Reviewerly/exHarmony huggingface dataset:
A knowledge-guided graph attention network for emotion-cause pair extraction

Emotion-Cause Pair Extraction (ECPE) is a research objective focused on identifying and extracting all emotion-clause and cause-clause pairs from unannotated emotional text. Traditional methodologies have predominantly employed attention mechanisms or joint learning techniques for feature information interaction. However, these approaches often overlook the aggregation of features under the guidance of external knowledge. To enhance performance in addressing this ECPE challenge, we propose a novel knowledge-guided graph attention network, i.e., GAT-ECPE. This model chiefly relies on an interclause dependency graph as a guiding principle. By employing this knowledge-guided graph attention network, we can proficiently combine semantic and structural information between clauses. In addition, an interpair possibility graph, derived from the outcomes of subtasks, is integrated as an additional guiding principle. As such, we are able to aggregate features between clause pairs, thereby facilitating interaction between multiple tasks. Extensive experiments were conducted to validate our proposed model, and the obtained results demonstrate its superiority when compared to 12 considered baselines. In terms of performance metrics, our model achieves an F1 score F1 of 74.92% and a recall R of 77.52%. These values significantly outperform those achieved by state-of-the-art approaches, indicating the effectiveness and superiority of our GAT-ECPE.
Blockchain-based access control architecture for multi-domain environments

Numerous users from diverse domains access information and perform various operations in multi-domain environments. These users have complex permissions that increase the risk of identity falsification, unauthorized access, and privacy breaches during cross-domain interactions. Consequently, implementing an access control architecture to prevent users from engaging in illicit activities is imperative. This paper proposes a novel blockchain-based access control architecture for multi-domain environments. By integrating the multi-domain environment within a federated chain, the architecture utilizes Decentralized Identifiers (DIDs) for user identification and relies on public/secret key pairs for operational execution. Verifiable credentials are used to authorize permissions and release resources, thereby ensuring authentication and preventing tampering and forgery. In addition, the architecture automates the authorization and access control processes through smart contracts, thereby eliminating human intervention. Finally, we performed a simulation evaluation of the architecture. The most time-consuming process had a runtime of 1074 ms, primarily attributed to interactions with the blockchain. Concurrent testing revealed that with a concurrency level of 2000 demonstrated that the response times for read and write operations were maintained within 1000 ms and 4600 ms, respectively. In terms of storage efficiency, except for user registration, which incurred two gas charges, all the other processes required only one charge.
Memory-enhanced appearance-motion consistency framework for video anomaly detection

Modern network communication systems extensively utilize video data for various applications, creating a pressing need for efficient Video Anomaly Detection (VAD) mechanisms. The essence of VAD lies in the detection of discrepancies in the appearance-motion dynamics of normal and abnormal events. However, existing methods primarily focus on the isolated analysis of appearance or motion anomalies, thereby overlooking the crucial aspect of appearance-motion consistency semantics. To this end, we introduce a Memory-enhanced Appearance-Motion Consistency (MAMC) framework, which is focused on understanding complex appearance-motion consistency patterns in video data for the discernment of anomalies. The first stage of our model involves the design of an Appearance-Motion Fusion (AMF) module, engineered to generate a robust representation of scene dynamics that captures appearance-motion consistency. This consistency data is then processed through the memory module, augmenting the distinction between normal and anomalous events. Experimental results on three benchmark datasets validate the effectiveness of our approach, which achieves AUCs of 96.7%, 87.6%, and 71.5% on three benchmark datasets (UCSD Ped2, CUHK Avenue, and ShanghaiTech datasets). Additional experiments and visualization analysis confirm the effectiveness of the proposed MAMC framework in anomaly detection scenarios.
Information gain ratio-based subfeature grouping empowers particle swarm optimization for feature selection

Feature selection is a critical preprocessing step in machine learning with significant real-world applications. Despite the widespread use of particle swarm optimization (PSO) for feature selection, owing to its robust global search capabilities, developing an effective PSO method for this task is still a substantial challenge. This study introduces a novel PSO variant, ISPSO, which integrates the information gain ratio for assessing feature importance. ISPSO's feature selection process involves partitioning features into distinct groups to establish the initial population. Recognizing that feature selection tasks are inherently binary, ISPSO replaces the traditional PSO velocity concept with a probabilistic approach. In addition, introducing a penalty term enhances the algorithm's ability to achieve superior results. Experimental evaluations on 16 datasets consistently show that ISPSO surpasses compared algorithms, highlighting its efficiency in eliminating redundant and irrelevant features.
Functional Mixed Membership Models

