Reviewerly/exHarmony huggingface dataset:
Style Consistent Image Generation for Nuclei Instance Segmentation

In medical image analysis, one limitation of the application of machine learning is the insufficient amount of data with detailed annotation, due primarily to high cost. Another impediment is the domain gap observed between images from different organs and different collections. The differences are even more challenging for the nuclei instance segmentation, where images have significant nuclei stain distribution variations and complex pleomorphisms (sizes and shapes). In this work, we generate style consistent histopathology images for nuclei instance segmentation. We set up a novel instance segmentation framework that integrates a generator and discriminator into the segmentation pipeline with adversarial training to generalize nuclei instances and texture patterns. A segmentation net detects and segments both real nuclei and synthetic nuclei and provides feedback so that the generator can synthesize images that can boost the segmentation performance. Experimental results on three public nuclei datasets indicate that our proposed method outperforms previous nuclei segmentation methods.
RHUPS

Databases that deal with the real world have various characteristics. New data is continuously inserted over time without limiting the length of the database, and a variety of information about the items constituting the database is contained. Recently generated data has a greater influence than the previously generated data. These are called the time-sensitive non-binary stream databases, and they include databases such as web-server click data, market sales data, data from sensor networks, and network traffic measurement. Many high utility pattern mining and stream pattern mining methods have been proposed so far. However, they have a limitation that they are not suitable to analyze these databases, because they find valid patterns by analyzing a database with only some of the features described above. Therefore, knowledge-based software about how to find meaningful information efficiently by analyzing databases with these characteristics is required. In this article, we propose an intelligent information system that calculates the influence of the insertion time of each batch in a large-scale stream database by applying the sliding window model and mines recent high utility patterns without generating candidate patterns. In addition, a novel list-based data structure is suggested for a fast and efficient management of the time-sensitive stream databases. Moreover, our technique is compared with state-of-the-art algorithms through various experiments using real datasets and synthetic datasets. The experimental results show that our approach outperforms the previously proposed methods in terms of runtime, memory usage, and scalability.
PID Controller-Guided Attention Neural Network Learning for Fast and Effective Real Photographs Denoising

Real photograph denoising is extremely challenging in low-level computer vision since the noise is sophisticated and cannot be fully modeled by explicit distributions. Although deep-learning techniques have been actively explored for this issue and achieved convincing results, most of the networks may cause vanishing or exploding gradients, and usually entail more time and memory to obtain a remarkable performance. This article overcomes these challenges and presents a novel network, namely, PID controller guide attention neural network (PAN-Net), taking advantage of both the proportional-integral-derivative (PID) controller and attention neural network for real photograph denoising. First, a PID-attention network (PID-AN) is built to learn and exploit discriminative image features. Meanwhile, we devise a dynamic learning scheme by linking the neural network and control action, which significantly improves the robustness and adaptability of PID-AN. Second, we explore both the residual structure and share-source skip connections to stack the PID-ANs. Such a framework provides a flexible way to feature residual learning, enabling us to facilitate the network training and boost the denoising performance. Extensive experiments show that our PAN-Net achieves superior denoising results against the state-of-the-art in terms of image quality and efficiency.
The Evolution of Chatbots in Tourism: A Systematic Literature Review

Abstract In the last decade, Information and Communication Technologies have revolutionized the tourism and hospitality sector. One of the latest innovations shaping new dynamics and fostering a remarkable behavioral change in the interaction between the service provider and the tourist is the employment of increasingly sophisticated chatbots. This work analyzes the most recent systems presented in the literature (since 2016) investigated via 12 research questions. The often appreciated quick evolution of such solutions is the primary outcome. However, such technological and financial fast-pace requires continuous investments, upskilling, and system innovation to tackle the eTourism challenges, which are shifting towards new dimensions.
Use of Blockchain and Internet of Things for Securing Data in Healthcare Systems

Advances in digital technologies have influenced the life of a human to a great extent. Among all the innovative digital technologies, blockchain, IoT are the most encouraging ones in the present era. Blockchain is a data-sharing platform which contains an open record that is decentralized. It allows multiple parties to involve in a logical decision-making process, which, however, does not trust each other individually. IoT is a network of networks in which real-time analysis of data takes place. If both these technologies are infused around, they will provide responses to a variety of problems, particularly in terms of health care where real-time monitoring of data has a huge role to play. Interoperability, security, liability are the significant worries of the healthcare industry that can be efficiently addressed by the use of blockchain. The goal of this chapter is to provide a remedy for the healthcare industry by using both blockchain, IoT technology. In this, we are proposing an architecture where IoT devices measures, collects real-time data which then is stored in the blockchain. Also, the various billing, insurance claim concerns could easily be addressed barring all fraudulent activities. In this way, reporting would be instant, data stored would be secured, the system would be transparent.
RETRACTED: Research on Fintech development issues based on embedded cloud computing and big data analysis

