Reviewerly/exHarmony huggingface dataset:
A Transformer-Based Coarse-to-Fine Wide-Swath SAR Image Registration Method under Weak Texture Conditions

As an all-weather and all-day remote sensing image data source, SAR (Synthetic Aperture Radar) images have been widely applied, and their registration accuracy has a direct impact on the downstream task effectiveness. The existing registration algorithms mainly focus on small sub-images, and there is a lack of available accurate matching methods for large-size images. This paper proposes a high-precision, rapid, large-size SAR image dense-matching method. The method mainly includes four steps: down-sampling image pre-registration, sub-image acquisition, dense matching, and the transformation solution. First, the ORB (Oriented FAST and Rotated BRIEF) operator and the GMS (Grid-based Motion Statistics) method are combined to perform rough matching in the semantically rich down-sampled image. In addition, according to the feature point pairs, a group of clustering centers and corresponding images are obtained. Subsequently, a deep learning method based on Transformers is used to register images under weak texture conditions. Finally, the global transformation relationship can be obtained through RANSAC (Random Sample Consensus). Compared with the SOTA algorithm, our method’s correct matching point numbers are increased by more than 2.47 times, and the root mean squared error (RMSE) is reduced by more than 4.16%. The experimental results demonstrate that our proposed method is efficient and accurate, which provides a new idea for SAR image registration.
An Exploratory Research on Blockchain in Aviation: The Case of Maintenance, Repair and Overhaul (MRO) Organizations

The aircraft maintenance sector has high complexity with many intermediaries, multiple actors sharing data and needs to ensure high data security. The implementation of Blockchain technology can significantly contribute to the aforementioned characteristics. This research explores the implementation of Blockchain technology in Maintenance, Repair and Overhaul (MRO). For this research, a mixed-method approach was followed to obtain a holistic view of the adoption of Blockchain technology in an aircraft maintenance facility. Semi-structured interviews and a case study were used. The interview findings related to the current status of maintenance records management. The findings also highlighted the value of data storage within MRO’s and the benefits of Blockchain. The paper also discusses the readiness/willingness of aircraft maintenance facilities to implement Blockchain and the barriers to implementation. Recommendations and areas for further research are identified.
A comparative evaluation and analysis of three generations of Distributional Semantic Models

Abstract Distributional semantics has deeply changed in the last decades. First, predict models stole the thunder from traditional count ones, and more recently both of them were replaced in many NLP applications by contextualized vectors produced by neural language models. Although an extensive body of research has been devoted to Distributional Semantic Model (DSM) evaluation, we still lack a thorough comparison with respect to tested models, semantic tasks, and benchmark datasets. Moreover, previous work has mostly focused on task-driven evaluation, instead of exploring the differences between the way models represent the lexical semantic space. In this paper, we perform a large-scale evaluation of type distributional vectors, either produced by static DSMs or obtained by averaging the contextualized vectors generated by BERT. First of all, we investigate the performance of embeddings in several semantic tasks, carrying out an in-depth statistical analysis to identify the major factors influencing the behavior of DSMs. The results show that (i) the alleged superiority of predict based models is more apparent than real, and surely not ubiquitous and (ii) static DSMs surpass BERT representations in most out-of-context semantic tasks and datasets. Furthermore, we borrow from cognitive neuroscience the methodology of Representational Similarity Analysis (RSA) to inspect the semantic spaces generated by distributional models. RSA reveals important differences related to the frequency and part-of-speech of lexical items.
Pushing boundaries of co-design by going online: Lessons learned and reflections from three perspectives

The global COVID-19 pandemic made significant changes to our day-to-day lives, which impacted how we conduct research and design - including co-design. In this article, we present case studies from three different co-design groups that pushed the boundaries of traditional co-design, and conducted multiple co-design sessions (more than 150 total) over the last year and a half. The case studies for each team include: the transition to online co-design; the pros and cons of logistics and design tools utilized during the co-design sessions; and the advances, challenges, and surprises. We compare and contrast themes that emerged from the case studies and present additional dimensions that need to be addressed as researchers utilize online co-design and advance methods to conduct online co-design.
Blockchain Based Authentication and Cluster Head Selection Using DDR-LEACH in Internet of Sensor Things

