Reviewerly/exHarmony huggingface dataset:
Research on Personal Health Data Provenance and Right Confirmation with Smart Contract

Data provenance technology and right confirmation technology have received great attention in recent years, because data exposure and abuse have been a serious problem with the rapid development of smart device and hospital. Based on the traceability and unchangeable properties of blockchain, we propose a model of personal health-related application data provenance and use smart contract to ensure right confirmation. We find that the user condition and use-right of personal health data can be effectively confirmed by using data provenance. In addition, the possible future extensions to personal health data on provenance are discussed.
DASNet: Dynamic Activation Sparsity for Neural Network Efficiency Improvement

To improve the execution speed and efficiency of neural networks in embedded systems, it is crucial to decrease the model size and computational complexity. In addition to conventional compression techniques, e.g., weight pruning and quantization, removing unimportant activations can reduce the amount of data communication and the computation cost. Unlike weight parameters, the pattern of activations is directly related to input data and thereby changes dynamically. To regulate the dynamic activation sparsity (DAS), in this work, we propose a generic low-cost approach based on winners-take-all (WTA) dropout technique. The network enhanced by the proposed WTA dropout, namely DASNet, features structured activation sparsity with an improved sparsity level. Compared to the static feature map pruning methods, DASNets provide better computation cost reduction. The WTA technique can be easily applied in deep neural networks without incurring additional training variables. Our experiments on various networks and datasets present significant run-time speedups with negligible accuracy loss.
Multiple Human Objects Tracking and Classification Directly in Compressive Measurement Domain for Long Range Infrared Videos

In this paper, we summarize new results in human target tracking and classification using compressive measurements directly. Two deep learning algorithms known as You Only Look Once (YOLO) and residual network (ResNet) have been applied. YOLO was used for object detection and tracking and ResNet was used for human classification. Extensive experiments using low quality and long range mid-wave infrared (MWIR) videos in the SENSIAC database showed that the proposed approach is promising.
Real-Time Two Way Communication System for Speech and Hearing Impaired Using Computer Vision and Deep Learning

Sign Language is the most expressive form of communication for speech and hearing impaired people to communicate with normal person but a normal person cannot understand sign language. So in order to break this barrier of communication there needs to be a system that can enable conversion of sign language to voice or text and voice or text to sign language and do it in real time. The systems that currently exist are not real time, do not facilitate two-way communication, require static surrounding conditions or have low recognition accuracy. There exist systems that have good accuracy but require external hardware like gloves [3] which increases the cost. Our contribution to solving this problem consists of a Sign Language Communication System. It is a real-time communications system built using the advancements in Image Processing, Deep Learning and Computer Vision that provides real-time sign language to text and text to sign language conversion. The project is software-based which can be installed on any computer with good specifications. It is also a two-way communication system allowing not just speech and hearing impaired to communicate with normal people but also other way around. The primary goal of our system is to enable hearing and speech impaired people to communicate with people that are not disabled in real time by interpreting alphabets, numbers and words in the Indian sign language. The system is able to predict 17600 test images in 14 seconds with an average prediction time of 0.000805 seconds with an accuracy of 99%.
Fast scheme for determination of direct normal irradiance. Part I: New aerosol parameterization and performance assessment

A new aerosol direct transmittance parameterization is developed based on detailed radiative transfer model calculations for improving direct normal irradiance (DNI) for the solar energy forecast in the fast radiation scheme SUNFLUX. The modified SUNFLUX scheme is evaluated against observational datasets collected from the 14 Baseline Surface Radiation Network (BSRN) stations. The aerosol data from AERONET stations collocated with the BSRN network are used in the evaluations. The evaluation results have indicated that the accuracy of the calculated DNI using the new version of SUNFLUX has been improved significantly compared to the old version. The relative mean bias difference (MBD) and relative root mean square difference (RMSD) are −3.03% and 4.95%, respectively. The performance of the modified DNI model is also compared with those from the other five models. The results predicted by the current model are comparable with those from the best reference models. More importantly, the evaluation results show that the new aerosol scheme developed in this study can also be applied to other models to improve their performance.
An Extensive Survey on Some Deep-Learning Applications