Mixed membership models, or partial membership models, are a flexible unsupervised learning method that allows each observation to belong to multiple clusters. In this article, we propose a Bayesian mixed membership model for functional data. By using the multivariate Karhunen-Loève theorem, we are able to derive a scalable representation of Gaussian processes that maintains data-driven learning of the covariance structure. Within this framework, we establish conditional posterior consistency given a known feature allocation matrix. Compared to previous work on mixed membership models, our proposal allows for increased modeling flexibility, with the benefit of a directly interpretable mean and covariance structure. Our work is motivated by studies in functional brain imaging through electroencephalography (EEG) of children with autism spectrum disorder (ASD). In this context, our work formalizes the clinical notion of "spectrum" in terms of feature membership proportions. Supplementary materials, including proofs, are available online. The R package BayesFMMM is available to fit functional mixed membership models.
Recovering a clean background: A parallel deep network architecture for single-image deraining

Deep convolutional neural networks have been popularly applied in single-image deraining recently. Nevertheless, as the network becomes deeper, it is easy to cause training over-fitting and performance saturation, particularly in the case of insufficient training data. In this paper, we report the design of a new network, namely parallel deraining convolutional neural network (PARDNet), for single-image deraining. Specifically, PARDNet adopts two parallel residual sub-networks based on different receptive fields to extract more comprehensive characteristics of the rain streaks, as well as decrease the depth of the network. The hybrid dilated convolution is employed to enlarge the sub-network's receptive field to capture more context information. The efficient channel attention module is integrated into the proposed PARDNet to capture rain streaks more effectively and preserve more background details. Furthermore, to facilitate the network training, the residual learning is also fused into PARDNet in a holistic way. Extensive experiments on synthetic and real-world rainy image datasets demonstrate the superiority of PARDNet for single-image deraining.
Energy-latency trade-off analysis for scientific workflow in cloud environments: The role of processor utilization ratio and mean grey wolf optimizer

Cloud computing has demonstrated its effectiveness in handling complex data that requires substantial computational power, immediate responsiveness, and ample storage capacity. However, there are significant challenges when it comes to distributing intricate tasks, such as workflows structured in a Directed Acyclic Graph (DAG), across cloud resources. These challenges include increased energy consumption and delays experienced by end-users due to geographical disparities between users and service providers. To address these issues, this research work introduces an innovative solution that utilizes a multi-objective Grey Wolf Optimizer (GWO) algorithm for optimizing the allocation of complex workflows. The primary objective of this workflow optimization is to achieve the highest possible processor utilization ratio while adhering to latency constraints. This approach consists of a two-step process. In the initial step, it assesses processor utilization by calculating the number of completed transactions within a specified period divided by the average power consumption. This evaluation ensures that processors operate at the most power-efficient utilization levels, resulting in the highest service profit. In the second step, an optimal mapping algorithm assigns workflow tasks to the most efficient destinations. This is accomplished by applying the Mean Grey Wolf Optimization (GWO) algorithm, which is a metaheuristic technique. The goal is to find an optimal pairing of tasks and virtual machines (VMs) that balances multiple objectives. The simulation of this approach was conducted using the Java-based open-source CloudSim simulator version 5.0. The experimental results from a series of trials indicate that the ELSCiW approach leads to various improvements, including a reduction in latency ranging from approximately 5.35% to 12.92%, a decrease in end-to-end delay costs of about 5.03% to 13.80%, a reduction in energy consumption within the range of 4.71% to 11.19%, an enhancement in load balancing of around 14.84% to 37.30%, an improvement in performance degradation of about 12.52% to 35.14%, a decrease in overhead by approximately 17.27% to 26.92%, and an increase in the quality of service of roughly 56.21% to 64.85%. Furthermore, the ELSCiW approach outperforms recent state-of-the-art metaheuristic methods, as confirmed by statistical findings that emphasize the robustness of the proposed algorithm.
Knowledge-based dynamic prompt learning for multi-label disease diagnosis

Pretrained language models (PLMs) have been developed rapidly which establish impressive performance on many open-domain downstream tasks. However, conducting these pretrained models directly without additional network architectures on special domain tasks like multi-label disease diagnosis cannot perform well. Recently, prompt learning has been a new paradigm in PLM field which is more convenient and well-performed than the traditional fine-tuning approach for different domain tasks. However, prompt engineering is challenging because it takes time and experience. In this paper, we propose a new prompt learning method named Knowledge-based Dynamic PrompT (KBDPT) to deal with these problems. Firstly, we import medical knowledge into PLMs by prompt templates which make results of the disease diagnosis more reasonable and qualified. Compared with the fine-tuning approach, this method needs fewer trainable parameters and less training data but achieve better performance. Secondly, unlike most existing pre-defined prompt methods, KBDPT dynamically generates prompts based on personal medical information and a large-scale medical knowledge graph, which can provide more valuable guidance information for disease diagnosis. Lastly, the proposed model also ensembles multiple prompts from all possible diseases to introduce more knowledge and obtain differential diagnosis results. Experiments of multi-label disease diagnosis are conduct on three real-world EMR datasets. Results demonstrate that our model can be used in various pretrained models and outperform both classical deep learning methods and fine-tuning PLMs. The source code of our proposed model has been released at: https://github.com/loxs123/KBDPT.
Multiscale cascaded domain-based approach for Arabic fake reviews detection in e-commerce platforms

