nils-herrmann/clean_scirepeval huggingface dataset:
Unsupervised biomedical named entity recognition: Experiments with clinical and biological texts

Named entity recognition is a crucial component of biomedical natural language processing, enabling information extraction and ultimately reasoning over and knowledge discovery from text. Much progress has been made in the design of rule-based and supervised tools, but they are often genre and task dependent. As such, adapting them to different genres of text or identifying new types of entities requires major effort in re-annotation or rule development. In this paper, we propose an unsupervised approach to extracting named entities from biomedical text. We describe a stepwise solution to tackle the challenges of entity boundary detection and entity type classification without relying on any handcrafted rules, heuristics, or annotated data. A noun phrase chunker followed by a filter based on inverse document frequency extracts candidate entities from free text. Classification of candidate entities into categories of interest is carried out by leveraging principles from distributional semantics. Experiments show that our system, especially the entity classification step, yields competitive results on two popular biomedical datasets of clinical notes and biological literature, and outperforms a baseline dictionary match approach. Detailed error analysis provides a road map for future work.
Basis Learning as an Algorithmic Primitive

A number of important problems in theoretical computer science and machine learning can be interpreted as recovering a certain basis. These include certain tensor decompositions, Independent Component Analysis (ICA), spectral clustering and Gaussian mixture learning. Each of these problems reduces to an instance of our general model, which we call a “Basis Encoding Function” (BEF). We show that learning a basis within this model can then be provably and efficiently achieved using a first order iteration algorithm (gradient iteration). Our algorithm goes beyond tensor methods, providing a function-based generalization for a number of existing methods including the classical matrix power method, the tensor power iteration as well as cumulant-based FastICA. Our framework also unifies the unusual phenomenon observed in these domains that they can be solved using efficient non-convex optimization. Specifically, we describe a class of BEFs such that their local maxima on the unit sphere are in one-to-one correspondence with the basis elements. This description relies on a certain “hidden convexity” property of these functions. We provide a complete theoretical analysis of gradient iteration even when the BEF is perturbed. We show convergence and complexity bounds polynomial in dimension and other relevant parameters, such as perturbation size. Our perturbation results can be considered as a non-linear version of the classical Davis-Kahan theorem for perturbations of eigenvectors of symmetric matrices. In addition we show that our algorithm exhibits fast (superlinear) convergence and relate the speed of convergence to the properties of the BEF. Moreover, the gradient iteration algorithm can be easily and efficiently implemented in practice. Finally we apply our framework by providing the first provable algorithm for recovery in a general perturbed ICA model. ar X iv :1 41 1. 14 20 v3 [ cs .L G ] 3 N ov 2 01 5
Determining the Veracity of Rumours on Twitter

While social networks can provide an ideal platform for upto-date information from individuals across the world, it has also proved to be a place where rumours fester and accidental or deliberate misinformation often emerges. In this article, we aim to support the task of making sense from social media data, and specifically, seek to build an autonomous message-classifier that filters relevant and trustworthy information from Twitter. For our work, we collected about 100 million public tweets, including users’ past tweets, from which we identified 72 rumours (41 true, 31 false). We considered over 80 trustworthiness measures including the authors’ profile and past behaviour, the social network connections (graphs), and the content of tweets themselves. We ran modern machine-learning classifiers over those measures to produce trustworthiness scores at various time windows from the outbreak of the rumour. Such time-windows were key as they allowed useful insight into the progression of the rumours. From our findings, we identified that our model was significantly more accurate than similar studies in the literature. We also identified critical attributes of the data that give rise to the trustworthiness scores assigned. Finally we developed a software demonstration that provides a visual user interface to allow the user to examine the analysis.
Taxonomy Induction Using Hierarchical Random Graphs

This paper presents a novel approach for inducing lexical taxonomies automatically from text. We recast the learning problem as that of inferring a hierarchy from a graph whose nodes represent taxonomic terms and edges their degree of relatedness. Our model takes this graph representation as input and fits a taxonomy to it via combination of a maximum likelihood approach with a Monte Carlo Sampling algorithm. Essentially, the method works by sampling hierarchical structures with probability proportional to the likelihood with which they produce the input graph. We use our model to infer a taxonomy over 541 nouns and show that it outperforms popular flat and hierarchical clustering algorithms.
A review of cyber security risk assessment methods for SCADA systems

This paper reviews the state of the art in cyber security risk assessment of Supervisory Control and Data Acquisition (SCADA) systems. We select and in-detail examine twenty-four risk assessment methods developed for or applied in the context of a SCADA system. We describe the essence of the methods and then analyse them in terms of aim; application domain; the stages of risk management addressed; key risk management concepts covered; impact measurement; sources of probabilistic data; evaluation and tool support. Based on the analysis, we suggest an intuitive scheme for the categorisation of cyber security risk assessment methods for SCADA systems. We also outline five research challenges facing the domain and point out the approaches that might be taken.
Dual Quaternions for Rigid Transformation Blending

