nils-herrmann/clean_scirepeval huggingface dataset:
Efficient Region Tracking With Parametric Models of Geometry and Illumination

As an object moves through the field of view of a camera, the images of the object may change dramatically. This is not simply due to the translation of the object across the image plane. Rather, complications arise due to the fact that the object undergoes changes in pose relative to the viewing camera, changes in illumination relative to light sources, and may even become partially or fully occluded. In this paper, we develop an efficient, general framework for object tracking—one which addresses each of these complications. We first develop a computationally efficient method for handling the geometric distortions produced by changes in pose. We then combine geometry and illumination into an algorithm that tracks large image regions using no more computation than would be required to track with no accommodation for illumination changes. Finally, we augment these methods with techniques from robust statistics and treat occluded regions on the object as statistical outliers. Throughout, we present experimental results performed on live video sequences demonstrating the effectiveness and efficiency of our methods.
Dynamic Capabilities , Talent Development and Firm Performance

All firms aim at high performance which leads to high profits and sustainable competitive advantage. However all firms face a challenge on how to create sustainable competitive advantage especially on the rapid changing environment. This conceptual study paper hence looks at the dynamic capabilities which have been identified to assist firms in creating and sustaining competitiveness in the ever changing environment. Specifically the paper looks at the nature of the sensing, seizing, transforming and managerial dynamic capabilities and how their interconnection influence firm performance. The study is anchored on the dynamic capability theory. The main objectives of the study are to understand how interconnectivity of various clusters of dynamic capabilities influences firm performance. The paper also looks at the role of dynamism in talent development. This conceptual study paper looks at the paradigm shifts in the area of sustainable competitive advantage from resource based view, knowledge based view and dynamic capability view. The paper identifies a number of knowledge and research gaps in relation to performance by reviewing previous scholarly work in the study area. A conceptual model attempting to address the identified gaps is development and highlights the relationship among various clusters of dynamic capabilities and their influence to each other and to performance. The study views dynamic capabilities as an emerging paradigm of the modern business firm that propels sustainable high performance in rapidly changing environment.
Be Precise or Fuzzy: Learning the Meaning of Cardinals and Quantifiers from Vision

People can refer to quantities in a visual scene by using either exact cardinals (e.g. one, two, three) or natural language quantifiers (e.g. few, most, all). In humans, these two processes underlie fairly different cognitive and neural mechanisms. Inspired by this evidence, the present study proposes two models for learning the objective meaning of cardinals and quantifiers from visual scenes containing multiple objects. We show that a model capitalizing on a ‘fuzzy’ measure of similarity is effective for learning quantifiers, whereas the learning of exact cardinals is better accomplished when information about number is provided.
Unsupervised Multilingual Word Embeddings

Multilingual Word Embeddings (MWEs) represent words from multiple languages in a single distributional vector space. Unsupervised MWE (UMWE) methods acquire multilingual embeddings without cross-lingual supervision, which is a significant advantage over traditional supervised approaches and opens many new possibilities for low-resource languages. Prior art for learning UMWEs, however, merely relies on a number of independently trained Unsupervised Bilingual Word Embeddings (UBWEs) to obtain multilingual embeddings. These methods fail to leverage the interdependencies that exist among many languages. To address this shortcoming, we propose a fully unsupervised framework for learning MWEs1 that directly exploits the relations between all language pairs. Our model substantially outperforms previous approaches in the experiments on multilingual word translation and cross-lingual word similarity. In addition, our model even beats supervised approaches trained with cross-lingual resources.
An Outlier Detection Model Based on Cross Datasets Comparison for Financial Surveillance

Outlier detection is a key element for intelligent financial surveillance systems which intend to identify fraud and money laundering by discovering unusual customer behaviour pattern. The detection procedures generally fall into two categories: comparing every transaction against its account history and further more, comparing against a peer group to determine if the behavior is unusual. The later approach shows particular merits in efficiently extracting suspicious transaction and reducing false positive rate. Peer group analysis concept is largely dependent on a cross-datasets outlier detection model. In this paper, we propose a new cross outlier detection model based on distance definition incorporated with the financial transaction data features. An approximation algorithm accompanied with the model is provided to optimize the computation of the deviation from tested data point to the reference dataset. An experiment based on real bank data blended with synthetic outlier cases shows promising results of our model in reducing false positive rate while enhancing the discriminative rate remarkably
Business Process Mining

