Reviewerly/exHarmony huggingface dataset:
Deep-Learning-Based Character Recognition from Handwriting Motion Data Captured Using IMU and Force Sensors

Digitizing handwriting is mostly performed using either image-based methods, such as optical character recognition, or utilizing two or more devices, such as a special stylus and a smart pad. The high-cost nature of this approach necessitates a cheaper and standalone smart pen. Therefore, in this paper, a deep-learning-based compact smart digital pen that recognizes 36 alphanumeric characters was developed. Unlike common methods, which employ only inertial data, handwriting recognition is achieved from hand motion data captured using an inertial force sensor. The developed prototype smart pen comprises an ordinary ballpoint ink chamber, three force sensors, a six-channel inertial sensor, a microcomputer, and a plastic barrel structure. Handwritten data of the characters were recorded from six volunteers. After the data was properly trimmed and restructured, it was used to train four neural networks using deep-learning methods. These included Vision transformer (ViT), DNN (deep neural network), CNN (convolutional neural network), and LSTM (long short-term memory). The ViT network outperformed the others to achieve a validation accuracy of 99.05%. The trained model was further validated in real-time where it showed promising performance. These results will be used as a foundation to extend this investigation to include more characters and subjects.
Deep Impact: A Study on the Impact of Data Papers and Datasets in the Humanities and Social Sciences

The humanities and social sciences (HSS) have recently witnessed an exponential growth in data-driven research. In response, attention has been afforded to datasets and accompanying data papers as outputs of the research and dissemination ecosystem. In 2015, two data journals dedicated to HSS disciplines appeared in this landscape: Journal of Open Humanities Data (JOHD) and Research Data Journal for the Humanities and Social Sciences (RDJ). In this paper, we analyse the state of the art in the landscape of data journals in HSS using JOHD and RDJ as exemplars by measuring performance and the deep impact of data-driven projects, including metrics (citation count; Altmetrics, views, downloads, tweets) of data papers in relation to associated research papers and the reuse of associated datasets. Our findings indicate: that data papers are published following the deposit of datasets in a repository and usually following research articles; that data papers have a positive impact on both the metrics of research papers associated with them and on data reuse; and that Twitter hashtags targeted at specific research campaigns can lead to increases in data papers’ views and downloads. HSS data papers improve the visibility of datasets they describe, support accompanying research articles, and add to transparency and the open research agenda.
A Narrative Review of Immersive Technology Enhanced Learning in Healthcare Education

Immersive technology is a growing field in healthcare education—attracting educationalists to evaluate its utility. There has been a trend of increasing research in this field; however, a lack of quality assurance surrounding the literature prompted the narrative review. Web Of Science database searches were undertaken from 2002 to the beginning of 2022. The studies were divided into three mixed reality groups: virtual reality (VR), augmented reality (AR), 360 videos, and learning theory subgroups. Appraising 246 studies with the Medical Education Research Study Quality Instrument (MERSQI) indicated a gap in the validation of measures used to evaluate the technology. Although, those conducted in VR or those detailing learning theories scored higher according to MERSQI. There is an educational benefit to immersive technology in the healthcare setting. However, there needs to be caution in how the findings are interpreted for application beyond the initial study and a greater emphasis on research methods.
Differential attention net: Multi-directed differential attention based hybrid deep learning model for solar power forecasting

Solar power plants provide a clean alternative to conventional thermal power plants. However, solar plant dependency on environmental factors threatens existing energy grids in terms of power quality and management. So, accurate solar power prediction is indispensable for designing and managing solar power plants. This work introduces a novel multi-directed differential attention-based differential attention net for accurate solar power prediction. The proposed model in the first step utilizes the feature of convolution neural network (CNN) and bidirectional long-short memory (BiLSTM) based hybrid deep learning model in parallel branches to extract the local dependencies in the latent space. However, long-term temporal dependencies in this time-series problem deteriorate prediction performance. So, an attention-based deep learning model is applied in the parallel branches to tap the long-term dependencies. Moreover, Bayesian optimization is used to fine-tune the hyperparameter of the proposed model, as it provides better results with the gaussian backend process. The proposed model is tested on two different datasets, one is real-time solar plant dataset located in the Australian desert, and other one is from Delhi, India. The proposed net is compared with other hybrid deep learning models (e.g., comprising of the recurrent neural network, gated recurrent unit, and LSTM). And the findings of the work confirms the effectiveness of the proposed model.
Threshold detection statistics of bosonic states

