Reviewerly/exHarmony huggingface dataset:
Improving Multimodal fusion via Mutual Dependency Maximisation

Multimodal sentiment analysis is a trending area of research, and the multimodal fusion is one of its most active topic.Acknowledging humans communicate through a variety of channels (i.e visual, acoustic, linguistic), multimodal systems aim at integrating different unimodal representations into a synthetic one.So far, a consequent effort has been made on developing complex architectures allowing the fusion of these modalities.However, such systems are mainly trained by minimising simple losses such as L 1 or cross-entropy.In this work, we investigate unexplored penalties and propose a set of new objectives that measure the dependency between modalities.We demonstrate that our new penalties lead to a consistent improvement (up to 4.3 on accuracy) across a large variety of state-of-the-art models on two well-known sentiment analysis datasets: CMU-MOSI and CMU-MOSEI.Our method not only achieves a new SOTA on both datasets but also produces representations that are more robust to modality drops.Finally, a by-product of our methods includes a statistical network which can be used to interpret the high dimensional representations learnt by the model.
Lattice-based public key searchable encryption with fine-grained access control for edge computing

As a bridge between cloud computing platforms and the Internet of Things (IoT) devices, edge computing provides various on-demand data services to reduce latency and network congestion. To ensure data security in edge computing, sensitive data should be encrypted before being outsourced to edge servers. Public key encryption with keyword search (PEKS) can provide search service for encrypted data. Nevertheless, most existing PEKS schemes are susceptible to quantum attacks because their security assumptions are based on traditional hardness assumptions. Moreover, many lattice-based PEKS schemes only apply to the single-user scenario, limiting the range of applications. In this paper, we present an efficient lattice-based public key searchable encryption with fine-grained access control for edge computing. Our scheme achieves post-quantum security and highly flexible access control policies for multi-user applications, by utilizing the learning with errors (LWE) assumption and subset predicate encryption. The security proof illustrates that our proposed scheme is secure under chosen plaintext attacks and chosen keyword attacks. Finally, extensive experiments on real-world datasets illustrate that our encryption algorithm is faster than existing approaches.
FR-Tree: A novel rare association rule for big data problem

In some situations, finding the rare association rule is of higher importance than the frequent itemset. Unique rules represent rare cases, activities, or events in real-world applications. It is essential to extract exceptional critical activity from vast routine data. This paper proposes a new algorithm called FR-Tree to mine the association rules and produce essential rules. This work aims to demonstrate that this algorithm is suitable for extracting rare association rules with high confidence. The proposed algorithm generates, filters, and classifies the all-important rules, either frequent or rare. The rare rules were produced without needing to set an additional threshold. Therefore, the proposed algorithm has an advantage incomparable with the other rare association rule techniques. The generated rules were tested using well-known datasets, and the performance was compared with the other rare association rule techniques. The results proved that our method outperformed the existing rare association rule techniques.
Self-Supervised Graph Learning With Hyperbolic Embedding for Temporal Health Event Prediction

Electronic health records (EHRs) have been heavily used in modern healthcare systems for recording patients' admission information to health facilities. Many data-driven approaches employ temporal features in EHR for predicting specific diseases, readmission times, and diagnoses of patients. However, most existing predictive models cannot fully utilize EHR data, due to an inherent lack of labels in supervised training for some temporal events. Moreover, it is hard for the existing methods to simultaneously provide generic and personalized interpretability. To address these challenges, we propose Sherbet, a self-supervised graph learning framework with hyperbolic embeddings for temporal health event prediction. We first propose a hyperbolic embedding method with information flow to pretrain medical code representations in a hierarchical structure. We incorporate these pretrained representations into a graph neural network (GNN) to detect disease complications and design a multilevel attention method to compute the contributions of particular diseases and admissions, thus enhancing personalized interpretability. We present a new hierarchy-enhanced historical prediction proxy task in our self-supervised learning framework to fully utilize EHR data and exploit medical domain knowledge. We conduct a comprehensive set of experiments on widely used publicly available EHR datasets to verify the effectiveness of our model. Our results demonstrate the proposed model's strengths in both predictive tasks and interpretable abilities.
Chinese sentiment classification based on Word2vec and vector arithmetic in human–robot conversation

With the aim of enhancing human–chatbot interactions, this paper presents a novel sentiment classification framework that recognizes both semantics emotion terms and emojis. Underpinned by Word2vec and vector arithmetic, a deep model learns the vectors of emotion words and emojis from a large-scale corpus of conversations. The vectors are then assembled into a dictionary, evaluated with an improved k-means similarity calculation, and assigned to a category of emotions. To accommodate negative grammar constructions, e.g., "not happy" as opposed to "unhappy", a separate module combines the vectors of negatives and emotion words using vector addition. The final sentiment classification results are derived by calculating the similarity between an utterance and the emotion categories. Experiments show this strategy results in multi-emotion and polarity classifications between 3 and 4% more precise than the next best performing baseline classifier.
Towards Unified Metrics for Accuracy and Diversity for Recommender Systems

