Reviewerly/exHarmony huggingface dataset:
EGC2: Enhanced graph classification with easy graph compression

Graph classification is crucial in network analyses. Networks face potential security threats, such as adversarial attacks. Some defense methods may trade off the algorithm complexity for robustness, such as adversarial training, whereas others may trade off clean example performance, such as smoothing-based defense. Most suffer from high complexity or low transferability. To address this problem, we proposed EGC2, an enhanced graph classification model with easy graph compression. EGC2 captures the relationship between the features of different nodes by constructing feature graphs and improving the aggregation of the node-level representations. To achieve lower-complexity defense applied to graph classification models, EGC2 utilizes a centrality-based edge-importance index to compress the graphs, filtering out trivial structures and adversarial perturbations in the input graphs, thus improving the model's robustness. Experiments on ten benchmark datasets demonstrate that the proposed feature read-out and graph compression mechanisms enhance the robustness of multiple basic models, resulting in a state-of-the-art performance in terms of accuracy and robustness against various adversarial attacks.
Study of Word Embeddings for Enhanced Cyber Security Named Entity Recognition

A vast majority of cyber security information is in the form of unstructured text. A much-needed task is to have a machine-assisted analysis of such information. Named Entity Recognition (NER) provides a vital step towards this conversion. However, cyber security named entities are not restricted to classical entity types like people, location, organisation, miscellaneous etc but comprise a large set of domain-specific entities. Word embedding has emerged as the dominant choice for the initial transfer of semantics to downstream NLP tasks and impacts performance. Though several word embeddings learned using general purpose large corpus like Google News, Wikipedia etc. are available as pre-trained embeddings and have shown good performance on NER tasks; this trend is not consistent when it comes to domain-specific NER. This work explores the relative performances and suitability of prominent word embeddings for cyber security NER task. Embeddings considered include both general-purpose pre-trained word embeddings (non-contextual and contextual) available in the public domain and task-adapted embedding generated by fine-tuning these pre-trained embeddings on a task-specific supervised dataset. The results indicate that when it comes to using pre-trained embeddings for cyber security NER, fastText performs better than GloVe and BERT. However, when embeddings are further fine-tuned for the cyber-NER task, the performance of all the fine-tuned embeddings improved by +2-7%. Further, BERT embedding fine-tuned using position-wise FFN (Feed Forward Network) produced the state-of-the-art 0.974 F1-Score on the cyber security NER dataset.
Towards Deep Learning for Efficient Image Encryption

Deep learning has become a topic of great concern in many domains and also in end-to-end image encryption. Traditional image encryption techniques employ rounds of diffusion and confusion to obtain an explicit trade-off between security and efficiency. With deep learning approach adequate solutions are obtained for current challenges in image encryption schemes such as efficiency, cryptographic strength, etc. In this paper, the latest trends in end-to-end encryption schemes based on deep learning are summarized. Firstly, the existing deep learning-based encryption systems are categorized into three categories: encryption with style transfer, Style transfer with enhanced diffusion properties, and Combining Deep Neural networks with chaotic systems. Each of these methodologies is discussed and relevant conclusions are made. Secondly, these methods are compared and analyzed for their achievements and drawbacks in terms of cryptographic properties of generated cipher images and quality of the recovered images. Third, the possibility of new cryptographic attacks are discussed such as Hidden Factors Leakage and Network Architecture Leakage as consequences of combination of deep learning approaches with an encryption system. Finally, conclusions are made based on comparison and analysis of deep learning approach in end-to-end encryption/decryption systems, providing basis for further research.
Exploring the Impact of Avatar Customization in Metaverse: The Role of the Class Mode on Task Engagement and Expectancy-Value Beliefs for Fashion Education

This study aims to explore the impact of avatar customization in metaverse environments, especially for fashion education. Considering the unique nature of fashion as an educational field in which theories and practices are equally significant, the impact of class modes (theoretical versus practical) on expectancy and value toward the class in the metaverse environment is empirically investigated, with task engagements as mediators. Students’ creative self-efficacy, an individual characteristic, was considered as a moderator. A total of 38 female undergraduate students participated voluntarily. In the experimental session, the participants were randomly allocated to one of the two class mode conditions, theoretical or practical. They were then asked to customize their own avatars for use in the metaverse class and write a descriptive essay about the avatar they made. The results showed that the practical class mode evoked higher engagements (i.e., dedication and absorption), which demonstrates the participants’ positive expectancy (i.e., self-efficacy for learning and performance) and value (i.e., task value belief) toward learning in the metaverse class. Interestingly, participants’ creative self-efficacy played a moderating role in the impact of dedication on expectancy and value in different directions, while the impact of absorption was positive regardless of participants’ creative self-efficacy level. Additionally, we found that expectancy and value toward learning led to the participants’ positive class engagement intention.
Multiple Types of Cancer Classification Using CT/MRI Images Based on Learning Without Forgetting Powered Deep Learning Models

