Reviewerly/exHarmony huggingface dataset:
Path Planning for Multi-Arm Manipulators Using Soft Actor-Critic Algorithm with Position Prediction of Moving Obstacles via LSTM

This paper presents a deep reinforcement learning-based path planning algorithm for the multi-arm robot manipulator when there are both fixed and moving obstacles in the workspace. Considering the problem properties such as high dimensionality and continuous action, the proposed algorithm employs the SAC (soft actor-critic). Moreover, in order to predict explicitly the future position of the moving obstacle, LSTM (long short-term memory) is used. The SAC-based path planning algorithm is developed using the LSTM. In order to show the performance of the proposed algorithm, simulation results using GAZEBO and experimental results using real manipulators are presented. The simulation and experiment results show that the success ratio of path generation for arbitrary starting and goal points converges to 100%. It is also confirmed that the LSTM successfully predicts the future position of the obstacle.
Bat algorithm optimised extreme learning machine (Bat‐ELM): A novel approach for daily river water temperature modelling

Abstract Here, the capability of the Bat algorithm optimised extreme learning machines ELM (Bat‐ELM) is demonstrated for river water temperature ( T w ) modelling in the Orda River, Poland. Results using the multilayer perceptron neural network (MLPNN), the classification and regression Tree (CART) and the multiple linear regression (MLR) models were presented for comparison. The models were developed according to two scenarios: (1) using air temperature ( T a ) as input for predicting T w , and (2) using T a and the periodicity (i.e., day, month and year number). River T w calibration and validation results derived from air temperature and the periodicity show its potential application. The Bat‐ELM accurately predicts the T w and surpassed all other models with coefficient of correlation ( R ) values ranging within the limits of 0.973 to 0.981, and the Nash‐Sutcliffe efficiency (NSE) values will fall within the interval of 0.947 to 0.963. Findings from this research also highlight the robustness of the Bat‐ELM using the periodicity by enhancing its ability to estimate river T w .
Stand-Alone Self-Attention in Vision Models

Convolutions are a fundamental building block of modern computer vision systems. Recent approaches have argued for going beyond convolutions in order to capture long-range dependencies. These efforts focus on augmenting convolutional models with content-based interactions, such as self-attention and non-local means, to achieve gains on a number of vision tasks. The natural question that arises is whether attention can be a stand-alone primitive for vision models instead of serving as just an augmentation on top of convolutions. In developing and testing a pure self-attention vision model, we verify that self-attention can indeed be an effective stand-alone layer. A simple procedure of replacing all instances of spatial convolutions with a form of self-attention applied to ResNet model produces a fully self-attentional model that outperforms the baseline on ImageNet classification with 12% fewer FLOPS and 29% fewer parameters. On COCO object detection, a pure self-attention model matches the mAP of a baseline RetinaNet while having 39% fewer FLOPS and 34% fewer parameters. Detailed ablation studies demonstrate that self-attention is especially impactful when used in later layers. These results establish that stand-alone self-attention is an important addition to the vision practitioner's toolbox.
What is Non-fungible token (NFT)? A short discussion about NFT Terms used in NFT

Non-fungible tokens (NFTs) may initially sound like a fancy jargon, but they are actually rather simple to comprehend.Blockchain-based non-fungible tokens (NFTs) are digital items that are often linked to one-of-a-kind digital material, like music or photographs.A multi-billion dollar NFT market has appeared overnight thanks to a recent flurry of public interest.NFT collections are being produced by brands like Coca-Cola, Nike, and conventional artists like Damien Hirst and Grimes, as well as mainstream consumer goods businesses.Individual NFTs may fetch millions or tens of millions of dollars [1].
Blockchain-Enabled Decentralized Secure Big Data of Remote Sensing

