Reviewerly/exHarmony huggingface dataset:
Medical Text and Image Processing: Applications, Issues and Challenges

Text and image analysis are playing very important role in healthcare and medical domain. The whole clinical process is getting affected positively by text and image processing. Many datasets, algorithms, models and tools are available for extracting useful information and for applying natural language processing, machine learning and deep learning algorithms. But there exist many challenges in healthcare data for successful implementation of text and image based machine learning models, which include: (i) storage and retrieval of high resolution images, (ii) scarcity of data (iii) dataset generation and validation, (iv) appropriate algorithms and models for extracting hidden information from images and texts, (v) use of modern concepts like deep neural networks, recurrent neural network, (vi) data wrangling and (vii) processing capacity of processors. This chapter will: (i) establish a background for the research work in the area of machine learning and deep learning, (ii) provide the brief about various types of medical texts and images, (iii) discuss the various existing models and applications of natural language processing, machine learning, computer vision and deep learning in medical domain and (iv) discuss various issues and challenges on applying natural language processing, machine learning and deep learning on medical data.
Adversarial Hypothesis Testing and a Quantum Stein’s Lemma for Restricted Measurements

Recall the classical hypothesis testing setting with two sets of probability distributions P and Q.One receives either n i.i.d.samples from a distribution p ∈ P or from a distribution q ∈ Q and wants to decide from which set the points were sampled.It is known that the optimal exponential rate at which errors decrease can be achieved by a simple maximum-likelihood ratio test which does not depend on p or q, but only on the sets P and Q.We consider an adaptive generalization of this model where the choice of p ∈ P and q ∈ Q can change in each sample in some way that depends arbitrarily on the previous samples.In other words, in the k th round, an adversary, having observed all the previous samples in rounds 1, . . ., k -1, chooses p k ∈ P and q k ∈ Q, with the goal of confusing the hypothesis test.We prove that even in this case, the optimal exponential error rate can be achieved by a simple maximum-likelihood test that depends only on P and Q.We then show that the adversarial model has applications in hypothesis testing for quantum states using restricted measurements.For example, it can be used to study the problem of distinguishing entangled states from the set of all separable states using only measurements that can be implemented with local operations and classical communication (LOCC).The basic idea is that in our setup, the deleterious effects of entanglement can be simulated by an adaptive classical adversary.We prove a quantum Stein's Lemma in this setting: In many circumstances, the optimal hypothesis testing rate is equal to an appropriate notion of quantum relative entropy between two states.In particular, our arguments yield an alternate proof of Li and Winter's recent strengthening of strong subadditivity for von Neumann entropy.
Results of the Fast-Music Project—Five Contributions to the Domain of Distributed Music

Due to the conventional “best-effort” approach that had no guarantee of packet delivery, the Internet was not initially developed for the purpose of sending real-time traffic. However, within the last 20 years and as part of the evolving trend of globalization along with distributed work processes, IP-based telecommunication has become a widely accepted and commonly used service. In that context a number of researchers have been investigating how far distributed communication on the Internet can be applied in terms of artistic music performances. Such a scenario exhibits signal delay boundaries tenfold less than the common video conferencing thresholds of 250 ms or more. Several successful results and actual implementations exist. However, apart from minor details, all of them share the same or at least similar approaches. In that context we established the fast-music research project in order to identify and develop novel approaches within this domain. In this paper we will present the final results of this project, which took place from 2016 to 2019. The target of fast-music was divided into five main goals: With respect to audio, we aimed for the development of a versatile streaming solution, the creation of a synchronizable standalone hardware and the installation of a server-based streaming solution. In terms of video, a latency-optimized capture/display component and an alternative IR-tracking based technology with 3D support was developed.
Application of multivariate geostatistics for local-scale lithological mapping – case study of pelagic surface sediments from the Clarion-Clipperton Fracture Zone, north-eastern equatorial Pacific (Interoceanmetal claim area)

Multivariate geostatistical methods were employed to describe the local facies of surface pelagic sediments from the Interoceanmetal IOM H11 and H22 perspective areas. Using these methods, we analyze spatial variability in seafloor features, sediment grain size, and mineralogical and geochemical composition. The data were acquired from the seafloor sediments sampled by box corer. Laboratory methods included laser grain size analysis, X-Ray diffractometry and Inducted Coupled Plasma Mass Spectrometry. Universal kriging was applied in order to generate lithofacies maps, following the “classification probability kriging” concept based on a Bayesian theorem. Under this concept, the class membership probabilities are regarded regionalized variables, so that methodical tool of geostatistics can be applied. The final result is a series of thematic maps, showing the spatial distribution of classified sediments facies, expressed by class membership probabilities, which provide information on reliability of mapping. The applied models can be useful for geological exploration of Clarion-Clipperton polymetallic nodules deposit and beyond. The lithofacies maps presented here provide information on formation processes of sediments, induced by several factors, such as diagenetic transformation of sediments and pathways of biogenic, hydrogenous or hydrothermal material.
Aesthetic Image Captioning From Weakly-Labelled Photographs

