Reviewerly/exHarmony huggingface dataset:
A Privacy-Preserving Biometric Recognition System with Visual Cryptography

The popularity of more powerful and smarter digital devices has improved the quality of life and poses new challenges to the privacy protection of personal information. In this paper, we propose a biometric recognition system with visual cryptography, which preserves the privacy of biometric features by storing biometric features in separate databases. Visual cryptography combines perfect ciphers and secret sharing in cryptography with images, thus eliminating the complex operations in existing privacy-preserving schemes based on cryptography or watermarking. Since shares do not reveal any feature about biometric information, we can efficiently transmit sensitive information among sensors and smart devices in plain. To abate the influence of noise in visual cryptography, we leverage the generalization ability of transfer learning to train a visual cryptography-based recognition network. Experimental results show that our proposed method keeps the high accuracy of the feature recognition system when providing security.
Adaptive search strategy based chemical reaction optimization scheme for task scheduling in discrete multiphysical coupling applications

Parallel computing problems of multiphysical coupling applications based on discrete grids can be equivalently transformed into parallel computing problems based on a directed acyclic graph (DAG). Due to the problem of the discrete high-dimensional grids of the multiphysical coupling application, the directed data dependencies usually have parallelism. Moreover, this kind of scheduling problem based on DAG is NP-hard problem. Heuristic algorithms are often used to achieve optimal execution order scheduling of tasks and mapping between tasks and processors. In this paper, we propose an improved chemical reaction optimization algorithm based on adaptive search strategy (ASSCRO), which is used to solve the DAG task scheduling problem of discrete multiphysical coupling applications. ASSCRO is divided into two phases. The first phase is used to search the directed execution order of the tasks, and the second phase aims to use the heuristic strategy to map the tasks to the processors efficiently. In the four basic reactions of CRO, the algorithm can cover a larger search solution space by using an adaptive search strategy, it can obtain a better solution, and achieve less overhead and superior performance than the state-of-the-art. We conducted the experiment that applied our ASSCRO to deal with the multiphysical coupling applications. The experimental results showed that the proposed algorithm outperforms other algorithms in multiple metrics when dealing with DAG scheduling problems.
Virtual Machine Resource Allocation Optimization in Cloud Computing Based on Multiobjective Genetic Algorithm

Cloud computing is an important milestone in the development of distributed computing as a commercial implementation, and it has good prospects. Infrastructure as a service (IaaS) is an important service mode in cloud computing. It combines massive resources scattered in different spaces into a unified resource pool by means of virtualization technology, facilitating the unified management and use of resources. In IaaS mode, all resources are provided in the form of virtual machines (VM). To achieve efficient resource utilization, reduce users' costs, and save users' computing time, VM allocation must be optimized. This paper proposes a new multiobjective optimization method of dynamic resource allocation for multivirtual machine distribution stability. Combining the current state and future predicted data of each application load, the cost of virtual machine relocation and the stability of new virtual machine placement state are considered comprehensively. A multiobjective optimization genetic algorithm (MOGANS) was designed to solve the problem. The simulation results show that compared with the genetic algorithm (GA-NN) for energy saving and multivirtual machine redistribution overhead, the virtual machine distribution method obtained by MOGANS has a longer stability time. Aiming at this shortage, this paper proposes a multiobjective optimization dynamic resource allocation method (MOGA-C) based on MOEA/D for virtual machine distribution. It is illustrated by experimental simulation that moGA-D can converge faster and obtain similar multiobjective optimization results at the same calculation scale.
A Storytelling Methodology to Facilitate User-Centered Co-Ideation between Scientists and Designers

Successful implementation of sustainable innovation requires strong collaborative ecosystems. In particular, collaboration between scientific and people-centered expertise (e.g., designers) is essential to bring technical innovation through contextualized, meaningful and attractive experiences. However, in practice, these types of expertise are siloed and struggle to communicate and think together. We present a creative design method based on participatory story building to support collaborative user-centered ideation between technology scientists and designers. The core of the method is a new story creation model, the three-tension framework, that facilitates the exploration of users’ experiences and needs during ideation. To evaluate the method’s effectiveness, we conducted open-ended interviews with participants. We found that the method facilitates the expression of different perspectives and outside-the-box creative thinking. An originality and strength of our method is that it favors the discovery of new issues and pain points—rather than only solutions. This, combined with idea enrichment by multidisciplinary expertise, contributes to generating ideas in a broader range of application areas than usual. Our results indicate that participatory storytelling has the potential to facilitate multidisciplinary collaboration and to bring user-centered thinking to non-design stakeholders in order to envision user needs in future scenarios and new ecosystems.
Estimating 3D Finger Pose via 2D-3D Fingerprint Matching

