Reviewerly/exHarmony huggingface dataset:
OutfitTransformer: Learning Outfit Representations for Fashion Recommendation

Learning an effective outfit-level representation is critical for predicting the compatibility of items in an outfit, and retrieving complementary items for a partial outfit. We present a framework, OutfitTransformer, that uses the pro-posed task-specific tokens and leverages the self-attention mechanism to learn effective outfit-level representations en-coding the compatibility relations between all items in the entire outfit for addressing both compatibility prediction and complementary item retrieval. For compatibility pre-diction, we design an outfit token to capture a global out-fit representation and train the framework using a classification loss. For complementary item retrieval, we design a target item token that additionally takes the target item specification (in the form of a category or text description) into consideration. We train our framework using a pro-posed set-wise outfit ranking loss to generate a target item embedding given an outfit, and a target item specification as inputs. The generated target item embedding is then used to retrieve compatible items that match the rest of the out-fit. Additionally, we adopt a pre-training approach and a curriculum learning strategy to improve retrieval performance. Experiments show that our approach outperforms state-of-the-art methods on compatibility prediction, fill-in-the-blank, and complementary item retrieval tasks.
Metacognitive computations for information search: Confidence in control.

The metacognitive sense of confidence can play a critical role in regulating decision making. In particular, a lack of confidence can justify the explicit, potentially costly, instrumental acquisition of extra information that might resolve uncertainty. Human confidence is highly complex, and recent computational work has suggested a statistically sophisticated tapestry behind the information that governs both the making and monitoring of choices. However, the consequences of the form of such confidence computations for search have yet to be understood. Here, we reveal extra richness in the use of confidence for information seeking by formulating joint models of action, confidence, and information search within a Bayesian and reinforcement learning framework. Through detailed theoretical analysis of these models, we show the intricate normative downstream consequences for search arising from more complex forms of metacognition. For example, our results highlight how the ability to monitor errors or general metacognitive sensitivity impact seeking decisions and can generate diverse relationships between action, confidence, and the optimal search for information. We also explore whether empirical search behavior enjoys any of the characteristics of normatively derived prescriptions. More broadly, our work demonstrates that it is crucial to treat metacognitive monitoring and control as closely linked processes. (PsycInfo Database Record (c) 2023 APA, all rights reserved).
When the servicescape becomes intelligent: Conceptualization, assessment, and implications for hospitableness

Recently, artificial intelligence (AI) has become widely used in the hospitality industry and has gradually been extended to the notion of the servicescape. However, how servicescapes have become "intelligent" has received little academic attention. Moreover, how intelligent servicescapes influence service outcomes, such as hospitableness, has not been discussed. In this study, we fill this gap by conceptualizing and modeling the intelligent servicescape to analyze its potential influence on service hospitableness. Netnography and constructivist grounded theory are adopted to collect and analyze data from online reviews and short videos. Our results suggest that the intelligent servicescape is understood through four subcategories: servicescape atmosphere, technology attributes, customer–technology interactions, and employee assistance. Additionally, the intelligent servicescape can lead to hospitableness through enhanced user-friendliness; opportunities for relaxation and immersion; and rapport among employees, customers, and technology. Our findings therefore extend servicescape theory and improve the understanding of hospitableness in the AI application context. Their practical implications can also be used to design better servicescapes and improve hospitableness.
A review of wearable sensors based fall-related recognition systems

Falls are an important factor in significantly deteriorating quality of life of older adults, consequently leading to both physical and psychological harm. A wearable-based fall-related recognition system (WFRS) indeed facilitates the prediction, detection, and classification of fall events in helping fallers. Previous studies have provided a relatively comprehensive introduction to WFRSs from the perspective of sensor types and recognition algorithms. However, while these studies provide a clear technical direction, how to choose the appropriate technology for each phase of the experiment is a stumbling block for newly interested researchers. Accordingly, a comprehensive review article covering the mainstream technologies of WFRSs is imperative and meaningful. This review analyzes 48 state-of-the-art researches in WFRSs from three databases (i.e., IEEE Explorer, ScienceDirect, and MDPI) and introduces the pipeline techniques that consist of data acquisition, preprocessing, feature extraction, model training, and evaluation. Specifically, we first analyze the pros and cons of the use of different number of sensors for data collection. We then introduce the widely used preprocessing techniques including filtering and data augmentation. Afterwards, we detail the extraction of various features and illustrate methods for the selection, training, and evaluation of fall recognition models. We finally discuss factors affecting the overall performance of a model and offer suggestions for future research.
Autokeras Approach: A Robust Automated Deep Learning Network for Diagnosis Disease Cases in Medical Images

