Reviewerly/exHarmony huggingface dataset:
Exposing unseen GAN-generated image using unsupervised domain adaptation

Benefiting from the tremendous success of Generative Adversarial Networks (GANs), we are entering a world where it will be increasingly difficult for people to distinguish whether an image is synthetic, which puts new demands on digital image forensics. To resolve this issue, several fake image detectors based on supervised binary classification have been designed. However, current methods remain vulnerable when testing samples are generated by an unseen GAN model. In this work, an unsupervised domain adaptation strategy is introduced to achieve the detection of GAN-generated images in an unknown domain. A Self-Attention block and novel loss function have been constructed to optimize the domain adaptation process, thus obtaining a better generalization. Experimental results demonstrate that the proposed scheme obtains high detection accuracy on the generalization problem and is robust to common post-processing in reality, which shows that unsupervised methods can enhance the generalization ability of detection models to some extent.
Understanding decentralization of decision-making power in proof-of-stake blockchains: an agent-based simulation approach

Blockchain systems allow for securely keeping shared records of transactions in a decentralised way. This is enabled by algorithms called consensus mechanisms. Proof-of-work is the most prominent consensus mechanism, but environmentally unsustainable. Here, we focus on proof-of-stake, its best-known alternative. Importantly, decentralised decision-making power is not an inherent feature of blockchain systems, but a technological possibility. Numerous security incidents illustrate that decentralised control cannot be taken for granted. We therefore study how key parameters affect the degree of decentralisation in proof-of-stake blockchain systems. Based on a real-world implementation of a proof-of-stake blockchain system, we conduct agent-based simulations to study how a range of parameters impact decentralisation. The results suggest that high numbers of initial potential validator nodes, large transactions, a high number of transactions, and a very high or very low positive validator network growth rate increase decentralisation. We find weak support for an impact of changes in transaction fees and initial stake distributions. Our study highlights how blockchain challenges our understanding of decentralisation in information systems research, and contributes to understanding the governance mechanisms that lead to decentralisation in proof-of-stake blockchain systems as well as to designing proof-of-stake blockchain systems that are prone to decentralisation and therefore more secure.
ASSERT: A Blockchain-Based Architectural Approach for Engineering Secure Self-Adaptive IoT Systems

Internet of Things (IoT) systems are complex systems that can manage mission-critical, costly operations or the collection, storage, and processing of sensitive data. Therefore, security represents a primary concern that should be considered when engineering IoT systems. Additionally, several challenges need to be addressed, including the following ones. IoT systems' environments are dynamic and uncertain. For instance, IoT devices can be mobile or might run out of batteries, so they can become suddenly unavailable. To cope with such environments, IoT systems can be engineered as goal-driven and self-adaptive systems. A goal-driven IoT system is composed of a dynamic set of IoT devices and services that temporarily connect and cooperate to achieve a specific goal. Several approaches have been proposed to engineer goal-driven and self-adaptive IoT systems. However, none of the existing approaches enable goal-driven IoT systems to automatically detect security threats and autonomously adapt to mitigate them. Toward bridging these gaps, this paper proposes a distributed architectural Approach for engineering goal-driven IoT Systems that can autonomously SElf-adapt to secuRity Threats in their environments (ASSERT). ASSERT exploits techniques and adopts notions, such as agents, federated learning, feedback loops, and blockchain, for maintaining the systems' security and enhancing the trustworthiness of the adaptations they perform. The results of the experiments that we conducted to validate the approach's feasibility show that it performs and scales well when detecting security threats, performing autonomous security adaptations to mitigate the threats and enabling systems' constituents to learn about security threats in their environments collaboratively.
Weakly-Supervised Salient Object Detection on Light Fields

Most existing salient object detection (SOD) methods are designed for RGB images and do not take advantage of the abundant information provided by light fields. Hence, they may fail to detect salient objects of complex structures and delineate their boundaries. Although some methods have explored multi-view information of light field images for saliency detection, they require tedious pixel-level manual annotations of ground truths. In this paper, we propose a novel weakly-supervised learning framework for salient object detection on light field images based on bounding box annotations. Our method has two major novelties. First, given an input light field image and a bounding-box annotation indicating the salient object, we propose a ground truth label hallucination method to generate a pixel-level pseudo saliency map, to avoid heavy cost of pixel-level annotations. This method generates high quality pseudo ground truth saliency maps to help supervise the training, by exploiting information obtained from the light field (including depths and RGB images). Second, to exploit the multi-view nature of the light field data in learning, we propose a fusion attention module to calibrate the spatial and channel-wise light field representations. It learns to focus on informative features and suppress redundant information from the multi-view inputs. Based on these two novelties, we are able to train a new salient object detector with two branches in a weakly-supervised manner. While the RGB branch focuses on modeling the color contrast in the all-in-focus image for locating the salient objects, the Focal branch exploits the depth and the background spatial redundancy of focal slices for eliminating background distractions. Extensive experiments show that our method outperforms existing weakly-supervised methods and most fully supervised methods.
Label smoothing and task-adaptive loss function based on prototype network for few-shot learning

