologies to facilitate XBRL consumption in conjunction with non-XBRL Open Data, report on current activities and highlight remaining challenges in terms of information consolidation faced by both XBRL and Web technologies.
Robust De-anonymization of Large Sparse Datasets

We present a new class of statistical de- anonymization attacks against high-dimensional micro-data, such as individual preferences, recommendations, transaction records and so on. Our techniques are robust to perturbation in the data and tolerate some mistakes in the adversary's background knowledge. We apply our de-anonymization methodology to the Netflix Prize dataset, which contains anonymous movie ratings of 500,000 subscribers of Netflix, the world's largest online movie rental service. We demonstrate that an adversary who knows only a little bit about an individual subscriber can easily identify this subscriber's record in the dataset. Using the Internet Movie Database as the source of background knowledge, we successfully identified the Netflix records of known users, uncovering their apparent political preferences and other potentially sensitive information.
Attributed Signed Network Embedding

The major task of network embedding is to learn low-dimensional vector representations of social-network nodes. It facilitates many analytical tasks such as link prediction and node clustering and thus has attracted increasing attention. The majority of existing embedding algorithms are designed for unsigned social networks. However, many social media networks have both positive and negative links, for which unsigned algorithms have little utility. Recent findings in signed network analysis suggest that negative links have distinct properties and added value over positive links. This brings about both challenges and opportunities for signed network embedding. In addition, user attributes, which encode properties and interests of users, provide complementary information to network structures and have the potential to improve signed network embedding. Therefore, in this paper, we study the novel problem of signed social network embedding with attributes. We propose a novel framework SNEA, which exploits the network structure and user attributes simultaneously for network representation learning. Experimental results on link prediction and node clustering with real-world datasets demonstrate the effectiveness of SNEA.
Automated Classification of Stance in Student Essays: An Approach Using Stance Target Information and the Wikipedia Link-Based Measure

We present a new approach to the automated classification of document-level argument stance, a relatively underresearched sub-task of Sentiment Analysis. In place of the noisy online debate data currently used in stance classification research, a corpus of student essays annotated for essaylevel stance is constructed for use in a series of classification experiments. A novel set of features designed to capture the stance, stance targets, and topical relationships between the essay prompt and the student’s essay is described. Models trained on this feature set showed significant increases in accuracy relative to two high baselines.
Adaptive gait pattern control of a quadruped locomotion robot

The authors have proposed a control system of a quadruped locomotion robot by using nonlinear oscillators. It is composed of a leg motion controller and a gait pattern controller. The leg motion controller drives the actuators of the legs by using local feedback control. The gait pattern controller involves nonlinear oscillators with mutual interactions. In this paper, capability of adaptation of the proposed control system against variance of the environment is verified through numerical simulations and hardware experiments: With the input signals from the touch sensors at the tips of the legs, the nonlinear oscillators tune the phase differences among them through mutual entrainments. As a result, a gait pattern corresponding to the states of the system or to the properties of the environment emerges. And the robot changes its gait pattern adaptively to variance of the environment and establishes a stable locomotion while suppressing the energy consumption.
Information Technology and Corporate Strategy: A Research Perspective

The use of information technology (IT) as a competitive weapon has become a popular cliché; but there is still a marked lack of understanding of the issues that determine the influence of information technology on a particular organization and the processes that will allow a smooth coordination of technology and corporate strategy. This article surveys the major efforts to arrive at a relevant framework and attempts to integrate them in a more comprehensive viewpoint. The focus then turns to the major research issues in understanding the impact of information technology on competitive strategy.
Generalizing Case Frames Using a Thesaurus and the MDL Principle

A new method for automatically acquiring case frame patterns from large corpora is proposed. In particular, the problem of generalizing values of a case frame slot for a verb is viewed as that of estimating a conditional probability distribution over a partition of words, and a new generalization method based on the Minimum Description Length (MDL) principle is proposed. In order to assist with efficiency, the proposed method makes use of an existing thesaurus and restricts its attention to those partitions that are present as "cuts" in the thesaurus tree, thus reducing the generalization problem to that of estimating a "tree cut model" of the thesaurus tree. An efficient algorithm is given, which provably obtains the optimal tree cut model for the given frequency data of a case slot, in the sense of MDL. Case frame patterns obtained by the method were used to resolve PP-attachment ambiguity. Experimental results indicate that the proposed method improves upon or is at least comparable with existing methods.
Head Reconstruction from Internet Photos

3D face reconstruction from Internet photos has recently produced exciting results. A person’s face, e.g., Tom Hanks, can be modeled and animated in 3D from a completely uncalibrated photo collection. Most methods, however, focus solely on face area and mask out the rest of the head. This paper proposes that head modeling from the Internet is a problem we can solve. We target reconstruction of the rough shape of the head. Our method is to gradually “grow” the head mesh starting from the frontal face and extending to the rest of views using photometric stereo constraints. We call our method boundary-value growing algorithm. Results on photos of celebrities downloaded from the Internet are presented.
A MINIATURIZED PRINTED DIPOLE ANTENNA WITH V-SHAPED GROUND FOR 2 . 45 GHZ RFID

