Reviewerly/exHarmony huggingface dataset:
Face Mask Detection Classifier and Model Pruning with Keras-Surgeon

Multidisciplinary initiatives in the new world of coronavirus were combined to limit the spread of the pandemic.Interestingly, the AI group was a part of those efforts.This result-based approach is used to help scan, assess, predict and track current patients and possibly potential patients.Developments for tracking social distances or recognizing face masks have made headlines in particular.Most current advanced approaches to face mask recognition are built based on deep learning which is dependent on a large number of face samples.Nearly everybody wears a mask during corona virus outbreak in order to effectively avoid the spread of COVID-19 virus.Our goal is to train a customized deep learning model that helps to detect even if or not a person wears a mask and study the concept of model pruning with Keras-Surgeon.Model pruning can be efficient in reducing model size, so that it can be easily implemented and inferred on embedded systems.
Choosing a Chatbot Development Tool

Chatbots are programs that supply services to users via conversation in natural language, acting as virtual assistants within social networks or web applications. Here, we review the most representative chatbot development tools with a focus on technical and managerial aspects.
Syntax-Guided Hierarchical Attention Network for Video Captioning

Video captioning is a challenging task that aims to generate linguistic description based on video content. Most methods only incorporate visual features (2D/3D) as input for generating visual and non-visual words in the caption. However, generating non-visual words usually depends more on sentence-context than visual features. The wrong non-visual words can reduce the sentence fluency and even change the meaning of sentence. In this paper, we propose a syntax-guided hierarchical attention network (SHAN), which leverages semantic and syntax cues to integrate visual and sentence-context features for captioning. First, a globally-dependent context encoder is designed to extract the global sentence-context feature that facilitates generating non-visual words. Then, we introduce hierarchical content attention and syntax attention to adaptively integrate features in terms of temporality and feature characteristics respectively. Content attention helps focus on time intervals related to the semantic of current word, while cross-modal syntax attention uses syntax information to model importance of different features for target word&#x2019;s generation. Moreover, such hierarchical attention can enhance the model interpretability for captioning. Experiments on MSVD and MSR-VTT datasets show the comparable performance of our method compared with current methods.
Enhancing Traceability of Infectious Diseases: A Blockchain-Based Approach

The global pandemic of COVID-19 has brought significant attentions to three important features of disease direct reporting systems: traceability, reliability, and effectiveness. A traditional disease direct reporting system has a central node of control, with a hierarchical structure that goes up from locals (cities and counties) to regions and eventually reaches a central data repository. Such systems are often prone to easy data loss, arbitrary or unauthorized data changes, and unreliable traceability to individual nodes. Blockchain, as a new disruptive technology, provides a potential solution. Leveraging blockchain's features of decentralization, unforgeability, whole-process traceability, we develop a method for disease information tracing with key components including infectious disease information collection, information chain-style storage, and information query. Our blockchain-based infectious disease traceability method can promptly collect disease information and form the disease information time series blockchain. We demonstrate that the information chain constructed is authentic and transparent, and it can be queried and maintained at any node in the system. Consequently, the infectious disease information on the blockchain can be monitored and queried any time, thereby greatly facilitating the tracing of the propagation paths of infectious diseases.
Artificial intelligence for breast ultrasound: An adjunct tool to reduce excessive lesion biopsy

Purpose To determine whether adding an artificial intelligence (AI) system to breast ultrasound (US) can reduce unnecessary biopsies. Methods Conventional US and AI analyses were prospectively performed on 173 suspicious breast lesions before US-guided core needle biopsy or vacuum-assisted excision. Conventional US images were retrospectively reviewed according to the BI-RADS 2013 lexicon and categories. Two downgrading stratifications based on AI assessments were manually used to downgrade the BI-RADS category 4A lesions to category 3. Stratification A was used to downgrade if the assessments of both orthogonal sections of a lesion from AI were possibly benign. Stratification B was used to downgrade if the assessment of any of the orthogonal sections was possibly benign. The effects of AI-based diagnosis on lesions to reduce unnecessary biopsy were analyzed using histopathological results as reference standards. Results Forty-three lesions diagnosed as BI-RADS category 4A by conventional US received AI-based hypothetical downgrading. While downgrading with stratification A, 14 biopsies were correctly avoided. The biopsy rate for BI-RADS category 4A lesions decreased from 100 % to 67.4 % (P < 0.001). While downgrading with stratification B, 27 biopsies could be avoided with two malignancies missed, and the biopsy rate would decrease to 37.2 % (P < 0.05, compared with conventional US and stratification A). Conclusion Adding an AI system to breast US could reduce unnecessary lesion biopsies. Downgrading stratification A was recommended for its lower misdiagnosis rate.
On the quest for supply chain transparency through Blockchain: Lessons learned from two serialized data projects