Touchscreens have become the primary input devices for smartphones, tablet computers, and other intelligent devices over the past decades. While for the most pervasive commercial devices, only 2D touch positions on the screen are utilized as interaction inputs. To extend the richness of the input vocabulary, some researchers have proposed several innovative interaction techniques, e.g. finger pose. However, due to the low resolution and lacking in information of capacitive images, only two angles, pitch and yaw, are considered in most finger pose estimation algorithms, and the accuracy is not sufficiently high for large scale applications in smartphones. With the rapid development of under-screen fingerprint sensing technology, a new input modality, fingerprint image, for 3D finger pose estimation is available from these fingerprint sensors. In this paper, we propose a finger specific algorithm for estimating 3D finger pose including roll, pitch, and yaw from fingerprint images. 3D finger surface is first reconstructed based on sequential fingerprint images captured in enrollment, and given this 3D surface model, 3D finger pose of a test fingerprint is estimated by matching keypoints between the 2D image and 3D point cloud and minimizing the projection error. The proposed approach is a non-learning algorithm with good generalization ability and robustness in real applications. To evaluate the performance of our method, a dataset of fingerprint images with their corresponding ground truth 3D angles is collected. Experimental results on this dataset demonstrate the effectiveness of introducing reconstructed 3D finger surface shape in 3D finger pose estimation. The average absolute errors of three angles are 10.74 for roll, 8.25 for pitch, and 7.38 for yaw, respectively. Extensive experiments are also conducted to explore the impact of touching area size and gallery size on performance.
Corpus-Based Japanese Reading Teaching Database Cloud Service Model

With the widespread application of mobile Internet technology, big data, and artificial intelligence, the demand for computing resources in the whole society is growing rapidly. Database cloud service is an online managed high-availability database service built on a cloud computing platform. It can be used as a basic service of the cloud computing platform and has the characteristics of high service availability and high data reliability. The purpose of this paper is to design a corpus-based Japanese reading teaching database cloud service system and integrate a machine learning-based Structure Query Language (SQL) injection detection function for the system. The proposed work is as follows: (1) Design the basic framework of database cloud service. The module of cloud database service is introduced, and the realization logic of main system functions is analyzed. (2) A SQL injection classifier based on feature engineering and machine learning is designed for database cloud services, which can determine whether the input SQL command is an injection statement. The classifier is deployed between the database instance and the front-end server to integrate the SQL injection detection function into the cloud database instance. Then use the support vector machine algorithm to perform simulation experiments on the classifier model to compare the classification performance of the support vector machine algorithm when using different kernel functions. It is found that when the classification decision threshold is 0.5, the classifier using linear kernel function or radial basis kernel function has better classification performance for SQL sentences.
Topic Categorization of Tamil News Articles

This project aims to develop a model in deep learning for &#x2018;TOPIC CATEGORIZATION OF TAMIL NEWS ARTICLES&#x2019;. The process of learning for regional languages such as Tamil, Hindi differs from that of English. The task of categorising Tamil news article by topic is a text classification task. Most of the applications, such as searching in web, filtering information, recognising language, readability rating, and analysis of sentiment, already use text categorization. These tasks heavily rely on neural networks. We did multiclass text classification in this study. Different techniques, ranging from machine learning to Deep Learning, are used to handle almost all NLP difficulties. Even so, language localization remains a mystery. For languages other than English, NLP issues are uncertain. Entity Extraction, prediction, classification, and OCR in sequence modelling are examples of difficulties. Because the number of individuals utilising local languages like Tamil, Hindi, Telegu in social media network is growing, it&#x0027;s critical to generate automatic classification process. The goal is to categorise Tamil news stories into similar subjects (Sports, Cinema, Politics). NB, CNN, SVM algorithms are implemented to classify the content of Tamil News articles based on the category done in the previous work. The performance of these algorithms are compared with RNN using Precision, Recall, F1-score are reported in this study.
Expanding the field: using digital to diversify learning in outdoor science

