Reviewerly/exHarmony huggingface dataset:
Phishing Attack Detection: An Improved Performance Through Ensemble Learning

Phishing is a cybercrime that deceives online users and steals their confidential information by impersonating a legitimate website or URL. Several machine learning-based strategies have been proposed to detect phishing websites. These techniques utilize a set of features extracted from website samples, the structure and syntax of URLs, the content of the pages, and querying external resources. In this work, we use a dataset of 11,430 samples with 87 extracted features that are designed to be used as a benchmark for machine learning-based phishing detection systems. Our classification is based on an ensemble learning technique, which is further optimized using grid search. The experiments provide a detailed description of tuning the model’s hyperparameters and its optimization. We evaluate the model using well-known evaluation metrics of accuracy, precision, recall, F1-score, and area under the ROC curve. The findings indicate that our optimized ensemble model classifies legitimate and phishing URLs with an accuracy of 95.36%, a precision of 96.29%, a recall of 94.24%, an F1 score of 95.26%, and the area under the ROC curve of 0.9876.
Towards Industry 5.0: Empowering SMEs with Blockchain-Based Supplier Collaboration Network

Industry 5.0 integrates human-centered production systems with cyber-physical systems, the Internet of Things (IoT), and machine learning technologies to create flexible, efficient, and environmentally friendly manufacturing processes. Small and medium-sized enterprises (SMEs), despite their importance in the manufacturing sector, typically need more resources and skills to implement Industry 5.0 technology. This article presents a blockchain-based supplier collaboration network to support small and medium-sized enterprises migrating to Industry 5.0. Small suppliers, manufacturers, and other supply chain stakeholders can engage securely and transparently within the proposed framework. The underlying blockchain technology allows for secure, immutable storage and real-time data sharing and collaboration. The proposed approach uses smart contracts to automate cooperation procedures and integrates blockchain with emerging technologies like IoT and machine intelligence. Moreover, the underlying method can boost SMEs businesses’ competitiveness and market access. The proposed approach will therefore be of benefit to companies that have limited resources to build their own supply chain management systems. Finally, we discuss the challenges and limitations of the proposed system for implementing a blockchain-enabled supplier collaboration network for SMEs in Industry 5.0.
Improved Barnacles Movement Optimizer (IBMO) Algorithm for Engineering Design Problems

A better understanding of natural behavior modeling in mathematical systems has enabled a new class of stochastic optimization algorithms that can estimate optimal solutions using reasonable computational resources for problems where exact algorithms show poor performance. The position up-dating mechanism in various optimization algorithms utilizes similar chaotic random behavior which impedes the performance of the search for a globally optimum solution in monotonic nonlinear search space. In this work, an approach is proposed that tackles these issues on an already established algorithm; Improved Barnacle Mating Optimization (IBMO) Algorithm, inspired by the movement and mating of Gooseneck Barnacles. The algorithm introduces the mimicry of the movement and mating behavior in nature to model an optimization process. Several benchmark functions are employed to gauge the performance of the proposed optimization technique. Results are compared with several meta-heuristics and conventional optimization algorithms. It is observed that the IBMO algorithm performs generally better and provides a huge potential for solving real-world problems.
A New Method of Verification of Dynamic Signatures Changing over Time with Decomposition and Selection of Characteristic Descriptors

Dynamic signature verification (DSV) has practical applications in banking, commerce, accounting, medicine, law, etc. DSV is important in comparison to the verification of other biometric attributes because the dynamic signature is one of the most widespread and commonly used biometric features. DSV methods are being systematically developed and improved. At the same time, expectations regarding these methods are changing. For example, they are expected to take into account changes of the individual’s signature appearing over time. In this paper, we propose a new DSV method that uses partitioning supported by partitions’ selection and characterized by the use of signatures created in different acquisition sessions in the training phase. This algorithm has been tested using a publically available, known xLongSignDB, dynamic signature database. In the simulations very good results were obtained.
Challenges in Designing and Implementing Augmented Reality-Based Decision Support Systems for Intralogistics: A Multiple Case Study

Numerous Augmented Reality-based prototypes and proof of concepts of assistance systems have been developed for industrial application scenarios that have not yet reached operational use. Alongside remaining technical challenges, the human-centered development of the systems and the associated improvement of the human-machine interfaces are seen as critical challenges for successful development and introduction. Therefore, this contribution addresses the challenges of designing and implementing augmented reality-based decision support systems for intralogistics work processes focusing on system users. The authors compared the qualitative results of a multiple case study conducted in ten intralogistics companies with challenges for industrial AR systems based on a literature review and the resulting focus areas regarding a human-centered design process. The view of the experts from the companies confirms the literature-based challenges on many points. However, some aspects pose particular challenges in intralogistics, such as the high-efficiency requirements for work processes. Overall, the results underscore the importance of involving operators in system development and highlight factors like usability, workload, and user acceptance, which can be evaluated through user research methods.
Examining Effects of Class Imbalance on Conditional GAN Training

