Reviewerly/exHarmony huggingface dataset:
Ensemble-NQG-T5: Ensemble Neural Question Generation Model Based on Text-to-Text Transfer Transformer

Deep learning chatbot research and development is exploding recently to offer customers in numerous industries personalized services. However, human resources are used to create a learning dataset for a deep learning chatbot. In order to augment this, the idea of neural question generation (NQG) has evolved, although it has restrictions on how questions can be expressed in different ways and has a finite capacity for question generation. In this paper, we propose an ensemble-type NQG model based on the text-to-text transfer transformer (T5). Through the proposed model, the number of generated questions for each single NQG model can be greatly increased by considering the mutual similarity and the quality of the questions using the soft-voting method. For the training of the soft-voting algorithm, the evaluation score and mutual similarity score weights based on the context and the question–answer (QA) dataset are used as the threshold weight. Performance comparison results with existing T5-based NQG models using the SQuAD 2.0 dataset demonstrate the effectiveness of the proposed method for QG. The implementation of the proposed ensemble model is anticipated to span diverse industrial fields, including interactive chatbots, robotic process automation (RPA), and Internet of Things (IoT) services in the future.
Adaptive Differential Privacy Mechanism Based on Entropy Theory for Preserving Deep Neural Networks

Recently, deep neural networks (DNNs) have achieved exciting things in many fields. However, the DNN models have been proven to divulge privacy, so it is imperative to protect the private information of the models. Differential privacy is a promising method to provide privacy protection for DNNs. However, existing DNN models based on differential privacy protection usually inject the same level of noise into parameters, which may lead to a balance between model performance and privacy protection. In this paper, we propose an adaptive differential privacy scheme based on entropy theory for training DNNs, with the aim of giving consideration to the model performance and protecting the private information in the training data. The proposed scheme perturbs the gradients according to the information gain of neurons during training, that is, in the process of back propagation, less noise is added to neurons with larger information gain, and vice-versa. Rigorous experiments conducted on two real datasets demonstrate that the proposed scheme is highly effective and outperforms existing solutions.
A question answering system based on mineral exploration ontology generation: A deep learning methodology

Mineral exploration reports and documents are a rich data source that contains a large amount of geological environments in which mineral deposits form. Among them, it is difficult to extract the required answers from the large amount of geological data. Despite the availability of search engines and digital databases that can be used to store geological data, users are unable to retrieve the information needed for a specific field in a timely manner. As a result, users usually have to contend with the burden of browsing and filtering information, which can be a time-consuming process. To address this issue, we propose a robust end-to-end approach that can improve the efficiency and effectiveness of retrieving queries related to mineral exploration terms. First, we present an automated workflow for constructing automatic question-and-answer datasets based on the names and definitions in the mineral exploration ontology. The Bidirectional Encoder Representation from Transformers (BERT) model is trained to test the answers generated from the user input question. Finally, a prototype chatbot system based on the WeChat platform and constructed experiments for evaluation is presented. Our proposed method has powerful feature representation and learning capabilities and thus has the potential to be adopted by other specialized fields (especially where a large number of mineral exploration ontologies already exist).
AERF: Adaptive ensemble random fuzzy algorithm for anomaly detection in cloud computing

The abnormality of system behavior is inevitable in cloud computing because of its complexity and scale. How to perform anomaly detection on the system’s operating data to discover abnormal behavior has become a popular research field. However, anomaly detection is a challenging research problem because data in cloud computing scenarios is continuous, imbalanced, and cannot be acquired at once. In this work, an adaptive ensemble random fuzzy (AERF) algorithm is proposed for anomaly detection in cloud computing systems. The random fuzzy rule-based method in the AERF selects samples randomly to enhance the diversity of base classifiers for dealing with disturbances caused by abnormal sample distribution effectively. A dynamic weighting strategy is used for fuzzy classifier ensembles to improve processing efficiency and anomaly detection accuracy. Experimental results show that the AERF yields 10% higher AUC and G-mean than existing methods on SMD and EMOSCloud datasets and 20% higher AUC and G-mean on five benchmarking datasets. The AERF yields 20% higher F1 than existing methods on EMOSCloud dataset. For all datasets, the AERF yields 50% faster training time than other methods.
Encipher GAN: An End-to-End Color Image Encryption System Using a Deep Generative Model

