Reviewerly/exHarmony huggingface dataset:
CODE-EHR best practice framework for the use of structured electronic healthcare records in clinical research

Abstract Big data is central to new developments in global clinical science aiming to improve the lives of patients. Technological advances have led to the routine use of structured electronic healthcare records with the potential to address key gaps in clinical evidence. The covid-19 pandemic has demonstrated the potential of big data and related analytics, but also important pitfalls. Verification, validation, and data privacy, as well as the social mandate to undertake research are key challenges. The European Society of Cardiology and the BigData@Heart consortium have brought together a range of international stakeholders, including patient representatives, clinicians, scientists, regulators, journal editors and industry. We propose the CODE-EHR Minimum Standards Framework as a means to improve the design of studies, enhance transparency and develop a roadmap towards more robust and effective utilisation of healthcare data for research purposes.
MediaPipe’s Landmarks with RNN for Dynamic Sign Language Recognition

Communication for hearing-impaired communities is an exceedingly challenging task, which is why dynamic sign language was developed. Hand gestures and body movements are used to represent vocabulary in dynamic sign language. However, dynamic sign language faces some challenges, such as recognizing complicated hand gestures and low recognition accuracy, in addition to each vocabulary’s reliance on a series of frames. This paper used MediaPipe in conjunction with RNN models to address dynamic sign language recognition issues. MediaPipe was used to determine the location, shape, and orientation by extracting keypoints of the hands, body, and face. RNN models such as GRU, LSTM, and Bi-directional LSTM address the issue of frame dependency in sign movement. Due to the lack of video-based datasets for sign language, the DSL10-Dataset was created. DSL10-Dataset contains ten vocabularies that were repeated 75 times by five signers providing the guiding steps for creating such one. Two experiments are carried out on our dataset (DSL10-Dataset) using RNN models to compare the accuracy of dynamic sign language recognition with and without the use of face keypoints. Experiments revealed that our model had an accuracy of more than 99%.
Reducing the Vision and Language Bias for Temporal Sentence Grounding

Temporal sentence grounding (TSG) is an important yet challenging task in multimedia information retrieval. Although previous TSG methods have achieved decent performance, they tend to capture the selection biases of frequently appeared video-query pairs in the dataset rather than present robust multimodal reasoning abilities, especially for the rarely appeared pairs. In this paper, we study the above issue of selection biases and accordingly propose a Debiasing-TSG (D-TSG) model to filter and remove the negative biases in both vision and language modalities for enhancing the model generalization ability. Specifically, we propose to alleviate the issue from two perspectives: 1) Feature distillation. We built a multi-modal debiasing branch to firstly capture the vision and language biases, and then apply a bias identification module to explicitly recognize the true negative biases and remove them from the benign multi-modal representations. 2) Contrastive sample generation. We construct two types of negative samples to enforce the model to accurately learn the aligned multi-modal semantics and make complete semantic reasoning. We apply the proposed model to both commonly and rarely appeared TSG cases, and demonstrate its effectiveness by achieving the state-of-the-art performance on three benchmark datasets (ActivityNet Caption, TACoS, and Charades-STA).
AesUST: Towards Aesthetic-Enhanced Universal Style Transfer

Recent studies have shown remarkable success in universal style transfer which transfers arbitrary visual styles to content images. However, existing approaches suffer from the aesthetic-unrealistic problem that introduces disharmonious patterns and evident artifacts, making the results easy to spot from real paintings. To address this limitation, we propose AesUST, a novel Aesthetic-enhanced Universal Style Transfer approach that can generate aesthetically more realistic and pleasing results for arbitrary styles. Specifically, our approach introduces an aesthetic discriminator to learn the universal human-delightful aesthetic features from a large corpus of artist-created paintings. Then, the aesthetic features are incorporated to enhance the style transfer process via a novel Aesthetic-aware Style-Attention (AesSA) module. Such an AesSA module enables our AesUST to efficiently and flexibly integrate the style patterns according to the global aesthetic channel distribution of the style image and the local semantic spatial distribution of the content image. Moreover, we also develop a new two-stage transfer training strategy with two aesthetic regularizations to train our model more effectively, further improving stylization performance. Extensive experiments and user studies demonstrate that our approach synthesizes aesthetically more harmonious and realistic results than state of the art, greatly narrowing the disparity with real artist-created paintings. Our code is available at https://github.com/EndyWon/AesUST.
Anthropomorphic response: Understanding interactions between humans and artificial intelligence agents

