Reviewerly/exHarmony huggingface dataset:
Context-aware Self-Attention Networks for Natural Language Processing

Recently, Self-Attention Networks (SANs) have shown its flexibility in parallel computation and effectiveness of modeling both short- and long-term dependencies. However, SANs face two problems: 1) the weighted averaging inhibits relations among neighboring words (i.e., local context); and 2) it calculates dependencies between representations without considering contextual information (i.e., global context). Both local and global contexts have proven useful for modeling dependencies among neural representations in a variety of natural language processing tasks. Accordingly, we augment SANs with the ability of capturing usefully local and global context, and meanwhile maintain their simplicity and flexibility. Firstly, we cast local context modeling as a learnable Gaussian bias, which indicates the central and scope of the local region to be paid more attention. The bias is then incorporated into the original attention distribution to form a revised version. Secondly, we leverage the internal representations that embed sentence-level information as the global context. Specifically, we propose to contextualize the transformations of query and key layers, which are used to calculate the relevance between elements. Since the two approaches are potentially complementary to each other, we propose to combine them to further improve the performance. Empirical results on machine translation and linguistics probing tasks demonstrate the effectiveness and universality of the proposed approaches. Further analyses confirm that our approaches successfully capture contextual information as expected.
Rethinking Text Segmentation: A Novel Dataset and A Text-Specific Refinement Approach

Text segmentation is a prerequisite in many real-world text-related tasks, e.g., text style transfer, and scene text removal. However, facing the lack of high-quality datasets and dedicated investigations, this critical prerequisite has been left as an assumption in many works, and has been largely overlooked by current research. To bridge this gap, we proposed TextSeg, a large-scale fine-annotated text dataset with six types of annotations: word- and character-wise bounding polygons, masks, and transcriptions. We also introduce Text Refinement Network (TexRNet), a novel text segmentation approach that adapts to the unique properties of text, e.g. non-convex boundary, diverse texture, etc., which often impose burdens on traditional segmentation models. In our TexRNet, we propose text-specific network designs to address such challenges, including key features pooling and attention-based similarity checking. We also introduce trimap and discriminator losses that show significant improvement in text segmentation. Extensive experiments are carried out on both our TextSeg dataset and other existing datasets. We demonstrate that TexRNet consistently improves text segmentation performance by nearly 2% compared to other state-of-the-art segmentation methods. Our dataset and code can be found at https://github.com/SHI-Labs/Rethinking-TextSegmentation.
Graph Adversarial Attack via Rewiring

Graph Neural Networks (GNNs) have demonstrated their powerful capability in learning representations for graph-structured data. Consequently, they have enhanced the performance of many graph-related tasks such as node classification and graph classification. However, it is evident from recent studies that GNNs are vulnerable to adversarial attacks. Their performance can be largely impaired by deliberately adding carefully created unnoticeable perturbations to the graph. Existing attacking methods often produce perturbation by adding/deleting a few edges, which might be noticeable even when the number of modified edges is small. In this paper, we propose a graph rewiring operation to perform the attack. It can affect the graph in a less noticeable way compared to existing operations such as adding/deleting edges. We then utilize deep reinforcement learning to learn the strategy to effectively perform the rewiring operations. Experiments on real-world graphs demonstrate the effectiveness of the proposed framework. To understand the proposed framework, we further analyze how its generated perturbation impacts the target model and the advantages of the rewiring operations. The implementation of the proposed framework is available at https://github.com/alge24/ReWatt.
An intelligent ensemble classification method based on multi-layer perceptron neural network and evolutionary algorithms for breast cancer diagnosis

Nowadays, breast cancer is one of the leading causes of women’s death in the world. If breast cancer is detected at the initial stages, it can ensure long-term survival. Numerous methods have been proposed for the early prediction of such cancer. However, efforts are still ongoing, given the importance of the problem. Artificial Neural Networks (ANN) are a prevalent machine learning algorithm, which is very popular for prediction and classification problems. In this paper, an Intelligent Ensemble Classification method based on Multi-Layer Perceptron neural network (IEC-MLP) is proposed for breast cancer diagnosis. The proposed method consists of two stages: parameters optimisation and ensemble classification. In the first stage, the MLP Neural Network (MLP-NN) parameters, including optimal features, hidden layers, hidden nodes and weights, are optimised with the help of an Evolutionary Algorithm (EA), aiming at maximising the classification accuracy. In the second stage, an ensemble classification algorithm of MLP-NN with optimised parameters is applied to classify the patients. Our proposed IEC-MLP method not only reduces the complexity of MLP-NN and effectively selects the optimal subset of features but also minimises the misclassification cost. The classification results have been evaluated using the IEC-MLP over different breast cancer datasets, and the prediction results have been auspicious (98.74% accuracy on the WBCD dataset). It is noteworthy that the proposed method outperforms the GAANN and CAFS algorithms and other state-of-the-art classifiers. In addition, IEC-MLP is also capable of being employed in diagnosing other cancer types.
Exploiting 5G and Blockchain for Medical Applications of Drones