Deep learning prospered as a distinct era of research and fragment of a wider family of machine learning, based on a set of algorithms that strengthen to model high-level abstractions in data. It tries to imitate the human intellect and learns from complicated input data and resolve different types of difficult and complex tasks. Because of Deep Learning, it was successful to deal with different input data types such as text, sound, and images in various fields. Improvement in deep-learning research has already influenced the search for speech recognition, automatic navigation systems, parallel computations, image processing, ImageNet, natural language processing, representation learning, Google translate, etc. Here, we present a review of DL and its applications including the recent development in natural language processing (NLP).
C-POS: A Context-Aware Adaptive Part-of-Speech Language Learning Framework

In the current age of technology, comprehension of the English language is very important. This is because that often innovation and discoveries are done by English speaking countries. And thus, it is considered as being the most important and widely used international language. Further, the need for learning an additional language has increased due to globalization in an unprecedented manner. Therefore, English is being taught as an additional language at various levels of education. In traditional classrooms of non-English speaking countries, students only learn English in their language classes whereas they have few opportunities to practice English outside the classroom. However, the specialized nature of mobile technologies has opened a new horizon for learners that make information available to people at any time, at any place, and in any form.The number of smartphone users is increasing every year and more than 4.78 billion smartphone users are in the world today.It can be understood that learning increases when the learning process is integrated with daily life routine activities. The context-awareness is one of the commonly used techniques that provide specialized content to different learners based on their context parameters. The word 'context' refers to any information that can be used to characterize the situation of a particular entity. The context parameter includes location, time, concentration level, Interest and learner's knowledge. Based on these context parameters, a Context-aware Part-Of-Speech (C-POS) learning system is designed, implemented and evaluated. Different experiments are conducted to evaluate the performance of learners' which is increased by 9%. The evaluation of results provides evidence that the C-POS learning system can be successfully adapted by the non-natives speakers to learn the English language effectively.
Privacy-Preserving Technologies

Abstract This chapter introduces privacy and data protection by design, and reviews privacy-enhancing techniques (PETs). Although privacy by design includes both technical and operational measures, the chapter focuses on the technical measures. First, it enumerates design strategies. Next, it considers privacy-enhancing techniques that directly address the hide strategy, but also aid in implementing the separate , control and enforce strategies. Specifically, it addresses PETs for: (1) identification, authentication and anonymity; (2) private communications; (3) privacy-preserving computations; (4) privacy in databases; and (5) discrimination prevention in data mining.
Possibilities of Block Chain in Indian Market and Notably In Advertising Industry

In this paper, the writers propose a new era of the Indian market and the advertising industry, by introducing a new platform for this industry, the block chain. It has a great potential to change the existing models and it will the next big thing as internet is today. Many entrepreneurs and people are unaware of this technology; this paper guides them and makes them familiarize with the broad use of block chain. The paper emphasizes mainly on the gap, which is between the advertisers & publishers, it will eliminate the intermediaries from the market and with block chain, which will effect in a drastic reduction for operational costs. Blockchain will discard the problems like fraud, security issues, hidden taxes, and most importantly, censorship will be gone forever. It will become the best alternative for real-time settlements also.
An Exploratory Study on Machine Learning to Combine Security Vulnerability Alerts from Static Analysis Tools

Due to time-to-market needs and cost of manual validation techniques, software systems are often deployed with vulnerabilities that may be exploited to gain illegitimate access/control, ultimately resulting in non-negligible consequences. Static Analysis Tools (SATs) are widely used for vulnerability detection, where the source code is analyzed without executing it. However, the performance of SATs varies considerably and a high detection rate usually comes with significant false alarms. Recent studies considered combining various SATs to improve the overall detection ability, but they do not allow exploring different performance trade-offs, as basic and rigid rules are normally followed. Machine Learning (ML) algorithms have shown promising results in several complex problems, due to their ability to fit specific needs. This paper presents an exploratory study on the combination of the output of SATs through ML algorithms to improve vulnerability detection while trying to reduce false alarms. The dataset consists of SQL Injection (SQLi) and Cross-Site Scripting (XSS) vulnerabilities detected by five different SATs in a large set of WordPress plugins developed in PHP. Results show that, for the case of SQLi, a false alarm reduction is possible without compromising the vulnerabilities detected, and that using ML allows trade-offs (e.g., reduction in false alarms at the expense of a few vulnerabilities) that are not possible with existing techniques. The paper also proposes a regression-based approach for ranking source code files considering estimates of vulnerabilities computed using the output of SATs. Results show that the approach allows creating a ranking of the source code files that largely overlaps the real ranking (based on real known vulnerabilities).
Design of an Autonomous Mobile Robot based on the Sensor Data Fusion of Lidar 360, Ultrasonic sensor and Wheel Speed Encoder

