nils-herrmann/clean_scirepeval huggingface dataset:
CRITICAL QUESTIONS FOR BIG DATA

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Keyphrase Extraction Based on Prior Knowledge

Keyphrase is an important way to quickly get the topic of a document by providing highly-summative information. The previous approaches for keyphrase extraction simply rank keyphrases according to statistics-based model or graph-based model, which ignore the influence of external knowledge. In this paper, we take prior knowledge, which contains controlled vocabulary of keyphrases and their prior probability, into consideration to enhance previous methods. First, we build a controlled vocabulary of keyphrases introduced by keyphrases from existing collections and a keyphrase candidate set is filtered from a given document by it. Then, we use prior probability to represent the importance of keyphrases candidate with TF-IDF and TextRank. Finally, a supervised learning algorithm is used to learn optimal weights of these three features. Experiments on four benchmark datasets show the great advantages of prior knowledge on keyphrase extraction. Furthermore, we achieve competitive performance compared with the state-of-the art methods.
From Argument Diagrams to Argumentation Mining in Texts: A Survey

In this paper, we consider argument mining as the task of buil ding a formal representation for an argumentative piece of text. Our goal is to provide a criti cal survey of the literature on both the resulting representations (i.e., argument diagrammin g techniques) and on the various aspects of the automatic analysis process. For representation, we a lso provide a synthesized proposal of a scheme that combines advantages from several of the earlier approaches; in addition, we discuss the relationship between representing argument structure and the rhetorical structure of texts in the sense of Mann and Thompsons (1988) RST. Then, for the argu ment mining problem, we also cover the literature on closely-related tasks that have bee n tackled in Computational Linguistics, because we think that these can contribute to more powerful a rg ment mining systems than the first prototypes that were built in recent years. The paper co ncludes with our suggestions for the major challenges that should be addressed in the field of argu ment mining.
Content analysis schemes to analyze transcripts of online asynchronous discussion groups: A review

Research in the field of Computer Supported Collaborative Learning (CSCL) is based on a wide variety of methodologies. In this paper, we focus upon content analysis, which is a technique often used to analyze transcripts of asynchronous, computer mediated discussion groups in formal educational settings. Although this research technique is often used, standards are not yet established. The applied instruments reflect a wide variety of approaches and differ in their level of detail and the type of analysis categories used. Further differences are related to a diversity in their theoretical base, the amount of information about validity and reliability, and the choice for the unit of analysis. This article presents an overview of different content analysis instruments, building on a sample of models commonly used in the CSCL-literature. The discussion of 15 instruments results in a number of critical conclusions. There are questions about the coherence between the theoretical base and the operational translation of the theory in the instruments. Instruments are hardly compared or contrasted with one another. As a consequence the empirical base of the validity of the instruments is limited. The analysis is rather critical when it comes to the issue of reliability. The authors put forward the need to improve the theoretical and empirical base of the existing instruments in order to promote the overall quality of CSCL-research.
Underwater Image Restoration Based on Image Blurriness and Light Absorption

Underwater images often suffer from color distortion and low contrast, because light is scattered and absorbed when traveling through water. Such images with different color tones can be shot in various lighting conditions, making restoration and enhancement difficult. We propose a depth estimation method for underwater scenes based on image blurriness and light absorption, which can be used in the image formation model (IFM) to restore and enhance underwater images. Previous IFM-based image restoration methods estimate scene depth based on the dark channel prior or the maximum intensity prior. These are frequently invalidated by the lighting conditions in underwater images, leading to poor restoration results. The proposed method estimates underwater scene depth more accurately. Experimental results on restoring real and synthesized underwater images demonstrate that the proposed method outperforms other IFM-based underwater image restoration methods.
Impact of Brand Related Attributes on Consumer ’ s Purchase Intention Towards Private Label Apparels Brands : A Study About the Consumers of Ahmedabad

