Reviewerly/exHarmony huggingface dataset:
Data dimensionality reduction techniques for Industry 4.0: Research results, challenges, and future research directions

Summary From the last few years, we have witnessed the fourth generation industrial revolution (Industry 4.0), impact of which will be seen in the years to come in various disciplines such as healthcare, transportation, IoT, smart grid, autonomous vehicles, and image processing. These applications in Industry 4.0 may have data in the form of images, speech signals, videos having high dimensions containing multiple dimensions to represent data along different axis. So, the complexity of data processing increases with an increase in the dimensions of the dataset. Complexity can be viewed in terms of detecting and exploiting the relationships among different features of the dataset. These complexities among different attributes can be reduced with the help of dimensionality reduction techniques. These techniques reduce the dimensions from the original input dataset to a lower dimensional dataset. Dimensionality reduction methods are broadly categorized into two types as feature extraction and feature selection . In feature selection method, out of the original set, a subset of features are identified to get a smaller subset which can be used to build the model whereas, the feature extraction method reduces the dataset of high dimensions to a lower dimension space, that is, a space with a less number of features having different values in comparison to the original dataset. Keeping focus on these points, in this article, we have compared and analyzed different data dimensionality reduction techniques which reduce the dimensions of a large and complex dataset during data processing. In addition, we have discussed various data dimensionality reduction techniques and compared these techniques with respect to various parameters. The comparison among various techniques provides insights to the readers about the applicability of a specific technique to the stand‐alone or a group of applications.
A novel image compression technology based on vector quantisation and linear regression prediction

In the information age, a digital image is an important media for people's daily interactions. Looking to maintain the quality of the restored image, how to maximise the compression of images has become a challenging topic. Vector quantisation (VQ) compression is an easy-operating image compression method that can compress images to 1/16th of the original size. Based on VQ compresses, a novel image compression method is proposed in this paper. The proposed scheme compresses the image depending on the result of linear regression prediction which can significantly increase the compression ratio.
Hierarchical fusion of common sense knowledge and classifier decisions for answer selection in community question answering

The goal of answer selection is to select the most applicable answers from an answer candidate pool. It plays an essential role in numerous applications in information retrieval (IR) and natural language processing (NLP). In this paper, we introduce a novel Knowledge-enhanced Hierarchical Attention mechanism for Answer Selection (KHAAS), which fully exploits the common sense knowledge from knowledge bases (KBs) and input textual information. Specifically, we first devise a three-stage knowledge-enhanced hierarchical attention mechanism, including the word-level attention, the phrase-level attention, and the document-level attention to learn the fact-aware intra-document features within questions and answers by fusing the knowledge from both the question/answer and KB. Hence, we can leverage the semantic compositionality of the question/answer and learn more holistic knowledge-enhanced intra-document features of the question/answer at three levels of granularity. Second, after obtaining the knowledge-enhanced question and answer representations, we employ a multi-perspective co-attention network to learn the complex inter-document relationships between the question and answer representations from different representation subspaces, which can capture the interactive semantics of the question and answer representations at three levels. Finally, we propose an adaptive decision fusion method to learn a more effective and robust ensemble answer selection model by adaptively combining multiple classifiers learned with different levels of features. Experimental results on three large-scale answer selection datasets demonstrate that KHAAS consistently outperforms the compared methods.
Collaborative List-and-Pairwise Filtering from Implicit Feedback

The implicit feedback based collaborative filtering (CF) has attracted much attention in recent years, mainly because users implicitly express their preferences in many real-world scenarios. The current mainstream pairwise methods optimize the Area Under the Curve (AUC) and are empirically proved to be helpful to exploit binary relevance data, but lead to either not address the ranking problem, or not specifically focus on top- <inline-formula><tex-math notation="LaTeX">$k$</tex-math></inline-formula> recommendation. Although there exists the listwise method maximizes the Mean Reciprocal Rank (MRR), it has low efficiency and is not particularly adequate for general implicit feedback situations. To that end, in this paper, we propose a new framework, namely <i>Collaborative List-and-Pairwise Filtering (CLAPF)</i> , which aims to introduce pairwise thinking into listwise methods. Specifically, we smooth another well-known rank-biased measure called Mean Average Precision (MAP), and respectively combine two rank-biased metrics (MAP, MRR) with the pairwise objective function to capture the performance of top- <inline-formula><tex-math notation="LaTeX">$k$</tex-math></inline-formula> recommendation. Furthermore, the sampling scheme for CLAPF is discussed to accelerate the convergence speed. Our CLAPF framework is a new hybrid model that provides an idea of utilizing rank-biased measures in a pairwise way on implicit feedback. Empirical studies demonstrated CLAPF outperforms state-of-the-art approaches on real-world datasets.
Event-centric Tourism Knowledge Graph—A Case Study of Hainan

