Reviewerly/exHarmony huggingface dataset:
Rapid health data repository allocation using predictive machine learning

Health-related data is stored in a number of repositories that are managed and controlled by different entities. For instance, Electronic Health Records are usually administered by governments. Electronic Medical Records are typically controlled by health care providers, whereas Personal Health Records are managed directly by patients. Recently, Blockchain-based health record systems largely regulated by technology have emerged as another type of repository. Repositories for storing health data differ from one another based on cost, level of security and quality of performance. Not only has the type of repositories increased in recent years, but the quantum of health data to be stored has increased. For instance, the advent of wearable sensors that capture physiological signs has resulted in an exponential growth in digital health data. The increase in the types of repository and amount of data has driven a need for intelligent processes to select appropriate repositories as data is collected. However, the storage allocation decision is complex and nuanced. The challenges are exacerbated when health data are continuously streamed, as is the case with wearable sensors. Although patients are not always solely responsible for determining which repository should be used, they typically have some input into this decision. Patients can be expected to have idiosyncratic preferences regarding storage decisions depending on their unique contexts. In this paper, we propose a predictive model for the storage of health data that can meet patient needs and make storage decisions rapidly, in real-time, even with data streaming from wearable sensors. The model is built with a machine learning classifier that learns the mapping between characteristics of health data and features of storage repositories from a training set generated synthetically from correlations evident from small samples of experts. Results from the evaluation demonstrate the viability of the machine learning technique used.
Secure Sharing of Health Data Using Hyperledger Fabric Based on Blockchain Technology

Permissioned block chain network ensures the trust and security with the help of key features of block-chain technology like distributed ledger, smart contract and consensus. Currently the sharing of patient data between health organisation is not safe without trusting each other may leads to data privacy violation. Block chain provides the trust in sharing of patient's medical information between health organizations. In this paper hyperledger fabric frame work based permissioned block chain network is proposed and established among patents and medical institutes to achieve the secured and reliable sharing of the patient's data. Block chain reliably manage the electronic health records eefficiently using Hyper ledger fabric frame work. Implementation results shows that, the hyperledger fabric based Block chain removes the unreliability in sharing of data among health care centres, doctors, public health departments and hospitals. This network also allows the organizations to quickly and safely move medical data in a legally compliant manner as it is a transparent system. The blockchain implemented has achieved the transparent and secure transfer and also controls the access of health data of patients by user policies with smart contracts as chain code. Hyper ledger Fabric provides high levels of performance, protection, and transaction privacy and the results have proved.
Deep Hybrid Knowledge Graph Embedding for Top-N Recommendation

In knowledge graph (KG) based recommender systems, path-based methods make recommendations by building user-item graphs and exploiting connectivity patterns between the entities in the graph. To overcome the limitations of traditional meta-path based methods that rely heavily on handcrafted meta-paths, recent deep neural network based methods, such as the Recurrent Knowledge Graph Embedding (RKGE) approach, can automatically mine the connectivity patterns between entities in the KG, thereby improving recommendation performance. However, these methods usually use only one type of neural network to encode path embeddings, which cannot fully extract path features, limiting performance improvement of the recommender system. In this paper, we propose a Deep Hybrid Knowledge Graph Embedding (DHKGE) method for top-N recommendation. DHKGE encodes embeddings of paths between users and items by combining convolutional neural network (CNN) and the long short-term memory (LSTM) network. Furthermore, it uses an attention mechanism to aggregate the encoded path representations and generate a final hidden state vector, which is used to calculate the proximity between the target user and candidate items, thus generating top-N recommendation. Experiments on the MovieLens 100K and Yelp datasets show that DHKGE overall outperforms RKGE and several typical recommendation methods in terms of Precision@N, MRR@N, and NDCG@N.
Two-Level LSTM for Sentiment Analysis With Lexicon Embedding and Polar Flipping