In this work, we investigate the impact of class imbalance on the accuracy and diversity of synthetic samples generated by conditional generative adversarial networks (CGAN) models. Though many studies utilizing GANs have seen extraordinary success in producing realistic image samples, these studies generally assume the use of well-processed and balanced benchmark image datasets, including MNIST and CIFAR-10. However, well-balanced data is uncommon in real world applications such as detecting fraud, diagnosing diabetes, and predicting solar flares. It is well known that when class labels are not distributed uniformly, the predictive ability of classification algorithms suffers significantly, a phenomenon known as the “class-imbalance problem.” We show that the imbalance in the training set can also impact sample generation of CGAN models. We utilize the well known MNIST datasets, controlling the imbalance ratio of certain classes within the data through sampling. We are able to show that both the quality and diversity of generated samples suffer in the presence of class imbalances and propose a novel framework named Two-stage CGAN to produce high-quality synthetic samples in such cases. Our results indicate that the proposed framework provides a significant improvement over typical oversampling and undersampling techniques utilized for class imbalance remediation.
Human-Centric Industrial Augmented Reality: Requirements and Design Guidelines for Usability

Industrial Augmented Reality (IAR) gains more traction as a means of improving and enabling human-centric manufacturing operations. IAR does not replace the human operator but rather it promotes a human-centric work system design that empowers users to perform their tasks more easily and effectively. While usability of IAR solutions improved over the years, it remains an issue preventing wider adoption. Therefore, this paper explores the current usability concerns encountered by industry and translates these into a design framework that specifies common requirements and design guidelines. The purpose of this framework is to extend the current understanding of usability in the context of IAR, helping software engineers in designing more usable, intuitive and easily accepted systems. Relevant literature and standards were reviewed to derive preliminary usability requirements. Subsequently, an IAR system prototype was created and validated via design science research at a global shipyard operating company. The study identified key design concerns of usable IAR systems, and context-specific ones, such as point selection precision and rendering performance.
A New Hybrid Particle Swarm Optimization and Evolutionary Algorithm with Self-Adaptation Mechanism

Particle Swarm Optimization (PSO) has demonstrated remarkable convergence capabilities in various optimization problems. It is increasingly employed in conjunction with gradient-based methods to enhance the learning process of deep neural networks. However, PSO suffers from a significant drawback, namely the propensity to become trapped in local minima, particularly in challenging multi-modal problems. To address this issue, this study introduces a novel Hybrid Particle Swarm Optimization and Genetic Algorithm with a Self-Adaptation mechanism. Self-adaptation mechanism enables the hybrid algorithm to dynamically adjust its behavior according to the current state of the global best solution search. Initial simulations conducted in this research validate the effectiveness of the proposed approach.
Learning Activation Functions for Adversarial Attack Resilience in CNNs

Adversarial attacks on convolutional neural networks (CNNs) have been a serious concern in recent years, as they can cause CNNs to produce inaccurate predictions. Through our analysis of training CNNs with adversarial examples, we discovered that this was primarily caused by naïvely selecting ReLU as the default choice for activation functions. In contrast to the focus of recent works on proposing adversarial training methods, we study the feasibility of an innovative alternative: learning novel activation functions to make CNNs more resilient to adversarial attacks. In this paper, we propose a search framework that combines simulated annealing and late acceptance hill-climbing to find activation functions that are more robust against adversarial attacks in CNN architectures. The proposed search method has superior search convergence compared to commonly used baselines. The proposed method improves the resilience to adversarial attacks by achieving up to 17.1%, 22.8%, and 16.6% higher accuracy against BIM, FGSM, and PGD attacks, respectively, over ResNet-18 trained on the CIFAR-10 dataset.
Machine Learning Assisted Interactive Multi-objectives Optimization Framework: A Proposed Formulation and Method for Overtime Planning in Software Development Projects

Software development project requires proper planning to mitigate risk and uncertainty. Overtime planning within software project management has been receiving attention recently from search-based software engineering researchers. Multi-objective evolutionary algorithms are used to build automated tools that could effectively help Project Managers (PM) plan overtime on project schedules. Existing models however lack applicability by the PMs due to their disregard for expert knowledge in planning overtime. This study proposes a new interactive problem formulation for software overtime planning and presents a framework for building a machine learning-based interactive multi-objective optimization algorithm for overtime planning in software development projects. The framework is designed to train a priori a machine learning model to mimic the PM’s subjective judgment of overtime plans within the project schedule. The machine learning model is integrated with a memetic multi-objective optimization algorithm via an interactive module. Also, the memetic algorithm incorporates a preference-based w-dominance method for selecting non-dominated solutions. The proposed framework will be developed to assist software project managers to better plan overtime in order to prevent the expected risk of software development overrun.
Support Learning Vovinam Exercises Based on Computer Vision

