Reviewerly/exHarmony huggingface dataset:
Improved the Performance of the K-Means Cluster Using the Sum of Squared Error (SSE) optimized by using the Elbow Method

Abstract K-Means is a simple clustering algorithm that has the ability to throw large amounts of data, partition datasets into several clusters k. The algorithm is quite easy to implement and run, relatively fast and efficient. Another division of K-Means still has several weaknesses, namely in determining the number of clusters, determining the cluster center. The results of the cluster formed from the K-means method is very dependent on the initiation of the initial cluster center value provided. This causes the results of the cluster to be a solution that is locally optimal. This research was conducted to overcome the weaknesses in the K-Means algorithm, namely: improvements to the K-Means algorithm produce better clusters, namely the application of Sum Of Squared Error (SSE) to help K-Means Clustering in determining the optimum number of clusters, From this modification process, it is expected that the cluster center obtained will produce clusters, where the cluster members have a high level of similarity. Improving the performance of the K-Means cluster will be applied to determining the number of clusters using the elbow method.
Automated acquisition of explainable knowledge from unannotated histopathology images

Abstract Deep learning algorithms have been successfully used in medical image classification. In the next stage, the technology of acquiring explainable knowledge from medical images is highly desired. Here we show that deep learning algorithm enables automated acquisition of explainable features from diagnostic annotation-free histopathology images. We compare the prediction accuracy of prostate cancer recurrence using our algorithm-generated features with that of diagnosis by expert pathologists using established criteria on 13,188 whole-mount pathology images consisting of over 86 billion image patches. Our method not only reveals findings established by humans but also features that have not been recognized, showing higher accuracy than human in prognostic prediction. Combining both our algorithm-generated features and human-established criteria predicts the recurrence more accurately than using either method alone. We confirm robustness of our method using external validation datasets including 2276 pathology images. This study opens up fields of machine learning analysis for discovering uncharted knowledge.
Multi-Scale Deep Residual Learning-Based Single Image Haze Removal via Image Decomposition

Images/videos captured from outdoor visual devices are usually degraded by turbid media, such as haze, smoke, fog, rain, and snow. Haze is the most common one in outdoor scenes due to the atmosphere conditions. In this paper, a novel deep learning-based architecture (denoted by MSRL-DehazeNet) for single image haze removal relying on multi-scale residual learning (MSRL) and image decomposition is proposed. Instead of learning an end-to-end mapping between each pair of hazy image and its corresponding haze-free one adopted by most existing learningbased approaches, we reformulate the problem as restoration of the image base component. Based on the decomposition of a hazy image into the base and the detail components, haze removal (or dehazing) can be achieved by both of our multi-scale deep residual learning and our simplified U-Net learning only for mapping between hazy and haze-free base components, while the detail component is further enhanced via the other learned convolutional neural network (CNN). Moreover, benefited by the basic building block of our deep residual CNN architecture and our simplified UNet structure, the feature maps (produced by extracting structural and statistical features), and each previous layer can be fully preserved and fed into the next layer. Therefore, possible color distortion in the recovered image would be avoided. As a result, the final haze-removed (or dehazed) image is obtained by integrating the haze-removed base and the enhanced detail image components. Experimental results have demonstrated good effectiveness of the proposed framework, compared with state-ofthe-art approaches.
KNN-BLOCK DBSCAN: Fast Clustering for Large-Scale Data

Large-scale data clustering is an essential key for big data problem. However, no current existing approach is “optimal” for big data due to high complexity, which remains it a great challenge. In this article, a simple but fast approximate DBSCAN, namely, KNN-BLOCK DBSCAN, is proposed based on two findings: 1) the problem of identifying whether a point is a core point or not is, in fact, a kNN problem and 2) a point has a similar density distribution to its neighbors, and neighbor points are highly possible to be the same type (core point, border point, or noise). KNN-BLOCK DBSCAN uses a fast approximate kNN algorithm, namely, FLANN, to detect core-blocks (CBs), noncore-blocks, and noise-blocks within which all points have the same type, then a fast algorithm for merging CBs and assigning noncore points to proper clusters is also invented to speedup the clustering process. The experimental results show that KNN-BLOCK DBSCAN is an effective approximate DBSCAN algorithm with high accuracy, and outperforms other current variants of DBSCAN, including ρ-approximate DBSCAN and AnyDBC.
A Full Stage Data Augmentation Method in Deep Convolutional Neural Network for Natural Image Classification