Financial technology will be widespread attention. It is growing rapidly. As a result of its continued development, new regulations have already been introduced in this field. The word "fintech" is one such word. Financial technology will be used in the field for various activities. Usually, these procedures are performed by the company or organization that provides information technology-based service that is requested by using an application. Various other important issues, such as network attack security restrictions and privacy threat accounts, should not be left unattended. It integrates various state-of-the-art technologies such as mobile, embedded systems, mobile networks, mobile computing, cloud, big data, data analysis technology, and embedded cloud computing, including FinTech development. It is important to Embedded Finance (EF) for its seamless integration and finance of its customers' business services, just as ride-sharing companies Uber and Lyft have integrated very successful payments. Refers to the provision of services. Embedded finance is a financial service on the terms of the customer, from anywhere, anytime. There is no longer a need to visit banks that have access to their money. In some cases, embedded finance (in more specifically embedded banks) eliminates traditional banks.
Sequence encoding incorporated CNN model for Email document sentiment classification

Document sentiment classification is an area of study that has been developed for decades. However, sentiment classification of Email data is rather a specialized field that has not yet been thoroughly studied. Compared to typical social media and review data, Email data has characteristics of length variance, duplication caused by reply and forward messages, and implicitness in sentiment indicators. Due to these characteristics, existing techniques are incapable of fully capturing the complex syntactic and relational structure among words and phrases in Email documents. In this study, we introduce a dependency graph-based position encoding technique enhanced with weighted sentiment features, and incorporate it into the feature representation process. We combine encoded sentiment sequence features with traditional word embedding features as input for a revised deep CNN model for Email sentiment classification. Experiments are conducted on three sets of real Email data with adequate label conversion processes. Empirical results indicate that our proposed SSE-CNN model obtained the highest accuracy rate of 88.6%, 74.3% and 82.1% for three experimental Email datasets over other comparative state-of-the-art algorithms. Furthermore, our performance evaluations on the preprocessing and sentiment sequence encoding justify the effectiveness of Email preprocessing and sentiment sequence encoding with dependency-graph based position and SWN features on the improvement of Email document sentiment classification.
End-to-end video text detection with online tracking

Text in videos usually acts as important semantic cues, which is helpful to video analysis. Video text detection is considered as one of the most difficult tasks in document analysis due to the following two challenges: 1) the difficulties caused by video scenes, i.e., motion blur, illumination changes, and occlusion; 2) the properties of text including variants of fonts, languages, orientations, and shapes. Most existing methods try to improve the video text detection through video text tracking, but treat these two tasks separately. This can significantly increase the amount of calculations and cannot take full advantage of the supervisory information of both tasks. In this work, we introduce explainable descriptor, combines appearance, geometry and PHOC features, to establish a bridge between detection and tracking and build an end-to-end video text detection model with online tracking to address these challenges together. By integrating these two branches into one trainable framework, they can promote each other and the computational cost is significantly reduced. Besides, the introduce explainable descriptor also make our end-to-end model have inherent interpretability. Experiments on existing video text benchmarks including ICDAR 2013 Video, DOST, Minetto and YVT verify the role of explainable descriptors in improving model expression ability and the proposed method significantly outperforms state-of-the-art methods. Our method improves F-score by more than 2% on all datasets and achieves 81.52% on the MOTA of the Minetto dataset.
Enablers of Augmented Reality in the Food Supply Chain: A Systematic Literature Review

The food industry represents one of the most critically important sectors of the economy. Food is a basic necessity for human life and requires specialized handling, preparation, and logistics to be safe for human consumption. Moreover, food supply chains have become more globalized, fragmented, and complex. Food industry stakeholders are forced to reconsider conventional ways of managing food chains and coping with the latest technology innovation trends. As technology unlocks several opportunities in supply chains, food businesses have a vested interest in exploring Augmented Reality (AR). AR denotes the technology of overlaying virtual objects upon the real-world environment. Despite being a well-established technology, AR possibilities in the food supply chain remain an unexplored research area. Therefore, to fill this knowledge gap and capture the latest developments in this field, we conducted a systematic literature review. Fifty-one (51) publications were thoroughly analyzed to identify the enablers of AR in the food industry. Findings revealed five main areas where AR potentially offers substantial business value. These include food process efficiencies, food decision-making, food marketing, food training, and food safety. Finally, research contributions, trajectories and limitations were highlighted.
TypeNet: Scaling up Keystroke Biometrics