Automated deep learning is promising in artificial intelligence (AI). However, a few applications of automated deep learning networks have been made in the clinical medical fields. Therefore, we studied the application of an open-source automated deep learning framework, Autokeras, for detecting smear blood images infected with malaria parasites. Autokeras is able to identify the optimal neural network to perform the classification task. Hence, the robustness of the adopted model is due to it not needing any prior knowledge from deep learning. In contrast, the traditional deep neural network methods still require more construction to identify the best convolutional neural network (CNN). The dataset used in this study consisted of 27,558 blood smear images. A comparative process proved the superiority of our proposed approach over other traditional neural networks. The evaluation results of our proposed model achieved high efficiency with impressive accuracy, reaching 95.6% when compared with previous competitive models.
Constrained disorder principle-based second-generation algorithms implement quantified variability signatures to improve the function of complex systems

Improving the efficacy and overcoming the malfunctions of systems are significant challenges. Variability characterizes all levels of complex biological systems. We reviewed the relevant publications and described a method for improving the systems' function. The constrained disorder principle (CDP) defines the function of living systems based on their degree of variability. Per the CDP, the boundaries of a system define its function and efficiency. The present paper aims to describe the role of variability in biological systems and the generation of CDP-based second-generation artificial intelligence (AI) algorithms designed to improve effectiveness and correct malfunctions of biological organisms by focusing on implementing personalized variability signatures. The paper describes some of the challenges of first-generation AI systems, focusing on the three steps process of establishing the second-generation platforms comprising: the use of a pseudorandom number generator in an openloop system, implementing variability based on feedback in a closed-loop system, and quantifying variability signatures in a personalized way for improving algorithm' output. Examples of its use in humans are provided. The CDP provides a platform for improving disturbed systems' functions using second-generation AI systems.
Designing an Educational Intelligent System with Natural Language Processing Based on Fuzzy Logic

This paper presents the implementation of an educational intelligent system with natural language processing based on fuzzy logic and spatial data visualization using geographic information technology. With our development we contribute to the development of modern information technologies in the educational process, namely intelligent linguistic resources with subsequent visualization and processing of spatial data. For the development of the web interface of the shell and the server part we used the Django framework of the Python programming language. Pandas and Folium libraries were used for data processing and visualization. To implement the fuzzy logic module the Levenshtein distance algorithm was used.
Artificial Intelligence and Machine Learning in Insurance: A Bibliometric Analysis

Purpose: The aim of this chapter is to provide a quantitative literature review on machine learning (ML) and artificial intelligence (AI) in the Insurance Sector.Need for the Study: The current study maps the literature regarding AI and ML in the insurance sector through bibliometric tools to identify the significant gaps in the available literature, considerable insights that emerged, and a scientific evaluation of AI and ML in the Insurance sector.Methodology: The VOS viewer method was used to conduct the depth and quantitative analysis of the AI and ML in Insurance. The study of 450 articles has been retrieved through the Scopus database from 2012 to 2021. The implication of performance analysis methods has helped to explore influential journals, authors, countries, Keywords, and affiliations, elevating the literature in AI and the Insurance Sector.Finding: This study conducts an exploratory analysis and identifies the prominent authors, sources, countries, affiliations, and articles using modern bibliometric analysis (BA) tools. The geographic scattering of the study indicates that the USA and the UK have highly influential publications and contribute to AI and Insurance. East and Southern Asia countries are far behind.Practical Implication: Furthermore, this chapter can be used as a reference paper to explore the new field of study in the insurance sector using AI. The search criteria were set in the study to limit the sample published papers/articles included in Scopus data based on the AI and ML in Insurance.
Short communication: The Wasserstein distance as a dissimilarity metric for comparing detrital age spectra and other geological distributions