Nowadays, deep learning has achieved remarkable results in many computer vision related tasks, among which the support of big data is essential. In this paper, we propose a full stage data augmentation framework to improve the accuracy of deep convolutional neural networks, which can also play the role of implicit model ensemble without introducing additional model training costs. Simultaneous data augmentation during training and testing stages can ensure network optimization and enhance its generalization ability. Augmentation in two stages needs to be consistent to ensure the accurate transfer of specific domain information. Furthermore, this framework is universal for any network architecture and data augmentation strategy and therefore can be applied to a variety of deep learning based tasks. Finally, experimental results about image classification on the coarse-grained dataset CIFAR-10 (93.41%) and fine-grained dataset CIFAR-100 (70.22%) demonstrate the effectiveness of the framework by comparing with state-of-the-art results.
Aspect-based sentiment analysis using adaptive aspect-based lexicons

Reviews expressed in e-commerce websites have formed an important source of information for both consumers and enterprises. Text sentiment analysis approaches aim to detect the sentiments of written reviews in order to achieve a better understanding of public opinion towards entities. Aspect-based sentiment analysis deals with capturing sentiments expressed towards each aspect of entities. A common approach in sentiment analysis problems is to take advantage of lexicons to generate features for classification of reviews. Existing aspect-based approaches fail to properly adapt general lexicons to the context of aspect-based datasets which results in reduced performance. To address this problem, this paper proposes extensions of two lexicon generation methods for aspect-based problems; one using statistical methods, and another using a genetic algorithm presented in our previous works. The aforementioned lexicons are then fused with prominent static lexicons to classify the aspects in reviews; this outperforms our previous works according to the t-test results (with a p-value of less than 0.001). Experimental results indicate that the proposed approach outperforms baseline methods in aspect-based polarity classification on Bing Liu's customer review datasets and improves precision, recall and F-measure by 6.0, 1.0, and 7.4 percentage points respectively.
Rethinking Table Recognition using Graph Neural Networks

Document structure analysis, such as zone segmentation and table recognition, is a complex problem in document processing and is an active area of research. The recent success of deep learning in solving various computer vision and machine learning problems has not been reflected in document structure analysis since conventional neural networks are not well suited to the input structure of the problem. In this paper, we propose an architecture based on graph networks as a better alternative to standard neural networks for table recognition. We argue that graph networks are a more natural choice for these problems, and explore two gradient-based graph neural networks. Our proposed architecture combines the benefits of convolutional neural networks for visual feature extraction and graph networks for dealing with the problem structure. We empirically demonstrate that our method outperforms the baseline by a significant margin. In addition, we identify the lack of large scale datasets as a major hindrance for deep learning research for structure analysis and present a new large scale synthetic dataset for the problem of table recognition. Finally, we open-source our implementation of dataset generation and the training framework of our graph networks to promote reproducible research in this direction.
Modeling Relation Paths for Knowledge Graph Completion

Knowledge graphs (KG) often encounter knowledge incompleteness. The path reasoning that predicts the unknown path relation between pairwise entities based on existing facts is one of the most promising approaches to the knowledge graph completion. However, most conventional path reasoning methods exclusively consider the entity description included in fact triples, ignoring both the type information of entities and the interaction between different semantic representations. In this study, we propose a novel method, Type-aware Attentive Path Reasoning (TAPR), to complete the knowledge graph by simultaneously considering KG structural information, textual information, and type information. More specifically, we first leverage types to enrich the representational learning of entities and relationships. Next, we describe a type-level attention to select the most relevant type of given entity in a specific triple without any predefined rules or patterns to reduce the impact of noisy types. After learning the distributed representation of all paths, path-level attention assigns different weights to paths, from which relations among entity pairs are calculated. We conduct a series of experiments on a real-world dataset to demonstrate the effectiveness of TAPR. Experimental results show that our method significantly outperforms all baselines on link prediction and entity prediction tasks.
Privacy-Aware Cross-Platform Service Recommendation Based on Enhanced Locality-Sensitive Hashing

Recommender systems are a promising way for users to quickly find the valuable information that they are interested in from massive data. Concretely, by capturing the user's personalized preferences, a recommender system can return a list of recommended items that best match the user preferences by using collaborative filtering. However, in the big data environment, the heavily fragmented distribution of the QoS (Quality of Services) data for recommendation decision- making presents a large challenge when integrating the QoS data from different platforms while ensuring that the sensitive user information contained in the QoS data is secure. Furthermore, due to the common tradeoff between data availability and privacy in data-driven applications, protecting the sensitive user information contained in the QoS data will probably decrease the availability of QoS data and finally produce inaccurate recommendation results. Considering these challenges, we enhance the classic Locality-Sensitive Hashing (LSH) technique, after which we propose an approach based on enhanced LSH for accurate and less-sensitive cross-platform recommendation decision-makings. Finally, extensive experiments are designed and tested on the reputable WS-DREAM dataset. The test reports prove the benefits of our work compared to other competitive approaches in the aspects of recommendation accuracy, efficiency and privacy protection performances.
Issues and Challenges of Aspect-based Sentiment Analysis: A Comprehensive Survey