This study of anthropomorphic response to artificial intelligence begins with an extensive review of the literature and an identification of conceptual distinctions between anthropomorphism and anthropomorphic response. The authors develop an instrument for measuring how users form anthropomorphic response to interactions with AI chatbots. Amazon MTurk is used to recruit 120 users for a pilot study and 303 users for the main study. Participants respond to six scenarios depicting interactions with banking service chatbots of varying appearance and intelligence. Results show that anthropomorphic response depend on perceptions of agent appearance, cognitive intelligence, and emotional intelligence. Users perceive more humanness in highly intelligent but disembodied agents rather than in highly intelligent agents that have poorly designed appearances. And users who have strong tendencies to anthropomorphize non-sentient entities are less likely to form anthropomorphic response when interacting with agents with high cognitive intelligence. The study enhances understandings about human/AI interactions. It provides directions for future research regarding anthropomorphic response and provide directions for future research on designing and using artificial agents.
Artificial intelligence and business education: What should be taught

• Examines the increasing influence of Artificial Intelligence. • The role of Artificial Intelligence as a part of Business Education. • Posits the need to integrate Artificial Intelligence into Business Curriculum.
A collaboration-based hybrid GWO-SCA optimizer for engineering optimization problems

Grey Wolf Optimizer (GWO) tends to converge prematurely when dealing with multimodal problems. Using the benefits of hybridizing algorithm to boost the performance of GWO is a recent trend. Therefore, a novel improved GWO called collaboration-based Hybrid GWO-SCA optimizer (cHGWOSCA) is developed. Given the powerful exploration of Sine Cosine Algorithm (SCA), SCA is incorporated into the position update of leading wolves in GWO. Then a collaboration between the personal best and leading wolves is applied in the hybridized position update, which can improve the global exploration. To balance the exploitation, weight-based individual position update and crossover with personal best are used to guide the exploitation of promising areas. The factor a→ modified by a sine function is employed to equilibrate exploration and exploitation. In addition, the global convergence of cHGWOSCA is proved. IEEE CEC 2013, 2014 and 2019 are applied to verify the validity of cHGWOSCA. PV model parameter extraction and three constrained engineering design problems are used to further demonstrate the performance of cHGWOSCA. Experimental results indicate that cHGWOSCA is a high-performing algorithm in global optimization.
Disentangled Contrastive Learning for Social Recommendation

Social recommendations utilize social relations to enhance the representation learning for recommendations. Most social recommendation models unify user representations for the user-item interactions (collaborative domain) and social relations (social domain). However, such an approach may fail to model the users' heterogeneous behavior patterns in two domains, impairing the expressiveness of user representations. In this work, to address such limitation, we propose a novel Disentangled contrastive learning framework for social Recommendations (DcRec). More specifically, we propose to learn disentangled users' representations from the item and social domains. Moreover, disentangled contrastive learning is designed to perform knowledge transfer between disentangled users' representations for social recommendations. Comprehensive experiments on various real-world datasets demonstrate the superiority of our proposed model.
Review of healthcare industry 4.0 application-based blockchain in terms of security and privacy development attributes: Comprehensive taxonomy, open issues and challenges and recommended solution

This paper has been contributed on the basis of three main goals, namely, to identify the research gaps, challenges and open issues in the academic literature related to the evaluation and benchmarking of healthcare industry 4.0 applications; to determine the theory gap and the recommended solution of healthcare industry 4.0 application evaluation and benchmarking; and to summarise the current validation and evaluation procedures used for the recommended solution in the literature. For these purposes, this study provided a new representation of taxonomy on the basis of crossover between the ‘security and privacy development attribute aspect’ and ‘healthcare industry 4.0 applications based on blockchain aspect’. This review also identified the research gap, challenges and open issues regarding the benchmarking and evaluation of healthcare industry 4.0 applications. This study presented the gap of the current theory and recommended a solution for handling the evaluation and benchmarking processes and overcoming the identified issues. Finally, common validation and evaluation procedures were reviewed to validate the proposed benchmarking solutions.
Design of hyperchaotic system based on multi-scroll and its encryption algorithm in color image

Chaotic systems are suitable for image encryption due to their numerous intrinsic properties. However, the low-dimensional chaotic systems used in many existing chaos-based image encryption algorithms suffer from various drawbacks. To overcome such problems, a new four-dimensional multi-scroll hyperchaotic system is constructed in this paper. By studying the stability of equilibrium point, dissipation attractors, Lyapunov exponent spectrum, and bifurcation diagram of the system, these are proved that the system has rich dynamical behaviors. Then, based on the system, a new color image encryption algorithm is designed by using the classical structure of the 'scrambling-diffusion-scrambling' algorithm. In this image encryption algorithm, an optimized Arnold transform is used and the plaintext pixel sum is applied to the scrambling parameters so that different plaintext image pixels correspond to different scrambling parameters and are fully resistant to plaintext attacks. Then for the three Red, Green, Blue (RGB) channels, three different diffusion directions are adopted. Finally, the correlation between the pixels is further broken by an ascending permutation scrambling operation to get the final encrypted image. The experimental results show that the key space is up to 2207, the information entropy is up to 7.9998, the Number of Pixels Change Rate (NPCR) and the Unified Average Changing Intensity (UACI) values are close to the ideal values, the scheme can resist exhaustive and differential attacks, and the scheme has good robustness through noise attacks and crop attacks.
Interpretable temporal-spatial graph attention network for multi-site PV power forecasting

