nils-herrmann/clean_scirepeval huggingface dataset:
The devil is in the details: an evaluation of recent feature encoding methods

A large number of novel encodings for bag of visual words models have been proposed in the past two years to improve on the standard histogram of quantized local features. Examples include locality-constrained linear encoding [23], improved Fisher encoding , super vector encoding , and kernel codebook encoding [20]. While several authors have reported very good results on the challenging PASCAL VOC classification data by means of these new techniques, differences in the feature computation and learning algorithms, missing details in the description of the methods, and different tuning of the various components, make it impossible to compare directly these methods and hard to reproduce the results reported. This paper addresses these shortcomings by carrying out a rigorous evaluation of these new techniques by: (1) fixing the other elements of the pipeline (features, learning, tuning); (2) disclosing all the implementation details, and (3) identifying both those aspects of each method which are particularly important to achieve good performance, and those aspects which are less critical. This allows a consistent comparative analysis of these encoding methods. Several conclusions drawn from our analysis cannot be inferred from the original publications.
Personality Recognition on Social Media With Label Distribution Learning

Personality is an important psychological construct accounting for individual differences in people. To reliably, validly, and efficiently recognize an individual’s personality is a worthwhile goal; however, the traditional ways of personality assessment through self-report inventories or interviews conducted by psychologists are costly and less practical in social media domains, since they need the subjects to take active actions to cooperate. This paper proposes a method of big five personality recognition (PR) from microblog in Chinese language environments with a new machine learning paradigm named label distribution learning (LDL), which has never been previously reported to be used in PR. One hundred and thirteen features are extracted from 994 active Sina Weibo users’ profiles and micro-blogs. Eight LDL algorithms and nine non-trivial conventional machine learning algorithms are adopted to train the big five personality traits prediction models. Experimental results show that two of the proposed LDL approaches outperform the others in predictive ability, and the most predictive one also achieves relatively higher running efficiency among all the algorithms.
Unsupervised Learning of Sentence Embeddings using Compositional n-Gram Features

The recent tremendous success of unsupervised word embeddings in a multitude of applications raises the obvious question if similar methods could be derived to improve embeddings (i.e. semantic representations) of word sequences as well. We present a simple but efficient unsupervised objective to train distributed representations of sentences. Our method outperforms the state-of-the-art unsupervised models on most benchmark tasks, highlighting the robustness of the produced general-purpose sentence embeddings.
Scene Coordinate and Correspondence Learning for Image-Based Localization

Scene coordinate regression has become an essential part of current camera re-localization methods. Different versions, such as regression forests and deep learning methods, have been successfully applied to estimate the corresponding camera pose given a single input image. In this work, we propose to regress the scene coordinates pixel-wise for a given RGB image by using deep learning. Compared to the recent methods, which usually employ RANSAC to obtain a robust pose estimate from the established point correspondences, we propose to regress confidences of these correspondences, which allows us to immediately discard erroneous predictions and improve the initial pose estimates. Finally, the resulting confidences can be used to score initial pose hypothesis and aid in pose refinement, offering a generalized solution to solve this task.
Canonical Abstract Syntax Trees

This paper presents GOM, a language for describing abstract syntax trees and generating a Java implementation for those trees. GOM includes features allowing to specify and modify the interface of the data structure. These features provide in particular the capability to maintain the internal representation of data in canonical form with respect to a rewrite system. This explicitly guarantees that the client program only manipulates normal forms for this rewrite system, a feature which is only implicitly used in many implementations.
Expanded autoencoder recommendation framework and its application in movie recommendation

Automatic recommendation has become a popular research field: it allows the user to discover items that match their tastes. In this paper, we proposed an expanded autoencoder recommendation framework. The stacked autoencoders model is employed to extract the feature of input then reconstitution the input to do the recommendation. Then the side information of items and users is blended in the framework and the Huber function based regularization is used to improve the recommendation performance. The proposed recommendation framework is applied on the movie recommendation. Experimental results on a public database in terms of quantitative assessment show significant improvements over conventional methods.
Event Schema Induction with a Probabilistic Entity-Driven Model

Event schema induction is the task of learning high-level representations of complex events (e.g., a bombing) and their entity roles (e.g., perpetrator and victim) from unlabeled text. Event schemas have important connections to early NLP research on frames and scripts, as well as modern applications like template extraction. Recent research suggests event schemas can be learned from raw text. Inspired by a pipelined learner based on named entity coreference, this paper presents the first generative model for schema induction that integrates coreference chains into learning. Our generative model is conceptually simpler than the pipelined approach and requires far less training data. It also provides an interesting contrast with a recent HMM-based model. We evaluate on a common dataset for template schema extraction. Our generative model matches the pipeline’s performance, and outperforms the HMM by 7 F1 points (20%).
Incorporating Relation Paths in Neural Relation Extraction

