nils-herrmann/clean_scirepeval huggingface dataset:
Analyzing Co-training Style Algorithms

Co-training is a semi-supervised learning paradigm which trains two learners respectively from two different views and lets the learners label some unlabeled examples for each other. In this paper, we present a new PAC analysis on co-training style algorithms. We show that the co-training process can succeed even without two views, given that the two learners have large difference, which explains the success of some co-training style algorithms that do not require two views. Moreover, we theoretically explain that why the co-training process could not improve the performance further after a number of rounds, and present a rough estimation on the appropriate round to terminate co-training to avoid some wasteful learning rounds.
Using Decision Trees to Improve Case-Based Learning

This paper shows that decision trees can be used to improve the performance of case-based learning (CBL) systems. We introduce a performance task for machine learning systems called semi-exible prediction that lies between the classiication task performed by decision tree algorithms and the exible prediction task performed by conceptual clustering systems. In semi-exible prediction, learning should improve prediction of a spe-ciic set of features known a priori rather than a single known feature (as in classii-cation) or an arbitrary set of features (as in conceptual clustering). We describe one such task from natural language processing and present experiments that compare solutions to the problem using decision trees, CBL, and a hybrid approach that combines the two. In the hybrid approach, decision trees are used to specify the features to be included in k-nearest neighbor case retrieval. Results from the experiments show that the hybrid approach outperforms both the decision tree and case-based approaches as well as two case-based systems that incorporate expert knowledge into their case retrieval algorithms. Results clearly indicate that decision trees can be used to improve the performance of CBL systems and do so without reliance on potentially expensive expert knowledge.
Evidence based medicine: what it is and what it isn't.

Evidence based medicine, whose philosophical origins extend back to mid-19th century Paris and earlier, remains a hot topic for clinicians, public health practitioners, purchasers, planners, and the public. There are now frequent workshops in how to practice and teach it (one sponsored by the BMJ will be held in London on 24 April); undergraduate and postgraduate training programmes are incorporating it (or pondering how to do so); British centres for evidence based practice have been established or planned in adult medicine, child health, surgery, pathology, pharmacotherapy, nursing, general practice, and dentistry; the Cochrane Collaboration and Britain's Centre for Review and Dissemination in York are providing systematic reviews of the effects of health care; new evidence based practice journals are being launched; and it has become a common topic in the lay media. But enthusiasm has been mixed with some negative reaction. 5 6 Criticism has ranged from evidence based medicine being old hat to it being a dangerous innovation, perpetrated by the arrogant to serve cost cutters and suppress clinical freedom. As evidence based medicine continues to evolve and adapt, now is a useful time to refine the discussion of what it is and what it is not.
When does self-esteem relate to deviant behavior? The role of contingencies of self-worth.

Researchers have assumed that low self-esteem predicts deviance, but empirical results have been mixed. This article draws upon recent theoretical developments regarding contingencies of self-worth to clarify the self-esteem/deviance relation. It was predicted that self-esteem level would relate to deviance only when self-esteem was not contingent on workplace performance. In this manner, contingent self-esteem is a boundary condition for self-consistency/behavioral plasticity theory predictions. Using multisource data collected from 123 employees over 6 months, the authors examined the interaction between level (high/low) and type (contingent/noncontingent) of self-esteem in predicting workplace deviance. Results support the hypothesized moderating effects of contingent self-esteem; implications for self-esteem theories are discussed.
Large-scale factorization of type-constrained multi-relational data

The statistical modeling of large multi-relational datasets has increasingly gained attention in recent years. Typical applications involve large knowledge bases like DBpedia, Freebase, YAGO and the recently introduced Google Knowledge Graph that contain millions of entities, hundreds and thousands of relations, and billions of relational tuples. Collective factorization methods have been shown to scale up to these large multi-relational datasets, in particular in form of tensor approaches that can exploit the highly scalable alternating least squares (ALS) algorithms for calculating the factors. In this paper we extend the recently proposed state-of-the-art RESCAL tensor factorization to consider relational type-constraints. Relational type-constraints explicitly define the logic of relations by excluding entities from the subject or object role. In addition we will show that in absence of prior knowledge about type-constraints, local closed-world assumptions can be approximated for each relation by ignoring unobserved subject or object entities in a relation. In our experiments on representative large datasets (Cora, DBpedia), that contain up to millions of entities and hundreds of type-constrained relations, we show that the proposed approach is scalable. It further significantly outperforms RESCAL without type-constraints in both, runtime and prediction quality.
Using Mentoring and Storytelling to Transfer Knowledge in the Workplace