This paper proposes a blockchain-based node authentication model for the Internet of sensor things (IoST). The nodes in the network are authenticated based on their credentials to make the network free from malicious nodes. In IoST, sensor nodes gather the information from the environment and send it to the cluster heads (CHs) for additional processing. CHs aggregate the sensed information. Therefore, their energy rapidly depletes due to extra workload. To solve this issue, we proposed distance, degree, and residual energy-based low-energy adaptive clustering hierarchy (DDR-LEACH) protocol. DDR-LEACH is used to replace CHs with the ordinary nodes based on maximum residual energy, degree, and minimum distance from BS. Furthermore, storing a huge amount of data in the blockchain is very costly. To tackle this issue, an external data storage, named as interplanetary file system (IPFS), is used. Furthermore, for ensuring data security in IPFS, AES 128-bit is used, which performs better than the existing encryption schemes. Moreover, a huge computational cost is required using a proof of work consensus mechanism to validate transactions. To solve this issue, proof of authority (PoA) consensus mechanism is used in the proposed model. The simulation results are carried out, which show the efficiency and effectiveness of the proposed system model. The DDR-LEACH is compared with LEACH and the simulation results show that DDR-LEACH outperforms LEACH in terms of energy consumption, throughput, and improvement in network lifetime with CH selection mechanism. Moreover, transaction cost is computed, which is reduced by PoA during data storage on IPFS and service provisioning. Furthermore, the time is calculated in the comparison of AES 128-bit scheme with existing scheme. The formal security analysis is performed to check the effectiveness of smart contract against attacks. Additionally, two different attacks, MITM and Sybil, are induced in our system to show our system model's resilience against cyber attacks.
SketchAA: Abstract Representation for Abstract Sketches

What makes free-hand sketches appealing for humans lies with its capability as a universal tool to depict the visual world. Such flexibility at human ease, however, introduces abstract renderings that pose unique challenges to computer vision models. In this paper, we propose a purpose-made sketch representation for human sketches. The key intuition is that such representation should be abstract at design, so to accommodate the abstract nature of sketches. This is achieved by interpreting sketch abstraction on two levels: appearance and structure. We abstract sketch structure as a pre-defined coarse-to-fine visual block hierarchy, and average visual features within each block to model appearance abstraction. We then discuss three general strategies on how to exploit feature synergy across different levels of this abstraction hierarchy. The superiority of explicitly abstracting sketch representation is empirically validated on a number of sketch analysis tasks, including sketch recognition, fine-grained sketch-based image retrieval, and generative sketch healing. Our simple design not only yields strong results on all said tasks, but also offers intuitive feature granularity control to tailor for various downstream tasks. Code will be made publicly available.
Shuffle-invariant Network for Action Recognition in Videos

The local key features in video are important for improving the accuracy of human action recognition. However, most end-to-end methods focus on global feature learning from videos, while few works consider the enhancement of the local information in a feature. In this article, we discuss how to automatically enhance the ability to discriminate the local information in an action feature and improve the accuracy of action recognition. To address these problems, we assume that the critical level of each region for the action recognition task is different and will not change with the region location shuffle. We therefore propose a novel action recognition method called the shuffle-invariant network. In the proposed method, the shuffled video is generated by regular region cutting and random confusion to enhance the input data. The proposed network adopts the multitask framework, which includes one feature backbone network and three task branches: local critical feature shuffle-invariant learning, adversarial learning, and an action classification network. To enhance the local features, the feature response of each region is predicted by a local critical feature learning network. To train this network, an L 1-based critical feature shuffle-invariant loss is defined to ensure that the ordered feature response list of these regions remains unchanged after region location shuffle. Then, the adversarial learning is applied to eliminate the noise caused by the region shuffle. Finally, the action classification network combines these two tasks to jointly guide the training of the feature backbone network and obtain more effective action features. In the testing phase, only the action classification network is applied to identify the action category of the input video. We verify the proposed method on the HMDB51 and UCF101 action datasets. Several ablation experiments are constructed to verify the effectiveness of each module. The experimental results show that our approach achieves the state-of-the-art performance.
Block Chain Application in Insurance Services: A Systematic Review of the Evidence

The Insurance industry participates in various processes that are characterized by data exchange, which is modified or updated by many parties. Hence, the insurance industry can benefit from the adoption of blockchain technology. However, there is a lack of understanding of the technology, the legal implications and the issues in implementing the technology. This paper aims at finding potential opportunities for the insurance sector on the implementation of blockchain technology. It also discusses issues and concerns for insurance companies wanting to adopt block chain technologies. A search was carried out for relevant electronic bibliographic databases (searched by means of keywords), articles published in scientific journals, websites of consultancy firms and blockchain developers, and reference lists of relevant review articles. Articles were screened and eligibility was based on participants, procedures, interventions comparisons, outcomes (PICO) model and criteria for PRISMA (Preferred Reporting Items for Systematic Reviews). A total of 23 papers were finalized after scrutiny for this study whereby the results disclose that blockchain, as a single source of reality, has the potential to improve productivity and mitigate the complexity of the insurance processes. Examples of real-world applications and insurance use cases are presented to demonstrate the strengths &amp; capabilities of the technology. This study also considers the present-day issues, risks and concerns in the implementation of the blockchain technology. Finally, the challenges and obstacles in the application of Blockchain technology in the Insurance Sector is highlighted and presented.
Visual Distant Supervision for Scene Graph Generation