Distantly supervised relation extraction has been widely used to find novel relational facts from plain text. To predict the relation between a pair of two target entities, existing methods solely rely on those direct sentences containing both entities. In fact, there are also many sentences containing only one of the target entities, which also provide rich useful information but not yet employed by relation extraction. To address this issue, we build inference chains between two target entities via intermediate entities, and propose a path-based neural relation extraction model to encode the relational semantics from both direct sentences and inference chains. Experimental results on realworld datasets show that, our model can make full use of those sentences containing only one target entity, and achieves significant and consistent improvements on relation extraction as compared with strong baselines. The source code of this paper can be obtained from https:// github.com/thunlp/PathNRE.
Contextual domain classification in spoken language understanding systems using recurrent neural network

In a multi-domain, multi-turn spoken language understanding session, information from the history often greatly reduces the ambiguity of the current turn. In this paper, we apply the recurrent neural network (RNN) to exploit contextual information for query domain classification. The Jordan-type RNN directly sends the vector of output distribution to the next query turn as additional input features to the convolutional neural network (CNN). We evaluate our approach against SVM with and without contextual features. On our contextually labeled dataset, we observe a 1.4% absolute (8.3% relative) improvement in classification error rate over the non-contextual SVM, and 0.9% absolute (5.5% relative) improvement over the contextual SVM.
Metabolic and electric brain patterns during pleasant and unpleasant emotions induced by music masterpieces.

Brain correlates comparing pleasant and unpleasant states induced by three dissimilar masterpiece excerpts were obtained. Related emotional reactions to the music were studied using Principal Component Analysis of validated reports, fMRI, and EEG coherent activity. A piano selection by Bach and a symphonic passage from Mahler widely differing in musical features were used as pleasing pieces. A segment by Prodromidès was used as an unpleasing stimulus. Ten consecutive 30 s segments of each piece alternating with random static noise were played to 19 non-musician volunteers for a total of 30 min of auditory stimulation. Both brain approaches identified a left cortical network involved with pleasant feelings (Bach and Mahler vs. Prodromidès) including the left primary auditory area, posterior temporal, inferior parietal and prefrontal regions. While the primary auditory zone may provide an early affective quality, left cognitive areas may contribute to pleasant feelings when melodic sequences follow expected rules. In contrast, unpleasant emotions (Prodromidès vs. Bach and Mahler) involved the activation of the right frontopolar and paralimbic areas. Left activation with pleasant and right with unpleasant musical feelings is consistent with right supremacy in novel situations and left in predictable processes. When all musical excerpts were jointly compared to noise, in addition to bilateral auditory activation, the left temporal pole, inferior frontal gyrus, and frontopolar area were activated suggesting that cognitive and language processes were recruited in general responses to music. Sensory and cognitive integration seems required for musical emotion.
Deep Features for Text Spotting

The goal of this work is text spotting in natural images. This is divided into two sequential tasks: detecting words regions in the image, and recognizing the words within these regions. We make the following contributions: first, we develop a Convolutional Neural Network (CNN) classifier that can be used for both tasks. The CNN has a novel architecture that enables efficient feature sharing (by using a number of layers in common) for text detection, character case-sensitive and insensitive classification, and bigram classification. It exceeds the state-of-the-art performance for all of these. Second, we make a number of technical changes over the traditional CNN architectures, including no downsampling for a per-pixel sliding window, and multi-mode learning with a mixture of linear models (maxout). Third, we have a method of automated data mining of Flickr, that generates word and character level annotations. Finally, these components are used together to form an end-to-end, state-of-the-art text spotting system. We evaluate the text-spotting system on two standard benchmarks, the ICDAR Robust Reading data set and the Street View Text data set, and demonstrate improvements over the state-of-the-art on multiple measures.
Towards Developing a Comprehensive Business Intelligence Maturity Model for Malaysian Public Sector: Application of Mixed Methodology

