Reviewerly/exHarmony huggingface dataset:
A Novel Dual Confusion and Diffusion Approach for Grey Image Encryption using Multiple Chaotic Maps

With the exponential growth of the internet and social media, images have become a predominant form of information transmission, including confidential data. Ensuring the proper security of these images has become crucial in today’s digital age. This research study proposes a unique strategy for solving this demand by presenting a dual confusion and diffusion technique for encrypting gray-scale pictures. This method is presented as an innovative means of meeting this need. To im-prove the effectiveness of the encryption process, the encryption method uses several chaotic maps, including the logistic map, the tent map, and the Lorenz attractor. Python is used for the implementation of the suggested approach. Furthermore, a thorough assessment of the encryption mechanism is carried out to determine its efficacy and resilience. By employing the combined strength of chaotic maps and dual confusion and diffusion techniques, the proposed method aims to provide a high level of security for confidential image transmission. The experimental results demonstrate the algorithm’s effectiveness in terms of encryption speed, security, and resistance against common attacks. The encrypted images exhibit properties such as randomness, key sensitivity, and resilience against statistical analysis and differential attacks. Moreover, the proposed method maintains a reasonable computational efficiency, and it is compatible with real-time applications. This study makes a contribution to the growing area of picture encryption by presenting an original and effective encryption method that overcomes the shortcomings of previously used approaches. Future work can explore additional security features and extend the proposed approach to encrypt other forms of multimedia data.
D-STGCNT: A Dense Spatio-Temporal Graph Conv-GRU Network based on transformer for assessment of patient physical rehabilitation

This paper tackles the challenge of automatically assessing physical rehabilitation exercises for patients who perform the exercises without clinician supervision. The objective is to provide a quality score to ensure correct performance and achieve desired results. To achieve this goal, a new graph-based model, the Dense Spatio-Temporal Graph Conv-GRU Network with Transformer, is introduced. This model combines a modified version of STGCN and transformer architectures for efficient handling of spatio-temporal data. The key idea is to consider skeleton data respecting its non-linear structure as a graph and detecting joints playing the main role in each rehabilitation exercise. Dense connections and GRU mechanisms are used to rapidly process large 3D skeleton inputs and effectively model temporal dynamics. The transformer encoder's attention mechanism focuses on relevant parts of the input sequence, making it useful for evaluating rehabilitation exercises. The evaluation of our proposed approach on the KIMORE and UI-PRMD datasets highlighted its potential, surpassing state-of-the-art methods in terms of accuracy and computational time. This resulted in faster and more accurate learning and assessment of rehabilitation exercises. Additionally, our model provides valuable feedback through qualitative illustrations, effectively highlighting the significance of joints in specific exercises.
Human shape representations are not an emergent property of learning to classify objects.

Humans are particularly sensitive to relationships between parts of objects. It remains unclear why this is. One hypothesis is that relational features are highly diagnostic of object categories and emerge as a result of learning to classify objects. We tested this by analyzing the internal representations of supervised convolutional neural networks (CNNs) trained to classify large sets of objects. We found that CNNs do not show the same sensitivity to relational changes as previously observed for human participants. Furthermore, when we precisely controlled the deformations to objects, human behavior was best predicted by the number of relational changes while CNNs were equally sensitive to all changes. Even changing the statistics of the learning environment by making relations uniquely diagnostic did not make networks more sensitive to relations in general. Our results show that learning to classify objects is not sufficient for the emergence of human shape representations. Instead, these results suggest that humans are selectively sensitive to relational changes because they build representations of distal objects from their retinal images and interpret relational changes as changes to these distal objects. This inferential process makes human shape representations qualitatively different from those of artificial neural networks optimized to perform image classification. (PsycInfo Database Record (c) 2023 APA, all rights reserved).
Blockchain-Based Authentication and Explainable AI for Securing Consumer IoT Applications

The consumer Internet of Things (IoT) applications in particular smart cities are mostly equipped with Internet-connected networked devices to improve city operations by giving access to a massive amount of valuable information. However, these smart devices in a smart city environment mostly use public channels to access and share data among different participants. This has introduced a great interest in using authentication and key agreement (AKA) mechanisms and intrusion detection systems (IDS) based on artificial intelligence (AI) techniques. However, most of the AKA mechanisms have high computation and communication costs and cannot be trusted completely. On the other hand, the AI-based IDS are treated as blackbox by the security analyst due to their inability to explain the reasons behind the decision. In this direction, we have integrated blockchain-based AKA mechanism with explainable artificial intelligence (XAI) for securing smart city-based consumer applications. Specifically, first, the participating entities communicate with each other in a secure manner to exchange data using a blockchain-based AKA mechanism. On the other hand, we have used SHapley Additive exPlanations (SHAP) mechanism to explain and interpret the prominent features that constituent most in the decision. The practical implementation of the proposed framework proves the efficiency over other recent state-of-the-art techniques.
Coherently Remapping Toroidal Cells But Not Grid Cells are Responsible for Path Integration in Virtual Agents