Accurate forecasting of photovoltaic (PV) and wind production is crucial for the integration of more renewable energy sources into the power grid. To address the limited resolution and costs of methods based on numerical weather predictions (NWP), we take PV production data as main input for forecasting. Since PV power is affected by weather and cloud dynamics, we model spatio-temporal correlations between production data by representing PV systems as nodes of a dynamic graph and embedding production data, geographical information and clear-sky irradiance as signals on that graph. We introduce a new temporal-spatial multi-windows graph attention network (TSM-GAT) for predicting future PV power production. TSM-GAT can adapt to the dynamics of the problem, by learning different graphs over time. It consists of temporal attention with an overlapping-window mechanism that finds the temporal correlations and spatial attention with a multi-window mechanism, which captures different dynamical spatio-temporal correlations for different parts of the forecasting horizon. Thus, it is possible to interpret which PV stations have the most influence when making a prediction for short-, medium- and long-term intra-day forecasts. TSM-GAT outperforms multi-site state-of-the-art models for four to six hours ahead predictions, with average NRMSE 12.4% and 10.5% on a real and synthetic dataset, respectively. Furthermore, it outperforms state-of-the-art models that use NWP as inputs for up to five hours ahead predictions. TSM-GAT yields predicted signals with a closer shape to ground truth than state-of-the-art models, which indicates that it is better at capturing cloud motion and may lead to better generalization capabilities.
Constructing keyed strong S-Box with higher nonlinearity based on 2D hyper chaotic map and algebraic operation

As the only nonlinear component, S-Box plays an important role in cryptography. To overcome the problems in some 1D chaotic maps such as poor randomness and lacking ergodicity, we constructed a 2D exponential quadratic chaotic map (2D-EQCM), and analyzed its dynamic behavior through phase diagram, Lyapunov exponent, Kolmogorov entropy, correlation dimension and randomness testing. The results demonstrated that the 2D-EQCM with ergodicity and better randomness can be served as pseudo-random number generator (PRNG). Furthermore, to generate a large number of S-Boxes with higher nonlinearity, we designed a keyed strong S-Box construction algorithm using the 2D-EQCM and algebraic operation based on seed S-Boxes. Experimental results verified the effectiveness of the proposed keyed strong S-Box construction algorithm.
“I just like the stock”: The role of Reddit sentiment in the GameStop share rally

Abstract This paper investigates the role played by the social media platform Reddit in the events around the GameStop (GME) share rally in early 2021. In particular, we analyze the impact of discussions on the r/WallStreetBets subreddit on the price dynamics of the American online retailer GameStop. We customize a sentiment analysis dictionary for Reddit platform users based on the Valence Aware Dictionary and Sentiment Reasoner (VADER) sentiment analysis package and perform textual analysis on 10.8 million comments. The analysis of the relationships between Reddit sentiments and 1‐, 5‐, 10‐, and 30‐min GameStop returns contribute to the growing body of literature on “meme stocks” and the impact of discussions on investment forums on intraday stock price movements.
PSG-Yolov5: A Paradigm for Traffic Sign Detection and Recognition Algorithm Based on Deep Learning

With the gradual popularization of autonomous driving technology, how to obtain traffic sign information efficiently and accurately is very important for subsequent decision-making and planning tasks. Traffic sign detection and recognition (TSDR) algorithms include color-based, shape-based, and machine learning based. However, the algorithms mentioned above are insufficient for traffic sign detection tasks in complex environments. In this paper, we propose a traffic sign detection and recognition paradigm based on deep learning algorithms. First, to solve the problem of insufficient spatial information in high-level features of small traffic signs, the parallel deformable convolution module (PDCM) is proposed in this paper. PDCM adaptively acquires the corresponding receptive field preserving the integrity of the abstract information through symmetrical branches thereby improving the feature extraction capability. Simultaneously, we propose sub-pixel convolution attention module (SCAM) based on the attention mechanism to alleviate the influence of scale distribution. Distinguishing itself from other feature fusion, our proposed method can better focus on the information of scale distribution through the attention module. Eventually, we introduce GSConv to further reduce the computational complexity of our proposed algorithm, better satisfying industrial application. Experimental results demonstrate that our proposed methods can effectively improve performance, both in detection accuracy and mAP@0.5. Specifically, when the proposed PDCM, SCAM, and GSConv are applied to the Yolov5, it achieves 89.2% mAP@0.5 in TT100K, which exceeds the benchmark network by 4.9%.
A unifying and general account of fairness measurement in recommender systems

