Reviewerly/exHarmony huggingface dataset:
Path Planning Optimization of Automated Guided Vehicles using Chaotic Marine Predators Algorithm

This paper presents the collision-free path planning approach for automated guided vehicle (AGV) in an intelligent warehouse environment, optimized by means of recent well-known meta-heuristic algorithms. This novel approach is an assessment and possibilities for path planning and scheduling of the multi-AGVs to complete the given tasks in a minimal travel distance with optimal operation time. Six different metaheuristic algorithms such as PSO, MELGWO, GTO, SFS, MPA and chaotic-improved MPA are compared for the AGVs path optimization capability. In order to test the robustness of the proposed approaches, four different scenarios are presented which include a general obstacle avoidance and three tasks in simple maps that treated as an intelligent warehouse environment. In each scenario, the obstacles are placed in such a way to increase the overall path complexity for an AGV to reach the target destination. The exploration and exploitation phases in the MPA algorithm are enhanced simultaneously by replacing the conventional Gaussian random with chaotic operators to ensure its effectiveness in AGVs path planning optimization. The chaotic MPA algorithm outperforms other algorithms based on the statistical analysis results with overall improvement of 11.0171% in comparison to unoptimized probabilistic roadmap method (PRM) planner. In conclusion, the chaotic MPA algorithm can be efficiently optimized the AGVs path planning in all aforementioned environments.
Application of supervised learning classification modeling for predicting benthic sediment toxicity in the southern California bight: A test of concept

Benthic sediment toxicity is linked to harmful effects in marine organisms and humans, and an understanding of the link would require, in part, a comprehensive and exhaustive analysis of sediment toxicity data already in hand. One tool which could aid in the process is machine learning (ML), a supervised classification modeling technique that has transformed how actionable insight are acquired from large datasets. The current study is a test of concept in which an ML classifier is sought that can accurately extrapolate the characteristics of a 5437 California-wide coastal training dataset (assembled from 1635 samples) to predict sediment toxicity in southern California bight (SCB). Twelve classifiers were trained to recognize sediment toxicity using 70 % of the dataset and among them, a Gradient Boosting Classifier (GBC) model using latitude, longitude, and water depth was found to be the most accurate at predicting toxicity (83 %). Among the variables, latitude was found to be the most significant driver of prediction by GBC in this test ecosystem. The performance of the model was verified with the remaining 30 % of the dataset and found to be 83 % accurate. Presented with 884 unfamiliar data points assembled from 854 measurements at 346 stations across SCB, GBC was 87 % accurate post-training, thus demonstrating a role supervised learning can play in the southern California environmental analytics.
Low-light image enhancement by two-stream contrastive learning in both spatial and frequency domains

Images taken under low light conditions tend to suffer from poor visibility, which can decrease image quality and even reduce the performance of downstream tasks. It is hard for a CNN-based method to learn generalized features that can recover normal images from the ones under various unknown low light conditions. We propose to incorporate the contrastive learning into an illumination correction network to learn abstract representations to distinguish various low light conditions in the representation space, with the purpose of enhancing the generalizability of the network. Considering that light conditions can change the frequency components of the images, the representations are learned and compared in both spatial and frequency domains to make full advantage of the contrastive learning. Additionally, a grayscale self-weight perception method is used to preproccess the images to reduce the complexity of the model in coping with the uneven distribution of image illumination. The proposed method is evaluated on LOL and LOL-V2 datasets, and the results show that the proposed method achieves better qualitative and quantitative results compared with other state-of-the-art methods.
Enhancing Engagement and Learning Outcomes in Architectural Computing Design Education: A Study on the Implementation of Augmented Reality

Abstract Augmented reality (AR) technology is being increasingly used in the fields of architecture and design to provide new and engaging ways to visualize and communicate design concepts. Since architectural models are the simplest approach to emphasize a design concept, the tools, materials, and technology used to construct physical models in architecture and design education may vary. This paper describes the implementation of AR technology in an architectural computing design course. The purpose of this study is to ascertain an efficient method of model-making for architectural education through the use of AR. The study involved the development of an AR application that allowed students to visualize their design concepts in a virtual environment. The paper presents the design of the application, the methods used to integrate AR technology into the course curriculum, and the results of a survey conducted among students to evaluate their experience with the technology. Architecture students’ experiences using AR technology in place of physically constructed physical models for design projects are discussed in this study as well. The study concluded that the use of AR technology in the course had a positive impact on student engagement, learning outcomes, and overall satisfaction.
MTAFinder - Unified OSINT platform for efficient data gathering