Quaternions have been a popular tool in 3D computer graphics for more than 20 years. However, classical quaternions are restricted to the representation of rotations, whereas in graphical applications we typically work with rotation composed with translation (i.e., a rigid transformation). Dual quaternions represent rigid transformations in the same way as classical quaternions represent rotations. In this paper we show how to generalize established techniques for blending of rotations to include all rigid transformations. Algorithms based on dual quaternions are computationally more efficient than previous solutions and have better properties (constant speed, shortest path and coordinate invariance). For the specific example of skinning, we demonstrate that problems which required considerable research effort recently are trivial to solve using our dual quaternion formulation. However, skinning is only one application of dual quaternions, so several further promising research directions are suggested in the paper.  I.3.5 [Computer Graphics]: Computational Geometry and Object Modeling – Geometric Transformations— [I.3.7]: Computer Graphics—Three-Dimensional Graphics and Realism – Animation
Least Squares Generative Adversarial Networks

Unsupervised learning with generative adversarial networks (GANs) has proven hugely successful. Regular GANs hypothesize the discriminator as a classifier with the sigmoid cross entropy loss function. However, we found that this loss function may lead to the vanishing gradients problem during the learning process. To overcome such a problem, we propose in this paper the Least Squares Generative Adversarial Networks (LSGANs) which adopt the least squares loss function for the discriminator. We show that minimizing the objective function of LSGAN yields minimizing the Pearson X2 divergence. There are two benefits of LSGANs over regular GANs. First, LSGANs are able to generate higher quality images than regular GANs. Second, LSGANs perform more stable during the learning process. We evaluate LSGANs on LSUN and CIFAR-10 datasets and the experimental results show that the images generated by LSGANs are of better quality than the ones generated by regular GANs. We also conduct two comparison experiments between LSGANs and regular GANs to illustrate the stability of LSGANs.
Reverse Engineering Aggregation Queries

Query reverse engineering seeks to re-generate the SQL query that produced a given query output table from a given database. In this paper, we solve this problem for OLAP queries with group-by and aggregation. We develop a novel three-phase algorithm named REGAL 1 for this problem. First, based on a lattice graph structure, we identify a set of group-by candidates for the desired query. Second, we apply a set of aggregation constraints that are derived from the properties of aggregate operators at both the table-level and the group-level to discover candidate combinations of group-by columns and aggregations that are consistent with the given query output table. Finally, we find a multi-dimensional filter, i.e., a conjunction of selection predicates over the base table attributes, that is needed to generate the exact query output table. We conduct an extensive experimental study over the TPC-H dataset to demonstrate the effectiveness and efficiency of our proposal.
Crafting technology: Reimagining the processes, materials, and cultures of electronics

This article examines the practice of electronics building in the context of other crafts. We compare the experience of making electronics with the experiences of carving, sewing, and painting. Our investigation is grounded in a survey of 40 practicing craftspeople who are working in each of these disciplines. We then use this survey as a foundation for a discussion of hybrid craft—integrations of electronics with carving, sewing, and painting. We present examples of hybrid craft and discuss the ways in which blended practices can enrich and diversify technology.
Empirical evaluation of the tarantula automatic fault-localization technique

The high cost of locating faults in programs has motivated the development of techniques that assist in fault localization by automating part of the process of searching for faults. Empirical studies that compare these techniques have reported the relative effectiveness of four existing techniques on a set of subjects. These studies compare the rankings that the techniques compute for statements in the subject programs and the effectiveness of these rankings in locating the faults. However, it is unknown how these four techniques compare with Tarantula, another existing fault-localization technique, although this technique also provides a way to rank statements in terms of their suspiciousness. Thus, we performed a study to compare the Tarantula technique with the four techniques previously compared. This paper presents our study---it overviews the Tarantula technique along with the four other techniques studied, describes our experiment, and reports and discusses the results. Our studies show that, on the same set of subjects, the Tarantula technique consistently outperforms the other four techniques in terms of effectiveness in fault localization, and is comparable in efficiency to the least expensive of the other four techniques.
A Joint Framework for Coreference Resolution and Mention Head Detection

