Reviewerly/exHarmony huggingface dataset:
Hierarchical Capsule Based Neural Network Architecture for Sequence Labeling

Sequence Labeling is one of the most prominent tasks in NLP. The traditional text classification models do not carry context from one sentence to another and hence may not perform well on these tasks. These models lack a hierarchical structure that can aid them in dissecting the input structure at different levels to allow flow of context between sentences. In this paper, we propose a hierarchical neural network comprising of Bi-LSTMs, Dilated Convolution operation, Capsules and Conditional Random Field (CRF) to understand the discourse/ abstract structure and predict next probable label by using label history. We have performed experiments on 3 publicly available datasets through which we have demonstrated that our model has achieved state-of-art performance on these datasets.
Efficient Range-Trapdoor Functions and Applications: Rate-1 OT and More

Substantial work on trapdoor functions (TDFs) has led to many powerful notions and applications. However, despite tremendous work and progress, all known constructions have prohibitively large public keys. In this work, we introduce new techniques for realizing so-called range-trapdoor hash functions with short public keys. This notion, introduced by Döttling et al. [Crypto 2019], allows for encoding a range of indices into a public key in a way that the public key leaks no information about the range, yet an associated trapdoor enables recovery of the corresponding input part. We give constructions of range-trapdoor hash functions, where for a given range I the public key consists of O(n) group elements, improving upon O(n|I|) achieved by Döttling et al. Moreover, by designing our evaluation algorithm in a special way involving Toeplitz matrix multiplication and by showing how to perform fast-Fourier transforms in the exponent, we arrive at $$O(n \log n)$$ group operations for evaluation, improving upon $$O(n^2)$$ , required of previous constructions. Our constructions rely on power-DDH assumptions in pairing-free groups. As applications of our results we obtain
The Daunting Task of Real-World Textual Style Transfer Auto-Evaluation.

The difficulty of textual style transfer lies in the lack of parallel corpora. Numerous advances have been proposed for the unsupervised generation. However, significant problems remain with the auto-evaluation of style transfer tasks. Based on the summary of Pang and Gimpel (2018) and Mir et al. (2019), style transfer evaluations rely on three criteria: style accuracy of transferred sentences, content similarity between original and transferred sentences, and fluency of transferred sentences. We elucidate the problematic current state of style transfer research. Given that current tasks do not represent real use cases of style transfer, current auto-evaluation approach is flawed. This discussion aims to bring researchers to think about the future of style transfer and style transfer evaluation research.
Report of the Session “The State of Streaming Video in Academic Libraries,” Co-sponsored by the ALCTS Collection Management Section (CMS) Collection Development Librarians of Academic Libraries Interest Group and the Film and Media Round Table. American Library Association Midwinter Meeting, Seattle, January 2019

The ALCTS Collection Management Section (CMS) Collection Development Librarians of Academic Libraries Interest Group and the Film and Media Round Table co-hosted a session entitled, “The State of S...
Using ethnographic data to support preschool children's game design

Ethnography is a well-known instrument for studying the target user group and its context. In this paper, we present and discuss an ethnographic study within a kindergarten environment with the aim of gaining insight into the nature of children's behaviors, interpersonal relationships, and engagement with regular kindergarten resources. The ethnographic data collected is used as supporting material in the design of a memory game that uses Near Field Communication (NFC) technology. We conducted a Case Study to evaluate the memory game with the participation of 5-6 years old children and preschool teachers, in a real educational scenario. The results suggest that NFC technology may enhance the play experience of the classic memory game by providing emotional connections with the children. The work also contributes by showing the effectiveness of Ethnography in supporting the design of playful technology for a constantly changing and creative environment of kindergarten settings.
Conv2Warp: An Unsupervised Deformable Image Registration with Continuous Convolution and Warping

Recent successes in deep learning based deformable image registration (DIR) methods have demonstrated that complex deformation can be learnt directly from data while reducing computation time when compared to traditional methods. However, the reliance on fully linear convolutional layers imposes a uniform sampling of pixel/voxel locations which ultimately limits their performance. To address this problem, we propose a novel approach of learning a continuous warp of the source image. Here, the required deformation vector fields are obtained from a concatenated linear and non-linear convolution layers and a learnable bicubic Catmull-Rom spline resampler. This allows to compute smooth deformation field and more accurate alignment compared to using only linear convolutions and linear resampling. In addition, the continuous warping technique penalizes disagreements that are due to topological changes. Our experiments demonstrate that this approach manages to capture large non-linear deformations and minimizes the propagation of interpolation errors. While improving accuracy the method is computationally efficient. We present comparative results on a range of public 4D CT lung (POPI) and brain datasets (CUMC12, MGH10).
Learning from Longitudinal Mammography Studies