We study the suitability of keystroke dynamics to authenticate 100 K users typing free-text. For this, we first analyze to what extent our method based on a Siamese Recurrent Neural Network (RNN) is able to authenticate users when the amount of data per user is scarce, a common scenario in free-text keystroke authentication. With 1 K users for testing the network, a population size comparable to previous works, TypeNet obtains an equal error rate of 4.8% using only 5 enrollment sequences and 1 test sequence per user with 50 keystrokes per sequence. Using the same amount of data per user, as the number of test users is scaled up to 100K, the performance in comparison to 1 K decays relatively by less than 5%, demonstrating the potential of Type-Net to scale well at large scale number of users. Our experiments are conducted with the Aalto University keystroke database. To the best of our knowledge, this is the largest free-text keystroke database captured with more than 136M keystrokes from 168K users.
A Two-Stage Unsupervised Approach for Low Light Image Enhancement

As vision based perception methods are usually built on the normal light assumption, there will be a serious safety issue when deploying them into low light environments. Recently, deep learning based methods have been proposed to enhance low light images by penalizing the pixel-wise loss of low light and normal light images. However, most of them suffer from the following problems: 1) the need of pairs of low light and normal light images for training, 2) the poor performance for dark images, 3) the amplification of noise. To alleviate these problems, in this letter, we propose a two-stage unsupervised method that decomposes the low light image enhancement into a pre-enhancement and a post-refinement problem. In the first stage, we pre-enhance a low light image with a conventional Retinex based method. In the second stage, we use a refinement network learned with adversarial training for further improvement of the image quality. The experimental results show that our method outperforms previous methods on four benchmark datasets. In addition, we show that our method can significantly improve feature points matching and simultaneous localization and mapping in low light conditions.
ConceptExplorer: Visual Analysis of Concept Drifts in Multi-source Time-series Data

Time-series data is widely studied in various scenarios, like weather forecast, stock market, customer behavior analysis. To comprehensively learn about the dynamic environments, it is necessary to comprehend features from multiple data sources. This paper proposes a novel visual analysis approach for detecting and analyzing concept drifts from multi-sourced time-series. We propose a visual detection scheme for discovering concept drifts from multiple sourced time-series based on prediction models. We design a drift level index to depict the dynamics, and a consistency judgment model to justify whether the concept drifts from various sources are consistent. Our integrated visual interface, ConceptExplorer, facilitates visual exploration, extraction, understanding, and comparison of concepts and concept drifts from multi-source time-series data. We conduct three case studies and expert interviews to verify the effectiveness of our approach.
Deep Unsupervised Anomaly Detection

This paper proposes a novel method to detect anomalies in large datasets under a fully unsupervised setting. The key idea behind our algorithm is to learn the representation underlying normal data. To this end, we leverage the latest clustering technique suitable for handling high dimensional data. This hypothesis provides a reliable starting point for normal data selection. We train an autoencoder from the normal data subset, and iterate between hypothesizing normal candidate subset based on clustering and representation learning. The reconstruction error from the learned autoen-coder serves as a scoring function to assess the normality of the data. Experimental results on several public benchmark datasets show that the proposed method outperforms state-of-the-art unsupervised techniques and is comparable to semi-supervised techniques in most cases.
Examining Machine Learning for 5G and Beyond Through an Adversarial Lens

Spurred by the recent advances in deep learning to harness rich information hidden in large volumes of data and to tackle problems that are hard to model/solve (e.g., resource allocation problems), there is currently tremendous excitement in the mobile networks domain around the transformative potential of data-driven artificial intelligence/machine learning (AI/ML) based network automation, control and analytics for 5G and beyond. In this article, we present a cautionary perspective on the use of AI/ML in the 5G context by highlighting the adversarial dimension spanning multiple types of ML (supervised/unsupervised/reinforcement learning) and support this through three case studies. We also discuss approaches to mitigate this adversarial ML risk, offer guidelines for evaluating the robustness of ML models, and call attention to issues surrounding ML oriented research in 5G more generally.
The use of immersive 360° videos for foreign language learning: a study on usage and efficacy among high-school students

