Reviewerly/exHarmony huggingface dataset:
Discovering Limitations of Image Quality Assessments with Noised Deep Learning Image Sets

Image quality is important, and can affect overall performance in image processing and computer vision as well as for numerous other reasons. Image quality assessment (IQA) is consequently a vital task in different applications from aerial photography interpretation to object detection to medical image analysis. In previous research, the BRISQUE algorithm and the PSNR algorithm were evaluated with high resolution (≥ 512×384 pixels), but relatively small image sets (≤4,744 images). However, scientists have not evaluated IQA algorithms on low resolution (≤32×32 pixels), multi-perturbation, big image sets (for example, ≥60,000 different images not counting their perturbations). This study explores these two IQA algorithms through experimental investigation. We first chose two deep learning image sets, CIFAR-10 and MNIST. Then, we added 68 perturbations that add noise to the images in specific sequences and noise intensities. In addition, we tracked the performance outputs of the two IQA algorithms with singly and multiply noised images. After quantitatively analyzing experimental results, we report the limitations of the two IQAs with these noised CIFAR-10 and MNIST image sets. We also explain three potential root causes for performance degradation. These findings point out weaknesses of the two IQA algorithms. The research results provide guidance to scientists and engineers developing accurate, robust IQA algorithms. All source codes, related image sets, and figures are shared on the website (https://github.com/caperock/imagequality) to support future scientific and industrial projects.
Alignment with a combination of deflectometry and the sine condition test

We propose a new alignment method that is based on relative measurements with an on-axis test setup composed of a pixelated camera and a monitor. By combining deflectometry and the sine condition test, the new method eliminates the necessity of moving a test instrument to multiple field points but still estimates the state of alignment by measuring both the off-axis and on-axis performances of the system. Additionally, it can be a very cost-effective option for certain projects as a monitor, and a camera may be substituted for the return optic and the interferometer required in a conventional interferometric method. We explain the concept of the new alignment method using a meter-class Ritchey–Chrétien telescope. Additionally, we present a new metric, the Metric for Misalignment Indicators (MMI), which represents the transmitted wavefront error caused by misalignment in the system. Then we demonstrate the validity of the concept using simulations where a poorly aligned telescope is the starting point to show that this method has a larger dynamic range compared to the interferometric method. Even considering realistic levels of noise, the new alignment method works successfully, as there is an improvement of two orders of magnitude in the final MMI after three iterations of alignment. The MMI of perturbed telescope models is about 10 µm but, after alignment, the MMI converges to one-tenth of a micrometer.
On the Convergence of Blockchain and IoT for Enhanced Security

Internet of Things (IoT) is the group of intelligent “objects” in a network that collects information and makes smart assessments. Due to the blend of IoT and the Internet, heterogeneous intelligent devices might handle real-time information and transactions. However, there are certain concerns with IoT, such as security, privacy, and data integrity that need to be addressed. Blockchain technology with its “secure by design” can overcome these challenges of IoT and address the major IoT security requirements. The key characteristics of blockchain are decentralization, immutability, data encryption, smart contracts, and consensus mechanism that can improve IoT security performance and avoid potential threats. In this book chapter, we are going to talk about the various security issues with IoT and how the blockchain integration can address those security risks. Additionally, we will also present the challenges that need to be addressed by integrating IoT with blockchain technology.
A cyber resilience analysis case study of an industrial operational technology environment

Cyber resilience is an active research area offering a novel approach to Cyber Security. The term appeared due to the concerning number of cyber-attacks on critical infrastructure. The National Institute of Standards and Technology (NIST) developed a framework to assist organisations with techniques and approaches to improving cyber resilience. However, there are a sparsity of case studies that speak to the adoption or measurement of these novel approaches within a complex industrial control environment. This paper presents a case study analysis of a manufacturing plant assessment drawing on key themes from the NIST literature. The paper presents how well NIST constructs can be adopted to find cyber-resilient enhancement opportunities and to decide if an evaluation of the results could supply a quantitative baseline measure of an organisation’s overall resilience. Conclusions drawn show that although the framework did partially aid with the analysis process, the frameworks ease of adoption assume an organisation has a conventional cyber security foundation; NIST should make this clear within their guidance. Furthermore, the accompanying evaluation process was not sufficient to quantitatively measure the overall cyber resilience maturity for this case study.
A web-based distributed system for integrating mobile music in choral performance

