Reviewerly/exHarmony huggingface dataset:
Explainable Artificial Intelligence in Sustainable Smart Healthcare

Artificial Intelligence (AI) is the capability of a system to execute tasks similar to decisions taken by human intelligence. AI has been certainly the hotspot for Internet of Health Things (IoHT) and has brought revolutionary changes in the health community. But yet the healthcare providers and the researchers' demand for explanation of the resulting predictions made by the system on the basis of the health data trained in the Machine Learning model was not satisfied. Thus, the field of Explainable Artificial Intelligence (XAI) has been explored by the researcher community to provide explanation to the predictions made by the machines and ensure accuracy in the absolute healthcare infrastructure. Since blindly relying on the decisions made by the machine for saving a human soul without proper understanding of the underlying logic is inappropriate, in this condition XAI assists the medical care team to understand the logic and counter check the decisions before implementing on the patient for a better cause. Our aim is to highlight the reasons of adopting XAI in the healthcare domain in this book chapter and discuss the basic concept behind it on how it can contribute towards reliant AI-based solutions to the healthcare.
Forecasting solar photosynthetic photon flux density under cloud cover effects: novel predictive model using convolutional neural network integrated with long short-term memory network

Abstract Forecast models of solar radiation incorporating cloud effects are useful tools to evaluate the impact of stochastic behaviour of cloud movement, real-time integration of photovoltaic energy in power grids, skin cancer and eye disease risk minimisation through solar ultraviolet (UV) index prediction and bio-photosynthetic processes through the modelling of solar photosynthetic photon flux density (PPFD). This research has developed deep learning hybrid model (i.e., CNN-LSTM) to factor in role of cloud effects integrating the merits of convolutional neural networks with long short-term memory networks to forecast near real-time (i.e., 5-min) PPFD in a sub-tropical region Queensland, Australia. The prescribed CLSTM model is trained with real-time sky images that depict stochastic cloud movements captured through a total sky imager (TSI-440) utilising advanced sky image segmentation to reveal cloud chromatic features into their statistical values, and to purposely factor in the cloud variation to optimise the CLSTM model. The model, with its competing algorithms (i.e., CNN, LSTM, deep neural network, extreme learning machine and multivariate adaptive regression spline), are trained with 17 distinct cloud cover inputs considering the chromaticity of red, blue, thin, and opaque cloud statistics, supplemented by solar zenith angle (SZA) to predict short-term PPFD . The models developed with cloud inputs yield accurate results, outperforming the SZA -based models while the best testing performance is recorded by the objective method (i.e., CLSTM) tested over a 7-day measurement period. Specifically, CLSTM yields a testing performance with correlation coefficient r = 0.92, root mean square error RMSE = 210.31 μ mol of photons m −2 s −1 , mean absolute error MAE = 150.24 μ mol of photons m −2 s −1 , including a relative error of RRMSE = 24.92% MAP E = 38.01%, and Nash Sutcliffe’s coefficient E NS = 0.85, and Legate and McCabe’s Index LM = 0.68 using cloud cover in addition to the SZA as an input. The study shows the importance of cloud inclusion in forecasting solar radiation and evaluating the risk with practical implications in monitoring solar energy, greenhouses and high-value agricultural operations affected by stochastic behaviour of clouds. Additional methodological refinements such as retraining the CLSTM model for hourly and seasonal time scales may aid in the promotion of agricultural crop farming and environmental risk evaluation applications such as predicting the solar UV index and direct normal solar irradiance for renewable energy monitoring systems.
Multiview Subspace Clustering With Multilevel Representations and Adversarial Regularization

Multiview subspace clustering has turned into a promising technique due to its encouraging ability to discover the underlying subspace structure. In recent studies, a lot of subspace clustering methods have been developed to strengthen the clustering performance of multiview data, but these methods rarely consider simultaneously the nonlinear structure and multilevel representation (MLR) information in multiview data as well as the data distribution of latent representation. To address these problems, we develop a new Multiview Subspace Clustering with MLRs and Adversarial Regularization (MvSC-MRAR), where multiple deep auto-encoders are utilized to model nonlinear structure information of multiview data, multiple self-expressive layers are introduced into each deep auto-encoder to extract multilevel latent representations of each view data, and diversity regularizations are designed to preserve complementary information contained in different layers and different views. Furthermore, a universal discriminator based on adversarial training is developed to enforce the output of each encoder to obey a given prior distribution, so that the affinity matrix for spectral clustering (SPC) is more realistic. Comprehensive empirical evaluation with nine real-world multiview datasets indicates that our proposed MvSC-MRAR achieves significant improvements than several state-of-the-art methods in terms of clustering accuracy (ACC) and normalized mutual information (NMI).
Robotic Manipulation in Dynamic Scenarios via Bounding-Box-Based Hindsight Goal Generation