Abstract. Distributional data such as detrital age populations or grain size distributions are common in the geological sciences. As analytical techniques become more sophisticated, increasingly large amounts of distributional data are being gathered. These advances require quantitative and objective methods, such as multidimensional scaling (MDS), to analyse large numbers of samples. Crucial to such methods is choosing a sensible measure of dissimilarity between samples. At present, the Kolmogorov–Smirnov (KS) statistic is the most widely used of these dissimilarity measures. However, the KS statistic has some limitations such as high sensitivity to differences between the modes of two distributions and insensitivity to their tails. Here, we propose the Wasserstein-2 distance (W2) as an additional and alternative metric for use in geochronology. Whereas the KS distance is defined as the maximum vertical distance between two empirical cumulative distribution functions, the W2 distance is a function of the horizontal distances (i.e. age differences) between observations. Using a variety of synthetic and real datasets, we explore scenarios where the W2 may provide greater geological insight than the KS statistic. We find that in cases where absolute time differences are not relevant (e.g. mixing of known, discrete age peaks), the KS statistic can be more intuitive. However, in scenarios where absolute age differences are important (e.g. temporally and/or spatially evolving sources, thermochronology, and overcoming laboratory biases), W2 is preferable. The W2 distance has been added to the R package, IsoplotR, for immediate use in detrital geochronology and other applications. The W2 distance can be generalized to multiple dimensions, which opens opportunities beyond distributional data.
Reinforcement learning-based AI assistant and VR play therapy game for children with Down syndrome bound to wheelchairs

&lt;abstract&gt; &lt;p&gt;Some of the most significant computational ideas in neuroscience for learning behavior in response to reward and penalty are reinforcement learning algorithms. This technique can be used to train an artificial intelligent (AI) agent to serve as a virtual assistant and a helper. The goal of this study is to determine whether combining a reinforcement learning-based Virtual AI assistant with play therapy. It can benefit wheelchair-bound youngsters with Down syndrome. This study aims to employ play therapy methods and Reinforcement Learning (RL) agents to aid children with Down syndrome and help them enhance their abilities like physical and mental skills by playing games with them. This Agent is designed to be smart enough to analyze each patient's lack of ability and provide a specific set of challenges in the game to improve that ability. Increasing the game's difficulty can help players develop these skills. The agent should be able to assess each player's skill gap and tailor the game to them accordingly. The agent's job is not to make the patient victorious but to boost their morale and skill sets in areas like physical activities, intelligence, and social interaction. The primary objective is to improve the player's physical activities such as muscle reflexes, motor controls and hand-eye coordination. Here, the study concentrates on the employment of several distinct techniques for training various models. This research focuses on comparing the reinforcement learning algorithms like the Deep Q-Learning Network, QR-DQN, A3C and PPO-Actor Critic. This study demonstrates that when compared to other reinforcement algorithms, the performance of the AI helper agent is at its highest when it is trained with PPO-Actor Critic and A3C. The goal is to see if children with Down syndrome who are wheelchair-bound can benefit by combining reinforcement learning with play therapy to increase their mobility.&lt;/p&gt; &lt;/abstract&gt;
Improving Fairness in AI Models on Electronic Health Records: The Case for Federated Learning Methods

Developing AI tools that preserve fairness is of critical importance, specifically in high-stakes applications such as those in healthcare. However, health AI models’ overall prediction performance is often prioritized over the possible biases such models could have. In this study, we show one possible approach to mitigate bias concerns by having healthcare institutions collaborate through a federated learning paradigm (FL; which is a popular choice in healthcare settings). While FL methods with an emphasis on fairness have been previously proposed, their underlying model and local implementation techniques, as well as their possible applications to the healthcare domain remain widely underinvestigated. Therefore, we propose a comprehensive FL approach with adversarial debiasing and a fair aggregation method, suitable to various fairness metrics, in the healthcare domain where electronic health records are used. Not only our approach explicitly mitigates bias as part of the optimization process, but an FL-based paradigm would also implicitly help with addressing data imbalance and increasing the data size, offering a practical solution for healthcare applications. We empirically demonstrate our method’s superior performance on multiple experiments simulating large-scale real-world scenarios and compare it to several baselines. Our method has achieved promising fairness performance with the lowest impact on overall discrimination performance (accuracy). Our code is available at https://github.com/healthylaife/FairFedAvg.
Robot swarms neutralize harmful Byzantine robots using a blockchain-based token economy