Cancer is the second biggest cause of death worldwide, accounting for one of every six deaths. On the other hand, early detection of the disease significantly improves the chances of survival. The use of Artificial Intelligence (AI) to automate cancer detection might allow us to evaluate more cases in less time. In this research, AI-based deep learning models are proposed to classify the images of eight kinds of cancer, such as lung, brain, breast, and cervical cancer. This work evaluates the deep learning models, namely Convolutional Neural Networks (CNN), against classifying images with cancer traits. Pre-trained CNN variants such as MobileNet, VGGNet, and DenseNet are employed to transfer the knowledge they learned with the ImageNet dataset to detect different kinds of cancer cells. We use Bayesian Optimization to find the suitable values for the hyperparameters. However, transfer learning could make it so that models can no longer classify the datasets they were initially trained. So, we use Learning without Forgetting (LwF), which trains the network using only new task data while keeping the network’s original abilities. The results of the experiments show that the proposed models based on transfer learning are more accurate than the current state-of-the-art techniques. We also show that LwF can better classify both new datasets and datasets that have been trained before.
Artificial Identification: A Novel Privacy Framework for Federated Learning Based on Blockchain

To provide off-chain federations with complete privacy services to realize on-chain federated learning (FL), this article proposes a novel privacy framework for FL based on blockchain and smart contracts, named Artificial Identification. It consists of two modules: private peer-to-peer identification and private FL, using two scalable smart contracts to manage the identification and learning process, respectively. Based on Ethereum and interplenary file systems (IPFS), we implement our framework and comprehensively analyze its performance. Experiments show that the proposed framework has acceptable collaboration costs and offers advantages in terms of privacy, security, and decentralization. Furthermore, combined with radio frequency identification (RFID) technology, the framework has the potential to realize automatic on-chain identification and autonomous FL of machine clusters composed of Internet of Things (IoT) devices or distributed participants.
A Blockchain-Driven Food Supply Chain Management Using QR Code and XAI-Faster RCNN Architecture

The availability of food in a country and the capacity of its citizens to access, acquire, and receive enough food are both referred to as having food security. A crucial component of food security is ensuring and maintaining safe and high-quality goods, which the supply chain process should take into due deliberation. To enhance the food supply chain, organic and wholesome food items should be encouraged. Although packaged goods are evaluated and approved by legal authorities, there is no mechanism in place for testing and assessing the market’s available supply on a regular basis. As a result, food manufacturers are compelled to provide nutritious and healthy products. In this research, we propose an explainable artificial intelligence-based faster regions with convolutional neural networks (XAI-based Faster RCNN) model to evaluate the contents of the food items through user-friendly web-based front-end design and QR code. To validate each communication token in the network, an elliptic curve integrated encrypted scheme (ECIES) based on blockchain technology is utilized. Additionally, artificial rabbit optimization (ARO) is used to register each user and assign him a key. The user will gain a deeper understanding of machine learning (ML) and AI applications using the XAI technique. An EAI-based Faster RCNN model is proposed to help digitize information about food products, rapidly retrieve the information, and discover any hidden information in the quick response (QR) code that could have impacted the safety and quality of the food. The results of the experiments indicated that the proposed method requires less response time than other existing methods with the increase of payload and users. The Shapley additive explanation is used to obtain a legal plea for the laboratory test based on the nutritional information present in the QR code. The benefits provided by ECIES-based blockchain technology assist policymakers, manufacturers, and merchants in efficient decision-making, minimizing public health hazards, and improving welfare. This paper also shows that the accuracy achieved by the proposed method reached 99.53%, with the lowest processing time.
Unsupervised extreme multi label classification of stack overflow posts

Knowing the topics of a software forum post, such as those on StackOverflow, allows for greater analysis and understanding of the large amounts of data that come from these communities. One approach to this problem is using extreme multi label classification (XMLC) to predict the topic (or "tag") of a post from a potentially very large candidate label set. While previous work has trained these models on data which has explicit text-to-tag information, we assess the classification ability of embedding models which have not been trained using such structured data (and are thus "unsupervised") to assess the potential applicability to other forums or domains in which tag data is not available.
Heterogeneous Graph Neural Network for Privacy-Preserving Recommendation