By relabeling past experience with heuristic or curriculum goals, state-of-the-art reinforcement learning (RL) algorithms such as hindsight experience replay (HER), hindsight goal generation (HGG), and graph-based HGG (G-HGG) have been able to solve challenging robotic manipulation tasks in multigoal settings with sparse rewards. HGG outperforms HER in challenging tasks in which goals are difficult to explore by learning from a curriculum, in which intermediate goals are selected based on the Euclidean distance to target goals. G-HGG enhances HGG by selecting intermediate goals from a precomputed graph representation of the environment, which enables its applicability in an environment with stationary obstacles. However, G-HGG is not applicable to manipulation tasks with dynamic obstacles, since its graph representation is only valid in static scenarios and fails to provide any correct information to guide the exploration. In this article, we propose bounding-box-based HGG (Bbox-HGG), an extension of G-HGG selecting hindsight goals with the help of image observations of the environment, which makes it applicable to tasks with dynamic obstacles. We evaluate Bbox-HGG on four challenging manipulation tasks, where significant enhancements in both sample efficiency and overall success rate are shown over state-of-the-art algorithms. The videos can be viewed at https://videoviewsite.wixsite.com/bbhgg.
Competitive Swarm Optimization with Encryption Based Steganography for Digital Image Security

Digital image security is a fundamental and tedious process on shared communication channels.Several methods have been employed for accomplishing security on digital image transmission, such as encryption, steganography, and watermarking.Image stenography and encryption are commonly used models to achieve improved security.Besides, optimal pixel selection process (OPSP) acts as a vital role in the encryption process.With this motivation, this study designs a new competitive swarm optimization with encryption based stenographic technique for digital image security, named CSOES-DIS technique.The proposed CSOES-DIS model aims to encrypt the secret image prior to the embedding process.In addition, the CSOES-DIS model applies a double chaotic digital image encryption (DCDIE) technique to encrypt the secret image, and then embedding method was implemented.Also, the OPSP can be carried out by the design of CSO algorithm and thereby increases the secrecy level.In order to portray the enhanced outcomes of the CSOES-DIS model, a comparative examination with recent methods is performed and the results reported the betterment of the CSOES-DIS model based on different measures.
NomMer: Nominate Synergistic Context in Vision Transformer for Visual Recognition

Recently, Vision Transformers (ViT), with the self-attention (SA) as the de facto ingredients, have demon-strated great potential in the computer vision community. For the sake of trade-off between efficiency and performance, a group of works merely perform SA operation within local patches, whereas the global contextual information is abandoned, which would be indispensable for visual recognition tasks. To solve the issue, the subsequent global-local ViTs take a stab at marrying local SA with global one in parallel or alternative way in the model. Nevertheless, the exhaustively combined local and global context may exist redundancy for various visual data, and the receptive field within each layer is fixed. Alternatively, a more graceful way is that global and local context can adaptively contribute per se to accommodate different visual data. To achieve this goal, we in this paper propose a novel ViT architecture, termed NomMer, which can dynamically Nominate the synergistic global-local context in vision transforMer. By investigating the working pattern of NomMer, we further explore what context information is focused. Beneficial from this “dynamic nomination” mechanism, without bells and whistles, the NomMer can not only achieve 84.5% Top-1 classification accuracy on ImageNet with only 73M parameters, but also show promising performance on dense prediction tasks, i.e., object detection and semantic segmentation. The code and models are publicly available at https://github.com/TencentYoutuResearch/VisualRecognition-NomMer.
Learning Pneumatic Non-Prehensile Manipulation With a Mobile Blower

We investigate pneumatic non-prehensile manipulation (i.e., blowing) as a means of efficiently moving scattered objects into a target receptacle. Due to the chaotic nature of aerodynamic forces, a blowing controller must (i) continually adapt to unexpected changes from its actions, (ii) maintain fine-grained control, since the slightest misstep can result in large unintended consequences (e.g., scatter objects already in a pile), and (iii) infer long-range plans (e.g., move the robot to strategic blowing locations). We tackle these challenges in the context of deep reinforcement learning, introducing a multi-frequency version of the spatial action maps framework. This allows for efficient learning of vision-based policies that effectively combine high-level planning and low-level closed-loop control for dynamic mobile manipulation. Experiments show that our system learns efficient behaviors for the task, demonstrating in particular that blowing achieves better downstream performance than pushing, and that our policies improve performance over baselines. Moreover, we show that our system naturally encourages emergent specialization between the different subpolicies spanning low-level fine-grained control and high-level planning. On a real mobile robot equipped with a miniature air blower, we show that our simulation-trained policies transfer well to a real environment and can generalize to novel objects.
A Tree-Structure Analysis Network on Handwritten Chinese Character Error Correction