Sentiment analysis is a key component in various text mining applications. Numerous sentiment classification techniques, including conventional and deep-learning-based methods, have been proposed in the literature. In most existing methods, a high-quality training set is assumed to be given. Nevertheless, constructing a high-quality training set that consists of highly accurate labels is challenging in real applications. This difficulty stems from the fact that text samples usually contain complex sentiment representations, and their annotation is subjective. We address this challenge in this study by leveraging a new labeling strategy and utilizing a two-level long short-term memory network to construct a sentiment classifier. Lexical cues are useful for sentiment analysis, and they have been utilized in conventional studies. For example, polar and negation words play important roles in sentiment analysis. A new encoding strategy, that is, ρ -hot encoding, is proposed to alleviate the drawbacks of one-hot encoding and, thus, effectively incorporate useful lexical cues. Moreover, the sentimental polarity of a word may change in different sentences due to label noise or context. A flipping model is proposed to model the polar flipping of words in a sentence. We compile three Chinese datasets on the basis of our label strategy and proposed methodology. Experiments demonstrate that the proposed method outperforms state-of-the-art algorithms on both benchmark English data and our compiled Chinese data.
Mining Shape Expressions From Positive Examples

Shape expressions (SEs) is a novel specification language that was recently introduced to express behavioral patterns over real-valued signals observed during the execution of cyber-physical systems. An SE is a regular expression composed of arbitrary parameterized shapes, such as lines, exponential curves, and sinusoids as atomic symbols with symbolic constraints on the shape parameters. SEs enable a natural and intuitive specification of complex temporal patterns over possibly noisy data. In this article, we propose a novel method for mining a broad and interesting fragment of SEs from time-series data using a combination of techniques from linear regression, unsupervised clustering, and learning finite automata from positive examples. The learned SE for a given dataset provides an explainable and intuitive model of the observed system behavior. We demonstrate the applicability of our approach on two case studies from different application domains and experimentally evaluate the implemented specification mining procedure.
GLCM and GLRLM Based Texture Analysis: Application to Brain Cancer Diagnosis Using Histopathology Images

Objective This work focuses on design and development of an automated diagnostic system using brain cancer histopathology images. Background Detection and classification of cancerous tissues are one of the challenging tasks in histopathology image analysis. The features based on cell morphology, cell distribution, and randomness in growth and placements have been considered as important aspects of the cancer diagnosis. Results of the conventional methods are subjective and dependent on the skills of histopathologists. The computer-assisted diagnosis (CAD) offers the diagnostic results based on standard algorithms and standard test database. Material and Methods This work shows the contribution of gray-level co-occurrence matrix (GLCM) based Haralick features and gray-level run length matrix (GLRLM) features in analysis and classification of Brain histopathology images. The primary focus of this study is to analyze the complex random field of cancer images with co-occurrence matrix features, run length matrix features, and also the fusion of both features. These features are used for classifying healthy and malignant tissue. The SVM classifier with RBF kernel has been used for classification. The Confusion matrix is formed showing true positive (TP), true negative (TN), false positive (FP), false negative (FN) information. The classifier performance has been described with sensitivity, specificity, precision, accuracy, and F-score measures. Results Outcome of this work shows that GLCM-based and GLRLM-based features offer excellent discriminating features for statistical study of histopathology images and can be useful for cancer detection. Overall accuracy improvement is seen by fusing the GLCM and GLRLM based texture features. Conclusion The work describes an innovative way of using GLCM and GLRLM based textural features to extract underlying information in brain cancer imagery.
Toward Reliable Models For Authenticating Multimedia Content: Detecting Resampling Artifacts With Bayesian Neural Networks

In multimedia forensics, learning-based methods provide state-of the-art performance in determining origin and authenticity of images and videos. However, most existing methods are challenged by out-of-distribution data, i.e., with characteristics that are not covered in the training set. This makes it difficult to know when to trust a model, particularly for practitioners with limited technical background.In this work, we make a first step toward redesigning forensic algorithms with a strong focus on reliability. To this end, we propose to use Bayesian neural networks (BNN), which combine the power of deep neural networks with the rigorous probabilistic formulation of a Bayesian framework. Instead of providing a point estimate like standard neural networks, BNNs provide distributions that express both the estimate and also an uncertainty range.We demonstrate the usefulness of this framework on a classical forensic task: resampling detection. The BNN yields state-of-the-art detection performance, plus excellent capabilities for detecting out-of-distribution samples. This is demonstrated for three pathologic issues in resampling detection, namely unseen resampling factors, unseen JPEG compression, and unseen resampling algorithms. We hope that this proposal spurs further research toward reliability in multimedia forensics.
Illumination Adaptive Person Reid Based on Teacher-Student Model and Adversarial Training