When reading imaging studies, radiologists often compare the acquired images to one or more prior studies of the patient. Machine learning algorithms that assist in identifying abnormalities in medical images usually do not analyze prior images. This work describes a deep-learning classification framework for mammography studies, which incorporates prior image information using four approaches: (1) late fusion of prediction scores; (2) early fusion of input layers; (3) feature fusion combining a convolutional neural network (CNN) and gradient boosting trees; and (4) feature fusion using CNN and long-short term memory (LSTM) architecture. We demonstrate the advantages and limitations of each approach and compare their performance in identifying biopsy-proven malignancies in mammography screening studies. On an evaluation cohort of 439 patients, adding prior studies to the analysis improved the diagnostic performance of the classification framework. The CNN-LSTM architecture achieved the highest area under the ROC curve of 0.88, with sensitivity and specificity of 0.87 and 0.78, respectively. The methods that were trained using information from prior studies achieved better results than the baseline classifier, with up to 45% reduction in false-positive rate at the same sensitivity. The major advantage of the CNN-LSTM approach is in its flexibility and scalability; it allows to use the same network to classify sequences of multiple priors with variable length. The study demonstrates that longitudinal analysis of images can potentially improve the ability of machine learning algorithms to accurately and reliably interpret imaging studies, thus providing value to the radiology community.
Variability Modelling and Analysis During 30 Years

Variability modelling and analysis are among the most important activities in software engineering in general and in software product line engineering in particular. In 1990, the FODA report supposed a revolution in the importance of modelling and analysing of variability. In 2020, 30 years of variability modelling and analysis will be celebrated. In this paper, a short overview of the history and the importance of variability modelling and analysis is given, in concordance to that anniversary and on the occasion of Stefania Gnesi’s retirement. She was part of this amazing history.
情绪词加工过程及其情绪效应特点：ERP的证据

<p id="C2">Language plays an important role in the exchange of emotional information. The processing of emotional language has always been the focus of emotional research. Some main views and results on emotional words processing from ERP data are analyzed and summarized: including ERP components and relationship between valence and arousal, as well as emotional effect characteristics. ERP components related to emotional word processing encompass very early P1-N1 components, early EPN components, and late LPP components. Emotional valence and arousal dimensions have shown to function as two significant factors in emotional word processing. Their relationship, however, remains undetermined. Compared with emotional pictures, emotional word processing exerts a weaker emotional effect, showing a bias towards positive valence. Future studies are required to explore relevant factors influencing emotional effect of emotional word processing, to distinguish emotional-laden words from emotional-label words, and to analyze the processing of these two types of emotional words. In addition, it is also an important topic for a future research to elucidate the emotional word processing mechanism in natural context reading by using eye-tracking technique.
Construction of Sharing Model for Network Digital Teaching Resource Oriented to Big Data

Aiming at the problems of low resource utilisation rate, low model reliability and poor feasibility of current network resource sharing model, this paper proposes and constructs a sharing model for network digital teaching resource based on semantic web. The game model is used to analyse the behaviour evolution of teaching resource sharing model. Based on ontology relationship, a general framework of metadata for teaching resources is constructed to realise interoperability between metadata. Taking XML as the basic grammar, according to the details of the application of teaching resources, the related application based on XML is created, and the sharing of network digital teaching resources is realised by defining the pattern layer by layer. The experimental results show that the utilisation rate of teaching resources under this model is high, and the fitting degree between the model and the actual situation is high.
What Number of Features is Optimal

Selecting a text representation model faces a crucial problem of choosing an optimal number of features. The optimality criterion is the minimum number of features, which allows to achieve (or preserve) the maximum performance. The article suggests a new method of determining the optimal number of features, in which both components of the optimality criterion are taken into consideration. Using the proposed method, we first construct the dependence of task performance on the number of features, then the obtained dependence is approximated on the basis of Weibull distribution function, and the optimal number of features is determined by analyzing the growth rate of this function. We called this method DOFNAF (Determining the Optimal Feature Number by the Approximating Function). The proposed method is tested on stance detection task, consisting in identifying the position ("for" or "against"), which the author of the text supports towards the object (or objects) under discussion. The comparison involves constant methods, a method based on the function of the total number of features, a method of performance maximum, as well as Recursive Feature Elimination with Cross-Validation (RFECV) and Correlation-based Feature Selection (CFS) methods. The DOFNAF method allows to determine the minimum number of features compared with the existing methods and at the same time to maintain the classification performance.
BPO: The Building Product Ontology for Assembled Products

