nils-herrmann/clean_scirepeval huggingface dataset:
The Pulse of News in Social Media: Forecasting Popularity

News articles are extremely time sensitive by nature. There is also intense competition among news items to propagate as widely as possible. Hence, the task of predicting the popularity of news items on the social web is both interesting and challenging. Prior research has dealt with predicting eventual online popularity based on early popularity. It is most desirable, however, to predict the popularity of items prior to their release, fostering the possibility of appropriate decision making to modify an article and the manner of its publication. In this paper, we construct a multi-dimensional feature space derived from properties of an article and evaluate the efficacy of these features to serve as predictors of online popularity. We examine both regression and classification algorithms and demonstrate that despite randomness in human behavior, it is possible to predict ranges of popularity on twitter with an overall 84% accuracy. Our study also serves to illustrate the differences between traditionally prominent sources and those immensely popular on the social web.
Performance Prediction Models for Hard Rock Tunnel Boring Machines

Tunnel Boring Machine (TBM) is one of the most popular tunneling equipment in the industry. Modern hard rock TBMs are very versatile and have been used very favorably in various ground conditions while setting advance rate records of over 170 m a day. There has been a lot of research on development of models to allow accurate prediction of machine rate of penetration in given ground conditions. These models, although successful in prediction of machine performance in many cases, are short of accounting for some of the parameters affecting machine performance in a variety of grounds. Moreover, with more accurate predictive capabilities and better understanding of operational parameters, accurate planning and cost estimation is possible, which allows for wider area application for TBMs. This justifies the initial high investments for the machine and facilitates increased productivity by proper planning of the back up system, matching machine specifications to the jobs site conditions, and reduces the risks involved in using a machine for a particular project. Study and analysis of the rock cutting process by disc cutters is very important since discs are the most common cutting tools used on hard rock TBMs. The previous studies on this subject have been instrumental in developing models for estimation of rock cutting forces needed for design analysis and optimization as well as performance prediction of machines such as TBMs. This paper, offers a brief review of the previous research works in this field including an overview of rock indentation by disc cutter, failure mechanism, important parameters influencing on the performance. Also, models developed for performance prediction of hard rock TBMs based on the cutting forces will be introduced along with empirical models used for an overall estimation of the machine rate of penetration in a given ground condition. The paper will finally review the current efforts underway by the authors for improving the accuracy of the models.
Integrated WiFi/PDR/Smartphone Using an Adaptive System Noise Extended Kalman Filter Algorithm for Indoor Localization

Wireless signal strength is susceptible to the phenomena of interference, jumping, and instability, which often appear in the positioning results based on Wi-Fi field strength fingerprint database technology for indoor positioning. Therefore, a Wi-Fi and PDR (pedestrian dead reckoning) real-time fusion scheme is proposed in this paper to perform fusing calculation by adaptively determining the dynamic noise of a filtering system according to pedestrian movement (straight or turning), which can effectively restrain the jumping or accumulation phenomena of wireless positioning and the PDR error accumulation problem. Wi-Fi fingerprint matching typically requires a quite high computational burden: To reduce the computational complexity of this step, the affinity propagation clustering algorithm is adopted to cluster the fingerprint database and integrate the information of the position domain and signal domain of respective points. An experiment performed in a fourth-floor corridor at the School of Environment and Spatial Informatics, China University of Mining and Technology, shows that the traverse points of the clustered positioning system decrease by 65%–80%, which greatly improves the time efficiency. In terms of positioning accuracy, the average error is 4.09 m through the Wi-Fi positioning method. However, the positioning error can be reduced to 2.32 m after integration of the PDR algorithm with the adaptive noise extended Kalman filter (EKF).
Intelligence Science I

The brain acquires the ability of pattern recognition through learning. Understanding neural circuit mechanisms underlying learning and memory is thus essential for understanding how the brain recognizes patterns. Much progress has been made in this area of neuroscience during the past decades. In this lecture, I will summarize three distinct features of neural circuits that provide the basis of learning and memory of neural information, and pattern recognition. First, the architecture of the neural circuits is continuously modified by experience. This process of experience‐induced sculpting (pruning) of connections is most prominent early in development and decreases gradually to a much more limited extent in the adult brain. Second, the efficacy of synaptic transmission could be modified by neural activities associated with experience, in a manner that depends on the pattern (frequency and timing) of spikes in the pre‐ and postsynaptic neurons. This activity‐induced circuit alteration in the form of long‐term potentiation (LTP) and long‐term depression (LTD) of existing synaptic connections is the predominant mechanism underlying learning and memory of the adult brain. Third, learning and memory of information containing multiple modalities, e.g., visual, auditory, and tactile signals, involves processing of each type of signals by different circuits for different modalities, as well as binding of processed multimodal signals through mechanisms that remain to be elucidated. Two potential mechanisms for binding of multimodal signals will be discussed: binding of signals through converging connections to circuits specialized for integration of multimodal signals, and binding of signals through correlated firing of neuronal assemblies that are established in circuits for processing signals of different modalities. Incorporation of these features into artificial neural networks may help to achieve more efficient pattern recognition, especially for recognition of time‐varying multimodal signals. Interactive Granular Computing: Toward Computing Model for Turing Test
Software release planning challenges in software development : An empirical study