Recommender systems evaluation has evolved rapidly in recent years. However, for offline evaluation, accuracy is the de facto standard for assessing the superiority of one method over another, with most research comparisons focused on tasks ranging from rating prediction to ranking metrics for top-n recommendation. Simultaneously, recommendation diversity and novelty have become recognized as critical to users' perceived utility, with several new metrics recently proposed for evaluating these aspects of recommendation lists. Consequently, the accuracy-diversity dilemma frequently shows up as a choice to make when creating new recommendation algorithms.
Conditional GAN with an Attention-Based Generator and a 3D Discriminator for 3D Medical Image Generation

Conditional Generative Adversarial Networks (cGANs) are a set of methods able to synthesize images that match a given condition. However, existing models designed for natural images are impractical to generate high-quality 3D medical images due to enormous computation. To address this issue, most cGAN models used in the medical field process either 2D slices or small 3D crops and join them together in subsequent steps to reconstruct the full-size 3D image. However, these approaches often cause spatial inconsistencies in adjacent slices or crops, and the changes specified by the target condition may not consider the 3D image as a whole. To address these problems, we propose a novel cGAN that can synthesize high-quality 3D MR images at different stages of the Alzheimer's disease (AD). First, our method generates a sequence of 2D slices using an attention-based 2D generator with a disease condition for efficient transformations depending on brain regions. Then, consistency in 3D space is enforced by the use of a set of 2D and 3D discriminators. Moreover, we propose an adaptive identity loss based on the attention scores to properly transform features relevant to the target condition. Our experiments show that the proposed method can generate smooth and realistic 3D images at different stages of AD, and the image change with respect to the condition is better than the images generated by existing GAN-based methods.
On the Validity of Pre-Trained Transformers for Natural Language Processing in the Software Engineering Domain

Transformers are the current state-of-the-art of natural language processing in many domains and are using traction within software engineering research as well. Such models are pre-trained on large amounts of data, usually from the general domain. However, we only have a limited understanding regarding the validity of transformers within the software engineering domain, i.e., how good such models are at understanding words and sentences within a software engineering context and how this improves the state-of-the-art. Within this article, we shed light on this complex, but crucial issue. We compare BERT transformer models trained with software engineering data with transformers based on general domain data in multiple dimensions: their vocabulary, their ability to understand which words are missing, and their performance in classification tasks. Our results show that for tasks that require understanding of the software engineering context, pre-training with software engineering data is valuable, while general domain models are sufficient for general language understanding, also within the software engineering domain.
Performance evaluation of metaheuristics algorithms for workload prediction in cloud environment

The smooth operation of a cloud data center along with the best user experience is one of the prime objectives of a resource management scheme that must be achieved at low cost in terms of resource wastage, electricity consumption, security and many others. The workload prediction has proved to be very useful in improving these schemes as it provides the prior estimation of upcoming demands. These predictions help a cloud system in assigning the resources to new and existing applications on low cost. Machine learning has been extensively used to design the predictive models. This article aims to study the performance of different nature-inspired based metaheuristic algorithms on workload prediction in cloud environment. We conducted an in-depth analysis using eight widely used algorithms on five different data traces. The performance of each approach is measured using Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE). In addition, the statistical analysis is also carried out using Wilcoxon signed rank and Friedman with Finner post-hoc multiple comparison tests. The study finds that Blackhole Algorithm (BhA) reduced the RMSE by 23.60%, 6.51%, 21.21%, 60.45% and 38.30% relative to the worst performing algorithm for 5 min forecasts of all five data traces correspondingly. Moreover, Friedman test confirms that the results of these approaches have a significant difference with 95% confidence interval (CI) and ranks show that the BhA and FSA received best ranks for Google Cluster trace (CPU and Memory Requests) while second best ranks for NASA and Saskatchewan HTTP server requests.
Costly Features Classification using Monte Carlo Tree Search

In many real-world tasks, acquiring features requires a certain cost, which gives rise to the costly features classification problem. In this study, We formulate the problem in the reinforcement learning framework and sequentially select the subset of features to make a balance between the classification error and the feature cost. Specifically, advantage actor critic algorithm is firstly used to solve it. Furthermore, to improve the learned policy and make it explainable, we employ the Monte Carlo Tree Search to update the policy iteratively. During the procedure, we also consider its performance on imbalanced datasets. Our empirical evaluation shows that our method performs well in comparison with other traditional methods.
Risk-Aware Motion Planning in Partially Known Environments

