Reviewerly/exHarmony huggingface dataset:
A Blockchain assisted fog computing framework for secure data sharing mechanism in smart home

The demand and popularity of Internet of Things (IoT) has been growing with exponential rate due to the rapid growth of various smart devices in the IoT ecosystem. The request generated through various smart devices is generally stochastic and unscheduled. The existing IoT-Cloud paradigm does not provide the security services efficiently and effectively. In order to provide better security services to different IoT applications domain particularly in smart home domain, it introduces the blockchain technology. So, in the present article, it proposes BlockCFS framework, which is an innovative blockchain assisted fog computing framework, as a safe datasharing mechanism for smart homes. It also illustrates the methodical sequence diagram for the BlockCFS, for registering and transmitting data from a variety of smart devices that are connected in a smart home.
Toxic Comment Classification Using S-BERT Vectorization and Random Forest Algorithm

The growing popularity of social media platforms and microblogging websites has led to an increase in the expression of views and opinions. However, conversations and debates on these platforms often lead to the use of toxic comments, which consists of insulting and hateful remarks. To address this issue, it is important for social media systems to be able to recognize harmful comments. With the rising incidence of cyberbullying, it is crucial to study the classification of toxic comments using various algorithms. This study compares the effectiveness of different word and sentence embedding methods, including TF-IDF, InferSent, Bert, and T5 for toxic comments classification. A comparative study is also conducted on the impact of using SMOTE to balance the highly imbalanced dataset. The results of these models are compared and analysed. It is observed that T5 embedding with Random Forest Classifier works best at 0.91 F1-Score.
BloomBERT: A Deep Learning-Based Cognitive Complexity Classifier of Assessment Questions

We propose a novel automated approach using deep learning techniques to classify assessment questions based on their cognitive complexity. To this end, we first heuristically annotated a dataset of ∼111.6K questions based on Revised Bloom's Taxonomy. Subsequently, we used transfer learning to train an attention-based model for question classification. Our experimental results indicate that our proposed approach outperforms existing classification techniques in terms of F1-Score. Moreover, our proposed model could correctly classify 74.04% of real-life questions.
TGCN-Bert Emoji Prediction in Information Systems Using TCN and GCN Fusing Features Based on BERT

In recent studies, graph convolutional neural networks (GCNs) have been used to solve different natural language processing (NLP) tasks. However, few researches apply graph convolutional networks to short text classification. Emoji prediction, as a complex sentiment analysis task, has received even less attention. In this work, the authors propose TGCN-Bert which combines pre-trained BERT temporal convolutional networks (TCNs) and graph convolutional networks for short text classification and emoji prediction. They initialize the nodes with the help of BERT and define the edges in text graph based on the term frequency-inverse document frequency (TF-IDF) and positive point-wise mutual information (PPMI). They employ the model for emoji prediction task, and a metric based on emoji clustering is developed to better measure the validity of emoji prediction results. To validate the performance of TGCN-Bert, they compare it with other GCN variants on short text classification datasets and emoji prediction datasets; experiments show that TGCN-Bert achieves better performance.
A Semantic Feature Enhancement-Based Aerial Image Target Detection Method Using Dense RFB-FE

Aerial image target detection is a challenging task due to the complex backgrounds, dense target distribution, and large-scale differences often present in aerial images. Existing methods often struggle to effectively extract detailed features and address the issue of imbalanced positive and negative samples. To tackle these challenges, an aerial image target detection method (dense RFB-FE-CGAM) based on dense RFB-FE and channel-global attention mechanism (CGAM) was proposed. First, the authors design a shallow feature enhancement module using dense RFB feature multiplexing and expand convolution within an SSD network, improving detailed feature extraction. Second, they introduce CGAM, a global attention module, to enhance semantic feature extraction in backbone networks. Finally, they incorporate a focal loss function for joint training, addressing sample imbalance. In experiments, the method achieved an mAP of 0.755 on the DOTA dataset and recall/AP values of 0.889/0.906 on HRSC2016, confirming the effectiveness of dense RFB-FE-CGAM for aerial image target detection.
An IoT and machine learning enhanced framework for real-time digital human modeling and motion simulation