Aiming at solving the problems of prototype network that the label information is not reliable enough and that the hyperparameters of the loss function cannot follow the changes of image feature information, we propose a method that combines label smoothing and hyperparameters. First, the label information of an image is processed by label smoothing regularization. Then, according to different classification tasks, the distance matrix and logarithmic operation of the image feature are used to fuse the distance matrix of the image with the hyperparameters of the loss function. Finally, the hyperparameters are associated with the smoothed label and the distance matrix for predictive classification. The method is validated on the miniImageNet, FC100 and tieredImageNet datasets. The results show that, compared with the unsmoothed label and fixed hyperparameters methods, the classification accuracy of the flexible hyperparameters in the loss function under the condition of few-shot learning is improved by 2%-3%. The result shows that the proposed method can suppress the interference of false labels, and the flexibility of hyperparameters can improve classification accuracy.
An AI-in-Loop Fuzzy-Control Technique for UAV’s Stabilization and Landing

In this paper, an adaptable fuzzy control mechanism for an Unmanned Aerial Vehicle (UAV) to manipulate its mechanical actuators is provided.The mission (landing) for the UAV is defined to track (land on) an object that is detected by a deep learning object detection algorithm.The inputs of the controller are the location and speed of the UAV that has been calculated based on the location of the detected object.Two separate fuzzy controllers are proposed to control the UAV motor throttle and its roll and pitch over the mission and landing time.Fuzzy logic controller (FLC) is an intelligent controller that can be used to compensate for the non-linearity behaviour of the UAV by designing a specific fuzzy rule base .These rules will be utilized to adjust the control parameters during the mission and landing period in runtime.To add the effect of the ground for tuning the FLC membership function over the landing operation, a computational flow dynamic (CFD) modeling has been investigated.The proposed techniques is evaluated on MATLAB/Simulink simulation platform and real environment.Statistical analysis of the UAV location reported during stabilization and landing process, on both simulation and real platform, shows that the proposed technique outperforms the similar state-of-art control techniques for both mission and landing control.
Enhancing Cluster Analysis With Explainable AI and Multidimensional Cluster Prototypes

Explainable Artificial Intelligence (XAI) aims to introduce transparency and intelligibility into the decision-making process of AI systems. Most often, its application concentrates on supervised machine learning problems such as classification and regression. Nevertheless, in the case of unsupervised algorithms like clustering, XAI can also bring satisfactory results. In most cases, such application is based on the transformation of an unsupervised clustering task into a supervised one and providing generalised global explanations or local explanations based on cluster centroids. However, in many cases, the global explanations are too coarse, while the centroid-based local explanations lose information about cluster shape and distribution. In this paper, we present a novel approach called CLAMP (Cluster Analysis with Multidimensional Prototypes) that aids experts in cluster analysis with human-readable rule-based explanations. The developed state-of-the-art explanation mechanism is based on cluster prototypes represented by multidimensional bounding boxes. This allows representing of arbitrary shaped clusters and combines the strengths of local explanations with the generality of global ones.We demonstrate and evaluate the use of our approach in a real-life industrial case study from the domain of steel manufacturing as well as on the benchmark datasets. The explanations generated with CLAMP were more precise than either centroid-based or global ones.
Survey on Blockchain-Based IoT Payment and Marketplaces

