Reviewerly/exHarmony huggingface dataset:
A review on multimodal zero‐shot learning

Abstract Multimodal learning provides a path to fully utilize all types of information related to the modeling target to provide the model with a global vision. Zero‐shot learning (ZSL) is a general solution for incorporating prior knowledge into data‐driven models and achieving accurate class identification. The combination of the two, known as multimodal ZSL (MZSL), can fully exploit the advantages of both technologies and is expected to produce models with greater generalization ability. However, the MZSL algorithms and applications have not yet been thoroughly investigated and summarized. This study fills this gap by providing an objective overview of MZSL's definition, typical algorithms, representative applications, and critical issues. This article will not only provide researchers in this field with a comprehensive perspective, but it will also highlight several promising research directions. This article is categorized under: Algorithmic Development &gt; Multimedia Technologies &gt; Classification Technologies &gt; Machine Learning
n&lt;SUP&gt;th&lt;/SUP&gt; Power Root Fuzzy Sets and Its Topology

Tareq M. Al-shami, Hariwan Z. Ibrahim, Abdelwaheb Mhemdi, and Radwan Abu-Gdairi. International Journal of Fuzzy Logic and Intelligent Systems 2022;22:350-65. https://doi.org/10.5391/IJFIS.2022.22.4.350
Memorizing Complementation Network for Few-Shot Class-Incremental Learning

Few-shot Class-Incremental Learning (FSCIL) aims at learning new concepts continually with only a few samples, which is prone to suffer the catastrophic forgetting and overfitting problems. The inaccessibility of old classes and the scarcity of the novel samples make it formidable to realize the trade-off between retaining old knowledge and learning novel concepts. Inspired by that different models memorize different knowledge when learning novel concepts, we propose a Memorizing Complementation Network (MCNet) to ensemble multiple models that complements the different memorized knowledge with each other in novel tasks. Additionally, to update the model with few novel samples, we develop a Prototype Smoothing Hard-mining Triplet (PSHT) loss to push the novel samples away from not only each other in current task but also the old distribution. Extensive experiments on three benchmark datasets, e.g., CIFAR100, miniImageNet and CUB200, have demonstrated the superiority of our proposed method.
AUTO-HAR: An adaptive human activity recognition framework using an automated CNN architecture design

Convolutional neural networks (CNNs) have demonstrated exceptional results in the analysis of time- series data when used for Human Activity Recognition (HAR). The manual design of such neural architectures is an error-prone and time-consuming process. The search for optimal CNN architectures is considered a revolution in the design of neural networks. By means of Neural Architecture Search (NAS), network architectures can be designed and optimized automatically. Thus, the optimal CNN architecture representation can be found automatically because of its ability to overcome the limitations of human experience and thinking modes. Evolution algorithms, which are derived from evolutionary mechanisms such as natural selection and genetics, have been widely employed to develop and optimize NAS because they can handle a blackbox optimization process for designing appropriate solution representations and search paradigms without explicit mathematical formulations or gradient information. The Genetic optimization algorithm (GA) is widely used to find optimal or near-optimal solutions for difficult problems. Considering these characteristics, an efficient human activity recognition architecture (AUTO-HAR) is presented in this study. Using the evolutionary GA to select the optimal CNN architecture, the current study proposes a novel encoding schema structure and a novel search space with a much broader range of operations to effectively search for the best architectures for HAR tasks. In addition, the proposed search space provides a reasonable degree of depth because it does not limit the maximum length of the devised task architecture. To test the effectiveness of the proposed framework for HAR tasks, three datasets were utilized: UCI-HAR, Opportunity, and DAPHNET. Based on the results of this study, it has been found that the proposed method can efficiently recognize human activity with an average accuracy of 98.5% (∓1.1), 98.3%, and 99.14% (∓0.8) for UCI-HAR, Opportunity, and DAPHNET, respectively.
Optimal Strategies of Quantum Metrology with a Strict Hierarchy

One of the main quests in quantum metrology is to attain the ultimate precision limit with given resources, where the resources are not only of the number of queries, but more importantly of the allowed strategies. With the same number of queries, the restrictions on the strategies constrain the achievable precision. In this Letter, we establish a systematic framework to identify the ultimate precision limit of different families of strategies, including the parallel, the sequential, and the indefinite-causal-order strategies, and provide an efficient algorithm that determines an optimal strategy within the family of strategies under consideration. With our framework, we show there exists a strict hierarchy of the precision limits for different families of strategies.
Path Planning for Autonomous Drones: Challenges and Future Directions

