Reviewerly/exHarmony huggingface dataset:
Generalizing Aggregation Functions in GNNs: Building High Capacity and Robust GNNs via Nonlinear Aggregation

The main aspect powering GNNs is the multi-layer network architecture to learn the nonlinear representation for graph learning task. The core operation in GNNs is the message propagation in which each node updates its information by aggregating the information from its neighbors. Existing GNNs usually adopt either linear neighborhood aggregation (e.g. mean, sum) or max aggregator in their message propagation. 1) For linear aggregators, the whole nonlinearity and network's capacity of GNNs are generally limited because deeper GNNs usually suffer from the over-smoothing issue due to their inherent information propagation mechanism. Also, linear aggregators are usually vulnerable to the spatial perturbations. 2) For max aggregator, it usually fails to be aware of the detailed information of node representations within neighborhood. To overcome these issues, we re-think the message propagation mechanism in GNNs and develop the new general nonlinear aggregators for neighborhood information aggregation in GNNs. One main aspect of our nonlinear aggregators is that they all provide the optimally balanced aggregator between max and mean/sum aggregators. Thus, they can inherit both i) high nonlinearity that enhances network's capacity, robustness and ii) detail-sensitivity that is aware of the detailed information of node representations in GNNs' message propagation. Promising experiments show the effectiveness, high capacity and robustness of the proposed methods.
Fast Task Allocation of Heterogeneous Robots With Temporal Logic and Inter-Task Constraints

This work develops a fast task allocation framework for heterogeneous multi-robot systems subject to both temporal logic and inter-task constraints. The considered inter-task constraints include unrelated tasks, compatible tasks, and exclusive tasks. To specify such inter-task relationships, we extend conventional atomic proposition to batch atomic propositions, which gives rise to the LTL <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^{\mathcal {T}}$</tex-math></inline-formula> formula. The Task Batch Planning Decision Tree (TB-PDT) is then developed, which is a variant of conventional decision tree specialized for temporal logic and inter-task constraints. The TB-PDT is built incrementally to represent the task progress and does not require sophisticated product automaton, which significantly reduces the search space. Based on TB-PDT, the search algorithm, namely Intensive Inter-task Relationship Tree Search (IIRTS), is developed for the fast task allocation of heterogeneous multi-robot systems. It is shown that the solution time of finding a satisfactory task allocation grows almost quadratically with the number of automaton states. Extensive simulation and experiment demonstrate the validity, the effectiveness, and the transferability of IIRTS.
OCOsense<sup>TM</sup> Smart Glasses for Analyzing Facial Expressions Using Optomyographic Sensors

This article introduces the Emteq's OCOsenseTM smart glasses equipped with a novel noncontact OCOTM sensor technology for measuring facial muscle activation and expressions based on high-resolution tracking of skin movement. We demonstrate that the OCOTM sensor technology based on optomyography is a sensitive and accurate approach for assessing skin movement in three dimensions, providing a means for measuring the facial expressions used to assess emotional valence such as smile, frown, and eyebrow raise. We propose that glasses-based optomyography sensing has the potential to herald a paradigm shift in real-world facial expression monitoring, thus enabling real-time emotional analytics with healthcare and research applications.
A modified convolutional neural network framework for categorizing lung cell histopathological image based on residual network

Computational technology has drastically improved the medical and Healthcare fields in the last few years. Many new diagnostic techniques are introduced with the help of computer technology. Cancer causes most death worldwide. Lung cancer is the most common and gender-independent cancer among other types of cancer. The timely detection of cancer increases survival for the patient. Cancer may be automatically diagnosed using the power of Machine Learning (ML) with medical images. ML can classify cancer cell images more accurately with less time and lower cost. This research modifies the Convolutional Neural Network (CNN) model as pre-trained Visual Geometry Group19 (VGG19) for classifying lung cancer biopsy images with improved augmentation technique. The results observed with fine-tuned VGG19 model with improved augmentation technique are up to 97.73% accuracy. The experiment results by the proposed method show better performance than existing methods.
Multivariate statistical analysis of the alluvial aquifer of Tadjenanet-Chelghoum Laid (Eastern Algeria)