Through cooperation, robot swarms can perform tasks or solve problems that a single robot from the swarm could not perform/solve by itself. However, it has been shown that a single Byzantine robot (such as a malfunctioning or malicious robot) can disrupt the coordination strategy of the entire swarm. Therefore, a versatile swarm robotics framework that addresses security issues in inter-robot communication and coordination is urgently needed. Here, we show that security issues can be addressed by setting up a token economy between the robots. To create and maintain the token economy, we used blockchain technology, originally developed for the digital currency Bitcoin. The robots were given crypto tokens that allowed them to participate in the swarm’s security-critical activities. The token economy was regulated via a smart contract that decided how to distribute crypto tokens among the robots depending on their contributions. We designed the smart contract so that Byzantine robots soon ran out of crypto tokens and could therefore no longer influence the rest of the swarm. In experiments with up to 24 physical robots, we demonstrated that our smart contract approach worked: The robots could maintain blockchain networks, and a blockchain-based token economy could be used to neutralize the destructive actions of Byzantine robots in a collective-sensing scenario. In experiments with more than 100 simulated robots, we studied the scalability and long-term behavior of our approach. The obtained results demonstrate the feasibility and viability of blockchain-based swarm robotics.
YOLOv7-CSAW for maritime target detection

Introduction The issue of low detection rates and high false negative rates in maritime search and rescue operations has been a critical problem in current target detection algorithms. This is mainly due to the complex maritime environment and the small size of most targets. These challenges affect the algorithms' robustness and generalization. Methods We proposed YOLOv7-CSAW, an improved maritime search and rescue target detection algorithm based on YOLOv7. We used the K-means++ algorithm for the optimal size determination of prior anchor boxes, ensuring an accurate match with actual objects. The C2f module was incorporated for a lightweight model capable of obtaining richer gradient flow information. The model's perception of small target features was increased with the non-parameter simple attention module (SimAM). We further upgraded the feature fusion network to an adaptive feature fusion network (ASFF) to address the lack of high-level semantic features in small targets. Lastly, we implemented the wise intersection over union (WIoU) loss function to tackle large positioning errors and missed detections. Results Our algorithm was extensively tested on a maritime search and rescue dataset with YOLOv7 as the baseline model. We observed a significant improvement in the detection performance compared to traditional deep learning algorithms, with a mean average precision (mAP) improvement of 10.73% over the baseline model. Discussion YOLOv7-CSAW significantly enhances the accuracy and robustness of small target detection in complex scenes. This algorithm effectively addresses the common issues experienced in maritime search and rescue operations, specifically improving the detection rates and reducing false negatives, proving to be a superior alternative to current target detection algorithms.
A super-resolution network for medical imaging via transformation analysis of wavelet multi-resolution

In recent years, deep learning super-resolution models for progressive reconstruction have achieved great success. However, these models which refer to multi-resolution analysis basically ignore the information contained in the lower subspaces and do not explore the correlation between features in the wavelet and spatial domain, resulting in not fully utilizing the auxiliary information brought by multi-resolution analysis with multiple domains. Therefore, we propose a super-resolution network based on the wavelet multi-resolution framework (WMRSR) to capture the auxiliary information contained in multiple subspaces and to be aware of the interdependencies between spatial domain and wavelet domain features. Initially, the wavelet multi-resolution input (WMRI) is generated by combining wavelet sub-bands obtained from each subspace through wavelet multi-resolution analysis and the corresponding spatial domain image content, which serves as input to the network. Then, the WMRSR captures the corresponding features from the WMRI in the wavelet domain and spatial domain, respectively, and fuses them adaptively, thus learning fully explored features in multi-resolution and multi-domain. Finally, the high-resolution images are gradually reconstructed in the wavelet multi-resolution framework by our convolution-based wavelet transform module which is suitable for deep neural networks. Extensive experiments conducted on two public datasets demonstrate that our method outperforms other state-of-the-art methods in terms of objective and visual qualities.
Many-Body Superradiance and Dynamical Mirror Symmetry Breaking in Waveguide QED

The many-body decay of extended collections of two-level systems remains an open problem. Here, we investigate whether an array of emitters coupled to a one-dimensional bath undergoes Dicke superradiance. This is a process whereby a completely inverted system becomes correlated via dissipation, leading to the release of all the energy in the form of a rapid photon burst. We derive the minimal conditions for the burst to happen as a function of the number of emitters, the chirality of the waveguide, and the single-emitter optical depth, both for ordered and disordered ensembles. Many-body superradiance occurs because the initial fluctuation that triggers the emission is amplified throughout the decay process. In one-dimensional baths, this avalanchelike behavior leads to a spontaneous mirror symmetry breaking, with large shot-to-shot fluctuations in the number of photons emitted to the left and right. Superradiant bursts may thus be a smoking gun for the generation of correlated photon states of exotic quantum statistics.