It is widely believed that grid cells provide cues for path integration, with place cells encoding an animal's location and environmental identity. When entering a new environment, these cells remap concurrently, sparking debates about their causal relationship. Using a continuous attractor recurrent neural network, we study spatial cell dynamics in multiple environments. We investigate grid cell remapping as a function of global remapping in place-like units through random resampling of place cell centers. Dimensionality reduction techniques reveal that a subset of cells manifest a persistent torus across environments. Unexpectedly, these toroidal cells resemble band-like cells rather than high grid score units. Subsequent pruning studies reveal that toroidal cells are crucial for path integration while grid cells are not. As we extend the model to operate across many environments, we delineate its generalization boundaries, revealing challenges with modeling many environments in current models.
Research on Fine-Grained Image Recognition of Birds Based on Improved YOLOv5

Birds play a vital role in maintaining biodiversity. Accurate identification of bird species is essential for conducting biodiversity surveys. However, fine-grained image recognition of birds encounters challenges due to large within-class differences and small inter-class differences. To solve this problem, our study took a part-based approach, dividing the identification task into two parts: part detection and identification classification. We proposed an improved bird part detection algorithm based on YOLOv5, which can handle partial overlap and complex environmental conditions between part objects. The backbone network incorporates the Res2Net-CBAM module to enhance the receptive fields of each network layer, strengthen the channel characteristics, and improve the sensitivity of the model to important information. Additionally, in order to boost data on features extraction and channel self-regulation, we have integrated CBAM attention mechanisms into the neck. The success rate of our suggested model, according to experimental findings, is 86.6%, 1.2% greater than the accuracy of the original model. Furthermore, when compared with other algorithms, our model’s accuracy shows noticeable improvement. These results show how useful the method we suggested is for quickly and precisely recognizing different bird species.
FedFA: Federated Learning with Feature Anchors to Align Features and Classifiers for Heterogeneous Data

Federated learning allows multiple clients to collaboratively train a model without exchanging their data, thus preserving data privacy. Unfortunately, it suffers significant performance degradation due to heterogeneous data at clients. Common solutions involve designing an auxiliary loss to regularize weight divergence or feature inconsistency during local training. However, we discover that these approaches fall short of the expected performance because they ignore the existence of a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">vicious cycle</i> between feature inconsistency and classifier divergence across clients. This <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">vicious cycle</i> causes client models to be updated in inconsistent feature spaces with more diverged classifiers. To break the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">vicious cycle</i> , we propose a novel framework named <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"/> <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Fed</b> <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">erated learning with</i> <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">F</b> <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">eature</i> <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">A</b> <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">nchors</i> (FedFA). FedFA utilizes feature anchors to align features and calibrate classifiers across clients simultaneously. This enables client models to be updated in a shared feature space with consistent classifiers during local training. Theoretically, we analyze the non-convex convergence rate of FedFA. We also demonstrate that the integration of feature alignment and classifier calibration in FedFA brings a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">virtuous cycle</i> between feature and classifier updates, which breaks the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">vicious cycle</i> existing in current approaches. Extensive experiments show that FedFA significantly outperforms existing approaches on various classification datasets under label distribution skew and feature distribution skew.
A Novel Approach for Earthquake Prediction Using Random Forest and Neural Networks

