Reviewerly/exHarmony huggingface dataset:
The Battle of the Video Codecs in the Healthcare Domain - A Comparative Performance Evaluation Study Leveraging VVC and AV1

Video compression is the core technology in mobile (mHealth) and electronic (eHealth) health video streaming applications. With global video traffic projected to reach 82% of all Internet traffic by 2022, there is a strong need to develop efficient compression algorithms to accommodate expected future growth. For the first time in decades, and especially since ISO/IEC MPEG and ITU-T VCEG expert groups strategically joined forces to develop the highly successful H.264/AVC standard, we have two distinct initiatives competing for the best performing video codec. On the one hand, we have the Alliance for Open Media (AOM) that support a new, royalty free video codec generation, termed AV1, based on VP8 and VP9 efforts. On the other hand, the Joint Video Exploration Team (JVET) has been developing the Versatile Video Codec (VVC) as the successor of the High Efficiency Video Coding (HEVC) standard. At the same time, the breadth of applications utilizing video codecs, involving significant content variability and moving across the video resolution ladder, to satisfy different constraints, have resulted in mixed literature results, with respect to the best performing codec. In this paper, we compare the performance of emerging VVC and AV1 codecs, along with popular HEVC implementations, namely the HEVC Test Model (HM) and x265, as well as earlier, VP9 codec, and investigate their suitability for medical applications. To the best of our knowledge, this is the first performance comparison of emerging VVC and AV1 video codecs for use in the healthcare domain. Experimental evaluation based on three datasets (ultrasound, emergency scenery, and general-purpose videos) demonstrate that VVC outperforms all rival codecs while AV1 achieves better compression efficiency than HEVC in all cases but low-resolution ($560\times448$ @40Hz) ultrasound videos of the common carotid artery. Furthermore, the use of video despeckling prior to ultrasound video compression can provide significant bitrate savings.
Multi-agent actor centralized-critic with communication

Multiple real-world problems are naturally modeled as cooperative multi-agent systems, ranging from satellite formation to traffic monitoring. These systems require algorithms that can learn successful policies with independent agents that rely solely on local partial-observations of the environment. However, multi-agent environments are more complex, due to their partial-observability and non-stationarity from an agent’s perspective, as well as the structural credit assignment problem and the curse of dimensionality, and achieving coordination in such systems remains a complex challenge. To this end, we propose a multi-agent actor-critic algorithm called Asynchronous Advantage Actor Centralized-Critic with Communication (A3C3). A3C3 uses a centralized critic to estimate a value function, decentralized actors to approximate each agent’s policy function, and decentralized communication networks for each agent to share relevant information with its team. The critic can incorporate additional information, like the environment’s global state, when available, and optimizes the actor networks. The actor networks of an agent’s teammates optimize that agent’s communication network, such that each agent learns to output information that is relevant to the policies of others. A3C3 supports a dynamic amount of agents, noisy communication mediums, and can be horizontally scaled to shorten its learning phase. We evaluate A3C3 in two partially-observable multi-agent suites where agents benefit from communicating local information to each other. A3C3 outperforms state-of-the-art multi-agent algorithms, independent approaches, and centralized controllers with access to all agents’ observations.
Shape Information Improves the Cross-Cohort Performance of Deep Learning-Based Segmentation of the Hippocampus

Performing an accurate segmentation of the hippocampus from brain magnetic resonance images is a crucial task in neuroimaging research, since its structural integrity is strongly related to several neurodegenerative disorders, including Alzheimer's disease (AD). Some automatic segmentation tools are already being used, but, in recent years, new deep learning (DL)-based methods have been proven to be much more accurate in various medical image segmentation tasks. In this work, we propose a DL-based hippocampus segmentation framework that embeds statistical shape of the hippocampus as context information into the deep neural network (DNN). The inclusion of shape information is achieved with three main steps: (1) a U-Net-based segmentation, (2) a shape model estimation, and (3) a second U-Net-based segmentation which uses both the original input data and the fitted shape model. The trained DL architectures were tested on image data of three diagnostic groups [AD patients, subjects with mild cognitive impairment (MCI) and controls] from two cohorts (ADNI and AddNeuroMed). Both intra-cohort validation and cross-cohort validation were performed and compared with the conventional U-net architecture and some variations with other types of context information (i.e., autocontext and tissue-class context). Our results suggest that adding shape information can improve the segmentation accuracy in cross-cohort validation, i.e., when DNNs are trained on one cohort and applied to another. However, no significant benefit is observed in intra-cohort validation, i.e., training and testing DNNs on images from the same cohort. Moreover, compared to other types of context information, the use of shape context was shown to be the most successful in increasing the accuracy, while keeping the computational time in the order of a few minutes.
Machine learning with sklearn