In quantum photonics, threshold detectors, distinguishing between vacuum and one or more photons, such as superconducting nanowires and avalanche photodiodes, are routinely used to measure Fock and Gaussian states of light. Despite being the standard measurement scheme, there is no general closed form expression for measurement probabilities with threshold detectors, unless accepting coarse approximations or combinatorially scaling summations. Here, we present new matrix functions to fill this gap. We develop the Bristolian and the loop Torontonian functions for threshold detection of Fock and displaced Gaussian states, respectively, and connect them to each other and to existing matrix functions. By providing a unified picture of bosonic statistics for most quantum states of light, we provide novel tools for the design and analysis of photonic quantum technologies.
High-performance reversible data hiding based on ridge regression prediction algorithm

An effective error prediction algorithm is the key to improving the embedding performance of reversible data hiding schemes. In this paper, high-performance ridge regression predictor-based reversible data hiding (RDH) is proposed. The ridge regression predictor is an adaptive predictor that adds L2 regularisation to minimise the residual sum of squares between the prediction pixels and the target pixels. The L2 norm as a penalty function decreases the weights for the prediction coefficients of unimportant pixels. In other words, the ridge regression predictor limits prediction coefficients that have negative or no influence on predicting the target pixels (abnormal samples). The ridge regression predictor allows the prediction coefficients to be small, which avoids the overfitting problem and enhances tamper-resistance and generalisation ability. In addition, to increase the prediction accuracy of the ridge regression predictor, the proposed method employs small samples to obtain more accurate prediction values. The neighbouring pixels closest to the target pixels are selected as the training sets and supported sets during the prediction process. In summary, the ridge regression predictor can generate an error plane that is more suitable for embedding, thereby improving the embedding performance of RDH. Extensive experimental results also show that the proposed method is superior to the state-of-the-art RDH schemes in terms of prediction accuracy and embedding performance.
Deep NMF topic modeling

Nonnegative matrix factorization (NMF) based topic modeling methods do not rely on model- or data-assumptions much. However, they are usually formulated as difficult optimization problems, which may suffer from bad local minima and high computational complexity. In this paper, we propose a deep NMF (DNMF) topic modeling framework to alleviate the aforementioned problems. It first applies an unsupervised deep learning method to learn latent hierarchical structures of documents, under the assumption that if we could learn a good representation of documents by, e.g. a deep model, then the topic word discovery problem can be boosted. Then, it takes the output of the deep model to constrain a topic-document distribution for the discovery of the discriminant topic words, which not only improves the efficacy but also reduces the computational complexity over conventional unsupervised NMF methods. We constrain the topic-document distribution in three ways, which takes the advantages of the three major sub-categories of NMF—basic NMF, structured NMF, and constrained NMF respectively. To overcome the weaknesses of deep neural networks in unsupervised topic modeling, we adopt a non-neural-network deep model—multilayer bootstrap network. To our knowledge, this is the first time that a deep NMF model is used for unsupervised topic modeling. We have compared the proposed method with a number of representative references covering major branches of topic modeling on a variety of real-world text corpora. Experimental results illustrate the effectiveness of the proposed method under various evaluation metrics.
A Novel Gradient-guided Post-processing Method for Adaptive Image Steganography

Designing an effective cost function has always been the key in image steganography after the development of the near-optimal encoders. To learn the cost maps automatically, the Generative Adversarial Networks (GAN) are often trained from the given cover images. However, this needs to train two Convolutional Neural Networks (CNN) in theory and is thus very time-consuming. In this paper, without modifying the original stego image and the associated cost function of the steganography, and no need to train a GAN, we proposed a novel post-processing method for adaptive image steganography. The post-processing method aims at the embedding cost, hence it is called Post-cost-optimization in this paper. Given a cover image, its gradient map is learned from a pre-trained CNN, which is further smoothed by a low-pass filter. The elements of the cost map derived from the original steganography are projected to 0,1 for separating embeddable and non-embeddable areas. For embeddable areas, the elements will be further screened by the gradient map, according to the magnitudes of the gradients, to produce a new cost map. Finally, the new cost map is used to generate new stego images. Comprehensive experiments have validated the efficacy of the proposed method, which has outperformed several state-of-the-art approaches, whilst the computational cost is also significantly reduced.
Cold-start active learning for image classification