The region of Chelghoum Laid - Tadjenanet is located in eastern Algeria, in the high plains. This area is characterized by a varied age (Mio-Plio-Quaternary) formations and the human demand is answered by the groundwater hosted into the superficial aquifer of the Mio-Plio-Quaternary formations. However, the overexploitation of this aquifer leads to a drop in the piezometric surface and an increase in salinity. of this aquifer leads to a drop in the piezometric surface and an increase in the salinity of the water. A hydrochemical study using characteristic ratios and statistical tools such as principal component analysis and hierarchical ascending classification, was performed using 28 water samples. Chemical analyzes show that the waters of this aquifer are of the calcium bicarbonate type on the limits of the study area, and calcium sulphate to sodium chloride in the centre. The spatial evolution of chemical water facies is explained by the phenomenon of dissolution and ion exchange between the alkali metals and alkaline earth metals coinciding with the West-East flow direction. The application of the various tools shows that the aquifer is recharged by precipitation at the plains border, where the water acquires its original mineralization from the carbonate formations, and becomes more mineralized in chlorides, sodium and sulphates concentrations in contact with the salt-bearing terrigenous formations of the Mio-Plio-Quaternary. The salinization of the waters seems to be mainly due to the dissolution of gypsum, halite and epsomite. The application of the ascending hierarchical classification and the principal component analysis shows the existence of two groups of water whose salinity increases from the borders towards the center of the plain following the main flow direction.
Vision-Based Real-Time Motion Estimation of Ground Vehicles from Aerial Imagery

Abstract This research presents vision-based maneuvering object motion estimation in case of occlusion. Unequal dimension Interactive Multiple Model (UDIMM) approach is applied to increase the motion prediction accuracy when no measurement is available. Current deep learning-based multi-object tracking algorithms cannot track the objects when an occlusion exists, even for quite a short time. The main reason is the motion model used in tracking algorithms. If the measurement coming from the object detector is not available, the prediction model propagates the Kalman Filter’s motion model. In this period, the object may maneuver or accelerate/decelerate so that the tracker cannot assign the tracklets successfully. Eventually, the ID switch occurs, and object tracking gets lost. Using the UDIMM approach, track loss due to the ID switch is highly reduced compared to the simple constant velocity motion model used in the StrongSort multi-object tracker algorithm. A two-dimensional simulation environment is created to compare the ground truth data with the estimation results. Performance results of different filters are compared.
A Novel Underwater Image Enhancement Using Optimal Composite Backbone Network

Continuous exploration of the ocean has made underwater image processing an important research field, and plenty of CNN (convolutional neural network)-based underwater image enhancement methods have emerged over time. However, the feature-learning ability of existing CNN-based underwater image enhancement is limited. The networks were designed to be complicated or embed other algorithms for better results, which cannot simultaneously meet the requirements of suitable underwater image enhancement effects and real-time performance. Although the composite backbone network (CBNet) was introduced in underwater image enhancement, we proposed OECBNet (optimal underwater image-enhancing composite backbone network) to obtain a better enhancement effect and shorten the running time. Herein, a comprehensive study of different composite architectures in an underwater image enhancement network was carried out by comparing the number of backbones, connection strategies, pruning strategies for composite backbones, and auxiliary losses. Then, a CBNet with optimal performance was obtained. Finally, cross-sectional research of the obtained network with the state-of-the-art underwater enhancement network was performed. The experiments showed that our optimized composite backbone network achieved better-enhanced images than those of existing CNN-based methods.
Version <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" id="d1e68" altimg="si1.svg"><mml:mrow><mml:mo>[</mml:mo><mml:mn>2</mml:mn><mml:mo>.</mml:mo><mml:mn>0</mml:mn><mml:mo>]</mml:mo></mml:mrow></mml:math>- [AMLBID: An auto-explained Automated Machine Learning tool for Big Industrial Data]

We report a new release of the self-explainable AutoML software AMLBID. The software package is a meta-learning based AutoML decision support system that assists machine learning(ML) experts and neophytes in building well performing ML pipelines and makes the outcomes transparent and interpretable. The release 2.0 of the software introduces an enhanced efficiency of algorithms recommendation. The performance improvement is mainly achieved by the integration of the AeKNN meta-model. This implementation makes datasets meta-features more informative and further improve the accuracy. The release 2.0 also introduces the standalone AMLBIDesc, a desktop version of the AMLBID software which makes the tool more accessible to non-expert users.
EcoEnsemble: A general framework for combining ecosystem models in R

