Reviewerly/exHarmony huggingface dataset:
Artistic robotic pencil sketching using closed-loop force control

This study introduces an artistic robotic painting system. The main purpose was to use a force sensor combined with a 6-axis robotic arm to create pencil sketches. Compared with other painting robots, our system has additional tactile sensing that can overcome the problem of pencil wear and can simulate the feeling of human sketches. To provide the robot with the capability to create paintings, our experiment used neural style transfer technology to extract the content and style image features and recomposed them into a new artwork. The newly generated image was processed for edge detection and further layered using methods, such as k-means clustering and image grayscale quantisation, resulting in a contour layer and multiple background layers. We carried out path planning of different layers according to the layering result, and we sorted the pixels by finding the shortest distance between them to plan a feasible path for the robotic arm at a higher speed. Finally, in the robotic arm painting experiment, we conducted two types of art creation: Chinese brush painting and pencil sketching. In the first type, we used position control to create a Chinese brush painting, and in the second type, we used a proportional-integral-derivative (PID) controller for force control to create pencil sketching. The experimental results show the validity of the proposed techniques in artistic robotic painting, both in Chinese brush painting and pencil sketching, with painting times of less than 30 min.
Unmanned aerial vehicles object detection based on image haze removal under sea fog conditions

Unmanned aerial vehicles (UAVs) have gradually become a major air threat to ships because of small size, good maneuverability, and low cost. Vision-based UAV detection offers one of the main ways to identify and protect against UAVs. Unlike land environment, the weather is complicated at sea. The visibility of an object is undermined by such factors as sea fog and sunlight, which makes it difficult to detect UAVs at sea through vision-based object detection. For the purpose of object detection at sea, this paper proposes a UAV object detection method based on image haze removal. In the proposed method, an improved dark channel haze removal (DCHR) algorithm is utilized to remove haze for and restore video images. Additionally, co-ordinate attention (CoordAttention, CA) is introduced to the lightweight algorithms of You Only Look Once (YOLO) for the object detection in restored video images, so as to improve the precision and speed of detection and reduce the miss rate. Some video images are also taken for detection experiments to verify the feasibility and effectiveness of the proposed method.
Feature-Aligned Video Raindrop Removal With Temporal Constraints

Existing adherent raindrop removal methods focus on the detection of the raindrop locations, and then use inpainting techniques or generative networks to recover the background behind raindrops. Yet, as adherent raindrops are diverse in sizes and appearances, the detection is challenging for both single image and video. Moreover, unlike rain streaks, adherent raindrops tend to cover the same area in several frames. Addressing these problems, our method employs a two-stage video-based raindrop removal method. The first stage is the single image module, which generates initial clean results. The second stage is the multiple frame module, which further refines the initial results using temporal constraints, namely, by utilizing multiple input frames in our process and applying temporal consistency between adjacent output frames. Our single image module employs a raindrop removal network to generate initial raindrop removal results, and create a mask representing the differences between the input and initial output. Once the masks and initial results for consecutive frames are obtained, our multiple-frame module aligns the frames in both the image and feature levels and then obtains the clean background. Our method initially employs optical flow to align the frames, and then utilizes deformable convolution layers further to achieve feature-level frame alignment. To remove small raindrops and recover correct backgrounds, a target frame is predicted from adjacent frames. A series of unsupervised losses are proposed so that our second stage, which is the video raindrop removal module, can self-learn from video data without ground truths. Experimental results on real videos demonstrate the state-of-art performance of our method both quantitatively and qualitatively.
Is Permissioned Blockchain the Key to Support the External Audit Shift to Entirely Open Innovation Paradigm?

Open Innovation (OI) models have been studied in many fields. However, the challenges and opportunities of a possible OI paradigm application in external auditing have been under-researched. Recent corporate scandals are currently triggering changes and improvements in the regulatory framework by targeting, in particular, the dominance of the so-called "Big Four". The main research question is whether a permissioned blockchain ecosystem could better enhance an OI paradigm and prove more suitable than the Semi-Open Innovation (SOI) paradigm that currently shapes the external audit field. Some challenges are considered in this article. Notably, blockchain requires suitable legal frameworks to ensure legally binding transactions. Moreover, multidisciplinary teams and high investments are required to develop efficient blockchain ecosystems and exploit the power of data analytics. Systematic analysis is performed based on a relevant literature review, along with abductive reasoning and applied modelling methodologies. The analyses demonstrate that the current Semi-Open Innovation external audit model is inefficient because it has led to market concentration, conflicting interests, and even fraud. Therefore, the regulators' role in promoting fully Open Innovation models in the audit industry is essential to ensure transparency, information sharing, fair competition, innovation, and collaboration among audit professionals. Hence, this research aims at providing a different perspective by focusing on the necessary assumptions needed to ensure successful application of technologies in the audit field. The innovative introduction of a permissioned blockchain-based audit system is also suggested to ensure the feasibility of the shift from Semi-Open to Open Innovation.
Position-aware image captioning with spatial relation