Software development is a complicated process and requires careful planning to produce high quality software. In large software development projects, release planning may involve a lot of unique challenges. Due to time, budget and some other constraints, potentially there are many problems that may possibly occur. Subsequently, project managers have been trying to identify and understand release planning, challenges and possible resolutions which might help them in developing more effective and successful software products. This paper presents the findings from an empirical study which investigates release planning challenges. It takes a qualitative approach using interviews and observations with practitioners and project managers at five large software banking projects in Informatics Services Corporation (ISC) in Iran. The main objective of this study is to explore and increase the understanding of software release planning challenges in several software companies in a developing country. A number of challenges were elaborated and discussed in this study within the domain of software banking projects. These major challenges are classified into two main categories: the human-originated including people cooperation, disciplines and abilities; and the system-oriented including systematic approaches, resource constraints, complexity, and interdependency among the systems.
Lorify: A Knowledge Base from Scratch

In this paper we discuss our approach to the task of Cold-Start Knowledge Base Population and the challenges associated with it. We describe our knowledge base system Lorify and each of the components necessary to populate it from unstructured text. The pivotal component for building a large-scale knowledge base is scalable cross-document coreference. We address this with a novel clustering algorithm based on Markov-Chain MonteCarlo, and show that it is capable of scaling to much larger sets of entities than typical algorithms. Finally, we detail the performance of this system on the TAC KBP 2012 evaluation.
Iris recognition: an emerging biometric technology

This paper examines automated iris recognition as a biometri· ca/ly based technology for personal identification and verification. The motivation for this endeavor stems from the observation that the human iris provides a particularly interesting structure on which to base a technology for noninvasive biometric assessment. In particular, the biomedical literature suggests that irises are as distinct as fingerprints or patterns of retinal blood vessels. Further, since the iris is an overt body, its appearance is amenable to remate examination with the aid of a machine vision system. The body of this paper details issues in the design and operation of such systems. For the sake of illustration, extant systems are described in some amount of detail.
A systematic literature review on features of deep learning in big data analytics

Deep Learning (DL) is a division of machine learning techniques that based on algorithms for learning multiples level of representations. Big Data Analytics (BDA) is the process of examining large scale of data and variety of data types.  The aims of this study are to identify the existing features of DL approaches for using in BDA and identify the key features that affect the effectiveness of DL approaches.  A Systematic Literature Review (SLR) was carried out and reported based on the preferred reporting items for systematic reviews. 4065 papers were retrieved by manual search in four databases which are Google Scholar, Taylor & Francis,  34 primary studies were finally included.  From these studies, 70% were journal articles, 25% were conference papers and 5% were contributions from the studies consisted of book chapters. Five features of DL were identified and analyzed. The features are (1) hierarchical layer, (2) high-level abstraction, (3) process high volume of data, (4) universal model and (5) does not over fit the training data.  This review delivers the evidence that DL in BDA is an active research area. The review provides researchers with some guidelines for future research on this topic. It also provides broad information on DL in BDA which could be useful for practitioners.
Differential Evolution - A Simple and Efficient Heuristic for global Optimization over Continuous Spaces

A new heuristic approach for minimizing possibly nonlinear and non-differentiable continuous space functions is presented. By means of an extensive testbed it is demonstrated that the new method converges faster and with more certainty than many other acclaimed global optimization methods. The new method requires few control variables, is robust, easy to use, and lends itself very well to parallel computation.
Graph Neural Networks with Generated Parameters for Relation Extraction

Recently, progress has been made towards improving relational reasoning in machine learning field. Among existing models, graph neural networks (GNNs) is one of the most effective approaches for multi-hop relational reasoning. In fact, multi-hop relational reasoning is indispensable in many natural language processing tasks such as relation extraction. In this paper, we propose to generate the parameters of graph neural networks (GP-GNNs) according to natural language sentences, which enables GNNs to process relational reasoning on unstructured text inputs. We verify GPGNNs in relation extraction from text. Experimental results on a human-annotated dataset and two distantly supervised datasets show that our model achieves significant improvements compared to baselines. We also perform a qualitative analysis to demonstrate that our model could discover more accurate relations by multi-hop relational reasoning.
A Survey of Machine Learning Based Approaches for Parkinson Disease Prediction

