Reviewerly/exHarmony huggingface dataset:
Web XR User Interface Research: Design 3D Layout Framework in Static Websites

Advances in digital interactive technologies have created a range of innovative products and services for the well-being of society. Extended Reality (XR) technology has shown enormous potential in the educational, commercial, and medical fields. Considering the potential of XR, we analyzed the use of XR technology for the improvement of web service user experiences. The paper discusses Web Extended Reality (XR) and its current circumstances. The study also discusses the advantages of web componentization and the Page Builder System, which is a famous framework for web componentization. Furthermore, the study analyzes the characteristics of XR. The research designs the Web XR User Interface framework with XR characteristics and componentization design. A 3D UI framework is proposed for providing an immersive, explorative, and readable user experience. The framework covers three aspects, including main content, a scrollbar, and navigation. In order to evaluate the performance efficiency of the framework, a proof-of-concept prototype was developed to examine the concept. We conducted a user study with 60 participants to evaluate the reading performance. Compared with the traditional web layout, the experimental results showed that the framework can improve the user experience in static websites. The paper indicates possible future views based on the experimental study.
Conversational DevBots for Secure Programming: An Empirical Study on SKF Chatbot

Conversational agents or chatbots are widely investigated and used across different fields including healthcare, education, and marketing. Still, the development of chatbots for assisting secure coding practices is in its infancy. In this paper, we present the results of an empirical study on SKF chatbot, a software-development bot (DevBot) designed to answer queries about software security. To the best of our knowledge, SKF chatbot is one of the very few of its kind, thus a representative instance of conversational DevBots aiding secure software development. In this study, we collect and analyse empirical evidence on the effectiveness of SKF chatbot, while assessing the needs and expectations of its users (i.e., software developers). Furthermore, we explore the factors that may hinder the elaboration of more sophisticated conversational security DevBots and identify features for improving the efficiency of state-of-the-art solutions. All in all, our findings provide valuable insights pointing towards the design of more context-aware and personalized conversational DevBots for security engineering.
Hybrid Smart Contracts for Securing IoMT Data

Data management becomes essential component of patient healthcare.Internet of Medical Things (IoMT) performs a wireless communication between E-medical applications and human being.Instead of consulting a doctor in the hospital, patients get health related information remotely from the physician.The main issues in the E-Medical application are lack of safety, security and privacy preservation of patient's health care data.To overcome these issues, this work proposes block chain based IoMT Processed with Hybrid consensus protocol for secured storage.Patients health data is collected from physician, smart devices etc.The main goal is to store this highly valuable health related data in a secure, safety, easy access and less cost-effective manner.In this research we combine two smart contracts such as Practical Byzantine Fault Tolerance with proof of work (PBFT-PoW).The implementation is done using cloud technology setup with smart contracts (PBFT-PoW).The accuracy rate of PBFT is 90.15%, for PoW is 92.75% and our proposed work PBFT-PoW is 99.88%.
Deniable encryption in a Quantum world

(Sender-)Deniable encryption provides a very strong privacy guarantee: a sender who is coerced by an attacker into “opening” their ciphertext after-the-fact is able to generate “fake” local random choices that are consistent with any plaintext of their choice. The only known fully-efficient constructions of public-key deniable encryption rely on indistinguishability obfuscation (iO) (which currently can only be based on sub-exponential hardness assumptions).
Neural network interpolation operators optimized by Lagrange polynomial

In this paper, we introduce a new type of interpolation operators by using Lagrange polynomials of degree r, which can be regarded as feedforward neural networks with four layers. The approximation rate of the new operators can be estimated by the (r+1)-th modulus of smoothness of the objective functions. By adding some smooth assumptions on the activation function, we establish two important inequalities of the derivatives of the operators. With these two inequalities, by using the K-functional and Berens-Lorentz lemma in approximation theory, we establish the converse theorem of approximation. We also give the Voronovskaja-type asymptotic estimation of the operators for smooth functions. Furthermore, we extend our operators to the multivariate case, and investigate their approximation properties for multivariate functions. Finally, some numerical examples are given to demonstrate the validity of the theoretical results obtained and the superiority of the operators.
Experience on Using ArchiMate Models for Modelling Blockchain-Enhanced Value Chains

