Reviewerly/exHarmony huggingface dataset:
Enhanced Intrusion Detection with Data Stream Classification and Concept Drift Guided by the Incremental Learning Genetic Programming Combiner

Concept drift (CD) in data streaming scenarios such as networking intrusion detection systems (IDS) refers to the change in the statistical distribution of the data over time. There are five principal variants related to CD: incremental, gradual, recurrent, sudden, and blip. Genetic programming combiner (GPC) classification is an effective core candidate for data stream classification for IDS. However, its basic structure relies on the usage of traditional static machine learning models that receive onetime training, limiting its ability to handle CD. To address this issue, we propose an extended variant of the GPC using three main components. First, we replace existing classifiers with alternatives: online sequential extreme learning machine (OSELM), feature adaptive OSELM (FA-OSELM), and knowledge preservation OSELM (KP-OSELM). Second, we add two new components to the GPC, specifically, a data balancing and a classifier update. Third, the coordination between the sub-models produces three novel variants of the GPC: GPC-KOS for KA-OSELM; GPC-FOS for FA-OSELM; and GPC-OS for OSELM. This article presents the first data stream-based classification framework that provides novel strategies for handling CD variants. The experimental results demonstrate that both GPC-KOS and GPC-FOS outperform the traditional GPC and other state-of-the-art methods, and the transfer learning and memory features contribute to the effective handling of most types of CD. Moreover, the application of our incremental variants on real-world datasets (KDD Cup '99, CICIDS-2017, CSE-CIC-IDS-2018, and ISCX '12) demonstrate improved performance (GPC-FOS in connection with CSE-CIC-IDS-2018 and CICIDS-2017; GPC-KOS in connection with ISCX2012 and KDD Cup '99), with maximum accuracy rates of 100% and 98% by GPC-KOS and GPC-FOS, respectively. Additionally, our GPC variants do not show superior performance in handling blip drift.
Fast Frequency Domain Screen-Shooting Watermarking Algorithm Based on ORB Feature Points

With high performances of image capturing tools, image information can be easily obtained by screenshots that make image copyright protection a challenging task. The existing screen-shooting watermarking algorithms suffer from a huge running time, in addition to their low robustness against different screenshot attacks, such as different distances and capturing angles of the screenshots. In this paper, a fast and robust high-capacity flexible watermarking algorithm for screenshot images is proposed. Firstly, Oriented FAST and Rotated BRIEF (ORB) feature points are extracted from the input image. Secondly, the feature points are then sorted in a descending order according to their response values. Then, the first five non-overlapping feature points are selected for the embedding by using Hamming window-based filtering method. Furthermore, we exploit the multi-resolution property of Discrete Wavelet Transform (DWT) and energy compaction property of Singular Value Decomposition (SVD) to embed the watermark. Therefore, the classical DWT combined with Singular Value Decomposition (SVD) are adopted to improve the robustness and capacity of the proposed watermarking algorithm. At the extraction side, the sum of the response values for the three RGB channels of the color-ripped image is calculated to improve the feature point localization accuracy. Experimental results show that the proposed screen-shooting watermarking algorithm improves running speed while ensuring the robustness. Furthermore, it has less time complexity and high robustness compared with the state-of-the-art watermarking algorithms against different screenshot attacks.
Symbolic knowledge extraction for explainable nutritional recommenders

This paper focuses on nutritional recommendation systems (RS), i.e. AI-powered automatic systems providing users with suggestions about what to eat to pursue their weight/body shape goals. A trade-off among (potentially) conflictual requirements must be taken into account when designing these kinds of systems, there including: (i) adherence to experts' prescriptions, (ii) adherence to users' tastes and preferences, (iii) explainability of the whole recommendation process. Accordingly, in this paper we propose a novel approach to the engineering of nutritional RS, combining machine learning and symbolic knowledge extraction to profile users-hence harmonising the aforementioned requirements. MethodsOur contribution focuses on the data processing workflow. Stemming from neural networks (NN) trained to predict user preferences, we use CART Breiman et al.(1984) to extract symbolic rules in Prolog Körner et al.(2022) form, and we combine them with expert prescriptions brought in similar form. We can then query the resulting symbolic knowledge base via logic solvers, to draw explainable recommendations. ResultsExperiments are performed involving a publicly available dataset of 45,723 recipes, plus 12 synthetic datasets about as many imaginary users, and 6 experts' prescriptions. Fully-connected 4-layered NN are trained on those datasets, reaching ∼86% test-set accuracy, on average. Extracted rules, in turn, have ∼80% fidelity w.r.t. those NN. The resulting recommendation system has a test-set precision of ∼74%. The symbolic approach makes it possible to devise how the system draws recommendations. ConclusionsThanks to our approach, intelligent agents may learn users' preferences from data, convert them into symbolic form, and extend them with experts' goal-directed prescriptions. The resulting recommendations are then simultaneously acceptable for the end user and adequate under a nutritional perspective, while the whole process of recommendation generation is made explainable.
Effects of Anthropomorphic Design Cues of Chatbots on Users’ Perception and Visual Behaviors