Parkinson disease (PD) is a universal public health problem of massive measurement. Machine learning based method is used to classify between healthy people and people with Parkinson’s disease (PD). This paper presents a comprehensive review for the prediction of Parkinson disease buy using machine learning based approaches. The brief introduction of various computational intelligence techniques based approaches used for the prediction of Parkinson diseases are presented .This paper also presents the summary of results obtained by various researchers available in literature to predict the Parkinson diseases.  Parkinson’s disease, classification, random forest, support vector machine, machine learning, signal processing, artificial neural network.
Fonduer: Knowledge Base Construction from Richly Formatted Data

We focus on knowledge base construction (KBC) from richly formatted data. In contrast to KBC from text or tabular data, KBC from richly formatted data aims to extract relations conveyed jointly via textual, structural, tabular, and visual expressions. We introduce Fonduer, a machine-learning-based KBC system for richly formatted data. Fonduer presents a new data model that accounts for three challenging characteristics of richly formatted data: (1) prevalent document-level relations, (2) multimodality, and (3) data variety. Fonduer uses a new deep-learning model to automatically capture the representation (i.e., features) needed to learn how to extract relations from richly formatted data. Finally, Fonduer provides a new programming model that enables users to convert domain expertise, based on multiple modalities of information, to meaningful signals of supervision for training a KBC system. Fonduer-based KBC systems are in production for a range of use cases, including at a major online retailer. We compare Fonduer against state-of-the-art KBC approaches in four different domains. We show that Fonduer achieves an average improvement of 41 F1 points on the quality of the output knowledge base---and in some cases produces up to 1.87x the number of correct entries---compared to expert-curated public knowledge bases. We also conduct a user study to assess the usability of Fonduer's new programming model. We show that after using Fonduer for only 30 minutes, non-domain experts are able to design KBC systems that achieve on average 23 F1 points higher quality than traditional machine-learning-based KBC approaches.
Deep Neural Network for Real-Time Autonomous Indoor Navigation

Autonomous indoor navigation of Micro Aerial Vehicles (MAVs) possesses many challenges. One main reason is because GPS has limited precision in indoor environments. The additional fact that MAVs are not able to carry heavy weight or power consuming sensors, such as range finders, makes indoor autonomous navigation a challenging task. In this paper, we propose a practical system in which a quadcopter autonomously navigates indoors and finds a specific target, i.e. a book bag, by using a single camera. A deep learning model, Convolutional Neural Network (ConvNet), is used to learn a controller strategy that mimics an expert pilot’s choice of action. We show our system’s performance through real-time experiments in diverse indoor locations. To understand more about our trained network, we use several visualization techniques.
Neural Machine Translation with Reconstruction

Although end-to-end Neural Machine Translation (NMT) has achieved remarkable progress in the past two years, it suffers from a major drawback: translations generated by NMT systems often lack of adequacy. It has been widely observed that NMT tends to repeatedly translate some source words while mistakenly ignoring other words. To alleviate this problem, we propose a novel encoder-decoder-reconstructor framework for NMT. The reconstructor, incorporated into the NMT model, manages to reconstruct the input source sentence from the hidden layer of the output target sentence, to ensure that the information in the source side is transformed to the target side as much as possible. Experiments show that the proposed framework significantly improves the adequacy of NMT output and achieves superior translation result over state-of-theart NMT and statistical MT systems.  Past several years have observed a significant progress in Neural Machine Translation (NMT) (Kalchbrenner and Blunsom 2013; Cho et al. 2014; Sutskever, Vinyals, and Le 2014; Bahdanau, Cho, and Bengio 2015). Particularly, NMT has significantly enhanced the performance of translation between a language pair involving rich morphology prediction and/or significant word reordering (Luong and Manning 2015; Bentivogli et al. 2016). Long Short-Term Memory (Hochreiter and Schmidhuber 1997) enables NMT to conduct long-distance reordering, which is a significant challenge for Statistical Machine Translation (SMT) (Brown et al. 1993;  Och, and Marcu 2003). Unlike SMT which employs a number of components, NMT adopts an end-to-end encoder-decoder framework to model the entire translation process. The role of encoder is to summarize the source sentence into a sequence of latent vectors, and the decoder acts as a language model to generate a target sentence word by word by selectively leveraging the information from the latent vectors at each step. In learning, NMT essentially estimates the likelihood of a target sentence given a source sentence. However, conventional NMT faces two main problems: 1 Translations generated by NMT systems often lack of adequacy. When generating target words, the decoder often  Many airports were forced to close . duoge jichang beipo guanbi . translation reconstruction
Efficient Dense Modules of Asymmetric Convolution for Real-Time Semantic Segmentation