Social networks are considered to be heterogeneous graph neural networks (HGNNs) with deep learning technological advances. HGNNs, compared to homogeneous data, absorb various aspects of information about individuals in the training stage. That means more information has been covered in the learning result, especially sensitive information. However, the privacy-preserving methods on homogeneous graphs only preserve the same type of node attributes or relationships, which cannot effectively work on heterogeneous graphs due to the complexity. To address this issue, we propose a novel heterogeneous graph neural network privacy-preserving method based on a differential privacy mechanism named HeteDP, which provides a double guarantee on graph features and topology. In particular, we first define a new attack scheme to reveal privacy leakage in the heterogeneous graphs. Specifically, we design a two-stage pipeline framework, which includes the privacy-preserving feature encoder and the heterogeneous link reconstructor with gradients perturbation based on differential privacy to tolerate data diversity and against the attack. To better control the noise and promote model performance, we utilize a bi-level optimization pattern to allocate a suitable privacy budget for the above two modules. Our experiments on four public benchmarks show that the HeteDP method is equipped to resist heterogeneous graph privacy leakage with admirable model generalization.
Review of Methodologies and Metrics for Assessing the Quality of Random Number Generators

Random number generators are a key element for various applications, such as computer simulation, statistical sampling, and cryptography. They are used to generate/derive cryptographic keys and non-repeating values, e.g., for symmetric or public key cyphers. The strength of a data protection system against cyber attacks corresponds to the strength of the weakest point in the security chain. Therefore, from a mathematical point of view, the security chain can be compromised even if the strongest algorithm is implemented. In fact, if the system requires keys or other random values and the generation process shows a certain vulnerability, the security of the system itself can be compromised. In this article, we present the most reliable tools and methodologies and the main standardisation efforts in the field of computer security to assess the quality of random number generators and ensure that they can be applied to computer security applications by offering adequate security strength. We offer a comprehensive guide that can be used as a quick and practical reference by developers of random number generators of any type to evaluate the random bit streams generated by implemented modules and determine whether or not they can be used in cybersecurity applications. Finally, we also present some use cases to which we applied the presented approach.
GitHub issue classification using BERT-style models

Recent innovations in natural language processing techniques have led to the development of various tools for assisting software developers. This paper provides a report of our proposed solution to the issue report classification task from the NL-Based Software Engineering workshop. We approach the task of classifying issues on GitHub repositories using BERT-style models [1, 2, 6, 8]. We propose a neural architecture for the problem that utilizes contextual embeddings for the text content in the GitHub issues. Besides, we design additional features for the classification task. We perform a thorough ablation analysis of the designed features and benchmark various BERT-style models for generating textual embeddings. Our proposed solution performs better than the competition organizer's method and achieves an F1 score of 0.8653. Our code and trained models are available at https://github.com/Kadam-Tushar/Issue-Classifier.
Federated Learning for improved prediction of failures in Autonomous Guided Vehicles

Autonomous Guided Vehicles (AGVs) are nowadays an indispensable component of production lines in smart manufacturing. Managing the fleet of AGVs covers not only the delegation of operational tasks but also the monitoring of AGVs activity and health condition by applying tailored Machine Learning-based methods to detect anomalies in various signals gathered by edge IoT devices mounted on board. Detecting anomalies requires appropriate prediction of selected signals based on multiple types of sensor readings. Momentary energy consumption is one of the signals that can indicate abnormal states in AGVs. In this paper, we show that the prediction of this signal can be improved with the Federated Learning (FL) approach that involves exchanging experience gained by particular AGVs. This paper significantly extends the conference paper (Shubyn et al., 2022) with the new multi-round approach to building global prediction models and recent experiments on real data streams produced by AGVs designed by the AIUT company. The results of our experiments prove that in the AGV operational environments with distributed knowledge Federated Learning performs better than traditional centralized approaches and that frequent synchronization of experience may lead to better prediction quality.
Effects of communication style on relational outcomes in interactions between customers and embodied conversational agents

