Reviewerly/exHarmony huggingface dataset:
FLRA: A Reference Architecture for Federated Learning Systems

Federated learning is an emerging machine learning paradigm that enables multiple devices to train models locally and formulate a global model, without sharing the clients’ local data. A federated learning system can be viewed as a large-scale distributed system, involving different components and stakeholders with diverse requirements and constraints. Hence, developing a federated learning system requires both software system design thinking and machine learning knowledge. Although much effort has been put into federated learning from the machine learning perspectives, our previous systematic literature review on the area shows that there is a distinct lack of considerations for software architecture design for federated learning. In this paper, we propose FLRA, a reference architecture for federated learning systems, which provides a template design for federated learning-based solutions. The proposed FLRA reference architecture is based on an extensive review of existing patterns of federated learning systems found in the literature and existing industrial implementation. The FLRA reference architecture consists of a pool of architectural patterns that could address the frequently recurring design problems in federated learning architectures. The FLRA reference architecture can serve as a design guideline to assist architects and developers with practical solutions for their problems, which can be further customised.
Synthesis of Polyurethane Membranes Derived from Red Seaweed Biomass for Ammonia Filtration

The development of membrane technology is rapidly increasing due to its numerous advantages, including its ease of use, chemical resistant properties, reduced energy consumption, and limited need for chemical additives. Polyurethane membranes (PUM) are a particular type of membrane filter, synthesized using natural organic materials containing hydroxy (-OH) groups, which can be used for water filtration, e.g., ammonia removal. Red seaweed (Rhodophyta) has specific molecules which could be used for PUM. This study aimed to ascertain PUM synthesis from red seaweed biomass (PUM-RSB) by using toluene diisocyanate via the phase inversion method. Red seaweed biomass with a particle size of 777.3 nm was used as starting material containing abundant hydroxy groups visible in the FTIR spectrum. The PUM-RSB produced was elastic, dry, and sturdy. Thermal analysis of the membrane showed that the initial high degradation temperature was 290.71 °C, while the residue from the thermogravimetric analysis (TGA) analysis was 4.88%. The PUM-RSB section indicates the presence of cavities on the inside. The mechanical properties of the PUM-RSB have a stress value of 53.43 MPa and a nominal strain of 2.85%. In order to optimize the PUM-RSB synthesis, a Box–Behnken design of Response Surface Methodology was conducted and showed the value of RSB 0.176 g, TDI 3.000 g, and glycerin 0.200 g, resulting from the theoretical and experimental rejection factor, i.e., 31.3% and 23.9%, respectively.
A New Random Forest Applied to Heavy Metal Risk Assessment

As soil heavy metal pollution is increasing year by year, the risk assessment of soil heavy metal pollution is gradually gaining attention. Soil heavy metal datasets are usually imbalanced datasets in which most of the samples are safe samples that are not contaminated with heavy metals. Random Forest (RF) has strong generalization ability and is not easy to overfit. In this paper, we improve the Bagging algorithm and simple voting method of RF. A W-RF algorithm based on adaptive Bagging and weighted voting is proposed to improve the classification performance of RF on imbalanced datasets. Adaptive Bagging enables trees in RF to learn information from the positive samples, and weighted voting method enables trees with superior performance to have higher voting weights. Experiments were conducted using G-mean, recall and F1-score to set weights, and the results obtained were better than RF. Risk assessment experiments were conducted using W-RF on the heavy metal dataset from agricultural fields around Wuhan. The experimental results show that the RW-RF algorithm, which use recall to calculate the classifier weights, has the best classification performance. At the end of this paper, we optimized the hyperparameters of the RW-RF algorithm by a Bayesian optimization algorithm. We use G-mean as the objective function to obtain the optimal hyperparameter combination within the number of iterations.
Visual Explanation for Deep Metric Learning

This work explores the visual explanation for deep metric learning and its applications. As an important problem for learning representation, metric learning has attracted much attention recently, while the interpretation of the metric learning model is not as well-studied as classification. To this end, we propose an intuitive idea to show where contributes the most to the overall similarity of two input images by decomposing the final activation. Instead of only providing the overall activation map of each image, we propose to generate point-to-point activation intensity between two images so that the relationship between different regions is uncovered. We show that the proposed framework can be directly applied to a wide range of metric learning applications and provides valuable information for model understanding. Both theoretical and empirical analyses are provided to demonstrate the superiority of the proposed overall activation map over existing methods. Furthermore, our experiments validate the effectiveness of the proposed point-specific activation map on two applications, i.e. cross-view pattern discovery and interactive retrieval. Code is available at https://github.com/Jeff-Zilence/Explain Metric Learning.
From Ensemble Clustering to Subspace Clustering: Cluster Structure Encoding