The ever-growing smart applications will expand the Internet of Things (IoT) ecosystem to connect over 75 billion devices by 2025.IoT ecosystems comprise sensors that act as data suppliers and applications where monetary transactions are required to compensate the data producers.This signifies the importance of IoT payments and marketplaces to facilitate micro-transactions of billions of connected devices in the IoT ecosystem, which is heterogeneous, decentralized, and diverse.However, realizing such an ecosystem raises multiple challenges such as overcoming poor inter-operability, resource constraints, and security and privacy vulnerabilities of IoT devices and platforms.Blockchain is a Distributed Ledger Technology (DLT) that can be identified as a potential solution to overcome the challenges in realizing IoT payments and marketplaces.This is due to the characteristics of blockchain such as decentralization, traceability, immutability, and non-repudiation.This paper presents a comprehensive survey on blockchainbased IoT payments and marketplaces.This paper provides a brief introduction to the concepts of IoT payments and IoT marketplaces.Then the technical challenges involved in realizing IoT payment and marketplaces are discussed by highlighting the blockchain-based solutions.Furthermore, blockchain-based smart applications which use IoT marketplace and IoT payment concepts are presented marking the role of blockchain in each of the application.Subsequently, the paper discussed the integration challenges while also highlighting possible solutions.It is envisaged that this paper would shed light on the development of blockchain-based solutions to realize IoT payments and marketplaces.
Knowledge Base 4.0: Using Crowdsourcing Services for Mimicking the Knowledge of Domain Experts

Intelligence is the ability to learn from experience. Knowledge workers in knowledge-intensive processes develop invaluable domain-specific expertise and knowledge over time. Accordingly, it is vital for organizations to capture this knowledge (which is hidden in the biological Neural Network of subject-matter experts) and enable novices/inexperienced knowledge workers to benefit from that in choosing the best next steps. This position paper proposes linking weak supervision and crowd-sourcing techniques to incorporate knowledge in a continual fashion based on estimating uncertainty or errors from the existing knowledge and learning models. Applications may include handling cold start and concept drift situations. We discuss the design of an intelligent Knowledge Base (KB), namely KB 4.0, for mimicking the knowledge of domain experts in knowledge-intensive processes, and using this knowledge to facilitate auto labelling of the data to be used in learning algorithms. We present a motivating scenario in police investigation processes, and argue how inexperienced investigators can benefit from such a domain-specific KB in law enforcement.
A Deep Learning based Personalized QoE/QoS Correlation Model for Composite Services

Classical services computing tasks such as service design and service recommendation need to comprehensively consider objective Quality of Services (QoS) and subjective Quality of Experiences (QoE) of users. There are close relationships between QoS and QoE, and how to construct an accurate QoS/QoE correlation model has been a hot topic in academia for years. Particularly, it is a challenge to construct such a model for complex composite services that are composed of services and their corresponding providers from multiple domains. This is because the number of QoS parameters is huge while the number of QoE parameters is comparatively smaller, and consequently, to reasonably encode the imbalanced QoS and QoE parameters of composite services becomes challenging. In addition, different users have different concerns and personalized experiences on the same service, and the QoS/QoE correlation model should be personalized, too; however, traditional end-to-end models which simply use QoS as input and QoE as output ignore such personalized preferences of different users, thus the model accuracy is not high enough. Based on the transformer pre-trained language model, this paper mines users’ fine-grained concerns and their sentiment polarity from comments. Then, personalized preferences of users are encoded with CNN, QoS of composite services are encoded with multi-layer Bi-LSTM, and the QoS/QoE correlation is established based on the attention mechanism. In the experiments, our model achieves the highest on the accuracy of sentiment polarity prediction of user concerns, and the QoS and QoE encoded by the proposed model can accurately express the differentiated preferences of different users in a concrete composite service scenario. Potential downstream applications of the proposed QoS/QoE correlation model are comprehensively discussed.
An Anomaly Detection Method for Satellites Using Monte Carlo Dropout

Recently, there has been a significant amount of interest in satellite telemetry anomaly detection (AD) using neural networks (NN). For AD purposes, the current approaches focus on either forecasting or reconstruction of the time series, and they cannot measure the level of reliability or the probability of correct detection. Although the Bayesian neural network (BNN)-based approaches are well known for time series uncertainty estimation, they are computationally intractable. In this paper, we present a tractable approximation for BNN based on the Monte Carlo (MC) dropout method for capturing the uncertainty in the satellite telemetry time series, without sacrificing accuracy. For time series forecasting, we employ an NN, which consists of several Long Short-Term Memory (LSTM) layers followed by various dense layers. We employ the MC dropout inside each LSTM layer and before the dense layers for uncertainty estimation. With the proposed uncertainty region and by utilizing a post-processing filter, we can effectively capture the anomaly points. Numerical results show that our proposed time series AD approach outperforms the existing methods from both prediction accuracy and AD perspectives.
Public Key Authenticated Encryption with Keyword Search from LWE

