Reviewerly/exHarmony huggingface dataset:
Two-stage aware attentional Siamese network for visual tracking

Siamese networks have achieved great success in visual tracking with the advantages of speed and accuracy. However, how to track an object precisely and robustly still remains challenging. One reason is that multiple types of features are required to achieve good precision and robustness, which are unattainable by a single training phase. Moreover, Siamese networks usually struggle with online adaption problem. In this paper, we present a novel two-stage aware attentional Siamese network for tracking (Ta-ASiam). Concretely, we first propose a position-aware and an appearance-aware training strategy to optimize different layers of Siamese network. By introducing diverse training patterns, two types of required features can be captured simultaneously. Then, following the rule of feature distribution, an effective feature selection module is constructed by combining both channel and spatial attention networks to adapt to rapid appearance changes of the object. Extensive experiments on various latest benchmarks have well demonstrated the effectiveness of our method, which significantly outperforms state-of-the-art trackers.
Smart Learning Environment (SLE) in the Fourth Industrial Revolution (IR 4.0)

This paper is attempted to examine the explanatory approach in dealing with SLE by advancing online learning sources. The systematic approach of searching for the relevant articles on SLE in IR 4.0 has been widely identified through two electronic databases, Scopus and Web of Sciences. Through adopting such digitally systematic search program, identification was made on the various elements in terms of online learning resources (OLR). This attempts to propose the SLE framework model with an innovative approach in enhancing the learning through incorporating IR 4.0 platform to utilize the variety of information sources together with knowledge attribution in the higher education (HE). The contribution provides theoretical framework with the guideline of well-adapted performance in the educational activities as the new normal trend. In achieving this attainment, the readiness of both instruction facilities and accessibility procedure is significantly the main basis in ensuring the process flow in enlarging the digital learning.
Research on global path planning of electric disinfection vehicle based on improved A* algorithm

Aiming at the shortcomings of A* algorithm in the path planning process of electric disinfection vehicles, such as long planned paths, low one-way search efficiency, and unsmooth inflection points, an improved two-way A* search algorithm is proposed from the heuristic function and search direction. First, the cosine factor is introduced into the heuristic function of the A* algorithm to optimize the direction of the path search. Second, the search neighborhood is expanded, and the 24-neighbor search is used to obtain a better path. Finally, the one-way search from the start point to the end point is changed to a two-way simultaneous search. The experimental results show that the path distance planned by the improved A* algorithm is shortened by 9.13%, the time is shortened by 38.21%, and the inflection point is reduced by an average of 30.34%.
Sequential Transformer via an Outside-In Attention for image captioning

Attention-based approaches have been firmly established the state of the art in image captioning tasks. However, both the recurrent attention in recurrent neural network (RNN) and the self attention in transformer have limitations. Recurrent attention only takes the external state to decide where to look, while ignoring to discover the internal relationships between image regions. Self attention is just the opposite. To fill this gap, we firstly introduce an Outside-in Attention that makes the external state participate in the interaction of the image regions. And, it prompts the model to learn the dependency inside the image regions, as well as the dependency between image regions and the external state. Then, we investigate a Sequential Transformer Framework (S-Transformer) based on the original Transformer structure, where the decoder is incorporated with the Outside-in Attention and RNN. This framework can help the model to inherit the advantages of both the transformer and recurrent network in sequence modeling. When tested on COCO dataset, the proposed approaches achieve competitive results in single-model and ensemble configurations on both MSCOCO Karpathy test split and the online test server.
High-order approximation rates for shallow neural networks with cosine and ReLU activation functions

We study the approximation properties of shallow neural networks with an activation function which is a power of the rectified linear unit. Specifically, we consider the dependence of the approximation rate on the dimension and the smoothness in the spectral Barron space of the underlying function f to be approximated. We show that as the smoothness index s of f increases, shallow neural networks with ReLUk activation function obtain an improved approximation rate up to a best possible rate of O(n−(k+1)log⁡(n)) in L2, independent of the dimension d. The significance of this result is that the activation function ReLUk is fixed independent of the dimension, while for classical methods the degree of polynomial approximation or the smoothness of the wavelets used would have to increase in order to take advantage of the dimension dependent smoothness of f. In addition, we derive improved approximation rates for shallow neural networks with cosine activation function on the spectral Barron space. Finally, we prove lower bounds showing that the approximation rates attained are optimal under the given assumptions.
Weakly-supervised semantic segmentation with superpixel guided local and global consistency

