Reviewerly/exHarmony huggingface dataset:
An efficient data integrity auditing protocol for cloud computing

Cloud data storage has brought a revolution in Information Technology (IT) sector with its inherent benefits like ease of use, cost efficiency, location independence etc. By outsourcing data on cloud servers, users can reduce the hardware expenditures and can simplify the data management process. A Cloud Service Provider (CSP) is responsible for offering secure data storage services. However, using these services in a secured manner and ensuring data integrity in these remote cloud servers remains to be an issue for the clients. In this paper, we propose an efficient approach for data integrity auditing in cloud computing. The objective of proposed protocol is to minimize the computational complexity of client during system setup phase of the auditing protocol. Based on the properties of bilinear pairings, the proposed protocol is publicly verifiable and supports dynamic operations on data. The security of proposed protocol relies on the stability of Computational Diffie Hellman Problem (CDHP) in a Random Oracle Model (ROM). Finally, the performance of proposed protocol has been evaluated by implementing a prototype of the protocol and conducting experiments. The results obtained have been compared with state-of-the-art protocols and demonstrates the high efficiency and adaptability of proposed protocol by clients with limited resources.
Attentive Cutmix: An Enhanced Data Augmentation Approach for Deep Learning Based Image Classification

Convolutional neural networks (CNN) are capable of learning robust representation with different regularization methods and activations as convolutional layers are spatially correlated. Based on this property, a large variety of regional dropout strategies have been proposed, such as Cutout [1], DropBlock [2], CutMix [3], etc. These methods aim to promote the network to generalize better by partially occluding the discriminative parts of objects. However, all of them perform this operation randomly, without capturing the most important region(s) within an object. In this paper, we propose Attentive CutMix, a naturally enhanced augmentation strategy based on CutMix [3]. In each training iteration, we choose the most descriptive regions based on the intermediate attention maps from a feature extractor, which enables searching for the most discriminative parts in an image. Our proposed method is simple yet effective, easy to implement and can boost the baseline significantly. Extensive experiments on CIFAR-10/100, ImageNet datasets with various CNN architectures (in a unified setting) demonstrate the effectiveness of our proposed method, which consistently outperforms the baseline CutMix and other methods by a significant margin.
Flow-TTS: A Non-Autoregressive Network for Text to Speech Based on Flow

In this work, we propose Flow-TTS, a non-autoregressive end-to-end neural TTS model based on generative flow. Unlike other non-autoregressive models, Flow-TTS can achieve high-quality speech generation by using a single feed-forward network. To our knowledge, Flow-TTS is the first TTS model utilizing flow in spectrogram generation network and the first non-autoregssive model which jointly learns the alignment and spectrogram generation through a single network. Experiments on LJSpeech show that the speech quality of Flow-TTS heavily approaches that of human and is even better than that of autoregressive model Tacotron 2 (outperforms Tacotron 2 with a gap of 0.09 in MOS). Meanwhile, the inference speed of Flow-TTS is about 23 times speed-up over Tacotron 2, which is comparable to FastSpeech. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>
Recent development in face recognition

Face stands out as a preferable biometric trait for automatic human authentication as it is intuitive and non-intrusive. This paper investigates various feature-based automatic face recognition approaches in detail. High degree of freedom in head movement and human emotion leads a face recognition system to face critical challenges in terms of pose, illumination and expression. Human face also undergoes irreversible changes due to aging. These factors makes the process of face recognition non trivial and hard. This paper also provides a review of the facial recognition approaches individually dealing with these issues. Applications of face recognition in the forensic domain sometimes needs identification using a scanned facial image. The scenario is quite useful to get investigative leads. Important approaches for the same are also been discussed in the manuscript. Recent developments in the low-cost image capturing devices has flooded the facial image databases with a lot of images, at the same time availability of GPU based compute power has helped develop deep learning approaches to handle the face recognition at a very accurate and massive level. The same has also been surveyed and analyzed in the manuscript.
Effectiveness of Self-Supervised Pre-Training for ASR

We compare self-supervised representation learning algorithms which either explicitly quantize the audio data or learn representations without quantization. We find the former to be more accurate since it builds a good vocabulary of the data through vq-wav2vec [1] self-supervision approach to enable learning of effective representations in subsequent BERT training. Different to previous work, we directly fine-tune the pre-trained BERT models on transcribed speech using a Connectionist Temporal Classification (CTC) loss instead of feeding the representations into a task-specific model. We also propose a BERT-style model learning directly from the continuous audio data and compare pre-training on raw audio to spectral features. Fine-tuning a BERT model on 10 hour of labeled Librispeech data with a vq-wav2vec vocabulary is almost as good as the best known reported system trained on 100 hours of labeled data on test-clean, while achieving a 25% WER reduction on test-other. When using only 10 minutes of labeled data, WER is 25.2 on test-other and 16.3 on test-clean. This demonstrates that self-supervision can enable speech recognition systems trained on a near-zero amount of transcribed data.
ResNet-SCDA-50 for Breast Abnormality Classification