In this study, we propose a novel algorithm to encode the cluster structure by incorporating ensemble clustering (EC) into subspace clustering (SC). First, the low-rank representation (LRR) is learned from a higher order data relationship induced by ensemble K-means coding, which exploits the cluster structure in a co-association matrix of basic partitions (i.e., clustering results). Second, to provide a fast predictive coding mechanism, an encoding function parameterized by neural networks is introduced to predict the LRR derived from partitions. These two steps are jointly proceeded to seamlessly integrate partition information and original features and thus deliver better representations than the ones obtained from each single source. Moreover, an alternating optimization framework is developed to learn the LRR, train the encoding function, and fine-tune the higher order relationship. Extensive experiments on eight benchmark datasets validate the effectiveness of the proposed algorithm on several clustering tasks compared with state-of-the-art EC and SC methods.
Swarming Behavior of Harris Hawks Optimizer for Arabic Opinion Mining

At present, the immense development of social networks allows generating a significant amount of textual data, which has facilitated researchers to explore the field of opinion mining. In addition, the processing of textual o... | Find, read and cite all the research you need on Tech Science Press
COP: customized correlation-based Filter level pruning method for deep CNN compression

As deep CNNs get larger, it becomes more challenging to deploy them on resource-restricted mobile devices. Filter-level pruning is one of the most popular methods to compress deep models for mobile deployment. It prunes unimportant filters in the pre-trained CNN to reduce its storage and computational cost, yielding a smaller and more efficient model. Though having made some progress, most filter-level pruning methods still suffer from at least one of the following two problems: 1) High redundancy: some methods pick out unimportant filters without considering their correlations, and thus many highly correlated filters are not pruned, yielding a model still with high redundancy. 2) Sub-optimal: all existing pruning methods achieve sub-optimal pruning plan because they neglect that a high parameters reduction ratio (PRR) does not always mean a high FLOPs reduction ratio (FRR). In this paper, we propose our customized correlation-based pruning (COP) to solve these problems. In particular, we observe redundant filters through their correlations. Moreover, to achieve the optimal pruning plan, PRR and FRR are considered when evaluating filters’ importance. Besides, we also propose a new pruning pipeline, which improves the accuracy of the pruned model. Extensive experiments show that our proposed method has outperformed the state-of-the-art on several popular architectures and datasets.
DRGI: Deep Relational Graph Infomax for Knowledge Graph Completion

Recently, many knowledge graph embedding models for knowledge graph completion have been proposed, ranging from the initial translation-based models such as TransE to recent convolutional neural network (CNN) models such as ConvE. However, these models only focus on semantic information of knowledge graph and neglect the natural graph structure information. Although graph convolutional network (GCN)-based models for knowledge graph embedding have been introduced to address this issue, they still suffer from fact incompleteness, resulting in the unconnectedness of knowledge graph. To solve this problem, we propose a novel model called deep relational graph infomax (DRGI) with mutual information (MI) maximization which takes the benefit of complete structure information and semantic information together. Specifically, the proposed DRGI consists of two encoders which are two identical adaptive relational graph attention networks (ARGATs), corresponding to catching semantic information and complete structure information respectively. Our method establishes new state-of-the-art on the standard datasets for knowledge graph completion. In addition, by exploring the complete structure information, DRGI embraces the merits of faster convergence speed over existing methods and better predictive performance for entities with small indegree.
Automated Anomaly Detection via Curiosity-Guided Search and Self-Imitation Learning

Anomaly detection is an important data mining task with numerous applications, such as intrusion detection, credit card fraud detection, and video surveillance. However, given a specific complicated task with complicated data, the process of building an effective deep learning-based system for anomaly detection still highly relies on human expertise and laboring trials. Also, while neural architecture search (NAS) has shown its promise in discovering effective deep architectures in various domains, such as image classification, object detection, and semantic segmentation, contemporary NAS methods are not suitable for anomaly detection due to the lack of intrinsic search space, unstable search process, and low sample efficiency. To bridge the gap, in this article, we propose AutoAD, an automated anomaly detection framework, which aims to search for an optimal neural network model within a predefined search space. Specifically, we first design a curiosity-guided search strategy to overcome the curse of local optimality. A controller, which acts as a search agent, is encouraged to take actions to maximize the information gain about the controller's internal belief. We further introduce an experience replay mechanism based on self-imitation learning to improve the sample efficiency. Experimental results on various real-world benchmark datasets demonstrate that the deep model identified by AutoAD achieves the best performance, comparing with existing handcrafted models and traditional search methods.
Surface measuring coherence scanning interferometry beyond the specular reflection limit

