Reviewerly/exHarmony huggingface dataset:
The evolution of online ideological communities

Online communities are virtual spaces for users to share interests, support others, and to exchange knowledge and information. Understanding user behavior is valuable to organizations and has applications from marketing to security, for instance, identifying leaders within a community or predicting future behavior. In the present research, we seek to understand the various roles that users adopt in online communities-for instance, who leads the conversation? Who are the supporters? We examine user role changes over time and the pathways that users follow. This allows us to explore the differences between users who progress to leadership positions and users who fail to develop influence. We also reflect on how user role proportions impact the overall health of the community. Here, we examine two online ideological communities, RevLeft and Islamic Awakening (N = 1631; N = 849), and provide a novel approach to identify various types of users. Finally, we study user role trajectories over time and identify community "leaders" from meta-data alone. Study One examined both communities using K-MEANS cluster analysis of behavioral meta-data, which revealed seven user roles. We then mapped these roles against Preece and Schneiderman's (2009) Reader-to-Leader Framework (RtLF). Both communities aligned with the RtLF, where most users were "contributors", many were "collaborators", and few were "leaders". Study Two looked at one community over a two-year period and found that, despite a high churn rate of users, roles were stable over time. We built a model of user role transitions over the two years. This can be used to predict user role changes in the future, which will have implications for community managers and security focused contexts (e.g., analyzing behavioral meta-data from forums and websites known to be associated with illicit activity).
Efficient Layout Hotspot Detection via Binarized Residual Neural Network

Layout hotspot detection is of great importance in the physical verification flow. Deep neural network models have been applied to hotspot detection and achieved great successes. The layouts can be viewed as binary images. The binarized neural network can thus be suitable for the hotspot detection problem. In this paper we propose a new deep learning architecture based on binarized neural networks (BNNs) to speed up the neural networks in hotspot detection. A new binarized residual neural network is carefully designed for hotspot detection. Experimental results on ICCAD 2012 Contest benchmarks show that our architecture outperforms all previous hotspot detectors in detecting accuracy and has an 8x speedup over the best deep learning-based solution.
Blockchain in Internet-of-Things: Architecture, Applications and Research Directions

The emergence of Internet-of-Things (IoT) has attracted a massive response from different stakeholders including industry, academia and research community due to the wide range of applications it provides. IoT enables devices with storage, computation and communication capabilities to connect to the Internet, thus providing various applications such as smart transportation, smart healthcare, smart industry, logistics and smart homes, to name a few. Since, IoT devices generates sensitive information, therefore, security is an important issue in IoT networks. Recently, Blockchain (BC) is proposed as a revolutionary technology which can be integrated within IoT to provide desired level of security and privacy. However, the integration of BC within IoT networks is extremely challenging. The major contribution of this study is three-fold. (1) Firstly, we proposed a tier-based architecture “Blockchain in Internet-of-ThingS (BITS)” which can support BC within IoT networks. (2) Secondly, we highlighted various applications of BC in IoT networks, and (3) Lastly, we provided various challenges and future research directions in the realization of BC in IoT networks.
Adaptive Credit Card Fraud Detection Techniques Based on Feature Selection Method

Credit card fraud is a crucial issue that has been faced by cardholder and card issuing companies for decades. Credit card frauds are performed at two levels, application-level frauds and transaction-level frauds. This paper focus on credit cards fraud detection at application level using features selection methods. In this paper, J48 decision tree, AdaBoost, Random Forest, Naive Bayes, and PART machine learning techniques have been used for detection of financial frauds of a credit card and the performance of these techniques are compared on the basis of the five parameters namely sensitivity, specificity, precision, recall, MCC, and accuracy. A German credit dataset is used to evaluate these machines learning techniques efficiency based on filter and wrapper features selection method. The experiment outcomes show that the prediction accuracy of J48 and PART has been increased after applying filter and wrapper methods. Finally, precision and sensitivity of J48, AdaBoost, and the random forest have been enhanced.
Data Curator’s Roles and Responsibilities: An International Perspective

