Reviewerly/exHarmony huggingface dataset:
Prediction-Based Reversible Watermarking of CT Scan Images for Content Authentication and Copyright Protection

In the application of teleradiology, radiologists located at different geographical locations around the globe, exchange Computed Tomography (CT) scan images along with relevant patient information for diagnosis as well as therapeutic measures. However, when these images are exchanged over public networks like the Internet, two important issues are created: content authentication and copyright protection. In this paper, a novel high capacity reversible watermarking scheme for CT Scan images is presented which is based on the region of interest (ROI) and region of non-interest (RONI) watermarking. The scheme embeds and extracts the secret data into the input CT scan image as well as restores the image to its pristine state at the receiving end. After segmenting the host image into the ROI and RONI regions, the scheme implants a fragile watermark (FW) into the ROI to check the integrity of the ROI and a robust watermark (RW) into the RONI for copyright protection. The novelty of the proposed system is in two fold. First, it takes advantage of the segmentation algorithm to avoid the additional overhead of information about the vertices usually required at the receiving end in order to select the ROI. Secondly, it takes advantage of the prediction-based reversible watermarking to avoid additional overhead of location map, which is usually required for reversibility in most of the reversible medical image watermarking techniques. Experimental results show that the proposed system outperforms medical image watermarking techniques recently reported in the literature in terms of embedding capacity and imperceptibility of watermarked image.
A Survey on Learning-Based Approaches for Modeling and Classification of Human–Machine Dialog Systems

With the rapid development from traditional machine learning (ML) to deep learning (DL) and reinforcement learning (RL), dialog system equipped with learning mechanism has become the most effective solution to address human-machine interaction problems. The purpose of this article is to provide a comprehensive survey on learning-based human-machine dialog systems with a focus on the various dialog models. More specifically, we first introduce the fundamental process of establishing a dialog model. Second, we examine the features and classifications of the system dialog model, expound some representative models, and also compare the advantages and disadvantages of different dialog models. Third, we comb the commonly used database and evaluation metrics of the dialog model. Furthermore, the evaluation metrics of these dialog models are analyzed in detail. Finally, we briefly analyze the existing issues and point out the potential future direction on the human-machine dialog systems.
Using Quantum Metrological Bounds in Quantum Error Correction: A Simple Proof of the Approximate Eastin-Knill Theorem

We present a simple proof of the approximate Eastin-Knill theorem, which connects the quality of a quantum error-correcting code (QECC) with its ability to achieve a universal set of transversal logical gates. Our derivation employs powerful bounds on the quantum Fisher information in generic quantum metrological protocols to characterize the QECC performance measured in terms of the worst-case entanglement fidelity. The theorem is applicable to a large class of decoherence models, including independent erasure and depolarizing noise. Our approach is unorthodox, as instead of following the established path of utilizing QECCs to mitigate noise in quantum metrological protocols, we apply methods of quantum metrology to explore the limitations of QECCs.
Viewport-Dependent Saliency Prediction in 360° Video

Saliency prediction in traditional images and videos has drawn extensive research interests in recent years. Few works have been proposed for saliency prediction over 360° videos. They focus on directly predicting fixations over the whole panorama. When viewing 360° videos, a person can only observe the content in her viewport, which means that only a fraction of the 360° scene can be seen at any given time. In this paper, we study human attention over viewport of 360° videos and propose a novel visual saliency model, dubbed viewport saliency, to predict fixations over 360° videos. Two contributions are introduced. First, we find that where people look is affected by the content and location of the viewport in 360° video. We study this over 200+ 360° videos viewed by 30+ subjects over two recent benchmark databases. Second, we propose a Multi-Task Deep Neural Network (MT-DNN) method for Viewport Saliency (VS) prediction in 360° video, which considers the input content and location of the viewport. Extensive experiments and analyses show that our method outperforms other state-of-the-art methods in this task. In particular, over the two recent 360° video databases, our MT-DNN raises the average CC score by 0.149 and 0.205, compared to SalGAN and DeepVS methods, respectively.
Using Deep Learning to Understand and Mitigate the Qubit Noise Environment

A neural network is constructed to learn the environment of an arbitrary qubit, allowing the underlying noise spectrum to be extracted in a manner that is more accurate and experimentally efficient than existing methods.
A secured land registration framework on Blockchain

