Reviewerly/exHarmony huggingface dataset:
New boosting approaches for improving cluster-based undersampling in problems with imbalanced data

Class unbalanced datasets are frequently encountered in a variety of areas including health, security, and finance. Often these datasets create bias in the supervised learning models trained for the prediction task. One of the most successful techniques to handle imbalanced data is undersampling. Experiments demonstrate that cluster-based undersampling improves over random undersampling in many cases. In this paper, we propose three new boosting approaches to improve the performance of cluster-based undersampling technique: (i) inject unlabelled data into training data for improved clustering; (ii) keep the instances close to cluster boundary and centroid while undersampling and (iii) remove the majority samples in the neighborhood of minority data in each cluster. We experimented with our boosting methods over 49 standard benchmark datasets and analyzed the performances in terms of standard evaluation metrics. Experimental results suggest these boosting techniques are promising and significantly improve over cluster-based undersampling strategies.
A Blockchain-Based Model for the Prevention of Superannuation Fraud: A Study of Australian Super Funds

Superannuation is the fund set aside by employers to provide their employees with a dignified retirement. Studies highlight that issues can arise with retirement funds from employers, such as failure to make required contributions to an employee’s superannuation fund, incorrect payments, or debiting the wrong fund, contrary to legal or contractual obligations. To address these issues, the Australian Government has implemented laws and regulations to ensure employers fulfil their contribution obligations. Despite these safeguards and highly secured information systems, there has been a significant increase in fraudulent activity in the finance sector, and there have been several instances of employers not making contributions, misusing retirement funds, or reporting incorrectly in their systems. The current process restricts employees from viewing recent data until the contributions reach their super fund, which opens the doors for fraud. Recently, blockchain technology has gained popularity because of its ability to improve security and prevent fraud across many sectors, including finance. Prior studies have shed limited light on how superannuation fraud can be prevented. Moreover, there is limited literature on the possibility of utilizing blockchain technology to address this issue. Therefore, this paper aims to review the current superannuation contribution process and identify the factors contributing to non-payment, incorrect payments, misallocation of funds and communication gaps. This study presents a novel process model and develops a blockchain-based application to mitigate fraudulent practices. This research provides valuable insights into the design of innovative process models that utilize blockchain technology to address superannuation challenges. Furthermore, the paper presents a sample simulated smart contract to explore additional implications and advancements in this domain.
Virtual Reality in E-commerce: Brief Review of Current State

Social media and existing platforms on the Internet are resources that have improved the level of exposure of brands, products, and services; small and large companies have used these resources to the point of becoming monotonous over the years at the time of entering the digital market. This inconvenience, added to the excessive number of social profiles, the mismanagement of these platforms and the great technological advancement forces different companies to use new alternatives to innovate the process of buying and selling products or services. Virtual reality (VR) has become the tool to be used to grant a unique experience at the moment of interaction between consumers and suppliers. In this sense, this work proposes a review of the current state of the inclusion of VR in e-commerce, it has been identified that virtual reality is a resource that allows a close approximation to reality in which users have the possibility of having a unique experience with a product or service in a fast, modern, and safe way.
Enabling a Secure IoT Environment Using a Blockchain-Based Local-Global Consensus Manager

The Internet of Things (IoT) refers to the network of interconnected devices that can communicate and share data over the Internet. The widespread adoption of smart devices within Internet of Things (IoT) networks poses considerable security challenges for their communication. To address these issues, blockchain technology, known for its decentralized and distributed nature, offers potential solutions within consensus-based authentication in IoT networks. This paper presents a novel approach called the local and global layer blockchain model, which aims to enhance security while simplifying implementation. The model leverages the concept of clustering to establish a local-global architecture, with cluster heads assuming responsibility for local authentication and authorization. Implementing a local private blockchain facilitates seamless communication between cluster heads and relevant base stations. This blockchain implementation enhances credibility assurance, strengthens security, and provides an effective network authentication mechanism. Simulation results indicate that the proposed algorithm outperforms previously reported methods. The proposed model achieved an average coverage per node of 0.9, which is superior to baseline models. Additionally, the lightweight blockchain model proposed in this paper demonstrates superior capabilities in achieving balanced network latency and throughput compared to traditional global blockchain approaches.
Variational Bayesian Inference for Exponentiated Weibull Right Censored Survival Data