Computer vision has many applications which has attracted many researchers, especially with the problems of recognizing actions, postures, movements. This study offers a method to support students to perform correct postures during martial arts practice. We have collected and labeled data the movements of a traditional Vietnamese martial art called Vovinam. The deep learning architecture is based on the ST-GCN model with an additional processing stage for the input. On our dataset, adding the input processing stage to the recognition model has yielded much better results than applying the original model. The final accuracy is 99.23%, showing that the model has the potential to be applied in practice.
Federated Learning for Human Activity Recognition on the MHealth Dataset

Human activity recognition (HAR) tasks are essential for developing context-aware features due to their capacity to generate higher-level contexts and personalisable services. The traditional centralised training approach requires clients to share their data with a server that trains the HAR algorithms. This data transfer raises privacy concerns on the clients’ side regarding data leaks that can be used to obtain personal data. This research work studies Federated Learning (FL) as a privacy-preserving alternative to developing HAR algorithms. The centralised and FL approaches are implemented to train a Multi-Layer Perceptron neural network using three different activation functions (Tanh, ReLU and Sigmoid). The experiments use the MHealth dataset to train and test the HAR algorithms. The results are analysed to compare the performance of the centralised and FL approaches, the activation functions outcomes, and the communication rounds needed between the server and the clients to reach competitive results for the FL models. The outcomes are positive and encourage further work in using of FL for HAR.
Application of Object Detection Models for the Detection of Kitchen Furniture - A Comparison

Object detection is being applied in an increasing number of areas. In this paper, the authors investigate the application of object detection in a use case for the kitchen industry. The main goal of the use case is to extract information from kitchen scenes that can be used for kitchen planning. The use case is located in a medium-sized company that has little experience with the application of deep learning models. Therefore, this paper proposes a methodology that ensures fast and reliable testing of different object detection models to identify a suitable model for the given use case. In the first step, a dataset with kitchen images is built. Further, augmentation methods are applied to the dataset, to increase the amount and variety of the data. For object detection, there is a variety of models that are freely available, and the question of which model is best for the use case cannot be answered easily. A selection of models (Faster R-CNN, SSD, and EfficentDet) from the TensorFlow Object Detection API will therefore be tested on the image dataset created. The achieved mean average precision (mAP) of the trained models will be used as a metric to determine the best model for the use case. The purpose of this work is to provide an approximate solution, that proves that object detection and the methodology work for the use case.
Expansion Rate Parametrization and K-Fold Based Inference with U-Net Neural Networks for Multiclass Medical Image Segmentation

In this paper, authors seek a way to improve the performance of state-of-art U-Net models in the context of multiclass segmentation of Human Gastro-Intestinal (GI) Magnetic Resonance Imaging (MRI) by introducing the expansion rate parameter R, which regulates the sizes of layers and allows an increase in the depth of the network while still meeting the model’s size limitations and giving better control over model’s size. For the inference, a method has been used based on k-fold cross-validation which combines an output of each of the k-models. By combining changes in expansion rate, model depth, and using a proposed inference method, it was possible to boost performance by 6% with larger models and reach comparable results with 2x smaller models.
An Improvement of Graph Neural Network for Multi-behavior Recommendation

One of the most extensively adapted paradigms for building a recommender system is embedding users and items into a low-dimensional latent space based on their interactions. While traditional Collaborative Filtering is designed for only one type of user-item action, real-world scenarios observe multiple activities of a user such as browsing and favorites, which can serve as an effective enhancement to the method. In addition, early efforts towards multi-behavior recommendation have two main limitations: first, they fail to calculate the influence strength of users’ behaviors on target behavior; second, they also ignore that behavior semantics. Therefore, taking advantage of Graph Neural Network, this work further improves the graph-based recommender system by adding residual blocks and a behavior-learnable weight for each user. The effectiveness of our model is confirmed by empirical results of a real-world e-commerce dataset. Our model outperforms the baseline in both Recall@k and NDGC@k statistics.
A Novel ConvMixer Transformer Based Architecture for Violent Behavior Detection