Recent trends envisage robots being deployed in areas deemed dangerous to humans, such as buildings with gas and radiation leaks. In such situations, the model of the underlying hazardous process might be unknown to the agent a priori, giving rise to the problem of planning for safe behaviour in partially known environments. We employ Gaussian process regression to create a probabilistic model of the hazardous process from local noisy samples. The result of this regression is then used by a risk metric, such as the Conditional Value-at-Risk, to reason about the safety at a certain state. The outcome is a risk function that can be employed in optimal motion planning problems. We demonstrate the use of the proposed function in two approaches. First is a sampling-based motion planning algorithm with an event-based trigger for online replanning. Second is an adaptation to the incremental Gaussian Process motion planner (iGPMP2), allowing it to quickly react and adapt to the environment. Both algorithms are evaluated in representative simulation scenarios, where they demonstrate the ability of avoiding high-risk areas.
Benchmarking and optimization of robot motion planning with motion planning pipeline

Abstract Algorithms have been designed for robot motion planning with various adaptability to different problems. However, how to choose the most suitable planner in a scene has always been a problem worthy of research. This paper aims to find the most suitable motion planner for each query under three different scenes and six different queries. The work lies in optimization of sampling-based motion planning algorithms through motion planning pipeline and planning request adapter. The idea is to use the pre-processing of the planning request adapter, to run OMPL as a pre-processer for the optimized CHOMP or STOMP algorithm, and connect through the motion planning pipeline, to realize the optimization of the motion trajectory. The optimized trajectories are compared with original trajectories through benchmarking. The benchmarking determines the most suitable motion planning algorithm for different scenarios and different queries. Experimental results show that after optimization, the planning time of the algorithm is longer, but the efficiency is significantly improved. In the low-complexity scenes, STOMP optimizes the sampling algorithm very well, improves the trajectory quality greatly, and has a higher success rate. CHOMP also has a good optimization of the sampling algorithm, but it reduces the success rate of the original algorithm. However, in more complex scenes, optimization performance of the two optimization methods may not be as good as the original algorithm. In future work, we need to find better algorithms and better optimization algorithms to tackle with complex scenes.
Aggregated independent forecasters of half-hourly global horizontal irradiance

In this study, single and aggregated forecasters of half-hourly global horizontal irradiance are assessed. The models are the standard persistent model and four newly proposed static, dynamic, moving average, and amplified persistent models. These sub-forecasters are aggregated using equal, annual optimal, and monthly optimal weights. A particle swarm optimizer was used to find those weights. Measured data, obtained from two desert sites for the years 2015–2018, was used for fitting and training the different models, while the data of the year 2019 was used to test their prediction capabilities. For the single forecasters, the dynamic model is the most accurate, followed by the static and average models. When the aggregated model of annual optimal weights was tested, the three contributing forecasters were the dynamic, average, and amplified models. The dynamic forecaster held the largest weight due to its prediction superiority during overcast and partially cloudy days. When monthly optimal weights were used, all forecasters contributed, and the dynamic model held the largest weight during winter but not in the summer when the clear sky condition is dominant. The aggregated model was the most precise, with relative mean square errors lower than 15.0% and coefficients of determination higher than 98.8%.
Conceptual Design and Multi-Disciplinary Computational Investigations of Multirotor Unmanned Aerial Vehicle for Environmental Applications

This study focuses on establishing a conceptual design for a multirotor unmanned aerial vehicle (UAV). The objectives of this octocopter are to reduce the number of flight cancelations and car accidents owing to low-visibility issues and to improve abnormal environmental conditions due to the presence of smoke. The proposed octocopter contains a convergent–divergent [CD] duct-based storage tank, which provides a platform to store saltwater and allows it to fly in foggy zones. Fine saltwater is sprayed from the octocopter and dispersed into the low clouds, thereby altering the vapor’s microphysical processes to break it up and improve visibility. The nature of the seawater and its enhanced fluid properties, due to the involvement of octocopter, creates the fluid flow mixing between atmospheric fluids and spraying particles, which increases the settling of foggy and smokey content groundward. For deployment, the conceptual design of the octocopter was initially constructed through analytical approaches. Additionally, three unique historical relationships were created. The standard engineering approaches involved in this work were stability analysis through MATLAB and fluid-property analysis through computational fluid dynamics (CFD) cum multiple reference frame (MRF) tools. The systematic model of this octocopter was developed by CATIA, and thereafter CFD and fluid–structure-interaction (FSI) analyses were computed, in ANSYS Workbench, on the octocopter for various environmental conditions. The aerodynamic forces on the drone, the enhancement of dynamic pressure by the presence of high amounts of rotors and nozzle sprayer, suitable material to resist aerodynamic loadings, and tests on the efficiency of the controller and its electronic components were investigated in detail. Finally, the proposed octocopter-based dynamic system was conceptually constructed.
Real-time End-to-End Federated Learning: An Automotive Case Study