INTRODUCTION: This research paper presents an innovative method that merges neural networks and random forest algorithms to enhance earthquake prediction.&#x0D; OBJECTIVES: The primary objective of the study is to improve the precision of earthquake prediction by developing a hybrid model that integrates seismic wave data and various extracted features as inputs.&#x0D; METHODS: By training a neural network to learn the intricate relationships between the input features and earthquake magnitudes and employing a random forest algorithm to enhance the model's generalization and robustness, the researchers aim to achieve more accurate predictions. To evaluate the effectiveness of the proposed approach, an extensive dataset of earthquake records from diverse regions worldwide was employed.&#x0D; RESULTS: The results revealed that the hybrid model surpassed individual models, demonstrating superior prediction accuracy. This advancement holds profound implications for earthquake monitoring and disaster management, as the prompt and accurate detection of earthquake magnitudes is vital for effective mitigation and response strategies.&#x0D; CONCLUSION: The significance of this detection technique extends beyond theoretical research, as it can directly benefit organizations like the National Disaster Response Force (NDRF) in their relief efforts. By accurately predicting earthquake magnitudes, the model can facilitate the efficient allocation of resources and the timely delivery of relief materials to areas affected by natural disasters. Ultimately, this research contributes to the growing field of earthquake prediction and reinforces the critical role of data-driven approaches in enhancing our understanding of seismic events, bolstering disaster preparedness, and safeguarding vulnerable communities.
Zonotope-Based Distributed Set-Membership Fusion Estimation for Artificial Neural Networks Under the Dynamic Event-Triggered Mechanism

This article is concerned with the distributed set-membership fusion estimation problem for a class of artificial neural networks (ANNs), where the dynamic event-triggered mechanism (ETM) is utilized to schedule the signal transmission from sensors to local estimators to save resource consumption and avoid data congestion. The main purpose of this article is to design a distributed set-membership fusion estimation algorithm that ensures the global estimation error resides in a zonotope at each time instant and, meanwhile, the radius of the zonotope is ultimately bounded. By means of the zonotope properties and the linear matrix inequality (LMI) technique, the zonotope restraining the prediction error is first calculated to improve the prediction accuracy and subsequently, the zonotope enclosing the local estimation error is derived to enhance the estimation performance. By taking into account the side-effect of the order reduction technique (utilized in designing the local estimation algorithm) of the zonotope, a sufficient condition is derived to guarantee the ultimate boundedness of the radius of the zonotope that encompasses the local estimation error. Furthermore, parameters of the local estimators are obtained via solutions to certain bilinear matrix inequalities. Moreover, the zonotope-based distributed fusion estimator is obtained through minimizing certain upper bound of the radius of the zonotope (that contains the global estimation error) according to the matrix-weighted fusion rule. Finally, the effectiveness of the proposed distributed fusion estimation method is illustrated via a numerical example.
Sentiment Difficulty in Aspect-Based Sentiment Analysis

Subjectivity is a key aspect of natural language understanding, especially in the context of user-generated text and conversational systems based on large language models. Natural language sentences often contain subjective elements, such as opinions and emotions, that make them more nuanced and complex. The level of detail at which the study of the text is performed determines the possible applications of sentiment analysis. The analysis can be done at the document or paragraph level, or, even more granularly, at the aspect level. Many researchers have studied this topic extensively. The field of aspect-based sentiment analysis has numerous data sets and models. In this work, we initiate the discussion around the definition of sentence difficulty in this context of aspect-based sentiment analysis. To assess and quantify the difficulty of the aspect-based sentiment analysis, we conduct an experiment using three data sets: “Laptops”, “Restaurants”, and “MTSC” (Multi-Target-dependent Sentiment Classification), along with 21 learning models from scikit-learn. We also use two textual representations, TF-IDF (Terms frequency-inverse document frequency) and BERT (Bidirectional Encoder Representations from Transformers), to analyze the difficulty faced by these models in performing aspect-based sentiment analysis. Additionally, we compare the models with a fine-tuned version of BERT on the three data sets. We identify the most challenging sentences using a combination of classifiers in order to better understand them. We propose two strategies for defining sentence difficulty. The first strategy is binary and considers sentences as difficult when the classifiers are unable to correctly assign the sentiment polarity. The second strategy uses a six-level difficulty scale based on how many of the top five best-performing classifiers can correctly identify sentiment polarity. These sentences with assigned difficulty classes are then used to create predictive models for early difficulty detection. The purpose of estimating the difficulty of aspect-based sentiment analysis is to enhance performance while minimizing resource usage.
Value creation in the metaverse age: a thematic analysis of press releases