Land is an immovable and non-liquid asset having high value. The integrity and proper track of ownership/transfer records of land is a highly challenging task. As the ownership of land can constantly change over time and that too sometimes very frequently, it poses a daunting task of keeping elaborate and long ownership transfer records. The problem further escalates due to presence of fraudulent or incomplete registries which are very difficult to trace back through time. Thus ownership disputes in the system, lead to litigation running for years, leading to wastage of valuable time, energy and resource for solving these disputes. Most of the issues root from the problem of the current land registration systems being either having legacy paper document trails or from poorly kept non-transparent centralized systems. Fraudulent users may try to forge paper documents or modify electronic records to change the land ownership record. This paper proposes a secure record keeping mechanism that addresses these issues using a Blockchain based system which can create record for the physical assets into an immutable liquid Blockchain based token asset. This new block chain token asset can now be used to keep a digitally secured and selectively visible record of ownership, solving the mentioned issues. An implementation of this system has been done using Ethereum and the benchmark data shows that the transaction processing time of such a system is reasonably low thereby making it suitable for practical implementation.
Development of an augmented reality‐based scaffold to improve the learning experience of engineering students in embedded system course

Abstract The Embedded system is the core unit of every digital device. The design and development of a new digital device or system require a good understanding of the embedded system embed in it. In the context of engineering education, learning embedded systems remains a challenge for the millennial. As, it involves the implementation of mathematics, programming, and working knowledge of electronic and electrical components rooted in it. Traditional teaching pedagogy has become less useful for new‐gen learners to satisfy their demand for knowledge of embedded systems. So, the teaching techniques have to be modified and equipped with the latest technological tools and practices. Augmented reality (AR) is emerging as a new technology in the field of engineering education. With the help of computer‐generated 3D data, animation, visual effects, and immersion, this technology has become more prevalent in the education domain to make complex concepts easier to understand. In this paper, an augmented reality‐based framework designed to teach embedded systems to engineering students. The proposed framework utilizes a tangible user interface comprising of AR markers, a USB camera, a display device, and the processing unit to give AR learning experience to the students. The system so developed tested for its usability with the faculty members of engineering education. Twenty faculty members, as participants, share their system usability experience and feedback using google form. The overall system usability score is 79.5%, which makes it suitable for further deployment on students for exploratory work.
Scene Graph Generation With Hierarchical Context

Scene graph generation has received increasing attention in recent years. Enhancing the predicate representations is an important entry point to this task. There are various methods to fully investigate the context of representation enhancement. In this brief, we analyze the decisive factors that can significantly affect the relation detection results. Our analysis shows that spatial correlations between objects, focused regions of objects, and global hints related to the relations have strong influences in relation prediction and contradiction elimination. Based on our analysis, we propose a hierarchical context network (HCNet) to generate a scene graph. HCNet consists of three contexts, including interaction context, depression context, and global context, which integrates information from pair, object, and graph levels. The experiments show that our method outperforms the state-of-the-art methods on the Visual Genome (VG) data set.
HBoost: A heterogeneous ensemble classifier based on the Boosting method and entropy measurement

In recent years, ensemble classifiers have attracted a lot of attention in the field of machine learning. The main challenges with these classifiers are 1) to select the base classifiers and 2) to combine the outputs. The key point for an ensemble to be successful is the diversity and accuracy of the base classifiers. This paper proposes a heterogeneous Boosting-based ensemble classifier (HBoost) which is inspired by Boosting algorithm and aims at increasing the diversity by recruiting distinct learning algorithms. In this approach, for each learning algorithm, several classifiers are generated by using the Boosting method and a matrix of heterogeneous classifiers is formed. Since all generated classifiers may not be appropriate for generating the final output, the subset of those that make the most diverse and accurate classifiers is selected and the rest of the classifiers are eliminated. Finally, the classifiers in the subset are combined based on the weight assigned to each classifier. HBoost is a self-configured algorithm i.e., unlike existing methods, it does not require a human expert to specify the type of base classifiers manually. It can also automatically determine the optimum number of base classifiers which should be combined. For evaluating the HBoost, two evaluation metrics have been used: Accuracy and Geometric Mean which are the most popular metrics to measure the performance of a classifier. We compare the proposed approach in three different scenarios 1) all the base classifiers are in the ensemble, 2) four traditional ensemble methods including Bagging, Boosting, Stacking, and StackingC, 3) two state-of-the-art approaches in the literature including Random Forest and Classifier Subset Selection (CSS). HBoost is performed on 20 datasets from the UCI repository and experimental results reveal how adequate the HBoost is in comparison to other approaches.
Crime Prediction Using K-Nearest Neighboring Algorithm