Due to the digital era and rapid advancements in technology, an enormous amount of publicly available information is generated. Open Source Intelligence (OSINT) is a term used to describe the search, collection, analysis and use of information from open sources. OSINT has emerged as a valuable tool in cybersecurity, transitioning from its basic usage in social engineering to becoming a powerful tool in red teaming exercises and seamlessly integrating into the fabric of digital forensics. OSINT environment is constantly evolving with the emergence of new use cases and complex tools. This paper presents MTAFinder, an application for automated data collection that integrates OSINT open source tools and free online services. The platform is modular, scalable and provides a web API designed to gather data about people, phone numbers, web domains, e-mail addresses and IP addresses.
Use of Yolo Algorithm for Traffic Sign Detection in Autonomous Vehicles and Improvement Using Data Replication Methods

Autonomous vehicles use many technologies and methods to detect and act on surrounding objects. The most common among these technologies is an algorithm called YOLO (You Only Look Once). This algorithm quickly detects objects in an image and classifies these objects accurately. This study examines the use of the YOLO algorithm for signage detection in autonomous vehicles and how this algorithm can be improved. First of all, the basic principles and working mechanisms of the YOLO algorithm are explained. Then, it is explained in detail how this algorithm can be used for plate detection in autonomous vehicles. Various models were trained using the YOLO algorithm and the data set created with real data, and the trained models were tested on real-time systems. Finally, suggestions for the improvement of the YOLO algorithm are presented and how this algorithm can be improved further in the future is discussed.
On Sub-Gaussian Concentration of Missing Mass

The statistical inference on missing mass aims to estimate the weight of elements not observed during sampling. Since the pioneer work of Good and Turing, the problem has been studied in many areas, including statistical linguistics, ecology, and machine learning. Proving the sub-Gaussian behavior of the missing mass has been notoriously hard, and a number of complicated arguments have been proposed: logarithmic Sobolev inequalities, thermodynamic approaches, and information-theoretic transportation methods. Prior works have argued that the difficulty is inherent, and classical tools are inadequate. We show that this common belief is false, and all that we need to establish the sub-Gaussian concentration is the classical inequality of Bernstein. The strong educational value of our work is in its demonstration of this inequality in its full generality, an aspect not well recognized by researchers.
Scalable Web-based Application for Dynamic Multi-Tenant Hotel Reservation Management

Conducting the work to set up a dynamic hotel reservation management website requires not only technical skills but also a modern information systems approach and various processing and dissemination tools. The major software companies are targeting their offers mainly to classic hotel management. One market underserved by these companies is the bed and breakfast community. Our application is targeting those small companies or even individuals offering a geographically dispersed, multi-tenant management platform. Our paper presents a hosted architecture that is designed having as first requirement a highly scalable application. The application has to handle an important load represented by the volume of user traffic, from here resulting the technical decisions taken.
Transfer Learning and Dual Attention Network Based Nuclei Segmentation in Head and Neck Digital Cancer Histology Images

Histology analysis is currently a gold standard in analyzing cancer. Nuclei segmentation is vital in histopathology analysis. However, it is challenging due to limited data and extreme conditions. With the advent of transfer learning methods, the solution to this problem is possible. We propose a transfer learning-based approach for segmenting the nuclei in Head and Neck (H&N) cancer histology images. The suggested technique comprises two stages. In the first stage, we train our previously proposed architecture, DAN-Nuc Net, on generic histology data to achieve generic nuclei segmentation in histology. We use the PanNuke dataset, which has over 8000 histology images, to train DAN-Nuc Net. In the second stage, we use transfer learning techniques to optimize our network for two types of histology mages, i.e., Hematoxylin and Eosin (H&E), and P63 independently. Selected deep layers of the pre-trained DAN-Nuc Net are frozen. Then the model is re-trained on the new datasets. Compared to the state-of-the-art, our method has shown superior performance in DSC and JI (0.8702 and 0.7596).
Analysis, Combination and Integration of Neuroevolution and Backpropagation Algorithms for Gaming Environment

This paper provides a method of combining Neu-roevolution with Backpropagation to achieve lower training times than Neuroevolution when training agents in a video game environment. The combination of these algorithms is reproduced by an alteration of the step of creating a new generation from the most capable agents with the creation of a new generation through the Backpropagation method using the preventively saved data of the most capable agent from the environment. Thus, for the new generation, a Neural Network trained with backpropagation is assigned instead of the best-performing Neural Network from the previous generation. As a result, the Neuroevolution with the Backpropagation method shows better performance when increasing the target of the environmental performance of the agent.
Study on Omnidirectional Cooperative Trasnport System Using Multiple Dual-wheeled Mobile Robots with Active-Caster Control