With the current trend of using Linked Data to describe buildings during their entire lifecycles, the importance of product descriptions in a Semantic Web context is growing while most product ontologies are designed for mass-produced goods of little variance. But especially in the construction industry, products are often innovative and individually manufactured and previous attempts to depict them with ontologies failed to include meaningful alignments to already existing approaches. Thus, this paper gives an overview of existing product ontologies in general and analyses previous approaches in more detail to identify potential improvements that can be made. Based on this analysis, this paper presents the Building Product Ontology (BPO), including its concepts and alignments. To obtain a modular ontology, the BPO focuses on the non-geometric description without defining templates by determination of taxonomies and includes concepts to model assembly structures, interconnections of components, and complex properties and property values. The BPO enables manufacturers to freely model their products while still benefitting from the Semantic Web in respects of findability and availability of product data. By going through the given examples and demonstrations, inexperienced users are supported to apply the BPO and exploit its benefits.
Machine-Learning Based Spark and Hadoop Workload Classification Using Container Performance Patterns

Big data Hadoop and Spark applications are deployed on infrastructure managed by resource managers such as Apache YARN, Mesos, and Kubernetes, and run in constructs called containers. These applications often require extensive manual tuning to achieve acceptable levels of performance. While there have been several promising attempts to develop automatic tuning systems, none are currently robust enough to handle realistic workload conditions. Big data workload analysis research performed to date has focused mostly on system-level parameters, such as CPU and memory utilization, rather than higher-level container metrics. In this paper we present the first detailed experimental analysis of container performance metrics in Hadoop and Spark workloads. We demonstrate that big data workloads show unique patterns of container creation, completion, response-time and relative standard deviation of response-time. Based on these observations, we built a machine-learning-based workload classifier with a workload classification accuracy of 83% and a workload change detection accuracy of 74%. Our observed experimental results are an important step towards developing automatically tuned, fully autonomous cloud infrastructure for big data analytics.
Translingual negotiation strategies in CMC contexts: English-medium communication in online marketplaces

Abstract This study explores how interlocutors from different linguistic and cultural backgrounds employ the five translingual negotiation strategies of envoicing, recontextualization, interactional activity, entextualization and transmodality to communicate in online marketplaces. Informed by conversation analysis and translingualism, three computer-mediated communication (CMC) data sets were analyzed. Findings are as follows: 1) Korean speaker’s envoicing strategy was used to reveal his non-native English speaker status, which softened the impression of discourtesy. It set the co-constructed conversation stage where English speaking counterparts would be implicitly requested to speak more intelligibly; 2) recontextualization strategy was presented through emojis, emoticons, transliteration and translation to turn a business transaction into a friendly conversation between translingual speakers from different cultures where non-verbal cues of hospitality can be delivered in written forms in online one-on-one chats; 3) interactional strategy was observed when speakers did not share a culturally-bounded speech act of “conversation closers.” Misalignment was partially solved by mutual efforts to close their online conversation; 4) entextualization strategy was shown from Korean speakers’ asynchronous communication to confer on appropriate English expression by seeking help from other online community members; and, 5) transmodal strategy refers to the practice of shuttling among different on/offline modes and platforms to encode and decode languages, emotions, and cultural perspectives by depending on a wide range of resources and multiple strategies to accomplish meaningful interaction in online marketplaces. This study shows the translingual characteristics of English-medium online communication, suggesting an extended model of translingual meaning negotiation strategies in CMC contexts.
An efficient EM algorithm for the mixture of negative binomial models

Abstract Overdispersion is a widespread phenomenon in most count data sets. The negative binomial distribution is commonly adopted to fit over-dispersed count data. On the other hand, the mixture model always plays an important role in unsupervised classification. However, when estimating the parameters in the mixture of negative binomial models, the typical generalized Expectation Maximization (EM) algorithm which involves additional iterative procedures in M-step increases computational time. Hence, there remains a need for an efficient algorithm that can speed up the procedure of parameter estimation. For this purpose, here we develop a novel EM algorithm that successfully avoids the typical numerical solution in M-step for the mixture of negative binomial models. We extend further this EM algorithm to the zero-inflated negative binomial model. In the simulation studies, we focus on the runtimes and the classification performance of our proposed algorithm implemented in the mixture of negative binomial model. We found that our proposed EM algorithm can reduce the runtime of maximum likelihood estimation effectively, while achieving the similar classification performance in comparison with the typical EM algorithm. The mixture of negative binomial model and the proposed EM algorithm finally illustrates their good performance of fitting the real earthquake count data.
Secure Design and Development Cybersecurity Capability Maturity Model (SD2-C2M2)