Real-time semantic segmentation plays an important role in practical applications such as self-driving and robots. Most research working on semantic segmentation focuses on accuracy with little consideration for efficiency. Several existing studies that emphasize high-speed inference often cannot produce high-accuracy segmentation results. In this paper, we propose a novel convolutional network named Efficient Dense modules with Asymmetric convolution (EDANet), which employs an asymmetric convolution structure incorporating the dilated convolution and the dense connectivity to attain high efficiency at low computational cost, inference time, and model size. Compared to FCN, EDANet is 11 times faster and has 196 times fewer parameters, while it achieves a higher the mean of intersection-over-union (mIoU) score without any additional decoder structure, context module, post-processing scheme, and pretrained model. We evaluate EDANet on Cityscapes and CamVid datasets to evaluate its performance and compare it with the other state-of-art systems. Our network can run on resolution 512×1024 inputs at the speed of 108 and 81 frames per second on a single GTX 1080Ti and Titan X, respectively.
Modeling of nonlinear audio effects with end-to-end deep neural networks

In the context of music production, distortion effects are mainly used for aesthetic reasons and are usually applied to electric musical instruments. Most existing methods for nonlinear modeling are often either simplified or optimized to a very specific circuit. In this work, we investigate deep learning architectures for audio processing and we aim to find a general purpose end-to-end deep neural network to perform modeling of nonlinear audio effects. We show the network modeling various nonlinearities and we discuss the generalization capabilities among different instruments.
A neural network architecture for detecting grammatical errors in statistical machine translation

In this paper we present a Neural Network (NN) architecture for detecting grammatical errors in Statistical Machine Translation (SMT) using monolingual morpho-syntactic word representations in combination with surface and syntactic context windows. We test our approach on two language pairs and two tasks, namely detecting grammatical errors and predicting overall post-editing effort. Our results show that this approach is not only able to accurately detect grammatical errors but it also performs well as a quality estimation system for predicting overall post-editing effort, which is characterised by all types of MT errors. Furthermore, we show that this approach is portable to other languages.
Conceptual metaphor theory meets the data: a corpus-based human annotation study

Metaphor makes our thoughts more vivid and fills our communication with richer imagery. Furthermore, according to the Conceptual Metaphor Theory (CMT) of [30], metaphor also plays an important structural role in the organization and processing of conceptual knowledge. According to this account, the phenomenon of metaphor is not restricted to similarity-based extensions of meanings of individual words, but instead involves activating fixed mappings that reconceptualize one whole area of experience in terms of another. CMT produced a significant resonance in the fields of philosophy, linEkaterina Shutova Computer Laboratory, University of Cambridge, William Gates Building, 15 JJ Thomson Avenue, Cambridge CB3 0FD, UK.  Ekaterina.Shutova@cl.cam.ac.uk Barry Devereux Centre for Speech, Language and the Brain, Department of Experimental Psychology, University of Cambridge. E-mail: barry@csl.psychol.cam.ac.uk Anna Korhonen Computer Laboratory, University of Cambridge, William Gates Building, 15 JJ Thomson Avenue, Cambridge CB3 0FD, UK.  Anna.Korhonen@cl.cam.ac.uk 2 Ekaterina Shutova et al. guistics, cognitive science and artificial intelligence and still underlies a large proportion of modern research on metaphor. However, there has to date been no comprehensive corpus-based study of conceptual metaphor, which would provide an empirical basis for evaluating the CMT using real-world linguistic data. The annotation scheme and the empirical study we present in this paper is a step towards filling this gap. We test our annotation procedure in an experimental setting involving multiple annotators and estimate their agreement on the task. The goal of the study is to investigate (1) how intuitive the conceptual metaphor explanation of linguistic metaphors is for human annotators and whether it is possible to consistently annotate interconceptual mappings; (2) what are the main difficulties that the annotators experience during the annotation process; (3) whether one conceptual metaphor is sufficient to explain a linguistic metaphor or whether a chain of conceptual metaphors is needed. The resulting corpus annotated for conceptual mappings provides a new, valuable dataset for linguistic, computational and cognitive experiments on metaphor.