Blockchain is an emerging disruptive technology with a great potential to impact business value creation. Yet it is challenging to understand how blockchain can be utilised to improve enterprises’ performance and value creation. A formalised conceptual and enterprise modelling may help bridge the communication gap between domain experts and blockchain system designers. The goal of this study is to explore how can modelling facilitate blockchain-enhanced value chain design from a motivation viewpoint. We report our experience from modelling a blockchain-enhanced seafood supply chain using ArchiMate motivation and strategy models, where stakeholders have diverse concerns and goals. Preliminary results have indicated that such a modelling approach facilitates the common understanding, and the decision and prioritisation of the business strategies, as well as the identification of the blockchain benefits for the enterprises. It may help stakeholders along the value chain to align their business strategies and priorities, and can be an effective tool for value co-creation and enhancing the collaboration and communication among stakeholders and with blockchain application developers.
Application of Visitor Eye Movement Information to Museum Exhibit Analysis

The motivation of this study is that after the COVID-19 epidemic, museum exhibition visits have also been significantly affected. The purpose of this research is to better understand the visual cognition of visitors, so as to improve the application of physical field or online exhibitions. Currently, no research is available on the differences in the visitor’s viewing and cognitive process with eye movements sequence analysis that stem from the exhibition planning and design of different museums. This study tracks and analyzes the eye movement trajectories of visitors and studies its relation to learning and cognition and finds the key to influencing cognition through behavioral sequence analysis of displayed content. The results show that those interested in the displayed content have better cognitive performance, are immersed in reading text, and have a substantial shift in eye movement. Contrarily, those not interested in the displayed content are distracted and often turn their attention back to the title of the content. In this study, eye movement and fixation are indicators that can be used as a reference for the future design of displays to improve the effectiveness of presenting information to a visitor. Furthermore, this research can also provide future applications in integrating the virtual world and cognitive information, in the application of AR, VR, or metaverse environment, to provide people’s cognition of rapid information in the virtual environment.
Facial expression recognition in facial occlusion scenarios: A path selection multi-network

The occlusion scenarios including wearing mask, wearing sunglasses, wearing hat, etc. Thus, parts of face are occluded, e.g. nose and mouth are obscured if a mask is worn. Facial expression recognition (FER) tasks have been widely researched. However, less attention has been paid to FER in occlusion scenarios, which are not uncommon in the real world. In this paper, we propose a method that structures a path selection multi-network model to achieve the FER in the above three types of facial occlusion scenarios. The method contains two parts. For the multi-network, we segment the labels in one database which results in three new sub-databases to train three Subnets, respectively. For the path selection, which is an integration method of multi-network, we merge groups of labels in one database to train an initial network called BeginNet. The prediction of BeginNet selects one of the Subnets to make the final prediction. We concatenate four popular databases Fer2013, JAFFE, KDEF and Raf-DB databases into one larger database, and use the combined database to verify our method effectiveness. The experimental results show that our method has better results in coping with the expression recognition task in multiple types of facial occlusion scenarios.
Optimizing Parallel Recursive Datalog Evaluation on Multicore Machines

Over the past years, there has been a resurgence of interest in Datalog due to its superior ability of expressing applications that require recursive computations. However, in addition to expressive power, supporting analytical tasks with ever-increasing volume of data requires high performance and scalability. In this paper, we present DCDatalog, an in-memory Datalog engine specifically designed for modern shared-memory multicore architectures. Our key contribution is a novel system architecture that supports a wide scope of Datalog applications with a light-weight coordination scheme during parallel evaluation. To this end, we propose a dynamic scheduling strategy that can generate the parallel execution plan on-the-fly while reducing concurrent accesses to the shared memory. Experimental results on several large datasets show that our system significantly outperforms existing parallel Datalog engines and also scales well with increasing amount of data.
Survey on Meta Learning Algorithms for Few Shot Learning

In Meta learning auto learning algorithms are applied to machine learning experiments. The Meta learning is trying to solve problem of learning to learn as there is a significant lack in data as well as experts. There are many novel approaches developed in field of meta learning in past few years. This paper is summary of ongoing research in field of meta learning. It describes current trends and development in field of meta learning and with tactic knowledge how the meta learning can be applied to achieve few shot learning. It is believe that the Meta learning will perform well to overcome the challenges of few shot learning.
The Cost of Culture

Mobile banking applications have become a primary mode of sharing and managing financial resources. However, prior work has questioned its role in serving Black communities amid legacies of structural racial inequity. To investigate this critical topic, we conducted a 30-day diary study with 21 participants and an interview study with 15 participants, all Black American users of the mobile payment platform Cash App. We observed that addressing hurdles to financial inclusion requires the navigation of institutional distrust, banking barriers, and financial literacy disparities. Our findings suggest that Black Americans persist and navigate contemporary financial systems through legacies of social and cultural capacities. We offer grounded-truth considerations for the continued design of fin-tech that could have equitable impact among historically marginalized communities. Reflecting on the tenor of financial services to lack empathy, we urge designers and scholars of financial technology to reflect on their power to promote racial justice and financial inclusion.
Using an Improved Residual Network to Identify PIK3CA Mutation Status in Breast Cancer on Ultrasound Image