With the development and the increasing interests in ML/DL fields, companies are eager to apply Machine Learning/Deep Learning approaches to increase service quality and customer experience. Federated Learning was implemented as an effective model training method for distributing and accelerating time-consuming model training while protecting user data privacy. However, common Federated Learning approaches, on the other hand, use a synchronous protocol to conduct model aggregation, which is inflexible and unable to adapt to rapidly changing environments and heterogeneous hardware settings in real-world scenarios. In this paper, we present an approach to real-time end-to-end Federated Learning combined with a novel asynchronous model aggregation protocol. Our method is validated in an industrial use case in the automotive domain, focusing on steering wheel angle prediction for autonomous driving. Our findings show that asynchronous Federated Learning can significantly improve the prediction performance of local edge models while maintaining the same level of accuracy as centralized machine learning. Furthermore, by using a sliding training window, the approach can minimize communication overhead, accelerate model training speed and consume real-time streaming data, proving high efficiency when deploying ML/DL components to heterogeneous real-world embedded systems.
FR-FMSS: Federated Recommendation via Fake Marks and Secret Sharing

With the implementation of privacy protection laws such as GDPR, it is increasingly difficult for organizations to legally collect user data. However, a typical recommendation algorithm based on machine learning requires user data to learn user preferences. In order to protect user privacy, a lot of recent works turn to develop federated learning-based recommendation algorithms. However, some of these works can only protect the users’ rating values, some can only protect the users’ rating behavior (i.e., the engaged items), and only a few works can protect the both types of privacy at the same time. Moreover, most of them can only be applied to a specific algorithm or a class of similar algorithms. In this paper, we propose a generic cross-user federated recommendation framework called FR-FMSS. Our FR-FMSS can not only protect the two types of user privacy, but can also be applied to most recommendation algorithms for rating prediction, item ranking, and sequential recommendation. Specifically, we use fake marks and secret sharing to modify the data uploaded by the clients to the server, which protects user privacy without loss of model accuracy. We take three representative recommendation algorithms, i.e., MF-MPC, eALS, and Fossil, as examples to show how to apply our FR-FMSS to a specific algorithm.
Residual Feedback Network for Breast Lesion Segmentation in Ultrasound Image

Accurate lesion segmentation in breast ultrasound (BUS) images is of great significance for the clinical diagnosis and treatment of breast cancer. However, precise segmentation on missing/ambiguous boundaries or confusing regions remains challenging. In this paper, we proposed a novel residual feedback network, which enhances the confidence of the inconclusive pixels to boost breast lesion segmentation performance. In the proposed network, a residual representation module is introduced to learn the residual representation of missing/ambiguous boundaries and confusing regions, which promotes the network to make more efforts on those hardly-predicted pixels. Moreover, a residual feedback transmission strategy is designed to update the input of the encoder blocks by combining the residual representation with original features. This strategy could enhance the regions including hardly-predicted pixels, which makes the network can further correct the errors in initial segmentation results. Experimental results on three datasets (3813 images in total) demonstrate that our proposed network outperforms the state-of-the-art segmentation methods. Our code is available at https://github.com/mniwk/RF-Net.
Unified Batch All Triplet Loss for Visible-Infrared Person Re-identification

Visible-Infrared cross-modality person reidentification (VI-ReID), whose aim is to match person images between visible and infrared modality, is a challenging cross-modality image retrieval task. Batch Hard Triplet loss is widely used in person re-identification tasks, but it does not perform well in the Visible-Infrared person re-identification task. Because it only optimizes the hardest triplet for each anchor image within the mini-batch, samples in the hardest triplet may all belong to the same modality, which will lead to the imbalance problem of modality optimization. To address this problem, we adopt the batch all triplet selection strategy, which selects all the possible triplets among samples to optimize instead of the hardest triplet. Furthermore, we introduce Unified Batch All Triplet loss and Cosine Softmax loss to collaboratively optimize the cosine distance between image vectors. Similarly, we modify the Hetero Center Triplet loss, which is proposed for VI-ReID task, into a batch all form to improve model performance. Extensive experiments indicate the effectiveness of the proposed methods, which outperform state-of-the-art methods by a wide margin.