This paper is a part of an ongoing study on developing an extensive business intelligence (BI) maturity model for government agencies in Malaysia. In our endeavor to achieve this objective, we highlight the need of new BI maturity model to provide a holistic understanding towards BI maturity as existing models are generic, incomprehensive, and lack of tools to support its practical usage. To address this issue, we proposed a new structure of maturity model and an unconventional mixed-methodological framework to construct the respective model.
The semantics of space: Integrating linguistic typology and cognitive neuroscience

In the cognitive neuroscience literature on the distinction between categorical and coordinate spatial relations, it has often been observed that categorical spatial relations are referred to linguistically by words like English prepositions, many of which specify binary oppositions-e.g., above/below, left/right, on/off, in/out. However, the actual semantic content of English prepositions, and of comparable word classes in other languages, has not been carefully considered. This paper has three aims. The first and most important aim is to inform cognitive neuroscientists interested in spatial representation about relevant research on the kinds of categorical spatial relations that are encoded in the 6000+ languages of the world. Emphasis is placed on cross-linguistic similarities and differences involving deictic relations, topological relations, and projective relations, the last of which are organized around three distinct frames of reference--intrinsic, relative, and absolute. The second aim is to review what is currently known about the neuroanatomical correlates of linguistically encoded categorical spatial relations, with special focus on the left supramarginal and angular gyri, and to suggest ways in which cross-linguistic data can help guide future research in this area of inquiry. The third aim is to explore the interface between language and other mental systems, specifically by summarizing studies which suggest that although linguistic and perceptual/cognitive representations of space are at least partially distinct, language nevertheless has the power to bring about not only modifications of perceptual sensitivities but also adjustments of cognitive styles.
Ju n 20 03 DECOHERENCE , EINSELECTION , AND THE QUANTUM ORIGINS OF THE CLASSICAL

Decoherence is caused by the interaction with the environment which in effect monitors certain observables of the system, destroying coherence between the pointer states corresponding to their eigenvalues. This leads to environment-induced superselection or einselection, a quantum process associated with selective loss of information. Einselected pointer states are stable. They can retain correlations with the rest of the Universe in spite of the environment. Einselection enforces classicality by imposing an effective ban on the vast majority of the Hilbert space, eliminating especially the flagrantly nonlocal “Schrödinger cat” states. Classical structure of phase space emerges from the quantum Hilbert space in the appropriate macroscopic limit: Combination of einselection with dynamics leads to the idealizations of a point and of a classical trajectory. In measurements, einselection replaces quantum entanglement between the apparatus and the measured system with the classical correlation. Only the preferred pointer observable of the apparatus can store information that has predictive power. When the measured quantum system is microscopic and isolated, this restriction on the predictive utility of its correlations with the macroscopic apparatus results in the effective “collapse of the wavepacket”. Existential interpretation implied by einselection regards observers as open quantum systems, distinguished only by their ability to acquire, store, and process information. Spreading of the correlations with the effectively classical pointer states throughout the environment allows one to understand ‘classical reality’ as a property based on the relatively objective existence of the einselected states: They can be “found out” without being re-prepared, e.g, by intercepting the information already present in the environment. The redundancy of the records of pointer states in the environment (which can be thought of as their ‘fitness’ in the Darwinian sense) is a measure of their classicality. A new symmetry appears in this setting: Environment assisted invariance or envariance sheds a new light on the nature of ignorance of the state of the system due to quantum correlations with the environment, and leads to Born’s rules and to the reduced density matrices, ultimately justifying basic principles of the program of decoherence and einselection.
The Use of Essential Oils as a Complementary Treatment for Anxiety

Mental disorders occur in people in all countries, societies and in all ethnic groups, regardless socio-economic order with more frequent anxiety disorders. Through the process of time many treatment have been applied in order to address this complex mental issue. People with anxiety disorders can benefit from a variety of treatments and services. Following an accurate diagnosis, possible treatments include psychological treatments and mediation. Complementary and alternative medicine (CAM) plays a significant role in health care systems. Patients with chronic pain conditions, including arthritis, chronic neck and backache, headache, digestive problems and mental health conditions (including insomnia, depression, and anxiety) were high users of CAM therapies. Aromatherapy is a holistic method of treatment, using essential oils. There are several essential oils that can help in reducing anxiety disorders and as a result the embodied events that they may cause.
Psychopathy increases perceived moral permissibility of accidents.