Unmanned aerial vehicles (UAV), or drones, have gained a lot of popularity over the last decade. The use of autonomous drones appears to be a viable and low-cost solution to problems in many applications. Path planning capabilities are essential for autonomous control systems. An autonomous drone must be able to rapidly compute feasible and energy-efficient paths to avoid collisions. In this study, we review two key aspects of path planning: environmental representation and path generation techniques. Common path planning techniques are analyzed, and their key limitations are highlighted. Finally, we review thirty-five highly cited publications to identify current trends in drone path planning research. We then use these results to identify factors that need to be addressed in future studies in order to develop a practical path planner for autonomous drones.
TEXT COMPLEXITY AS INTERDISCIPLINARY PROBLEM

The article is the first state-of-the-art review in Russian and foreign (mostly English) studies on text complexity in which we concentrate on both articles and books, addressing text complexity theories and methods of assessment. “Text complexity” is viewed as a construct computed with the help of linguo-mathematical, cognitive, and machine learning methods. The interdisciplinary approach implemented in the study enabled the authors to integrate the research of the problem in a new discipline, i.e. textual complexology viewed as an integral research paradigm focused on assessment of objective and individual complexity of text. Textual complexology provides methods and develops tools of text leveling thus empowering the process of selecting texts appropriate for a specific reader. During the two periods of its timeline, i.e. before and after computer technologies, textual complexology accumulated numerous linguo-mathematical, cognitive, and machine learning methods of text complexity assessment and text leveling. The latter are based on linguo-cognitive abilities of readers and their readiness to be exposed to texts of certain complexity. The research results can be implemented in various pragmalinguistic situations to predict text readability dependency on certain age, year of schooling, and professional specialization.
Social Distance Monitoring Framework Using YOLO V5 Deep Architecture

Due to the current outburst and speedy spread of the COVID-19 pandemic, there is a need to comply with social distancing rules by the general public. The public needs to, at minimum, hold a distance of 3 ft or 1 m among one another to follow strict social distancing as instructed by using the World Health Organization for general public safety. Researchers have proposed many solutions based on deep learning to reduce the current pandemic, including COVID-19 screening, diagnosis, social distancing monitoring, etc. This work focuses explicitly on social distancing monitoring by a deep learning approach. Here we employ the YOLOV5 object detection technique upon different images and videos to develop a strategy to assist and put strict social distancing in public. The YOLOV5 algorithm is more robust and has a quicker detection pace than its competitors. The suggested object detection framework shows an average precision rating of 94.75%. Some of the existing analyses suffer to identify humans within a range. A few identification blunders happen because of overlapping video frames or humans taking walks too near each other. This detection mistake is due to the overlapping structures, and human beings are standing too close to each other. This paper focuses on correctly identifying humans by using and overcoming the flaws of frame overlapping. Following the proposed social distancing technique also yields positive results in numerous variable eventualities.
BarrierNet: Differentiable Control Barrier Functions for Learning of Safe Robot Control

Many safety-critical applications of neural networks, such as robotic control, require safety guarantees. This article introduces a method for ensuring the safety of learned models for control using differentiable control barrier functions (dCBFs). dCBFs are end-to-end trainable and guarantee safety. They improve over classical control barrier functions (CBFs), which are usually overly conservative. Our dCBF solution relaxes the CBF definitions by: 1) using environmental dependencies; 2) embedding them into differentiable quadratic programs. These novel safety layers are called a BarrierNet. They can be used in conjunction with any neural network-based controller. They are trained by gradient descent. With BarrierNet, the safety constraints of a neural controller become adaptable to changing environments. We evaluate BarrierNet on the following several problems: 1) robot traffic merging; 2) robot navigation in 2-D and 3-D spaces; 3) end-to-end vision-based autonomous driving in a sim-to-real environment and in physical experiments; 4) demonstrate their effectiveness compared to state-of-the-art approaches.
No-reference quality assessment for low-light image enhancement: Subjective and objective methods

