nils-herrmann/clean_scirepeval huggingface dataset:
Sentiment analysis of product reviews: A review

Now a day's internet is the most valuable source of learning, getting ideas, reviews for a product or a service. Everyday millions of reviews are generated in the internet about a product, person or a place. Because of their huge number and size it is very difficult to handle and understand such reviews. Sentiment analysis is such a research area which understands and extracts the opinion from the given review and the analysis process includes natural language processing (NLP), computational linguistics, text analytics and classifying the polarity of the opinion. In the field of sentiment analysis there are many algorithms exist to tackle NLP problems. Each algorithm is used by several applications. In this paper we have shown the taxonomy of various sentiment analysis methods. This paper also shows that Support vector machine (SVM) gives high accuracy compared to Naïve bayes and maximum entropy methods.
Data profiling for data quality improvement with OpenRefine

Data profiling is an information analysis technique on data stored inside database. Data profiling purpose is to ensure data quality by detecting whether the data in the data source compiles with the established business rules. Profiling could be performed using multiple analysis techniques depending on the data element to be analyzed. The analysis process also influenced by the data profiling tool being used. This paper describes tehniques of profiling analysis using open-source tool OpenRefine. The method used in this paper is case study method, using data retrieved from BPOM Agency website for checking commodity traditional medicine permits. Data attributes that became the main concern of this paper is Nomor Ijin Edar (NIE / distribution permit number) and registrar company name. The result of this research were suggestions to improve data quality on NIE and company name, which consists of data cleansing and improvement to business process and applications.
Claude Bernard and the heart–brain connection: Further elaboration of a model of neurovisceral integration

The intimate connection between the brain and the heart was enunciated by Claude Bernard over 150 years ago. In our neurovisceral integration model we have tried to build on this pioneering work. In the present paper we further elaborate our model. Specifically we review recent neuroanatomical studies that implicate inhibitory GABAergic pathways from the prefrontal cortex to the amygdala and additional inhibitory pathways between the amygdala and the sympathetic and parasympathetic medullary output neurons that modulate heart rate and thus heart rate variability. We propose that the default response to uncertainty is the threat response and may be related to the well known negativity bias. We next review the evidence on the role of vagally mediated heart rate variability (HRV) in the regulation of physiological, affective, and cognitive processes. Low HRV is a risk factor for pathophysiology and psychopathology. Finally we review recent work on the genetics of HRV and suggest that low HRV may be an endophenotype for a broad range of dysfunctions.
The fundamentals of standardizing global marketing strategy

To examine issues of standardization and adaptation in global marketing strategy and to explain the dynamics of standardization.  This is a conceptual research paper that has been developed based on gaps in prior frameworks of standardization/adaptation. A three-factor model of standardization/adaptation of global marketing strategy was developed. The three factors include homogeneity of customer response to the marketing mix, transferability of competitive advantage, and similarities in the degree of economic freedom.  The model through the use of feedback effects explains the dynamics of standardization.  Future research needs to empirically test the model. To enable empirical validation, reliable and valid measures of the three factors proposed in the model need to be developed. Additionally, the model may be used in future research to delineate the impact a variable may have on the ability of a firm to follow a standardized global marketing strategy.  The three-factor model aids decisions relating to standardization in a global marketing context.  The paper furthers the discussion on the issue of standardization. Through the identification of three factors that impact standardization/adaptation decisions, and the consideration of feedback effects, the paper provides a foundation for future research addressing the issue.
Corporate social responsibility organizational identification and motivation

The purpose of this paper is to explore the impact of corporate social responsibility (CSR) engagement on employee motivation, job satisfaction and organizational identification as well as employee citizenship in voluntary community activities.  Employees (n 1⁄4 224) of a major airline carrier participated in the study based on a 54-item questionnaire, containing four different sets of items related to volunteering, motivation, job satisfaction and organizational identification. The employee sample consisted of two sub-samples drawn randomly from the company pool of employees, differentiating between active participants in the company’s CSR programs (APs) and non participants (NAPs).  Significant differences were found between APs and NAPs on organizational identification and motivation, but not for job satisfaction. In addition, positive significant correlations between organizational identification, volunteering, job satisfaction, and motivation were obtained. These results are interpreted within the broader context that ties social identity theory (SIT) and organizational identification increase.  The paper contributes to the understanding of the interrelations between CSR and other organizational behavior constructs. Practitioners can learn from this study how to increase job satisfaction and organizational identification. Both are extremely important for an organization’s sustainability.  This is a first attempt to investigate the relationship between CSR, organizational identification and motivation, comparing two groups from the same organization. The paper discusses the questions: ‘‘Are there potential gains at the intra-organizational level in terms of enhanced motivation and organizational attitudes on the part of employees?’’ and ‘‘Does volunteering or active participation in CSR yield greater benefits for involved employees in terms of their motivation, job satisfaction and identification?’’.
CAYLEYNETS: SPECTRAL GRAPH CNNS WITH COMPLEX RATIONAL FILTERS