Scene graph generation aims to identify objects and their relations in images, providing structured image representations that can facilitate numerous applications in computer vision. However, scene graph models usually require supervised learning on large quantities of labeled data with intensive human annotation. In this work, we propose visual distant supervision, a novel paradigm of visual relation learning, which can train scene graph models without any human-labeled data. The intuition is that by aligning commonsense knowledge bases and images, we can automatically create large-scale labeled data to provide distant supervision for visual relation learning. To alleviate the noise in distantly labeled data, we further propose a framework that iteratively estimates the probabilistic relation labels and eliminates the noisy ones. Comprehensive experimental results show that our distantly supervised model outperforms strong weakly supervised and semi-supervised baselines. By further incorporating human-labeled data in a semi-supervised fashion, our model outperforms state-of-the-art fully supervised models by a large margin (e.g., 8.3 micro- and 7.8 macro-recall@50 improvements for predicate classification in Visual Genome evaluation). We make the data and code for this paper publicly available at https://github.com/thunlp/VisualDS.
Unsupervised machine learning for identifying important visual features through bag-of-words using histopathology data from chronic kidney disease

Pathologists use visual classification to assess patient kidney biopsy samples when diagnosing the underlying cause of kidney disease. However, the assessment is qualitative, or semi-quantitative at best, and reproducibility is challenging. To discover previously unknown features which predict patient outcomes and overcome substantial interobserver variability, we developed an unsupervised bag-of-words model. Our study applied to the C-PROBE cohort of patients with chronic kidney disease (CKD). 107,471 histopathology images were obtained from 161 biopsy cores and identified important morphological features in biopsy tissue that are highly predictive of the presence of CKD both at the time of biopsy and in one year. To evaluate the performance of our model, we estimated the AUC and its 95% confidence interval. We show that this method is reliable and reproducible and can achieve 0.93 AUC at predicting glomerular filtration rate at the time of biopsy as well as predicting a loss of function at one year. Additionally, with this method, we ranked the identified morphological features according to their importance as diagnostic markers for chronic kidney disease. In this study, we have demonstrated the feasibility of using an unsupervised machine learning method without human input in order to predict the level of kidney function in CKD. The results from our study indicate that the visual dictionary, or visual image pattern, obtained from unsupervised machine learning can predict outcomes using machine-derived values that correspond to both known and unknown clinically relevant features.
From Missing Data Imputation to Data Generation

Real datasets often lack values, compromising the quality of data analyses. Adequate data may be synthetically imputed to replace missing values – a technique known as missing data imputation – avoiding deletion of incomplete observations. Several data imputation methods have been proposed and generative methods based on Artificial Neural Networks (ANN) are successful alternatives to discriminative methods. In this extended version of our work presented at the International Conference on Computational Science Neves et al. (2021), we propose three novel data imputation methods based on Generative Adversarial Networks (GAN): SGAIN, WSGAIN-CP, and WSGAIN-GP. We further studied how data imputation methods can be used to generate fully synthetic datasets. Among other benefits, the generation of synthetic data can help to mitigate legal, ethical, and data privacy issues, as well as to augment original data. In this context, we introduce tabulator , which is a novel meta-method for synthetic data generation that uses the data imputation methods as back-end engines for tabular data generation. We evaluated our data imputation methods using datasets with different amputation rates following the Missing Completely At Random (MCAR) setting. The results show that our methods are en-par or outperform state-of-the-art imputation methods in terms of response time and the quality of imputed data. We further evaluated and compared our data generation methods, which were derived from tabulator , with a state-of-the-art approach, the Conditional Tabular GAN (CTGAN). The evaluation results show that our tabulator methods outperform CTGAN in many cases, for example regarding the accuracy of machine learning tasks (e.g., prediction or classification) performed on the synthetic output data. • SGAIN , WSGAIN-CP , and WSGAIN-GP are novel GAN methods for missing data imputation. • These methods outperform state-of-the-art data imputation methods (e.g., GAIN ). • tabulator is a novel GAN meta-method for data generation that uses data imputation. • Methods derived from tabulator outperform state-of-the-art methods (e.g., CTGAN ).
Solidity Code Generation From UML State Machines in Model-Driven Smart Contract Development