The exponential, Weibull, log-logistic and lognormal distributions represent the class of light and heavy-tailed distributions that are often used in modelling time-to-event data. The exponential distribution is often applied if the hazard is constant, while the log-logistic and lognormal distributions are mainly used for modelling unimodal hazard functions. The Weibull distribution is on the other hand well-known for modelling monotonic hazard rates. Recently, in practice, survival data often exhibit both monotone and non-monotone hazards. This gap has necessitated the introduction of Exponentiated Weibull Distribution (EWD) that can accommodate both monotonic and non-monotonic hazard functions. It also has the strength of adapting unimodal functions with bathtub shape. Estimating the parameter of EWD distribution poses another problem as the flexibility calls for the introduction of an additional parameter. Parameter estimation using the maximum likelihood approach has no closed-form solution, and thus, approximation techniques such as Newton-Raphson is often used. Therefore, in this paper, we introduce another estimation technique called Variational Bayesian (VB) approach. We considered the case of the accelerated failure time (AFT) regression model with covariates. The AFT model was developed using two comparative studies based on real-life and simulated data sets. The results from the experiments reveal that the Variational Bayesian (VB) approach is better than the competing Metropolis-Hasting Algorithm and the reference maximum likelihood estimates.
An Enhanced Adaptive Differential Evolution Algorithm With Multi-Mutation Schemes and Weighted Control Parameter Setting

Differential evolution (DE) algorithm is one of the most effective and efficient heuristic approaches for solving complex black box problems. But it still easily suffers from premature convergence and stagnation. To alleviate these defects, this paper presents a novel DE variant, named enhanced adaptive differential evolution algorithm with multi-mutation schemes and weighted control parameter setting (MWADE), to further strengthen its search capability. In MWADE, a multi-schemes mutation strategy is first proposed to properly exploit or explore the promising information of each individual. Herein, the whole population are dynamically grouped into three subpopulations according to their fitness values and search performance, and three different mutant operators with various search characteristics are respectively adopted for each subpopulation. Meanwhile, in order to ensure the exploration of algorithm at the later evolutionary stage, a weight-controlled parameter setting is proposed to suitably assign scale factors for different differential vectors. Moreover, a random opposition mechanism with greedy selection is introduced to avoid trapping in local optima or stagnation, and an adaptive population size reduction scheme is devised to further promote the search effectiveness of algorithm. Finally, to illustrate the performance of MWADE, thirteen typical algorithms are adopted and compared with MWADE on 30 functions from IEEE CEC 2017 test suite with different dimensions, and the effectiveness of its proposed components are also investigated. Numerical results indicate that the proposed algorithm has a better search performance.
Minimizing Energy Consumption of Deep Learning Models by Energy-Aware Training

Deep learning models undergo a significant increase in the number of parameters they possess, leading to the execution of a larger number of operations during inference. This expansion significantly contributes to higher energy consumption and prediction latency. In this work, we propose EAT, a gradient-based algorithm that aims to reduce energy consumption during model training. To this end, we leverage a differentiable approximation of the $$\ell _0$$ norm, and use it as a sparse penalty over the training loss. Through our experimental analysis conducted on three datasets and two deep neural networks, we demonstrate that our energy-aware training algorithm EAT is able to train networks with a better trade-off between classification performance and energy efficiency.
Augmentation Based on Artificial Occlusions for Resilient Instance Segmentation

Real-world instance segmentation applications usually demand real-time identification of objects that are small in size, occluded from other objects, appearing and disappearing in quick succession. For these reasons, instance segmentation requires a lot of representative data to grasp the subtle changes that occur in a scene. In this paper, we propose an augmentation methodology that allows for sufficient training relying on a small number of annotated data. Additionally, we provide two new datasets for instance segmentation including the semantic class firearm, for security applications. By applying the proposed augmentation technique on three datasets, the performance of instance segmentation methods is improved as indicated by the experimental results.
Federated learning with hyper-parameter optimization

Federated Learning is a new approach for distributed training of a deep learning model on data scattered across a large number of clients while ensuring data privacy. However, this approach faces certain limitations, including a longer convergence time compared to typical deep learning models. Existing federated optimization algorithms often employ the same hyper-parameters for all clients, disregarding potential system heterogeneity and varying local data availability, which contributes to an even longer convergence time and more communication rounds. To address this challenge, we propose FedHPO1, a new federated optimization technique that adaptively modifies hyper-parameters of each client’s local model during training, such as learning rate and epochs. This adaptability facilitates quicker convergence of the client’s local model, which in turn helps the global model converge faster, consequently reducing overall convergence time and the required communication rounds. In addition, FedHPO does not require any additional complexity since each client adjusts hyper-parameters independently based on the training results obtained in each epoch. In our evaluation, we compare FedHPO with other algorithms, such as FedAVG, FedAVGM, FedProx, and FedYogi, using both IID and non-IID distributed datasets. The results demonstrate the promising outcomes of FedHPO, showcasing reduced convergence time and fewer required communication rounds in comparison to alternative algorithms.
Random Forest Algorithm based Device Authentication in IoT