Blockchain technology has emerged as a promising candidate for space exploration and sustainable energy systems. This transformative technology offers secure and decentralized strategies to process and manipulate space resources. Remote sensing provides viable potential with the coexistence of open data from various sources, such as short-range sensors on unmanned aerial vehicles (UAVs) or Internet-of-Things (IoT) tags and far-range sensors incorporated on satellites. Open data resources have most recently emerged as attractive connecting parties where owners have shown consent to share data. However, most data owners are anonymous and untrustworthy, which makes shared data likely insecure and unreliable. At present, there are several tools that distribute open data, serving as an intermediate party to link users with data owners. However, these platforms are operated by central authorities who develop guidelines for data ownership, integrity, and access, consequently restricting both users and data owners. Therefore, the need and feasibility of a decentralized system arise for data sharing and retrieving without involving these intermediate limiting parties. This study proposes a blockchain-based system without any central authority to share and retrieve data. Our proposed system features (i) data sharing, (ii) maintaining the historical data, and (iii) retrieving and evaluation of data along with enhanced security. We have also discussed the use of blockchain algorithms based on smart contracts to track space transactions and communications in a secure, verifiable, and transparent manner. We tested the suggested framework in the Windows environment by writing smart contracts prototype on an Ethereum TESTNET blockchain. The results of the study showed that the suggested strategy is efficient, practicable, and free of common security attacks and vulnerabilities.
Fast Method Based on Fuzzy Logic for Gaussian-Impulsive Noise Reduction in CT Medical Images

To remove Gaussian-impulsive mixed noise in CT medical images, a parallel filter based on fuzzy logic is applied. The used methodology is structured in two steps. A method based on a fuzzy metric is applied to remove the impulsive noise at the first step. To reduce Gaussian noise, at the second step, a fuzzy peer group filter is used on the filtered image obtained at the first step. A comparative analysis with state-of-the-art methods is performed on CT medical images using qualitative and quantitative measures evidencing the effectiveness of the proposed algorithm. The parallel method is parallelized on shared memory multiprocessors. After applying parallel computing strategies, the obtained computing times indicate that the introduced filter enables to reduce Gaussian-impulse mixed noise on CT medical images in real-time.
A Reverse Positional Encoding Multi-Head Attention-Based Neural Machine Translation Model for Arabic Dialects

Languages with a grammatical structure that have a free order for words, such as Arabic dialects, are considered a challenge for neural machine translation (NMT) models because of the attached suffixes, affixes, and out-of-vocabulary words. This paper presents a new reverse positional encoding mechanism for a multi-head attention (MHA) neural machine translation (MT) model to translate from right-to-left texts such as Arabic dialects (ADs) to modern standard Arabic (MSA). The proposed model depends on an MHA mechanism that has been suggested recently. The utilization of the new reverse positional encoding (RPE) mechanism and the use of sub-word units as an input to the self-attention layer improve this sublayer for the proposed model’s encoder by capturing all dependencies between the words in right-to-left texts, such as AD input sentences. Experiments were conducted on Maghrebi Arabic to MSA, Levantine Arabic to MSA, Nile Basin Arabic to MSA, Gulf Arabic to MSA, and Iraqi Arabic to MSA. Experimental analysis proved that the proposed reverse positional encoding MHA NMT model was efficiently able to handle the open grammatical structure issue of Arabic dialect sentences, and the proposed RPE MHA NMT model enhanced the translation quality for right-to-left texts such as Arabic dialects.
Significant Features for Human Activity Recognition Using Tri-Axial Accelerometers

Activity recognition using wearable sensors has become essential for a variety of applications. Tri-axial accelerometers are the most widely used sensor for activity recognition. Although various features have been used to capture patterns and classify the accelerometer signals to recognise activities, there is no consensus on the best features to choose. Reducing the number of features can reduce the computational cost and complexity and enhance the performance of the classifiers. This paper identifies the signal features that have significant discriminative power between different human activities. It also investigates the effect of sensor placement location, the sampling frequency, and activity complexity on the selected features. A comprehensive list of 193 signal features has been extracted from accelerometer signals of four publicly available datasets, including features that have never been used before for activity recognition. Feature significance was measured using the Joint Mutual Information Maximisation (JMIM) method. Common significant features among all the datasets were identified. The results show that the sensor placement location does not significantly affect recognition performance, nor does it affect the significant sub-set of features. The results also showed that with high sampling frequency, features related to signal repeatability and regularity show high discriminative power.
Few-Shot Fine-Grained Image Classification via GNN

Traditional deep learning methods such as convolutional neural networks (CNN) have a high requirement for the number of labeled samples. In some cases, the cost of obtaining labeled samples is too high to obtain enough samples. To solve this problem, few-shot learning (FSL) is used. Currently, typical FSL methods work well on coarse-grained image data, but not as well on fine-grained image classification work, as they cannot properly assess the in-class similarity and inter-class difference of fine-grained images. In this work, an FSL framework based on graph neural network (GNN) is proposed for fine-grained image classification. Particularly, we use the information transmission of GNN to represent subtle differences between different images. Moreover, feature extraction is optimized by the method of meta-learning to improve the classification. The experiments on three datasets (CIFAR-100, CUB, and DOGS) have shown that the proposed method yields better performances. This indicates that the proposed method is a feasible solution for fine-grained image classification with FSL.
Predicting the Occurrence of a Data Breach