In this paper, a miniaturized printed dipole antenna with the V-shaped ground is proposed for radio frequency identification (RFID) readers operating at the frequency of 2.45 GHz. The principles of the microstrip balun and the printed dipole are analyzed and design considerations are formulated. Through extending and shaping the ground to reduce the coupling between the balun and the dipole, the antenna’s impedance bandwidth is broadened and the antenna’s radiation pattern is improved. The 3D finite difference time domain (FDTD) Electromagnetic simulations are carried out to evaluate the antenna’s performance. The effects of the extending angle and the position of the ground are investigated to obtain the optimized parameters. The antenna was fabricated and measured in a microwave anechoic chamber. The results show that the proposed antenna achieves a broader impedance bandwidth, a higher forward radiation gain and a stronger suppression to backward radiation compared with the one without such a ground.
A patch-based convolutional neural network for remote sensing image classification

Availability of accurate land cover information over large areas is essential to the global environment sustainability; digital classification using medium-resolution remote sensing data would provide an effective method to generate the required land cover information. However, low accuracy of existing per-pixel based classification methods for medium-resolution data is a fundamental limiting factor. While convolutional neural networks (CNNs) with deep layers have achieved unprecedented improvements in object recognition applications that rely on fine image structures, they cannot be applied directly to medium-resolution data due to lack of such fine structures. In this paper, considering the spatial relation of a pixel to its neighborhood, we propose a new deep patch-based CNN system tailored for medium-resolution remote sensing data. The system is designed by incorporating distinctive characteristics of medium-resolution data; in particular, the system computes patch-based samples from multidimensional top of atmosphere reflectance data. With a test site from the Florida Everglades area (with a size of 771 square kilometers), the proposed new system has outperformed pixel-based neural network, pixel-based CNN and patch-based neural network by 24.36%, 24.23% and 11.52%, respectively, in overall classification accuracy. By combining the proposed deep CNN and the huge collection of medium-resolution remote sensing data, we believe that much more accurate land cover datasets can be produced over large areas.
Personalized PageRank Estimation and Search: A Bidirectional Approach

We present new algorithms for Personalized PageRank estimation and Personalized PageRank search. First, for the problem of estimating Personalized PageRank (PPR) from a source distribution to a target node, we present a new bidirectional estimator with simple yet strong guarantees on correctness and performance, and 3x to 8x speedup over existing estimators in experiments on a diverse set of networks. Moreover, it has a clean algebraic structure which enables it to be used as a primitive for the Personalized PageRank Search problem: Given a network like Facebook, a query like "people named John," and a searching user, return the top nodes in the network ranked by PPR from the perspective of the searching user. Previous solutions either score all nodes or score candidate nodes one at a time, which is prohibitively slow for large candidate sets. We develop a new algorithm based on our bidirectional PPR estimator which identifies the most relevant results by sampling candidates based on their PPR; this is the first solution to PPR search that can find the best results without iterating through the set of all candidate results. Finally, by combining PPR sampling with sequential PPR estimation and Monte Carlo, we develop practical algorithms for PPR search, and we show via experiments that our algorithms are efficient on networks with billions of edges.
Coreset-Based Adaptive Tracking

We propose a method for learning from streaming visual data using a compact, constant size representation of all the data that was seen until a given moment. Specifically, we construct a “coreset” representation of streaming data using a parallelized algorithm, which is an approximation of a set with relation to the squared distances between this set and all other points in its ambient space. We learn an adaptive object appearance model from the coreset tree in constant time and logarithmic space and use it for object tracking by detection. Our method obtains excellent results for object tracking on three standard datasets over more than 100 videos. The ability to summarize data efficiently makes our method ideally suited for tracking in long videos in presence of space and time constraints. We demonstrate this ability by outperforming a variety of algorithms on the TLD dataset with 2685 frames on average. This coreset based learning approach can be applied for both real-time learning of small, varied data and fast learning of big data.
Association of genes to genetically inherited diseases using data mining

Although approximately one-quarter of the roughly 4,000 genetically inherited diseases currently recorded in respective databases (LocusLink, OMIM) are already linked to a region of the human genome, about 450 have no known associated gene. Finding disease-related genes requires laborious examination of hundreds of possible candidate genes (sometimes, these are not even annotated; see, for example, refs 3,4). The public availability of the human genome draft sequence has fostered new strategies to map molecular functional features of gene products to complex phenotypic descriptions, such as those of genetically inherited diseases. Owing to recent progress in the systematic annotation of genes using controlled vocabularies, we have developed a scoring system for the possible functional relationships of human genes to 455 genetically inherited diseases that have been mapped to chromosomal regions without assignment of a particular gene. In a benchmark of the system with 100 known disease-associated genes, the disease-associated gene was among the 8 best-scoring genes with a 25% chance, and among the best 30 genes with a 50% chance, showing that there is a relationship between the score of a gene and its likelihood of being associated with a particular disease. The scoring also indicates that for some diseases, the chance of identifying the underlying gene is higher.