The core capabilities of an organization include critical skills of employees, management systems, and norms and values. Core capabilities may be transferred fonnally and explicitly. However, much knowledge, particularly knowledge with rich tacit dimensions, is transferred informally through processes of socialization and intemaiization. We focus on two transfer mechanisms—mentoring and storytelling—that can leverage the knowledge of an organization, particularly its tacit knowledge, to build core capabilities. We draw on relevant research in leaming and cognitive psychology to clarify the conditions under which mentoring and storytelling can be most effective as carriers of knowledge. Finally, we present recommendations for specific managerial practices that follow from our analysis.
Neuro-symbolic representation learning on biological knowledge graphs

Biological data and knowledge bases increasingly rely on Semantic Web technologies and the use of knowledge graphs for data integration, retrieval and federated queries. In the past years, feature learning methods that are applicable to graph-structured data are becoming available, but have not yet widely been applied and evaluated on structured biological knowledge.  We develop a novel method for feature learning on biological knowledge graphs. Our method combines symbolic methods, in particular knowledge representation using symbolic logic and automated reasoning, with neural networks to generate embeddings of nodes that encode for related information within knowledge graphs. Through the use of symbolic logic, these embeddings contain both explicit and implicit information. We apply these embeddings to the prediction of edges in the knowledge graph representing problems of function prediction, finding candidate genes of diseases, protein-protein interactions, or drug target relations, and demonstrate performance that matches and sometimes outperforms traditional approaches based on manually crafted features. Our method can be applied to any biological knowledge graph, and will thereby open up the increasing amount of Semantic Web based knowledge bases in biology to use in machine learning and data analytics.


Availability and implementation
https://github.com/bio-ontology-research-group/walking-rdf-and-owl.



robert.hoehndorf@kaust.edu.sa.



Supplementary data are available at Bioinformatics online.
Subsampling for efficient and effective unsupervised outlier detection ensembles

Outlier detection and ensemble learning are well established research directions in data mining yet the application of ensemble techniques to outlier detection has been rarely studied. Here, we propose and study subsampling as a technique to induce diversity among individual outlier detectors. We show analytically and experimentally that an outlier detector based on a subsample per se, besides inducing diversity, can, under certain conditions, already improve upon the results of the same outlier detector on the complete dataset. Building an ensemble on top of several subsamples is further improving the results. While in the literature so far the intuition that ensembles improve over single outlier detectors has just been transferred from the classification literature, here we also justify analytically why ensembles are also expected to work in the unsupervised area of outlier detection. As a side effect, running an ensemble of several outlier detectors on subsamples of the dataset is more efficient than ensembles based on other means of introducing diversity and, depending on the sample rate and the size of the ensemble, can be even more efficient than just the single outlier detector on the complete data.
End-to-end scene text recognition

This paper focuses on the problem of word detection and recognition in natural images. The problem is significantly more challenging than reading text in scanned documents, and has only recently gained attention from the computer vision community. Sub-components of the problem, such as text detection and cropped image word recognition, have been studied in isolation [7, 4, 20]. However, what is unclear is how these recent approaches contribute to solving the end-to-end problem of word recognition. We fill this gap by constructing and evaluating two systems. The first, representing the de facto state-of-the-art, is a two stage pipeline consisting of text detection followed by a leading OCR engine. The second is a system rooted in generic object recognition, an extension of our previous work in . We show that the latter approach achieves superior performance. While scene text recognition has generally been treated with highly domain-specific methods, our results demonstrate the suitability of applying generic computer vision methods. Adopting this approach opens the door for real world scene text recognition to benefit from the rapid advances that have been taking place in object recognition.
Teaching Senti-Lexicon for Automated Sentiment Polarity Definition in Teaching Evaluation

This research significantly achieved the construction of a teaching evaluation sentiment lexicon and an automated sentiment orientation polarity definition in teaching evaluation. The Teaching Senti-lexicon will compute the weights of terms and phrases obtained from student opinions, which are stored in teaching evaluation suggestions in the form of open-ended questions. This Teaching Senti-lexicon consists of three main attributes, including: teaching corpus, category and sentiment weight score. The sentiment orientation polarity was computed with its mean function being sentiment class definitions. A number of 175 instances were randomised using teaching feedback responses which were posted by students studying at Loei Raja hat University. The contributions of this paper propose an effective teaching sentiment analysis method, especially for teaching evaluation. In this paper, the experimented model employed SVM, ID3 and Naïve Bayes algorithms, which were implemented in order to analyse sentiment classifications with a 97% highest accuracy of SVM. This model is also applied to improve upon their teaching as well.
Tensor space model for document analysis