The domain of Aspect-based Sentiment Analysis, in which aspects are extracted, their sentiments are analysed and sentiments are evolved over time, is getting much attention with increasing feedback of public and customers on social media. The immense advancements in this field urged the researchers to devise new techniques and approaches, each sermonizing a different research analysis/question, that cope with upcoming issues and complex scenarios of Aspect-based Sentiment Analysis. Therefore, this survey emphasized on the issues and challenges that are related to extraction of different aspects and their relevant sentiments, relational mapping between aspects, interactions, dependencies, and contextual-semantic relationships between different data objects for improved sentiment accuracy, and prediction of sentiment evolution dynamicity. A rigorous overview of the recent progress is summarized based on whether they contributed towards highlighting and mitigating the issue of Aspect Extraction, Aspect Sentiment Analysis or Sentiment Evolution. The reported performance for each scrutinized study of Aspect Extraction and Aspect Sentiment Analysis is also given, showing the quantitative evaluation of the proposed approach. Future research directions are proposed and discussed, by critically analysing the presented recent solutions, that will be helpful for researchers and beneficial for improving sentiment classification at aspect-level.
Defining Blockchain Governance: A Framework for Analysis and Comparison

In this article, we introduce a blockchain governance framework that defines the governance of a blockchain as a combination of six dimensions and three layers. An evaluation through eight expert interviews confirms the perceived usefulness and operational feasibility of the presented framework. Furthermore, the framework, is demonstrated by an application in two case studies. The introduced blockchain governance framework establishes a shared understanding and discussion surrounding the topic of blockchain governance.
Sarcasm detection in mash-up language using soft-attention based bi-directional LSTM and feature-rich CNN

Analyzing explicit and clear sentiment is challenging owing to the growing use of emblematic and multilingual language constructs. This research proposes sarcasm detection using deep learning in code-switch tweets, specifically the mash-up of English with Indian native language, Hindi. The proposed model is a hybrid of bidirectional long short-term memory with a softmax attention layer and convolution neural network for real-time sarcasm detection. To evaluate the performance of the proposed model, real-time mash-up tweets are extracted on the trending political (#government) and entertainment (#cricket, #bollywood) posts on Twitter. The randomly sampled dataset contains 3000 sarcastic and 3000 non-sarcastic bilingual Hinglish (Hindi + English) tweets. Feature engineering is done using pre-trained GloVe word embeddings to extract English semantic context vector, hand-crafted features using subjective lexicon Hindi-SentiWordNet to generate the SentiHindi feature vector and an auxiliary pragmatic feature vector depicting the count of pragmatic markers in tweet. Performance analysis is done to compare and validate the proposed softAttBiLSTM-feature-richCNN model. The model outperforms the baseline deep learning models with a superior classification accuracy of 92.71% and F-measure of 89.05%.
PMF

With the popularity of mobile devices and location-based social network, understanding and modelling the human mobility becomes an important topic in the field of ubiquitous computing. With the model developing from personal models with own information to the joint models with population information, the prediction performance of proposed models become better and better. Meanwhile, the privacy issues of these models come into the view of community and the public: collecting and uploading private data to the centralized server without enough regulation. In this paper, we propose PMF, a privacy-preserving mobility prediction framework via federated learning, to solve this problem without significantly sacrificing the prediction performance. In our framework, based on the deep learning mobility model, no private data is uploaded into the centralized server and the only uploaded thing is the updated model parameters which are difficult to crack and thus more secure. Furthermore, we design a group optimization method for the training on local devices to achieve better trade-off between performance and privacy. Finally, we propose a fine-tuned personal adaptor for personal modelling to further improve the prediction performance. We conduct extensive experiments on three real-life mobility datasets to demonstrate the superiority and effectiveness of our methods in privacy protection settings.
Artificial Intelligence in Business: From Research and Innovation to Market Deployment

For the last few years, one can see the emergence of a large number of intelligent products and services, their commercial availability and the socioeconomic impact, this raises the question if the present emergence of AI is just hype or does it really have the capability of transforming the world. The paper investigates the wide range of implications of artificial intelligence (AI), and delves deeper into both positive and negative impacts on governments, communities, companies, and individuals. This paper investigates the overall impact of AI - from research and innovation to deployment. The paper addresses the influential academic achievements and innovations in the field of AI; their impact on the entrepreneurial activities and thus on the global market. The paper also contributes in investigating factors responsible for the advancement of AI. For the exploration of entrepreneurial activities towards AI, two lists of top 100 AI start-ups are considered. The inferences obtained from the research will provide an improved understanding of the innovations and the impact of AI on businesses and society in general. It will also provide a better understanding of how AI can transform the business operations and thus the global economy.
EasyBand: A Wearable for Safety-Aware Mobility During Pandemic Outbreak