Measurement of users’ perception and visual behaviors to anthropomorphic design cues of chatbots can improve our understanding of chatbots and potentially optimize chatbot design. However, as two typical and basic features, how chatbot appearances and conversational styles jointly affect users’ perception and visual behaviors remains unclear. Therefore, this study conducted an eye-tracking experiment to explore users’ perception and visual behaviors. Results indicate that anthropomorphic appearances and human-like conversational styles jointly increased users’ perception of chatbots’ social presence, trust in chatbots, and satisfaction with chatbots. In contrast, on users’ visual behaviors, such a joint effect was not found, although chatbots with higher anthropomorphic appearances and human-like conversational styles triggered more fixation counts and longer dwell time. These findings suggest that anthropomorphic appearance and human-like conversational style can improve users’ perception and attract more visual attention to chatbots. These findings provide theoretical contributions and practical implications for relevant researchers and designers.
SkillBot: Towards Data Augmentation using Transformer language model and linguistic evaluation

Creating accurate, closed-domain, and machine learning-based chatbots that perform language understanding (intent prediction/detection) and language generation (response generation) requires significant datasets derived from specific knowledge domains. The common challenge in developing a closed-domain chatbot application is the lack of a comprehensive dataset. Such scarcity of the dataset can be complemented by augmenting the dataset with the use of state-of-the-art technologies existing in the field of Natural Language Processing, called ‘Transformer Models’. Our applied computing project experimented with a ‘Generative Pre-trained Transformer’ model, a unidirectional transformer decoder model for augmenting an original dataset limited in size and manually authored. This model uses unidirectional contextual representation i.e., text input is processed from left to right while computing embeddings corresponding to the input sentences. The primary goal of the project was to leverage the potential of a pre-trained transformer-based language model in augmenting an existing, but limited dataset. Additionally, the idea for using the model for text generation and appending the generated embedding to the input embedding supplied was to preserve the intent for the augmented utterances as well as to find a different form of expressions for the same intent which could be expressed by the potential users in the future. Our experiment showed improved performance for understanding language and generation for the chatbot model trained on the augmented dataset indicating that a pre-trained language model can be beneficial for the effective working of natural language-based applications such as a chatbot model trained on the augmented dataset indicating that a pre-trained language model can be beneficial for the effective working of natural language-based applications such as a chatbot.
Deep-Reinforcement-Learning-Assisted Client Selection in Nonorthogonal-Multiple-Access-Based Federated Learning

To reap the benefit of big data generated by the massive number of Internet of Things (IoT) devices while preserving data privacy, federated learning (FL) has been proposed to enable IoT devices to train machine learning models locally. That is, instead of sharing the local data sets, different clients in terms of IoT devices only need to upload their local models to a centralized FL server. Client selection in FL is critical to maximize the number of qualified clients, who can successfully upload their local models to the FL server before the predefined deadline. Normally, client selection is coupled with wireless resource management owing to the fact that different clients need to share the same spectrum to upload their local models. The existing solutions of joint optimizing client selection and resource management are designed based on frequency-division multiple access (FDMA) or time-division multiple access (TDMA), which do not consider the dynamics of the clients and lead to low bandwidth utilization. In this article, we propose the Nonorthogonal-Multiple-Access (NOMA)-based resource allocation for client selection in FL to dynamically and jointly optimize client selection for each global iteration as well as the transmission power of each selected client in each time slot within a global iteration. We design the deep-reinforcement-learn-based client selection in NOMA-based federated learning (DREAM-FL) algorithm to solve the problem. Extensive simulations are conducted to demonstrate that DREAM-FL can select more qualified clients and has higher model accuracy than FDMA and TDMA-based solutions.
Hybrid Post-Training Quantization for Super-Resolution Neural Network Compression