For a developing country like India, it is not new that people hear of crimes happening quite often. With the rapid urbanization of cities, we have to constantly be aware of our surroundings. In order to avoid the unfortunate, we will try to observe crime rates by the KNN prediction method. It will predict, tentatively, the type of crime, when, where and at what time it may take place. This data will give the behaviors in crime over an area which might be helpful for criminal investigations. It will also provide us with the most committed crime in a particular region. In this paper, we will use the k-nearest neighbor algorithm of machine learning.
Text-to-SQL Generation for Question Answering on Electronic Medical Records

Electronic medical records (EMR) contain comprehensive patient information and are typically stored in a relational database with multiple tables. Effective and efficient patient information retrieval from EMR data is a challenging task for medical experts. Question-to-SQL generation methods tackle this problem by first predicting the SQL query for a given question about a database, and then, executing the query on the database. However, most of the existing approaches have not been adapted to the healthcare domain due to a lack of healthcare Question-to-SQL dataset for learning models specific to this domain. In addition, wide use of the abbreviation of terminologies and possible typos in questions introduce additional challenges for accurately generating the corresponding SQL queries. In this paper, we tackle these challenges by developing a deep learning based TRanslate-Edit Model for Question-to-SQL (TREQS) generation, which adapts the widely used sequence-to-sequence model to directly generate the SQL query for a given question, and further performs the required edits using an attentive-copying mechanism and task-specific look-up tables. Based on the widely used publicly available electronic medical database, we create a new large-scale Question-SQL pair dataset, named MIMICSQL, in order to perform the Question-to-SQL generation task in healthcare domain. An extensive set of experiments are conducted to evaluate the performance of our proposed model on MIMICSQL. Both quantitative and qualitative experimental results indicate the flexibility and efficiency of our proposed method in predicting condition values and its robustness to random questions with abbreviations and typos.
RES: Real-Time Video Stream Analytics Using Edge Enhanced Clouds

With increasing availability and use of Internet of Things (IoT) devices such as sensors and video cameras, large amounts of streaming data is now being produced at high velocity. Applications which require low latency response such as video surveillance, augmented reality and autonomous vehicles demand a swift and efficient analysis of this data. Existing approaches employ cloud infrastructure to store and perform machine learning-based analytics on this data. This centralized approach has limited ability to support real-time analysis of large-scale streaming data due to network bandwidth and latency constraints between data source and cloud. We propose RealEdgeStream (RES) an edge enhanced stream analytics system for large-scale, high performance data analytics. The proposed approach investigates the problem of video stream analytics by proposing (i) filtration and (ii) identification phases. The filtration phase reduces the amount of data by filtering low-value stream objects using configurable rules. The identification phase uses deep learning inference to perform analytics on the streams of interest. The phases consist of stages which are mapped onto available in-transit and cloud resources using a placement algorithm to satisfy the Quality of Service (QoS) constraints identified by a user. We demonstrate that for a 10K element data streams, with a frame rate of 15&#x2013;100 per second, the job completion in the proposed system takes 49 percent less time and saves 99 percent bandwidth compared to a centralized cloud-only based approach.
Gradient-Enhanced Softmax for Face Recognition

This letter proposes a gradient-enhanced softmax supervisor for face recognition (FR) based on a deep convolutional neural network (DCNN). The proposed supervisor conducts the constant-normalized cosine to obtain the score for each class using a combination of the intra-class score and the soft maximum of the inter-class scores as the objective function. This mitigates the vanishing gradient problem in the conventional softmax classifier. The experiments on the public Labeled Faces in the Wild (LFW) database denote that the proposed supervisor achieves better results when compared with those achieved using the current state-of-the-art softmax-based approaches for FR.
AutoCaption: An Approach to Generate Natural Language Description from Visualization Automatically

In this paper, we propose a novel approach to generate captions for visualization charts automatically. In the proposed method, visual marks and visual channels, together with the associated text information in the original charts, are first extracted and identified with a multilayer perceptron classifier. Meanwhile, data information can also be retrieved by parsing visual marks with extracted mapping relationships. Then a 1-D convolutional residual network is employed to analyze the relationship between visual elements, and recognize significant features of the visualization charts, with both data and visual information as input. In the final step, the full description of the visual charts can be generated through a template-based approach. The generated captions can effectively cover the main visual features of the visual charts and support major feature types in commons charts. We further demonstrate the effectiveness of our approach through several cases.
Straggler-Resistant Distributed Matrix Computation via Coding Theory: Removing a Bottleneck in Large-Scale Data Processing