The capability of optical surface topography measurement methods for measurement of steep and tilted surfaces is investigated through modelling of a coherence scanning interferometer. Of particular interest is the effect on the interference signal and measured topography when tilting the object at angles larger than the numerical aperture slope limit (i.e. the specular reflection limit) of the instrument. Here we use theoretical modelling to predict the results across a range of tilt angles for a blazed diffraction grating. The theoretically predicted interference patterns and surface height measurements are then verified directly with experimental measurements. Results illustrate the capabilities, limitations and modelling methods for interferometers to measure beyond the specular reflection limit.
RFPruning: A retraining-free pruning method for accelerating convolutional neural networks

Network pruning has been developed as a remedy for accelerating the inference of deep convolutional neural networks (DCNNs). The mainstream methods retrain the pruned models, which maintain the performance of the pruned models but consume a great deal of time. While the other methods reduce the time consumption by omitting to retrain, they lose the performance. To resolve the above conflicts, we propose a two-stage Retraining-Free pruning method, named RFPruning, which embeds the rough screening of channels into training and fine-tunes the structures during pruning, to achieve both good performance and low time consumption. In the first stage, the network training is reformulated as an optimization problem with constraints and solved by a sparse learning approach for rough channel selection. In the second stage, the pruning process is regarded as a multiobjective optimization problem, where the genetic algorithm is applied to carefully select channels for a trade-off between the performance and model size. The proposed method is evaluated against several DCNNs on CIFAR-10 and ImageNet datasets. Extensive experiments demonstrate that such a retraining-free pruning method obtains 43.0% ∼ 88.4% compression on model size and maintains the accuracy as the methods with retraining while achieving 3× speed up in pruning.
BPLight-CNN: A Photonics-Based Backpropagation Accelerator for Deep Learning

Training deep learning networks involves continuous weight updates across the various layers of the deep network while using a backpropagation (BP) algorithm. This results in expensive computation overheads during training. Consequently, most deep learning accelerators today employ pretrained weights and focus only on improving the design of the inference phase. The recent trend is to build a complete deep learning accelerator by incorporating the training module. Such efforts require an ultra-fast chip architecture for executing the BP algorithm. In this article, we propose a novel photonics-based backpropagation accelerator for high-performance deep learning training. We present the design for a convolutional neural network (CNN), BPLight-CNN , which incorporates the silicon photonics-based backpropagation accelerator. BPLight-CNN is a first-of-its-kind photonic and memristor-based CNN architecture for end-to-end training and prediction. We evaluate BPLight-CNN using a photonic CAD framework (IPKISS) on deep learning benchmark models, including LeNet and VGG-Net. The proposed design achieves (i) at least 34× speedup, 34× improvement in computational efficiency, and 38.5× energy savings during training; and (ii) 29× speedup, 31× improvement in computational efficiency, and 38.7× improvement in energy savings during inference compared with the state-of-the-art designs. All of these comparisons are done at a 16-bit resolution, and BPLight-CNN achieves these improvements at a cost of approximately 6% lower accuracy compared with the state-of-the-art.
Enhancing emotion inference in conversations with commonsense knowledge

Existing studies on emotion analysis in conversations have mainly focused on recognizing the emotion of a given utterance. This paper investigates the task of emotion inference in conversations, which explores how the utterances affect the addressee's emotion, without knowing the addressee's response yet. While it is straightforward for humans to perceive and reason about the feelings of others in conversations, it is a severe challenge for machines, mainly due to the lack of commonsense knowledge. In this work, we propose to leverage external inferential knowledge to enhance the emotion inference in conversations. Specifically, a conversation modeling module is designed to accumulate information from the conversation history based on the emotional interaction between the addressee and writers. In addition, a knowledge integration strategy is also proposed to integrate the conversation-related commonsense knowledge generated from the event-based knowledge graph. The experiments on three different benchmark conversational datasets demonstrate the effectiveness of the proposed models, and prove the benefits of commonsense knowledge for emotion inference in conversations.
A Cooperative Multi-Agent Reinforcement Learning Method Based on Coordination Degree

Multi-agent reinforcement learning (MARL) has become a prevalent method for solving cooperative problems owing to its tractable implementation and task distribution. The goal of the MARL algorithms for fully cooperative scenarios is to obtain the optimal joint strategy that maximizes the expected common cumulative reward for all agents. However, to date, the analysis of MARL dynamics has focused on repeated games with few agents and actions. To this end, we propose a cooperative MARL algorithm based on the coordination degree (CMARL-CD) and analyze its dynamics in more general cases in which repeated games with more agents and actions are considered. Theoretical analysis shows that if the component action of every optimal joint action is unique, all optimal joint actions are asymptotically stable critical points. The CMARL-CD algorithm realizes coordination among agents without the need to estimate the global Q-value function. Each agent estimates the coordination degree of its own action, which represents the potential of being the optimal action. The efficacy of the CMARL-CD algorithm is studied through repeated games and stochastic games.
An improved helmet detection method for YOLOv3 on an unbalanced dataset