(Aim) Breast cancer is the most common cancer in women and the second most common cancer worldwide. With the rapid advancement of deep learning, the early stages of breast cancer development can be accurately detected by radiologists with the help of artificial intelligence systems. (Method) Based on mammographic imaging, a mainstream clinical breast screening technique, we present a diagnostic system for accurate classification of breast abnormalities based on ResNet-50. To improve the proposed model, we created a new data augmentation framework called SCDA (Scaling and Contrast limited adaptive histogram equalization Data Augmentation). In its procedure, we first conduct the scaling operation to the original training set, followed by applying contrast limited adaptive histogram equalisation (CLAHE) to the scaled training set. By stacking the training set after SCDA with the original training set, we formed a new training set. The network trained by the augmented training set, was coined as ResNet-SCDA-50. Our system, which aims at a binary classification on mammographic images acquired from INbreast and MINI-MIAS, classifies masses, microcalcification as "abnormal", while normal regions are classified as "normal". (Results) We present the first attempt to use the image contrast enhancement method as the data augmentation method, resulting in an averaged 98.55 percent specificity and 92.83 percent sensitivity, which gives our best model an overall accuracy of 95.74 percent. (Conclusion) Our proposed method is effective in classifying breast abnormality.
FlyClient: Super-Light Clients for Cryptocurrencies

To validate transactions, cryptocurrencies such as Bitcoin and Ethereum require nodes to verify that a blockchain is valid. This entails downloading and verifying all blocks, taking hours and requiring gigabytes of bandwidth and storage. Hence, clients with limited resources cannot verify transactions independently without trusting full nodes. Bitcoin and Ethereum offer light clients known as simplified payment verification (SPV) clients, that can verify the chain by downloading only the block headers. Unfortunately, the storage and bandwidth requirements of SPV clients still increase linearly with the chain length. For example, as of July 2019, an SPV client in Ethereum needs to download and store about 4 GB of data.Recently, Kiayias et al. proposed a solution known as noninteractive proofs of proof-of-work (NIPoPoW) that allows a light client to download and store only a polylogarithmic number of block headers in expectation. Unfortunately, NIPoPoWs are succinct only as long as no adversary influences the honest chain, and can only be used in chains with fixed block difficulty, contrary to most cryptocurrencies which adjust block difficulty frequently according to the network hashrate.We introduce FlyClient, a novel transaction verification light client for chains of variable difficulty. FlyClient is efficient both asymptotically and practically and requires downloading only a logarithmic number of block headers while storing only a single block header between executions. Using an optimal probabilistic block sampling protocol and Merkle Mountain Range (MMR) commitments, FlyClient overcomes the limitations of NIPoPoWs and generates shorter proofs over all measured parameters. In Ethereum, FlyClient achieves a synchronization proof size of less than 500 KB which is roughly 6,600x smaller than SPV proofs. We finally discuss how FlyClient can be deployed with minimal changes to the existing cryptocurrencies via an uncontentious velvet fork.
Deep Learning Based Single Image Super-resolution: A Survey

Single image super-resolution has attracted increasing attention and has a wide range of applications in satellite imaging, medical imaging, computer vision, security surveillance imaging, remote sensing, objection detection, and recognition. Recently, deep learning techniques have emerged and blossomed, producing “the state-of-the-art” in many domains. Due to their capability in feature extraction and mapping, it is very helpful to predict high-frequency details lost in low-resolution images. In this paper, we give an overview of recent advances in deep learning-based models and methods that have been applied to single image super-resolution tasks. We also summarize, compare and discuss various models from the past and present for comprehensive understanding and finally provide open problems and possible directions for future research.
Rationalized fruit fly optimization with sine cosine algorithm: A comprehensive analysis