Weakly supervised semantic segmentation task aims to learn a segmentation model with only image-level annotations. Existing methods generally refine the initial seeds to obtain pseudo labels for training a fully supervised model. In recent years, some affinity-based methods perform well in this task. However, most of these methods only focus on the localization information from class activation map, while ignoring rule-based appearance information. In this paper, we find that the superpixel guidance is helpful for mining semantic affinities between pixels because pixels belonging to the same superpixel often have the same class label. As such, we propose a Superpixel Guided Weakly Segmentation framework, which alternately learns two modules to fuse superpixel information and localization information. The semantic segmentation results are more consistent with the image's local and global consistency through our framework. Experiments show that the proposed method achieves state-of-the-art performance, with mIoU at 70.5% on the PASCAL VOC 2012 test set and mIoU at 34.4% on the MS-COCO 2014 val set.
DBLCNN: Dependency-based lightweight convolutional neural network for multi-classification of breast histopathology images

Breast histopathology analysis is the gold standard for diagnosing breast cancer. Convolutional neural network-based methods for breast histology image classification have emerged in recent years to make the analysis process simple and fast. Due to the limitation of hardware devices, these classification methods still face the problem of difficult balance recognition performance and computational efficiency. In this paper, we propose the Dependency-based lightweight convolutional neural network (DBLCNN) for the multi-classification task of breast histopathology images. Firstly, we design a new network in which dependencies (magnification and binary classification probability) were used to guide subspecies features for better recognition. Secondly, we redesign the backbone MobileNet to greatly reduce the model parameters and computation while ensuring excellent recognition performance. At the same time, transfer learning based on ImageNet is applied to the DBLCNN network. Extensive experiments on the BreakHis dataset have shown that the DBLCNN network has state-of-the-art effects in terms of recognition performance and computational utilization.
Multiobjective Differential Evolution for Feature Selection in Classification

Feature selection aims to reduce the number of features and improve the classification accuracy, which is an essential step in many real-world problems. Multiple feature subsets with different features selected can achieve similar or even the same objective values (e.g., maximize the classification accuracy and minimize the number of selected features). This means the optimal feature subsets of a classification problem may not be unique. However, most existing feature selection methods do not take into consideration finding multiple optimal feature subsets. In this article, a multiobjective differential evolution approach is developed to search for multiple optimal feature subsets. The contributions are three-fold. First, to provide a good starting point, an initialization method considering feature relevance is proposed. Second, a clustering method is used to divide the whole population into multiple subpopulations. In each of these subpopulations, a subarchive utilizes a developed crowding distance to ensure diversity by considering both the search space and the objective space. Finally, the nondominated solutions from all the subarchives are retained in another archive to guide the evolutionary feature selection process, together with an improved hypervolume contribution indicator. The experiments on 14 datasets of varying difficulty show that the proposed approach can evolve a better Pareto front of feature subsets compared with seven other state-of-the-art methods as well as find different feature subsets with similar or the same classification performance.
Highly optimized Q‐learning‐based bees approach for mobile robot path planning in static and dynamic environments

Abstract This paper proposes a new novel approach to find an optimal path for a mobile robot in a two‐dimensional environment. Finding the optimal path is done using the Bees Algorithm (BA) with the Q‐Learning Algorithm. A new method to build the initial population is proposed to find the initial population regardless of the number and location of obstacles in the environment. Q‐Learning is implemented as a local search function of the BA. The hybridization of the BA and the Q‐Learning aims to find the optimal path with a fewer number of iterations of the BA. This method takes advantage of the BA to solve the problem without constraints and the sterilization in the Q‐Learning to find the shortest path. The experiment is run on some different maps to validate the proposed method in the static and dynamic case. The experimental results show the robustness and effectiveness of the proposed method in finding the optimal path. The comparison is executed to view the superiority of this method in finding the shortest path in the comparison of the results of other algorithms.
Hierarchical BERT with an adaptive fine-tuning strategy for document classification

Pretrained language models (PLMs) have achieved impressive results and have become vital tools for various natural language processing (NLP) tasks. However, there is a limitation that applying these PLMs to document classification when the document length exceeds the maximum acceptable length of the PLM since the excess portion is truncated in these models. If the keywords are in the truncated part, then the performance of the model declines. To address this problem, this paper proposes a hierarchical BERT with an adaptive fine-tuning strategy (HAdaBERT). It consists of a BERT-based model as the local encoder and an attention-based gated memory network as the global encoder. In contrast to existing PLMs that directly truncate documents, the proposed model uses a part of the document as a region, dividing input document into several containers. This allows the useful information in each container to be extracted by a local encoder and composed by a global encoder according to its contribution to the classification. To further improve the performance of the model, this paper proposes an adaptive fine-tuning strategy, which dynamically decides the layers of BERT to be fine-tuned instead of fine-tuning all layers for each input text. Experimental results on different corpora indicated that this method outperformed existing neural networks for document classification. • The hierarchical BERT model consist of local encoder and global encoder. • An adaptive fine-tuning strategy improve the performance of the PLMs. • An attention-based gated memory network model global information.
A Survey on Explainable Artificial Intelligence Techniques and Challenges