Abstract The ability to look into the supply chain has long enticed SCM scholars and practitioners. The possibilities created by such visibility are endless—from risk reduction and continuity planning to inventory management and cost reduction, nothing is off the table when end‐to‐end visibility is a possibility. Because of such enticements, there is usually much buzz in the industry every time a new technology that promises visibility and transparency is brought forward. Yet, years later, stories sometimes emerge that said technologies either failed to deliver or were not everything they were made out to be. Blockchain is yet another emerging technology in this space. Some consultants promise that it will be the final answer to the transparency and visibility woes that companies currently face. Yet, there is little empirical investigation regarding how the technology may benefit adopters, what the bottlenecks may be, and to what extent it may be able to deliver on these promises, without massive system‐wide upgrades of extant hardware and computing prowess. The current study takes a step in this direction by investigating a blockchain‐driven proof of concept across an industry consortium to identify promises, possibilities, and challenges of blockchain.
DIDarknet: A Contemporary Approach to Detect and Characterize the Darknet Traffic using Deep Image Learning

Darknet traffic classification is significantly important to categorize real-time applications. Although there are notable efforts to classify darknet traffic which rely heavily on existing datasets and machine learning classifiers, there are extremely few efforts to detect and characterize darknet traffic using deep learning. This work proposes a novel approach, named DeepImage, which uses feature selection to pick the most important features to create a gray image and feed it to a two-dimensional convolutional neural network to detect and characterize darknet traffic. Two encrypted traffic datasets are merged to create a darknet dataset to evaluate the proposed approach which successfully characterizes darknet traffic with 86% accuracy.
Instance-level Image Retrieval using Reranking Transformers

Instance-level image retrieval is the task of searching in a large database for images that match an object in a query image. To address this task, systems usually rely on a retrieval step that uses global image descriptors, and a subsequent step that performs domain-specific refinements or reranking by leveraging operations such as geometric verification based on local features. In this work, we propose Reranking Transformers (RRTs) as a general model to incorporate both local and global features to rerank the matching images in a supervised fashion and thus replace the relatively expensive process of geometric verification. RRTs are lightweight and can be easily parallelized so that reranking a set of top matching results can be performed in a single forward-pass. We perform extensive experiments on the Revisited Oxford and Paris datasets, and the Google Landmarks v2 dataset, showing that RRTs outperform previous reranking approaches while using much fewer local descriptors. Moreover, we demonstrate that, unlike existing approaches, RRTs can be optimized jointly with the feature extractor, which can lead to feature representations tailored to downstream tasks and further accuracy improvements. The code and trained models are publicly available at https://github.com/uvavision/RerankingTransformer.
Bidirectional stochastic configuration network for regression problems

To adapt to the reality of limited computing resources of various terminal devices in industrial applications, a randomized neural network called stochastic configuration network (SCN), which can conduct effective training without GPU, was proposed. SCN uses a supervisory random mechanism to assign its input weights and hidden biases, which makes it more stable than other randomized algorithms but also leads to time-consuming model training. To alleviate this problem, we propose a novel bidirectional SCN algorithm (BSCN) in this paper, which divides the way of adding hidden nodes into two modes: forward learning and backward learning. In the forward learning mode, BSCN still uses the supervisory mechanism to configure the parameters of the newly added nodes, which is the same as SCN. In the backward learning mode, BSCN calculates the parameters at one time based on the residual error feedback of the current model. The two learning modes are performed iteratively until the prediction error of the model reaches an acceptable level or the number of hidden nodes reaches its maximum value. This semi-random learning mechanism greatly speeds up the training efficiency of the BSCN model and significantly improves the quality of the hidden nodes. Extensive experiments on ten benchmark regression problems, two real-life air pollution prediction problems, and a classical image processing problem show that BSCN can achieve faster training speed, higher stability, and better generalization ability than SCN.
GPDBN: deep bilinear network integrating both genomic data and pathological images for breast cancer prognosis prediction