• Multiple methods are provided for predicting the occurrence of a data breach • Probability distribution of a data breach could be used for information risk assessment • Predicting the size of data breaches in terms of the number of personal records involved is more challenging The problem of data breach incidents is prevalent in the media and has attracted the attention of researchers and practitioners working diligently on solutions. Commercial entities, non-profit organizations, and government agencies that obtain, process, and store personal or business-related information face the risks of the breach of client personal information and organizational intellectual property, which can result in legal, reputational, and financial damages. This study aims to estimate the probability of the occurrence of a breach and its size using a predictive model with historical data as input. We used a dataset of all reported data breaches in the U.S. collected by Privacy Rights Clearinghouse (PRC). The results show that the trend of data breaches has not been increasing despite all the attention and warnings. Our Poisson and Negative binomial models proved promising and can predict data breach incidents with low deviance from actual numbers.
Automatic Correction of Indonesian Grammatical Errors Based on Transformer

Grammatical error correction (GEC) is one of the major tasks in natural language processing (NLP) which has recently attracted great attention from researchers. The performance of universal languages such as English and Chinese in the GEC system has improved significantly. This could be attributed to the large number of powerful applications supported by neural network models and pretrained language models. Referring to the satisfactory results of the universal language in the GEC task and the lack of research on the GEC task for low-resource languages, especially Indonesian, this paper proposes an automatic model for Indonesian grammar correction based on the Transformer architecture which can be applied to other low-resource language texts. Furthermore, we build a large corpus of the Indonesian language that can be utilized for evaluating the next Indonesian GEC task. We evaluate the models in this dataset, and the results show that the Transformer-based automatic error correction model achieved significant and satisfactory results compared with the results of previous research models.
DiffuSeq: Sequence to Sequence Text Generation with Diffusion Models

Recently, diffusion models have emerged as a new paradigm for generative models. Despite the success in domains using continuous signals such as vision and audio, adapting diffusion models to natural language is under-explored due to the discrete nature of texts, especially for conditional generation. We tackle this challenge by proposing DiffuSeq: a diffusion model designed for sequence-to-sequence (Seq2Seq) text generation tasks. Upon extensive evaluation over a wide range of Seq2Seq tasks, we find DiffuSeq achieving comparable or even better performance than six established baselines, including a state-of-the-art model that is based on pre-trained language models. Apart from quality, an intriguing property of DiffuSeq is its high diversity during generation, which is desired in many Seq2Seq tasks. We further include a theoretical analysis revealing the connection between DiffuSeq and autoregressive/non-autoregressive models. Bringing together theoretical analysis and empirical evidence, we demonstrate the great potential of diffusion models in complex conditional language generation tasks. Code is available at \url{https://github.com/Shark-NLP/DiffuSeq}
The paradigm and future value of the metaverse for the intervention of cognitive decline

Cognitive decline is a gradual neurodegenerative process that is affected by genetic and environmental factors. The doctor-patient relationship in the healthcare for cognitive decline is in a "shallow" medical world. With the development of data science, virtual reality, artificial intelligence, and digital twin, the introduction of the concept of the metaverse in medicine has brought alternative and complementary strategies in the intervention of cognitive decline. This article technically analyzes the application scenarios and paradigms of the metaverse in medicine in the field of mental health, such as hospital management, diagnosis, prediction, prevention, rehabilitation, progression delay, assisting life, companionship, and supervision. The metaverse in medicine has made primary progress in education, immersive consultation, dental disease, and Parkinson's disease, bringing revolutionary prospects for non-pharmacological complementary treatment of cognitive decline and other mental problems. In particular, with the demand for non-face-to-face communication generated by the global COVID-19 epidemic, the needs for uncontactable healthcare service for the elderly have increased. The paradigm of self-monitoring, self-healing, and healthcare experienced by the elderly through the metaverse in medicine, especially from meta-platform, meta-community, and meta-hospital, will be generated, which will reconstruct the service modes for the elderly people. The future map of the metaverse in medicine is huge, which depends on the co-construction of community partners.
Do open data impact citizens’ behavior? Assessing face mask panic buying behaviors during the Covid-19 pandemic

