Reviewerly/exHarmony huggingface dataset:
Polarisation Imaging of Soft Tissue Membranes

Polarisation imaging of biological media has a wide range of applications including cancer identification in tissues, such as colon, liver, cervix, breast, and skin; non-invasive glucose sensing; and non-destructive characterisation of the structural anisotropy in tissues containing fibrous components, such as collagen and actin-myosin. In this talk I will present my PhD work on the development of a wide-field Mueller matrix imaging system that has been integrated with a mechanical tester to enable structure-function studies of soft tissue membranes, such as pericardium, and discuss its potential applications in tissue engineering.
Heterogeneous-PAKE: Bridging the Gap between PAKE Protocols and Their Real-World Deployment

Two entities, who only share a password and communicate over an insecure channel, authenticate each other and agree on a large session key for protecting their subsequent communication. This is called the password-authenticated key exchange (PAKE) protocol. PAKE protocol has been considered a suitable substitute for the prevailing hash-based authentication which is vulnerable to various attacks. However, vendors are discouraged by both its prohibitively computational overheads as well as integrating costs, leading to its limited use since being proposed.
URL Links Malicious Classification Towards Autonomous Threat Detection Systems

Cyber threat behaviors can take different forms, approaches, and goals. For threat detection systems, it is essential to monitor URLs known for previous malicious attempts. It is also vital to study attack behaviors for the ultimate goal of designing autonomous threat detection systems. We collected a large dataset of URL links annotated toward that goal, manually as benign or malicious links. Several features are collected about those links related to lexicons and structure. Two classification algorithms were employed to extract the best features to predict whether a link is malicious or benign. We were applied three preprocessing techniques, including handling missing values, dealing with outliers, and categorical variables. For two evaluated classifiers, the results’ bias is avoided using proper data splitting methods. The quality of the classifiers is evaluated using classifiers’ accuracy. The results indicated that Random Forest continuously reported better accuracy than the Decision Tree classifier, which could predict URL link type, whether malicious or not, with the highest accuracy with 98%.
Improving customer decisions in web-based e-commerce through guerrilla modding

The provisioning of information about product attributes in e-commerce environments is today left entirely to owners of online platforms. Product transparency in online stores can be increased by client-side enrichment of retailer Web pages.
FP-Growth Algorithm for Discovering Region-Based Association Rule in the IoT Environment

With the development of the Internet of things (IoT), both types and amounts of spatial data collected from heterogeneous IoT devices are increasing. The increased spatial data are being actively utilized in the data mining field. The existing association rule mining algorithms find all items with high correlation in the entire data. Association rules that may appear differently for each region, however, may not be found when the association rules are searched for all data. In this paper, we propose region-based frequent pattern growth (RFP-Growth) to search for association rules by dense regions. First, RFP-Growth divides item transaction included position data into regions by a density-based clustering algorithm. Second, frequent pattern growth (FP-Growth) is performed for each transaction divided by region. The experimental results show that RFP-Growth discovers new association rules that the original FP-Growth cannot find in the whole data.
A Blockchain-Based Secure Healthcare Application

With the significantly growing Internet of Things (IoT), there has recently been much focus on the remote tracking of health information to achieve smart healthcare. Nonetheless, because of the low processing power and storage space of IoT devices, user medical information is usually saved with a centralized third party, like a clinical repository or cloud. It makes users lose control of their medical information and results in security disclosure and a single point of failure. An advanced solution is necessary to improve data usage while restricting it in terms of security. Blockchain is a relatively advanced technology that stores data in a distributed manner and enhances security. This chapter proposes an integrated distributed application, a data protection system focused on an IoT framework and blockchain technology, where healthcare data are encrypted to monitor access control. The proposed application’s objective is to provide a permanent database for the management of healthcare data. The smart contract defines the basic operations of the application. Also, the implementation of the proposed application ensures that both IoT data and physician evaluations can never be removed or manipulated to prevent health conflicts. Implementation outcomes and results indicate that the proposed application applies to intelligent medical systems.
Context-aware deep kernel networks for image annotation