Active learning (AL) aims to select valuable samples for labeling from an unlabeled sample pool to build a training dataset with minimal annotation cost. Traditional methods always require partially and initially labeled samples to start active selection and then query annotations of samples incrementally through several iterations. However, this scheme is not effective in the deep learning scenario. On the one hand, initially labeled sample sets are not always available in the beginning. On the other hand, the performance of the traditional model is usually poor in the early iterations due to limited training feedback. For the first time, we propose a cold-start AL model based on representative (CALR) sampling, which selects valuable samples without the need for an initial labeled set or the iterative feedback of the target models. Experiments on three image classification datasets, CIFAR-10, CIFAR-100 and Caltech-256, showed that CALR achieved a new state-of-the-art performance of AL in cold-start settings. Especially in low annotation budget conditions, our method can achieve up to a 10% performance increase compared to traditional methods. Furthermore, CALR can be combined with warm-start methods to improve the start-up efficiency while further breaking the performance ceiling of AL, which makes CALR have a broader application scenario.
LARFNet: Lightweight asymmetric refining fusion network for real-time semantic segmentation

In this paper, we propose a lightweight asymmetric refining fusion network (LARFNet) for real-time semantic segmentation to solve the problem that some existing models cannot achieve good segmentation accuracy with real-time inference speed in mobile devices due to the huge computational overhead. Specifically, LARFNet adopts an asymmetric encoder–decoder structure. The depth-wise separable asymmetric interaction module (DSAI module) is designed in the encoding process, which effectively extracted local and surrounding information under different receptive fields with optimized convolution in the condition of ensuring communication between channels. In the decoder, we design the bilateral pyramid pooling attention module (BPPA module) and the multi-stage refinement fusion module (MRF Module). The BPPA module is used to integrate the high-level output multi-scale context information. Based on spatial and channel attention mechanisms, the MRF module is proposed to refine the feature maps of different resolutions and guide the feature fusion. Experimental results show that LARFNet achieves 69.2% mIoU and 65.6% mIoU on Cityscapes and Camvid datasets at 127 FPS and 222 FPS respectively, only using a single NVIDIA GeForce GTX2080Ti GPU and 0.72M parameters without any pre-training or pre-processing. Compared with most of the existing state-of-the-art models, the proposed method realizes the efficient use of network parameters at a faster speed, reduces the number of network parameters, and still achieves the accuracy of good segmentation.
Image Classification for Identifying Social Gathering Types

Convolutional neural networks are current times state-of-art algorithms widely used in image classification. This paper has explored the image classification of social gatherings with state-of-the-art neural network models. We introduce image classification with the modified VGG16 model and the modified InceptionV3 model. Images are first pre-processed and then given input to the models for multi-class classification. We have modified layers in the models, resulting in the best accuracy for our dataset. Data augmentation and layer modification schemes are applied in this paper. The algorithm learns to identify the classes of an image by performing feature extraction and data augmentations of each image. Throughout this research, we discovered that the approaches suggested in this paper improve the performance of the models. Our task was based on four classes of social gathering images. We concluded that the layer-modified VGG16 model with augmentation gives us the best results with a training accuracy of 90.99% and validation accuracy of 87.18%.
Machine Learning Strategy for Solar Energy optimisation in Distributed systems

Using renewable energies such as wind and solar energy, two types of renewable energy, we may adjust the structure of the energy system, addressing both energy and environmental challenges at the same time. As a result of its impact on the environment, wind and solar energy generation are inherently unreliable sources of energy. Lithium batteries, a technology that is becoming increasingly mature in terms of energy storage, are a critical component of the answer to the problem of instability. In order to avoid waste and expense increases, the capacity should not be too large or too small, respectively. Power consumption restricts the amount of energy that may be stored, but industrial power usage is unpredictable and non-periodic. This is a significant task that needs the development of a model that can dispatch while still providing a reasonable amount of storage. In this paper, we develop a KNN classification model that considers the test cyclic of photovoltaic (PV) generation that includes battery installation, data on electricity consumption and data on PV generation in India. These metrics are used to develop an energy management model. The model aims at the reduction of operation cost and optimal storage of energy that should satisfy the grid demands. The results of simulation and the comparison of the theoretical results shows that the proposed model has higher optimisation of energy in the storage devices in case of distributed systems.
ExS-GAN: Synthesizing Anti-Forensics Images via Extra Supervised GAN