Fairness is fundamental to all information access systems, including recommender systems. However, the landscape of fairness definition and measurement is quite scattered with many competing definitions that are partial and often incompatible. There is much work focusing on specific – and different – notions of fairness and there exist dozens of metrics of fairness in the literature, many of them redundant and most of them incompatible. In contrast, to our knowledge, there is no formal framework that covers all possible variants of fairness and allows developers to choose the most appropriate variant depending on the particular scenario. In this paper, we aim to define a general, flexible, and parameterizable framework that covers a whole range of fairness evaluation possibilities. Instead of modeling the metrics based on an abstract definition of fairness, the distinctive feature of this study compared to the current state of the art is that we start from the metrics applied in the literature to obtain a unified model by generalization. The framework is grounded on a general work hypothesis: interpreting the space of users and items as a probabilistic sample space, two fundamental measures in information theory (Kullback–Leibler Divergence and Mutual Information) can capture the majority of possible scenarios for measuring fairness on recommender system outputs. In addition, earlier research on fairness in recommender systems could be viewed as single-sided, trying to optimize some form of equity across either user groups or provider/procurer groups, without considering the user/item space in conjunction, thereby overlooking/disregarding the interplay between user and item groups. Instead, our framework includes the notion of statistical independence between user and item groups. We finally validate our approach experimentally on both synthetic and real data according to a wide range of state-of-the-art recommendation algorithms and real-world data sets, showing that with our framework we can measure fairness in a general, uniform, and meaningful way.
Visible-Infrared Person Re-Identification via Partially Interactive Collaboration

Visible-infrared person re-identification (VI-ReID) task aims to retrieve the same person between visible and infrared images. VI-ReID is challenging as the images captured by different spectra present large cross-modality discrepancy. Many methods adopt a two-stream network and design additional constraint conditions to extract shared features for different modalities. However, the interaction between the feature extraction processes of different modalities is rarely considered. In this paper, a partially interactive collaboration method is proposed to exploit the complementary information of different modalities to reduce the modality gap for VI-ReID. Specifically, the proposed method is achieved in a partially interactive-shared architecture: collaborative shallow layers and shared deep layers. The collaborative shallow layers consider the interaction between modality-specific features of different modalities, encouraging the feature extraction processes of different modalities constrain each other to enhance feature representations. The shared deep layers further embed the modality-specific features to a common space to endow them the same identity discriminability. To ensure the interactive collaborative learning implement effectively, the conventional loss and collaborative loss are utilized jointly to train the whole network. Extensive experiments on two publicly available VI-ReID datasets verify the superiority of the proposed PIC method. Specifically, the proposed method achieves a rank-1 accuracy of 83.6% and 57.5% on RegDB and SYSU-MM01 datasets, respectively.
Machine learning in medicine: a practical introduction to techniques for data pre-processing, hyperparameter tuning, and model comparison

There is growing enthusiasm for the application of machine learning (ML) and artificial intelligence (AI) techniques to clinical research and practice. However, instructions on how to develop robust high-quality ML and AI in medicine are scarce. In this paper, we provide a practical example of techniques that facilitate the development of high-quality ML systems including data pre-processing, hyperparameter tuning, and model comparison using open-source software and data.We used open-source software and a publicly available dataset to train and validate multiple ML models to classify breast masses into benign or malignant using mammography image features and patient age. We compared algorithm predictions to the ground truth of histopathologic evaluation. We provide step-by-step instructions with accompanying code lines.Performance of the five algorithms at classifying breast masses as benign or malignant based on mammography image features and patient age was statistically equivalent (P > 0.05). Area under the receiver operating characteristics curve (AUROC) for the logistic regression with elastic net penalty was 0.89 (95% CI 0.85 - 0.94), for the Extreme Gradient Boosting Tree 0.88 (95% CI 0.83 - 0.93), for the Multivariate Adaptive Regression Spline algorithm 0.88 (95% CI 0.83 - 0.93), for the Support Vector Machine 0.89 (95% CI 0.84 - 0.93), and for the neural network 0.89 (95% CI 0.84 - 0.93).Our paper allows clinicians and medical researchers who are interested in using ML algorithms to understand and recreate the elements of a comprehensive ML analysis. Following our instructions may help to improve model generalizability and reproducibility in medical ML studies.