For the development of blockchain smart contracts, a structured approach based on the principles of the Model Driven Architecture can be beneficial and facilitate the implementation of smart contracts.This paper presents such an approach, which, in combination with Unified Modeling Language (UML) Class and State machine diagrams, allows the smart contract structure and behavior logic to be modeled in several abstraction layers.This paper delves into details on how the model-to-model transformations from the specified Blockchain Platform Independent Model (PIM) with specified state-like behavior can be used to produce a Solidity Platform Specific Model (PSM).Subsequently, we elaborate on how the Solidity PSM is used for Solidity smart contract code generation by employing model-to-text transformations.The paper also demonstrates the process of our proposed transformations and code generation using smart contract code examples from Solidity documentation.Based on the examples, a Blockchain PIM is specified and transformed to Solidity PSM, which is then used for Solidity smart contract code generation.The generated smart contract code is then compiled, deployed on the Ethereum blockchain JavaScript virtual machine, and compared to the original smart contract code in terms of Solidity code metrics, similarity scores, and execution costs.The evaluation results indicate that our approach could be successfully used to model and later generate smart contract code.
A novel multilevel framework based contrast enhancement for uniform and non-uniform background images using a suitable histogram equalization

The histogram equalization approach, which is employed for image enhancement, reduces the number of pixel intensities, resulting in detail loss and an unnatural impression. This research proposes a strategy to improve the contrast of an image based on its nature. The images' statistical parameters mean, median and kurtosis are extracted and utilized to classify them into uniform and non-uniform background images. Initially, the image is decomposed using a multilevel decomposition based on the l1−l0 minimization model to extract its significant edge information. Later, the retrieved edge information is employed in proper histogram equalization to produce an improved result. Variational histogram equalization is proposed here to overcome the problem of over-amplification and artifacts in the homogeneous zone caused by histogram spikes in the uniform background images. Non-uniform background images are enhanced via two-dimensional histogram equalization, which takes advantage of the joint occurrences of edge information and pixel intensities in the low contrast image. The proposed technique is tested on the five databases: CSIQ, TID2013, LOL, DRESDEN, and FLICKR. SD, CII, DE, NIQE, and AMBE are the performance metrics used to validate the algorithm's effectiveness. Experimental analysis shows that the proposed technique outperforms the other algorithms, including deep learning architectures in high CII, SD, DE, and low NIQE values.
Student engagement and authentic language use on WeChat for learning Chinese as a foreign language

In this study, the use of a popular mobile social networking platform (WeChat) is investigated for learning Chinese as a foreign language (CFL) in a beginners' course at an Australian university. Driven by practical considerations and major issues with previous literature, student engagement and authentic language used in a WeChat class group are investigated. Data was collected through weekly conversation episodes over a semester and from a student survey. Conversation episodes were analysed both quantitatively (by frequencies) and qualitatively (using a discourse approach). The study found that student engagement increased in frequency and complexity towards the end of the semester, and students used Chinese for various authentic purposes in the semi-formal context of class group chats. A microanalysis of transcripts illustrated how the use of Chinese in multilingual and multimodal practices achieved authentic social functions. However, individual differences were observed, and English was frequently used in chats. Recommendations are provided for scaffolding and for utilising different semiotic resources in dynamic group interactions.
A fast and flexible attribute-based searchable encryption scheme supporting multi-search mechanism in cloud computing

How to use efficient search mechanisms to realize data sharing while ensuring data security has become an important research topic at present. In this paper, we propose a new searchable algorithm based on LSSS (Linear Secret Sharing Scheme) access policy, which is superior to previous work in computational and storage efficiency. Our scheme supports various search mechanisms, such as exact search, boolean search and range search mechanism. We use 0,1-coding theory to reduce the number of the numeric keywords, thus improving the range search mechanism. We also prove that our scheme is secure against the selectively chosen-keyword attack (SCKA) in the generic bilinear group model. Furthermore, we evaluate the performance of our scheme by theoretical analysis and experimental simulation, and the result show that our scheme achieves significant improvement in both computational and storage efficiency. To enhance the security and practicality of our scheme, we also deploy our scheme on a private chain and conduct extensive experiments to demonstrate its practical performance.