The research in improving the efficiency of the navigation of autonomous mobile robot is escalating in the field of robotics. Path planning and Obstacle avoidance are important aspects of autonomous mobile robot navigation. This requires highly accurate and robust sensors for ranging and obstacle detection. Error reduction in these sensors can be achieved using various statistical methods. The proposed technique for error reduction utilizes ANFIS system for reducing error in ultrasonic sensor and wheel speed encoder. The ANFIS models for the sensors are evaluated with different membership function for finding the one with the best RMSE. The trained ultrasonic sensor and wheel speed encoder are integrated with LiDAR, used for building an autonomous mobile robot.
Closing the Loop in Cyber-Physical Systems using Blockchain: Microgrid Frequency Control Example

Closed-loop Cyber-Physical Systems (CPSs) are significant constituent elements for smart city applications. However, security and resiliency of closed-loop CPSs can be compromised due to the centralized control structure, network interdependency and power/computational constraints. In this paper, towards addressing this problem, we propose a blockchain based de-centralized closed-loop CPS framework. Sensed measurements are stored on the blockchain and controller implementation and actuation is realized using smart contracts. The feasibility of the proposed approach is demonstrated via its simulative implementation on a distributed frequency control system within an islanded microgrid. A co-simulation framework is developed that incorporates a microgrid simulated in Matlab interfaced with Ethereum blockchain. Actuated signals from smart contracts embedded with a distributed frequency control algorithm dictate the microgrid's operating frequency to its nominal value. The effectiveness of the proposed method is demonstrated through the convergence of the time-dependent signals to their expected nominal values. In addition, the feedback delays involved in transacting the sensed data and generating the actuation signals are characterized and found to be of the order of a few seconds, which is acceptable for the purpose of secondary frequency control and does not lead to instability. The effect of the block size and the crypto puzzle difficulty level on the delays is also investigated and while the difficulty does not affect the delay significantly, the increase in block size can lead to excessive delay values.
Orthogonality Constrained Multi-Head Attention for Keyword Spotting

Multi-head attention mechanism is capable of learning various representations from sequential data while paying attention to different subsequences, e.g., word-pieces or syllables in a spoken word. From the subsequences, it retrieves richer information than a single-head attention which only summarizes the whole sequence into one context vector. However, a naive use of the multi-head attention does not guarantee such richness as the attention heads may have positional and representational redundancy. In this paper, we propose a regularization technique for multi-head attention mechanism in an end-to-end neural keyword spotting system. Augmenting regularization terms which penalize positional and contextual non-orthogonality between the attention heads encourages to output different representations from separate subsequences, which in turn enables leveraging structured information without explicit sequence models such as hidden Markov models. In addition, intra-head contextual non-orthogonality regularization encourages each attention head to have similar representations across keyword examples, which helps classification by reducing feature variability. The experimental results demonstrate that the proposed regularization technique significantly improves the keyword spotting performance for the keyword “Hey Snapdragon”.
Deep Active Learning for Image Regression

Image regressionRanganathan, Hiranmayi is an important problem in computer vision and is useful in a variety of applications. However, training a robust regression model necessitates large amounts of labeled training data, whichVenkateswara, Hemanth is time-consuming and expensive to acquire. Active learning algorithms automatically identify the salient and exemplar instances from large amounts of unlabeled data and tremendously reduceChakraborty, Shayok human annotation effort in inducing a machine learning model. Further, deep learning models like Convolutional Neural Networks (CNNs) have gained popularity to automatically learn representativePanchanathan, Sethuraman features from a given dataset and have depicted promising performance in a variety of classification and regression applications. In this chapter, we exploit the feature learning capabilities of deep neural networks and propose a novel framework to address the problem of active learning for regression. We formulate a loss function (based on the expected model output change) relevant to the research task and exploit the gradient descent algorithm to optimize the loss and train the deep CNN. To the best of our knowledge, this is the first research effort to learn a discriminative set of features using deep neural networks to actively select informative samples in the regression setting. Our extensive empirical studies on five benchmark regression datasets (from three different application domains: rotation angle estimation of handwritten digits, age, and head pose estimation) demonstrate the merit of our framework in tremendously reducing human annotation effort to induce a robust regression model.
Co-Representation Learning Framework For the Open-Set Data Classification