So far, researchers have proposed many forensics tools to protect the authenticity and integrity of digital information. However, with the explosive development of machine learning, existing forensics tools may compromise against new attacks anytime. Hence, it is always necessary to investigate anti-forensics to expose the vulnerabilities of forensics tools. It is beneficial for forensics researchers to develop new tools as countermeasures. To date, one of the potential threats is the generative adversarial networks (GANs), which could be employed for fabricating or forging falsified data to attack forensics detectors. In this article, we investigate the anti-forensics performance of GANs by proposing a novel model, the ExS-GAN, which features an extra supervision system. After training, the proposed model could launch anti-forensics attacks on various manipulated images. Evaluated by experiments, the proposed method could achieve high anti-forensics performance while preserving satisfying image quality. We also justify the proposed extra supervision via an ablation study.
ACGAN: Age‐compensated makeup transfer based on homologous continuity generative adversarial network model

Abstract The authors focus on the makeup transformation problem, which refers to the transfer of makeup from a reference face to a source face image while maintaining the source makeup‐free face image. In recent years, makeup transformation has become a hot issue and a lot of research has been conducted on this basis, but there are some limitations in the existing methods, mainly due to the lack of consideration of age factor, which makes the final generated face makeup images appear not natural and lack appearance attractiveness. In order to further solve this problem, an age‐compensated makeup transformation framework based on homology continuity is proposed. In order to achieve a stable and controllable age‐compensation effect, the authors design a new coding module that can map the face makeup semantic vector into the higher feature space and achieve age compensation by adjusting the direction of the semantic vector. Finally, in order to comprehensively evaluate the effectiveness of the authors’ proposed method, a large number of qualitative and quantitative experiments have been conducted, and the experimental results show that the authors’ proposed framework outperforms existing methods.
“I know how you feel”

Abstract In this article, we aim to evaluate the role of robots’ personality-driven behavioural patterns on users’ intention to use in an entertainment scenario. Toward such a goal, we designed two personalities: one introverted with an empathic and self-comparative interaction style, and the other extroverted with a provocative and other-comparative interaction style. To evaluate the proposed technology acceptance model, we conducted an experiment ( N = 209) at a public venue where users were requested to play a game with the support of the TIAGo robot. Our findings show that the robot personality affects the acceptance model and three relevant drivers: perceived enjoyment, perceived usefulness, and social influence. The extroverted robot was perceived as more useful than the introverted, and participants who interacted with it were faster at solving the game. On the other hand, the introverted robot was perceived as more enjoyable but less useful than the extroverted, and participants who interacted with it made fewer mistakes. Taken together, these findings support the importance of designing proper robot personalities in influencing users’ acceptance, featuring that a given style can elicit a different driver of acceptance.
Belief Evolution Network-based Probability Transformation and Fusion

Smets proposes the Pignistic Probability Transformation (PPT) as the decision layer in the Transferable Belief Model (TBM), which argues when there is no more information, we have to make a decision using a Probability Mass Function (PMF). In this paper, the Belief Evolution Network (BEN) and the full causality function are proposed by introducing causality in Hierarchical Hypothesis Space (HHS). Based on BEN, we interpret the PPT from an information fusion view and propose a new Probability Transformation (PT) method called Full Causality Probability Transformation (FCPT), which has better performance under Bi-Criteria evaluation. Besides, we heuristically propose a new probability fusion method based on FCPT. Compared with Dempster Rule of Combination (DRC), the proposed method has more reasonable result when fusing same evidence. • A Belief Evolution Network (BEN) is proposed to model Basic Probability Assignment. • A BEN-based Probability Transformation (PT) method is proposed. • Based on the PT method in BEN, a Probability Combination Rule (PCR) is proposed. • The proposed PT method and PCR has better performance than existing methods.
Measuring the impact of digital exchange cyberattacks on Bitcoin Returns

We investigate the impact on Bitcoin returns arising from cyberattacks on digital exchanges over the 2012–2021 period. In particular, we test the hypothesis that Bitcoin experiences lower returns on the dates associated with cybersecurity breaches of cryptocurrency exchanges. We find a negative and statistically significant impact where Bitcoin price declines by about 1.513 percent on the cyberattack days. However, a subsample analysis suggests that while the estimated effect is somewhat larger over the 2012–2018 period, it has lessened and become statistically insignificant in the more recent 2019–2021 subsample.