In recent years, there has been growing popularity of applying drones for medical services. Such applications always involve flying safety, data transmission, and personal privacy; therefore, reliable communication and enhanced information security should be addressed first. The 5G cellular network and blockchain technology emerge as promising candidates for solving these problems. This article first categorizes the medical applications of drones, and then presents some key challenges in these applications. Then we give preliminaries of 5G and blockchain, and explore their intrinsic characteristics to provide reliable communication and enhanced information security. In the scenario of disaster relief, an illustrative example is presented and discussed. Analytical results demonstrate the great potential of using 5G and blockchain for promoting the medical applications of drones.
Acute Myeloid Leukemia (AML) Detection Using AlexNet Model

Acute Myeloid Leukemia (AML) is a kind of fatal blood cancer with a high death rate caused by abnormal cells’ rapid growth in the human body. The usual method to detect AML is the manual microscopic examination of the blood sample, which is tedious and time-consuming and requires a skilled medical operator for accurate detection. In this work, we proposed an AlexNet-based classification model to detect Acute Myeloid Leukemia (AML) in microscopic blood images and compared its performance with LeNet-5-based model in Precision, Recall, Accuracy, and Quadratic Loss. The experiments are conducted on a dataset of four thousand blood smear samples. The results show that AlexNet was able to identify 88.9% of images correctly with 87.4% precision and 98.58% accuracy, whereas LeNet-5 correctly identified 85.3% of images with 83.6% precision and 96.25% accuracy.
Multistability and circuit implementation of tabu learning two-neuron model: application to secure biomedical images in IoMT

In this paper, the dynamics of a non-autonomous tabu learning two-neuron model is investigated. The model is obtained by building a tabu learning two-neuron (TLTN) model with a composite hyperbolic tangent function consisting of three hyperbolic tangent functions with different offsets. The possibility to adjust the compound activation function is exploited to report the sensitivity of non-trivial equilibrium points with respect to the parameters. Analysis tools like bifurcation diagram, Lyapunov exponents, phase portraits, and basin of attraction are used to explore various windows in which the neuron model under the consideration displays the uncovered phenomenon of the coexistence of up to six disconnected stable states for the same set of system parameters in a TLTN. In addition to the multistability, nonlinear phenomena such as period-doubling bifurcation, hysteretic dynamics, and parallel bifurcation branches are found when the control parameter is tuned. The analog circuit is built in PSPICE environment, and simulations are performed to validate the obtained results as well as the correctness of the numerical methods. Finally, an encryption/decryption algorithm is designed based on a modified Julia set and confusion-diffusion operations with the sequences of the proposed TLTN model. The security performances of the built cryptosystem are analyzed in terms of computational time (CT = 1.82), encryption throughput (ET = 151.82 MBps), number of cycles (NC = 15.80), NPCR = 99.6256, UACI = 33.6512, χ2-values = 243.7786, global entropy = 7.9992, and local entropy = 7.9083. Note that the presented values are the optimal results. These results demonstrate that the algorithm is highly secured compared to some fastest neuron chaos-based cryptosystems and is suitable for a sensitive field like IoMT security.
Learning How to Propagate Messages in Graph Neural Networks

This paper studies the problem of learning message propagation strategies for graph neural networks (GNNs). One of the challenges for graph neural networks is that of defining the propagation strategy. For instance, the choices of propagation steps are often specialized to a single graph and are not personalized to different nodes. To compensate for this, in this paper, we present learning to propagate, a general learning framework that not only learns the GNN parameters for prediction but more importantly, can explicitly learn the interpretable and personalized propagate strategies for different nodes and various types of graphs. We introduce the optimal propagation steps as latent variables to help find the maximum-likelihood estimation of the GNN parameters in a variational Expectation- Maximization (VEM) framework. Extensive experiments on various types of graph benchmarks demonstrate that our proposed frame- work can significantly achieve better performance compared with the state-of-the-art methods, and can effectively learn personalized and interpretable propagate strategies of messages in GNNs.
A secure, private, and explainable IoHT framework to support sustainable health monitoring in a smart city