The Secure Design and Development Cybersecurity Capability Maturity Model (SD2-C2M2) provides a browser-based tool that allows hardware and software developers to assess the maturity level of their design and development processes, allows management to determine desired maturity levels in seven domains, and allows developers to monitor process maturity improvements against management goals. The tool can be used by commercial developers as well as internal development organizations. This paper provides an overview of the tool and domains and presents a hypothetical case study of the tool to reduce software buffer overflow attack vulnerabilities by improving the software development process.
Product Recommendation System Design

Along with the continuous scale expansion of the e-commerce market, it provides a wide variety of goods as well as choices for the users. The consequent " information overload " is causing information producers to worry about how to make their products stand out, while also worrying information consumers about how to get the information they want. This paper puts forward product recommendation system based on the electronic commerce. It collects e-commerce transaction data, analyzes and mines the rich data information, recommends goods based on user preference and interest. It reduces the time to select the interesting goods and improves the efficiency of purchasing for the customers, and improves the success rate of sales to create a greater economic benefit for the enterprises.
Moderate Resolution Imaging Spectroradiometer Products Classification Using Deep Learning

During the last years, the algorithms based on Artificial Intelligence have increased their popularity thanks to their application in multiple areas of knowledge. Nowadays with the increase of storage capacities and computing power, as well as the incorporation of new technologies for massively parallel processing (GPUs and TPUs) and Cloud Computing, it is increasingly common to incorporate this kind of algorithms and technology in tasks with a deep social and technological impact. In the present work a new Convolutional Neural Network specialized in the automatic classification of Moderate Resolution Imaging Spectroradiometer satellite products is proposed. The proposed architecture has shown a high-generalization by classifying more than 250,000 images with 99.99% accuracy. The methodology designed also can be extended, with other types of images, to make detection of Sargassum, oil spills, red tide, etc.
A Survey on Devanagari Character Recognition

Development of optical character recognition (OCR) algorithm for printed and handwritten characters is a challenging area for research. Plentiful research on OCR techniques for scripts such as Roman, Japanese, Korean, and Chinese has already been carried out. OCR research activities related to Indian script is limited. Devanagari script is used by about 500 million people in India. Remarkable research has been done in the last few years for Devanagari script. In this paper, a survey of the various research efforts done by various groups of researchers for the development of printed as well as handwritten Devanagari character recognition system is presented. Comparison of various methods in terms of feature extraction techniques, classifiers, datasets, and accuracy values is also described.
Social platform based interval valued intuitionistic fuzzy location recommendation system

The improvements in mobile technologies led to the wide adaptation and triggered the demand for location based services. In this respect, examining user similarities enable the analysis of user interests in terms of the determination of purchasing preferences and actual needs. User similarities are generally extracted from consumer life style, demographical information or the reflections from previously sent messages. In spite of the fact that these factors may not directly influence the purchasing decision, uncertain or lack of information can be encountered while establishing recommendation systems. Thus, researchers try to search other indicators that can reflect customer characteristics from spatial data, digital contribution in social media and search history for preferable representation of the changes in purchasing tendency. In this study, social platform based interval valued intuitionistic fuzzy location recommendation system is proposed by considering three common social platforms: Trip Advisor, Zomato and Foursquare. To perform restaurant offers to appropriate social platform users, a sentiment analysis is conducted to selected restaurants and number of negative, positive and neutral comments are extracted. After that, restaurant and location information are examined by using user, restaurant and location clustering via fuzzy clustering. Finally, intuitionistic fuzzy similarity matrix based collaborative filtering is used for restaurant offers to similar users.
Does Size Matter? An Evaluation of Institutional Internet Ranking Metrics

Institutional Internet presence and popularity ranking systems—such as Webometrics—are novel and purport to consider institutional size (in workforce and Internet volume) when ranking institutions of higher education. Using statistical methods and the Google Trends database, this study found influence of institutional size and Internet presence as a major contributing factor to institutional ranking. As a result, smaller, less-wealthy institutions are disadvantaged by these ranking systems. We conclude by proposing feasible methods of normalizing and equalizing these factors, allowing prospective students and other educational stakeholders a more accurate evaluation of an institution’s Internet presence and popularity.