Context plays a crucial role in visual recognition as it provides complementary clues for different learning tasks including image classification and annotation. As the performances of these tasks are currently reaching a plateau, any extra knowledge, including context, should be leveraged in ordficant leaps in these performances. In the particular scenario of kernel machines, context-aware kernel design aims at learning positive semi-definite similarity functions which return high values not only when data share similar contents, but also similar structures (a.k.a. contexts). However, the use of context in kernel design has not been fully explored; indeed, context in these solutions is handcrafted instead of being learned. In this paper, we introduce a novel deep network architecture that learns context in kernel design. This architecture is fully determined by the solution of an objective function mixing a content term that captures the intrinsic similarity between data, a context criterion which models their structure and a regularization term that helps designing smooth kernel network representations. The solution of this objective function defines a particular deep network architecture whose parameters correspond to different variants of learned contexts including layerwise, stationary and classwise; larger values of these parameters correspond to the most influencing contextual relationships between data. Extensive experiments conducted on the challenging ImageCLEF Photo Annotation, Corel5k and NUS-WIDE benchmarks show that our deep context networks are highly effective for image classification and the learned contexts further enhance the performance of image annotation.
Gamification in modern education

Purpose of the article is analysis of the experience of gamification in modern professional education. The article presents an analysis of the project activities of senior students of higher educational institutions against the background of gamification of the educational process. The study made it possible to determine the role of gamification in modern vocational education. Gamification acts as a means of promoting deeper involvement of students in the educational process, their achievement of higher indicators and results. Its development in modern education expands the opportunities for training students.
Towards natural object-based image recoloring

Abstract Existing color editing algorithms enable users to edit the colors in an image according to their own aesthetics. Unlike artists who have an accurate grasp of color, ordinary users are inexperienced in color selection and matching, and allowing non-professional users to edit colors arbitrarily may lead to unrealistic editing results. To address this issue, we introduce a palette-based approach for realistic object-level image recoloring. Our data-driven approach consists of an offline learning part that learns the color distributions for different objects in the real world, and an online recoloring part that first recognizes the object category, and then recommends appropriate realistic candidate colors learned in the offline step for that category. We also provide an intuitive user interface for efficient color manipulation. After color selection, image matting is performed to ensure smoothness of the object boundary. Comprehensive evaluation on various color editing examples demonstrates that our approach outperforms existing state-of-the-art color editing algorithms.
Sample Average Approximation for Stochastic Optimization with Dependent Data: Performance Guarantees and Tractability

Sample average approximation (SAA), a popular method for tractably solving stochastic optimization problems, enjoys strong asymptotic performance guarantees in settings with independent training samples. However, these guarantees are not known to hold generally with dependent samples, such as in online learning with time series data or distributed computing with Markovian training samples. In this paper, we show that SAA remains tractable when the distribution of unknown parameters is only observable through dependent instances and still enjoys asymptotic consistency and finite sample guarantees. Specifically, we provide a rigorous probability error analysis to derive 1 - beta confidence bounds for the out-of-sample performance of SAA estimators and show that these estimators are asymptotically consistent. We then, using monotone operator theory, study the performance of a class of stochastic first-order algorithms trained on a dependent source of data. We show that approximation error for these algorithms is bounded and concentrates around zero, and establish deviation bounds for iterates when the underlying stochastic process is phi-mixing. The algorithms presented can be used to handle numerically inconvenient loss functions such as the sum of a smooth and non-smooth function or of non-smooth functions with constraints. To illustrate the usefulness of our results, we present several stochastic versions of popular algorithms such as stochastic proximal gradient descent (S-PGD), stochastic relaxed Peaceman-Rachford splitting algorithms (S-rPRS), and numerical experiment.
Evaluation of a True Random Number Generator Utilizing Timing Jitters in RSFQ Logic Circuits

We experimentally evaluated random number sequences generated by a superconducting hardware random number generator composed of a Josephson-junction oscillator, a rapid-single-flux-quantum (RSFQ) toggle flip-flop (TFF), and an RSFQ AND gate. Test circuits were fabricated using a 10 kA/cm2 Nb/AlOx/Nb integration process. Measurements were conducted in a liquid helium bath. The random numbers were generated for a trigger frequency of 500 kHz under the oscillating Josephson-junction at 29 GHz. 26 random number sequences of 20 kb length were evaluated for bias voltages between 2.0 and 2.7 mV. The NIST FIPS PUBS 140-2 tests were used for the evaluation. 100% pass rates were confirmed at the bias voltages of 2.5 and 2.6 mV. We found that the Monobit test limited the pass rates. As numerical simulations suggested, a detailed evaluation for the probability of obtaining “1” demonstrated the monotonical dependence on the bias voltage.
AI-Driven Fraud Detection and Mitigation in e-Commerce Transactions