Distributed devices and data management systems are major components of IoT network. IoT security and privacy are of the utmost significance because there are already billions of IoT devices, applications, and services in use and more are going online every day. Authentication, confidentiality, integrity and attacks are some important security and privacy issues in IoT systems. Authentication is a crucial security issue in order to secure the Internet’s devices and data. The process of identifying users or devices is known as authentication. In this paper, a non-linear machine learning algorithm is used for device authentication in the network. The proposed scheme adopts Belief-Desire-Intention (BDI) server agency with the Random Forest (RF) algorithm, using the context information of the IoT objects for authentication. The proposed authentication scheme operates as follows: Authentication request from IoT device to authentication server agency, generation of session key, upon request from an IoT device, validation of the IoT device using a username/password and session key. The authentication server calls the BDI model, which uses context based information of device to make authentication decisions using a random forest (classification and regression) algorithm. Simulation results show that the performance of the proposed intelligent authentication approach is better in terms of authentication accuracy, computational delay etc.
Underwater Image Dehazing using Non-Local Prior Method and Air-Light Estimation

Water absorbs light differently than air and this can have a significant impact on the visual quality of underwater images. All of these factors combine to make underwater photography and videography resources challenging. Underwater image enhancement technology has numerous applications in various fields such as marine biology, oceanography and underwater exploration. Many underwater image dehazing algorithms have been introduced in the literature to reconstruct the pictographic information of the underwater images. But these methods still suffered from generating optimal results. In order to solve these problems, this study presents a competent underwater image dehazing method based on non-local prior and air-light estimation (NLP-ALE). First, non-local prior is used where it contains four major steps such as finding haze line, estimate initial transmission, regularization and initial dehazing operation. Next, we used the air-light estimation method, which depends on the prior for haze lines. This prior is based on the finding that lines in RGB space intersecting at the air-light can be used to describe the pixel values of a hazy image. To choose the location of the air-light, we apply the Hough transform in RGB space. The experimental findings show that the proposed algorithm produces results comparable to and even superior to those of recent state-of-the-art techniques along with the benefit of being appropriate for real time applications. The proposed research technique exhibits ideal performance in evaluation metrics such as PSNR and SSIM comparted to other competing methods.
Analyzing the Use of Large Language Models for Content Moderation with ChatGPT Examples

Content moderation systems are crucial in Online Social Networks (OSNs). Indeed, their role is to keep platforms and their users safe from malicious activities. However, there is an emerging consensus that such systems are unfair to fragile users and minorities. Furthermore, content moderation systems are difficult to personalize and lack effective communication between users and platforms. In this context, we propose an enhancement of the current framework of content moderation, integrating Large Language Models (LLMs) in the enforcing pipeline.
Data Fusion Analysis and Synthesis Framework for Improving Disaster Situation Awareness

To carry out required aid operations efficiently and effectively after an occurrence of a disaster such as an earthquake, emergency control centers must determine the effect of disasters precisely and and in a timely manner. Different kinds of data-gathering techniques can be used to collect data from disaster areas, such as sensors, cameras, and unmanned aerial vehicles (UAVs). Furthermore, data-fusion techniques can be adopted to combine the data gathered from different sources to enhance the situation awareness. Recent research and development activities on advanced air mobility (AAM) and related unmanned aerial systems (UASs) provide new opportunities. Unfortunately, designing these systems for disaster situation analysis is a challenging task due to the topological complexity of urban areas, and multiplicity and variability of the available data sources. Although there are a considerable number of research publications on data fusion, almost none of them deal with estimating the optimal set of heterogeneous data sources that provide the best effectiveness and efficiency value in determining the effect of disasters. Moreover, existing publications are generally problem- and system-specific. This article proposes a model-based novel analysis and synthesis framework to determine the optimal data fusion set among possibly many alternatives, before expensive implementation and installation activities are carried out.
Integrating the Opposition Nelder–Mead Algorithm into the Selection Phase of the Genetic Algorithm for Enhanced Optimization