The rise of graph-structured data such as social networks, regulatory networks, citation graphs, and functional brain networks, in combination with resounding success of deep learning in various applications, has brought the interest in generalizing deep learning models to non-Euclidean domains. In this paper, we introduce a new spectral domain convolutional architecture for deep learning on graphs. The core ingredient of our model is a new class of parametric rational complex functions (Cayley polynomials) allowing to efficiently compute spectral filters on graphs that specialize on frequency bands of interest. Our model generates rich spectral filters that are localized in space, scales linearly with the size of the input data for sparsely-connected graphs, and can handle different constructions of Laplacian operators. Extensive experimental results show the superior performance of our approach on spectral image classification, community detection, vertex classification and matrix completion tasks.
Biofertilizers function as key player in sustainable agriculture by improving soil fertility, plant tolerance and crop productivity

Current soil management strategies are mainly dependent on inorganic chemical-based fertilizers, which caused a serious threat to human health and environment. The exploitation of beneficial microbes as a biofertilizer has become paramount importance in agriculture sector for their potential role in food safety and sustainable crop production. The eco-friendly approaches inspire a wide range of application of plant growth promoting rhizobacteria (PGPRs), endo- and ectomycorrhizal fungi, cyanobacteria and many other useful microscopic organisms led to improved nutrient uptake, plant growth and plant tolerance to abiotic and biotic stress. The present review highlighted biofertilizers mediated crops functional traits such as plant growth and productivity, nutrient profile, plant defense and protection with special emphasis to its function to trigger various growth- and defense-related genes in signaling network of cellular pathways to cause cellular response and thereby crop improvement. The knowledge gained from the literature appraised herein will help us to understand the physiological bases of biofertlizers towards sustainable agriculture in reducing problems associated with the use of chemicals fertilizers.
Anomalies in Intertemporal Choice : Evidence and an Interpretation

Research on decision making under uncertainty has been strongly influenced by the documentation of numerous expected utility (EU) anomalies-behaviors that violate the expected utility axioms. The relative lack of progress on the closely related topic of intertemporal choice is partly due to the absence of an analogous set of discounted utility (DU) anomalies. We enumerate a set of DU anomalies analogous to the EU anomalies and propose a model that accounts for the anomalies, as well as other intertemporal choice phenomena incompatible with DU. We discuss implications for savings behavior, estimation of discount rates, and choice framing effects.
From Physical Marketing to Web Marketing: The Web-Marketing Mix

Reviews the criticism on the 4P Marketing Mix framework as the basis of traditional and virtual marketing planning. Argues that the customary marketing management approach, based on the popular Marketing Mix 4Ps paradigm, is inadequate in the case of virtual marketing. Identifies two main limitations of the Marketing Mix when applied in online environments namely the role of the Ps in a virtual commercial setting and the lack of any strategic elements in the model. Identifies the critical factors of the Web marketing and argues that the basis for successful E-Commerce is the full integration of the virtual activities into the company’s physical strategy, marketing plan and organisational processes. The 4S elements of the Web Marketing Mix framework offer the basis for developing and commercialising Business to Consumer online projects. The model was originally developed for educational purposes and has been tested and refined by means of three case studies.
Spotting opinion spammers using behavioral footprints

Opinionated social media such as product reviews are now widely used by individuals and organizations for their decision making. However, due to the reason of profit or fame, people try to game the system by opinion spamming (e.g., writing fake reviews) to promote or to demote some target products. In recent years, fake review detection has attracted significant attention from both the business and research communities. However, due to the difficulty of human labeling needed for supervised learning and evaluation, the problem remains to be highly challenging. This work proposes a novel angle to the problem by modeling spamicity as latent. An unsupervised model, called Author Spamicity Model (ASM), is proposed. It works in the Bayesian setting, which facilitates modeling spamicity of authors as latent and allows us to exploit various observed behavioral footprints of reviewers. The intuition is that opinion spammers have different behavioral distributions than non-spammers. This creates a distributional divergence between the latent population distributions of two clusters: spammers and non-spammers. Model inference results in learning the population distributions of the two clusters. Several extensions of ASM are also considered leveraging from different priors. Experiments on a real-life Amazon review dataset demonstrate the effectiveness of the proposed models which significantly outperform the state-of-the-art competitors.
i-LAND: An Interactive Landscape for Creativity and Innovation