Most existing works in Person Re-identification (ReID) focus on settings where illumination either is kept the same or has very little fluctuation. However, the changes in the illumination degree may affect the robustness of a ReID algorithm significantly. To address this problem, we proposed a Two-Stream Network that can separate ReID features from lighting features to enhance ReID performance. Its innovations are threefold: (1) A discriminative entropy loss to ensure the ReID features contain no lighting information. (2) A ReID Teacher model trained by images under “neutral” lighting conditions to guide ReID classification. (3) An illumination Teacher model trained by the differences between the illumination-adjusted and original images to guide illumination classification. We construct two augmented datasets by synthetically changing a set of predefined lighting conditions in two of the most popular ReID benchmarks: Market1501 and DukeMTMC-ReID. Experiments demonstrate that our algorithm outperforms other state-of-the-art works and particularly potent in handling images under extremely low light.
A Generative Approach Towards Improved Robotic Detection of Marine Litter

This paper presents an approach to address data scarcity problems in underwater image datasets for visual detection of marine debris. The proposed approach relies on a two-stage variational autoencoder (VAE) and a binary classifier to evaluate the generated imagery for quality and realism. From the images generated by the two-stage VAE, the binary classifier selects "good quality" images and augments the given dataset with them. Lastly, a multi-class classifier is used to evaluate the impact of the augmentation process by measuring the accuracy of an object detector trained on combinations of real and generated trash images. Our results show that the classifier trained with the augmented data outperforms the one trained only with the real data. This approach will not only be valid for the underwater trash classification problem presented in this paper, but it will also be useful for any data-dependent task for which collecting more images is challenging or infeasible.
A hardware-aware CPU power measurement based on the power-exponent function model for cloud servers

The energy consumption of cloud servers accounts for about 25% of the total energy of cloud data centers. Reducing and optimizing this energy consumption is thus extremely important in energy saving in cloud data centers. Power model is fundamental in energy efficiency optimization scheduling for cloud computing. However, systems and tools for power measurement in the cloud computing environment are relatively scarce, and power models of cloud servers cannot keep up with the times. Therefore, we propose a new CPU power model named power-exponent function model (PEFM) is proposed, which provides higher accuracy in estimating the CPU power of the latest cloud servers than the current linear, polynomial and power function models. A novel hardware-aware CPU power measurement (HCPM) is also proposed, that can select an appropriate CPU power model through the launch year of CPU without power model training. For validating the efficacy of PEFM and HCPM, a set of experiments including OpenStack cluster experiment, based on a distributed energy meter (DEM) implemented by our team were conducted. The experimental results indicate that the proposed PEFM and HCPM not only improve the accuracy of CPU power estimation in cloud servers in cloud environment, but also reduce the difficulty of model training and simplify system deployment.
Building an Online Learning Module for Satellite Remote Sensing Applications in Hydrologic Science

This article presents an online teaching tool that introduces students to basic concepts of remote sensing and its applications in hydrology. The learning module is intended for junior/senior undergraduate students or junior graduate students with no (or little) prior experience in remote sensing, but with some basic background of environmental science, hydrology, statistics, and programming. This e-learning environment offers background content on the fundamentals of remote sensing, but also integrates a set of existing online tools for visualization and analysis of satellite observations. Specifically, students are introduced to a variety of satellite products and techniques that can be used to monitor and analyze changes in the hydrological cycle. At completion of the module, students are able to visualize remote sensing data (both in terms of time series and spatial maps), detect temporal trends, interpret satellite images, and assess errors and uncertainties in a remote sensing product. Students are given the opportunity to check their understanding as they progress through the module and also tackle complex real-life problems using remote sensing observations that professionals and scientists commonly use in practice. The learning tool is implemented in HydroLearn, an open-source, online platform for instructors to find and share learning modules and collaborate on developing teaching resources in hydrology and water resources.
Understanding and Supporting Academic Literature Review Workflows with LitSense

It is increasingly difficult for researchers to navigate and reach an understanding of a growing body of literature in a field of research. While past works in HCI and data visualization sought to support such activities, few investigated how these workflows are conducted in practice and how practices change in view of support tools. This work contributes a more holistic understanding of this space via a user-centered approach encompassing (a) a formative study on literature review practices of 15 researchers which informed (b) the design of LitSense, a proof-of-concept tool to support literature review workflows, and (c) a week-long study with 12 researchers performing a literature review with Litsense.
Human interaction behavior modeling using Generative Adversarial Networks