Abstract The data-intensive research environment and the movement towards open science create demand for information professionals with knowledge of the research process and skills in managing and curating data. This paper is reporting the findings from a multiyear study entitled “Data curator: who is s/he?” initiated by the Library Theory and Research (LTR) Section of the International Federation of Library Associations (IFLA). The study aimed to identify the roles and responsibilities of data curators around the world and also focused on the terminology used to describe the new professional roles. The following questions were posed: R1: How is data curation defined by practitioners / professional working in the field? R2: What terms are used to describe the roles for professionals in data curation area? R3: What are primary roles and responsibilities of data curators? R4: What are educational qualifications and competencies required of data curators? To answer the research questions, the research team performed a comprehensive literature review and vocabulary analysis and conducted an empirical study using mixed-methods design. The study consisted of three stages: 1. Literature review and vocabulary analysis 2. Content analysis of position announcements 3. Interviews with professionals working in data curation and research data management- Findings confirm the results from previous research about the lack of common terminology and a variability of the position titles. The concept of data lifecycle highlighted the important role of data curators. However this study also found that many positions in practice were held by non library professionals. The findings indicate that data curation is an evolving sociotechnical practice that involves not only technical systems and services structured around research data life cycle but also a range of social activities around community building.
An overview of multirate multisensor systems: Modelling and estimation

In this paper, research advances in modelling and estimation algorithms for multirate multisensor systems are reviewed. Multirate multisensor sampling schemes can be classified into two cases: uniform sampling and nonuniform sampling. A general method to solve an asynchronous estimation problem for multirate multisensor systems is to transform the asynchronous problem into a synchronous one. Then, synchronous estimation algorithms can be applied. Therefore, modelling synchronisation approaches are summarised for uniform and nonuniform sampling cases. Meanwhile, the corresponding estimation algorithms are reviewed according to different synchronised models. Last, future research topics are discussed.
Dependence structure of Gabor wavelets based on copula for face recognition

Low resolution, difficult illumination and noise are the important factors that affect the performance of face recognition system. In order to counteract these adverse factors, in this paper we propose copula probability models based on Gabor wavelets for face recognition. Gabor wavelets have robust performance under lighting and noise conditions. The strong dependencies exist in the domain of Gabor wavelets due to their non-orthogonal property. In the light of the structure characteristic of Gabor wavelet subbands, the proposed methods use copula to capture the dependencies to represent the face image. Three probability-model-based methods CF-GW (Copula Function of Gabor Wavelets), LCM-GW (Lightweight Copula Model of Gabor Wavelets) and LCM-GW-PSO (Lightweight Copula Model of Gabor Wavelets with Particle Swarm Optimization) are proposed for face recognition. Experiments of face recognition show our proposed methods are more robust under the conditions of low resolution, lighting and noise than the popular methods such as the LBP-based methods and other Gabor-based methods. The face features extracted by our methods belong to the Riemannian manifold which is different to Euclidean space. In order to deal the issue of face recognition in complex environment, we can combine the face features in Riemannian manifold with the face features in Euclidean space to obtain the more robust face recognition system by using expert system technologies such as reasoning model and multi-classifier fusion.
Application of Transfer Learning Using Convolutional Neural Network Method for Early Detection of Terry’s Nail

Abstract Nails are one part of the fingers and toes, by observing the shape and the condition of the nails, health expert can find out information about a person’s health. However, this sometimes not realized and ignored by society, even though many diseases that can be seen through the condition of the nails and the shape of the nails are one of the systemic diseases. This research was conducted to detect abnormalities in the nail based on digital images. The detected abnormalities are terry’s nails in the hand which can represent systemic diseases, while the method used is the Convolutional Neural Network (CNN) method. This research uses Tensorflow Inception-V3 architecture model with the transfer learning method where the results of the experiments that have been done are obtained with 95.24% accuracy.
Object Detection With Location-Aware Deformable Convolution and Backward Attention Filtering

Multi-class and multi-scale object detection for autonomous driving is challenging because of the high variation in object scales and the cluttered background in complex street scenes. Context information and high-resolution features are the keys to achieve a good performance in multi-scale object detection. However, context information is typically unevenly distributed, and the high-resolution feature map also contains distractive low-level features. In this paper, we propose a location-aware deformable convolution and a backward attention filtering to improve the detection performance. The location-aware deformable convolution extracts the unevenly distributed context features by sampling the input from where informative context exists. Different from the original deformable convolution, the proposed method applies an individual convolutional layer on each input sampling grid location to obtain a wide and unique receptive field for a better offset estimation. Meanwhile, the backward attention filtering module filters the high-resolution feature map by highlighting the informative features and suppressing the distractive features using the semantic features from the deep layers. Extensive experiments are conducted on the KITTI object detection and PASCAL VOC 2007 datasets. The proposed method shows an average 6% performance improvement over the Faster R-CNN baseline, and it has the top-3 performance on the KITTI leaderboard with the fastest processing speed.
The construction of internet + piano intelligent network teaching system model