This is an empirical study of teacher experiences with school learners (7-18 years) engaging in cross-curricular environmental science during the COVID-19 pandemic. The study focuses on #FieldworkLive, a programme of live-streamed outdoor science lessons produced by the Field Studies Council and Encounter Edu during the UK lockdown (April - May 2020). The experiences of approximately 377,000 teachers and students from 32 countries were captured using an online survey and direct staff consultation. This delivery method allowed us to reach untapped audiences and to provide learners with a virtual fieldwork experience during the constraints of lockdown. Teachers were highly positive about the technology-enhanced learning which provided them with novel perspectives and approaches for the classroom. We propose a model for the affordances provided by this delivery approach based on the Technological Pedagogical Content Knowledge framework. The Field Studies Council has developed a flexible package of multimedia resources for secondary schools as a route to enriching outdoor experience and learning despite the constraints imposed by the pandemic.The online version contains supplementary material available at 10.1186/s43031-022-00047-0.
A Cluster-then-label Approach for Few-shot Learning with Application to Automatic Image Data Labeling

Few-shot learning (FSL) aims at learning to generalize from only a small number of labeled examples for a given target task. Most current state-of-the-art FSL methods typically have two limitations. First, they usually require access to a source dataset (in a similar domain) with abundant labeled examples, which may not always be possible due to privacy concerns and copyright issues. Second, they typically do not offer any estimation of the generalization error on the target FSL task, because the handful of labeled examples must be used for training and cannot spare a validation subset. In this article, we propose a cluster-then-label approach to perform few-shot learning. Our approach does not require access to the labeled source dataset and provides an estimation of generalization error. We show empirically, on four benchmark datasets, that our approach provides competitive predictive performance to state-of-the-art FSL approaches and our generalization error estimation is accurate. Finally, we explore the application of our proposed method to automatic image data labeling. We compare our method with existing automatic data labeling systems. The end-to-end performance of our method outperforms the state-of-the-art automatic data labeling system Snuba by 26% and is only 7% away from the fully supervised upper bound.
Hospital Readmission Prediction Using Clinical Admission Notes

Clinical notes contain contextualised information beyond structured data relating to patients' past and current health conditions. Despite the richness, their unstructured, long, and high dimensional nature presents challenges to traditional text representation techniques. The advancement of deep contextual representation techniques in natural language processing (NLP) has shown remarkable performance in the biomedical and clinical domains for various information extraction and predictive tasks, including hospital readmission. However, most previous works have proposed discharge summary models where on-site medical intervention is impossible, and readmission could still occur. This paper utilises clinical notes recorded during admissions to study the risk of 30-day hospital readmissions. We employ clinical notes from MIMIC-III and consider competing baselines for clinical text representation, where a set of machine learning and deep learning algorithms are used to classify hospital readmission. The study demonstrates that notes captured during admissions play a crucial role to recognise potential readmission risk supporting healthcare practitioners for practical therapeutic intervention and discharge planning.
The Dawn of Metamaterial Engineering Predicted via Hyperdimensional Keyword Pool and Memory Learning

Abstract Metamaterials research has been ongoing for more than 20 years and it has gained much public and scientific interest. There have been many experts forecasting on the road ahead for metamaterials, notably, in lieu of “knowledge tree” in 2010. Ten years on, it is proposed to re‐examine these claims by using automated computer tools, such as natural language processing (NLP), to extract research information for processing and analyzing from unstructured texts in publications. In this study, a fully auto‐generated database of 43 678 abstracts related to metamaterials published between 2000 and 2021 using Scopus Search API (Application Programming Interface) is built. Applying word embedding, each keyword is studied in a hyperdimensional vector space and clusters so that their relationships can be visualized for assessing the popularity and trends of research themes. A neural network model developed based on the encoder–decoder long short‐term memory (LSTM) architecture is finally trained to predict future directions and theme evolutions in the next four years for selected topics. This study not only provides vital information in terms of impact of metamaterials research but also lays down a solid foundation for the development of future metamaterial research roadmap in the form of Gartner hype cycle.
Shapley Idioms: Analysing BERT Sentence Embeddings for General Idiom Token Identification

This article examines the basis of Natural Language Understanding of transformer based language models, such as BERT. It does this through a case study on idiom token classification. We use idiom token identification as a basis for our analysis because of the variety of information types that have previously been explored in the literature for this task, including: topic, lexical, and syntactic features. This variety of relevant information types means that the task of idiom token identification enables us to explore the forms of linguistic information that a BERT language model captures and encodes in its representations. The core of this article presents three experiments. The first experiment analyzes the effectiveness of BERT sentence embeddings for creating a general idiom token identification model and the results indicate that the BERT sentence embeddings outperform Skip-Thought. In the second and third experiment we use the game theory concept of Shapley Values to rank the usefulness of individual idiomatic expressions for model training and use this ranking to analyse the type of information that the model finds useful. We find that a combination of idiom-intrinsic and topic-based properties contribute to an expression's usefulness in idiom token identification. Overall our results indicate that BERT efficiently encodes a variety of information from topic, through lexical and syntactic information. Based on these results we argue that notwithstanding recent criticisms of language model based semantics, the ability of BERT to efficiently encode a variety of linguistic information types does represent a significant step forward in natural language understanding.
Image Stitching With Manifold Optimization