Abstract Often there are several complex ecosystem models available to address a specific question. However, structural differences, systematic discrepancies and uncertainties mean that they typically produce different outputs. Rather than selecting a single ‘best’ model, it is desirable to combine them to give a coherent answer to the question at hand. Many methods of combining ecosystem models assume that one of the models is exactly correct, which is unlikely to be the case. Furthermore, models may not be fitted to the same data, have the same outputs, nor be run for the same time period, making many common methods difficult to implement. In this paper, we use a statistical model to describe the relationship between the ecosystem models, prior beliefs and observations to make coherent predictions of the true state of the ecosystem with robust quantification of uncertainty. We introduce EcoEnsemble , an R package that takes advantage of the statistical model's structure to efficiently fit the ensemble model, either sampling from the posterior distribution or maximising the posterior density. We demonstrate EcoEnsemble by investigating what would happen to four fish species in the North Sea under future management scenarios. Although developed for applications in ecology, EcoEnsemble can be used to combine any group of mechanistic models, for example in climate modelling, epidemiology or biology.
AKAASH: A realizable authentication, key agreement, and secure handover approach for controller-pilot data link communications

Controller-Pilot Data Link Communications (CPDLC) are rapidly replacing voice-based Air Traffic Control (ATC) communications worldwide. Being digital, CPDLC is highly resilient and bandwidth efficient, which makes it the best choice for traffic-congested airports. Although CPDLC initially seems to be a perfect solution for modern-day ATC operations, it suffers from serious security issues. For instance, eavesdropping, spoofing, man-in-the-middle, message replay, impersonation attacks, etc. Cyber attacks on the aviation communication network could be hazardous, leading to fatal aircraft incidents and causing damage to individuals, service providers, and the aviation industry. Therefore, we propose a new security model called AKAASH, enabling several paramount security services, such as efficient and robust mutual authentication, key establishment, and a secure handover approach for the CPDLC-enabled aviation communication network. We implement the approach on hardware to examine the practicality of the proposed approach and verify its computational and communication efficiency and efficacy. We investigate the robustness of AKAASH through formal (proverif) and informal security analysis. The analysis reveals that the AKAASH adheres to the CPDLC standards and can easily integrate into the CPDLC framework.
A multi-label Continual Learning framework to scale deep learning approaches for packaging equipment monitoring

Continual Learning aims to learn from a stream of tasks, being able to remember at the same time both new and old tasks. We propose a scenario that holds immense appeal for various real-world applications, where a model adapts to handle a stream of machines with distribution shifts Tests on real packaging data proved the feasibility of Continual Learning for addressing such problems. Our study uncovers the limitations of previous algorithms in the Domain Incremental Learning. Our research presents a novel approach for tackling multi-label tasks in Continual Learning, achieving superior performance compared to existing approaches found in the literature. Our method not only achieves optimal performance but also has logarithmic complexity, significantly reducing computation times.
Human Action Recognition in Smart Living Services and Applications: Context Awareness, Data Availability, Personalization, and Privacy

Smart living, an increasingly prominent concept, entails incorporating sophisticated technologies in homes and urban environments to elevate the quality of life for citizens. A critical success factor for smart living services and applications, from energy management to healthcare and transportation, is the efficacy of human action recognition (HAR). HAR, rooted in computer vision, seeks to identify human actions and activities using visual data and various sensor modalities. This paper extensively reviews the literature on HAR in smart living services and applications, amalgamating key contributions and challenges while providing insights into future research directions. The review delves into the essential aspects of smart living, the state of the art in HAR, and the potential societal implications of this technology. Moreover, the paper meticulously examines the primary application sectors in smart living that stand to gain from HAR, such as smart homes, smart healthcare, and smart cities. By underscoring the significance of the four dimensions of context awareness, data availability, personalization, and privacy in HAR, this paper offers a comprehensive resource for researchers and practitioners striving to advance smart living services and applications. The methodology for this literature review involved conducting targeted Scopus queries to ensure a comprehensive coverage of relevant publications in the field. Efforts have been made to thoroughly evaluate the existing literature, identify research gaps, and propose future research directions. The comparative advantages of this review lie in its comprehensive coverage of the dimensions essential for smart living services and applications, addressing the limitations of previous reviews and offering valuable insights for researchers and practitioners in the field.
Self-attention in vision transformers performs perceptual grouping, not attention

Recently, a considerable number of studies in computer vision involve deep neural architectures called vision transformers. Visual processing in these models incorporates computational models that are claimed to implement attention mechanisms. Despite an increasing body of work that attempts to understand the role of attention mechanisms in vision transformers, their effect is largely unknown. Here, we asked if the attention mechanisms in vision transformers exhibit similar effects as those known in human visual attention. To answer this question, we revisited the attention formulation in these models and found that despite the name, computationally, these models perform a special class of relaxation labeling with similarity grouping effects. Additionally, whereas modern experimental findings reveal that human visual attention involves both feed-forward and feedback mechanisms, the purely feed-forward architecture of vision transformers suggests that attention in these models cannot have the same effects as those known in humans. To quantify these observations, we evaluated grouping performance in a family of vision transformers. Our results suggest that self-attention modules group figures in the stimuli based on similarity of visual features such as color. Also, in a singleton detection experiment as an instance of salient object detection, we studied if these models exhibit similar effects as those of feed-forward visual salience mechanisms thought to be utilized in human visual attention. We found that generally, the transformer-based attention modules assign more salience either to distractors or the ground, the opposite of both human and computational salience. Together, our study suggests that the mechanisms in vision transformers perform perceptual organization based on feature similarity and not attention.
Optimization of computer programming based on mathematical models of artificial intelligence algorithms