Existing researches on handwritten Chinese characters are mainly based on recognition network designed to solve the complex structure and numerous amount characteristics of Chinese characters. In this paper, we investigate Chinese characters from the perspective of error correction, which is to diagnose a handwritten character to be right or wrong and provide a feedback on error analysis. For this handwritten Chinese character error correction task, we define a benchmark by unifying both the evaluation metrics and data splits for the first time. Then we design a diagnosis system that includes decomposition, judgement and correction stages. Specifically, a novel tree-structure analysis network (TAN) is proposed to model a Chinese character as a tree layout, which mainly consists of a CNN-based encoder and a tree-structure based decoder. Using the predicted tree layout for judgement, correction operation is performed for the wrongly written characters to do error analysis. The correction stage is composed of three steps: fetch the ideal character, correct the errors and locate the errors. Additionally, we propose a novel bucketing mining strategy to apply triplet loss at radical level to alleviate feature dispersion. Experiments on handwritten character dataset demonstrate that our proposed TAN shows great superiority on all three metrics comparing with other state-of-the-art recognition models. Through quantitative analysis, TAN is proved to capture more accurate spatial position information than regular encoder-decoder models, showing better generalization ability.
Trust Recommendation Based on Deep Deterministic Strategy Gradient Algorithm

Trust recommendation is a vital recommendation system application based on social networks.It can recommend items based on the trust between users, which can alleviate data sparseness and enhance the interpretability of results.A large number of recommendation algorithms have been proposed, but most of them believe that trust is fixed, ignoring the changes of trust in the process of interaction.In addition, deep learning models are good at solving complex tasks and processing high-dimensional data, and they can model recommendation algorithms, but they are insufficient in capturing changes in user preferences timely.Therefore, given the shortcomings of existing researches, we propose a DDPG-TR algorithm based on deep reinforcement learning to capture the changes in user preferences and update the trust between users.The algorithm uses deep deterministic policy gradient algorithm DDPG to model the user-item interaction process.Firstly, we improve a state representative structure to express the user's state, which is convenient to capture the changes in user preferences.Then, when the user accepts the recommendation, algorithm combines trust and similar information to predict item score, as well as calculates the difference of the score.Finally, Agent gets the score feedback and uses the difference to update trust.Experiments are conducted on three datasets, and they are verified that the DDPG-TR algorithm can provide more accurate recommendation results, compared with other recommendation algorithms.
Commitment capacity of classical-quantum channels

We study commitment scheme for classical-quantum channels. To accomplish this we define various notions of commitment capacity for these channels and prove matching upper and lower bound on it in terms of the conditional entropy. Our achievability (lower bound) proof is quantum generalisation of the work of one of the authors (arXiv:2103.11548) which studied the problem of secure list decoding and its application to bit-string commitment. The techniques we use in the proof of converse (upper bound) is similar in spirit to the techniques introduced by Winter, Nascimento and Imai (Cryptography and Coding 2003) to prove upper bound on the commitment capacity of classical channels. However, generalisation of this technique to the quantum case is not so straightforward and requires some new constructions, which can be of independent interest.
Robot Path Planning Using Improved Ant Colony Algorithm in the Environment of Internet of Things

It is a research topic of practical significance to study the path planning technology of mobile robot navigation technology. Aiming at the problems of slow convergence speed, redundant planning path, and easy to fall into local optimal value of ant colony algorithm in a complex environment, a robot path planning based on improved ant colony algorithm is proposed. First, the grid method is used to model the path environment, which marks each grid to make the ant colony move from the initial grid to the target grid for path search. Second, the ant colony is divided according to different planning tasks. Let some ants explore the way first, and carry out basic optimization planning for the map environment. The antecedent ants mark the basic advantage on a target value of the path with pheromone concentration so as to guide the subsequent route-finding operation of the main ant colony. Finally, in order to avoid the individual ants falling into a deadlock state in the early search, the obstacle avoidance factor is increased, the transition probability is improved, and the amount of information on each path is dynamically adjusted according to the local path information, so as to avoid the excessive concentration of pheromones. Experimental results show that the algorithm has high global search ability, significantly speeds up the convergence speed, and can effectively improve the efficiency of mobile robot in path planning.
Multi-Branch Distance-Sensitive Self-Attention Network for Image Captioning