Abstract Conversational agents are increasingly used to substitute or augment human service employees. Due to their ability to use human‐like communicative behaviors, these agents are theorized to establish social connections with customers. However, the existing literature is ambiguous on how conversational agents should verbally communicate with customers and whether they should be adaptive to customers' verbal behavior. The current study aims to address these gaps, by focusing on the effects of the conversational agent's communication style on the perceived social connection. Two experiments were conducted in which a virtual agent in a hedonic service (Experiment 1) or a utilitarian service (Experiment 2) was manipulated to adapt either a static task‐ or a social‐oriented communication style or mimic the communication style used by the customer in the previous turn. Guided by marketing and human–computer interaction literature, measures for engagement and rapport were used to reveal customers' perceived social connection. Results show that for the hedonic service, rapport was significantly affected by the presence of social cues in the agent's communication style (whether statically or mimicked), while engagement was significantly affected by mimicry. For the utilitarian service, only social cues significantly affected rapport. These findings enrich the Computers As Social Actors paradigm and provide clear guidelines for practitioners.
DGDNet: Deep Gradient Descent Network for Remotely Sensed Image Denoising

Gradient descent strategy, viewed as an important model optimization method, has been widely used for various tasks (such as model-based image denoising) of computer vision. In the gradient descent denoising model, the learning rate (LR) and residual component are two important parts to be adaptively estimated for its stable point. This letter proposes a deep gradient descent network (DGDNet), including two key points: one is that the LR is designed with eigenvalues of Hessian matrix of remotely sensed images (RSIs) and their local weighted factor (LWF), which can recognize structures from RSIs degraded by additive white Gaussian noise (AWGN). The other is that the residual part is calculated by an U-shaped network (USNet) to speed up the DGDNet convergent to a fixed point. Finally, the two components are plugged into the gradient descent scheme and contribute to an enjoyable result with a few iterations. Quantitatively and qualitatively experimental results demonstrate that the proposed DGDNet can obtain a stable solution efficiently, and produce competitive denoising performance which is even better than that yielded by the state-of-the-art noise reduction methods.
PTKE: Translation-based temporal knowledge graph embedding in polar coordinate system

Knowledge graph embedding has received widespread attention in recent years. Most existing models represent time-independent facts as low dimensional embeddings. Nevertheless, knowledge graphs with temporal information provide more accurate and timely data. Hence, we propose Polar Temporal Knowledge Graph Embedding (PTKE), a novel temporal knowledge graph (TKG) embedding model which belongs to the translation-based model family and embeds time-aware facts into polar coordinate system. PTKE defines time as a constraint of the entity and synchronously embeds the starting and ending timestamps. The fact is divided into the modulus and the angular parts to avoid generating similar time-constrained entities. We use the modulus part to distinguish different time-constrained entities, and the angular part to distinguish time-constrained entities with the same modulus. Experiments on the temporal datasets show that PTKE outperforms prior state-of-the-art static knowledge graph (SKG) embedding models and temporal knowledge graph (TKG) embedding models in the link prediction task and the relation prediction task. Furthermore, the analysis of different time units and semantic expressive ability test on time embeddings prove that PTKE has a great ability on time expression.
DT-SNE: t-SNE discrete visualizations as decision tree structures

Visualizations are powerful tools that are commonly used by data scientists to get more insights about their high dimensional data. One can for example cite t-SNE, which is probably one of the most famous and widely-used visualization techniques. However, t-SNE is a nonlinear and non-parametric technique that makes it suffer from a lack of interpretability. In this paper, we present a new technique inspired by t-SNE’s objective function that combines its ability to build nice visualizations with the interpretability of decision trees. This new visualization technique, called DT-SNE, can be seen as a discrete visualization technique where groups of instances are provided, as well as a ranking between them. The decision rules of the decision tree provide clear insights to interpret these different groups.
“I am the character, the character is me”: A thematic analysis of the user-avatar relationship in videogames

Far from merely being a tool to navigate a virtual world, individuals can often develop strong and complex relationships with their videogame characters. The present study examined the bond that can develop between a user and their avatar, as well as factors that can influence this relationship dynamic through the use of thematic analysis. Semi-structured interviews were carried out with 12 videogame players from the UK and US aged between 18 and 27 years. The results identified five major themes, comprising: (i) heterogeneity of game worlds; (ii) avatar attachment; (iii) game experiences affecting physical world behaviour and attitudes; (iv) types of self in a virtual world (with the sub-themes of actual self, idealised self, and utopian self); and (v) game difficulty affecting user-avatar relationship. The results showed gamers use their avatars as a means to insert a virtual version of themselves into the videogame or to attain a desired version of the self, sometimes including elements of fantasy. In addition, participants indicated that games with an advanced difficulty and avatar customisation appeared to facilitate stronger bonds to a character. Finally, several avenues of future research are discussed, in particular pertaining to the advancement of research relating to the Proteus effect.