Chaos-based image encryption schemes are applied widely for their cryptographic properties. However, chaos and cryptographic relations remain a challenge. The chaotic systems are defined on the set of real numbers and then normalized to a small group of integers in the range 0–255, which affects the security of such cryptosystems. This paper proposes an image encryption system developed using deep learning to realize the secure and efficient transmission of medical images over an insecure network. The non-linearity introduced with deep learning makes the encryption system secure against plaintext attacks. Another limiting factor for applying deep learning in this area is the quality of the recovered image. The application of an appropriate loss function further improves the quality of the recovered image. The loss function employs the structure similarity index metric (SSIM) to train the encryption/decryption network to achieve the desired output. This loss function helped to generate cipher images similar to the target cipher images and recovered images similar to the originals concerning structure, luminance and contrast. The images recovered through the proposed decryption scheme were high-quality, which was further justified by their PSNR values. Security analysis and its results explain that the proposed model provides security against statistical and differential attacks. Comparative analysis justified the robustness of the proposed encryption system.
Virtual reality technology in neuropsychological testing: A systematic review

Neuropsychological testing aims to measure individuals' cognitive abilities (e.g. memory, attention), analysing their performance on specific behavioural tasks. Most neuropsychological tests are administered in the so-called 'paper-and-pencil' modality or via computerised protocols. The adequacy of these procedures has been recently questioned, with more specific concerns about their ecological validity, i.e. the relation between test scores observed in the laboratory setting and the actual everyday cognitive functioning. In developing more ecological tasks, researchers started to implement virtual reality (VR) technology as an administration technique focused on exposing individuals to simulated but realistic stimuli and environments, maintaining at the same time a controlled laboratory setting and collecting advanced measures of cognitive functioning. This systematic review aims to present how VR procedures for neuropsychological testing have been implemented in the last years. We initially explain the rationale for supporting VR as an advanced assessment tool, but we also discuss the challenges and risks that can limit the widespread implementation of this technology. Then, we systematised the large body of studies adopting VR for neuropsychological testing, describing the VR tools' distribution amongst different cognitive functions through a PRISMA-guided systematic review. The systematic review highlighted that only very few instruments are ready for clinical use, reporting psychometric proprieties (e.g. validity) and providing normative data. Most of the tools still need to be standardised on large cohorts of participants, having published only limited data on small samples up to now. Finally, we discussed the possible future directions of the VR neuropsychological test development linked to technological advances.
Applying Grover's Algorithm to Hash Functions: A Software Perspective

Quantum software frameworks provide software engineers with the tools to study quantum algorithms as applied to practical problems. We implement classical hash functions MD5, SHA-1, SHA-2, and SHA-3 as quantum oracles to study the computational resource requirements of conducting a preimage attack with Grover's Algorithm. We introduce an improvement to the SHA-3 oracle that reduces the number of logical qubits required in the Keccak block permutation by 40%.
FedDM: Federated Weakly Supervised Segmentation via Annotation Calibration and Gradient De-Conflicting

Weakly supervised segmentation (WSS) aims to exploit weak forms of annotations to achieve the segmentation training, thereby reducing the burden on annotation. However, existing methods rely on large-scale centralized datasets, which are difficult to construct due to privacy concerns on medical data. Federated learning (FL) provides a cross-site training paradigm and shows great potential to address this problem. In this work, we represent the first effort to formulate federated weakly supervised segmentation (FedWSS) and propose a novel Federated Drift Mitigation (FedDM) framework to learn segmentation models across multiple sites without sharing their raw data. FedDM is devoted to solving two main challenges (i.e., local drift on client-side optimization and global drift on server-side aggregation) caused by weak supervision signals in FL setting via Collaborative Annotation Calibration (CAC) and Hierarchical Gradient De-conflicting (HGD). To mitigate the local drift, CAC customizes a distal peer and a proximal peer for each client via a Monte Carlo sampling strategy, and then employs inter-client knowledge agreement and disagreement to recognize clean labels and correct noisy labels, respectively. Moreover, in order to alleviate the global drift, HGD online builds a client hierarchy under the guidance of history gradient of the global model in each communication round. Through de-conflicting clients under the same parent nodes from bottom layers to top layers, HGD achieves robust gradient aggregation at the server side. Furthermore, we theoretically analyze FedDM and conduct extensive experiments on public datasets. The experimental results demonstrate the superior performance of our method compared with state-of-the-art approaches. The source code is available at https://github.com/CityU-AIM-Group/FedDM.
Semantic Communication for the Internet of Vehicles: A Multiuser Cooperative Approach