In this paper, we propose a omnidirectional transport system with multiple dual-wheeled mobile robots and a LiDAR-based robot self-orientation estimation method. The proposed transport system applies active caster control to the dual-wheeled mobile robots, enabling them to move the transport object in all directions. The orientation of the robot with respect to the object is very important in control of the robot used in this system. To estimate the orientation of the robot immediately, we propose a method to measure inner substructure of the object using LiDAR and estimate the orientation from a point cloud. In proposed method, the orientation is estimated from vertex location information obtained from the point cloud, and most of the points in the point cloud are not directly used for orientation estimation. Although this method cannot perform localization and mapping like SLAM, it is effective than SLAM in terms of computation time in estimating an orientation of an object in an invariant environment and position in the environment, as in our transport system. Through experiments, we confirmed that this estimation method works effectively in the proposed conveyance system.
Overview of System-Level Security Technologies based on ARM TrustZone

With the rapid development of embedded technology and the increasing complexity of system functionality, there is a growing need for a trusted computing environment to ensure the security, integrity, and reliability of sensitive information. Systems not only need to protect the security of sensitive application code but also ensure the isolation of their execution process to prevent attacks and data theft. Traditional system protection is achieved by using security mechanisms that run in the same address space and privilege level as the kernel. However, this approach is not sufficiently secure as attackers who compromise the kernel can also compromise these security mechanisms. To achieve true kernel and critical data protection, security mechanisms need to be isolated. Therefore, building a trusted isolation runtime environment in the system is crucial for system security. TrustZone technology, developed by ARM, is a system-level security isolation framework capable of defending against various potential attacks. this paper provides an overall overview of different security isolation technologies. By concentrating on the principles and characteristics of ARM TrustZone, the paper conducts an in-depth analysis of system security isolation technology based on TrustZone. Finally, considering the existing security issues in the field of trusted execution environments, the paper presents prospects for the future development of this technology.
Weighted Fuzzy Markov Chains for Regions Research on Rainfall Forecasting

Abstract The problem of rainfall prediction is of great importance for the study of the hydrological cycle of a watershed or region in China. Since rainfall series are not periodic as well as linear, rainfall series are measured based on Markov’s theorem, and the percentage of precipitation distance is used to classify the historical data. The traditional Markov chain was optimized and a weighted-fuzzy Markov rainfall prediction model was established. The results show that the average absolute error of the two prediction samples is 2.09%, except for 2019, when the rainfall in Guangzhou City is different from the past, and the model has high accuracy, which has good practical significance and extension value.
STAD-FEBTE, a shallow and supervised framework for time series anomaly detection by automatic feature engineering, balancing, and tree-based ensembles: An industrial case study

Modern industrial systems are equipped with multi-sensor units, and building anomaly detection modules to monitor their collected data has become a vital task. Missing such abnormal patterns may cause producing faulty products, unwanted shutdowns in the production line, or even catastrophic damages. Sensor measurements of different natures with different sampling frequencies build a multivariate heterogeneous time series data. Conventional machine learning models fail to capture the temporal characteristics of such data. Deep learning models can address this thanks to their internal network architecture, yet training such models requires large datasets with adequate samples from all anomaly classes. This is not the case in real-world problems where class imbalance is a major issue. Tree-based ensembles are reported to have the dominant performance when dealing with structured tabular data. Inspired by this, we propose a supervised framework that combines an automatic feature engineering pipeline converting the time series dataset into its tabular counterpart with tree-based ensembles. The suggested method tackles class imbalance by generating synthetic anomalies using balancing techniques. Moreover, it allows handling heterogeneous multivariate data and augmenting categorical features with sensor measurements. Two real-world industrial datasets of relatively small size from robotized screwing processes are benchmarked, showing better results for the suggested framework compared to commonly used deep learning architectures.
A Lightweight Dual-Branch Image Dehazing Network based on Associative Learning

Haze degrades the clarity, contrast, and details of images, resulting in a decrease in image quality. Image dehazing provides a means to obtain clearer and more accurate image information. Traditional methods for haze removal typically rely on manually designed features and models, limiting their performance in complex scenes. In recent years, the rapid advancement of deep learning has offered new insights into addressing the image dehazing problem. This paper proposes a lightweight dual-branch image dehazing network based on associative learning (LDANet). The network consists of a lightweight dehazing sub-network (LDSN) and a lightweight image enhancement subnetwork (LESN). To reduce computational and parameter complexity, the Tied Block Convolution (TBC) is employed, allowing for parameter sharing among components. Lastly, through associative learning, their distinctive features are mapped. Extensive experiments on synthetic and real-world datasets demonstrate the superiority of our approach in qualitative comparisons and quantitative evaluations compared to other state-of-the-art methods. Our method holds significant practical value for real-world image dehazing scenarios.
ConstraintMatch for Semi-constrained Clustering