We describe the i-LAND environment which constitutes an exampleof our vision of the workspaces of the future, in this casesupporting cooperative work of dynamic teams with changing needs.i-LAND requires and provides new forms of human-computerinteraction and new forms of computer-supported cooperative work.Its design is based on an integration of information andarchitectural spaces, implications of new work practices and anempirical requirements study informing our design. i-LAND consistsof several roomware components, i.e. computer-aug- mented objectsintegrating room elements with information technology. We presentthe current realization of i-LAND in terms of an interactiveelectronic wall, an interactive table, two computer-enhancedchairs, and two bridges for the Passage-mechanism. This iscomplemented by the description of the creativity supportapplication and the technological infrastructure. The paper isaccompanied by a video figure in the CHI99 video program.
Deep Multi-View Spatial-Temporal Network for Taxi Demand Prediction

Taxi demand prediction is an important building block to enabling intelligent transportation systems in a smart city. An accurate prediction model can help the city pre-allocate resources to meet travel demand and to reduce empty taxis on streets which waste energy and worsen the traffic congestion. With the increasing popularity of taxi requesting services such as Uber and Didi Chuxing (in China), we are able to collect large-scale taxi demand data continuously. How to utilize such big data to improve the demand prediction is an interesting and critical real-world problem. Traditional demand prediction methods mostly rely on time series forecasting techniques, which fail to model the complex non-linear spatial and temporal relations. Recent advances in deep learning have shown superior performance on traditionally challenging tasks such as image classification by learning the complex features and correlations from largescale data. This breakthrough has inspired researchers to explore deep learning techniques on traffic prediction problems. However, existing methods on traffic prediction have only considered spatial relation (e.g., using CNN) or temporal relation (e.g., using LSTM) independently. We propose a Deep Multi-View Spatial-Temporal Network (DMVST-Net) framework to model both spatial and temporal relations. Specifically, our proposed model consists of three views: temporal view (modeling correlations between future demand values with near time points via LSTM), spatial view (modeling local spatial correlation via local CNN), and semantic view (modeling correlations among regions sharing similar temporal patterns). Experiments on large-scale real taxi demand data demonstrate effectiveness of our approach over state-ofthe-art methods.
Privacy Preservation in e-Healthcare Environments: State of the Art and Future Directions

e-Healthcare promises to be the next big wave in healthcare. It offers all the advantages and benefits imaginable by both the patient and the user. However, current e-Healthcare systems are not yet fully developed and mature, and thus lack the degree of confidentiality, integrity, privacy, and user trust necessary to be widely implemented. Two primary aspects of any operational healthcare enterprise are the quality of healthcare services and patient trust over the healthcare enterprise. Trust is intertwined with issues like confidentiality, integrity, accountability, authenticity, identity, and data management, to name a few. Privacy remains one of the biggest obstacles to ensuring the success of e-Healthcare solutions in winning patient trust as it indirectly covers most security concerns. Addressing privacy concerns requires addressing security issues like access control, authentication, non-repudiation, and accountability, without which end-to-end privacy cannot be ensured. Achieving privacy from the point of data collection in wireless sensor networks, to incorporating the Internet of Things, to communication links, and to data storage and access, is a huge undertaking and requires extensive work. Privacy requirements are further compounded by the fact that the data handled in an enterprise are of an extremely personal and private nature, and its mismanagement, either intentionally or unintentionally, could seriously hurt both the patient and future prospects of an e-Healthcare enterprise. Research carried out in order to address privacy concerns is not homogenous in nature. It focuses on the failure of certain parts of the e-Healthcare enterprise to fully address all aspects of privacy. In the middle of this ongoing research and implementation, a gradual shift has occurred, moving e-Healthcare enterprise controls away from an organizational level toward the level of patients. This is intended to give patients more control and authority over decision making regarding their protected health information/electronic health record. A lot of works and efforts are necessary in order to better assess the feasibility of this major shift in e-Healthcare enterprises. Existing research can be naturally divided on the basis of techniques used. These include data anonymization/pseudonymization and access control mechanisms primarily for stored data privacy. This, however, results in giving a back seat to certain privacy requirements (accountability, integrity, non-repudiation, and identity management). This paper reviews research carried out in this regard and explores whether this research offers any possible solutions to either patient privacy requirements for e-Healthcare or possibilities for addressing the (technical as well as psychological) privacy concerns of the users.
Dynamic Functional Segregation and Integration in Human Brain Network During Complex Tasks