One of the most valuable assets of an organization is its organizational data. The analysis and mining of this potential hidden treasure can lead to much added-value for the organization. Process mining is an emerging area that can be useful in helping organizations understand the status quo, check for compliance and plan for improving their processes. The aim of process mining is to extract knowledge from event logs of today's organizational information systems. Process mining includes three main types: discovering process models from event logs, conformance checking and organizational mining. In this paper, we briefly introduce process mining and review some of its most important techniques. Also, we investigate some of the applications of process mining in industry and present some of the most important challenges that are faced in this area.
Quicksilver: Fast predictive image registration – A deep learning approach

This paper introduces Quicksilver, a fast deformable image registration method. Quicksilver registration for image-pairs works by patch-wise prediction of a deformation model based directly on image appearance. A deep encoder-decoder network is used as the prediction model. While the prediction strategy is general, we focus on predictions for the Large Deformation Diffeomorphic Metric Mapping (LDDMM) model. Specifically, we predict the momentum-parameterization of LDDMM, which facilitates a patch-wise prediction strategy while maintaining the theoretical properties of LDDMM, such as guaranteed diffeomorphic mappings for sufficiently strong regularization. We also provide a probabilistic version of our prediction network which can be sampled during the testing time to calculate uncertainties in the predicted deformations. Finally, we introduce a new correction network which greatly increases the prediction accuracy of an already existing prediction network. We show experimental results for uni-modal atlas-to-image as well as uni-/multi-modal image-to-image registrations. These experiments demonstrate that our method accurately predicts registrations obtained by numerical optimization, is very fast, achieves state-of-the-art registration results on four standard validation datasets, and can jointly learn an image similarity measure. Quicksilver is freely available as an open-source software.
Highway-LSTM and Recurrent Highway Networks for Speech Recognition

Recently, very deep networks, with as many as hundreds of layers, have shown great success in image classification tasks. One key component that has enabled such deep models is the use of “skip connections”, including either residual or highway connections, to alleviate the vanishing and exploding gradient problems. While these connections have been explored for speech, they have mainly been explored for feed-forward networks. Since recurrent structures, such as LSTMs, have produced state-of-the-art results on many of our Voice Search tasks, the goal of this work is to thoroughly investigate different approaches to adding depth to recurrent structures. Specifically, we experiment with novel Highway-LSTM models with bottlenecks skip connections and show that a 10 layer model can outperform a state-of-the-art 5 layer LSTM model with the same number of parameters by 2% relative WER. In addition, we experiment with Recurrent Highway layers and find these to be on par with Highway-LSTM models, when given sufficient depth.
Ensuring the Quality of the Findings of Qualitative Research : Looking at Trustworthiness Criteria

This paper identifies that masters of education students at the University of Dar es Salaam School of Education and Open University of Tanzania are faced with a challenge of deciding which trustworthiness criteria to employ between the qualitative and quantitative criteria in ensuring the genuineness of qualitative enquiry. This paper used 323 students‘ masters of education dissertations employing the qualitative research methodology and examined the trustworthiness criteria used by students to ensure the rigour of the findings of those dissertations. The findings indicated that most of the students in their dissertations incorrectly employed the quantitative trustworthiness criteria such as reliability and validity to ensure the rigour of their dissertations findings employing the qualitative research methodology. In the sampled masters of education dissertations only 21 out 323 employed the correct qualitative trustworthiness criteria, such as credibility, transferability, confirmability and dependability. This study finding suggests that the authenticity of some dissertations submitted for master‘s degree award their findings are questionable. The study recommendations were that research methodology course lecturers are encouraged to strengthen teaching of the qualitative research approach as well as dissertation supervision to guide students to apply correct trustworthiness criteria for qualitative research methodologies. _________________________________________________________________________________________
Making sense of complexity in context and implementation: the Context and Implementation of Complex Interventions (CICI) framework

The effectiveness of complex interventions, as well as their success in reaching relevant populations, is critically influenced by their implementation in a given context. Current conceptual frameworks often fail to address context and implementation in an integrated way and, where addressed, they tend to focus on organisational context and are mostly concerned with specific health fields. Our objective was to develop a framework to facilitate the structured and comprehensive conceptualisation and assessment of context and implementation of complex interventions.



The Context and Implementation of Complex Interventions (CICI) framework was developed in an iterative manner and underwent extensive application. An initial framework based on a scoping review was tested in rapid assessments, revealing inconsistencies with respect to the underlying concepts. Thus, pragmatic utility concept analysis was undertaken to advance the concepts of context and implementation. Based on these findings, the framework was revised and applied in several systematic reviews, one health technology assessment (HTA) and one applicability assessment of very different complex interventions. Lessons learnt from these applications and from peer review were incorporated, resulting in the CICI framework.