In coreference resolution, a fair amount of research treats mention detection as a preprocessed step and focuses on developing algorithms for clustering coreferred mentions. However, there are significant gaps between the performance on gold mentions and the performance on the real problem, when mentions are predicted from raw text via an imperfect Mention Detection (MD) module. Motivated by the goal of reducing such gaps, we develop an ILP-based joint coreference resolution and mention head formulation that is shown to yield significant improvements on coreference from raw text, outperforming existing state-ofart systems on both the ACE-2004 and the CoNLL-2012 datasets. At the same time, our joint approach is shown to improve mention detection by close to 15% F1. One key insight underlying our approach is that identifying and co-referring mention heads is not only sufficient but is more robust than working with complete mentions.
Coarse-and Fine-Grained Sentiment Analysis of Social Media Text

People make judgments about the world around them. They harbor positive and negative attitudes about people, organizations, places, events, and ideas. We regard these types of attitudes as sentiments. Sentiments are private states,1 cognitive phenomena that are not directly observable by others. However, expressions of sentiment can be manifested in actions, including written and spoken language. Sentiment analysis is the study of automated techniques for extracting sentiment from written language. This has been a very active area entiment analysis—the automated extraction of expressions of positive or negative attitudes from text—has received considerable attention from researchers during the past 10 years. During the same period, the widespread growth of social media has resulted in an explosion of publicly available, user-generated text on the World Wide Web. These data can potentially be utilized to provide real-time insights into the aggregated sentiments of people. The tools provided by statistical natural language processing and machine learning, along with exciting new scalable approaches to working with large volumes of text, make it possible to begin extracting sentiments from the web. We discuss some of the challenges of sentiment extraction and some of the approaches employed to address these challenges. In particular, we describe work we have done to annotate sentiment in blogs at the levels of sentences and subsentences (clauses); to classify subjectivity at the level of sentences; and to identify the targets, or topics, of sentiment at the level of clauses.
Functional Frank-Wolfe Boosting for General Loss Functions

Boosting is a generic learning method for classification and regression. Yet, as the number of base hypotheses becomes larger, boosting can lead to a deterioration of test performance. Overfitting is an important and ubiquitous phenomenon, especially in regression settings. To avoid overfitting, we consider using l1 regularization. We propose a novel Frank-Wolfe type boosting algorithm (FWBoost) applied to general loss functions. By using exponential loss, the FWBoost algorithm can be rewritten as a variant of AdaBoost for binary classification. FWBoost algorithms have exactly the same form as existing boosting methods, in terms of making calls to a base learning algorithm with different weights update. This direct connection between boosting and Frank-Wolfe yields a new algorithm that is as practical as existing boosting methods but with new guarantees and rates of convergence. Experimental results show that the test performance of FWBoost is not degraded with larger rounds in boosting, which is consistent with the theoretical analysis.
Microbiota Modulate Behavioral and Physiological Abnormalities Associated with Neurodevelopmental Disorders

Neurodevelopmental disorders, including autism spectrum disorder (ASD), are defined by core behavioral impairments; however, subsets of individuals display a spectrum of gastrointestinal (GI) abnormalities. We demonstrate GI barrier defects and microbiota alterations in the maternal immune activation (MIA) mouse model that is known to display features of ASD. Oral treatment of MIA offspring with the human commensal Bacteroides fragilis corrects gut permeability, alters microbial composition, and ameliorates defects in communicative, stereotypic, anxiety-like and sensorimotor behaviors. MIA offspring display an altered serum metabolomic profile, and B. fragilis modulates levels of several metabolites. Treating naive mice with a metabolite that is increased by MIA and restored by B. fragilis causes certain behavioral abnormalities, suggesting that gut bacterial effects on the host metabolome impact behavior. Taken together, these findings support a gut-microbiome-brain connection in a mouse model of ASD and identify a potential probiotic therapy for GI and particular behavioral symptoms in human neurodevelopmental disorders.
VANET security: Issues, challenges and solutions

Vehicular Ad-hoc Network (VANET) is an infrastructure less network. It provides enhancement in safety related techniques and comfort while driving. It enables vehicles to share information regarding safety and traffic analysis. The scope of VANET application has increased with the recent advances in technology and development of smart cities across the world. VANET provide a self aware system that has major impact in enhancement of traffic services and in reducing road accidents. Information shared in this system is time sensitive and requires robust and quick forming network connections. VANET, being a wireless ad hoc network, serves this purpose completely but is prone to security attacks. Highly dynamic connections, sensitive information sharing and time sensitivity of this network, make it an eye-catching field for attackers. This paper represents a literature survey on VANET with primary concern of the security issues and challenges with it. Features of VANET, architecture, security requisites, attacker type and possible attacks in VANET are considered in this survey paper.
Collaborative Filtering with Privacy