Psychopaths are notorious for their antisocial and immoral behavior, yet experimental studies have typically failed to identify deficits in their capacities for explicit moral judgment. We tested 20 criminal psychopaths and 25 criminal nonpsychopaths on a moral judgment task featuring hypothetical scenarios that systematically varied an actor's intention and the action's outcome. Participants were instructed to evaluate four classes of actions: accidental harms, attempted harms, intentional harms, and neutral acts. Psychopaths showed a selective difference, compared with nonpsychopaths, in judging accidents, where one person harmed another unintentionally. Specifically, psychopaths judged these actions to be more morally permissible. We suggest that this pattern reflects psychopaths' failure to appreciate the emotional aspect of the victim's experience of harm. These findings provide direct evidence of abnormal moral judgment in psychopathy.
THE PORTER ` S THEORY OF COMPETITIVE ADVANTAGE

In this item, we approached one of the new theories of the economic development, the theory of competitive advantage. The theory of competitive advantage was created by Michael E. Porter, starting from the actual economic reality which could no longer be explained on the basis of the model of comparative advantages, elaborated by David Ricardo. In order to conceive this theory, Porter analyzed four years, ten countries with important share in international commerce (Denmark, Germany, Italy, Japan, South Korea, Singapore, Sweden, Switzerland, Great Britain and USA), establishing the system of the determinants which determine the obtaining of the competitive advantage. Starting from describing the system of the determinants, the so-called “diamond”, we analyzed detailed these determinants of the diamond: the factorial ones, the demand ones, upstream and downstream industries and the domestic competition, and also the chance and the governmental policy. After analyzing and classifying the structure of these determinants, we approach their interaction the dynamics of the diamond by identifying the stages of the development which a country goes through and the features of each stage. In the last part of the article, we enumerated the causes that might lead to loosing the competitive advantage and the position as a leader on the market, and a few critics brought to this new theory. 1. The presentation of the theory of Porter The theory of the competitive advantage starts from the principle that the only important concept at the national level is the national productivity (Fota Constantin, 2004). In the elaboration of his theory, Porter starts from the following premises (Porter Michael, 1990): the nature of the competition and the sources of competitive advantage are very different among industries and even among the segments of the same industry, and a certain country can influence the obtaining of the competitive advantage within a certain sector of industry; the globalisation of the competition and the appearance of the trans-national companies do not eliminate the influence of a certain country for getting the competitive advantage ; a country can offer different compatitive advantages for a company, depending if it is an origin country or a host country; the competitivity has a dynamic character (Schumpeter); the innovations have a role of leading force in this permanent change and determine the companies to invest on order not to be eliminated from the market (Negriţoiu Mişu, 1997 ). Starting from these premises, Porter identifies a system of determinants which is the basis for getting competitive advantages by the nations. 2. The system of determinants The theory is based on the system of determinants, called by Porter „diamond”, which consists of:
Large scale author name disambiguation in digital libraries

Person name disambiguation is essential to distinguish between persons that share the same name where unique identifiers are not present. In many domains this is a common problem including digital libraries where the same name can refer to multiple unique authors. Correctly attributing work and citations requires the digital library's database to be disambiguated. In this work we describe a large scale framework for disambiguating author names efficiently and effectively. The framework uses a density based clustering algorithm with a random forest based distance function to clusters unique authors. Effective use of blocking functions allows the clustering algorithm to be run in parallel. In our experiments we show that the framework disambiguates authors of more than 4 million papers in 24 hours.
Generative Knowledge Transfer for Neural Language Models

In this paper, we propose a generative knowledge transfer technique that trains an RNN based language model (student network) using text and output probabilities generated from a previously trained RNN (teacher network). The text generation can be conducted by either the teacher or the student network. We can also improve the performance by taking the ensemble of soft labels obtained from multiple teacher networks. This method can be used for privacy conscious language model adaptation because no user data is directly used for training. Especially, when the soft labels of multiple devices are aggregated via a trusted third party, we can expect very strong privacy protection.
Urban ecology and sustainability : The state-ofthe-science and future directions

A conceptual diagram illustrating the relationships among biodiversity, ecosystem processes (or ecosystem functions), ecosystem services, and human well-being in an urban landscape. All the components and their relationships are influenced profoundly by the speed and spatiotemporal pattern of urbanization that is driven primarily by socioeconomic processes. Thus, understanding and improving the ecology and sustainability of urban landscapes and regions should not only consider how urbanization affects these key components but also how their relationships change in time. Human well-being is the primary focus for urban sustainability projects, whereas urban ecological studies often focus on biodiversity, ecological processes, and ecosystem services. In either case, the connections among the key components and their linkages across spatial (landscape–region–globe) and temporal (year–decade–century) scales should be