In this paper, we propose a novel methodology that combines the opposition Nelder–Mead algorithm and the selection phase of the genetic algorithm. This integration aims to enhance the performance of the overall algorithm. To evaluate the effectiveness of our methodology, we conducted a comprehensive comparative study involving 11 state-of-the-art algorithms renowned for their exceptional performance in the 2022 IEEE Congress on Evolutionary Computation (CEC 2022). Following rigorous analysis, which included a Friedman test and subsequent Dunn’s post hoc test, our algorithm demonstrated outstanding performance. In fact, our methodology exhibited equal or superior performance compared to the other algorithms in the majority of cases examined. These results highlight the effectiveness and competitiveness of our proposed approach, showcasing its potential to achieve state-of-the-art performance in solving optimization problems.
Digital vs. Analog Learning—Two Content-Similar Interventions and Learning Outcomes

The digitization of classrooms has enormously changed teaching during the COVID-19 lockdowns. The rapid introduction of tablet classes subsequently raised questions about potential learning outputs, as only a few studies had produced quite contradicting outcomes. Consequently, our study was set up to monitor cognitive learning outcomes of conventional and digital teaching interventions by explicitly paying attention to short- and long-term knowledge retention rates. Both modules covered the very same classroom content in focusing on the curricular content of the forest ecosystem. Subjects were eighth-graders from seven Bavarian secondary schools (analog: n = 74; digital: n = 225). We analyzed the knowledge gained by applying a multiple-choice questionnaire (online, 25 items) in a pre–post-retention design. For the statistical analyses SPSS was used, and a Rasch analysis was based on the ACERQuest software (Version 2.1). The Rasch calibration of the ad hoc knowledge items assured solid scores (Rel = 0.72). Both interventions significantly increased knowledge (analog and digital: p &lt; 0.001; Cohen’s d: danalog = 0.59, ddigital = 0.42) compared to the pre-test scores. Even after 6–9 weeks, there was no significant drop in the acquired knowledge scores (analog: p = 0.619; digital: p = 0.092) compared to the immediate post-test observed. Furthermore, there was no significant difference between the knowledge levels reached after both interventions. The knowledge scores showed typical learning profiles of earlier studies including its consistency even after several weeks. Since no significant differences appeared for the knowledge gain of both groups, the kind of teaching seemingly does not originate any influence independent of participation in the digital or analog module. The same seems to be valid for notebook entry options.
<scp>MapSafe</scp>: A complete tool for achieving geospatial data sovereignty

Abstract Sensitive geographic data are invaluable assets for the people to whom they belong and their disclosure should be decided by the sovereign data owner (SDO). Due to several high‐profile data breaches and business models that commercialize user data, the need for new approaches to geoprivacy and data sovereignty has grown. We propose MapSafe, a client web application that first obfuscates datasets using donut masking or hexagonal binning, separately, and thereafter implements a multi‐level encryption scheme that permits SDOs to share the final encrypted volume containing the geospatial information when they choose and at a level of detail which they are comfortable. The authenticity verification of the volume is facilitated by storing the hash value corresponding to the encrypted volume immutably on the Blockchain as a public record. Our approach places geoprivacy under data guardians'’ control, and its integration capabilities promote its adoption in existing and future geospatial web systems.
Sentence-level sentiment analysis based on supervised gradual machine learning

Abstract Sentence-level sentiment analysis (SLSA) aims to identify the overall sentiment polarity conveyed in a given sentence. The state-of-the-art performance of SLSA has been achieved by deep learning models. However, depending on the i.i.d (independent and identically distributed) assumption, the performance of these deep learning models may fall short in real scenarios, where the distributions of training and target data are almost certainly different to some extent. In this paper, we propose a supervised solution based on the non-i.i.d paradigm of gradual machine learning (GML) for SLSA. It begins with some labeled observations, and gradually labels target instances in the order of increasing hardness by iterative knowledge conveyance. It leverages labeled samples for supervised deep feature extraction, and constructs a factor graph based on the extracted features to enable gradual knowledge conveyance. Specifically, it employs a polarity classifier to detect polarity similarity between close neighbors in an embedding space, and a separate binary semantic network to extract implicit polarity relations between arbitrary instances. Our extensive experiments on benchmark datasets show that the proposed approach achieves the state-of-the-art performance on all benchmark datasets. Our work clearly demonstrates that by leveraging DNN for feature extraction, GML can easily outperform the pure DNN solutions.