This chapter’s goal is to show how to apply machine learning algorithms in a general setting using some classic methods. In particular, it demonstrates how to apply three important machine learning algorithms, a support vector classifier (SVC), a random forest classifier (RFC), and a multilayer perceptron (MLP). While many of the methods studied later go beyond these now classic methods, this does not mean that these methods are obsolete. Also, the algorithms discussed here provide some form of baseline to discuss advanced methods like probabilistic reasoning and deep learning. The aim here is to demonstrate that applying machine learning methods based on machine learning libraries is not very difficult. It offers an opportunity to discuss evaluation techniques that are very important in practice.
Text sentiment analysis using frequency-based vigorous features

Sentiment Analysis, an un-abating research area in text mining, requires a computational method for extracting useful information from text. In recent days, social media has become a really rich source to get information about the behavioral state of people (opinion) through reviews and comments. Numerous techniques have been aimed to analyze the sentiment of the text, however, they were unable to come up to the complexity of the sentiments. The complexity requires novel approach for deep analysis of sentiments for more accurate prediction. This research presents a three-step Sentiment Analysis and Prediction (SAP) solution of Text Trend through K-Nearest Neighbor (KNN). At first, sentences are transformed into tokens and stop words are removed. Secondly, polarity of the sentence, paragraph and text is calculated through contributing weighted words, intensity clauses and sentiment shifters. The resulting features extracted in this step played significant role to improve the results. Finally, the trend of the input text has been predicted using KNN classifier based on extracted features. The training and testing of the model has been performed on publically available datasets of twitter and movie reviews. Experiments results illustrated the satisfactory improvement as compared to existing solutions. In addition, GUI (Hello World) based text analysis framework has been designed to perform the text analytics.
Global Research on Big Data in Relation with Artificial Intelligence (A Bibliometric Study: 2008-2019)

&lt;p&gt;&lt;span style="font-family: Times New Roman; font-size: small;"&gt;The purpose of this paper is to analyze and explore the research studies on Big Data in relation with Artificial Intelligence domain, which published in Peer Review Journals and indexed in Web of Science Core Collection for the period of 2008-2019 years.&lt;/span&gt;&lt;/p&gt;&lt;p&gt;&lt;span style="font-family: Times New Roman;"&gt;&lt;span style="font-size: small;"&gt;The publication data for our research analysis “Big Data in relation with Artificial Intelligence” has been derived from the Web of Science (WoS) Core Collection database (Indexes included SCI Expanded and SSCI). &lt;/span&gt;&lt;span style="font-size: small;"&gt;The Bibliometric Analysis Methods is applied for the study in order to find out the relations between two domains and to investigate the status of scientific development level in the research era. Therefore, our research concentrates and highlights the current issues discussed and studied by the scholars around the globe. This paper would useful for researchers to show the publication trends on big data in relation with artificial intelligence research outcomes in highly reputable SCI-Exp and SSCI journal (ranked by WoS).&lt;/span&gt;&lt;span style="font-size: small;"&gt; &lt;/span&gt;&lt;/span&gt;&lt;/p&gt;
Sentiment Analysis in Turkish with Deep Learning

Social media (such as Twitter) helps users express their opinions easily through writings. With the increase in the use of social media, the amount of textual data coming from direct users has grown enormously. These data can help to extract meanings of the specific situations using text mining. For example, in moments of disaster, social media can be used to get important information about the current situation. Understanding the emotions of the users can help humanitarian aid to be allocated more effectively. Sentiment analysis is a method used to get emotion rank from the textual data. It helps get the numerical scale of the emotion from the unstructured data. This study aims to create sentiment analysis, also known as opinion mining, from the given data by applying both machine learning and deep learning methods for disaster moments. We intend to see which technique is more successful in terms of sentiment analysis through comparison of the methods. We created a Turkish sentiment analysis about the coup attempt that happened on July 15, 2016. In this work, the coup attempt is considered a disaster as it consisted of violence and happened suddenly. The dataset used is comprised of unstructured sentences and labels with sentiment points that are either positive or negative. In this work, we pre-process the dataset and apply Turkish Natural Language Processing (NLP). Three text-features (Bag of Words, Tf-Idf, Word 2Vec) are used to vectorize and prepare the dataset for the application of the models. Then, we apply machine learning and deep learning methods and compare them while looking at the results. Examining the results and comparison, we aim to see whether social media can help disaster management. In this comparison, we observed that deep learning has higher accuracy. Our findings indicated that social media can be used to understand sudden situations like disasters and may help to decide how to act.
Assessing Library Online Patrons Use of Resources to Improve Outreach and Marketing