Breast cancer is a very heterogeneous disease and there is an urgent need to design computational methods that can accurately predict the prognosis of breast cancer for appropriate therapeutic regime. Recently, deep learning-based methods have achieved great success in prognosis prediction, but many of them directly combine features from different modalities that may ignore the complex inter-modality relations. In addition, existing deep learning-based methods do not take intra-modality relations into consideration that are also beneficial to prognosis prediction. Therefore, it is of great importance to develop a deep learning-based method that can take advantage of the complementary information between intra-modality and inter-modality by integrating data from different modalities for more accurate prognosis prediction of breast cancer.We present a novel unified framework named genomic and pathological deep bilinear network (GPDBN) for prognosis prediction of breast cancer by effectively integrating both genomic data and pathological images. In GPDBN, an inter-modality bilinear feature encoding module is proposed to model complex inter-modality relations for fully exploiting intrinsic relationship of the features across different modalities. Meanwhile, intra-modality relations that are also beneficial to prognosis prediction, are captured by two intra-modality bilinear feature encoding modules. Moreover, to take advantage of the complementary information between inter-modality and intra-modality relations, GPDBN further combines the inter- and intra-modality bilinear features by using a multi-layer deep neural network for final prognosis prediction. Comprehensive experiment results demonstrate that the proposed GPDBN significantly improves the performance of breast cancer prognosis prediction and compares favorably with existing methods.GPDBN is freely available at https://github.com/isfj/GPDBN.Supplementary data are available at Bioinformatics online.
HNPU: An Adaptive DNN Training Processor Utilizing Stochastic Dynamic Fixed-Point and Active Bit-Precision Searching

This article presents HNPU, which is an energy-efficient deep neural network (DNN) training processor by adopting algorithm-hardware co-design. The HNPU supports stochastic dynamic fixed-point representation and layer-wise adaptive precision searching unit for low-bit-precision training. It additionally utilizes slice-level reconfigurability and sparsity to maximize its efficiency both in DNN inference and training. Adaptive bandwidth reconfigurable accumulation network enables reconfigurable DNN allocation and maintains its high core utilization even in various bit-precision conditions. Fabricated in a 28-nm process, the HNPU accomplished at least <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$5.9\times $ </tex-math></inline-formula> higher energy efficiency and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$2.5\times $ </tex-math></inline-formula> higher area efficiency in actual DNN training compared with the previous state-of-the-art on-chip learning processors.
A Survey on Security and Privacy Issues in Contact Tracing Application of Covid-19

In response to the coronavirus (COVID-19) pandemic, Government and public health authorities around the world are developing contact tracing apps as a way to trace and slow the unfold of the virus. There is major divergence among nations, however, between a "privacy-first" approach that protects citizens' information at the price of very restricted access for public health authorities and a "data-first" approach that stores massive amounts of knowledge that, whereas of immeasurable price to epidemiologists. Contact tracing apps work by gathering information from people who have tested positive for the virus and so locating and notifying individuals with whom those people are in shut contact, oftentimes by use of GPS, Bluetooth, or wireless technology. All of the user's information is employed and picked up, the study found that users' information would be created anonymous, encrypted, secured, and can be transmitted on-line and stored solely in an aggregated format. Contact tracing apps use either a centralized or a decentralized approach to work the user's information. Apps that use a centralized approach have high privacy risks. In this paper, the researcher's contributions related to the security and privacy of Contact tracing apps have been discussed and, later research gaps have been identified with proposed solutions.
An Improved Image Watermarking by Modifying Selected DWT-DCT Coefficients

Digital images can be easily copied or manipulated by irresponsible persons.The right property needs to be secured and protected from illegal copies and piracy.Digital watermarking is one of the solutions to protect the intellectual property of digital images.This paper proposed the adaptive scaling factor based on selected DWT-DCT coefficients of its image content.The adaptive scaling factor was generated based on the role of selected DWT-DCT coefficients against the average value of DWT-DCT coefficients.The watermark image was embedded by using a proposed set of rules that consider the adaptive scaling factor.The experimental results showed that the proposed scheme achieved high PSNR value of 47dB, SSIM value of about 0.987 and an embedded watermark resistance to several attacks in the watermarked image.
Concept Drift Adaptation Techniques in Distributed Environment for Real-World Data Streams

Real-world data streams pose a unique challenge to the implementation of machine learning (ML) models and data analysis. A notable problem that has been introduced by the growth of Internet of Things (IoT) deployments across the smart city ecosystem is that the statistical properties of data streams can change over time, resulting in poor prediction performance and ineffective decisions. While concept drift detection methods aim to patch this problem, emerging communication and sensing technologies are generating a massive amount of data, requiring distributed environments to perform computation tasks across smart city administrative domains. In this article, we implement and test a number of state-of-the-art active concept drift detection algorithms for time series analysis within a distributed environment. We use real-world data streams and provide critical analysis of results retrieved. The challenges of implementing concept drift adaptation algorithms, along with their applications in smart cities, are also discussed.
Socio-Technical Grounded Theory for Software Engineering