Internet of Health Things (IoHT) have allowed connected health paradigm ubiquitous. 5 G supported healthcare vertical allows IoHT to offer connected health monitoring with quality of service and ultra-low latency. Deep learning has shown potential in processing massive amount of IoHT data that are generated daily, automate connected healthcare workflows, and help in decision making processes. However, three important challenges need to be addressed to attain long term healthcare-related sustainability – data security, data privacy, and social acceptance of deep learning process. In this paper, we propose a framework that will allow healthcare sustainability through the following contributions 1) ensure privacy of training dataset, 2) support the aggregation of the global model gradients through a private Blockchain-brokered entity, 3) support trustworthiness and provenance of the federated clients by blockchain and off-chain, 4) share the dataset, train the model and share trained model among the federated clients in an encrypted fashion, and 5) add explainability and reasoning of deep learning process to make the model acceptable by the society. We will present the detailed design of our proposed sustainable system, the implementation details and test results. The test results show promising prospect of achieving sustainability of IoHT-enabled connected health applications.
Room-and-Object Aware Knowledge Reasoning for Remote Embodied Referring Expression

The Remote Embodied Referring Expression (REVERIE) is a recently raised task that requires an agent to navigate to and localise a referred remote object according to a high-level language instruction. Different from related VLN tasks, the key to REVERIE is to conduct goal-oriented exploration instead of strict instruction-following, due to the lack of step-by-step navigation guidance. In this paper, we propose a novel Cross-modality Knowledge Reasoning (CKR) model to address the unique challenges of this task. The CKR, based on a transformer-architecture, learns to generate scene memory tokens and utilise these informative history clues for exploration. Particularly, a Room-and-Object Aware Attention (ROAA) mechanism is devised to explicitly perceive the room- and object-type information from both linguistic and visual observations. Moreover, through incorporating commonsense knowledge, we propose a Knowledge-enabled Entity Relationship Reasoning (KERR) module to learn the internal-external correlations among room- and object-entities for agent to make proper action at each viewpoint. Evaluation on REVERIE benchmark demonstrates the superiority of the CKR model, which significantly boosts SPL and REVERIE-success rate by 64.67% and 46.05%, respectively. Code is available at: https://github.com/alloldman/CKR.
Hierarchical Transformer: Unsupervised Representation Learning for Skeleton-Based Human Action Recognition

The unsupervised representation learning for skeleton-based human action can be utilized in a variety of pose analysis applications. However, previous unsupervised methods focus on modeling the temporal dependencies in sequences, but take less effort in modeling the spatial structure in human action. To this end, we propose a novel unsupervised learning frame-work named Hierarchical Transformer for skeleton-based human action recognition. The Hierarchical Transformer consists of hierarchically aggregated self-attention modules for better capturing the spatial and temporal structure in the skeleton sequences. Furthermore, we propose to predict the motion between adjacent frames as a novel pre-training task for better capturing the long-term dependencies in sequences. Experimental results show that our method outperforms prior state-of-the-art unsupervised methods on NTU RGB+D and NW-UCLA datasets. Besides, our method also achieves state-of-the-art performance when the pre-trained model is transferred to SBU dataset, which demonstrates the generalizability of learned representation.
Designing a tourism business model on block chain platform

Today, blockchain is given attention in tourism, and research has begun about it. This study aimed to present a business model on blockchain platform. Blockchain is a shared distributed ledger that can lead to secure transactions in the tourism industry through a secure network. This research adopted a qualitative approach and relied upon the thematic analysis method. Data analysis showed that blockchain platform changes business models in tourism. That is to say the customer sector includes peer-to-peer exchanges and smart contracts, product and service sections include primary and secondary values, infrastructure management includes infrastructure as well as physical and human resources, the financial sector for revenues includes mining and earning national and international revenues and for costs includes the costs of setting up the platform on a blockchain platform, supporting and bringing about specialized development of the platform, and purchasing of a minor.
What Do You See?

EXplainable AI (XAI) methods have been proposed to interpret how a deep neural network predicts inputs through model saliency explanations that highlight the input parts deemed important to arrive at a decision for a specific target. However, it remains challenging to quantify the correctness of their interpretability as current evaluation approaches either require subjective input from humans or incur high computation cost with automated evaluation. In this paper, we propose backdoor trigger patterns--hidden malicious functionalities that cause misclassification--to automate the evaluation of saliency explanations. Our key observation is that triggers provide ground truth for inputs to evaluate whether the regions identified by an XAI method are truly relevant to its output. Since backdoor triggers are the most important features that cause deliberate misclassification, a robust XAI method should reveal their presence at inference time. We introduce three complementary metrics for the systematic evaluation of explanations that an XAI method generates. We evaluate seven state-of-the-art model-free and model-specific post-hoc methods through 36 models trojaned with specifically crafted triggers using color, shape, texture, location, and size. We found six methods that use local explanation and feature relevance fail to completely highlight trigger regions, and only a model-free approach can uncover the entire trigger region. We made our code available at https://github.com/yslin013/evalxai.
Towards asymmetric uncertainty modeling in designing General Type-2 Fuzzy classifiers for medical diagnosis