The Digital Human Model (DHM) is a computational technique employed for simulating human motion and behavior, finding applications across sectors such as manufacturing, healthcare, and education. Despite its potential, current DHM simulations grapple with challenges like high computational costs, significant latency, low precision, and limited realism. Addressing these challenges, this paper introduces a novel DHM simulation approach that synergizes the Kinect motion capture system, Internet of Things (IoT) devices, and advanced machine learning techniques. Distinctively, our method captures real-time human motion data through IoT devices and the Kinect system, followed by data preprocessing and feature extraction. In contrast to traditional methodologies, we harness deep learning to establish time-series models, integrating convolutional neural networks and dilated convolutional kernels to amplify processing capabilities and cater to multi-scale data. Furthermore, we employ virtual reality technology to craft a digital human structural model, streamlining motion simulation and optimization. Experimental evaluations underscore that our approach consistently outperforms conventional methods across all metrics, demonstrating robust real-time simulation capabilities. In essence, this paper pioneers an innovative technological paradigm for DHM simulation, heralding new avenues for research and applications in related domains.
Driving a key generation strategy with training-based optimization to provide safe and effective authentication using data sharing approach in IoT healthcare

Blockchain technology offers a solution to the interoperability issues that currently plague health information systems (BT). Additionally, blockchain technology provides the cutting-edge standard that ensures the safe exchange of computerized health data among healthcare providers, institutions, people, and medical specialists. Recently, both businesses and academics recognized the Internet of Things (IoT) as one of the top study fields. Nevertheless, a significant issue in IoT healthcare across several product domains is the secrecy of patient health data transmission. For secure and effective authentication with data sharing strategy in IoT healthcare, a method called Driving Training Based Optimization (DTBO) Key generation is presented to address these issues. The entities involved in the proposed DTBO_Key generation technique are the user, owner, authenticating minor, blockchain, and IoT server. The suggested technique consists of seven stages: initialization, registration phase, key creation, login phase, data protection, authentication process, and verification phase. A minimum memory consumption of 2.983 MB, a computation cost of 28.008%, and an accuracy of 0.9 were attained using the proposed method.
Cybersecurity Breach Case Study

This cybersecurity case study provides a comprehensive remediation plan for an organization that recently experienced a data breach and lacks a risk management strategy. Starting with a current state analysis, the plan includes strategies to support the new organizational behaviors, understanding and aligning company culture, supporting changes with ethical decision-making and strong leadership, and ensuring changes are maintained and reinforced. Foundation theories and models are used to support the plan: human factors, theory of constraints, the plan-do-check-act cycle, Schein's model of organizational culture, the Deal and Kennedy culture model, Lewin's change management model, nudge theory, the duty-based approach to ethical decision-making, and transformational leadership. The resulting plan ensures that the organization is able to prevent most cyberattacks and has a ready response plan for dealing with any future breaches.
Lumped Approach to Recognize Types of Construction Defect from Text with Hand-Drawn Circles

This study aims to improve the performance of optical character recognition (OCR), particularly in identifying printed Korean text marked by hand-drawn circles from images of construction defect tags. Despite advancements in mobile technologies, marking text on paper remains a prevalent practice. The typical approach for recognition in this context is to first detect the circles from the images and then identify the text entity within the region using OCR. Numerous OCR models have been developed to automatically identify various text types, but even a competition-winning multilingual model by Baek et al. does not perform well in recognizing circled Korean text, yielding a weighted F1 score of just 69%. The core idea of the lumped approach proposed in this study is to recognize circles and named entities as one instance. For this purpose, the YOLOv5 is fine tuned to detect 65 types of named entity overlapped with hand-drawn circles and yields a weighted F1 score of 94%, 25% higher than a typical approach using YOLOv5 for circle detection and a model by Baek et al. for subsequent OCR. This work thereby introduces a novel approach for developing advanced text information extraction methods and processing paper-based marked text in the construction industry.
Multimodal video retrieval with CLIP: a user study

Abstract Recent machine learning advances demonstrate the effectiveness of zero-shot models trained on large amounts of data collected from the internet. Among these, CLIP (Contrastive Language-Image Pre-training) has been introduced as a multimodal model with high accuracy on a number of different tasks and domains. However, the unconstrained nature of the model begs the question whether it can be deployed in open-domain real-word applications effectively in front of non-technical users. In this paper, we evaluate whether CLIP can be used for multimodal video retrieval in a real-world environment. For this purpose, we implemented impa , an efficient shot-based retrieval system powered by CLIP. We additionally implemented advanced query functionality in a unified graphical user interface to facilitate an intuitive and efficient usage of CLIP for video retrieval tasks. Finally, we empirically evaluated our retrieval system by performing a user study with video editing professionals and journalists working in the TV news media industry. After having the participants solve open-domain video retrieval tasks, we collected data via questionnaires, interviews, and UI interaction logs. Our evaluation focused on the perceived intuitiveness of retrieval using natural language, retrieval accuracy, and how users interacted with the system’s UI. We found that our advanced features yield higher task accuracy, user ratings, and more efficient queries. Overall, our results show the importance of designing intuitive and efficient user interfaces to be able to deploy large models such as CLIP effectively in real-world scenarios.
Sensing and Navigation for Multiple Mobile Robots Based on Deep Q-Network