No-Reference (NR) quality assessment is a crucial approach for evaluating the quality of low-light enhanced images, as it is often difficult to acquire high-quality reference images in applications such as night-time automatic driving. However, current NR evaluation methods for low-light enhanced images often lack consideration of important characteristics such as color, structure, and naturalness. This paper proposes a novel NR quality assessment method for NR low-light enhanced images from both subjective and objective aspects. On the subjective side, we construct the Low-light Enhanced Images Subjective Dataset (LEISD) containing 2040 images with 255 different image contents. Each image was evaluated based on the Single Stimulus (SS) method by 20 subjects. On the objective side, we propose Multi-Features Reconciliation-based Quality Assessment (MFRQA) methods for low-light enhanced images by observing the low-light enhanced images. The MFRQA summarized four key feature perspectives: brightness, color, structure and naturalness, and employed the traditional machine learning model to reconcile the multi-features. Experimental results on the LEISD dataset demonstrate competitive performance and low complexity of our method compared to the representative quality metrics.
Do Anthropomorphic Chatbots Increase Counseling Satisfaction and Reuse Intention? The Moderated Mediation of Social Rapport and Social Anxiety

The growing demand for mental health services and artificial intelligence chatbots to replace human agents have led to increased attention to chatbot anthropomorphizing. This study explored the effect of anthropomorphism on counseling satisfaction and reuse intention for chatbot-led mental health counseling and the mediating role of social rapport in the relationship. This study also examined the interaction effect of anthropomorphism and social anxiety on counseling satisfaction and reuse intention. A total of 374 U.S. adults were recruited from an online crowdsourcing company to simulate user-chatbot interactions in the context of mental health counseling. Two chatbots either with a high anthropomorphic design (i.e., human face) or low anthropomorphic design (i.e., robot face) were developed through Dialogflow—a natural language processing engine—to examine the hypotheses. The results revealed that the high anthropomorphic design produced higher counseling satisfaction and reuse intention than the low anthropomorphic design, while this relationship is mediated by the perceived social rapport between chatbot counselors and users. The results further revealed a significant interaction effect of anthropomorphism and social anxiety on counseling satisfaction and reuse intention. The findings of this study are expected to (a) enhance the understanding of the effect of anthropomorphic chatbots on counseling satisfaction and reuse intention, (b) clarify moderating effects of social rapport and social anxiety, and (c) help mental health practitioners and chatbot designers by providing a psychological mechanism of how anthropomorphism functions in the context of human–chatbot interaction.
Multi-objective reinforcement learning approach for trip recommendation

Trip recommendation is an intelligent service that provides personalized itinerary plans for tourists in unfamiliar cities. It aims to construct a series of ordered POIs that maximizes user travel experiences with temporal and spatial constraints. When appending a candidate POI to the recommended trip, it is critical to capture users’ dynamic preferences according to real-time context. Meanwhile, the diversity and popularity of the POIs in the personalized trip play an important role in users’ selections. To address these challenges, in this article, we propose a MORL-Trip (short for Multi-Objective Reinforcement Learning for Trip Recommendation) approach. MORL-Trip models the personalized trip recommendation as a Markov Decision Process (MDP), and implements it upon the Actor-Critic framework. MORL-Trip enhances the state representation with sequential information, geographic information and order information to learn user’s context from real-time location. In addition, MORL-Trip augments the standard Critic component by designing a composite reward function to enforce three principal objectives: accuracy, popularity and diversity. We conduct extensive experiments on the public datasets and compare the performance of MORL-Trip with the most advanced methods to verify its superiority, and show the importance of reinforcing popularity and diversity as complementary objectives in the personalized trip recommendation.
Progressive Feedback Residual Attention Network for Cardiac Magnetic Resonance Imaging Super-Resolution

Atrial fibrillation (AF) is an increasing medical burden worldwide, and its pathological manifestations are atrial tissue remodeling and low-pressure atrial tissue fibrosis. Due to the inherent defects of medical image data acquisition systems, the acquisition of high-resolution cardiac magnetic resonance imaging (CMRI) faces many problems. In response to these problems, we propose the Progressive Feedback Residual Attention Network (PFRN) for CMRI super-resolution. Specifically, we directly perform feature extraction on low-resolution images, retain feature information to a large extent, and then build multiple independent progressive feedback modules to extract high-frequency details. To accelerate network convergence and improve image reconstruction quality, we implement the MS-SSIM-L1 loss function. Furthermore, we utilize the residual attention stack module to explore the image's internal relevance and extract the low-resolution image's detailed features. Extensive benchmark evaluation shows that PFRN can improve the detailed information of the image SR reconstruction results, and the reconstructed CMRI has a better visual effect.
Online Bruxism‐related information: Can people understand what they read? A Cross‐Sectional Study