Server-based collaborative filtering systems have been very successful in e-commerce and in direct recommendation applications. In future, they have many potential applications in ubiquitous computing settings. But today’s schemes have problems such as loss of privacy, favoring retail monopolies, and with hampering diffusion of innovations. We propose an alternative model in which users control all of their log data. We describe an algorithm whereby a community of users can compute a public “aggregate” of their data that does not expose individual users’ data. The aggregate allows personalized recommendations to be computed by members of the community, or by outsiders. The numerical algorithm is fast, robust and accurate. Our method reduces the collaborative filtering task to an iterative calculation of the aggregate requiring only addition of vectors of user data. Then we use homomorphic encryption to allow sums of encrypted vectors to be computed and decrypted without exposing individual data. We give verification schemes for all parties in the computation. Our system can be implemented with untrusted servers, or with additional infrastructure, as a fully peer-to-peer (P2P) system.
RGBD-camera based get-up event detection for hospital fall prevention

In this work, we develop a computer vision based fall prevention system for hospital ward application. To prevent potential falls, once the event of patient get up from the bed is automatically detected, nursing staffs are alarmed immediately for assistance. For the detection task, we use a RGBD sensor (Microsoft Kinect). The geometric prior knowledge is exploited by identifying a set of task-specific feature channels, e.g., regions of interest. Extensive motion and shape features from both color and depth image sequences are extracted. Features from multiple modalities and channels are fused via a multiple kernel learning framework for training the event detector. Experimental results demonstrate the high accuracy and efficiency achieved by the proposed system.
Comparative Testing of Face Detection Algorithms

Face detection (FD) is widely used in interactive user interfaces, in advertising industry, entertainment services, video coding, is necessary first stage for all face recognition systems, etc. However, the last practical and independent comparisons of FD algorithms were made by Hjelmas et al. and by Yang et al. in 2001. The aim of this work is to propose parameters of FD algorithms quality evaluation and methodology of their objective comparison, and to show the current state of the art in face detection. The main idea is routine test of the FD algorithm in the labeled image datasets. Faces are represented by coordinates of the centers of the eyes in these datasets. For algorithms, representing detected faces by rectangles, the statistical model of eyes’ coordinates estimation was proposed. In this work the seven face detection algorithms were tested; article contains the results of their comparison.
Fascial plasticity - a new neurobiological explanation: Part 1

Part 1 of this two part article showed that immediate fascial responsiveness to manipulation cannot be explained by its mechanical properties alone. Fascia is densely innervated by mechanoreceptors which are responsive to myofascial manipulation. They are intimately connected with the central nervous system and specially with the autonomic nervous system. Part 2 of the article shows how stimulation of these receptors can trigger viscosity changes in the ground substance. The discovery and implications of the existence of fascial smooth muscle cells are of special interest in relation to fibromyalgia, amongst other conditions. An attitudinal shift is suggested, from a mechanical body concept towards a cybernetic model, in which the practitioner’s intervention are seen as stimulation for self-regulatory processes within the client’s organism. Practical implications of this approach in myofascial manipulation will be explored. r 2003
Scatter/Gather: A Cluster-based Approach to Browsing Large Document Collections

Document clustering has not been well received as an information retrieval tool. Objections to its use fall into two main categories: first, that clustering is too slow for large corpora (with running time often quadratic in the number of documents); and second, that clustering does not appreciably improve retrieval.
We argue that these problems arise only when clustering is used in an attempt to improve conventional search techniques. However, looking at clustering as an information access tool in its own right obviates these objections, and provides a powerful new access paradigm. We present a document browsing technique that employs document clustering as its primary operation. We also present fast (linear time) clustering algorithms which support this interactive browsing paradigm.
Cartman: The Low-Cost Cartesian Manipulator that Won the Amazon Robotics Challenge

Ahstract-The Amazon Robotics Challenge enlisted sixteen teams to each design a pick-and-place robot for autonomous warehousing, addressing development in robotic vision and manipulation. This paper presents the design of our custom-built, cost-effective, Cartesian robot system Cartman, which won first place in the competition finals by stowing 14 (out of 16) and picking all 9 items in 27 minutes, scoring a total of 272 points. We highlight our experience-centred design methodology and key aspects of our system that contributed to our competitiveness. We believe these aspects are crucial to building robust and effective robotic systems.
Analyza: Exploring Data with Conversation

We describe Analyza, a system that helps lay users explore data. Analyza has been used within two large real world systems. The first is a question-and-answer feature in a spreadsheet product. The second provides convenient access to a revenue/inventory database for a large sales force. Both user bases consist of users who do not necessarily have coding skills, demonstrating Analyza's ability to democratize access to data. We discuss the key design decisions in implementing this system. For instance, how to mix structured and natural language modalities, how to use conversation to disambiguate and simplify querying, how to rely on the ``semantics' of the data to compensate for the lack of syntactic structure, and how to efficiently curate the data.