Digital technology has created new channels of digital transaction for e-Commerce and financial services distribution companies. The COVID-19 has intensified the adoption of digital transactions. With increased load, digital transactions have become more vulnerable to frauds and have created a lucrative avenue for fraudsters. Digital transactions pose a significant challenge to enterprises and customers for having secure business and financial transactions. To enhance the ease and convenience of the customers and business enterprises, the e-Commerce and financial service providers are deploying sophisticated tools that employ data analytics, AI and machine learning techniques. However, such tools are susceptible to compromise and challenging to enterprises and customers for having secure business and financial transactions. In this paper, we present the issues and challenges in identifying spurious online payments and detecting fraud in e-Commerce transactions. We also present the AI, and data analytics techniques are employed for the detection, prevention and control of online frauds in e-Commerce and financial transactions.
Comparison of Convolutional Neural Network Architectures for Face Mask Detection

In 2020 World Health Organization (WHO) has declared that the Coronaviruses (COVID-19) pandemic is causing a worldwide health disaster.One of the most effective protections for reducing the spread of COVID-19 is by wearing a face mask in densely and close populated areas.In various countries, it has become mandatory to wear a face mask in public areas.The process of monitoring large numbers of individuals to comply with the new rule can be a challenging task.A costeffective method to monitor a large number of individuals to comply with this new law is through computer vision and Convolution Neural Network (CNN).This paper demonstrates the application of transfer learning on pre-trained CNN architectures namely; AlexNet, GoogleNet ResNet-18, ResNet-50, ResNet-101, to classify whether or not a person in the image is wearing a facemask.The number of training images are varied in order to compare the performance of these networks.It is found that AlexNet performed the worst and requires 400 training images to achieve Specificity, Accuracy, Precision, and F-score of more than 95%.Whereas, GoogleNet and Resnet can achieve the same level of performance with 10 times fewer number of training images.
Video-Based Inpatient Fall Risk Assessment: A Case Study

Inpatient falls are a serious safety issue in hospitals and healthcare facilities. Recent advances in video analytics for patient monitoring provide a non-intrusive avenue to reduce this risk through continuous activity monitoring. However, in- bed fall risk assessment systems have received less attention in the literature. The majority of prior studies have focused on fall event detection, and do not consider the circumstances that may indicate an imminent inpatient fall. Here, we propose a video-based system that can monitor the risk of a patient falling, and alert staff of unsafe behaviour to help prevent falls before they occur. We propose an approach that leverages recent advances in human localisation and skeleton pose estimation to extract spatial features from video frames recorded in a simulated environment. We demonstrate that body positions can be effectively recognised and provide useful evidence for fall risk assessment. This work highlights the benefits of video-based models for analysing behaviours of interest, and demonstrates how such a system could enable sufficient lead time for healthcare professionals to respond and address patient needs, which is necessary for the development of fall intervention programs.
Rauch-Tung-Striebel Smoothing Linear Multi-Target Tracking in Clutter

Most conventional data association based multi-target tracking (MTT) algorithms typically suffer from intractable computational complexities and could not perform in an environment where large number of closely spaced multiple targets move across each other in clutters. Unlike to the existing MTT systems, the linear multi-target (LM) algorithm modifies the measurement detection followed by neighbored tracks as a clutter, hence, it updates the track without the influence of other tracks. Thus, LM technique is a computationally efficient algorithm that allows the multi-target system to play like a single target tracking algorithm. Smoothing maximizes the state estimation accuracy and reduces estimation error based on future scan measurement. However, only few research paper focused on the LM algorithm without utilizing the benefits of the smoothing. This paper presents Rauch-Tung-Striebel Smoothing in the linear multi-target based on integrated probabilistic data association (RTS-LMIPDA). The RTS-LMIPDA algorithm fuses forward and backward LM track predictions to obtain the smoothing prediction which is required to calculate the smoothing multi-target state estimates in the forward track. Numerical analysis is presented to illustrate the estimation accuracy and false track discrimination (FTD) performances of RTS-LMIPDA in comparison to the existing MTT algorithms using the simulations.
Measurement and modeling of reflection characteristics of hole inner surface based on endoscopic image