Fake reviews in e-commerce can lead to customer deception and financial losses. Despite the importance of fake reviews detection, studies for Arabic language are scarce due to the lack of comprehensive datasets. This study addresses this gap by introducing a full-gold standard dataset, the Arabic Fake Reviews Detection (AFRD), across hotels, restaurants, and product domains. To identify the most effective model for each domain in the context of fake review detection, this research employed Bi-LSTM, Bi-GRU, CNN+Bi-LSTM, and CNN+Bi-GRU models. These models were then used in a cascading approach called Multiscale Cascaded domain-based (MCDB), which transfers knowledge from one domain to enhance results in other domains. Experimental results demonstrated that the MCDB approach improved the results of the models by 2.09% to 7.8% in terms of accuracy. The introduced dataset can be used to build effective models for Arabic e-commerce platforms, in addition to further Natural Language Processing applications. This study demonstrates that leveraging domain-specific datasets in a cascading manner can significantly improve performance, holding substantial implications for future research in problems with limited-size datasets.
Treatment of non-Markovian effects to investigate non-locality, dense coding and non-local information

In this contribution, we explore the time-evolution of Bell non-locality, dense coding capacity, local and non-local information with an exact solution in a two-qubit system, for the case in which the second qubit is coupled to a Markovian reservoir. Our results reveal a significant relationship between dense coding capacity and the behavior of Bell non-locality. In particular, we observe that optimal dense coding occurs when the violation of Bell non-locality is at its maximum. Furthermore, we study the dynamics of both local and non-local information, considering different initial states, including maximally/partially entangled states. However, our outcomes indicate that all quantities are sensitive to temperature variations, where a temperature threshold is obtained. Beyond this threshold, all proposed quantities exhibit unstable behavior. Specifically, Bell non-locality and dense coding capacity stabilize eventually to steady values below their classical limits. Moreover, the non-Markovian behavior is more prominent when using partially entangled states compared to those used maximally.
A novel approach for breast cancer detection using optimized ensemble learning framework and XAI

Breast cancer (BC) is a common and highly lethal ailment. It stands as the second leading contributor to cancer-related deaths in women worldwide. The timely identification of this condition is of utmost importance in mitigating mortality rates. This research paper presents a novel framework for the precise identification of BC, utilising a combination of image and numerical data features with explainable Artificial Intelligence (XAI). The utilisation of the U-NET transfer learning model is employed for image-based prediction. Additionally, an ensemble model is constructed by integrating characteristics from a customised convolutional neural network (CNN) model with an ensemble comprising random forest (RF) and support vector machine (SVM). The experiments aim to evaluate the influence of original features compared to convoluted features. A comparative analysis is carried out to assess the efficacy of various classifiers in accurately detecting BC, utilising the Wisconsin dataset. The model under consideration exhibits promising capabilities in enhancing BC diagnosis, with a remarkable accuracy rate of 99.99%. The present study contributes to the advancement of BC diagnosis by introducing a novel strategy based on machine learning and discussing the interpretation of the variables using XAI. The primary objective of this approach is to get a notable level of precision, hence facilitating the early and reliable identification of BC. Ultimately, the implementation of this approach is expected to enhance patient outcomes.
An iterated greedy heuristic for collaborative Human-UAV search of missing tourists

This paper investigates a problem of collaborative human-unmanned aerial vehicle (UAV) search for missing tourists. A mathematical model including detection stage and rescue stage is constructed to describe practical operation scenarios. The objective function is formulated using conditional probability theory by introducing the weighted probability that tourists being rescued at an allowable time limit. To solve this combinatorial optimization problem, an iterated greedy heuristic (IGH) is designed to search solution space. IGH starts with an initialization procedure based on problem-specific knowledge, then a neighborhood perturbation-based local search method is implemented on perturbed solution to intensify exploitation capability. Meanwhile, an acceptance criterion is presented to avoid premature convergence. Ultimately, Taguchi method is utilized to calibrate parameters of IGH for best performance. Comparison with other algorithms on objective function value and search performance validates the effectiveness of our proposed algorithm.
A federated learning system with data fusion for healthcare using multi-party computation and additive secret sharing