Purpose Since Zuckerberg's announcement to change Facebook's name to Meta Platforms Inc. on October 28, 2021, the concept of the metaverse has gained unprecedented popularity in the business world. Tech giants, SMEs and start-ups across various sectors are making substantial investments in metaverse-related technologies. Despite this, scholarly research in entrepreneurship and strategic management regarding the metaverse remains limited. This paper, grounded in value creation theory, aims to analyze how value is generated in the metaverse era. Design/methodology/approach This paper conducts a thematic analysis of 895 press releases published by LexisNexis between October 28, 2021, and October 28, 2022. The analysis identifies the primary emerging themes related to value creation in the metaverse age. Findings The thematic analysis reveals four significant emerging themes concerning value creation in the metaverse age: (1) factors enabling value creation, (2) digital resources contributing to value creation, (3) motives driving value creation and (4) practices of value creation. Originality/value This paper represents the inaugural attempt to analyze the metaverse through a value creation lens. Given the substantial investments and growing academic interest in the metaverse, understanding value creation in this context is a pressing concern. Additionally, this study provides valuable insights and suggests critical questions for future research on the metaverse.
A comparative study of YOLO models and a transformer-based YOLOv5 model for mass detection in mammograms

Breast cancer is a prevalent form of cancer across the globe, and if it is not diagnosed at an early stage it can be life-threatening. In order to aid in its diagnosis, detection, and classification, computer-aided detection (CAD) systems are employed. You Only Look Once (YOLO)-based CAD algorithms have become very popular owing to their highly accurate results for object detection tasks in recent years. Therefore, the most popular YOLO models are implemented to compare the performance in mass detection with various experiments on the INbreast dataset. In addition, a YOLO model with an integrated Swin Transformer in its backbone is proposed for mass detection in mammography images within the study. The performance of YOLOv5 models and a transformer-based YOLO model is compared to that of each other and YOLOv3 and YOLOv4 models using images with different sizes on the INbreast dataset. The best results are obtained by the transformer-based YOLO model of YOLOv5 for 832 × 832 image size. In another experiment, we compared the default anchors against the anchors provided by the YOLOv5 autoanchor function before training and saw that the anchors generated by the YOLOv5 autoanchor increased the success rates. Furthermore, various experiments were conducted to observe how data augmentation affects performance. Although a small amount of data was used in the study, high performance was obtained by YOLO algorithms, which are promising tools for cancer detection.
Searching for quasar candidates with periodic variations from the Zwicky Transient Facility: results and implications

Abstract We conduct a systematic search for quasars with periodic variations from the archival photometric data of the Zwicky Transient Facility by cross-matching with the quasar catalogs of the Sloan Digital Sky Survey and Véron-Cetty &amp; Véron. We first select out 184 primitive periodic candidates using the generalized Lomb-Scargle periodogram and auto-correlation function and then estimate their statistical significance of periodicity based on two red-noise models, i.e., damped random walk (DRW) and single power-law (SPL) models. As such, we finally identify 106 (DRW) and 86 (SPL) candidates with the most significant periodic variations out of 143,700 quasars. We further compare DRW and SPL models using Bayes factors, which indicate a relative preference of the SPL model for our primitive sample. We thus adopt the candidates identified with SPL as the final sample and summarize its basic properties. We extend the light curves of the selected candidates by supplying other archival survey data to verify their periodicity. However, only three candidates (with 6-8 cycles of periods) meet the selection criteria. This result clearly implies that, instead of being strictly periodic, the variability must be quasi-periodic or caused by stochastic red-noise. This exerts a challenge to the existing search approaches and calls for developing new effective methods.
Large-Scale Person Detection and Localization using Overhead Fisheye Cameras

Location determination finds wide applications in daily life. Instead of existing efforts devoted to localizing tourist photos captured by perspective cameras, in this article, we focus on devising person positioning solutions using overhead fisheye cameras. Such solutions are advantageous in large field of view (FOV), low cost, anti-occlusion, and unaggressive work mode (without the necessity of cameras carried by persons). However, related studies are quite scarce, due to the paucity of data. To stimulate research in this exciting area, we present LOAF, the first large-scale overhead fisheye dataset for person detection and localization. LOAF is built with many essential features, e.g., i) the data cover abundant diversities in scenes, human pose, density, and location; ii) it contains currently the largest number of annotated pedestrian, i.e., 457K bounding boxes with groundtruth location information; iii) the body-boxes are labeled as radius-aligned so as to fully address the positioning challenge. To approach localization, we build a fisheye person detection network, which exploits the fisheye distortions by a rotation-equivariant training strategy and predict radius-aligned human boxes end-to-end. Then, the actual locations of the detected persons are calculated by a numerical solution on the fisheye model and camera altitude data. Extensive experiments on LOAF validate the superiority of our fisheye detector w.r.t. previous methods, and show that our whole fisheye positioning solution is able to locate all persons in FOV with an accuracy of0.5 m, within 0.1 s.