Knowledge graphs have become increasingly important in tourism industry recently for their capability to power insights for applications like recommendations, question answering and so on. However, traditional tourism knowledge graph is a knowledge base which focuses on the static facts about entities, such as hotels, attractions, while ignoring events or activities of tourists' trips and temporal relations. In this paper, we first propose an Event-centric Tourism Knowledge Graph (ETKG) in order to model the temporal and spatial dynamics of tourists trips. ETKG is centered on activities during the trip and regards tourists' trajectories as carriers. We extract valuable information from over 18 thousand travel notes crawled from Internet, and define an ETKG schema to model tourism-related events and their key properties. An ETKG based on touristic data in Hainan is presented which incorporates 86977 events (50.61% of them have complete time, activity, location information) and 7132 journeys. To demonstrate the benefits of ETKG, we propose an Event-centric Tourism Knowledge Graph Convolutional Network (ETKGCN) for POI recommendations, which facilitates incorporating tourists behavior patterns obtained from ETKG, so as to capture the relations between users and POIs more efficiently. The offline experiment results show that our approach outperforms strong recommender baselines, so that it validates the effectiveness of ETKG.
A Case Study in Credit Fraud Detection With SMOTE and XGBoost

Abstract Credit fraud observations are minority in the sample set, variables tend to be seriously unbalanced, and the prediction results tend to be biased towards more observed classes. Common resolution usually constructs 1:1 data, either cutting off part of more classes (undersampling) or reducing classes for bootstrap sampling (oversampling). XGBoost is an efficient system implementation of Gradient Boosting, and also GB algorithm based on CART. Based on the real online transaction data of an Internet financial institution, this paper studies the performance of XGBoost algorithm on the original data set, the undersampling and SMOTE data sets respectively.
Attention-Based Convolutional Neural Network for Earthquake Event Classification

This letter presents a deep convolutional neural network (CNN) with attention module that improves the performance of the classification of various earthquake events. Addressing all possible earthquake events, including not only microearthquakes and artificial-earthquakes but also large-earthquakes, requires both suitable feature expression and a classifier that can effectively discriminate seismic waveforms under adverse conditions. To robustly classify earthquake events, a deep CNN with an attention module was proposed in raw seismic waveforms. Representative experimental results show that the proposed method provides an effective structure for earthquake events classification and, with the Korean peninsula earthquake database from 2016 to 2018, outperforms previous state-of-the-art methods.
A Comparative Study on Handwritten Digit Recognizer using Machine Learning Technique

Ability for accurate digit recognizer modelling and prediction is critical for pattern recognition and security. A variety of classification machine learning algorithms are known to be effective for digit recognition. The purpose of this experiment is rapid assessment of multiple types of classification models on digit recognition problem. The work offers an environment for comparing four types of classification models in a unified experiment: Multiclass decision forest, Multiclass decision jungle, Multiclass Neural Network and Multiclass Logistic Regression. The work presents assessment results using 6 performance metrics: Overall accuracy, Average accuracy, Micro-averaged precision, Macro-averaged precision, Micro-averaged recall and Macro-averaged recall. The experimental results showed that the highest accuracy was obtained by Multiclass Neural Network with the value of 97.14%. The operational tool has been published to the web and openly available at Azure Machine Learning Studio for experiments and extensions.
Self-Hosted Kubernetes: Deploying Docker Containers Locally With Minikube

Containerization is a cutting-edge DevOps technology which unifies the IT operations and Development domains. In recent times, virtualization using Virtual Machines has become an overkill for its large overhead on systems. As a lightweight alternative, containerization offers containers that constitute a package of an application along with all its dependencies that is required for it to execute. Containerization platforms help in building containers from images. Docker is a widely popular containerization platform. Containerization Orchestration tools manage these containers. Kubernetes is the front-runner of the emerging market of container orchestration tools. These software work together seamlessly in order to successfully implement containerization both locally and on the cloud. In this paper, we aim to deploy the container orchestration tool Kubernetes on a local system with a Docker sample container. The purpose of this is to ensure that all the configurations and management needed for a Docker container is set successfully on the local system before it is deployed onto the cloud or on the premise. The on-premise deployment use case is very important in domains such as finance and healthcare where organizations hesitate to upload confidential information on to the cloud for security reasons but still require scaling of their applications.
An ERNIE-Based Joint Model for Chinese Named Entity Recognition