The detection of phosphatidylinositol-3 kinase catalytic alpha (PIK3CA) gene mutations in breast cancer is a key step to design personalizing an optimal treatment strategy. Traditional genetic testing methods are invasive and time-consuming. It is urgent to find a non-invasive method to estimate the PIK3CA mutation status. Ultrasound (US), one of the most common methods for breast cancer screening, has the advantages of being non-invasive, fast imaging, and inexpensive. In this study, we propose to develop a deep convolutional neural network (DCNN) to identify PIK3CA mutations in breast cancer based on US images.We retrospectively collected 312 patients with pathologically confirmed breast cancer who underwent genetic testing. All US images (n=800) of breast cancer patients were collected and divided into the training set (n=600) and test set (n=200). A DCNN-Improved Residual Network (ImResNet) was designed to identify the PIK3CA mutations. We also compared the ImResNet model with the original ResNet50 model, classical machine learning models, and other deep learning models.The proposed ImResNet model has the ability to identify PIK3CA mutations in breast cancer based on US images. Notably, our ImResNet model outperforms the original ResNet50, DenseNet201, Xception, MobileNetv2, and two machine learning models (SVM and KNN), with an average area under the curve (AUC) of 0.775. Moreover, the overall accuracy, average precision, recall rate, and F1-score of the ImResNet model achieved 74.50%, 74.17%, 73.35%, and 73.76%, respectively. All of these measures were significantly higher than other models.The ImResNet model gives an encouraging performance in predicting PIK3CA mutations based on breast US images, providing a new method for noninvasive gene prediction. In addition, this model could provide the basis for clinical adjustments and precision treatment.
A decision tree-based method for ordinal classification problems

In ordinal classification problems, the class value exhibits a natural order. Usually, these problems are solved as multiclass classification problems while discarding the ordering form of the class. Recently, several research studies have proposed novel methods for ordinal classification problems while aiming to predict a predefined objective value. These methods are based on modification of the splitting criteria of decision tree-based algorithms. These methods consider the ordinal nature of the data and the magnitude of the potential classification error from a predefined predicted objective. This research aims to consolidate these methods and generalize them for any decision tree-based method while constructing decision trees according to any predefined objective value calculated from the training dataset. Furthermore, to evaluate the proposed method, a comprehensive experimental study is performed based on known ordinal datasets. The proposed method is found to significantly outperform its counterpart nonordinal models. This observation confirms that ordering information benefits ordinal methods and improves their performance. Additionally, the ordinal decision tree-based methods achieved competitive performance compared to state-of-the-art ordinal techniques. The results are validated with various performance measures that are commonly used for ordinal and non-ordinal classification problems.
Heterogenous affinity graph inference network for document-level relation extraction

Document-level relation extraction (Doc-level RE) is a more practical and challenging task, which provides a new perspective on obtaining factual knowledge from the more complex cross-sentence text. Recent Doc-level RE, based on pre-trained language models, uses graph neural networks to implicitly model relation reasoning in a document. However, it is not perfect that the model neglects explicit reasoning clues, leading to a weak ability and a lack of capability to model long-distance relationships. In this paper, we propose to explicitly model the heterogeneous affinity graph, HAG, including a mention graph (MG) and a coreference graph (CG). We first construct CG to cluster the expressions together as a coreference array. Then, MG and CG are incorporated to capture the reasoning clues from the adjacent affinity matrix. Moreover, HAG is aggregated into an isomorphic entity graph according to the noise suppression mechanism and RGCN. Finally, the classification is established on the normalized graph to infer the relations of entity pairs. Experimental results significantly outperform baselines by nearly 1.7% ∼ 2.0% in F1 on three public datasets, DocRED, DialogRE, and MPDD. We further conduct ablation experiments to demonstrate the effectiveness of the proposed approach.
Optical neuromorphic computing based on a large-scale laterally coupled laser array

We propose a neuromorphic photonic time-delayed reservoir computing (RC) scheme based on a large-scale laterally coupled laser array with optical feedback and injection. Here, the feasibility of this RC system for dealing with a complex task-image recognition is proved numerically. By the offline pre-processing using randomly initialized convolutional neural network, two common datasets in the visual domain, i.e., MNIST and Fashion-MNIST, are employed to evaluate the performance of the proposed RC. After enlarging the laser array size and optimizing the parameters, our RC system achieves identification accuracy of 98.2% for the MNIST dataset and of 89.9% for the Fashion-MNIST dataset. Since the feedback delay time and information mask period of each laser in the array are coincident and maintained at 0.2 ns, the information processing rate of the RC system reaches 5 Gbps. Besides, the potential situations of parameter mismatch are also analyzed.
Glo-In-One: holistic glomerular detection, segmentation, and lesion characterization with large-scale web image mining