The CICI framework comprises three dimensions-context, implementation and setting-which interact with one another and with the intervention dimension. Context comprises seven domains (i.e., geographical, epidemiological, socio-cultural, socio-economic, ethical, legal, political); implementation consists of five domains (i.e., implementation theory, process, strategies, agents and outcomes); setting refers to the specific physical location, in which the intervention is put into practise. The intervention and the way it is implemented in a given setting and context can occur on a micro, meso and macro level. Tools to operationalise the framework comprise a checklist, data extraction tools for qualitative and quantitative reviews and a consultation guide for applicability assessments.



The CICI framework addresses and graphically presents context, implementation and setting in an integrated way. It aims at simplifying and structuring complexity in order to advance our understanding of whether and how interventions work. The framework can be applied in systematic reviews and HTA as well as primary research and facilitate communication among teams of researchers and with various stakeholders.
On the identification of sales forecasting models in the presence of promotions

Shorter product life cycles and aggressive marketing, among other factors, have increased the complexity of sales forecasting. Forecasts are often produced using a Forecasting Support System that integrates univariate statistical forecasting with managerial judgment. Forecasting sales under promotional activity is one of the main reasons to use expert judgment. Alternatively, one can replace expert adjustments by regression models whose exogenous inputs are promotion features (price, display, etc.). However, these regression models may have large dimensionality as well as multicollinearity issues. We propose a novel promotional model that overcomes these limitations. It combines Principal Component Analysis to reduce the dimensionality of the problem and automatically identifies the demand dynamics. For items with limited history, the proposed model is capable of providing promotional forecasts by selectively pooling information across established products. The performance of the model is compared against forecasts provided by experts and statistical benchmarks, on weekly data; outperforming both substantially.
A Study on Social Network Metrics and Their Application in Trust Networks

Social network analysis has recently gained a lot of interest because of the advent and the increasing popularity of social media, such as blogs, social networks, micro logging, or customer review sites. Such media often serve as platforms for information dissemination and product placement or promotion. In this environment, influence and trust are becoming essential qualities among user interactions. In this work, we perform an extensive study of various metrics related to the aforementioned elements, and their effect in the process of information propagation in the virtual world. In order to better understand the properties of links and the dynamics of social networks, we distinguish between permanent and transient links and in the latter case, we consider the link freshness. Moreover, we distinguish between local and global influence and compare suggestions provided by locally or globally trusted users.
Improving Word Representations via Global Context and Multiple Word Prototypes

Unsupervised word representations are very useful in NLP tasks both as inputs to learning algorithms and as extra word features in NLP systems. However, most of these models are built with only local context and one representation per word. This is problematic because words are often polysemous and global context can also provide useful information for learning word meanings. We present a new neural network architecture which 1) learns word embeddings that better capture the semantics of words by incorporating both local and global document context, and 2) accounts for homonymy and polysemy by learning multiple embeddings per word. We introduce a new dataset with human judgments on pairs of words in sentential context, and evaluate our model on it, showing that our model outperforms competitive baselines and other neural language models.
Generator Reversal

We consider the problem of training generative models with deep neural networks as generators, i.e. to map latent codes to data points. Whereas the dominant paradigm combines simple priors over codes with complex deterministic models, we propose instead to use more flexible code distributions. These distributions are estimated non-parametrically by reversing the generator map during training. The benefits include: more powerful generative models, better modeling of latent structure and explicit control of the degree of generalization.
Chinese Medical Question Answer Matching Using End-to-End Character-Level Multi-Scale CNNs

This paper focuses mainly on the problem of Chinese medical question answer matching, which is arguably more challenging than open-domain question answer matching in English due to the combination of its domain-restricted nature and the language-specific features of Chinese. We present an end-to-end character-level multi-scale convolutional neural framework in which character embeddings instead of word embeddings are used to avoid Chinese word segmentation in text preprocessing, and multi-scale convolutional neural networks (CNNs) are then introduced to extract contextual information from either question or answer sentences over different scales. The proposed framework can be trained with minimal human supervision and does not require any handcrafted features, rule-based patterns, or external resources. To validate our framework, we create a new text corpus, named cMedQA, by harvesting questions and answers from an online Chinese health and wellness community. The experimental results on the cMedQA dataset show that our framework significantly outperforms several strong baselines, and achieves an improvement of top-1 accuracy by up to 19%.
Genomic resolution of linkages in carbon, nitrogen, and sulfur cycling among widespread estuary sediment bacteria

Estuaries are among the most productive habitats on the planet. Bacteria in estuary sediments control the turnover of organic carbon and the cycling of nitrogen and sulfur. These communities are complex and primarily made up of uncultured lineages, thus little is known about how ecological and metabolic processes are partitioned in sediments.