The fruit fly optimization algorithm (FOA) is a well-regarded algorithm for searching the global optimal solution by simulating the foraging behavior of fruit flies. However, when solving high dimensional mathematical and practical application problems, FOA is not competitive in convergence speed, and it may quickly fall into the local optimum. Therefore, in this paper, an enhanced fruit fly optimizer, termed SCA_FOA, is developed by introducing the logic of the sine cosine algorithm (SCA). Specifically, in the process of searching for food utilizing the osphresis organ, the individual fruit fly adopts the way inspired by the SCA to fly outward or inward to find the global optimum. A comprehensive set of 28 benchmark functions were used to measure the exploitation and exploration abilities of the proposed SCA_FOA. The results demonstrate that SCA_FOA is superior to other competitive algorithms. Moreover, 10 practical problems from IEEE CEC 2011, three engineering problems, three shifted and asymmetrical functions, and optimization problems of kernel extreme learning machines (KELM) were also solved, effectively. The results and observations indicate that not only the proposed SCA_FOA can be used for simulated problems as a very efficient method, but also it can be employed for real-world applications.
SECNLP: A survey of embeddings in clinical natural language processing

Traditional representations like Bag of words are high dimensional, sparse and ignore the order as well as syntactic and semantic information. Distributed vector representations or embeddings map variable length text to dense fixed length vectors as well as capture the prior knowledge which can transferred to downstream tasks. Even though embedding has become de facto standard for representations in deep learning based NLP tasks in both general and clinical domains, there is no survey paper which presents a detailed review of embeddings in Clinical Natural Language Processing. In this survey paper, we discuss various medical corpora and their characteristics, medical codes and present a brief overview as well as comparison of popular embeddings models. We classify clinical embeddings into nine types and discuss each embedding type in detail. We discuss various evaluation methods followed by possible solutions to various challenges in clinical embeddings. Finally, we conclude with some of the future directions which will advance the research in clinical embeddings.
SAR Image Despeckling by Noisy Reference-Based Deep Learning Method

Traditionally, clean reference images are needed to train the networks when applying the deep learning techniques to tackle image denoising tasks. However, this idea is impracticable for the task of synthetic aperture radar (SAR) image despeckling, since no real-world speckle-free SAR data exist. To address this issue, this article presents a noisy reference-based SAR deep learning filter, by using complementary images of the same area at different times as the training references. In the proposed method, to better exploit the information of the images, parameter-sharing convolutional neural networks are employed. Furthermore, to mitigate the training errors caused by the land-cover changes between different times, the similarity of each pixel pair between the different images is utilized to optimize the training process. The outstanding despeckling performance of the proposed method was confirmed by the experiments conducted on several multitemporal data sets, when compared with some of the state-of-the-art SAR despeckling techniques. In addition, the proposed method shows a pleasing generalization ability on single-temporal data sets, even though the networks are trained using finite input-reference image pairs at a different imaging area.
Variable selection in microbiome compositional data analysis

Abstract Though variable selection is one of the most relevant tasks in microbiome analysis, e.g. for the identification of microbial signatures, many studies still rely on methods that ignore the compositional nature of microbiome data. The applicability of compositional data analysis methods has been hampered by the availability of software and the difficulty in interpreting their results. This work is focused on three methods for variable selection that acknowledge the compositional structure of microbiome data: selbal, a forward selection approach for the identification of compositional balances, and clr-lasso and coda-lasso, two penalized regression models for compositional data analysis. This study highlights the link between these methods and brings out some limitations of the centered log-ratio transformation for variable selection. In particular, the fact that it is not subcompositionally consistent makes the microbial signatures obtained from clr-lasso not readily transferable. Coda-lasso is computationally efficient and suitable when the focus is the identification of the most associated microbial taxa. Selbal stands out when the goal is to obtain a parsimonious model with optimal prediction performance, but it is computationally greedy. We provide a reproducible vignette for the application of these methods that will enable researchers to fully leverage their potential in microbiome studies.
Structure-aware human pose estimation with graph convolutional networks

Human pose estimation is the task of localizing body key points from still images. As body key points are inter-connected, it is desirable to model the structural relationships between body key points to further improve the localization performance. In this paper, based on original graph convolutional networks, we propose a novel model, termed Pose Graph Convolutional Network (PGCN), to exploit these important relationships for pose estimation. Specifically, our model builds a directed graph between body key points according to the natural compositional model of a human body. Each node (key point) is represented by a 3-D tensor consisting of multiple feature maps, initially generated by our backbone network, to retain accurate spatial information. Furthermore, attention mechanism is presented to focus on crucial edges (structured information) between key points. PGCN is then learned to map the graph into a set of structure-aware key point representations which encode both structure of human body and appearance information of specific key points. Additionally, we propose two modules for PGCN, i.e., the Local PGCN (L-PGCN) module and Non-Local PGCN (NL-PGCN) module. The former utilizes spatial attention to capture the correlations between the local areas of adjacent key points to refine the location of key points. While the latter captures long-range relationships via non-local operation to associate the challenging key points. By equipping with these two modules, our PGCN can further improve localization performance. Experiments both on single- and multi-person estimation benchmark datasets show that our method consistently outperforms competing state-of-the-art methods.
Stochastic Super-Resolution for Downscaling Time-Evolving Atmospheric Fields With a Generative Adversarial Network