Image caption aims to generate a language description of a given image. The problem can be solved by learning semantic information of visual objects and generating descriptions based on extracted embedding. However, the spatial relationship between visual objects and their static position is not fully explored by existing methods. In this work, we propose a Position-Aware Transformer (PAT) model that extracts both regional and static global visual features and unify both the regional and global by incorporating spatial information aligned to each visual feature. To make a better representation of spatial information and correlation between extracted visual features, we propose and compare three subtle approaches to explore position embedding with spatial relation information explicitly. Moreover, we jointly consider the static global and regional embedding for spatial modeling. Experimental results illustrate that our proposed model achieves competitive performance on the COCO image captioning dataset, where the PAT model could respectively reach 38.7, 28.6, and 58.6 on BLEU-4, METEOR, and ROUGE-L respectively. Extensive experiments suggest that the proposed PAT model could also reach competitive performance on related visual-language tasks including visual question answering (VQA) and multi-modal retrieval. Detailed ablation studies are conducted to report how each part would contribute to the final performance, which could be a good reference for follow-up spatial information representation works.
A Novel Central Camera Calibration Method Recording Point-to-Point Distortion for Vision-Based Human Activity Recognition

The camera is the main sensor of vison-based human activity recognition, and its high-precision calibration of distortion is an important prerequisite of the task. Current studies have shown that multi-parameter model methods achieve higher accuracy than traditional methods in the process of camera calibration. However, these methods need hundreds or even thousands of images to optimize the camera model, which limits their practical use. Here, we propose a novel point-to-point camera distortion calibration method that requires only dozens of images to get a dense distortion rectification map. We have designed an objective function based on deformation between the original images and the projection of reference images, which can eliminate the effect of distortion when optimizing camera parameters. Dense features between the original images and the projection of the reference images are calculated by digital image correlation (DIC). Experiments indicate that our method obtains a comparable result with the multi-parameter model method using a large number of pictures, and contributes a 28.5% improvement to the reprojection error over the polynomial distortion model.
Data misrepresentation detection for insurance underwriting fraud prevention

Premium fraud concerns data misrepresentation committed by an insurance customer with the intent to benefit from an unduly low premium at the underwriting of a policy. In this paper, we propose a novel approach for evaluating the risk of underwriting premium fraud at the time of application in the presence of potentially misrepresented self-reported information. The aim of the approach is to support insurance companies in identifying fraudulent applications and their decisions to underwrite insurance contract propositions. Likewise, it can be use to make straight-through processing (i.e. automated) underwriting systems more fraudproof, by e.g., triggering a validation on applications prone to misrepresentations. Our approach is based on conditional density estimates for a set of validated contracts. The proposed approach does not require historical fraud labels and can adapt to changes in pricing policy. Moreover, the approach can be used to detect outliers in addition to predicting underwriting fraud and is extended to multivariate self-reported data. We further demonstrate a link between Shapley values in common conditional expectation problems and conditional density estimations to make our approach explainable. We report a case study involving motor insurance underwriting, in which a driver's identity and driving record can be misrepresented to benefit from an unduly low premium; the results indicate the effectiveness of the proposed approach for detecting and preventing underwriting fraud. • We introduce a flexible approach for detecting insurance premium fraud based on validated self-reported information. • Our approach estimates premium fraud based on the expected value of the self-reported information and its reported value. • We present evidence on the effectiveness of the proposed approach for detecting premium fraud on real insurance motor data. • We show how our model based on an orthonormal basis transformation of the self-reported variable(s) can be made explainable.
The Blockchain Technology Applied in the Development of Real Economy in Jiangsu under Deep Learning

This study focuses on the financing difficulties of small and medium enterprises (SMEs) in China to study the application of blockchain technology in developing the real economy. Deep learning neural network is applied to the vulnerability analysis and detection of smart contracts in blockchain technology by analyzing the connotation of blockchain technology and deep learning. A multiparty joint financial service platform based on blockchain technology is established to help SMEs financing institutions reduce transaction costs, thereby helping them reduce loan interest rates. Finally, Jiangsu Province is studied as a pilot unit. The results show that the Recall and F-score of Bidirectional Neural Network for smart contract vulnerability detection are higher than those of the original neural network. The Recall rate and F-score value of the Wide and Deep model are up to 96.2% and 94.7%, which are higher than those of other vulnerability detection schemes. The Timestamp vulnerability has the highest Recall rate, 94.2%, which can rely on a large amount of valid data to improve detection efficiency. The distribution of financing needs of SMEs in Jiangsu Province from 2020 to 2021 shows that the loan number of SMEs is generally not high. Still, financial institutions and enterprises must spend the same transaction cost. After a technology company in Nanjing made a loan through a blockchain financial service platform, its financing cost decreased by 0.5331%. Blockchain technology has played a great role in the financing process of SMEs, reducing intermediate links and credit costs, and promoting the development of SMEs and the real economy.
Metaheuristic Extreme Learning Machine for Improving Performance of Electric Energy Demand Forecasting