Named Entity Recognition (NER) is the fundamental task for Natural Language Processing (NLP) and the initial step in building a Knowledge Graph (KG). Recently, BERT (Bidirectional Encoder Representations from Transformers), which is a pre-training model, has achieved state-of-the-art (SOTA) results in various NLP tasks, including the NER. However, Chinese NER is still a more challenging task for BERT because there are no physical separations between Chinese words, and BERT can only obtain the representations of Chinese characters. Nevertheless, the Chinese NER cannot be well handled with character-level representations, because the meaning of a Chinese word is quite different from that of the characters, which make up the word. ERNIE (Enhanced Representation through kNowledge IntEgration), which is an improved pre-training model of BERT, is more suitable for Chinese NER because it is designed to learn language representations enhanced by the knowledge masking strategy. However, the potential of ERNIE has not been fully explored. ERNIE only utilizes the token-level features and ignores the sentence-level feature when performing the NER task. In this paper, we propose the ERNIE-Joint, which is a joint model based on ERNIE. The ERNIE-Joint can utilize both the sentence-level and token-level features by joint training the NER and text classification tasks. In order to use the raw NER datasets for joint training and avoid additional annotations, we perform the text classification task according to the number of entities in the sentences. The experiments are conducted on two datasets: MSRA-NER and Weibo. These datasets contain Chinese news data and Chinese social media data, respectively. The results demonstrate that the ERNIE-Joint not only outperforms BERT and ERNIE but also achieves the SOTA results on both datasets.
Score and Rank Level Fusion Algorithms for Social Behavioral Biometrics

The goal of a biometric system is to recognize individuals based on their unique physiological or behavioral traits. Online Social Networking (OSN) platforms have become an integral part of the daily life of individuals, where they leave a recognizable trail of behavioral information. Social Behavioral Biometric (SBB), being an emerging trend, focuses on such trails to distinguish between individuals. This research investigates the impact of users’ writing profiles on OSN to conclude whether such profiles contribute to SBB. The distinctiveness of the SBB features that are extracted from the social behavioral data of Twitter is studied. A person identification system that relies on users’ writing profiles, reply, retweet, shared weblink, trendy topic networks and temporal profiles is proposed. Score and rank level weighted fusion algorithm performance is compared on a social interaction database of 241 Twitter users. The experimental results establish that the users’ writing profiles have the highest impact over other social biometric features and that score level fusion algorithms perform better than rank level fusion on SBB. The proposed system has achieved recognition rate of 99.45% at rank-1 after cross-validation using genetic algorithm based score level fusion algorithm. The system outperformed all prior researches on SBB in terms of identification accuracy.
GHashing: Semantic Graph Hashing for Approximate Similarity Search in Graph Databases

Graph similarity search aims to find the most similar graphs to a query in a graph database in terms of a given proximity measure, say Graph Edit Distance (GED). It is a widely studied yet still challenging problem. Most of the studies are based on the pruning-verification framework, which first prunes non-promising graphs and then conducts verification on the small candidate set. Existing methods are capable of managing databases with thousands or tens of thousands of graphs, but fail to scale to even larger database, due to their exact pruning strategy. Inspired by the recent success of deep-learning-based semantic hashing in image and document retrieval, we propose a novel graph neural network (GNN) based semantic hashing, i.e. GHashing, for approximate pruning. We first train a GNN with ground-truth GED results so that it learns to generate embeddings and hash codes that preserve GED between graphs. Then a hash index is built to enable graph lookup in constant time. To answer a query, we use the hash codes and the continuous embeddings as two-level pruning to retrieve the most promising candidates, which are sent to the exact solver for final verification. Due to the approximate pruning strategy leveraged by our graph hashing technique, our approach achieves significantly faster query time compared to state-of-the-art methods while maintaining a high recall. Experiments show that our approach is on average 20x faster than the only baseline that works on million-scale databases, which demonstrates GHashing successfully provides a new direction in addressing graph search problem for large-scale graph databases.
QoE-Fair DASH Video Streaming Using Server-side Reinforcement Learning