Image stitching usually relies on spatial transformations to perform the overlap alignment and distortion mitigation. This paper presents a manifold optimization method to seek these transformations. The purpose is not to present a new formulation of image stitching, as the proposed method uses common transformations such as homography to align feature correspondences in the overlap and similarity transformations to preserve the shape. Instead, the proposed method is based on a new treatment of these transformations as elements of a prescribed matrix manifold. Its advantage lies in its more effective and efficient optimization in the manifold domain. Specifically, spatially varying homographies are computed by an efficient second-order minimization (ESM) of the geometric error of aligning feature correspondences, but with their intrinsic manifold parameterization. To mitigate the distortion, the interpolation between homography and similarity transformation is performed on a general matrix manifold. These on-manifold operations improve the stitching quality with fewer ghosting and distortion artifacts. The experiments show our manifold optimization for image stitching outperforms other methods.
d3p - A Python Package for Differentially-Private Probabilistic Programming

We present d3p, a software package designed to help fielding runtime efficient widely-applicable Bayesian inference under differential privacy guarantees. d3p achieves general applicability to a wide range of probabilistic modelling problems by implementing the differentially private variational inference algorithm, allowing users to fit any parametric probabilistic model with a differentiable density function. d3p adopts the probabilistic programming paradigm as a powerful way for the user to flexibly define such models. We demonstrate the use of our software on a hierarchical logistic regression example, showing the expressiveness of the modelling approach as well as the ease of running the parameter inference. We also perform an empirical evaluation of the runtime of the private inference on a complex model and find a $\sim$10 fold speed-up compared to an implementation using TensorFlow Privacy.
Secure and Efficient Communication in VANETs Using Level-Based Access Control

In recent years, the development of vehicular ad-hoc networks (VANETs) has received much attention in intelligent transportation systems (ITS). Unlike traditional ad-hoc networks, VANETs are emerging with unique characteristics that share similar technology with autonomous vehicles (AVs) and automated driving systems (ASDs). Communication between vehicles and the surrounding infrastructure unit, such as a roadside unit (RSU), must be secured, concise, and authentic. Hence, an access control system for the ad-hoc environment is required. We introduced a level-based controlled signcryption (LBS) scheme, which can be easily constructed and implemented into an access control system for VANETs environment. Our encrypted message has a short and constant size, which is better when compared with other attribute-based signcryption or encryption. Confidentiality, privacy, and authenticity are also provided in our scheme to ensure secure and authentic communication. Therefore, our scheme has addressed communication cost, scalability, security, and privacy issues in VANETs. This primitive can be applied to simplify attribute-based access control, as the only attribute required is an integer representing the security level. Our objective is to improve the quality and security of VANET communication. Moreover, an optional privacy mechanism in our scheme provides flexibility in controlling node privacy in VANETs.
Deep Regression Neural Networks for Proportion Judgment

Deep regression models are widely employed to solve computer vision tasks, such as human age or pose estimation, crowd counting, object detection, etc. Another possible area of application, which to our knowledge has not been systematically explored so far, is proportion judgment. As a prerequisite for successful decision making, individuals often have to use proportion judgment strategies, with which they estimate the magnitude of one stimulus relative to another (larger) stimulus. This makes this estimation problem interesting for the application of machine learning techniques. In regard to this, we proposed various deep regression architectures, which we tested on three original datasets of very different origin and composition. This is a novel approach, as the assumption is that the model can learn the concept of proportion without explicitly counting individual objects. With comprehensive experiments, we have demonstrated the effectiveness of the proposed models which can predict proportions on real-life datasets more reliably than human experts, considering the coefficient of determination (&gt;0.95) and the amount of errors (MAE &lt; 2, RMSE &lt; 3). If there is no significant number of errors in determining the ground truth, with an appropriate size of the learning dataset, an additional reduction of MAE to 0.14 can be achieved. The used datasets will be publicly available to serve as reference data sources in similar projects.
Prudent design principles for digital tampering experiments

We study the factors that lead to successful experiments in the field of digital evidence tampering, evaluating the studies conducted in the past and the mistakes that happened during the execution of our own experiments. We describe three lessons learned that arise from evaluating the experiments and provide advice on conducting future studies. We also report on qualitative results from our experiments and interviews with professional IT forensic experts.