Online learning and distance programs are a growing trend in higher education, and equitable access to library services and resources is vital for online students. While advertising to all patrons is important, marketing and outreach about electronic resources is crucial for online users, who often cannot physically come to the library for materials and rely on electronic collections for research and assignments. In Fall 2017, two academic librarians from public and technical services departments developed a marketing plan to promote electronic resources and online services for research by designing and distributing two virtual surveys to students taking courses online and instructors teaching online. The analysis of these surveys directly informed library promotion of electronic resources via the development of a marketing plan, as well as a more in-depth library integration in the university learning management system (LMS), with these strategies leading to an increase in e-resource usage.
Classification of User Comment Using Word2vec and SVM Classifier

Social media provides data that can be used for text classification, but these social media do not have a rating system.Comments taken from social media such as Twitter, does not provide a rating system which can help to classify comments based on their rating score.The goal of this study is to build a comment classification model using Word2vec and SVM classifier that can classify comments based on a rating scale from 1-5.The training data will be taken from user comments from play.google.comwebsite in which each comment already has a rating.The purpose of the model is to classify comments from social media about mobile network applications.Comment classification is performed in order to help these businesses, as well as the users, to know the overall satisfaction of users who use these applications.The best F1 score obtained from this research using class elimination and stop word removal is 0.795.
Cleaning decision model of MBR membrane based on Bandelet neural network optimized by improved Bat algorithm

The membrane fouling is an important factor of restricting wide application of MBR (Membrane Bio-Reactor), which causes the fall of membrane flux and reduces the membrane cleaning period. So the Bandelet neural network is proposed through combining Bandelet transform and neural network, which predicts membrane flux and its recovery rate for making proper membrane cleaning decision. Firstly, the main affecting factors of membrane fouling are discussed. Secondly, the architecture of Bandelet neural network is designed with Bandelet function and its scale function as activation functions of hidden and output layers respectively. Thirdly, the improved Bat algorithm is established, which is applied to improve the optimization effect of parameters of Bandelet neural network. Finally, the simulation analysis is carried out, the improved bat algorithm has higher performance than the traditional bat algorithm through analyzing the single objective optimization problem from 2018 CEC competition, the optimal number of nodes in hidden layer is confirmed based on comparison analysis and statistical tests. The proposed BNN-IBA has obvious superiority in prediction accuracy and speed according to prediction simulation results of membrane fouling of MBR, which has better prediction results than other state-of-art prediction models optimized by the novel optimal algorithms. In addition, the proper membrane cleaning period and method are confirmed according to the prediction results of membrane flux and its recovery rate.
The scenario coevolution paradigm: adaptive quality assurance for adaptive systems

Abstract Systems are becoming increasingly more adaptive, using techniques like machine learning to enhance their behavior on their own rather than only through human developers programming them. We analyze the impact the advent of these new techniques has on the discipline of rigorous software engineering, especially on the issue of quality assurance. To this end, we provide a general description of the processes related to machine learning and embed them into a formal framework for the analysis of adaptivity, recognizing that to test an adaptive system a new approach to adaptive testing is necessary. We introduce scenario coevolution as a design pattern describing how system and test can work as antagonists in the process of software evolution. While the general pattern applies to large-scale processes (including human developers further augmenting the system), we show all techniques on a smaller-scale example of an agent navigating a simple smart factory. We point out new aspects in software engineering for adaptive systems that may be tackled naturally using scenario coevolution. This work is a substantially extended take on Gabor et al. (International symposium on leveraging applications of formal methods, Springer, pp 137–154, 2018).
Quality Index for View Synthesis by Measuring Instance Degradation and Global Appearance