Aesthetic image captioning (AIC) refers to the multi-modal task of generating critical textual feedbacks for photographs. While in natural image captioning (NIC), deep models are trained in an end-to-end manner using large curated datasets such as MS-COCO, no such large-scale, clean dataset exists for AIC. Towards this goal, we propose an automatic cleaning strategy to create a benchmarking AIC dataset, by exploiting the images and noisy comments easily available from photography websites. We propose a probabilistic caption-filtering method for cleaning the noisy web-data, and compile a large-scale, clean dataset 'AVA-Captions', (~ 230, 000 images with ~ 5 captions per image). Additionally, by exploiting the latent associations between aesthetic attributes, we propose a strategy for training the convolutional neural network (CNN) based visual feature extractor, the first component of the AIC framework. The strategy is weakly supervised and can be effectively used to learn rich aesthetic representations, without requiring expensive ground-truth annotations. We finally show-case a thorough analysis of the proposed contributions using automatic metrics and subjective evaluations.
Differentially Private Deep Learning for Load Forecasting on Smart Grid

Load forecasting is vital for a reliable and sustainable smart grid as it is used to predict the demand and make price adjustment accordingly. Electric consumption data which is gathered from IoT devices like smart meter or smart appliances is a key input to improve the accuracy of the forecasting task. However, this data can leak private information of the householders as the consumption data reflects the behavioral patterns of the individuals. Providing privacy for the data without compromising the utility of the forecast is a challenging problem and this is where the differential privacy comes in to play. In this work, we present a practical implementation of the privacy preserving load forecasting with differential privacy techniques using Tensorflow Privacy library. We show that privacy guarantee for the data can be achieved to varying degrees with a tolerable degradation in the forecast results. We provide privacy-utility tradeoff values in our experiments for different privacy levels.
GPU-Accelerated Automatic Microseismic Monitoring Algorithm (GAMMA) and Its Application to the 2019 Ridgecrest Earthquake Sequence

Abstract Foreshocks and/or aftershocks play critical roles in improving our understanding of the processes of faulting, such as nucleation of earthquakes, earthquake triggering, and postseismic deformation. A rapid and accurate earthquake detection and location algorithm can provide timely information of seismic activities, thereby benefitting our understanding of physical mechanisms of faulting and seismic hazard assessment. We have developed a graphic processing unit (GPU)-accelerated automatic microseismic monitoring algorithm (GAMMA) for accurate and near real-time detection and location of earthquakes. GAMMA utilizes methods based on backprojection to automatically detect potential earthquakes, and then the waveforms of qualified earthquakes are selected as templates when searching for small earthquakes in continuous recordings using the template-matching algorithm. The use of GPUs has substantially accelerated the calculations and has made GAMMA capable of (near-)real-time earthquake monitoring. We have successfully applied GAMMA to the 2019 Ridgecrest earthquake sequence in southern California. The number of earthquakes detected by GAMMA is more than 21 times that documented in the regional catalog. The more complete catalog determined by GAMMA may provide crucial information for improving our understanding of the physical mechanisms of faulting and also supply useful constraints for a variety of types of studies, including dynamic rupture simulations and crustal deformation modeling.
Indian Sign Language converter using Convolutional Neural Networks

People with hearing and speech impairments have to face a lot of difficulties while communicating with the general public. Being a minority, the sign language used by them is not known to a majority of people. In this paper, an Indian sign language converter was developed using a Convolutional Neural Network algorithm with the aim to classify the 26 letters of the Indian Sign Language into their equivalent alphabet letters by capturing a real time image of that sign and converting it to its text equivalent. First a database was created in various backgrounds and various image pre-processing techniques were used to make the database ready for feature extraction. After feature extraction, the images were fed into the CNN using the python software. Several real time images were tested to find the accuracy and efficiency. The results showed a 96% accuracy for the testing images and an accuracy of 87.69% for real time images.
The Usefulness of Search Results