The current big data era routinely requires the processing of large-scale data on massive distributed computing clusters. In these applications, data sets are often so large that they cannot be housed in the memory and/or the disk of any one computer. Thus, the data and the processing are typically distributed across multiple nodes. Distributed computation is thus a necessity rather than a luxury. The widespread use of such clusters presents several opportunities and advantages over traditional computing paradigms. However, it also presents newer challenges where coding-theoretic ideas have recently had a significant impact. Large-scale clusters (which can be heterogeneous in nature) suffer from the problem of stragglers, which are slow or failed worker nodes in the system. Thus, the overall speed of a computation is typically dominated by the slowest node in the absence of a sophisticated assignment of tasks to the worker nodes.
Enhancing Security Mechanisms for Healthcare Informatics Using Ubiquitous Cloud

The most vital chores of the health care is the proper attention and sharing of the information’s about the patients record maintained whenever it is needed. The failure in proper maintenance of the patient’s particulars and proper sharing of information’s when required would lead to loss of privacy and reliability resulting in the inert and severe effects on the particulars of the patients as well as their lives respectively. So securing of the health care particulars in electronic form becomes a very important necessity nowadays. The constant monitoring of the centralized storage of health records that are prone to security threats are very difficult. So the paper puts forward a block chain technology to safeguard the reliability of the information’s stored and utilizes the digital signature with authentication to protect the private information’s in the patient’s record developing a model utilizing the cloud that enables the authenticity and the reliability of the information. The model put forth in the paper was examined and compared with the traditional techniques used for storing the medical records, on the terms of reply time, along with the cost of storing and retrieving.
Role of artificial intelligence in diagnostic oral pathology-A modern approach

Over the decades, new equipment was emerged in medical field, and we have witnessed the importance of medical imaging such as computed tomography, magnetic resonance imaging, ultrasound, mammography and X-ray and their contribution in successful diagnosis and treatment of various diseases. Now, we are in era of artificial intelligence (AI), where machines were modeled after human brain's ability to take inputs and produce outputs from given data. AI has a wide range of uses and applications in health services industry. Factors such as increase in workload, complexity of work and potential fatigue of doctors may compromise diagnostic ability and outcome. AI components in imaging machines would reduce this workload and drive greater efficiency. They also have access to a greater wealth of data than human counterparts and can detect cancer with more accuracy than humans. This study presented an overview of AI, its recent advances in pathology and future prospects.
Quantifying Gender Bias in Different Corpora

Word embedding models have been shown to be effective in performing a wide variety of Natural Language Processing (NLP) tasks such as identifying audiences for web advertisements, parsing resumés to select promising job candidates, and translating documents from one language to another. However, it has been demonstrated that NLP systems learn gender bias from the corpora of documents on which they are trained. It is increasingly common for pre-trained models to be used as a starting point for building applications in a wide range of areas including critical decision making applications. It is also very easy to use a pre-trained model as the basis for a new application without careful consideration of the original nature of the training set. In this paper, we quantify the degree to which gender bias differs with the corpora used for training. We look especially at the impact of starting with a pre-trained model and fine-tuning with additional data. Specifically, we calculate a measure of direct gender bias on several pre-trained models including BERT’s Wikipedia and Book corpus models as well as on several fine-tuned General Language Understanding Evaluation (GLUE) benchmarks. In addition, we evaluate the bias from several more extreme corpora including the Jigsaw identity toxic dataset that includes toxic speech biased against race, gender, religion, and disability and the RtGender dataset that includes speech specifically labelled by gender. Our results reveal that the direct gender bias of the Jigsaw toxic identity dataset is surprisingly close to that of the base pre-trained Google model, but the RtGender dataset has significantly higher direct gender bias than the base model. When the bias learned by an NLP system can vary significantly with the corpora used for training, it becomes important to consider and report these details, especially for use in critical decision-making applications.
What is Venmo? A descriptive analysis of social features in the mobile payment platform

This article describes the social features found in Venmo, a US-based payment app. Venmo is a social payments platform that allows users to broadcast transactions to a private social activity stream and a fully public transaction feed. After assessing and measuring the content and structure of public Venmo transaction messages (N = 328,769,355), we present findings related to the types of payment transactions, the frequency of emoji and text in messages used for labeling and earmarking payments, and trends in the temporality of payments from the first six years of the Venmo Public Feed. We explore how affordances in Venmo support social features typically seen in social media platforms with public feeds such as Facebook, Instagram, or Twitter. Our findings ultimately detail how mobile payment services like the Venmo support social practices, digital communication, and commerce with social features found in social media platforms. In addition to these descriptive findings, this early Venmo case study develops some implications for the design, study, and impact of researching mobile payment technologies as social media.