Electric energy demand forecasting is very important for electric utilities to procure and supply electric energy for consumers sufficiently, safely, reliably, and continuously. Consequently, the processing time and accuracy of the forecast system are essential to consider when applying in real power system operations. Nowadays, the Extreme Learning Machine (ELM) is significant for forecasting as it provides an acceptable value of forecasting and consumes less computation time when compared with the state-of-the-art forecasting models. However, the result of electric energy demand forecasting from the ELM was unstable and its accuracy was increased by reducing overfitting of the ELM model. In this research, metaheuristic optimization combined with the ELM is proposed to increase accuracy and reduce the cause of overfitting of three forecasting models, composed of the Jellyfish Search Extreme Learning Machine (JS-ELM), the Harris Hawk Extreme Learning Machine (HH-ELM), and the Flower Pollination Extreme Learning Machine (FP-ELM). The actual electric energy demand datasets in Thailand were collected from 2018 to 2020 and used to test and compare the performance of the proposed and state-of-the-art forecasting models. The overall results show that the JS-ELM provides the best minimum root mean square error compared with the state-of-the-art forecasting models. Moreover, the JS-ELM consumes the appropriate processing time in this experiment.
A fast face recognition based on image gradient compensation for feature description

Abstract To improve the efficiency of traditional face recognition techniques, this paper proposes a novel face recognition algorithm called Image Gradient Feature Compensation (IGFC). Based on the gradients along four directions in an image, a fusion algorithm and a compensation method are implemented to obtain features of the original image. In this study, gradient magnitude maps of a face image are calculated along four directions. Fusion gradients and differential fusion gradients are produced by fusing the four gradient magnitude maps of a face image in multiple ways, and they are used as compensation variables to compensate the appropriate coefficients on the original image and build IGFC feature maps of the original face image. Subsequently, IGFC feature maps are divided into several blocks to calculate the concatenated histogram over all blocks, which is in turn utilized as the feature descriptor for face recognition. Principal component analysis (PCA) is used to cut down the number of dimensions in high-dimensional features, which are recognized by the Support Vector Machine (SVM) classifier. Finally, the proposed IGFC method is superior to traditional methods as suggested by verification studies on YALE, ORL, CMU_PIE, and FERET face databases. When the LibSVM parameter was set to ‘-s 0 -t 2 -c 16 -g 0.0009765625’, the algorithm achieved 100% recognition on Yale and ORL data sets, 92.16% on CMU_PIE data sets, and 74.3% on FERET data sets. The average time for simultaneous completion of the data sets examined was 1.93 s, and the algorithm demonstrated a 70.71% higher running efficiency than the CLBP algorithm. Therefore, the proposed algorithm is highly efficient in feature recognition with excellent computational efficiency.
Towards CBDC-based machine-to-machine payments in consumer IoT

The technological advancement of the Internet of Things (IoT) is a well-known phenomenon that mainly affects industrial sectors but also consumers in everyday life. The use of Consumer IoT, i.e. CIoT, devices is increasing, and they are paving the way for a Machine-to-Machine (M2M) communication that could highly enrich consumer services. In this paper we position ourselves in the narrowing gap between the world of CIoT and the world of money, and we explore the emerging interaction between the payment needs of a M2M Economy and the "new ways of payment". Indeed, the advent of Distributed Ledger Technology and cryptocurrencies has introduced a tech-oriented dynamism in the monetary and financial sphere. Accordingly, central banks all over the world have started investigations into digital fiat money, i.e., "retail" Central Bank Digital Currencies (CBDCs). Against this backdrop, we analyze the integration of retail CBDC models into M2M and CIoT dynamics, while heeding regulation-by-design and compliance-by/through-design methodologies, and we propose a preliminary model of integration between a two-tier retail CBDC architecture and CIoT.
A Post-Quantum Secure Subscription Concealed Identifier for 6G