It has been observed that there is a great growth of the market share of PLB in developed countries. Earlier most of the people were using branded clothes, but now a days the companies have introduced their own private brands to increase their popularity and more profit. Companies are providing more discounts on private brands to get more customers. Retailers have not only customized and localized the products as per customer’s tastes and preference but also created PLBs’. At present customers are more intelligent and smart, they look for the product which gives them value, so today’s private label brands are more competitive, reasonable price and of better quality. The consumers prefer private label brands heavily because they can save money. The apparels are second most demanded product after FMCG. So, here we are focusing on the private label apparels brands. Big companies like Pantaloons, Wills Lifestyle, Reliance and many more retailers having their own brands. The researcher aimed to find and analyze the effect of various brand related attributes (Brand knowledge (brand image, brand awareness) on consumer’s purchase intention towards private label brands in apparels. Most of the customer purchase depends upon its brand image. The study is carried at some reputed stores of Ahmedabad like Pantaloons, Westside and Will Lifestyle. It tries to establish the relationship between brands related factors and their impact on consumers purchase
Attentive Pooling Networks

In this work, we propose Attentive Pooling (AP), a two-way attention mechanism for discriminative model training. In the context of pair-wise ranking or classification with neural networks, AP enables the pooling layer to be aware of the current input pair, in a way that information from the two input items can directly influence the computation of each other’s representations. Along with such representations of the paired inputs, AP jointly learns a similarity measure over projected segments (e.g. trigrams) of the pair, and subsequently, derives the corresponding attention vector for each input to guide the pooling. Our two-way attention mechanism is a general framework independent of the underlying representation learning, and it has been applied to both convolutional neural networks (CNNs) and recurrent neural networks (RNNs) in our studies. The empirical results, from three very different benchmark tasks of question answering/answer selection, demonstrate that our proposed models outperform a variety of strong baselines and achieve state-of-the-art performance in all the benchmarks.
Happiness in the Face of Adversity : Reformulating the Dynamic and Modular Bases of Subjective Well-Being

Addressing diversity and apparent contradictions in manifestations of happiness, this article delineates subjective well-being (SWB) as a dynamic system in the face of possible adversity. SWB constitutes a favorable psychological environment that regulates the hostile-world scenario, defined as one’s image of actual or potential threats to one’s life or integrity. SWB operates in various modules: experiential, wherein private awareness of SWB dwells on relevant core themes; declarative, wherein public selfreports of SWB function as social behavior; differential, wherein synchronic dimensions of SWB form well-being types; and narrative, wherein diachronic valences of SWB construct trajectories along one’s life story. By explicating the regulatory and configurational nature of SWB, the present conceptualization emphasizes the process, rather than the outcome, of pursuing happiness.
Pattern recognition techniques for the identification of Activities of Daily Living using mobile device accelerometer

This paper focuses on the recognition of Activities of Daily Living (ADL) applying pattern recognition techniques to the data acquired by the accelerometer available in the mobile devices. The recognition of ADL is composed by several stages, including data acquisition, data processing, and artificial intelligence methods. The artificial intelligence methods used are related to pattern recognition, and this study focuses on the use of Artificial Neural Networks (ANN). The data processing includes data cleaning, and the feature extraction techniques to define the inputs for the ANN. Due to the low processing power and memory of the mobile devices, they should be mainly used to acquire the data, applying an ANN previously trained for the identification of the ADL. The main purpose of this paper is to present a new method implemented with ANN for the identification of a defined set of ADL with a reliable accuracy. This paper also presents a comparison of different types of ANN in order to choose the type for the implementation of the final method. Results of this research probes that the best accuracies are achieved with Deep Learning techniques with an accuracy higher than 80%.
6 Energy Management for Intelligent Buildings

The increasing availability and affordability of wireless building and home automation networks has increased interest in residential and commercial building energy management. This interest has been coupled with an increased awareness of the environmental impact of energy generation and usage. Residential appliances and equipment account for 30% of all energy consumption in OECD countries and indirectly contribute to 12% of energy generation related carbon dioxide (CO2) emissions (International Energy Agency, 2003). The International Energy Association also predicts that electricity usage for residential appliances would grow by 12% between 2000 and 2010, eventually reaching 25% by 2020. These figures highlight the importance of managing energy use in order to improve stewardship of the environment. They also hint at the potential gains that are available through smart consumption strategies targeted at residential and commercial buildings. The challenge is how to achieve this objective without negatively impacting people’s standard of living or their productivity. The three primary purposes of building energy management are the reduction/management of building energy use; the reduction of electricity bills while increasing occupant comfort and productivity; and the improvement of environmental stewardship without adversely affecting standards of living. Building energy management systems provide a centralized platform for managing building energy usage. They detect and eliminate waste, and enable the efficient use electricity resources. The use of widely dispersed sensors enables the monitoring of ambient temperature, lighting, room occupancy and other inputs required for efficient management of climate control (heating, ventilation and air conditioning), security and lighting systems. Lighting and HVAC account for 50% of commercial and 40% of residential building electricity expenditure respectively, indicating that efficiency improvements in these two areas can significantly reduce energy expenditure. These savings can be made through two avenues: the first is through the use of energy-efficient lighting and HVAC systems; and the second is through the deployment of energy management systems which utilize real time price information to schedule loads to minimize energy bills. The latter scheme requires an intelligent power grid or smart grid which can provide bidirectional data flows between customers and utility companies. The smart grid is characterized by the incorporation of intelligenceand bidirectional flows of information and electricity throughout the power grid. These enhancements promise to revolutionize the grid by enabling customers to not only consume but also supply power.
Wireless Physical Layer Security with Imperfect Channel State Information: A Survey