Purpose: The quantitative detection, segmentation, and characterization of glomeruli from high-resolution whole slide imaging (WSI) play essential roles in the computer-assisted diagnosis and scientific research in digital renal pathology. Historically, such comprehensive quantification requires extensive programming skills to be able to handle heterogeneous and customized computational tools. To bridge the gap of performing glomerular quantification for non-technical users, we develop the Glo-In-One toolkit to achieve holistic glomerular detection, segmentation, and characterization via a single line of command. Additionally, we release a large-scale collection of 30,000 unlabeled glomerular images to further facilitate the algorithmic development of self-supervised deep learning. Approach: The inputs of the Glo-In-One toolkit are WSIs, while the outputs are (1) WSI-level multi-class circle glomerular detection results (which can be directly manipulated with ImageScope), (2) glomerular image patches with segmentation masks, and (3) different lesion types. In the current version, the fine-grained global glomerulosclerosis (GGS) characterization is provided, including assessed-solidified-GSS (associated with hypertension-related injury), disappearing-GSS (a further end result of the SGGS becoming contiguous with fibrotic interstitium), and obsolescent-GSS (nonspecific GGS increasing with aging) glomeruli. To leverage the performance of the Glo-In-One toolkit, we introduce self-supervised deep learning to glomerular quantification via large-scale web image mining. Results: The GGS fine-grained classification model achieved a decent performance compared with baseline supervised methods while only using 10% of the annotated data. The glomerular detection achieved an average precision of 0.627 with circle representations, while the glomerular segmentation achieved a 0.955 patch-wise Dice dimilarity coefficient. Conclusion: We develop and release an open-source Glo-In-One toolkit, a software with holistic glomerular detection, segmentation, and lesion characterization. This toolkit is user-friendly to non-technical users via a single line of command. The toolbox and the 30,000 web mined glomerular images have been made publicly available at https://github.com/hrlblab/Glo-In-One.
Convenient Access to Expert-Reviewed Health Information via an Alexa Voice Assistant Skill for Patients With Multiple Myeloma: Development Study

Patients with multiple myeloma (MM) have high information needs due to the complexity of the disease and variety of treatments. Digital voice assistants provide support in daily life and can be a convenient tool that even older patients can use to access health information. Voice assistants may therefore be useful in providing digital health services to meet the information needs of patients with MM.We aim to describe and report on the development, content, and functionality of the first Amazon Alexa voice assistant skill for patients with MM in Germany with the goal of empowering and educating patients. Further, we share data on skill usage and first learnings.In a cocreation workshop with MM patient organizations and MM medical experts in Germany, Takeda Oncology discussed the development and content of the Alexa skill Multiple Myeloma. Patient information on MM disease, diagnostics, and therapy was presented in a question-and-answer format, reviewed by experts, and programmed into the skill. Additionally, a search function for finding patient support groups within a perimeter of 200 km around the users and a myeloma quiz functionality with multiple-choice questions were integrated into the skill. Aggregated retrospective data on the total number of skill installations and skill usage were retrieved from an Amazon Alexa developer account, and a web-based patient survey was conducted on the Takeda Oncology website.The Alexa skill Multiple Myeloma was launched in September 2019. It was available free of charge on the German Amazon Alexa skill store between September 2019 and March 2022 and could be used with devices featuring the Amazon Alexa voice assistant. Since the launch in September 2019 and up to July 2021, a total of 141 users have installed the skill. Between July 2020 and July 2021, a total of 189 skill sessions with 797 utterances were analyzed. The most popular inquiries were searches for patient support groups near the users (58/797, 7.3%), followed by inquiries about information on MM disease (53/797, 6.6%) and the quiz (43/797, 5.4%). The web-based survey on voice assistant usage and the feedback on the Alexa skill Multiple Myeloma were collected from 24 participants and showed that 46% (11/24) of participants would recommend the Alexa skill. Nonusers of voice assistants (11/24, 46%) stated that data protection concerns (7/11, 64%) and a lack of need (6/11, 55%) were the most important factors of not using voice assistants.The Alexa skill Multiple Myeloma offers patient-friendly and expert-reviewed answers and explanations for medical terms related to MM disease, diagnostics, and therapy, as well as connections to patient support groups and a quiz functionality. In the future, the skill can be extended with new content and functionalities, such as medication adherence support.