In this paper, a novel DRL algorithm based on a DQN is proposed for multiple mobile robots to find optimized paths. The multiple robots’ states are the inputs of the DQN. The DQN estimates the Q-value of the agents’ actions. After selecting the action with the maximum Q-value, the multiple robots’ actions are calculated and sent to them. Then, the robots will explore the area and detect the obstacles. In the area, there are static obstacles. The robots should detect the static obstacles using a LiDAR sensor. The other moving robots are recognized as dynamic obstacles that need to be avoided. The robots will give feedback on the reward and the robots’ new states. A positive reward will be given when a robot successfully arrives at its goal point. If it is in a free space, zero reward will be given. If the robot collides with a static obstacle or other robots or reaches its start point, it will receive a negative reward. Multiple robots explore safe paths to the goals at the same time, in order to improve learning efficiency. If a robot collides with an obstacle or other robots, it will stop and wait for the other robots to complete their exploration tasks. The episode will end when all robots find safe paths to reach their goals or when all of them have collisions. This collaborative behavior can reduce the risk of collisions between robots, enhance overall efficiency, and help avoid multiple robots attempting to navigate through the same unsafe path simultaneously. Moreover, storage space is used to store the optimal safe paths of all robots. Finally, the multi-robots will learn the policy to find the optimized paths to go to the goal points. The goal of the simulations and experiment is to make multiple robots efficiently and safely move to their goal points.
Addressing the Transaction Validation Issue in IOTA Tangle: A Tip Selection Algorithm Based on Time Division

IOTA is a new public chain system specifically designed for the Internet of Things (IoT), which provides strong support for the high concurrency, scalability, and zero handling fees of the IoT. The distributed ledger of IOTA, called the tangle, adopts a Directed Acyclic Graph (DAG) structure. However, compared to the single-chain architecture, the tangle is more complex and highly vulnerable to security threats. The existing transaction verification methods still cannot simultaneously meet the need for accelerating approval speed and improving security to resist illegal transactions, such as lazy tips and permanent tips. In this work, we propose TDTS, a tip-selection algorithm based on time division to improve the efficiency of transaction verification. The main idea of the algorithm is to quickly determine two tips of an incoming transaction that need to be confirmed by sorting tip values in a time slot. It shortens the transaction verification time and reduces the number of lazy tips and permanent tips. A comprehensive theoretical analysis confirmed the effectiveness of our proposed algorithm. Based on 1000 IOTA nodes, the evaluations showed that TDTS can select tips quickly like URTS and resist lazy tips like MCMC.
MTLBORKS-CNN: An Innovative Approach for Automated Convolutional Neural Network Design for Image Classification

Convolutional neural networks (CNNs) have excelled in artificial intelligence, particularly in image-related tasks such as classification and object recognition. However, manually designing CNN architectures demands significant domain expertise and involves time-consuming trial-and-error processes, along with substantial computational resources. To overcome this challenge, an automated network design method known as Modified Teaching-Learning-Based Optimization with Refined Knowledge Sharing (MTLBORKS-CNN) is introduced. It autonomously searches for optimal CNN architectures, achieving high classification performance on specific datasets without human intervention. MTLBORKS-CNN incorporates four key features. It employs an effective encoding scheme for various network hyperparameters, facilitating the search for innovative and valid network architectures. During the modified teacher phase, it leverages a social learning concept to calculate unique exemplars that effectively guide learners while preserving diversity. In the modified learner phase, self-learning and adaptive peer learning are incorporated to enhance knowledge acquisition of learners during CNN architecture optimization. Finally, MTLBORKS-CNN employs a dual-criterion selection scheme, considering both fitness and diversity, to determine the survival of learners in subsequent generations. MTLBORKS-CNN is rigorously evaluated across nine image datasets and compared with state-of-the-art methods. The results consistently demonstrate MTLBORKS-CNN’s superiority in terms of classification accuracy and network complexity, suggesting its potential for infrastructural development of smart devices.
High-Quality Reversible Data Hiding Based on Multi-Embedding for Binary Images

Unlike histogram-based reversible data hiding (RDH), the general distortion-based framework considers pixel-by-pixel distortions, which is a new research direction in RDH. The advantage of the general distortion-based RDH method is that it can enhance the visual quality of the marked image by embedding data into visually insensitive regions (e.g., edges and textures). In this paper, following this direction, a high-capacity RDH approach based on multi-embedding is proposed. The cover image is decoupled to select the embedding sequence that can better utilize texture pixels and reduce the size of the reconstruction information, and a multi-embedding strategy is proposed to embed the secret data along with the reconstruction information by matrix embedding. The experimental results demonstrate that the proposed method provides a superior visual quality and higher embedding capacity than some state-of-the-art RDH works for binary images. With an embedding capacity of 1000 bits, the proposed method achieves an average PSNR of 49.45 dB and an average SSIM of 0.9705 on the test images. This marks an improvement of 1.1 dB in PSNR and 0.0242 in SSIM compared to the latest state-of-the-art RDH method.
Chinese Named Entity Recognition in Football Based on ALBERT-BiLSTM Model