Abstract This paper presents an Internet of Musical Things system designed to enhance the singing practices of conventional vocal ensembles with electronic sounds generated by smartphones. The system comprises a small loudspeaker connected to a smartphone running a web-based app that generates sounds locally to the chorister, who uses it while singing. An evaluation of the user experience was conducted through three experiments involving a small choir of 9 choristers and a conductor. In the first experiment, the system was utilized in a co-located setting, where choristers shared the same physical space. In the second experiment, the system was employed in a remote setting, where geographically displaced choristers were connected through a networked music performance system. In the third experiment, a hybrid condition was implemented where part of the choir was co-located and part was remotely connected. Overall, results show that the application can be successfully utilized to augment the practice and experience of choir singing, leading to novel forms of musical expression. We provide a critical reflection where we discuss the lessons learned, strengths, limitations, and possible future developments.
Universal Time-Dependent Control Scheme for Realizing Arbitrary Linear Bosonic Transformations

We study the implementation of arbitrary excitation-conserving linear transformations between two sets of N stationary bosonic modes, which are connected through a photonic quantum channel. By controlling the individual couplings between the modes and the channel, an initial N-partite quantum state in register A can be released as a multiphoton wave packet and, successively, be reabsorbed in register B. Here we prove that there exists a set of control pulses that implement this transfer with arbitrarily high fidelity and, simultaneously, realize a prespecified N×N unitary transformation between the two sets of modes. Moreover, we provide a numerical algorithm for constructing these control pulses and discuss the scaling and robustness of this protocol in terms of several illustrative examples. By being purely control-based and not relying on any adaptations of the underlying hardware, the presented scheme is extremely flexible and can find widespread applications, for example, for boson-sampling experiments, multiqubit state transfer protocols, or in continuous-variable quantum computing architectures.
Chinese Medical Text Classification with RoBERTa

Many existed Chinese text classification solutions are successful, but the gap is that they are also limited by the models applied by themselves, so it’s available to consider a solution for advancing the Chinese text classification performance, especially in TCM (Traditional Chinese Medicine) text classification task. Assembled by Encoder element and Decoder element, Transformer and others X-former models have shown an outstanding performance in different NLP tasks, and among them BERT has succeeded in text representation and text classification tasks, but it has the possibility to be improved. Here we show our solution and experiment. In many NLP tasks, RoBERTa, which is based on BERT, has s a state-of-the-art performance than BERT. The classified sample data is selected from TCM workbench and tokenized by the Tokenizer we build based on pretrained RoBERTa, which was processed by RoBERTa_TCM, the RoBERTa model fine-tuned with our own data. In order to evaluate the vectorization performance and text classification performance of our Tokenizer-RoBERTa_TCM solution, we select some wild-range-applied language model: Word2Vec, LSTM, Bi-LSTM, contributing 4 baselines: Word2Vec-LSTM, Word2Vec-BiLSTM, Tokenizer-LSTM, Tokenizer-BiLSTM. We find out that the Tokenizer-RoBERTa_TCM model has shown a state-of-the-art classification ability with 90.88% average precision, 91.05% average recall and 90.72% average F1. All of them were the highest results among the baselines. It means that compared to regular text classification models (LSTM, Bi-LSTM, etc.), our RoBERTa_TCM model has an obvious improvement. This solution has the potential application research value in the text classification of TCM text.
Analysis on AES encryption standard and safety

AES has replaced DES to become a widely used encryption algorithm since 2000s. This paper may insight the basic theory of AES and safety analysis. In this paper, the whole process of AES encryption is described. The procedures of plaintext processing firstly, substituting bytes secondly, then shifting rows, mixing columns and adding round keys. AES-128 is taken as the study example in this paper to introduce the features and operating steps of AES. The construction and use of a simple S-box is also mentioned to help understand the procedure of substituting bytes. Safety analysis of AES is also taken into consideration to test AES's resistance against different kinds of attacks. The results show that AES is free from brute force attack with time security analysis. AES with 128 or more bits of key length can resist square attacks according to reviews on research. A way of differential cryptanalysis attack with concrete operating steps is introduced as a potential attack method against AES encryption standard. The paper also casts view on an improved AES algorithm to increase efficiency and security proposed by other research.
A Novel Method for Measuring, Visualizing, and Monitoring E-Collaboration