Vector Space Model (VSM) has been at the core of information retrieval for the past decades. VSM considers the documents as vectors in high dimensional space.In such a vector space, techniques like Latent Semantic Indexing (LSI), Support Vector Machines (SVM), Naive Bayes, etc., can be then applied for indexing and classification. However, in some cases, the dimensionality of the document space might be extremely large, which makes these techniques infeasible due to the curse of dimensionality. In this paper, we propose a novel Tensor Space Model for document analysis. We represent documents as the second order tensors, or matrices. Correspondingly, a novel indexing algorithm called Tensor Latent Semantic Indexing (TensorLSI) is developed in the tensor space. Our theoretical analysis shows that TensorLSI is much more computationally efficient than the conventional Latent Semantic Indexing, which makes it applicable for extremely large scale data set. Several experimental results on standard document data sets demonstrate the efficiency and effectiveness of our algorithm.
The clinical profile and pathophysiology of atrial fibrillation: relationships among clinical features, epidemiology, and mechanisms.

Atrial fibrillation (AF) is the most common arrhythmia (estimated lifetime risk, 22%-26%). The aim of this article is to review the clinical epidemiological features of AF and to relate them to underlying mechanisms. Long-established risk factors for AF include aging, male sex, hypertension, valve disease, left ventricular dysfunction, obesity, and alcohol consumption. Emerging risk factors include prehypertension, increased pulse pressure, obstructive sleep apnea, high-level physical training, diastolic dysfunction, predisposing gene variants, hypertrophic cardiomyopathy, and congenital heart disease. Potential risk factors are coronary artery disease, kidney disease, systemic inflammation, pericardial fat, and tobacco use. AF has substantial population health consequences, including impaired quality of life, increased hospitalization rates, stroke occurrence, and increased medical costs. The pathophysiology of AF centers around 4 general types of disturbances that promote ectopic firing and reentrant mechanisms, and include the following: (1) ion channel dysfunction, (2) Ca(2+)-handling abnormalities, (3) structural remodeling, and (4) autonomic neural dysregulation. Aging, hypertension, valve disease, heart failure, myocardial infarction, obesity, smoking, diabetes mellitus, thyroid dysfunction, and endurance exercise training all cause structural remodeling. Heart failure and prior atrial infarction also cause Ca(2+)-handling abnormalities that lead to focal ectopic firing via delayed afterdepolarizations/triggered activity. Neural dysregulation is central to atrial arrhythmogenesis associated with endurance exercise training and occlusive coronary artery disease. Monogenic causes of AF typically promote the arrhythmia via ion channel dysfunction, but the mechanisms of the more common polygenic risk factors are still poorly understood and under intense investigation. Better recognition of the clinical epidemiology of AF, as well as an improved appreciation of the underlying mechanisms, is needed to develop improved methods for AF prevention and management.
Event-Based, 6-DOF Camera Tracking from Photometric Depth Maps

Event cameras are bio-inspired vision sensors that output pixel-level brightness changes instead of standard intensity frames. These cameras do not suffer from motion blur and have a very high dynamic range, which enables them to provide reliable visual information during high-speed motions or in scenes characterized by high dynamic range. These features, along with a very low power consumption, make event cameras an ideal complement to standard cameras for VR/AR and video game applications. With these applications in mind, this paper tackles the problem of accurate, low-latency tracking of an event camera from an existing photometric depth map (i.e., intensity plus depth information) built via classic dense reconstruction pipelines. Our approach tracks the 6-DOF pose of the event camera upon the arrival of each event, thus virtually eliminating latency. We successfully evaluate the method in both indoor and outdoor scenes and show that—because of the technological advantages of the event camera—our pipeline works in scenes characterized by high-speed motion, which are still inaccessible to standard cameras.
Multi-source Multi-scale Counting in Extremely Dense Crowd Images