Data are essential for digital solutions and supporting citizens' everyday behavior. Open data initiatives have expanded worldwide in the last decades, yet investigating the actual usage of open data and evaluating their impacts are insufficient. Thus, in this paper, we examine an exemplary use case of open data during the early stage of the Covid-19 pandemic and assess its impacts on citizens. Based on quasi-experimental methods, the study found that publishing local stores' real-time face mask stock levels as open data may have influenced people's purchase behaviors. Results indicate a reduced panic buying behavior as a consequence of the openly accessible information in the form of an online mask map. Furthermore, the results also suggested that such open-data-based countermeasures did not equally impact every citizen and rather varied among socioeconomic conditions, in particular the education level.
A Local-Sparse-Information-Aggregation Transformer with Explicit Contour Guidance for SAR Ship Detection

Ship detection in synthetic aperture radar (SAR) images has witnessed rapid development in recent years, especially after the adoption of convolutional neural network (CNN)-based methods. Recently, a transformer using self-attention and a feed forward neural network with a encoder-decoder structure has received much attention from researchers, due to its intrinsic characteristics of global-relation modeling between pixels and an enlarged global receptive field. However, when adapting transformers to SAR ship detection, one challenging issue cannot be ignored. Background clutter, such as a coast, an island, or a sea wave, made previous object detectors easily miss ships with a blurred contour. Therefore, in this paper, we propose a local-sparse-information-aggregation transformer with explicit contour guidance for ship detection in SAR images. Based on the Swin Transformer architecture, in order to effectively aggregate sparse meaningful cues of small-scale ships, a deformable attention mechanism is incorporated to change the original self-attention mechanism. Moreover, a novel contour-guided shape-enhancement module is proposed to explicitly enforce the contour constraints on the one-dimensional transformer architecture. Experimental results show that our proposed method achieves superior performance on the challenging HRSID and SSDD datasets.
Jones-matrix imaging based on two-photon interference

Abstract Two-photon interference is an important effect that is tightly related to the quantum nature of light. Recently, it has been shown that the photon bunching from the Hong–Ou–Mandel (HOM) effect can be used for quantum imaging in which sample properties (reflection/transmission amplitude, phase delay, or polarization) can be characterized at the pixel-by-pixel level. In this work, we perform Jones matrix imaging for an unknown object based on two-photon interference. By using a reference metasurface with panels of known polarization responses in pairwise coincidence measurements, the object’s polarization responses at each pixel can be retrieved from the dependence of the coincidence visibility as a function of the reference polarization. The post-selection of coincidence images with specific reference polarization in our approach eliminates the need in switching the incident polarization and thus parallelized optical measurements for Jones matrix characterization. The parallelization in preparing input states, prevalent in any quantum algorithms, is an advantage of adopting two-photon interference in Jones matrix imaging. We believe our work points to the usage of metasurfaces in biological and medical imaging in the quantum optical regime.
Defending against adversarial attacks on Covid-19 classifier: A denoiser-based approach

<h2>Abstract</h2> Covid-19 has posed a serious threat to the existence of the human race. Early detection of the virus is vital to effectively containing the virus and treating the patients. Profound testing methods such as the Real-time reverse transcription-polymerase chain reaction (RT-PCR) test and the Rapid Antigen Test (RAT) are being used for detection, but they have their limitations. The need for early detection has led researchers to explore other testing techniques. Deep Neural Network (DNN) models have shown high potential in medical image classification and various models have been built by researchers which exhibit high accuracy for the task of Covid-19 detection using chest X-ray images. However, it is proven that DNNs are inherently susceptible to adversarial inputs, which can compromise the results of the models. In this paper, the adversarial robustness of such Covid-19 classifiers is evaluated by performing common adversarial attacks, which include the Fast Gradient Sign Method (FGSM) and Projected Gradient Descent (PGD). Using these attacks, it is found that the accuracy of the models for Covid-19 samples decreases drastically. In the medical domain, adversarial training is the most widely explored technique to defend against adversarial attacks. However, using this technique requires replacing the original model and retraining it by including adversarial samples. Another defensive technique, High-Level Representation Guided Denoiser (HGD), overcomes this limitation by employing an adversarial filter which is also transferable across models. Moreover, the HGD architecture, being suitable for high-resolution images, makes it a good candidate for medical image applications. In this paper, the HGD architecture has been evaluated as a potential defensive technique for the task of medical image analysis. Experiments carried out show an increased accuracy of up to 82% in the white box setting. However, in the black box setting, the defense completely fails to defend against adversarial samples.