Generative adversarial networks (GANs) have been recently adopted for super-resolution, an application closely related to what is referred to as “downscaling” in the atmospheric sciences: improving the spatial resolution of low-resolution images. The ability of conditional GANs to generate an ensemble of solutions for a given input lends itself naturally to stochastic downscaling, but the stochastic nature of GANs is not usually considered in super-resolution applications. Here, we introduce a recurrent, stochastic super-resolution GAN that can generate ensembles of time-evolving high-resolution atmospheric fields for an input consisting of a low-resolution sequence of images of the same field. We test the GAN using two data sets: one consisting of radar-measured precipitation from Switzerland; the other of cloud optical thickness derived from the Geostationary Earth Observing Satellite 16 (GOES-16). We find that the GAN can generate realistic, temporally consistent super-resolution sequences for both data sets. The statistical properties of the generated ensemble are analyzed using rank statistics, a method adapted from ensemble weather forecasting; these analyses indicate that the GAN produces close to the correct amount of variability in its outputs. As the GAN generator is fully convolutional, it can be applied after training to input images larger than the images used to train it. It is also able to generate time series much longer than the training sequences, as demonstrated by applying the generator to a three-month data set of the precipitation radar data. The source code to our GAN is available at https://github.com/jleinonen/downscaling-rnn-gan.
Web Service QoS Prediction via Collaborative Filtering: A Survey

With the growing number of competing Web services that provide similar functionality, Quality-of-Service (QoS) prediction is becoming increasingly important for various QoS-aware approaches of Web services. Collaborative filtering (CF), which is among the most successful personalized prediction techniques for recommender systems, has been widely applied to Web service QoS prediction. In addition to using conventional CF techniques, a number of studies extend the CF approach by incorporating additional information about services and users, such as location, time, and other contextual information from the service invocations. There are also some studies that address other challenges in QoS prediction, such as adaptability, credibility, privacy preservation, and so on. In this survey, we summarize and analyze the state-of-the-art CF QoS prediction approaches of Web services and discuss their features and differences. We also present several Web service QoS datasets that have been used as benchmarks for evaluating the predition accuracy and outline some possible future research directions.
Answering Questions about Charts and Generating Visual Explanations

People often use charts to analyze data, answer questions and explain their answers to others. In a formative study, we find that such human-generated questions and explanations commonly refer to visual features of charts. Based on this study, we developed an automatic chart question answering pipeline that generates visual explanations describing how the answer was obtained. Our pipeline first extracts the data and visual encodings from an input Vega-Lite chart. Then, given a natural language question about the chart, it transforms references to visual attributes into references to the data. It next applies a state-of-the-art machine learning algorithm to answer the transformed question. Finally, it uses a template-based approach to explain in natural language how the answer is determined from the chart's visual features. A user study finds that our pipeline-generated visual explanations significantly outperform in transparency and are comparable in usefulness and trust to human-generated explanations.
A taxonomy of blockchain envisioned edge‐as‐a‐connected autonomous vehicles

Abstract In the past few decades, Intelligent Transport System (ITS) has gained the popularity in smart society. Global automotive stakeholders such as Google, Uber, and Toshiba are the driving force in that and also looking for the interconnection of autonomous vehicles (AVs) for road traffic management, that is, connected AVs (CAV). CAVs are vulnerable to various safety and security issues, for instance, denial‐of‐service, global positioning system (GPS) spoofing, man‐in‐the‐middle, and timing attacks, which raise serious concerns for their development in application specific areas. Although many solutions exist in the literature, most of them are focused on centralized systems, which is having a single‐point‐failure. In light of the above facts, we analyze and classify the security and privacy issues in CAV and proposed a solution taxonomy in this article. We emphasized the defense mechanism for these attacks on the CAV system along with their enactment on issues and discovered how emergent blockchain technology could handle it. Furthermore, we proposed a blockchain‐based secure and decentralized CAV architecture to mitigate security and privacy issues such as denial of service, man‐in‐the‐middle, hijacking, GPS spoofing, and replay attacks. Finally, we reconnoiter the open research challenges associated with the blockchain‐based CAV system. To validate the efficacy of the proposed system, a real‐time case study on smart city integration over edge computing using computation and communication costs is proposed in this article.