The Internet of Vehicles (IoV) is expected to become the central infrastructure to provide advanced services to connected vehicles and users for higher transportation efficiency and security. A variety of emerging applications/services bring explosively growing demands for mobile data traffic between connected vehicles and roadside units (RSUs), imposing the significant challenge of spectrum scarcity in the IoV. In this article, we propose a cooperative semantic-aware architecture to convey essential semantics from collaborated users to servers for lessening data traffic. In contrast to current solutions that are mainly based on piling up highly complex signal processing techniques and multiple access capabilities in terms of syntactic communications, this article puts forth the idea of semantic-aware content delivery in the IoV. Specifically, the successful transmission of essential semantics of the source data is pursued rather than the accurate reception of symbols regardless of their meaning, as in conventional syntactic communications. To assess the benefits of the proposed architecture, we provide a case study of an image retrieval task for vehicles in intelligent transportation systems (ITSs). Simulation results demonstrate that the proposed architecture outperforms existing solutions, with fewer radio resources, especially in a low-signal-to-noise-ratio (SNR) regime, which can shed light on the potential of the proposed architecture in extending applications in extreme environments.
Knowledge representation and reasoning using interconnected uncertain rules for describing workflows in complex systems

Knowledge representation and reasoning (KRR) in complex systems (CSs) usually require facts from multiple experts having complementary backgrounds to fuse together. Consequently, such KRR methods should provide universal modeling languages close to human reasoning, with increased expressiveness and efficient capabilities to describe uncertainties. In this context, this paper introduces a new modeling formalism entitled Hybrid Logic-Algebraic Relational Modeling which is based on combining logic, probabilities, numerical information and network representations. The behavior, facts and workflows in a CS can be described using an environment of interconnected models enclosing sets of logical rules with attached probabilistic trust factors and links regarding logical attributes and numerical parameters. The logical and probabilistic inference applied to the modeling environment gives valuable knowledge to designers and decision-makers so that they can develop procedures or take actions in managing the CS. In this article, the proposed approach is completely formalized, from concept to definition and proofs and up to implementation, while its usage is illustrated within a complex economic, logistical, economical and technical scenario.
Cyber risk assessment in small and medium‐sized enterprises: A multilevel decision‐making approach for small e‐tailors

The role played by information and communication technologies in today's businesses cannot be underestimated. While such technological advancements provide numerous advantages and opportunities, they are known to thread organizations with new challenges such as cyberattacks. This is particularly important for small and medium-sized enterprises (SMEs) that are deemed to be the least mature and highly vulnerable to cybersecurity risks. Thus, this research is set to assess the cyber risks in online retailing SMEs (e-tailing SMEs). Therefore, this article employs a sample of 124 small e-tailers in the United Kingdom and takes advantage of a multi-criteria decision analysis (MCDA) method. Indeed, we identified a total number of 28 identified cyber-oriented risks in five exhaustive themes of "security," "dependency," "employee," "strategic," and "legal" risks. Subsequently, an integrated approach using step-wise weight assessment ratio analysis (SWARA) and best-worst method (BWM) has been employed to develop a pathway of risk assessment. As such, the current study outlines a novel approach toward cybersecurity risk management for e-tailing SMEs and discusses its effectiveness and contributions to the cyber risk management literature.
Quantum grayscale image encryption and secret sharing schemes based on Rubik’s Cube

In order to encrypt and safely share images using quantum computer, a quantum grayscale image encryption scheme and a quantum grayscale image secret sharing scheme are proposed in this paper. According to Rubik's Cube rotation method, this paper initially puts forward the quantum Rubik's Cube rotation scrambling scheme (QRRSS) to permute the pixel position. Then, based on QRRSS, quantum XOR operation and quantum SWAP operation, we design the quantum image encryption scheme. Finally, we propose a quantum image secret sharing scheme. In the secret sharing scheme, one secret image and six cover images are encrypted. Quantum least significant bit (LSB) method is used to stego the secret image into four randomly chosen cover images in order to generate four shadow images. The shared secret image can be recovered if all the shadow images can be collected from all participants. We then design the whole quantum circuits of two proposed schemes and finish simulation experiments. The simulation results indicate that our encryption scheme and secret sharing scheme have good effect. It is worth noting that the computational complexity of our two proposed quantum algorithms is exponentially decreased than the classical counterparts.
Perceptual Learned Source-Channel Coding for High-Fidelity Image Semantic Transmission