To design an optimal adaptive video streaming method, video service providers need to consider both the efficiency and the fairness of the Quality of Experience (QoE) of their users. In Reference [8], we proposed a server-side QoE-fair rate adaptation method that considers both efficiency and fairness of the QoE. The server uses Reinforcement Learning (RL) to select a bitrate for each client sharing the same bottleneck link to the server in a way that achieves fairness among concurrent DASH clients and imposes that bitrate by dynamically modifying the client’s Media Presentation Description (MPD) file. In this article, we extend that work to minimize the number of actions the server needs to take to keep the system in its equilibrium state. By incorporating a Recurrent Neural Network, specifically an LSTM model, we modify the server’s training algorithm to achieve improvements in both the quality and the quantity of actions the server takes to guide the client. Performance evaluation of the modified algorithm for clients running both homogeneous and heterogeneous adaptation algorithms showed that the number of server actions dropped by 14% and 22%, respectively, while QoE-fairness improved by at least 6% and 10%, respectively.
Decentralized Industrial IoT Data Management Based on Blockchain and IPFS

The wide application of Internet of Things (IoT) has fostered the development of Industry 4.0. In manufacturing domain, Industrial IoT (IIoT) are key components of the Factories of the Future (FoF). The big IIoT data are the foundation of implementing data-driven strategies. In current industrial practice, most of these IIoT data are wasted or fragmented in data silos due to security and privacy concerns. Novel data management approaches are required to replace traditional centralized data management systems. The rapid development of blockchain technologies provides a novel solution for this challenge leveraging its unique characteristics such as decentralization, immutability and traceability. However, blockchain is inefficient for exchanging big data due to transaction throughput limits. The peer-to-peer InterPlanetary File System (IPFS) provides a suitable complement for blockchain. Therefore, this paper aims to propose a decentralized IIoT data management approach based on blockchain and IPFS technology. The architecture and enabling technologies of the proposed system are introduced. A proof-of-concept implementation is realized and relevant experiments are conducted. The results demonstrated the feasibility of the proposed approach.
The Menstruating Entrepreneur Kickstarting a New Politics of Women's Health

This article addresses itself to two developments in recent HCI research. One is the rising emphasis on women's health, a topic that is often seen as at least partly political. The other development in HCI research is the ongoing interest in supporting democracy and political activism. We present the case of a menstrual cup design project in Taiwan, called the Formoonsa Cup, whose product development led to the change in the legal status of menstrual cups and forcefully challenged a traditional value of hymen maintenance as an expression of “pure” and morally upright womanhood. We argue that this project is significant to HCI research for the following two reasons: first, because the design project is a successful, if complicated, case of political activism, and second, because the design and legalization processes were in part mediated by platform technologies, including social networking, crowdfunding, and direct democracy platforms. Using philosopher Michel Foucault's notion of “subjugated knowledge,” we analyze the case to improve understandings of how design can engage in emancipatory politics in the domain of women's health in HCI. In this case study, we begin with the idea that women's freedom of self-care is the knowledge that is subjugated, though by the end we suggest that subjugated knowledge is a more complex, and troubling, category than this initial evaluation suggests. We also argue that design can critique and intervene when it materializes previously subjugated knowledges and renders them both socially intelligible and politically efficacious.
Feature-Induced Manifold Disambiguation for Multi-View Partial Multi-label Learning

In conventional multi-label learning framework, each example is assumed to be represented by a single feature vector and associated with multiple valid labels simultaneously. Nonetheless, real-world objects usually exhibit complicated properties which can have multi-view feature representation as well as false positive labeling. Accordingly, the problem of multi-view partial multi-label learning (MVPML) is studied in this paper, where each example is assumed to be presented by multiple feature vectors while associated with multiple candidate labels which are only partially valid. To learn from MVPML examples, a novel approach named FIMAN is proposed which makes use of multi-view feature representation to tackle the noisy labeling information. Firstly, an aggregate manifold structure over training examples is generated by adaptively fusing affinity information conveyed by feature vectors of different views. Then, candidate labels of each training example are disambiguated by preserving the feature-induced manifold structure in label space. Finally, the resulting predictive models are learned by fitting modeling outputs with the disambiguated labels. Extensive experiments on a number of real-world data sets show that FIMAN achieves highly competitive performance against state-of-the-art approaches in solving the MVPML problem.
Learning Representations by Maximizing Mutual Information in Variational Autoencoders

Variational autoencoders (VAE) have ushered in an new era of unsupervised learning methods for complex distributions. Although these techniques are elegant in their approach, they are typically not useful for representation learning. In this work, we propose a simple yet powerful class of VAEs that simultaneously result in meaningful learned representations. Our solution is to combine traditional VAEs with mutual information maximization, with the goal to enhance amortized inference in VAEs using Information Theoretic techniques. We call this approach InfoMax-VAE, and such an approach can significantly boost the quality of learned high-level representations. We realize this through explicit maximization of information measures associated with the representation. Using extensive experiments on varied datasets and setups, we show that InfoMax-VAE outperforms contemporary popular approaches, including Info- VAE and β-VAE.