Self-attention (SA) based networks have achieved great success in image captioning, constantly dominating the leaderboards of online benchmarks. However, existing SA networks still suffer from distance insensitivity and low-rank bottleneck. In this paper, we aim to optimize SA in terms of two aspects, thereby addressing the above issues. First, we introduce a Distance-sensitive Self-Attention (DSA), which considers the raw geometric distances between query-key pairs in the 2D images during SA modeling. Second, we present a simple yet effective approach, named Multi-branch Self-Attention (MSA) to compensate for the low-rank bottleneck. MSA treats a multi-head self-attention layer as a branch and duplicates it multiple times to increase the expressive power of SA. To validate the effectiveness of the two designs, we apply them to the standard self-attention network, and conduct extensive experiments on the highly competitive MS-COCO dataset. We achieve new state-of-the-art performance on both the local and online test sets, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e</i> ., 135.1% CIDEr on the Karpathy split and 135.4% CIDEr on the official online split.
A Three-Step Fuzzy-Based BERT Model for Sentiment Analysis

The main aim of sentiment analysis is to understand the attitude of the audience towards a particular product. The attitudes are generally classified into positive, negative or neutral views. This paper proposes a new three-step fuzzy-based bidirectional encoder representations from transformers (BERT) sentiment analysis model to predict the sentiments of various types of datasets. The advantage of BERT is its both direction-wise training of context along with words. In the first step, the data pre-processing is done to clean the texts. The second step consists of passing the cleaned texts through the BERT module for classification. Finally, in the third step, the classified datasets are passed through a fuzzy logic module to help in better dealing of inconsistencies while finding sentiments. BERT has emerged to be one of the most competent deep learning models in recent times. The main aim of the model is to combine the advantages of both BERT and fuzzy logic mechanisms to identify sentiments accurately. The proposed model has been implemented on various datasets and has shown exemplary accuracy of over 90% in the highest epoch considered in the experiment. The model also has proved to perform better in terms of accuracy, precision and non-parametric measures.
Hybrid Single Image Super-Resolution Algorithm for Medical Images

High-quality medical microscopic images used for diseases detection are expensive and difficult to store.Therefore, low-resolution images are favorable due to their low storage space and ease of sharing, where the images can be enlarged when needed using Super-Resolution (SR) techniques.However, it is important to maintain the shape and size of the medical images while enlarging them.One of the problems facing SR is that the performance of medical image diagnosis is very poor due to the deterioration of the reconstructed image resolution.Consequently, this paper suggests a multi-SR and classification framework based on Generative Adversarial Network (GAN) to generate high-resolution images with higher quality and finer details to reduce blurring.The proposed framework comprises five GAN models: Enhanced SR Generative Adversarial Networks (ESRGAN), Enhanced deep SR GAN (EDSRGAN), Sub-Pixel-GAN, SRGAN, and Efficient Wider Activation-B GAN (WDSR-b-GAN).To train the proposed models, we have employed images from the famous BreakHis dataset and enlarged them by 4× and 16× upscale factors with the ground truth of the size of 256 × 256 × 3.Moreover, several evaluation metrics like Peak Signal-to-Noise Ratio (PSNR), Mean Squared Error (MSE), Structural Similarity Index (SSIM), Multiscale Structural Similarity Index (MS-SSIM), and histogram are applied to make comprehensive and objective comparisons to determine the best methods in terms of efficiency, training time, and storage space.The obtained results reveal the superiority of the proposed models over traditional and benchmark models in terms of color and texture restoration and detection by achieving an accuracy of 99.7433%.
Digital Formats of Learning Outcomes Assessment in the COVID-19 Paradigm: Survey Study

The global pandemic and emergency digitization measures have introduced systemic challenges to the university summative and formative assessment workflow. Various modes of assessment for University-level programs are a strict regimen that consists of different elements and stages (oral, hybrid, and written exams, tests of different types, project presentations, internal and external review, expert evaluation, and peering). This study aims to critically analyze the practices of Borys Grinchenko Kyiv University in various forms and modes of digital assessment for stakeholders of Liberal Arts, Education, and Computer Science major programs, implemented in the years 2020–2021 through quarantine induced digital learning. The survey analysis was conducted to evaluate ICT tools and digital competencies that are implemented to compare and contrast traditional and formative assessment practices, translated into the digital hybrid format. The investigation novelty is attained through systemic empirical findings on experiences and techniques of learning outcomes assessment in the emergency digitization measures, contrastive assessment of different modes in digital learning, evaluation of ICT tools and skills, implemented through different forms of assessment in the digital learning context.