Football is one of the most popular sports in the world, arousing a wide range of research topics related to its off- and on-the-pitch performance. The extraction of football entities from football news helps to construct sports frameworks, integrate sports resources, and timely capture the dynamics of the sports through visual text mining results, including the connections among football players, football clubs, and football competitions, and it is of great convenience to observe and analyze the developmental tendencies of football. Therefore, in this paper, we constructed a 1000,000-word Chinese corpus in the field of football and proposed a BiLSTM-based model for named entity recognition. The ALBERT-BiLSTM combination model of deep learning is used for entity extraction of football textual data. Based on the BiLSTM model, we introduced ALBERT as a pre-training model to extract character and enhance the generalization ability of word embedding vectors. We then compared the results of two different annotation schemes, BIO and BIOE, and two deep learning models, ALBERT-BiLSTM-CRF and ALBERT BiLSTM. It was verified that the BIOE tagging was superior than BIO, and the ALBERT-BiLSTM model was more suitable for football datasets. The precision, recall, and F-Score of the model were 85.4%, 83.47%, and 84.37%, correspondingly.
An Improved Dandelion Optimizer Algorithm for Spam Detection: Next-Generation Email Filtering System

Spam emails have become a pervasive issue in recent years, as internet users receive increasing amounts of unwanted or fake emails. To combat this issue, automatic spam detection methods have been proposed, which aim to classify emails into spam and non-spam categories. Machine learning techniques have been utilized for this task with considerable success. In this paper, we introduce a novel approach to spam email detection by presenting significant advancements to the Dandelion Optimizer (DO) algorithm. The DO is a relatively new nature-inspired optimization algorithm inspired by the flight of dandelion seeds. While the DO shows promise, it faces challenges, especially in high-dimensional problems such as feature selection for spam detection. Our primary contributions focus on enhancing the DO algorithm. Firstly, we introduce a new local search algorithm based on flipping (LSAF), designed to improve the DO’s ability to find the best solutions. Secondly, we propose a reduction equation that streamlines the population size during algorithm execution, reducing computational complexity. To showcase the effectiveness of our modified DO algorithm, which we refer to as the Improved DO (IDO), we conduct a comprehensive evaluation using the Spam base dataset from the UCI repository. However, we emphasize that our primary objective is to advance the DO algorithm, with spam email detection serving as a case study application. Comparative analysis against several popular algorithms, including Particle Swarm Optimization (PSO), the Genetic Algorithm (GA), Generalized Normal Distribution Optimization (GNDO), the Chimp Optimization Algorithm (ChOA), the Grasshopper Optimization Algorithm (GOA), Ant Lion Optimizer (ALO), and the Dragonfly Algorithm (DA), demonstrates the superior performance of our proposed IDO algorithm. It excels in accuracy, fitness, and the number of selected features, among other metrics. Our results clearly indicate that the IDO overcomes the local optima problem commonly associated with the standard DO algorithm, owing to the incorporation of LSAF and the reduction in equation methods. In summary, our paper underscores the significant advancement made in the form of the IDO algorithm, which represents a promising approach for solving high-dimensional optimization problems, with a keen focus on practical applications in real-world systems. While we employ spam email detection as a case study, our primary contribution lies in the improved DO algorithm, which is efficient, accurate, and outperforms several state-of-the-art algorithms in various metrics. This work opens avenues for enhancing optimization techniques and their applications in machine learning.
Dynamic Risk Assessment in Cybersecurity: A Systematic Literature Review

Traditional information security risk assessment (RA) methodologies and standards, adopted by information security management systems and frameworks as a foundation stone towards robust environments, face many difficulties in modern environments where the threat landscape changes rapidly and new vulnerabilities are being discovered. In order to overcome this problem, dynamic risk assessment (DRA) models have been proposed to continuously and dynamically assess risks to organisational operations in (near) real time. The aim of this work is to analyse the current state of DRA models that have been proposed for cybersecurity, through a systematic literature review. The screening process led us to study 50 DRA models, categorised based on the respective primary analysis methods they used. The study provides insights into the key characteristics of these models, including the maturity level of the examined models, the domain or application area in which these models flourish, and the information they utilise in order to produce results. The aim of this work is to answer critical research questions regarding the development of dynamic risk assessment methodologies and provide insights on the already developed methods as well as future research directions.