With its roots in the 1960s, e-collaboration has dramatically evolved and expanded over the past decades and became a globally adopted practice of teamwork. On the other hand, despite the development of e-collaboration technologies, the lack of true collaboration remains one of the main reasons for teamwork failures. However, traditional approaches to improving collaboration due to time-consuming, complicated, and expensive procedures do not meet the modern setup's requirements. This paper presents a new fast, simple, and low-cost method to improve e-collaboration through active engagement measures by analyzing data logs. The authors designed and ran a feedback system to mirror the participants' engagement during a collaborative engineering design course. The results of two case studies, including nine teams, suggest meaningful positive impacts of the method. The presented approach is applicable in upgrading e-collaboration platforms and further investigation on improving web-based collaborative learning and teamwork through monitoring dashboards and feedback systems.
Static Hovering Realization for Multirotor Aerial Vehicles With Tiltable Propellers

This paper presents a theoretical study on the ability of multi-rotor aerial vehicles (MRAVs) with tiltable propellers to achieve and sustain static hovering at different orientations. To analyze the ability of MRAVs with tiltable propellers to achieve static hovering, a novel linear map between the platform's control inputs and applied forces and moments is introduced. The relation between the introduced map and the platform's ability to hover at different orientations is developed. Correspondingly, the conditions for MRAVs with tiltable propellers to realize and sustain static hovering are detailed. A numerical metric is then introduced, which reflects the ability of MRAVs to sustain static hovering at different orientations. A subclass of MRAVs with tiltable propellers is defined as the Critically Statically Hoverable platforms (CSH), where CSH platforms are MRAVs that cannot sustain static hovering with fixed propellers, but can achieve static hovering with tilting propellers. Finally, extensive simulations are conducted to test and validate the above findings, and to demonstrate the effect of the proposed numerical metric on the platform's dynamics.
Potential of Homomorphic Encryption for Cloud Computing Use Cases in Manufacturing

Homomorphic encryption enables secure cloud computing over the complete data lifecycle. As so-called in-use encryption methodology, it allows using encrypted data for, e.g., data analysis—in contrast to classic encryption methods. In-use encryption enables new ways of value creation and an extensive use of cloud computing for manufacturing companies. However, homomorphic encryption is not widely implemented in practice yet. This is mainly since homomorphic encryption has higher computation times and is limited regarding its calculation operations. Nevertheless, for some use cases, the security requirements are a lot stricter than, e.g., timeliness requirements. Thus, homomorphic encryption might be beneficial. This paper, therefore, analyzes the potential of homomorphic encryption for cloud computing in manufacturing. First, the potential and limitations for both classic and homomorphic encryption are presented on the basis of a literature review. Second, to validate the limitations, simulations are executed, comparing the computation time and data transfer of classic and homomorphic encryption. The results show that homomorphic encryption is a tradeoff of security, time, and cost, which highly depends on the use case. Therefore, third, manufacturing use cases are identified; the two use cases of predictive maintenance and contract manufacturing are presented in detail, demonstrating how homomorphic encryption can be beneficial.
Dissecting Deep Metric Learning Losses for Image-Text Retrieval

Visual-Semantic Embedding (VSE) is a prevalent approach in image-text retrieval by learning a joint embedding space between the image and language modalities where semantic similarities would be preserved. The triplet loss with hard-negative mining has become the de-facto objective for most VSE methods. Inspired by recent progress in deep metric learning (DML) in the image domain which gives rise to new loss functions that outperform triplet loss, in this paper we revisit the problem of finding better objectives for VSE in image-text matching. Despite some attempts in designing losses based on gradient movement, most DML losses are defined empirically in the embedding space. Instead of directly applying these loss functions which may lead to sub-optimal gradient updates in model parameters, in this paper we present a novel Gradient-based Objective AnaLysis framework, or GOAL, to systematically analyze the combinations and reweighting of the gradients in existing DML functions. With the help of this analysis framework, we further propose a new family of objectives in the gradient space exploring different gradient combinations. In the event that the gradients are not integrable to a valid loss function, we implement our proposed objectives such that they would directly operate in the gradient space instead of on the losses in the embedding space. Comprehensive experiments have demonstrated that our novel objectives have consistently improved performance over baselines across different visual/text features and model frameworks. We also showed the generalizability of the GOAL framework by extending it to other models using triplet family losses including vision-language model with heavy cross-modal interactions and have achieved state-of-the-art results on the image-text retrieval tasks on COCO and Flick30K.
More Than Just Attention: Improving Cross-Modal Attentions with Contrastive Constraints for Image-Text Matching