5G saw the introduction of an encrypted user identifier, the Subscriber Concealed Identifier (SUCI), to provide confidentiality of the subscriber's whereabouts and identities. The SUCI protects the new generation of cellular networks against tracking devices, so-called IMSI-catchers, which have undermined users' confidentiality ever since the inception of cellular networks. However, the potential advent of large-scale quantum computers in the near future threatens to compromise the confidentiality provided by the SUCI yet again. The security of the public-key cryptography that underpins the SUCI relies on the hardness of the discrete logarithm problem. Using Shor's algorithm, a quantum adversary could break the SUCI's cryptography and once more gain the capability to track and identify users. Advancements in quantum computing are unpredictable, and a breakthrough might be only a decade away. Given the slow nature of standards and their implementation, it is thus necessary to already integrate now quantum-resistant cryptography into the current and also next-generation (6G) cellular networks. To contribute to this development, we propose a post-quantum secure scheme for the SUCI calculation, \textttKEMSUCI. To this end, we first analyze the weak points in the current SUCI calculation scheme when considering quantum attacks. We then describe an alternative SUCI calculation scheme based on post-quantum secure key-encapsulation mechanisms (KEMs). Our proposed scheme can use any of the KEMs submitted to the NIST call for standardization of post-quantum secure cryptography (PQC) schemes. For the usage in \textttKEMSUCI, the KEM should provide efficient execution on a SIM card and induce little network communication overhead. We evaluate all of the NIST PQC finalists under these aspects and identify Kyber and Saber as the best fit. Instantiated with these KEMs, \textttKEMSUCI can be integrated into 5G and 6G. Compared to the existing SUPI protection schemes, \textttKEMSUCI exhibits faster execution speed and only little communication overhead.
Trajectory Generation and Optimization Using the Mutual Learning and Adaptive Ant Colony Algorithm in Uneven Environments

Aiming at the trajectory generation and optimization of mobile robots in complex and uneven environments, a hybrid scheme using mutual learning and adaptive ant colony optimization (MuL-ACO) is proposed in this paper. In order to describe the uneven environment with various obstacles, a 2D-H map is introduced in this paper. Then an adaptive ant colony algorithm based on simulated annealing (SA) is proposed to generate initial trajectories of mobile robots, where based on a de-temperature function of the simulated annealing algorithm, the pheromone volatilization factor is adaptively adjusted to accelerate the convergence of the algorithm. Moreover, the length factor, height factor, and smooth factor are considered in the comprehensive heuristic function of ACO to adapt to uneven environments. Finally, a mutual learning algorithm is designed to further smooth and shorten initial trajectories, in which different trajectory node sequences learn from each other to acquire the shortest trajectory sequence to optimize the trajectory. In order to verify the effectiveness of the proposed scheme, MuL-ACO is compared with several well-known and novel algorithms in terms of running time, trajectory length, height, and smoothness. The experimental results show that MuL-ACO can generate a collision-free trajectory with a high comprehensive quality in uneven environments.
AdaSL: An Unsupervised Domain Adaptation framework for Arabic multi-dialectal Sequence Labeling

Dialectal Arabic (DA) refers to varieties of everyday spoken languages in the Arab world. These dialects differ according to the country and region of the speaker, and their textual content is constantly growing with the rise of social media networks and web blogs. Although research on Natural Language Processing (NLP) on standard Arabic, namely Modern Standard Arabic (MSA), has witnessed remarkable progress, research efforts on DA are rather limited. This is due to numerous challenges, such as the scarcity of labeled data as well as the nature and structure of DA. While some recent works have reached decent results on several DA sentence classification tasks, other complex tasks, such as sequence labeling, still suffer from weak performances when it comes to DA varieties with either a limited amount of labeled data or unlabeled data only. Besides, it has been shown that zero-shot transfer learning from models trained on MSA does not perform well on DA. In this paper, we introduce AdaSL, a new unsupervised domain adaptation framework for Arabic multi-dialectal sequence labeling, leveraging unlabeled DA data, labeled MSA data, and existing multilingual and Arabic Pre-trained Language Models (PLMs). The proposed framework relies on four key components: (1) domain adaptive fine-tuning of multilingual/MSA language models on unlabeled DA data, (2) sub-word embedding pooling, (3) iterative self-training on unlabeled DA data, and (4) iterative DA and MSA distribution alignment. We evaluate our framework on multi-dialectal Named Entity Recognition (NER) and Part-of-Speech (POS) tagging tasks. The overall results show that the zero-shot transfer learning, using our proposed framework, boosts the performance of the multilingual PLMs by 40.87% in macro-F1 score for the NER task, while it boosts the accuracy by 6.95% for the POS tagging task. For the Arabic PLMs, our proposed framework increases performance by 16.18% macro-F1 for the NER task and 2.22% accuracy for the POS tagging task, and thus, achieving new state-of-the-art zero-shot transfer learning performance for Arabic multi-dialectal sequence labeling.