The YOLOv3 target detection algorithm is widely used in industry due to its high speed and high accuracy, but it has some limitations, such as the accuracy degradation of unbalanced datasets. The YOLOv3 target detection algorithm is based on a Gaussian fuzzy data augmentation approach to pre-process the data set and improve the YOLOv3 target detection algorithm. Through the efficient pre-processing, the confidence level of YOLOv3 is generally improved by 0.01-0.02 without changing the recognition speed of YOLOv3, and the processed images also perform better in image localization due to effective feature fusion, which is more in line with the requirement of recognition speed and accuracy in production.
Blockchain-Based Information Management for Supply Chain Data-Platforms

Performance measurement and information management are vital assets for supply chain management. In this study, we analyzed the effective combination between blockchain technologies and supply chain management processes. We conducted eighteen interviews with international experts from different areas and analyzed the collected data following a grounded theory approach. We have identified five main categories in this area including accounting and administration, trust, data platform, interoperability, and disintermediation. The main findings concluded with a set of seven statements as key elements to summarize how blockchain-based supply chains fit in with supplier relationship management processes and financial business units for international trade. The seven statements also recommend future research activities and trajectories.
YOLOv2PD: An Efficient Pedestrian Detection Algorithm Using Improved YOLOv2 Model

Real-time pedestrian detection is an important task for unmanned driving systems and video surveillance. The existing pedestrian detection methods often work at low speed and also fail to detect smaller and densely distributed pedestrians by losing some of their detection accuracy in such cases. Therefore, the proposed algorithm YOLOv2 (“YOU ONLY LOOK ONCE Version 2”)-based pedestrian detection (referred to as YOLOv2PD) would be more suitable for detecting smaller and densely distributed pedestrians in real-time complex road scenes. The proposed YOLOv2PD algorithm adopts a Multi-layer Feature Fusion (MLFF) strategy, which helps to improve the model’s feature extraction ability. In addition, one repeated convolution layer is removed from the final layer, which in turn reduces the computational complexity without losing any detection accuracy. The proposed algorithm applies the K-means clustering method on the Pascal Voc-2007+2012 pedestrian dataset before training to find the optimal anchor boxes. Both the proposed network structure and the loss function are improved to make the model more accurate and faster while detecting smaller pedestrians. Experimental results show that, at image resolution, the proposed model achieves 80.7% average precision (AP), which is 2.1% higher than the YOLOv2 Model on the Pascal Voc-2007+2012 pedestrian dataset. Besides, based on the experimental results, the proposed model YOLOv2PD achieves a good trade-off balance between detection accuracy and real-time speed when evaluated on INRIA and Caltech test pedestrian datasets and achieves state-of-the-art detection results.
DANet

In this paper, we propose a dimension apart network (DANet) for radar object detection task. A Dimension Apart Module (DAM) is first designed to be lightweight and capable of extracting temporal-spatial information from the RAMap sequences. To fully utilize the hierarchical features from the RAMaps, we propose a multi-scale U-Net style network architecture termed DANet. Extensive experiments demonstrate that our proposed DANet achieves superior performance on the radar detection task at much less computational cost, compared to previous pioneer works. In addition to the proposed novel network, we also utilize a vast amount of data augmentation techniques. To further improve the robustness of our model, we ensemble the predicted results from a bunch of lightweight DANet variants. Finally, we achieve 82.2% on average precision and 90% on average recall of object detection performance and rank at 1st place in the ROD2021 radar detection challenge. Our code is available at: \urlhttps://github.com/jb892/ROD2021_Radar_Detection_Challenge_Baidu.
Unsupervised Video Summarization via Multi-source Features

Video summarization aims at generating a compact yet representative visual summary that conveys the essence of the original video. The advantage of unsupervised approaches is that they do not require human annotations to learn the summarization capability and generalize to a wider range of domains. Previous work relies on the same type of deep features, typically based on a model pre-trained on ImageNet data. Therefore, we propose to incorporate multiple feature sources with chunk and stride fusion to provide more information about the visual content. For a comprehensive evaluation on the two benchmarks TVSum and SumMe, we compare our method with four state-of-the-art approaches. Two of these approaches were implemented by ourselves to reproduce the reported results. Our evaluation shows that we obtain state-of-the-art results on both datasets while also highlighting the shortcomings of previous work with regard to the evaluation methodology. Finally, we perform error analysis on videos for the two benchmark datasets to summarize and spot the factors that lead to misclassifications.