With the advent of the Internet + era, traditional piano teaching is facing unprecedented opportunities and challenges. In order to realize the intellectualization of piano network teaching, this paper constructs a piano note recognition algorithm. According to MIDI files and corresponding piano au dio files, the algorithm builds training and test waveform data to calculate the corresponding annotation files, and recompiles the multi-note model to realize the multi-note recognition system. The multi tone HMM acoustic model and multi note model are established by using Internet technology. The matching degree between the test audio and the multi note models is calculated respectively, and then an Internet + piano intelligent network teaching system model is constructed. The test results show that when the number of states of the multi-note recognition system is 7, the correct recognition rate is the highest. Through this study, the piano intelligent network teaching system model has produced new inspiration, which can be more accurate when the multi-note recognition state is 7.
Rule-based chabot for student enquiries

Abstract Conversational agents, or chatbots, refer to computer programs that conduct conversations and deliver a natural language interface to its users. Due to the significant foothold messaging applications have begun receiving, chatbots have spawned a new revival and gaining vast popularity. The focus of this paper is to present the implementation of a rule-based enquiry chatbot that is designed exclusively toward students of Asia Pacific University (APU). The implemented chatbot known as ‘APU Admin Bot’ intends to provide students with a quicker solution to resolving their queries instead of heavily depending on the administrative offices. Relying on the rule-based approach of pattern recognition, certain words, phrases and even actions trigger an entire set of responses from the chatbot. Built entirely from the Chatfuel platform and hosted on Facebook Messenger, the implemented chatbot is reliant on a code-less authoring tool and a messaging platform, instead of traditional programming and architectural structures.
SVQ

Recent advances in video processing utilizing deep learning primitives achieved breakthroughs in fundamental problems in video analysis such as frame classification and object detection enabling an array of new applications.
How to Make a Pizza: Learning a Compositional Layer-Based GAN Model

A food recipe is an ordered set of instructions for preparing a particular dish. From a visual perspective, every instruction step can be seen as a way to change the visual appearance of the dish by adding extra objects (e.g., adding an ingredient) or changing the appearance of the existing ones (e.g., cooking the dish). In this paper, we aim to teach a machine how to make a pizza by building a generative model that mirrors this step-by-step procedure. To do so, we learn composable module operations which are able to either add or remove a particular ingredient. Each operator is designed as a Generative Adversarial Network (GAN). Given only weak image-level supervision, the operators are trained to generate a visual layer that needs to be added to or removed from the existing image. The proposed model is able to decompose an image into an ordered sequence of layers by applying sequentially in the right order the corresponding removing modules. Experimental results on synthetic and real pizza images demonstrate that our proposed model is able to: (1) segment pizza toppings in a weakly- supervised fashion, (2) remove them by revealing what is occluded underneath them (i.e., inpainting), and (3) infer the ordering of the toppings without any depth ordering supervision. Code, data, and models are available online.
Efficient Multi-Domain Learning by Covariance Normalization

The problem of multi-domain learning of deep networks is considered. An adaptive layer is induced per target domain and a novel procedure, denoted covariance normalization (CovNorm), proposed to reduce its parameters. CovNorm is a data driven method of fairly simple implementation, requiring two principal component analyzes (PCA) and fine-tuning of a mini-adaptation layer. Nevertheless, it is shown, both theoretically and experimentally, to have several advantages over previous approaches, such as batch normalization or geometric matrix approximations. Furthermore, CovNorm can be deployed both when target datasets are available sequentially or simultaneously. Experiments show that, in both cases, it has performance comparable to a fully fine-tuned network, using as few as 0.13% of the corresponding parameters per target domain.
Cascade interpolation learning with double subspaces and confidence disturbance for imbalanced problems

In this paper, a new ensemble framework named Cascade Interpolation Learning with Double subspaces and Confidence disturbance (CILDC) is designed for the imbalanced classification problems. Developed from the Cascade Forest of the Deep Forest which is the stacking based tree ensembles for big data issues with less hyper-parameters, CILDC aims to generalize the cascade model for more base classifiers. Specifically, CILDC integrates base classifiers through the double subspaces strategy and the random under-sampling preprocessing. Further, one simple but effective confidence disturbance technique is introduced to CILDC to tune the threshold deviation for imbalanced samples. In detail, the disturbance coefficients are multiplied to various confidence vectors before interpolating in each level of CILDC, and the ideal threshold can be adaptively learned through the cascade structure. Furthermore, both the Random Forest and the Naive Bayes are suitable to be the base classifier for CILDC. Subsequently, comprehensive comparison experiments on typical imbalanced datasets demonstrate both the effectiveness and generalization of CILDC.
Unsupervised Parallel Sentence Extraction with Parallel Segment Detection Helps Machine Translation