Artificial intelligence (AI) technology is a remarkable combination of computer science, biotechnology, and other disciplines. However, due to the late start of research on AI algorithms, the lack of practical experience, and an incomplete theoretical system, there are still several issues that need to be solved and improved upon further. This article uses the mathematical model of AI algorithms to optimize computer programing. The importance and practical role of mathematical model-optimization methods and mathematical algorithms based on AI algorithms in computer programing optimization are highlighted. The results show that optimizing computer programing based on mathematical models of AI algorithms can effectively improve the organization and hierarchy of code programing, thus improving the programing efficiency of the code. The application of mathematical models of AI algorithms improved the quality of computer programing, computer programing was optimized, and the utilization of programing space was increased by 5.75%.
A deep attention LSTM embedded aggregation network for multiple histopathological images

Recent advancements in computer vision and neural networks have facilitated the medical imaging survival analysis for various medical applications. However, challenges arise when patients have multiple images from multiple lesions, as current deep learning methods provide multiple survival predictions for each patient, complicating result interpretation. To address this issue, we developed a deep learning survival model that can provide accurate predictions at the patient level. We propose a deep attention long short-term memory embedded aggregation network (DALAN) for histopathology images, designed to simultaneously perform feature extraction and aggregation of lesion images. This design enables the model to efficiently learn imaging features from lesions and aggregate lesion-level information to the patient level. DALAN comprises a weight-shared CNN, attention layers, and LSTM layers. The attention layer calculates the significance of each lesion image, while the LSTM layer combines the weighted information to produce an all-encompassing representation of the patient's lesion data. Our proposed method performed better on both simulated and real data than other competing methods in terms of prediction accuracy. We evaluated DALAN against several naive aggregation methods on simulated and real datasets. Our results showed that DALAN outperformed the competing methods in terms of c-index on the MNIST and Cancer dataset simulations. On the real TCGA dataset, DALAN also achieved a higher c-index of 0.803±0.006 compared to the naive methods and the competing models. Our DALAN effectively aggregates multiple histopathology images, demonstrating a comprehensive survival model using attention and LSTM mechanisms.
The Art of Phenomena Construction: A Framework for Coming Up with Research Phenomena beyond ‘the Usual Suspects’

Abstract Despite the centrality of research phenomena, the process of their definition is often neglected and reduced to a simple choosing of pre‐established subjects of interest. However, good research not only includes empirical work aimed at more or less ‘given as fact’ phenomena. It also involves phenomena construction: that is, the process of generating and establishing phenomena to investigate and theorize. We contend that phenomena construction is not separate from, but integral to, both the empirical and theorizing phases in research. As few phenomena are truly ‘given’ or straightforward to observe, good research calls for careful and creative construction of the phenomenon under investigation. We propose and elaborate a framework that enables researchers to generate and establish research phenomena beyond those currently available in their specific area of interest and, based on this, to produce more imaginative and impactful research.
Enhancing Pre-Service Teachers' Technological Pedagogical Content Knowledge (TPACK) through the Learning by Design Framework: A Fink Taxonomy-based Study

This study aimed to develop Technological Pedagogical Content Knowledge (TPACK) through the use of the Learning by Design (LBD) framework in a teaching process for pre-service teachers. During a 12-week period that included both teaching and data collection, 19 pre-service teachers produced 10 unique instructional materials. TPACK levels and self-confidence were assessed using scales and qualitative data from interviews, with the Fink Taxonomy used to analyze the interviews and determine the significance of the LBD-TPACK teaching process in terms of learning outcomes. The results showed a significant increase in pre-service teachers’ TPACK scores and TPACK self-confidence scores after the teaching process (α=0.05, p=0.00). Qualitative data supported these findings, demonstrating that pre-service teachers had achieved significant learning outcomes by the end of the process. It is recommended that conducting comparable research across diverse teaching fields and larger sample sizes would lead to more robust and generalizable findings.