In evaluating search systems there is a growing trend to complement the established effectiveness indicator topical relevance by the usefulness of search results. Usefulness refers to the contribution of search results to a larger task generating information search. This study analyses articles on interactive information retrieval which either predict the usefulness of documents retrieved or evaluate search systems by the usefulness of search results. The aim is to systematize the findings of these studies by categorizing the types of usefulness and their predictor types. Significant empirical associations between predictor types and usefulness are systematized, too. The data consists of articles in journals or conference proceedings focusing on the usefulness of search results either in web or database environment. The results indicate that there is a growing trend to complement topical relevance by the usefulness of search results in evaluating search systems. Search tasks typically instruct participants to search information for a writing task. Perceived usefulness of search results is the established measure in studies, although there are alternative measures in use. Significant associations between predictors and usefulness do not accumulate much but vary notably. Growth of knowledge on the usefulness of search results is based on the increasing number and variety of proposition supported empirically.
Novel Adaptive Generative Adversarial Network for Voice Conversion

Voice Conversion (VC) converts the speaking style of a source speaker to the speaking style of a target speaker by preserving the linguistic content of a given speech utterance. Recently, Cycle Consistent Adversarial Network (CycleGAN), and its variants have become popular for non-parallel VC tasks. However, CycleGAN uses two different generators and discriminators. In this paper, we introduce a novel Adaptive Generative Adversarial Network (AdaGAN) for non-parallel VC task, which effectively requires single generator, and two discriminators for transferring the style from one speaker to another while preserving the linguistic content in the converted voices. To the best of authors' knowledge, this is the first study of its kind to introduce a new Generative Adversarial Network (GAN)-based architecture (i.e., AdaGAN) in machine learning literature, and the first attempt to apply this architecture for nonparallel VC task. In this paper, we compared the results of the AdaGAN w.r.t. state-of-the-art CycleGAN architecture. Detailed subjective and objective tests are carried out on the publicly available VC Challenge 2018 corpus. In addition, we perform three statistical analysis which show effectiveness of AdaGAN over CycleGAN for parallel-data free one-to-one VC. For inter-gender and intra-gender VC, We observe that the AdaGAN yield objective results that are comparable to the CycleGAN, and are superior in terms of subjective evaluation. A subjective evaluation shows that AdaGAN outperforms CycleGAN-VC in terms of naturalness, sound quality, and speaker similarity. AdaGAN was preferred 58.33% and 41% time more over CycleGAN in terms of speaker similarity and sound quality, respectively.
CNN based speaker recognition in language and text-independent small scale system

Speaker Recognition is the ability of the system to recognize the speaker from the set of speaker samples available in the system. It is of 2 types, one uses a keyword, called text-dependent systems, and another one can recognize the voice in any language/text, also called as text-independent speaker recognition. In this paper, a text-independent, language-independent speaker recognition system is implemented using dense & convolutional neural networks. Speaker recognition has found several applications in upcoming electronic products like personal/home assistants, telephone banking and biometric identification. In this paper, we explore a system that uses MFCC along with DNN and CNN as the model for building a speaker recognition system.
A Novel Application/Infrastructure Co-design Approach for Real-time Edge Video Analytics

Recent advances in machine learning and deep learning have enabled many existing applications in smart cities, autonomous systems, and wearable devices. These applications often demand scalable real-time cognitive intelligence and on-the-spot decision making. Current computer systems have been customized for a cloud computing paradigm which often does not meet latency constraints and scalability requirements. To address the limitations of the cloud computing paradigm, the general trend is toward shifting the computation next to data producers at the edge. However, the edge computing paradigm is in the very early stages. Many system-level aspects of edge computing, including algorithms mapping and partitioning across edge computing resources (edge server, and edge nodes) are unknown. New research is required to understand and quantify design dimensions for edge computing.This paper presents a novel edge computing infrastructure for distributed real-time video analytics. This paper presents a holistic solution for co-designing application and edge infrastructure, including edge nodes and edge servers, to enable scalable real-time Artificial Intelligence (AI)/Deep Learning (DL) video analytics across many cameras. For experimental results and evaluation, we focus on the case study of object re-identification across many cameras, which is composed of object detection/classification (TinyYOLOv3), feature extraction, local re-identification, and global re-identification kernels. We evaluate the edge system under three different task mapping and resource allocation configurations. The results present that with the edge nodes (video cameras) more than 32, the only scalable solution is to perform detection/classification (TinyYOLOv3), feature extraction, local re-identification on the edge nodes next to cameras, and execute global re-identification on edge server.
Sinhala and Tamil Speech Intent Identification From English Phoneme Based ASR