De novo assembly and binning resulted in the reconstruction of 82 bacterial genomes from different redox regimes of estuary sediments. These genomes belong to 23 bacterial groups, including uncultured candidate phyla (for example, KSB1, TA06, and KD3-62) and three newly described phyla (White Oak River (WOR)-1, WOR-2, and WOR-3). The uncultured phyla are generally most abundant in the sulfate-methane transition (SMTZ) and methane-rich zones, and genomic data predict that they mediate essential biogeochemical processes of the estuarine environment, including organic carbon degradation and fermentation. Among the most abundant organisms in the sulfate-rich layer are novel Gammaproteobacteria that have genes for the oxidation of sulfur and the reduction of nitrate and nitrite. Interestingly, the terminal steps of denitrification (NO3 to N2O and then N2O to N2) are present in distinct bacterial populations.



This dataset extends our knowledge of the metabolic potential of several uncultured phyla. Within the sediments, there is redundancy in the genomic potential in different lineages, often distinct phyla, for essential biogeochemical processes. We were able to chart the flow of carbon and nutrients through the multiple geochemical layers of bacterial processing and reveal potential ecological interactions within the communities.
Anaphora Resolution with the ARRAU Corpus

The ARRAU corpus is an anaphorically annotated corpus of English providing rich linguistic information about anaphora resolution. The most distinctive feature of the corpus is the annotation of a wide range of anaphoric relations, including bridging references and discourse deixis in addition to identity (coreference). Other distinctive features include treating all NPs as markables, including nonreferring NPs; and the annotation of a variety of morphosyntactic and semantic mention and entity attributes, including the genericity status of the entities referred to by markables. The corpus however has not been extensively used for anaphora resolution research so far. In this paper, we discuss three datasets extracted from the ARRAU corpus to support the three subtasks of the CRAC 2018 Shared Task– identity anaphora resolution over ARRAU-style markables, bridging references resolution, and discourse deixis; the evaluation scripts assessing system performance on those datasets; and preliminary results on these three tasks that may serve as baseline for subsequent research in these phenomena.
Entropy Production in a Cell and Reversal of Entropy Flow as an Anticancer Therapy

The entropy production rate of cancer cell is always higher than healthy cell under the case of no external field applied. Different entropy production between two kinds of cells determines the direction of entropy flow among cells. The entropy flow is the carrier of information flow. The entropy flow from cancer to healthy cell takes along the harmful information of cancerous cell, propagating its toxic action to healthy tissues. We demonstrate that a low-frequency and low-intensity electromagnetic field or ultrasound irradiation may increase the entropy production rate of a cell in normal tissue than that in cancer, consequently reverse the direction of entropy current between two kinds of cells. The modification of PH value of cells may also cause the reversal of the direction of entropy flow between healthy and cancerous cells. So, the biological tissue under the irradiation of electromagnetic field or ultrasound or under the appropriate change of cell acidity can avoid the propagation of harmful information from cancer cells. We suggest that this entropy mechanism possibly provides a basis for a novel approach to anticancer therapy. Thermodynamic entropy is expressed by (1) ln B S k W = where W is the number of microscopic states which are related to a given macroscopic thermodynamic state and kB is the Boltzmann constant. Entropy is a measure of disorder. From general physical principles, Schrodinger first indicated that life should remain in a low-entropy state or “an organism feeds with negative entropy”[1]. This means that entropy production in an organism is canceled by the outward entropy flow so that the system remains in a highly ordered state of low entropy. However, following our point of view, negative entropy (or negentropy) is only the first half of the story. The living organism is a chemical engine in which a series of chemical reactions take place one by one in an appropriate sequence. Accordingly, the energy transfer in an organism in the normal state is so efficient that the entropy production is minimized. Minimal entropy production in a healthy cell is the second half of the story . We shall compare qualitatively and demonstrate that the entropy production rate (or “entropy production” for short) of a healthy cell is lower than that of a cancerous cell if no external energy input [3-5]. However, when the appropriate external energy is applied to tissues, the rate of entropy production of normal cells may exceed that of cancerous cells. As an example, we shall discuss the entropy production of cells under irradiation of ultrasound and alternative electromagnetic fields. We shall prove the B