Quantization is a widely adopted technique to reduce the storage cost of neural networks. However, existing methods primarily focus on minimizing the quantization error of neural network parameters without considering the correlation between the quantization error and performance of quantized neural networks. Motivated by this consideration, we propose a hybrid post-training quantization (HPTQ) method for super-resolution neural networks. Layer-wise quantization and piecewise quantization are integrated based on error sensitivity and the quantization error of parameters. In HPTQ, we utilize Taylor expansion to demonstrate that the performance distortion of quantized neural networks is a weighted average of parameter quantization errors with respect to gradients. To reduce the quantization error, we apply uniform and clustered quantization to parameters in dense and sparse regions, respectively. Furthermore, we allocate larger bit-widths to layers with higher error sensitivity indicated by gradients. Numerical experiments show that the super-resolution neural networks perform better under the proposed quantization approach compared to existing quantization methods.
FPPNet: A Fixed-Perspective-Perception Module for Small Object Detection Based on Background Difference

A roadside sensing unit can provide over-the-horizon perception information for autonomous vehicles due to its high perception perspective. However, numerous challenges need to be overcome such as the missing detection of small objects and occluded objects. To this end, this study proposed a fixed perspective perception (FPP) module, which considered background subtraction and a fixed camera for small object detection. The proposed FPP module was divided into two parts: a grayscale background subtraction (GBS) submodule and a background–current image fusion (BCF) submodule. Specifically, the GBS submodule introduces background spatial information into a current frame, and the BCF submodule combines feature maps of a current frame and background by using channel attention. Moreover, we designed an object detection network called FPPNet which uses the FPP module to facilitate small object detection. Experimental results demonstrate that the FPPNet achieved 39.8% average precision small ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${\text {AP}}_{{S}}$ </tex-math></inline-formula> ) and 65.7% AP in a Dair-V2X-I dataset. Futhermore, we conducted an extension of the FPP module to mainstream object detection networks such as CenterNet, Faster-Rcnn, and RetinaNet. Experimental results show that the proposed module can effectively improve small object detection accuracy of the networks mentioned earlier.
Federated Ensemble Model-Based Reinforcement Learning in Edge Computing

Federated learning (FL) is a privacy-preserving distributed machine learning paradigm that enables collaborative training among geographically distributed and heterogeneous devices without gathering their data. Extending FL beyond the supervised learning models, federated reinforcement learning (FRL) was proposed to handle sequential decision-making problems in edge computing systems. However, the existing FRL algorithms directly combine model-free RL with FL, thus often leading to high sample complexity and lacking theoretical guarantees. To address the challenges, we propose a novel FRL algorithm that effectively incorporates model-based RL and ensemble knowledge distillation into FL for the first time. Specifically, we utilise FL and knowledge distillation to create an ensemble of dynamics models for clients, and then train the policy by solely using the ensemble model without interacting with the environment. Furthermore, we theoretically prove that the monotonic improvement of the proposed algorithm is guaranteed. The extensive experimental results demonstrate that our algorithm obtains much higher sample efficiency compared to classic model-free FRL algorithms in the challenging continuous control benchmark environments under edge computing settings. The results also highlight the significant impact of heterogeneous client data and local model update steps on the performance of FRL, validating the insights obtained from our theoretical analysis.
Advancements in 2D/3D Image Registration Methods

In this paper, we present methods for identifying an image from a given set of Radon projections.Given a suitably regular 2-D or 3-D function, we form a new function from using a linear transformation.We show how the Radon projections of and can be used to determine the transformation.The proposed algorithms introduce three major contributions, (1) Improvements on the 2-D setting using the moments of the Radon projections with only two orthogonal projections.(2) A natural extension of the 2-D setting to work with the 3-D setting.In particular, reducing the 3-D problem to a 2-D problem so that we can recover a translation, a scaling, or a rotation transformation of a 3-D object in the Radon domain.(3) An efficient method of recovering a rotation of a 3-D image around an arbitrary axis and an angle of rotation.
Hierarchical Contrastive Learning Enhanced Heterogeneous Graph Neural Network

Heterogeneous graph neural networks (HGNNs) as an emerging technique have shown superior capacity of dealing with heterogeneous information network (HIN). However, most HGNNs follow a semi-supervised learning manner, which notably limits their wide use in reality since labels are usually scarce in real applications. Recently, contrastive learning, a self-supervised method, becomes one of the most exciting learning paradigms and shows great potential when there are no labels. In this paper, we study the problem of self-supervised HGNNs and propose a novel co-contrastive learning mechanism for HGNNs, named HeCo. Different from traditional contrastive learning which only focuses on contrasting positive and negative samples, HeCo employs cross-view contrastive mechanism. Specifically, two views of a HIN (network schema and meta-path views) are proposed to learn node embeddings, so as to capture both of local and high-order structures simultaneously. Then the cross-view contrastive learning, as well as a view mask mechanism, is proposed, which is able to extract the positive and negative embeddings from two views. This enables the two views to collaboratively supervise each other and finally learn high-level node embeddings. Moreover, to further boost the performance of HeCo, two additional methods are designed to generate harder negative samples with high quality. The essence of HeCo is to make positive samples from different views close to each other by cross-view contrast, and learn the factors invariant to two proposed views. However, besides the invariant factors, view-specific factors complementally provide the diverse structure information between different nodes, which also should be contained into the final embeddings. Therefore, we need to further explore each view independently and propose a modified model, called HeCo++. Specifically, HeCo++ conducts hierarchical contrastive learning, including cross-view and intra-view contrasts, which aims to enhance the mining of respective structures. Extensive experiments conducted on a variety of real-world networks show the superior performance of the proposed methods over the state-of-the-arts.
Improved Abdominal Multi-Organ Segmentation via 3D Boundary-Constrained Deep Neural Networks