Reflection characteristic is the law of specular and diffuse reflection after receives light of the surface, usually expressed by the ratio of irradiance of incident direction to the emissivity of observation direction, namely bidirectional reflection distribution function (BRDF). A method to measure BRDF for hole inner surface based on the endoscope image and morphology is proposed, which established the relationship between BRDF and morphology. Taking stainless steel and galvanized internal threaded as an example, the axial images in the holes are collected, and the BRDF of internal surface is calculated through the image brightness and the normal direction. The distribution characteristics of BRDF on the inner surface of different materials are analyzed. Based on the Phong model, the inBRDF parameter model of the inner surface is established. Research shows that the inBRDF model is consistent with measured results, and the average fitting error of the two materials is only 6.22%. The inBRDF method of hole inner surface proposed in this paper has high measurement efficiency and strong universality of equipment, and it is preferred to be applied on hole inner surface quality detection accordingly.
Kubernetes Load-balancing and related network functions using P4

This paper highlights the use of the P4 language for the development of a Kubernetes load balancer and related network functions that address scale, security, and network performance requirements. Load balancers have multiple deployment scenarios from edge to data center clusters, including per-node application load distributions. A P4 data plane running on an Infrastructure Processing Unit (IPU) can serve as a highly performant, secure and flexible data plane for Container Network Interfaces (CNI) like Calico. Using P4, we can identify the packet headers and operator specific fields for load balancing with consistent service delivery across multi-cloud environments. Challenges like per flow monitoring, on-demand autoscaling and adding network policy ACLs (Access Control Lists) can be addressed with software and P4 data plane extensions on an IPU, eventually paving the path for modernized service mesh delivery.
Highly-accurate gesture recognition based on ResNet with low-budget data gloves

Gesture recognition has been widely used in various fields such as virtual reality (VR), human-computer interaction, and clinical medicine. Traditionally, gesture recognition requires expensive sensors to obtain high-precision data, but some applications–such as VR games–do not require high accuracy and users prefer a lower cost instead. Thus some researchers began to use Convolutional Neural Networks (CNN) to recognize gestures directly without other calibration equipment. However, using CNN encounters the degradation phenomenon of the neural network. In this paper, we propose a highly-accurate gesture recognition method for low-precision glove data based on Residual neural Networks (ResNet). Our method combines 1D convolution with 2D convolution and adopts a sliding window for sample size constraint to achieve data enhancement. Evaluation results indicate that compared to the CNN method, our method improves the classification accuracy rate from 94.4% to 99.2%.
Limitations on Realistic Hyperbolic Graph Drawing

We show that several types of graph drawing in the hyperbolic plane require features of the drawing to be separated from each other by sub-constant distances, distances so small that they can be accurately approximated by Euclidean distance. Therefore, for these types of drawing, hyperbolic geometry provides no benefit over Euclidean graph drawing.
Linguistically Driven Multi-Task Pre-Training for Low-Resource Neural Machine Translation

In the present study, we propose novel sequence-to-sequence pre-training objectives for low-resource machine translation (NMT): Japanese-specific sequence to sequence (JASS) for language pairs involving Japanese as the source or target language, and English-specific sequence to sequence (ENSS) for language pairs involving English. JASS focuses on masking and reordering Japanese linguistic units known as bunsetsu, whereas ENSS is proposed based on phrase structure masking and reordering tasks. Experiments on ASPEC Japanese–English &amp; Japanese–Chinese, Wikipedia Japanese–Chinese, News English–Korean corpora demonstrate that JASS and ENSS outperform MASS and other existing language-agnostic pre-training methods by up to +2.9 BLEU points for the Japanese–English tasks, up to +7.0 BLEU points for the Japanese–Chinese tasks and up to +1.3 BLEU points for English–Korean tasks. Empirical analysis, which focuses on the relationship between individual parts in JASS and ENSS, reveals the complementary nature of the subtasks of JASS and ENSS. Adequacy evaluation using LASER, human evaluation, and case studies reveals that our proposed methods significantly outperform pre-training methods without injected linguistic knowledge and they have a larger positive impact on the adequacy as compared to the fluency.
Event Detection in Social Media via Graph Neural Network

Recently, Graph Neural Networks (GNN) have been applied to many natural language processing tasks. However, few studies exploit GNN for event detection, especially event detection in social media. In this paper, we proposed a new Event Detection model based on GNN (EDGNN). EDGNN first utilizes a topic model to capture the topical information of the corpus, which is used to help the graph enrich the semantics of short texts. Then, a text-level graph with fewer edges and memory consumption is constructed for each input short text. Furthermore, we incorporate word embeddings trained by Bidirectional Encoder Representations from Transformers (BERT) into EDGNN, which greatly improves the performance of the proposed method. Experimental results on a real-world foodborne disease event dataset demonstrate our model outperforms state-of-the-art baselines.