Nowadays most of the streets, squares and buildings are monitored by a large number of surveillance cameras. Nevertheless, these cameras are used only to record scenes to be analyzed after crimes or thefts, and not to prevent violent actions in an automatic way. In few cases there may be a guard who checks the videos manually in real-time, but it is a very inefficient and expensive process. In this paper we proposes a novel approach to Violence Detection task using a recent architecture named ConvMixer, a simple CNN which uses patch-based embeddings in order to obtain superior performance with fewer parameters and computation resources. We also use an interesting technique that consists in arranging frames into super images to encode the temporal information into the spatial dimensions. Our tests on popular “Real Life Violence Situations” dataset highlight a remarkable accuracy of 0.95, placing our proposed model at the second position of the leader board on the same dataset.
A Distributed Ledger Technology Solution for Connecting E-mobility Partners

The work is focused on the e-mobility industry and proposes an innovative solution for handling roaming connections in the context of electric vehicles charging. We address the privacy and performance issues of the roaming hub approach for enabling roaming by designing a peer-to-peer platform for connecting e-mobility partners. We attempt to achieve interoperability by using the current industry standards as a foundation to our approach. The idea behind this solution is to record and verify transactions between the charging point operators (CPOs) and the e-mobility service providers (EMSPs) on a permissioned distributed ledger to increase the trust between the two parties. The agreements between the partners are signed and enforced via smart contracts.
Generating Image Captions in Polish Using Transformer Architecture

This paper presents an attention-based method for image captioning. Existing deep-learning methods mainly use recurrent neural networks for producing captions. We present Transformer based image captioning method dubbed CaptFormer. Our method use convolutional neural network for image feature extraction and Transformer for generating image captions. CaptFormer is meant for generating captions in a morphologically rich fusional language, such as Polish. For evaluation of our method, we created a custom multilingual dataset. Our multilingual dataset contains English and machine-translated Polish captions. Images and English captions in our dataset comes from Flick 30K and MS COCO.
Multi-population Algorithm Using Surrogate Models and Different Training Plans

Population-based algorithms (PBAs) are effective metaheuristic methods. Their important feature is their ability to define a complex objective function depending on a given problem need not be differentiable. This allows PBAs to be used to solve continuous and discrete problems for which it is difficult to use gradient algorithms. Another advantage of PBAs is the ability to search the problem domain using a population of individuals. Therefore, an important issue regarding PBAs is to reduce their complexity and increase their efficiency. In this article, we propose a new PBA that uses both surrogate and temporary subpopulation models to (a) reduce the number of evaluation function calls and (b) increase the efficiency of finding a solution. The underlying algorithm for the application of these mechanisms is the Particle Swarm Optimization (PSO) algorithm. For the initial testing of the proposed method, we used well known test functions. We consider the obtained results to be good.
Metaverse and Non-fungible Tokens—A Comparative Study

The term Metaverse refers to a unified network of virtual worlds enabling multiple users to connect, interact, play games and buy stuffs. In general, the Metaverse is an extensive concept wherein Non-Fungible Tokens may serve an essential concept in the broad ecosystem of Metaverse. In order to comprehensively understand the concepts of Metaverse and NFT’s, it is important to be versed with a few other concepts as well. Before proceeding further with the chapter, few a conceptual and contextual introductions are provided below for better understanding of the Metaverse ecosystem.
Towards Deep Anomaly Detection with Structured Knowledge Representations

Machine learning models tend to only make reliable predictions for inputs that are similar to the training data. Consequentially, anomaly detection, which can be used to detect unusual inputs, is critical for ensuring the safety of machine learning agents operating in open environments. In this work, we identify and discuss several limitations of current anomaly detection methods, such as their weak performance on tasks that require abstract reasoning, the inability to integrate background knowledge, and the opaqueness that undermines their trustworthiness in critical applications. Furthermore, we propose an architecture for anomaly detection models that aims to integrate structured knowledge representations to address these limitations. Our hypothesis is that this approach can improve performance and robustness, reduce the required resources (such as data and computation), and provide a higher degree of transparency. As a result, our work contributes to the increased safety of machine learning systems. Our code is publicly available. ( https://github.com/kkirchheim/sumnist )
Tango: Harmonious Management and Scheduling for Mixed Services Co-located among Distributed Edge-Clouds

Co-locating Latency-Critical (LC) and Best-Effort (BE) services in edge-clouds is expected to enhance resource utilization. However, this mixed deployment encounters unique challenges. Edge-clouds are heterogeneous, distributed, and resource-constrained, leading to intense competition for edge resources, making it challenging to balance fluctuating co-located workloads. Previous works in cloud datacenters are no longer applicable since they do not consider the unique nature of edges. Although very few works explicitly provide specific schemes for edge workload co-location, these solutions fail to address the major challenges simultaneously.