In the last decade, the world has envisioned tremendous growth in technology with improved accessibility of data, cloud-computing resources, and the evolution of machine learning (ML) algorithms. The intelligent system has achieved significant performance with this growth. The state-of-the-art performance of these algorithms in various domains has increased the popularity of artificial intelligence (AI). However, alongside these achievements, the non-transparency, inscrutability and inability to expound and interpret the majority of the state-of-the-art techniques are considered an ethical issue. These flaws in AI algorithms impede the acceptance of complex ML models in a variety of fields such as medical, banking and finance, security, and education. These shortcomings have prompted many concerns about the security and safety of ML system users. These systems must be transparent, according to the current regulations and policies, in order to meet the right to explanation. Due to a lack of trust in existing ML-based systems, explainable artificial intelligence (XAI)-based methods are gaining popularity. Although neither the domain nor the methods are novel, they are gaining popularity due to their ability to unbox the black box. The explainable AI methods are of varying strengths, and they are capable of providing insights to the system. These insights can be ranging from a single feature explanation to the interpretability of sophisticated ML architecture. In this paper, we present a survey of known techniques in the field of XAI. Moreover, we suggest future research routes for developing AI systems that can be responsible. We emphasize the necessity of human knowledge-oriented systems for adopting AI in real-world applications with trust and high fidelity.
Social Bots Detection via Fusing BERT and Graph Convolutional Networks

The online social media ecosystem is becoming more and more confused because of more and more fake information and the social media of malicious users’ fake content; at the same time, unspeakable pain has been brought to mankind. Social robot detection uses supervised classification based on artificial feature extraction. However, user privacy is also involved in using these methods, and the hidden feature information is also ignored, such as semi-supervised algorithms with low utilization rates and graph features. In this work, we symmetrically combine BERT and GCN (Graph Convolutional Network, GCN) and propose a novel model that combines large scale pretraining and transductive learning for social robot detection, BGSRD. BGSRD constructs a heterogeneous graph over the dataset and represents Twitter as nodes using BERT representations. Corpus learning via text graph convolution network is a single text graph, which is mainly built for corpus-based on word co-occurrence and document word relationship. BERT and GCN modules can be jointly trained in BGSRD to achieve the best of merit, training data and unlabeled test data can spread label influence through graph convolution and can be carried out in the large-scale pre-training of massive raw data and the transduction learning of joint learning representation. The experiment shows that a better performance can also be achieved by BGSRD on a wide range of social robot detection datasets.
Energy innovations through blockchain: Challenges, opportunities, and the road ahead

As a secure distributed ledger, blockchain has attracted a great deal of attention over the past decade. The power sector is not an exception to this phenomenon and industry leaders are looking into different ways to create value through this technology. Blockchain is a powerful enabling technology for decentralization of the energy systems of the future. Although this technology can be applied to a wide spectrum of applications in the power sector, many challenges and limitations are still needed to be addressed before its full implementation into grid operations. This paper presents energy innovations through the blockchain technology and addresses the barriers that preclude the power sector from large-scale, full-scope adoption of this technology. The emerging blockchain trends in near future will be further discussed and its potential in facilitating a secure, decentralized energy trading platforms will be investigated in this paper.
FPC: Filter pruning via the contribution of output feature map for deep convolutional neural networks acceleration

Pruning is a very effective solution to alleviate the difficulty of deploying neural networks on resource-constrained devices. However, most of the existing methods focus on the inherent parameters of the network itself, but rarely consider the contribution of the output feature map. In this paper, we propose the FPC, a novel filter pruning method based on the contribution of the output feature map, which considers the diverse information carried by different output feature maps. According to the above characteristic, FPC can evaluate the contribution of output feature maps and then effectively delete low contribution part without reducing the performance of the model. In this paper, we firstly use Singular Value Decomposition (SVD) to decompose the output feature map. Then we analyze the contribution of the output feature map to the model performance. Finally, we delete the filters with lower contribution output feature maps. Extensive experimental results show that our proposed FPC can produce excellent compression results. For example, with VGG-16, we can reduce the FLOPs by 65.62% and increase the accuracy by 0.25% on CIFAR-10. With ResNet-110, we can reduce FLOPs by 50.66% and increase the accuracy by 0.09% on CIFAR-100.
Identifying and evaluating clinical subtypes of Alzheimer’s disease in care electronic health records using unsupervised machine learning