Recently, considerable research has focused on personal assistant robots, and robots capable of rich human-like communication are expected. Among humans, non-verbal elements contribute to effective and dynamic communication. However, people use a wide range of diverse gestures, and a robot capable of expressing various human gestures has not been realized. In this study, we address human behavior modeling during interaction using a deep generative model. In the proposed method, to consider interaction motion, three factors, i.e., interaction intensity, time evolution, and time resolution, are embedded in the network structure. Subjective evaluation results suggest that the proposed method can generate high-quality human motions.
Healthcare Data Protection Based on Blockchain using Solidity

In today's digital world, information is exchanged between systems and it is expected that each interaction / transaction between the systems is secure and reliable. Block chain technology can be applied to protect the security of healthcare data. It is important to have access control mechanism that allows only authorized persons to share the medical data. In this work, we introduce a blockchain based system for securely sharing health records. The system includes a smart contract based data protection and accessibility mechanism.
Performance Analysis Between RunC and Kata Container Runtime

Kata container unifies the security advantages of virtual machines with the speed and flexibility of containers. It is designed along the Open Container Initiative's specification to perform like containers, enforcing the workload isolation. Since Docker containers share the kernel of the host operating system, it is exposed to potential security threats. Kata container mitigates these security issues by introducing a lightweight virtual machine with separate kernels to create an isolated environment for the container. This research work aims at providing a quantitative analysis between the Docker and Kata container runtimes. The performance of both the technologies has been evaluated based on boot tests, system utilizations, and benchmarks, showing a detailed analysis of the security and performance tradeoff between both the container technologies.
A large reproducible benchmark of ontology-based methods and word embeddings for word similarity

This work is a companion reproducibility paper of the experiments and results reported in Lastra-Diaz et al. (2019a), which is based on the evaluation of a companion reproducibility dataset with the HESML V1R4 library and the long-term reproducibility tool called Reprozip. Human similarity and relatedness judgements between concepts underlie most of cognitive capabilities, such as categorization, memory, decision-making and reasoning. For this reason, the research on methods for the estimation of the degree of similarity and relatedness between words and concepts has received a lot of attention in the fields of artificial intelligence and cognitive sciences. However, despite the huge research effort done, there is a lack of a self-contained, reproducible and extensible collection of benchmarks which being amenable to become a de facto standard for large scale experimentation in this line of research. In order to bridge this reproducibility gap, this work introduces a set of reproducible experiments on word similarity and relatedness by providing a detailed reproducibility protocol together with a set of software tools and a self-contained reproducibility dataset, which allow that all experiments and results in our aforementioned work to be reproduced exactly. Our aforementioned primary work introduces the largest, most detailed and reproducible experimental survey on word similarity and relatedness reported in the literature, which is based on the implementation of all evaluated methods into the same software platform. Our reproducible experiments evaluate most of methods in the families of ontology-based semantic similarity measures and word embedding models. We also detail how to extend our experiments to evaluate other unconsidered experimental setups. Finally, we provide a corrigendum for a mismatch in the MC28 similarity scores used in our original experiments.
Preoperative Prediction of Lymph Node Metastasis from Clinical DCE MRI of the Primary Breast Tumor Using a 4D CNN

In breast cancer, undetected lymph node metastases can spread to distal parts of the body for which the 5-year survival rate is only 27%, making accurate nodal metastases diagnosis fundamental to reducing the burden of breast cancer, when it is still early enough to intervene with surgery and adjuvant therapies. Currently, breast cancer management entails a time consuming and costly sequence of steps to clinically diagnose axillary nodal metastases status. The purpose of this study is to determine whether preoperative, clinical DCE MRI of the primary tumor alone may be used to predict clinical node status with a deep learning model. If possible then many costly steps could be eliminated or reserved for only those with uncertain or probable nodal metastases. This research develops a data-driven approach that predicts lymph node metastasis through the judicious integration of clinical and imaging features from preoperative 4D dynamic contrast enhanced (DCE) MRI of 357 patients from 2 hospitals. Innovative deep learning classifiers are trained from scratch, including 2D, 3D, 4D and 4D deep convolutional neural networks (CNNs) that integrate multiple data types and predict the nodal metastasis differentiating nodal stage N0 (non metastatic) against stages N1, N2 and N3. Appropriate methodologies for data preprocessing and network interpretation are presented, the later of which bolster radiologist confidence that the model has learned relevant features from the primary tumor. Rigorous nested 10-fold cross-validation provides an unbiased estimate of model performance. The best model achieves a high sensitivity of 72% and an AUROC of 71% on held out test data. Results are strongly supportive of the potential of the combination of DCE MRI and machine learning to inform diagnostics that could substantially reduce breast cancer burden.