The large-scale diffusion of tablets and smartphones in the last decades offered new opportunities to broaden educational strategies. In particular, the Mobile-Assisted Language Learning (MALL) emerged as a promising approach leveraging the widespread availability of portable devices. In this study we investigated the feasibility and efficacy of a self-administered MALL homework training based on immersive 360° videos, compared to a training based on not-immersive standard videos showing the same visual content and enriched by an auditory description of the environment. The knowledge of target words was assessed before and after the training. In addition, students’ attitudes toward the technology were assessed before the training. Results indicated that students with a more positive attitude toward technology watched the videos more than those with a less positive attitude. Furthermore, students who underwent the training with 360°videos learned more words than students belonging to the control group, even after controlling for the number of videos views.
Migrating Large Deep Learning Models to Serverless Architecture

Serverless computing platform is emerging as a solution for event-driven artificial intelligence applications. Function-as-a-Service (FaaS) using serverless computing paradigm provides high performance and low cost solutions for deploying such applications on cloud while minimizing the operational logic. Using FaaS for efficient deployment of complex applications, such as natural language processing (NLP) and image processing, containing large deep learning models will be an advantage. However, constrained resources and stateless nature of FaaS offers numerous challenges while deploying such applications. In this work, we discuss the methodological suggestions and their implementation for deploying pre-trained large size machine learning and deep learning models on FaaS. We also evaluate the performance and deployment cost of an enterprise application, consisting of suite of deep vision preprocessing algorithms and models, on VM and FaaS platform. Our evaluation shows that migration from monolithic to FaaS platform significantly improves the performance of the application at a reduced cost.
Trends in the Usage of Statistical Software and Their Associated Study Designs in Health Sciences Research: A Bibliometric Analysis

BackgroundThe development of statistical software in research has transformed the way scientists and researchers conduct their statistical analysis.Despite these advancements, it was not clear which statistical software is mainly used for which research design thereby creating confusion and uncertainty in choosing the right statistical tools.Therefore, this study aimed to review the trend of statistical software usage and their associated study designs in articles published in health sciences research. MethodsThis bibliometric analysis study reviewed 10,596 articles published in PubMed in three 10-year intervals (1997, 2007, and 2017).The data were collected through Google sheet and were analyzed using SPSS software.This study described the trend and usage of currently available statistical tools and the different study designs that are associated with them. ResultsOf the statistical software mentioned in the retrieved articles, SPSS was the most common statistical tool used (52.1%) in the three-time periods followed by SAS (12.9%) and Stata (12.6%).WinBugs was the least used statistical software with only 40(0.6%) of the total articles.SPSS was mostly associated with observational (61.1%) and experimental (65.3%) study designs.On the other hand, Review Manager (43.7%) and Stata (38.3%) were the most statistical software associated with systematic reviews and meta-analyses. ConclusionIn this study, SPSS was found to be the most widely used statistical software in the selected study periods.Observational studies were the most common health science research design.SPSS was associated with observational and experimental studies while Review Manager and Stata were mostly used for systematic reviews and meta-analysis.
K-Means clustering based Extreme Learning ANFIS with improved interpretability for regression problems

In this paper, a novel network called K-Means clustering based Extreme Learning ANFIS (KMELANFIS) with improved interpretability for regression problems is presented. Grid input space partitioning results in the exponential rise in the number of rules in Fuzzy Inference System (FIS) with an increase in the number of features, thus reducing the interpretability of the network and increasing the computational burden. In the proposed network, input partitioning is done using K-means clustering algorithm to avoid the computational complexity arising due to the large number of rules generated for problems with high input dimensionality. The cluster centers resulting from the clustering of input–output space are used for initializing the membership function parameters. Based on the similarity index between adjacent membership functions, the similar membership functions are merged, and membership function parameters are tuned to improve the distinguishability of membership functions. Extreme learning machine (ELM) technique is incorporated to compute the consequent parameters thus avoiding the computational complexity of the backpropagation algorithm used in the training of ANFIS network. For performance comparison, simulations for function approximation and real world benchmark regression problems are done for ANFIS, ELANFIS, LSSVR, and KMELANFIS networks. KMELANFIS network has improved interpretability with decent accuracy, lesser numbers of rules and parameters, and has faster training for most of the examples. Regularized KMELANFIS network have better accuracy and larger training time compared to KMELANFIS network.