Abstract Background Alzheimer’s disease (AD) is a highly heterogeneous disease with diverse trajectories and outcomes observed in clinical populations. Understanding this heterogeneity can enable better treatment, prognosis and disease management. Studies to date have mainly used imaging or cognition data and have been limited in terms of data breadth and sample size. Here we examine the clinical heterogeneity of Alzheimer's disease patients using electronic health records (EHR) to identify and characterise disease subgroups using multiple clustering methods, identifying clusters which are clinically actionable. Methods We identified AD patients in primary care EHR from the Clinical Practice Research Datalink (CPRD) using a previously validated rule-based phenotyping algorithm. We extracted and included a range of comorbidities, symptoms and demographic features as patient features. We evaluated four different clustering methods (k-means, kernel k-means, affinity propagation and latent class analysis) to cluster Alzheimer’s disease patients. We compared clusters on clinically relevant outcomes and evaluated each method using measures of cluster structure, stability, efficiency of outcome prediction and replicability in external data sets. Results We identified 7,913 AD patients, with a mean age of 82 and 66.2% female. We included 21 features in our analysis. We observed 5, 2, 5 and 6 clusters in k-means, kernel k-means, affinity propagation and latent class analysis respectively. K-means was found to produce the most consistent results based on four evaluative measures. We discovered a consistent cluster found in three of the four methods composed of predominantly female, younger disease onset (43% between ages 42–73) diagnosed with depression and anxiety, with a quicker rate of progression compared to the average across other clusters. Conclusion Each clustering approach produced substantially different clusters and K-Means performed the best out of the four methods based on the four evaluative criteria. However, the consistent appearance of one particular cluster across three of the four methods potentially suggests the presence of a distinct disease subtype that merits further exploration. Our study underlines the variability of the results obtained from different clustering approaches and the importance of systematically evaluating different approaches for identifying disease subtypes in complex EHR.
A Secure Anonymous D2D Mutual Authentication and Key Agreement Protocol for IoT

Internet of Things (IoT) is a developing technology in our time that is prone to security problems as it uses wireless and shared networks. A challenging scenario in IoT environments is Device-to-Device (D2D) communication where an authentication server, as a trusted third-party, does not participate in the Authentication and Key Agreement (AKA) process and only cooperates in the process of allocating and updating long-term secret keys. Various authentication protocols have been suggested for such situations but have not been able to meet security and efficiency requirements. This paper examined three related protocols and demonstrated that they failed to remain anonymous and insecure against Key Compromise Impersonation (KCI) and clogging attacks. To counter these pitfalls, a new D2D mutual AKA protocol that is anonymous, untraceable, and highly secure was designed that needed no secure channel to generate paired private and public keys in the registration phase. Formal security proof and security analysis using BAN logic, Real-Or-Random (ROR) model, and Scyther tool showed that our proposed protocol satisfied security requirements. The communication and computation costs and energy consumption comparisons denoted that our design had a better performance than existing protocols.
MBANet: Multi-branch aware network for kidney ultrasound images segmentation

Due to the influence of kidney morphology, heterogeneous structure and image quality, segmenting kidney in ultrasound images is challenging. To alleviate this challenge, we proposed a novel deep neural network architecture, namely Multi-branch Aware Network (MBANet), to segment kidney accurately and robustly. MBANet mainly consists of multi-scale feature pyramid (MSFP), multi-branch encoders (MBE) and master decoder. The design of MSFP can make the network more accessible to different kinds of class details at different scales. The information exchange between MBE can reduce the loss of feature information and improve the segmentation accuracy of the network. In addition, we designed a multi-scale fusion block (MFBlock) in the MBE to further extract and fuse more refined multi-scale image information. In order to further improve the robustness of MBANet, this paper also designed a step-by-step training mechanism. We validated the proposed approach and compared to several state-of-the-art approaches on the same kidney ultrasound datasets using six quantitative metrics. The results of our method on the six indicators of pixel accuracy (PA), intersection over union (IoU), precision, recall, specificity and F1-score (F1) are 98.83%, 92.38%, 97.10%, 95.03%, 99.46% and 0.9601, respectively. Compared with the comparison method, the average values on the six indicators are improved by about 2%. The evaluation results and segmentation results demonstrate that the proposed approach achieves the best overall performance on kidney ultrasound images segmentation.