One of the most studied application areas of intelligent systems is the classification area, and this is because classification covers a wide range of real-world problems. Some examples are fault-diagnosis, image segmentation, medical diagnosis, among others. In most cases, the intelligent systems designed for the solution of this kind of problems are based on supervised learning, which is based on learning how to classify with previous datasets for finding relations between the inputs and outputs. The main focus of the present paper is the supervised generation of general type-2 fuzzy classifiers with a new strategy for modeling data uncertainty. The proposed methodology includes a mix of concepts, such as the use of embedded type-1 membership functions, statistical concepts such as the quartiles, and nature inspired optimization methods. The classifiers generated with the proposed methodology are compared with respect to other general type-2 fuzzy classifiers based on symmetric uncertainty to evaluate their performance, in this way obtaining interesting results for medical diagnosis with benchmark data sets.
MoleculARweb: A Web Site for Chemistry and Structural Biology Education through Interactive Augmented Reality out of the Box in Commodity Devices

Augmented/virtual realities (ARs/VRs) promise to revolutionize STEM education. However, most easy-to-use tools are limited to static visualizations, which limits the approachable content, whereas more interactive and dynamic alternatives require costly hardware, preventing large-scale use and evaluation of pedagogical effects. Here, we introduce https://MoleculARweb.epfl.ch, a free, open-source web site with interactive AR webpage-based apps that work out-of-the-box in laptops, tablets, and smartphones, where students and teachers can naturally handle virtual objects to explore molecular structure, reactivity, dynamics, and interactions, covering topics from inorganic, organic, and biological chemistry. With these web apps, teachers and science communicators can develop interactive material for their lessons and hands-on activities for their students and target public, in person or online, as we exemplify. Thousands of accesses to moleculARweb attest to the ease of use; teacher feedback attests to the utility in online teaching and homework during a pandemic; and in-class plus online surveys show that users find AR engaging and useful for teaching and learning chemistry. These observations support the potential of AR in future education and show the large impact that modern web technologies have in democratizing access to digital learning tools, providing the possibility to mass-test the pedagogical effect of these technologies in STEM education.
Deep Learning Approach for Breast Cancer Diagnosis from Microscopy Biopsy Images

Breast Cancer is the abnormal growth of cells in the breast. According to the World Health Organisation, Breast Cancer is the most common type of cancer that women are diagnosed with worldwide. Breast cancer tests vary from Mammograms to CTs and Ultrasounds; however, the only way to tell for sure if the suspicious lesions found in the breast are cancerous or not is by performing a biopsy test. The main contribution of this paper is proposing a deep learning approach to diagnose breast cancer from biopsy microscopy images. Several types of Deep Convolution nets such as Vgg16, Alexnet, Inception, Resnet50, Resnet101, and Densenet169 are used. The three best models that achieved the highest accuracy without data preprocessing techniques are Densenet169, Resnet50, and Resnet101 with accuracy 62%, 68%, and 85% respectively. Additionally, the paper shows the impact of various data preprocessing techniques on the performance of the best models. The experimental results indicate that data augmentation and segmentation increase the accuracy of the best models by 20%, 17%, and 6% respectively. To further boost the accuracy of the models, the paper aggregates the best models using an ensemble learning technique. The results reveal that the best-achieved accuracy is 92.5%.
Research on the practice of college English classroom teaching based on Internet and artificial intelligence

In the face of the arrival of artificial intelligence era, English classroom teaching is bound to change in organizational form, activity design, teacher-student relationship and technology integration. This paper combines SPOC and rain classroom, from knowledge infusion to ability improvement, individual teaching to team teaching, static resources to dynamic resources, artificial evaluation to intelligent evaluation, which brings new development opportunities for education. This paper uses the combination of Internet and artificial intelligence to analyze the teaching mixed teaching mode based on SPOC+and WeChat Rain classroom on the Internet. Based on the analysis results, we novelty propose the mixed English teaching mode based on such platforms to reveal the combination of Internet and artificial intelligence. On the second basis, we propose that future English teachers should continue to develop in the integration of teaching resources, integration of educational technology, design of in-depth learning activities, improvement of professional quality and self-cultivation of personality charm, and actively adapt to the era of artificial intelligence for College English classroom teaching.