Bruxism patients often access the web to search for information. Unfortunately, the low text readability of online material and the limited medical literacy in the general population may prevent patients' understanding of health information.We aimed to assess the home page's readability of the top 10 patients-oriented bruxism-related websites and the educational level required to approach them.Using the word bruxism in the "no country redirect" extension of Google Chrome browser (www.google.com/ncr), we identified the first 10 patient-oriented English language websites. The readability of the material was determined using six commonly recommended readability tests (1): Gunning Fog Index (GFI), Coleman Liau Index (CLI), Automated Readability Index (ARI) Simple Measure of Gobbledygook (SMOG), Flesch Kincald Grade Level (FKGL), Flesh Reasing Ease (FRE).The USA National Institutes of Health requirements, which call for websites to be readable at a 6th to 7th-grade level, were not followed by any of the most popular websites.The average consumer frequently finds the health information on the Internet to be too complex to understand, which can lead to misinterpretation, a delay in diagnosis, and poorer health consequences.
Investigation of proper orthogonal decomposition for echo state networks

Echo State Networks (ESN) are a type of Recurrent Neural Network that yields promising results in representing time series and nonlinear dynamic systems. Although they are equipped with a very efficient training procedure, Reservoir Computing strategies, such as the ESN, require high-order networks, i.e., many neurons, resulting in a large number of states that are magnitudes higher than the number of model inputs and outputs. A large number of states not only makes the time-step computation more costly but also may pose robustness issues, especially when applying ESNs to problems such as Model Predictive Control (MPC) and other optimal control problems. One way to circumvent this complexity issue is through Model Order Reduction strategies such as the Proper Orthogonal Decomposition (POD) and its variants (POD-DEIM), whereby we find an equivalent lower order representation to an already trained high dimension ESN. To this end, this work aims to investigate and analyze the performance of POD methods in Echo State Networks, evaluating their effectiveness through the Memory Capacity (MC) of the POD-reduced network compared to the original (full-order) ESN. We also perform experiments on two numerical case studies: a NARMA10 difference equation and an oil platform containing two wells and one riser. The results show that there is little loss of performance comparing the original ESN to a POD-reduced counterpart and that the performance of a POD-reduced ESN tends to be superior to a normal ESN of the same size. Also, the POD-reduced network achieves speedups of around 80% compared to the original ESN.
An Extensive Comparative Analysis of Chatbot Technologies - ChatGPT, Google BARD and Microsoft Bing

This study presents an extensive comparative analysis of ChatGPT, Google BARD, and Microsoft Bing in natural language processing, machine learning, and user experience. Chatbot technologies have become increasingly popular due to their potential to enhance customer service, support, and engagement in various fields, including healthcare, education, and e-commerce. However, the performance of chatbots depends on their ability to understand natural language, learn from user interactions, and provide an engaging user experience. The study's objective was to compare the performance of three chatbot technologies in terms of their natural language processing, machine learning techniques, and user experience. A mixed-methods research design was employed, combining quantitative analysis of chatbot performance metrics and qualitative analysis of user feedback. The research variables included accuracy, response time, relevance, user satisfaction, and user engagement. The results of the study indicate significant differences between the chatbot technologies in terms of their performance metrics. ChatGPT outperformed the other chatbot technologies in terms of accuracy and relevance, while Google BARD had the fastest response time. Microsoft Bing demonstrated the highest user satisfaction and engagement. The study also revealed the importance of natural language processing and machine learning in enhancing chatbot performance. The findings of the study have important implications for chatbot development and implementation. The strengths and weaknesses of each chatbot technology were analyzed, providing insights into the areas where each technology could be improved. The study's limitations, such as the limited dataset, were discussed, and recommendations for future research were made. In conclusion, this study contributes to the understanding of chatbot technologies and their performance in natural language processing, machine learning, and user experience. The results provide valuable insights for developers and businesses looking to implement chatbot technologies to enhance their customer service, support, and engagement. The study's methodology and findings can also serve as a reference for future research in the field of chatbot technology.