Quantitative assessment of the abdominal region from CT scans requires the accurate delineation of abdominal organs. Therefore, automatic abdominal image segmentation has been the subject of intensive research for the past two decades. Recently, deep learning-based methods have resulted in state-of-the-art performance for the 3D abdominal CT segmentation. However, the complex characterization of abdominal organs with weak boundaries prevents the deep learning methods from accurate segmentation. Specifically, the voxels on the boundary of organs are more vulnerable to misprediction due to the highly-varying intensities. This paper proposes a method for improved abdominal image segmentation by leveraging organ-boundary prediction as a complementary task. We train 3D encoder-decoder networks to simultaneously segment the abdominal organs and their boundaries via multi-task learning. We explore two network topologies based on the extent of weights shared between the two tasks within a unified multi-task framework. In the first topology, the whole-organ prediction task and the boundary detection task share all the layers in the network except for the last task-specific layers. The second topology employs a single shared encoder but two separate task-specific decoders. The effectiveness of utilizing the organs’ boundary information for abdominal multi-organ segmentation is evaluated on two publically available abdominal CT datasets: Pancreas-CT and the BTCV dataset. The improvements shown in segmentation results reveal the advantage of the multi-task training that forces the network to pay attention to ambiguous boundaries of organs. A maximum relative improvement of 3.5% and 3.6% is observed in Mean Dice Score for Pancreas-CT and BTCV datasets, respectively.
An Efficient Online/Offline Heterogeneous Signcryption Scheme With Equality Test for IoVs

The privacy and authenticity of propagated messages are two important goals in Internet of Vehicles (IoVs). Most of existing signcryption schemes can satisfy above requirements, but they are rigidly compatible for a single cryptosystem rather than a heterogeneous system consisting of different cryptographic mechanisms. In this paper, we construct an efficient online/offline heterogeneous signcryption scheme with equality test (OOHSC-ET), in which the signcrypted message is delivered from certificateless cryptosystem (CLC) to public key infrastructure (PKI) system for information compatibility. Besides, this construction allows the cloud server to perform the equality test, to determine whether the different ciphertexts contain the same plaintext without unsigncryption, as a feasible solution for searching ciphertext in heterogeneous environment. In addition, the signcryption phase can be separated into online and offline manners, the former conducts the lightweight operation, while the latter initializes the heavy and preparative operation. In this way, the security and efficiency are both preserved. Finally, the security proof and performance analysis demonstrate that the OOHSC-ET scheme is more suitable for the payment operation in terms of IoVs.
A Differentially Private Method for Distributed Optimization in Directed Networks via State Decomposition

In this article, we study the problem of consensus-based distributed optimization, where a network of agents, abstracted as a directed graph, aims to minimize the sum of all agents' cost functions collaboratively. In the existing distributed optimization approaches (Push-Pull/AB) for directed graphs, all the agents exchange their states with neighbors to achieve the optimal solution with a constant step size, which may lead to the disclosure of sensitive and private information. For privacy preservation, we propose a novel state-decomposition-based gradient tracking approach (SD-Push-Pull) for distributed optimization over directed networks that preserves differential privacy, which is a strong notion that protects agents' privacy against an adversary with arbitrary auxiliary information. The main idea of the proposed approach is to decompose the gradient state of each agent into two substates. Only one substate is exchanged by the agent with its neighbors over time, and the other one is not shared. That is to say, only one substate is visible to an adversary, protecting the sensitive information from being leaked. It is proved that under certain decomposition principles, a bound for the suboptimality of the proposed algorithm can be derived, and the differential privacy is achieved simultaneously. Moreover, the tradeoff between differential privacy and the optimization accuracy is also characterized. Finally, a numerical simulation is provided to illustrate the effectiveness of the proposed approach.