As one novel approach to realize end-to-end wireless image semantic transmission, deep learning-based joint source-channel coding (deep JSCC) method is emerging in both deep learning and communication communities. However, current deep JSCC image transmission systems are typically optimized for traditional distortion metrics such as peak signal-to-noise ratio (PSNR) or multi-scale structural similarity (MS-SSIM). But for low transmission rates, due to the imperfect wireless channel, these distortion metrics lose significance as they favor pixel-wise preservation. To account for human visual perception in semantic communications, it is of great importance to develop new deep JSCC systems optimized beyond traditional PSNR and MS-SSIM metrics. In this paper, we introduce adversarial losses to optimize deep JSCC, which tends to preserve global semantic information and local texture. Our new deep JSCC architecture combines encoder, wireless channel, decoder/generator, and discriminator, which are jointly learned under both perceptual and adversarial losses. Our method yields human visually much more pleasing results than state-of-the-art engineered image coded transmission systems and traditional deep JSCC systems. A user study confirms that achieving perceptually similar end-to-end image transmission quality, the proposed method can save about 50% wireless channel bandwidth costs.
Vine Spread for Superpixel Segmentation

Superpixel is the over-segmentation region of an image, whose basic units "pixels" have similar properties. Although many popular seeds-based algorithms have been proposed to improve the segmentation quality of superpixels, they still suffer from the seeds initialization problem and the pixel assignment problem. In this paper, we propose Vine Spread for Superpixel Segmentation (VSSS) to form superpixel with high quality. First, we extract image color and gradient features to define the soil model that establishes a "soil" environment for vine, and then we define the vine state model by simulating the vine "physiological" state. Thereafter, to catch more image details and twigs of the object, we propose a new seeds initialization strategy that perceives image gradients at the pixel-level and without randomness. Next, to balance the boundary adherence and the regularity of the superpixel, we define a three-stage "parallel spreading" vine spread process as a novel pixel assignment scheme, in which the proposed nonlinear velocity for vines helps to form the superpixel with regular shape and homogeneity, the crazy spreading mode for vines and the soil averaging strategy help to enhance the boundary adherence of superpixel. Finally, a series of experimental results demonstrate that our VSSS offers competitive performance in the seed-based methods, especially in catching object details and twigs, balancing boundary adherence and obtaining regular shape superpixels.
Hierarchically stacked graph convolution for emotion recognition in conversation

Accurate emotion recognition can drive the robot to understand human affection intentions precisely and deliver the emotional response when communicating with a person. Recently, graph structure has been applied to explicitly capture the self and inter-dependencies of speakers in the conversation. However, the performance of the method is limited by inadequate discriminative information extraction based on naive graph convolution. In this paper, we propose a novel Hierarchically Stacked Graph Convolution Framework (HSGCF), which leverages hierarchical structure to extract emotional discriminative features. The proposed HSGCF uses five graph convolution layers connected hierarchically to establish a more discriminative emotional feature extractor. More importantly, to mitigate the over-smooth problem caused by deeper networks, Transformer structures with residual connection are introduced into HSGCF. Experimental results on the IEMOCAP benchmark dataset indicate the proposed framework achieves a 4.12% improvement in accuracy and a 4.80% improvement in F1 score compared with the baseline method.
DGSLN: Differentiable graph structure learning neural network for robust graph representations

Recently, graph neural networks (GNNs) have been widely used for graph representation learning, where the central idea is to recursively aggregate neighborhood information to update the node feature based on the graph topology. Therefore, an appropriate graph topology is crucial for effective graph representation learning in GNNs. However, most existing GNNs assume that the initial graph is complete and accurate, and utilize the fixed initial graph structure in the entire network, which may limit the learning representation capability of the model. In this work, we propose a novel differentiable graph structure learning neural network (DGSLN), which learns suitable graph structures for GNNs. Specifically, our DGSLN presents a general graph generation scheme that integrates various useful graph prior messages to generate normal structures. We describe the generation process with homophily, node degree, and sparsity as examples. Moreover, we develop a hybrid loss function to ensure the quality of learned graphs, which combines task-specific loss and graph regularization loss to optimize graph structures from both structural adaptive and task-driven aspects. Extensive experiments on graph classification and node classification have shown that our approach significantly improves performance on different benchmark datasets compared to state-of-the-art GNNs methods.
Detection of Moving Object Using Superpixel Fusion Network

Moving object detection is still a challenging task in complex scenes. The existing methods based on deep learning mainly use U-Nets and have achieved amazing results. However, they ignore the local continuity between pixels. In order to solve this problem, a method based on a superpixel fusion network (SF-Net) is proposed in this article. First, the median filter is used to extract the candidate foreground (called pixel features ) and the image sequence is segmented by superpixel. Then, the histogram features (called superpixel features ) of the candidate foreground superpixels are extracted. Next, the pixel features and the superpixel features are the inputs of SF-Net, respectively. Experiments show the effectiveness of SF-Net on 34 image sequences and the average F-measure reaches 0.84. SF-Net can remove more background noise and has stronger expression ability than a network with the same depth.