Physical layer security is an emerging technique to improve the wireless communication security, which is wide ly regarded as a complement to cryptographic technologies. To design physical layer security techniques under practical scenarios, the uncertainty and imperfections in the channel knowl edge need to be taken into consideration. This paper provides a survey of recent research and development in physical layer security considering the imperfect channel state informat ion (CSI) at communication nodes. We first present an overview of the main information-theoretic measures of the secrecy p erformance with imperfect CSI. Then, we describe several sign al processing enhancements in secure transmission designs, s uch as secure on-off transmission, beamforming with artificialnoise, and secure communication assisted by relay nodes or in cogni tive radio systems. The recent studies of physical layer securit y in large-scale decentralized wireless networks are also summ arized. Finally, the open problems for the on-going and future resea rch are discussed.
Solving Probability Problems in Natural Language

The ability to solve probability word problems such as those found in introductory discrete mathematics textbooks, is an important cognitive and intellectual skill. In this paper, we develop a two-step endto-end fully automated approach for solving such questions that is able to automatically provide answers to exercises about probability formulated in natural language. In the first step, a question formulated in natural language is analysed and transformed into a highlevel model specified in a declarative language. In the second step, a solution to the high-level model is computed using a probabilistic programming system. On a dataset of 2160 probability problems, our solver is able to correctly answer 97.5% of the questions given a correct model. On the end-toend evaluation, we are able to answer 12.5% of the questions (or 31.1% if we exclude examples not supported by design).
Smart Electricity Meter Data Intelligence for Future Energy Systems: A Survey

Smart meters have been deployed in many countries across the world since early 2000s. The smart meter as a key element for the smart grid is expected to provide economic, social, and environmental benefits for multiple stakeholders. There has been much debate over the real values of smart meters. One of the key factors that will determine the success of smart meters is smart meter data analytics, which deals with data acquisition, transmission, processing, and interpretation that bring benefits to all stakeholders. This paper presents a comprehensive survey of smart electricity meters and their utilization focusing on key aspects of the metering process, different stakeholder interests, and the technologies used to satisfy stakeholder interests. Furthermore, the paper highlights challenges as well as opportunities arising due to the advent of big data and the increasing popularity of cloud environments.
FacetGist: Collective Extraction of Document Facets in Large Technical Corpora

Given the large volume of technical documents available, it is crucial to automatically organize and categorize these documents to be able to understand and extract value from them. Towards this end, we introduce a new research problem called Facet Extraction. Given a collection of technical documents, the goal of Facet Extraction is to automatically label each document with a set of concepts for the key facets (e.g., application, technique, evaluation metrics, and dataset) that people may be interested in. Facet Extraction has numerous applications, including document summarization, literature search, patent search and business intelligence. The major challenge in performing Facet Extraction arises from multiple sources: concept extraction, concept to facet matching, and facet disambiguation. To tackle these challenges, we develop FacetGist, a framework for facet extraction. Facet Extraction involves constructing a graph-based heterogeneous network to capture information available across multiple local sentence-level features, as well as global context features. We then formulate a joint optimization problem, and propose an efficient algorithm for graph-based label propagation to estimate the facet of each concept mention. Experimental results on technical corpora from two domains demonstrate that Facet Extraction can lead to an improvement of over 25% in both precision and recall over competing schemes.
Procedural textures using tilings with Perlin Noise