We propose to leverage multiple sources of information to compute an estimate of the number of individuals present in an extremely dense crowd visible in a single image. Due to problems including perspective, occlusion, clutter, and few pixels per person, counting by human detection in such images is almost impossible. Instead, our approach relies on multiple sources such as low confidence head detections, repetition of texture elements (using SIFT), and frequency-domain analysis to estimate counts, along with confidence associated with observing individuals, in an image region. Secondly, we employ a global consistency constraint on counts using Markov Random Field. This caters for disparity in counts in local neighborhoods and across scales. We tested our approach on a new dataset of fifty crowd images containing 64K annotated humans, with the head counts ranging from 94 to 4543. This is in stark contrast to datasets used for existing methods which contain not more than tens of individuals. We experimentally demonstrate the efficacy and reliability of the proposed approach by quantifying the counting performance.
Law , Finance , and Economic Growth

This paper examines the connection between the legal environment and financial development, and then traces this link through to long-run economic growth. Countries with legal and regulatory systems that (1) give a high priority to creditors receiving the full present value of their claims on corporations, (2) enforce contracts effectively, and (3) promote comprehensive and accurate financial reporting by corporations have better-developed financial intermediaries. The data also indicate that the exogenous component of financial intermediary development – the component of financial intermediary development defined by the legal and regulatory environment – is positively associated with economic growth.  I thank Thorsten Beck, Maria Carkovic, Bill Easterly, Lant Pritchett, Andrei Shleifer, and seminar participants at the Board of Governors of the Federal Reserve System, the University of Virginia, and the World Bank for helpful comments.
A survey of collaborative filtering based social recommender systems

Recommendation plays an increasingly important role in our daily lives. Recommender systems automatically suggest to a user items that might be of interest to her. Recent studies demonstrate that information from social networks can be exploited to improve accuracy of recommendations. In this paper, we present a survey of collaborative filtering (CF) based social recommender systems. We provide a brief overview over the task of recommender systems and traditional approaches that do not use social network information. We then present how social network information can be adopted by recommender systems as additional input for improved accuracy. We classify CF-based social recommender systems into two categories: matrix factorization based social recommendation approaches and neighborhood based social recommendation approaches. For each category, we survey and compare several represen-
Answer Garden 2: Merging Organizational Memory with Collaborative Help

This research examines a collaborative solution to a common problem, that of providing help to distributed users. The Answer Garden 2 system provides a secondgeneration architecture for organizational and community memory applications. After describing the need for Answer Garden 2’s functionality, we describe the architecture of the system and two underlying systems, the Cafe ConstructionKit and Collaborative Refinery. We also present detailed descriptions of the collaborative help and collaborative refining facilities in the Answer Garden 2 system.
A magnetic cell-based sensor.

Cell-based sensing represents a new paradigm for performing direct and accurate detection of cell- or tissue-specific responses by incorporating living cells or tissues as an integral part of a sensor. Here we report a new magnetic cell-based sensing platform by combining magnetic sensors implemented in the complementary metal-oxide-semiconductor (CMOS) integrated microelectronics process with cardiac progenitor cells that are differentiated directly on-chip. We show that the pulsatile movements of on-chip cardiac progenitor cells can be monitored in a real-time manner. Our work provides a new low-cost approach to enable high-throughput screening systems as used in drug development and hand-held devices for point-of-care (PoC) biomedical diagnostic applications.
Learning relational affordance models for robots in multi-object manipulation tasks

Affordances define the action possibilities on an object in the environment and in robotics they play a role in basic cognitive capabilities. Previous works have focused on affordance models for just one object even though in many scenarios they are defined by configurations of multiple objects that interact with each other. We employ recent advances in statistical relational learning to learn affordance models in such cases. Our models generalize over objects and can deal effectively with uncertainty. Two-object interaction models are learned from robotic interaction with the objects in the world and employed in situations with arbitrary numbers of objects. We illustrate these ideas with experimental results of an action recognition task where a robot manipulates objects on a shelf.
Density-Driven Cross-Lingual Transfer of Dependency Parsers

We present a novel method for the crosslingual transfer of dependency parsers. Our goal is to induce a dependency parser in a target language of interest without any direct supervision: instead we assume access to parallel translations between the target and one or more source languages, and to supervised parsers in the source language(s). Our key contributions are to show the utility of dense projected structures when training the target language parser, and to introduce a novel learning algorithm that makes use of dense structures. Results on several languages show an absolute improvement of 5.51% in average dependency accuracy over the state-of-the-art method of (Ma and Xia, 2014). Our average dependency accuracy of 82.18% compares favourably to the accuracy of fully supervised methods.