In the Internet of medical things, data from a single source can be easily analyzed. Besides, it is paramount to collect data from multiple sources to provide consistent, accurate, and vital information as compared to that collected from a single source. Data fusion enables the combination of data from multiple sensors. However, privacy concerns need to be addressed during data aggregation. Furthermore, since medical data is becoming increasingly sophisticated via multimodal means, there is a high likelihood that a complex relationship among biological processes exists. This challenge motivates the study. This study is proposing a secure federated learning system based on data fusion using multi-party computation and additive secret-sharing method. The gradient parameters of the federated training model are protected rather than the actual data. The purpose of protecting the gradient parameters is to address the tradeoff between prediction accuracy and data privacy. In the proposed model, a convolutional neural network, which has an efficient weight-sharing approach, is employed for the prediction. The weight of the model is encrypted using a multi-computation and additive secret sharing. Extensive simulations are carried out to ascertain the efficiency of the proposed model. Besides, the proposed system model is compared with existing works in the literature in terms of accuracy, precision, recall and f1-score. Simulation results show that the proposed model has 97% accuracy as compared to 93% for random forest, 91% for Naive Bayes, 92% for logistic regression and 95% for decision tree. Furthermore, F1-score of the proposed model is 89% as compared to 85% for random forest, 80% for Naive Bayes, 82% for logistic regression and 79% for decision tree. Security analysis of the proposed system model is performed, which shows that the system is robust against weighted majority vote attack, honest-but-curious party and other security related attacks.
Post-training quantization for re-parameterization via coarse &amp; fine weight splitting

Although neural networks have made remarkable advancements in various applications, they require substantial computational and memory resources. Network quantization is a powerful technique to compress neural networks, allowing for more efficient and scalable AI deployments. Recently, Re-parameterization has emerged as a promising technique to enhance model performance while simultaneously alleviating the computational burden in various computer vision tasks. However, the accuracy drops significantly when applying quantization on the re-parameterized networks. We identify that the primary challenge arises from the large variation in weight distribution across the original branches. To address this issue, we propose a coarse & fine weight splitting (CFWS) method to reduce quantization error of weight, and develop an improved KL metric to determine optimal quantization scales for activation. To the best of our knowledge, our approach is the first work that enables post-training quantization applicable on re-parameterized networks. For example, the quantized RepVGG-A1 model exhibits a mere 0.3% accuracy loss. The code is in https://github.com/NeonHo/Coarse-Fine-Weight-Split.git
Three dimensional tracking of rigid objects in motion using 2D optical flows

Detecting and tracking objects in image sequences is paramount for any video analytic. While object detectors have become increasingly robust, motion estimates based on the straightforward approach of running a detector and linking detections are prone to bounding box noise. An algorithm for the monocular estimation of the 3D motion of rigid objects is presented, combining an object detector, minimal camera calibration, and 2D optical flows observed on the image plane. The algorithm utilizes the 2D Delaunay triangulation over geometrically consistent optical flow tracks to identify regions common to the same object. The algorithm is evaluated on the special case of image sequences of vehicles on roads. Experiments on BrnoCompSpeed dataset show that speed estimates from both detector-based tracking (mean error of 1.62 km/h) and optical flow based tracking (mean error of 2.19 km/h) perform competitively on straight roads. An extensive empirical evaluation is also conducted on a new dataset containing synthetic and real world scenes that also include vehicle trajectories involving rotation. A naive baseline bounding box track based method obtained a mean error of 5.29 km/h for vehicle speed, but optical flow tracks performed significantly better with mean error of 1.65 km/h on the new dataset including rotation. With the new method, video analytics such as vehicle speed estimation and lane change detection can obtain precise information about the 3D trajectory of rigid objects in motion.
Speech driven video editing via an audio-conditioned diffusion model

Taking inspiration from recent developments in visual generative tasks using diffusion models, we propose a method for end-to-end speech-driven video editing using a denoising diffusion model. Given a video of a talking person, and a separate auditory speech recording, the lip and jaw motions are re-synchronised without relying on intermediate structural representations such as facial landmarks or a 3D face model. We show this is possible by conditioning a denoising diffusion model on audio mel spectral features to generate synchronised facial motion. Proof of concept results are demonstrated on both single-speaker and multi-speaker video editing, providing a baseline model on the CREMA-D audiovisual data set. To the best of our knowledge, this is the first work to demonstrate and validate the feasibility of applying end-to-end denoising diffusion models to the task of audio-driven video editing. All code, datasets, and models used as part of this work are made publicly available here: https://danbigioi.github.io/DiffusionVideoEditing/.