Grounded Theory (GT), a sociological research method designed to study social phenomena, is increasingly being used to investigate the human and social aspects of software engineering (SE). However, being written by and for sociologists, GT is often challenging for a majority of SE researchers to understand and apply. Additionally, SE researchers attempting ad hoc adaptations of traditional GT guidelines for modern socio-technical (ST) contexts often struggle in the absence of clear and relevant guidelines to do so, resulting in poor quality studies. To overcome these research community challenges and leverage modern research opportunities, this paper presents Socio-Technical Grounded Theory (STGT) designed to ease application and achieve quality outcomes. It defines what exactly is meant by an ST research context and presents the STGT guidelines that expand GT's philosophical foundations, provide increased clarity and flexibility in its methodological steps and procedures, define possible scope and contexts of application, encourage frequent reporting of a variety of interim, preliminary, and mature outcomes, and introduce nuanced evaluation guidelines for different outcomes. It is hoped that the SE research community and related ST disciplines such as computer science, data science, artificial intelligence, information systems, human computer/robot/AI interaction, human-centered emerging technologies (and increasingly other disciplines being transformed by rapid digitalisation and AI-based augmentation), will benefit from applying STGT to conduct quality research studies and systematically produce rich findings and mature theories with confidence.
Super-Resolution of Cardiac MR Cine Imaging using Conditional GANs and Unsupervised Transfer Learning

High-resolution (HR), isotropic cardiac Magnetic Resonance (MR) cine imaging is challenging since it requires long acquisition and patient breath-hold times. Instead, 2D balanced steady-state free precession (SSFP) sequence is widely used in clinical routine. However, it produces highly-anisotropic image stacks, with large through-plane spacing that can hinder subsequent image analysis. To resolve this, we propose a novel, robust adversarial learning super-resolution (SR) algorithm based on conditional generative adversarial nets (GANs), that incorporates a state-of-the-art optical flow component to generate an auxiliary image to guide image synthesis. The approach is designed for real-world clinical scenarios and requires neither multiple low-resolution (LR) scans with multiple views, nor the corresponding HR scans, and is trained in an end-to-end unsupervised transfer learning fashion. The designed framework effectively incorporates visual properties and relevant structures of input images and can synthesise 3D isotropic, anatomically plausible cardiac MR images, consistent with the acquired slices. Experimental results show that the proposed SR method outperforms several state-of-the-art methods both qualitatively and quantitatively. We show that subsequent image analyses including ventricle segmentation, cardiac quantification, and non-rigid registration can benefit from the super-resolved, isotropic cardiac MR images, to produce more accurate quantitative results, without increasing the acquisition time. The average Dice similarity coefficient (DSC) for the left ventricular (LV) cavity and myocardium are 0.95 and 0.81, respectively, between real and synthesised slice segmentation. For non-rigid registration and motion tracking through the cardiac cycle, the proposed method improves the average DSC from 0.75 to 0.86, compared to the original resolution images.
EllSeg: An Ellipse Segmentation Framework for Robust Gaze Tracking

Ellipse fitting, an essential component in pupil or iris tracking based video oculography, is performed on previously segmented eye parts generated using various computer vision techniques. Several factors, such as occlusions due to eyelid shape, camera position or eyelashes, frequently break ellipse fitting algorithms that rely on well-defined pupil or iris edge segments. In this work, we propose training a convolutional neural network to directly segment entire elliptical structures and demonstrate that such a framework is robust to occlusions and offers superior pupil and iris tracking performance (at least 10% and 24% increase in pupil and iris center detection rate respectively within a two-pixel error margin) compared to using standard eye parts segmentation for multiple publicly available synthetic segmentation datasets.
A Security Management Framework for Big Data in Smart Healthcare

Big Data analytics in the medical sector can assist medical professionals to facilitate improvement in healthcare. With the help of data analysis, clinical images of patients can be used to detect certain medical conditions. In the COVID-19 pandemic, many integrated technologies are being used to remodel the healthcare systems. The management of an integrated healthcare solution necessitates the need for security of the medical data. In this paper, we propose a security framework based on the Logistic equation, Hyperchaotic equation, and Deoxyribonucleic Acid (DNA) encoding. Subsequently, a Lossless Computational Secret Image Sharing (CSIS) method is used to convert the encrypted secret image into shares for distributed storage in cloud-based servers. Hyperchaotic and DNA encryption is performed to improve the overall security of the system. Furthermore, Pseudorandom Numbers (PRN) generated by the logistic equation are XORed with the image sequence in two phases by changing the parameters slightly. Finally, the application of Secret Sharing (SS) generates completely noise-like cipher images that enhance the security of the cloud-based cryptosystem. The generated shares are small in size and require fewer resources like storage capacity and transmission bandwidth which is highly desirable in IoT-based systems. It is verified that the cryptosystem is highly secure against attacks as well as interferences and has a very strong key-sensitivity.