Mining parallel sentences from comparable corpora is important.Most previous work relies on supervised systems, which are trained on parallel data, thus their applicability is problematic in low-resource scenarios.Recent developments in building unsupervised bilingual word embeddings made it possible to mine parallel sentences based on cosine similarities of source and target language words.We show that relying only on this information is not enough, since sentences often have similar words but different meanings.We detect continuous parallel segments in sentence pair candidates and rely on them when mining parallel sentences.We show better mining accuracy on three language pairs in a standard shared task on artificial data.We also provide the first experiments showing that parallel sentences mined from real life sources improve unsupervised MT.Our code is available, we hope it will be used to support low-resource MT research.
Automatic CIN Grades Prediction of Sequential Cervigram Image Using LSTM With Multistate CNN Features

Cervical cancer ranks as the second most common cancer in women worldwide. In clinical practice, colposcopy is an indispensable part of screening for cervical intraepithelial neoplasia (CIN) grades and cervical cancer but exhibits high misdiagnosis rate. Existing computer-assisted algorithms for analyzing cervigram images have neglected that colposcopy is a sequential and multistate process, which is unsuitable for clinical applications. In this work, we construct a cervigram-based recurrent convolutional neural network (C-RCNN) to classify different CIN grades and cervical cancer. Convolutional neural networks are leveraged to extract spatial features. We develop a sequence-encoding module to encode discriminative temporal features and a multistate-aware convolutional layer to integrate features from different states of cervigram images. To train and evaluate the performance of C-RCNN, we leveraged a dataset of 4,753 real cervigrams and obtained 96.13% test accuracy with a specificity and sensitivity of 98.22% and 95.09%, respectively. Areas under each receiver operating characteristic curves are above 0.94, proving that visual representations and sequential dynamics can be jointly and effectively optimized in the training phase. Comparative analysis demonstrated the effectiveness of the proposed C-RCNN against competing methods, showing significant improvement over only focusing on a single frame. This architecture can be extended to other applications in medical image analysis
Path Planning of a Type of Porous Structures for Additive Manufacturing

Porous structures are widely used in our daily life due to their special properties such as higher rigidity and lightweight. With the popularity of additive manufacturing (AM), producing porous structures by AM shows great advantages over traditional manufacturing technologies. In this paper, we first introduce a framework to model a type of porous structures which contain lots of small holes. Then we propose an efficient path planning algorithm for this type of porous structures. The algorithm consists of the following steps: for each slice of a porous structure, we first subdivide the domain of the slice into subdomains by generalized Voronoi diagram such that each subdomain contains exactly one hole, and then we carry out dual operation to obtain a partition of the domain into a set of subregions. A route is then found to traverse every subregion by solving a traveling salesman problem (TSP) using genetic algorithm (GA), and along the route subregions are merged into larger ones to improve printing efficiency. Finally we fill each merged subregion with a single Fermat spiral curve and further optimize smoothness and uniformity of the filling path. We demonstrated a variety of testing examples and the experimental results showed the superiority and effectiveness of our method in terms of material cost, printing time and structure stability.
LIME

Personalized live video streaming is an increasingly popular technology that allows a broadcaster to share videos in real time with worldwide viewers. Compared to video-on-demand (VOD) streaming, experimenting with personalized live video streaming is harder due to its intrinsic live nature, the need for worldwide viewers, and a more complex data collection pipeline. In this paper, we make several contributions to both experimenting with and understanding today's commercial live video streaming services. First, we develop LIME (Live video MEasurement platform), a generic and holistic system allowing researchers to conduct crowd-sourced measurements on both commercial and experimental live streaming platforms. Second, we use LIME to perform, to the best of our knowledge, a first study of personalized 360° live video streaming on two commercial platforms, YouTube and Facebook. During a 7-day study, we have collected a dataset from 548 paid Amazon Mechanical Turk viewers from 35 countries who have watched more than 4,000 minutes of 360° live videos. Using this unique dataset, we characterize 360° live video streaming performance in the wild. Third, we conduct controlled experiments through LIME to shed light on how to make 360° live streaming (more) adaptive in the presence of challenging network conditions.