Constrained clustering allows the training of classi-fication models using pairwise constraints only, which are weak and relatively easy to mine, while still yielding full-supervision-level model performance. While they perform well even in the absence of the true underlying class labels, constrained clustering models still require large amounts of binary constraint annotations for training. In this paper, we propose a semi-supervised context whereby a large amount of unconstrained data is available alongside a smaller set of constraints, and propose ConstraintMatch to leverage such unconstrained data. While a great deal of progress has been made in semi-supervised learning using full labels, there are a number of challenges that prevent a naive application of the resulting methods in the constraint-based label setting. Therefore, we reason about and analyze these challenges, specifically 1) proposing a pseudo-constraining mechanism to overcome the confirmation bias, a major weakness of pseudo-labeling, 2) developing new methods for pseudo-labeling towards the selection of informative unconstrained samples, 3) showing that this also allows the use of pairwise loss functions for the initial and auxiliary losses which facilitates semi-constrained model training. In extensive experiments, we demonstrate the effectiveness of ConstraintMatch over relevant baselines in both the regular clustering and overclustering scenarios on five challenging benchmarks and provide analyses of its several components.
Encrypted Coordinate Transformation via Parallelized Somewhat Homomorphic Encryption for Robotic Teleoperation

This paper seeks to understand the viability of encrypted robot control. Controllers are susceptible to malicious attacks unless controller parameters are encrypted; however, homomorphic encryption is necessary in order to allow controller mathematical operations on encrypted text, but is limited due to heavy computational overhead. Encrypted control is accomplished via the implementation of Dyer’s somewhat homomorphic encryption scheme on multi and single threaded matrix transformations in order to telecommunicate movement commands between a virtual-reality joystick and a robot arm. Results find that encrypted teleoperation via the user interface is a viable encrypted controller technique, and is optimally produced on multi-threaded systems.
GEMM: A Graph Embedded Model for Memorability Prediction

The focus of this study is to predict human memorability - a person's ability to remember previously seen images or objects. Although recent works have employed deep learning-based approaches to address the problem, they do not utilize spatial structural information within the images. This work investigates Graph Convolutional Networks (GCNs) and Graph Attention Networks (GATs) to approach the problem. The object-centric features within the images are extracted using deep CNN-based models, which contain the structural information of the image. A generic baseline model is created and improved upon iteratively through structural data by constructing graphs and attention mechanisms on the graph edge connections. The constructed graph nodes represent the objects within the image, and the edge connections between the nodes represent the spatial relation to the objects. These graph embeddings are used to train our proposed Graph Embedded Memorability Model (GEMM), which shows significant improvements from the baseline as the attention improves the edge connections of the graph nodes. The model is then evaluated on the LaMem, SUN memorability, and FIGRIM datasets. Although existing state-of-the-art models perform well on one or two datasets, the proposed model generalizes over all three datasets with a Spearman's rank correlation of 0.71 on LaMem, 0.69 on SUN memorability, and 0.59 on the FIGRIM dataset. This model achieves a new state-of-the-art performance compared to the existing literature.
Visually Analysing the Fairness of Clustered Federated Learning with Non-IID Data

As a popular distributed privacy-preserving machine learning approach, federated learning (FL) trains a global model across numerous clients without directly sharing their private training data. A fundamental challenge of FL is non-independently and identically distributed (non-IID) data, which can be addressed by clustered FL (CFL) that explores inherent partitions among clients to capture heterogeneous local data distributions. Existing CFL methods, however, tend to optimise the mean accuracy across all clients, overlooking the important issue of fairness, that is, uniformity of clients' performance. Besides, prior FL- related visual analytics (VA) frameworks are developed based on the standard FL scenario of single global model and are hence inapplicable to the CFL case of multiple global (cluster) models. To fill the research gap, this study proposes a novel VA framework named cCFLvis to illustrate and analyse variation of fairness during the CFL learning process. A new metric, change of absolute deviation from the mean (ΔAD), is introduced to quantify such variation. Client embeddings are generated by contrastive learning-based dimensionality reduction techniques to facilitate 2-d visualisation of client disparity. Visual cues can be drawn to help understand fairness variation and to offer clues for improving learning outcomes. cCFL vis is a general framework applicable to various CFL methods. Its effectiveness is showcased in a simple demonstration and two case studies, each conducted with a distinct representative CFL method and a different federated non-IID dataset.