Cross-modal attention mechanisms have been widely applied to the image-text matching task. They have achieved remarkable improvements thanks to their capability of learning fine-grained relevance across different modalities. However, the cross-modal attention models of existing methods could be sub-optimal and inaccurate because there is no direct supervision provided during the training process. In this work, we propose two novel training strategies, namely Contrastive Content Resourcing (CCR) and Contrastive Content Swapping (CCS) constraints, to address such limitations. These constraints supervise the training of cross-modal attention models in a contrastive learning manner without requiring explicit attention annotations. They are plug-in training strategies and can be generally integrated into existing cross-modal attention models. Additionally, we introduce three metrics, including Attention Precision, Recall, and F1-Score, to quantitatively measure the quality of learned attention models. We evaluate the proposed constraints by incorporating them into four state- of-the-art cross-modal attention-based image-text matching models. Experimental results on both Flickr30k and MS-COCO datasets demonstrate that integrating these constraints generally improves the model performance in terms of both retrieval performance and attention metrics.
Online Learning for Vocational Education: Uncovering Emerging Themes on Perceptions and Experiences

The worldwide spread of the COVID19 pandemic has shifted the teaching of theory and practical sessions in all schools, including in the field of vocational education, to online learning. Theoretical and practical learning in vocational education become more flexible and is not confined to physical space. A systematic literature review of selected research articles was conducted using Systematic Reviews and Meta-Analyses (PRISMA) guidelines based on important keywords of online learning in vocational education. Initially, the research identified 89 articles from Scopus databases by using a specific keywords search. After the screening phase, ten articles were finalized to meet the criteria for review and discussion in this paper. The thematic analysis conducted reveals several exciting topics discussed in this paper; students' experiences, teaching and learning performances, and the teacher and students' perception of online learning for vocational education. The study implies the cruciality of understanding the role of teachers and students in vocational education so online learning can be optimized and conducted efficiently.
Multi-scale Triplet Hashing for Medical Image Retrieval

For medical image retrieval task, deep hashing algorithms are widely applied in large-scale datasets for auxiliary diagnosis due to the retrieval efficiency advantage of hash codes. Most of which focus on features learning, whilst neglecting the discriminate area of medical images and hierarchical similarity for deep features and hash codes. In this paper, we tackle these dilemmas with a new Multi-scale Triplet Hashing (MTH) algorithm, which can leverage multi-scale information, convolutional self-attention and hierarchical similarity to learn effective hash codes simultaneously. The MTH algorithm first designs multi-scale DenseBlock module to learn multi-scale information of medical images. Meanwhile, a convolutional self-attention mechanism is developed to perform information interaction of the channel domain, which can capture the discriminate area of medical images effectively. On top of the two paths, a novel loss function is proposed to not only conserve the category-level information of deep features and the semantic information of hash codes in the learning process, but also capture the hierarchical similarity for deep features and hash codes. Extensive experiments on the Curated X-ray Dataset, Skin Cancer MNIST Dataset and COVID-19 Radiography Dataset illustrate that the MTH algorithm can further enhance the effect of medical retrieval compared to other state-of-the-art medical image retrieval algorithms.
Quantum decomposition algorithm for master equations of stochastic processes: The damped spin case

In this paper, we introduce a quantum decomposition algorithm (QDA) that decomposes the problem [Formula: see text] into a summation of eigenvalues times phase–space variables. One interesting feature of QDA stems from its ability to simulate damped spin systems by means of pure quantum harmonic oscillators adjusted with the eigenvalues of the original eigenvalue problem. We test the proposed algorithm in the case of undriven qubit with spontaneous emission and dephasing.
Self-Supervised Tumor Segmentation With Sim2Real Adaptation

This paper targets on self-supervised tumor segmentation. We make the following contributions: (i) we take inspiration from the observation that tumors are often characterised independently of their contexts, we propose a novel proxy task “layer-decomposition”, that closely matches the goal of the downstream task, and design a scalable pipeline for generating synthetic tumor data for pre-training; (ii) we propose a two-stage Sim2Real training regime for unsupervised tumor segmentation, where we first pre-train a model with simulated tumors, and then adopt a self-training strategy for downstream data adaptation; (iii) when evaluating on different tumor segmentation benchmarks, e.g. BraTS2018 for brain tumor segmentation and LiTS2017 for liver tumor segmentation, our approach achieves state-of-the-art segmentation performance under the unsupervised setting. While transferring the model for tumor segmentation under a low-annotation regime, the proposed approach also outperforms all existing self-supervised approaches; (iv) we conduct extensive ablation studies to analyse the critical components in data simulation, and validate the necessity of different proxy tasks. We demonstrate that, with sufficient texture randomization in simulation, model trained on synthetic data can effortlessly generalise to datasets with real tumors.