Virtual view synthesis plays a vital role in the application of multi-view and free-viewpoint videos. Depth-image-based rendering (DIBR) is the most commonly used approach in view synthesis, and many DIBR algorithms have been proposed. However, how to evaluate the quality of DIBR-synthesized images and benchmark the DIBR algorithms are still very challenging, which may hinder the further development of the view synthesis technique. Hence, an effective quality metric for evaluating the distortions in view synthesis is urgently needed. With this motivation, this paper presents a quality index for view synthesis by simultaneously measuring local Instance DEgradation and global Appearance (IDEA). Due to the imperfection of rendering algorithms, local geometric distortions are easily introduced around instance contours, causing instance degradation, which is the dominant distortion in synthesized views. In this work, image instances are first detected and local instance degradation is measured based on discrete orthogonal moments. Meantime, we propose to measure the global appearance of synthesized images based on the superpixel representation. By integrating both local and global aspects of the distortions, a more accurate quality model is built for view synthesis. Extensive experiments and comparisons have demonstrated the superiority of the proposed method in evaluating the quality of DIBR-synthesized images and benchmarking the performance of view synthesis algorithms.
A Novel Facial Expression Intelligent Recognition Method Using Improved Convolutional Neural Network

Human facial expression is the core carrier of feedback. Facial expression recognition(FER) has been introduced into mickle fields, such as auxiliary medical care, safe driving, marketing assistance, distance education. However, in the real production process, facial expression image samples collected in different scenarios have problems such as complex backgrounds, which causes the FER model to train very slowly, low recognition rate, and insufficient generalization, so it cannot meet the actual production requirements. As the originator of the clustering algorithm, fuzzy C-means clustering(FCM) algorithm has stable performance and good results. It is applied to the convolutional layer of a convolutional neural network(CNN) to obtain a convolution kernel with an initial value, so as to extract the expression image features in the training set and the test set. This can solve the problem of random initialization of the convolution kernel. Based on the CNN, this paper introduces FCM to optimize the feature extraction (FE) capability of the model, and proposes a novel FER algorithm using an improved CNN(F-CNN). Because traditional CNN has problems such as irrational layer settings and too many parameters. The proposed F-CNN first adjusts the CNN network structure to improve the nonlinear expression ability of CNN. Then, replace the Softmax classifier that comes with CNN with a support vector machine (SVM) to improve the model's classification ability. The comparison experiments with other models show that the improved model improve the FER rate. The introduced FCM algorithm can effectively improve the model's FE performance and shorten the time of F-CNN during training. On the whole, F-CNN has reference value.
Multiple Knowledge Syncretic Transformer for Natural Dialogue Generation

Knowledge is essential for intelligent conversation systems to generate informative responses. This knowledge comprises a wide range of diverse modalities such as knowledge graphs (KGs), grounding documents and conversation topics. However, limited abilities in understanding language and utilizing different types of knowledge still challenge existing approaches. Some researchers try to enhance models' language comprehension ability by employing the pre-trained language models, but they neglect the importance of external knowledge in specific tasks. In this paper, we propose a novel universal transformer-based architecture for dialogue system, the Multiple Knowledge Syncretic Transformer (MKST), which fuses multi-knowledge in open-domain conversation. Firstly, the model is pre-trained on a large-scale corpus to learn commonsense knowledge. Then during fine-tuning, we divide the type of knowledge into two specific categories that are handled in different ways by our model. While the encoder is responsible for encoding dialogue contexts with multifarious knowledge together, the decoder with a knowledge-aware mechanism attentively reads the fusion of multi-knowledge to promote better generation. This is the first attempt that fuses multi-knowledge in one conversation model. The experimental results have been demonstrated that our model achieves significant improvement on knowledge-driven dialogue generation tasks than state-of-the-art baselines. Meanwhile, our new benchmark could facilitate the further study in this research area.
Research on the Automatic Extraction Method of Web Data Objects Based on Deep Learning

This paper represents a neural network model for the Web page information extraction based on the depth learning technology, and implements the model algorithm using the TensorFlow system. We then complete a detailed experimental analysis of the information extraction effect of Web pages on the same website, then show statistics on the accuracy index of the page information extraction, and optimize some parameters in the model according to the experimental results. On the premise of achieving ideal experimental results, an algorithm for migrating the model to the same pages of other websites for information extraction is proposed, and the experimental results are analyzed. Although the overall effect of the experiment is not as good as that of the page information extraction in different websites, it is far more effective than that of using the model directly on new websites. A new method is proposed to improve the portability of the information extraction system based on machine learning technology. At the same time, the deep nonlinear learning method of the depth learning model can prove deeper features, can have a more essential description of the abstract language, and can better express and understand sentences from the syntactic and semantic levels.