Today we can find many use cases for content-based speech classification. These include speech topic identification and spoken command recognition. Automatic Speech Recognition (ASR) sits underneath all of these applications to convert speech into textual format. However, creating an ASR system for a language is a resource-consuming task. Even though there are more than 6000 languages, all of these speech-related applications are limited to the most well-known languages such as English, because of the availability of data. There is some past research that looked into classifying speech while addressing the data scarcity. However, all of these methods have their own limitations. In this paper, we present an English language phoneme based speech intent classification methodology for Sinhala and Tamil languages. We use a pre-trained English ASR model to generate phoneme probability features and use them to identify intents of utterances expressed in Sinhala and Tamil, for which a rather small speech dataset is available. The experiment results show that the proposed method can have more than 80% accuracy for a 0.5-hour limited speech dataset in both languages.
Conditional generative adversarial network for synthesizing hyperspectral images of breast cancer cells from digitized histology

Hyperspectral imaging (HSI), which acquires up to hundreds of bands, has been proposed as a promising imaging modality for digitized histology beyond RGB imaging to provide more quantitative information to assist pathologists with disease detection in samples. While digitized RGB histology is quite standardized and easy to acquire, histological HSI often requires custom-made equipment and longer imaging times compared to RGB. In this work, we present a dataset of corresponding RGB digitized histology and histological HSI of breast cancer, and we develop a conditional generative adversarial network (GAN) to artificially synthesize HSI from standard RGB images of normal and cancer cells. The results of the GAN synthesized HSI are promising, showing structural similarity (SSIM) of approximately 80% and mean absolute error (MAE) of 6 to 11%. Further work is needed to establish the ability of generating HSI from RGB images on larger datasets.
Understanding Walking Meetings: Drivers and Barriers

There is increased interest in reducing sedentary behavior of office workers to combat the negative health effects of prolonged sitting. Walking meetings offer a promising solution to this problem as they facilitate a physically active way of working. To inform future development of technologies supporting these type of meetings, in-depth qualitative insights into people's experiences of walking meetings are needed. We conducted semi-structured walking interviews (N=16) to identify key drivers and barriers for walking meetings in a living lab setting by using the 'WorkWalk'. The 'WorkWalk' is a 1.8 km walking route indicated by a dotted blue line with outdoor meeting points, integrated into the room booking system. Our findings provide insights into how walking meetings are experienced and affect the set-up and social dynamics of meetings. We offer design recommendations for the development of future technologies and service design elements to support walking meetings and active ways of working.
A parallel fuzzy rule-base based decision tree in the framework of map-reduce

Decision trees are commonly used for learning and extracting classification rules from data. The fuzzy rule based decision tree (FRDT) is very representative owing to its better robustness and generalization. However, FRDT cannot work well on the analysis of large-scale data sets. One solution for this problem is parallel computing. A proved effective parallel computing model is Map-Reduce. Ensemble learning is an effective strategy which can significantly improve the generalization ability of machine learning systems. The objective of this paper is to develop a fuzzy rule-base based decision tree on the strategies of parallel computing and ensemble learning. First, we implement a parallel fusing fuzzy rule based classification system via Map-Reduce (MR-FFRCS) to display how to extract fuzzy rules from data in parallel and how to evaluate the fuzzy rules in an ensemble learning way. Then, taking MR-FFRCS as a fundamental module, we propose a parallel fuzzy rule-base based decision tree (MR-FRBDT) to improve the original FRDT algorithm. The experimental studies mainly focus on feasibility and parallelism. Compared with FRDT on 23 UCI benchmark data sets, the proposed MR-FRBDT algorithm with fewer parameters is effective and has the ability to handle large-scale data sets. Furthermore, some numerical experiments conducted on several large-scale data sets verify the parallel performance on reducing computing time and avoiding memory restrictions.
A Stacked Predictor and Dynamic Thresholding Algorithm for Anomaly Detection in Spacecraft

Anomaly or abnormal behavior detection in downlinked spacecraft telemetry is a key step for determining root cause of subsystem failures. Long Short-Term Memory networks (LSTMs) have been demonstrated to be useful for anomaly detection in time series, due to their predictive power, especially with long sequences that contain contextual information. However, there could be thousands of telemetry channels transmitted from the spacecraft with diverse signal characteristics. A single LSTM predictor may not perform well for all data channels. To enhance adaptability, we propose a stacked predictor framework. This framework stacks three LSTM based predictors with different neuron arrangements and a Support Vector Machine (SVM) based predictor in its first layer. A traditional fully connected layer is used as its second layer. We then estimate the distribution of the error of the predicted output using Kernel Density Estimation (KDE). Finally, a novel dynamic thresholding algorithm is applied to optimally extract anomalous behavior in the data. We present the Stacked Predictor results against the benchmark from the NASA MSL/SMAP anomaly dataset.