In this paper, we demonstrate the use of tiling with noise to generate rich procedural textures. We introduce the idea of storing tiles which consist of only the gradients stored at the integer lattice points and constructing a texture on the GPU from these tiles. We also introduce the idea of using mipmapped tiles to store gradients for turbulence. Finally we demonstrate a novel use of mipmaps to generate infinite aperiodic textures with varying frequency patterns.
IPOD: Intensive Point-based Object Detector for Point Cloud

We present a novel 3D object detection framework, named IPOD, based on raw point cloud. It seeds object proposal for each point, which is the basic element. This paradigm provides us with high recall and high fidelity of information, leading to a suitable way to process point cloud data. We design an end-to-end trainable architecture, where features of all points within a proposal are extracted from the backbone network and achieve a proposal feature for final bounding inference. These features with both context information and precise point cloud coordinates yield improved performance. We conduct experiments on KITTI dataset, evaluating our performance in terms of 3D object detection, Bird’s Eye View (BEV) detection and 2D object detection. Our method accomplishes new state-of-the-art , showing great advantage on the hard set.
Successive Randomization of Inception v 3 Weights Gradient Gradient-SG Gradient -

Explaining the output of a complicated machine learning model like a deep neural network (DNN) is a central challenge in machine learning. Several proposed local explanation methods address this issue by identifying what dimensions of a single input are most responsible for a DNN’s output. The goal of this work is to assess the sensitivity of local explanations to DNN parameter values. Somewhat surprisingly, we find that DNNs with randomly-initialized weights produce explanations that are both visually and quantitatively similar to those produced by DNNs with learned weights. Our conjecture is that this phenomenon occurs because these explanations are dominated by the lower level features of a DNN, and that a DNN’s architecture provides a strong prior which significantly affects the representations learned at these lower layers.
Effect of Corporate Social Responsibility on Organisation Performance ; Banking Industrykenya , Kakamega County

Corporate Social Responsibility is how business organization activities influences the stakeholder intrest.CSR plays a very important role in organizational performance. Most organizations have embraced corporate social responsibility without substantial increase in organization performance hence the research sought to find out the effect of CSR on organization performance. This research limited itself to selected commercial banks in Kakamega that’s equity and cooperative bank. A population of over 10,000 customers, the researcher picked on corporate customers of around 70 customers; a sample size of 50 was used to carry out the research. The questionnaires which were administered randomly for bank management, bank staff and customer among other stake holders in the banking industry. The internal consistency for performance independent variable was achieved through the use of reliability Cronbach’s Alpha coefficient which had an alpha of 0.915 implying that the instruments used were reliable for the study. Based on the results of this study, it was concluded that philanthropic responsibility of a bank has an impact on bank performance. The positive significant correlation coefficient 0.490, P<0.05 shows that any increase in philanthropic responsibility will increase the performance of the bank.The intervening variables government policy and priority both had significant impact on organisation performance as there was significant increase in R squared for both models though government policy had got the highest increase of the two variables..The study recommended that for banks to retain its customers, they should focus on more on their ethics of how they treat their employees, customers and other stakeholder Bank management should prioritise CSR activities in their institution and ensure enough resources and personnel are set aside to fund the CSR activities. They can co-operate CSR as of its core functions thereby implementing it seriously. Bank management should implement Government policy on CSR because it has a positive impact on customer retention and performance as some of the activities are appealing to customer. Government agencies on environment should create awareness to citizen so as to make bank customer identify those banks that adhere to environmental regulation and laws as many customer are unaware of the importance of green and clean environment
Part-based Graph Convolutional Network for Action Recognition

Human actions comprise of joint motion of articulated body parts or “gestures”. Human skeleton is intuitively represented as a sparse graph with joints as nodes and natural connections between them as edges. Graph convolutional networks have been used to recognize actions from skeletal videos. We introduce a part-based graph convolutional network (PB-GCN) for this task, inspired by Deformable Part-based Models (DPMs). We divide the skeleton graph into four subgraphs with joints shared across them and learn a recognition model using a part-based graph convolutional network. We show that such a model improves performance of recognition, compared to a model using entire skeleton graph. Instead of using 3D joint coordinates as node features, we show that using relative coordinates and temporal displacements boosts performance. Our model achieves state-of-the-art performance on two challenging benchmark datasets NTURGB+D and HDM05, for skeletal action recognition.