The analysis of the topology and organization of brain networks is known to greatly benefit from network measures in graph theory. However, to evaluate dynamic changes of brain functional connectivity, more sophisticated quantitative metrics characterizing temporal evolution of brain topological features are required. To simplify conversion of time-varying brain connectivity to a static graph representation is straightforward but the procedure loses temporal information that could be critical in understanding the brain functions. To extend the understandings of functional segregation and integration to a dynamic fashion, we recommend dynamic graph metrics to characterise temporal changes of topological features of brain networks. This study investigated functional segregation and integration of brain networks over time by dynamic graph metrics derived from EEG signals during an experimental protocol: performance of complex flight simulation tasks with multiple levels of difficulty. We modelled time-varying brain functional connectivity as multi-layer networks, in which each layer models brain connectivity at time window  $t+\Delta t$ . Dynamic graph metrics were calculated to quantify temporal and topological properties of the network. Results show that brain networks under the performance of complex tasks reveal a dynamic small-world architecture with a number of frequently connected nodes or hubs, which supports the balance of information segregation and integration in brain over time. The results also show that greater cognitive workloads caused by more difficult tasks induced a more globally efficient but less clustered dynamic small-world functional network. Our study illustrates that task-related changes of functional brain network segregation and integration can be characterized by dynamic graph metrics.
Using Deep Learning Towards Biomedical Knowledge Discovery

A vast amount of knowledge exists within biomedical literature, publications, clinical notes and online content. Identifying hidden, interesting or previously unknown biomedical knowledge from free text resources using an automated approach remains an important challenge. Towards this problem, we investigate the use of deep learning methods that have shown significant promise in identifying hidden patterns from large corpus of text in an unsupervised manner. For example, it can deduce that „husband‟ „man‟ + 'woman' = 'wife'. We use the text corpus from MRDEF file in the Unified Medical Language System (UMLS) dataset as training set to discover potential relationships. To evaluate our approach, we cross-verify new relationships against the UMLS MRREL dataset and conduct a manual evaluation from a sample of the non-overlapping set. The algorithm found 32% of new relationships not originally represented in the UMLS. The deep learning methods provide a promising approach in discovering potential new biomedical knowledge from free text.
Capsule Routing for Sound Event Detection

The detection of acoustic scenes is a challenging problem in which environmental sound events must be detected from a given audio signal. This includes classifying the events as well as estimating their onset and offset times. We approach this problem with a neural network architecture that uses the recently-proposed capsule routing mechanism. A capsule is a group of activation units representing a set of properties for an entity of interest, and the purpose of routing is to identify part-whole relationships between capsules. That is, a capsule in one layer is assumed to belong to a capsule in the layer above in terms of the entity being represented. Using capsule routing, we wish to train a network that can learn global coherence implicitly, thereby improving generalization performance. Our proposed method is evaluated on Task 4 of the DCASE 2017 challenge. Results show that classification performance is state-of-the-art, achieving an F-score of 58.6%. In addition, overfitting is reduced considerably compared to other architectures.
A Self-Attention Network for Hierarchical Data Structures with an Application to Claims Management

Insurance companies must manage millions of claims per year. While most of these claims are non-fraudulent, fraud detection is core for insurance companies. The ultimate goal is a predictive model to single out the fraudulent claims and pay out the non-fraudulent ones immediately. Modern machine learning methods are well suited for this kind of problem. Health care claims often have a data structure that is hierarchical and of variable length. We propose one model based on piecewise feed forward neural networks (deep learning) and another model based on self-attention neural networks for the task of claim management. We show that the proposed methods outperform bagof-words based models, hand designed features, and models based on convolutional neural networks, on a data set of two million health care claims. The proposed self-attention method performs the best.
Attention to Refine Through Multi Scales for Semantic Segmentation

This paper proposes a novel attention model for semantic segmentation, which aggregates multi-scale and context features to refine prediction. Specifically, the skeleton convolutional neural network framework takes in multiple different scales inputs, by which means the CNN can get representations in different scales. The proposed attention model will handle the features from different scale streams respectively and integrate them. Then location attention branch of the model learns to softly weight the multi-scale features at each pixel location. Moreover, we add an recalibrating branch, parallel to where location attention comes out, to recalibrate the score map per class. We achieve quite competitive results on PASCAL VOC 2012 and ADE20K datasets, which surpass baseline and related works.