COVID-19 (Corona Virus Disease 2019) is a pandemic, which has been spreading exponentially around the globe.Many countries adopted stay-at-home or lockdown policies to control its spreading.However, prolonged stay-at-home may cause worse effects like economical crises, unemployment, food scarcity, and mental health problems of individuals.This article presents a smart consumer electronics solution to facilitate safe and gradual opening after stay-at-home restrictions are lifted.An Internet of Medical Things enabled wearable called EasyBand is introduced to limit the growth of new positive cases by autocontact tracing and by encouraging essential social distancing. POST PANDEMIC MOBILITY IS A CHALLENGE& COVID-19 (CORONA VIRUS DISEASE 2019) is a pandemic caused by a newly discovered coronavirus. 1 It is spreading in human populations in an exponential manner.A single positive case of COVID-19 has the potential to spread across a nation in just a few days.It spreads through droplets generated by an infected person while coughing, sneezing, spitting, or even breathing air.The probability of the transmission of the disease is directly proportional to the number of Digital Object
A multi-level influence model of COVID-19 themed cybercrime

The recent severity and frequency of cybercrime has been dominated by a single theme – the COVID-19 pandemic. This research develops a multi-level influence model to explore how cybercriminals are exploiting the COVID-19 pandemic by assessing situational factors, identifying victims, impersonating trusted sources, selecting attack methods, and employing social engineering techniques. The model extends upon prior work on influence techniques and emotional appeals that cybercriminals employ, by bringing into sharper focus the role of situational factors in COVID-19 related cybercrime attacks. Content and thematic analysis was conducted on 185 distinct COVID-19 cybercrime scam incident documents, including text, images, and photos, provided by a global online fraud and cybersecurity company tracking COVID-19 related cybercrime. The analysis reveals interesting patterns about the sheer breadth and diversity of COVID-19 related cybercrime and how these crimes are continually evolving in response to changing situational factors. It is hoped that these insights and recommendations for end-users and organisations can contribute to a safer digital world as we cope with many other pressing challenges during the COVID-19 pandemic.
How can the digital competences of pre-service teachers be developed? Examining a case study through the lens of DigComp and DigCompEdu

In this study, a training program, which aimed to develop pre-service teachers' digital competences, was designed. After the training, pre-service teachers’ opinions in terms of digital competence development, how they planned to use the digital competence knowledge and skills that they gained in the training program for their future professional careers, and pros and cons of the training were examined. The training was conducted as a case study that involved 24 pre-service teachers. The pre-service teachers were provided 70 h of training on five competence areas of DigComp. Diaries and focus group interviews were used as data collection tools. The researchers followed two-phased qualitative data analysis procedure. Deductive and inductive analysis techniques were used. The results indicated that pre-service teachers should be trained for information and data literacy, communication and collaboration, digital content creation, safety and problem solving. In addition, digital competence trainings should be given in a way to include knowledge and practice about professional engagement, digital resources, teaching and learning, assessment and empowering learners. The study revealed that it is necessary for the digital competence trainings to be implemented effectively in which pre-service teachers collaborate on digital issues regardless of their previous experience. In addition, trainers should structure their courses so that pre-service teachers can see them as role models, and theoretical information as well as the possibility of learning by doing should be offered.
Federated Learning for Data Privacy Preservation in Vehicular Cyber-Physical Systems

Recent developments in technologies such as MEC and AI contribute significantly in accelerating the deployment of VCPS. Techniques such as dynamic content caching, efficient resource allocation, and data sharing play a crucial role in enhancing the service quality and user driving experience. Meanwhile, data leakage in VCPS can lead to physical consequences such as endangering passenger safety and privacy, and causing severe property loss for data providers. The increasing volume of data, the dynamic network topology, and the availability of limited resources make data leakage in VCPS an even more challenging problem, especially when it involves multiple users and multiple transmission channels. In this article, we first propose a secure and intelligent architecture for enhancing data privacy. Then we present our new privacy-preserving federated learning mechanism and design a two-phase mitigating scheme consisting of intelligent data transformation and collaborative data leakage detection. Numerical results based on a real-world dataset demonstrate the effectiveness of our proposed scheme and show that our scheme achieves good accuracy, efficiency, and high security.