Public key encryption with keyword search (PEKS) inherently suffers from the inside keyword guessing attack. To resist against this attack, Huang et al. proposed the public key authenticated encryption with keyword search (PAEKS), where the sender not only encrypts a keyword, but also authenticates it. To further resist against quantum attacks, Liu et al. proposed a generic construction of PAEKS and the first quantum-resistant PAEKS instantiation based on lattices. Later, Emura pointed out some issues in Liu et al.’s construction and proposed a new generic construction of PAEKS. The basic construction methodology of Liu et al. and Emura is the same. In this paper, we first analyze the schemes of Liu et al. and Emura, and point out some issues regarding their construction and security model. In short, in their lattice-based instantiations, the sender and receiver use a lattice-based word independent smooth projective hash functions (SPHF) to compute the same shared key to authenticate keywords, leading to a super-polynomial modulus q; their generic constructions need a trusted setup assumption or the designated-receiver setting; Liu et al. failed to provide convincing evidence that their scheme satisfies their claimed security. Then, we propose two new lattice-based PAEKS schemes with totally different construction methodology from Liu et al. and Emura. Specifically, in our PAEKS schemes, instead of using the shared key calculated by SPHF, the sender and receiver achieve keyword authentication by using their own secret key to sample a set of short vectors related to the keyword. In this way, the modulus q in our schemes could be of polynomial size, which results in much smaller size of the public key, ciphertext and trapdoor. In addition, our schemes need neither a trusted setup assumption nor the designated-receiver setting. Finally, our schemes can be proven secure in stronger security model, and thus provide stronger security guarantee for both ciphertext privacy and trapdoor privacy.
Can altcoins act as hedges or safe-havens for Bitcoin?

Bitcoin remains the most popular cryptocurrency and has attracted significant research attention, especially in the hedging and safe-haven literature. As many investors in bitcoin are concentrated heavily in cryptocurrencies as opposed to other assets, a question arises whether alternative cryptocurrencies (altcoins) can used as safe-havens and hedges against Bitcoin? We find that only meme coins offer hedging benefits but a wider range – Defi, meme coins, smart contracts, metaverse and privacy cryptocurrencies – can all act as safe-havens against bitcoin. We further show that their ability to act as hedges and safe-havens varies depending on whether the market is in a bubble or non-bubble period.
Look-ahead workflow scheduling with width changing trend in clouds

With the migration of more and more workflows to clouds, cloud workflow scheduling becomes the main bottleneck for meeting user’s Quality of Service (QoS) due to the dependency between tasks in a workflow and the elasticity of cloud resources. Besides, the pricing model of cloud resources causes the workflow execution time (WET) and workflow execution cost (WEC) to be critical in workflow scheduling. In this paper, we investigate how to optimize WEC under WET as a deadline constraint for workflow scheduling in clouds and propose a look-ahead workflow scheduling algorithm with width changing trend (W-LA) to solve it. First, we come up with the concept of the width changing trend of a workflow. On the basis of this concept, we define the priority of each task and design a novel deadline distribution strategy to distribute the deadline constraint to each task suitably. Then, we propose a look-ahead instance selection framework (LAISF), where selecting instance not only is based on the impact of the selection on the task being assigned, but also looks ahead in the scheduling to consider the impact of this selection on this task’s subsequent tasks. Finally, based on them, W-LA follows a three-step heuristic scheduling: rank tasks by their priorities, distribute the deadline constraint and select instances for tasks by LAISF. W-LA is compared with the state-of-the-art algorithms, including IC-PCP, ProLiS, PSO, ADBRKGA and L-ACO. Experimental results on five real-world scientific workflows demonstrate that W-LA outperforms the five algorithms on average by 41.33%, 33.29%, 96.88%, 86.37% and 14.36% in terms of WEC.
Cyberbullying-related Hate Speech Detection Using Shallow-to-deep Learning

Communication in society had developed within cultural and geographical boundaries prior to the invention of digital technology.The latest advancements in communication technology have significantly surpassed the conventional constraints for communication with regards to time and location.These new platforms have ushered in a new age of user-generated content, online chats, social network and comprehensive data on individual behavior.However, the abuse of communication software such as social media websites, online communities, and chats has resulted in a new kind of online hostility and aggressive actions.Due to widespread use of the social networking platforms and technological gadgets, conventional bullying has migrated from physical form to online, where it is termed as Cyberbullying.However, recently the digital technologies as machine learning and deep learning have been showing their efficiency in identifying linguistic patterns used by cyberbullies and cyberbullying detection problem.In this research paper, we aimed to evaluate shallow machine learning and deep learning methods in cyberbullying detection problem.We deployed three deep and six shallow learning algorithms for cyberbullying detection problems.The results show that bidirectional long-short-term memory is the most efficient method for cyberbullying detection, in terms of accuracy and recall.