Deep Neural Network (DNN) has been largely demonstrated to be effective for real-world classification problems. However, such model requires a huge amount of training samples to get more accurate result. When limited samples allowed for the training step, the model may perform weak generalization ability on the test set, especially when the novel/unseen class may occur during the test period (we call it open-set classification). This severely limits its further utility in many real-world large scale applications, such as the open-set image and text classification scenarios. In this paper, we focus on addressing this key challenge by developing a DNN based co-representation learning approach RLCN. It utilizes limited samples for training a model then applies it to classify normal instances and detect the emergence of novel class over time. The key novelty is that we design a weighted pairwise-constraint loss (WPC) function to learn an enhanced generalization and robust feature embedding, where the intra-class (same class) compactness and inter-class (different class) separation are achieved. Moreover, we apply the temperature scaling scheme on the softmax function to replace traditional softmax output in our open-world classifier to achieve the classification and novel class detection simultaneously. Our extensive empirical evaluation on benchmark datasets demonstrate the effectiveness of our framework compared to other competing techniques.
Image feature extraction in detection technology of breast tumor

Presently, detection technology of breast tumors is mostly influenced by external interference factors, leading to poor detection efficiency. To improve the detection efficiency of breast tumors and reduce the cost of breast tumor detection, the study was designed to analyze the characteristics of breast tumors and improved the traditional ultra-wideband microwave technology to construct a two-dimensional and three-dimensional detection model of breast tumors. In addition, the study used dimensional transformation to optimize the two-dimensional texture feature extraction method and employed three-dimensional texture feature extraction method to study the image. Finally, this paper verifies the validity of the feature model proposed by this study through comparison experiments and collects 2D and 3D detection effects images separately.
Acoustic Model Adaptation from Raw Waveforms with Sincnet

Raw waveform acoustic modelling has recently gained interest due to neural networks' ability to learn feature extraction, and the potential for finding better representations for a given scenario than hand-crafted features. SincNet has been proposed to reduce the number of parameters required in raw-waveform modelling, by restricting the filter functions, rather than having to learn every tap of each filter. We study the adaptation of the SincNet filter parameters from adults' to children's speech, and show that the parameterisation of the SincNet layer is well suited for adaptation in practice: we can efficiently adapt with a very small number of parameters, producing error rates comparable to techniques using orders of magnitude more parameters.
Using Blockchain Technology to Mitigate Challenges in Service Access for the Homeless and Data Exchange Between Providers: Qualitative Study

Background In the homeless population, barriers to housing and supportive services include a lack of control or access to data. Disparate data formats and storage across multiple organizations hinder up-to-date intersystem access to records and a unified view of an individual’s health and documentation history. The utility of blockchain to solve interoperability in health care is supported in recent literature, but the technology has yet to be tested in real-life conditions encompassing the complex regulatory standards in the health sector. Objective This study aimed to test the feasibility and performance of a blockchain system in a homeless community to securely store and share data across a system of providers in the health care ecosystem. Methods We performed a series of platform demonstrations and open-ended qualitative feedback interviews to determine the key needs and barriers to user and stakeholder adoption. Account creation and data transactions promoting organizational efficiency and improved health outcomes in this population were tested with homeless users and service providers. Results Persons experiencing homelessness and care organizations could successfully create accounts, grant and revoke data sharing permissions, and transmit documents across a distributed network of providers. However, there were issues regarding the security of shared data, user experience and adoption, and organizational preparedness for service providers as end users. We tested a set of assumptions related to these problems within the project time frame and contractual obligations with an existing blockchain-based platform. Conclusions Blockchain technology provides decentralized data sharing, validation, immutability, traceability, and integration. These core features enable a secure system for the management and distribution of sensitive information. This study presents a concrete evaluation of the effectiveness of blockchain through an existing platform while revealing limitations from the perspectives of user adoption, cost-effectiveness, scalability, and regulatory frameworks.
GR and BP neural network-based performance prediction of dual-antenna mobile communication networks

The performance of a dual-antenna mobile communication network in 2-Rayleigh fading is investigated in this paper. Exact average symbol error probability (SEP) expressions with selection combining (SC) are derived for q-ary phase-shift keying (PSK) and pulse-amplitude modulation (PAM). Exact expressions are also given for the channel capacity. It is important to predict the performance of mobile communication networks in complex wireless environments. Thus, we propose generalized regression (GR) and back-propagation (BP) neural network-based SEP prediction methods. The theoretical results are used to generate training data. The proposed prediction methods are compared to the extreme learning machine (ELM), locally weighted linear regression (LWLR), support vector machine (SVM), and radial basis function (RBF) neural network methods. The results obtained verify that the proposed methods provide better SEP predictions.