Reviewerly/exHarmony huggingface dataset:
Unsupervised Domain Adaptation via Deep Conditional Adaptation Network

• A novel domain adaptation (DA) framework is proposed by aligning the conditional distributions and simultaneously extracting discriminant information from both domains. • Conditional Maximum Mean Discrepancy is used to align the conditional distributions directly by their conditional embeddings in Reproducing Kernel Hilbert Space. • We further extend the proposed DA framework on partial DA scenarios. • The proposed method achieves state-of-the-art performance on both DA and partial DA scenarios. Unsupervised domain adaptation (UDA) aims to generalize the supervised model trained on a source domain to an unlabeled target domain. Previous works mainly rely on the marginal distribution alignment of feature spaces, which ignore the conditional dependence between features and labels, and may suffer from negative transfer. To address this problem, some UDA methods focus on aligning the conditional distributions of feature spaces. However, most of these methods rely on class-specific Maximum Mean Discrepancy or adversarial training, which may suffer from mode collapse and training instability. In this paper, we propose a Deep Conditional Adaptation Network (DCAN) that aligns the conditional distributions by minimizing Conditional Maximum Mean Discrepancy, and extracts discriminant information from the target domain by maximizing the mutual information between samples and the prediction labels. Conditional Maximum Mean Discrepancy measures the difference between conditional distributions directly through their conditional embedding in Reproducing Kernel Hilbert Space, thus DCAN can be trained stably and converge fast. Mutual information can be expressed as the difference between the entropy and conditional entropy of the predicted category variable, thus DCAN can extract the discriminant information of individual and overall distributions in the target domain, simultaneously. In addition, DCAN can be used to address a special scenario, Partial UDA, where the target domain category is a subset of the source domain category. Experiments on both UDA and Partial UDA show that DCAN achieves superior classification performance over state-of-the-art methods.
Virtual prompt pre-training for prototype-based few-shot relation extraction

Prompt tuning with pre-trained language models (PLM) has exhibited outstanding performance by reducing the gap between pre-training tasks and various downstream applications, which requires additional labor efforts in label word mappings and prompt template engineering. However, in a label intensive research domain, e.g., few-shot relation extraction (RE), manually defining label word mappings is particularly challenging, because the number of utilized relation label classes with complex relation names can be extremely large. Besides, the manual prompt development in natural language is subjective to individuals. To tackle these issues, we propose a virtual prompt pre-training method, projecting the virtual prompt to latent space, then fusing with PLM parameters. The pre-training is entity-relation-aware for RE, including the tasks of mask entity prediction, entity typing, distant supervised RE, and contrastive prompt pre-training. The proposed pre-training method can provide robust initialization for prompt encoding, while maintaining the interaction with the PLM. Furthermore, the virtual prompt can effectively avoid the labor efforts and the subjectivity issue in label word mapping and prompt template engineering. Our proposed prompt-based prototype network delivers a novel learning paradigm to model entities and relations via the probability distribution and Euclidean distance of the predictions of query instances and prototypes. The results indicate that our model yields an averaged accuracy gain of 4.21% on two few-shot datasets over strong RE baselines. Based on our proposed framework, our pre-trained model outperforms the strongest RE-related PLM by 6.52%.
Diagnosis of Oral Squamous Cell Carcinoma Using Deep Neural Networks and Binary Particle Swarm Optimization on Histopathological Images: An AIoMT Approach

Overall prediction of oral cavity squamous cell carcinoma (OCSCC) remains inadequate, as more than half of patients with oral cavity cancer are detected at later stages. It is generally accepted that the differential diagnosis of OCSCC is usually difficult and requires expertise and experience. Diagnosis from biopsy tissue is a complex process, and it is slow, costly, and prone to human error. To overcome these problems, a computer-aided diagnosis (CAD) approach was proposed in this work. A dataset comprising two categories, normal epithelium of the oral cavity (NEOR) and squamous cell carcinoma of the oral cavity (OSCC), was used. Feature extraction was performed from this dataset using four deep learning (DL) models (VGG16, AlexNet, ResNet50, and Inception V3) to realize artificial intelligence of medial things (AIoMT). Binary Particle Swarm Optimization (BPSO) was used to select the best features. The effects of Reinhard stain normalization on performance were also investigated. After the best features were extracted and selected, they were classified using the XGBoost. The best classification accuracy of 96.3% was obtained when using Inception V3 with BPSO. This approach significantly contributes to improving the diagnostic efficiency of OCSCC patients using histopathological images while reducing diagnostic costs.
UD_BBC: Named entity recognition in social network combined BERT-BiLSTM-CRF with active learning

With the rapid growth of Internet penetration, more and more people choose the Internet to express their views on topics of interest. In recent years, named entity recognition (NER) is becoming a popular task for the public to obtain structured information from public opinion text. At present, NER models with good results, such as deep learning model, need a lot of labeled data for training. However, this will give rise to a problem: labeling a large amount of data requires a lot of human resources, which is thankless in some areas. Therefore, in this paper, we propose a NER model combining active learning and deep learning methods. Firstly, the active learning method can solve the above problem. The strategy combines uncertainty-based sampling and diversity-based sampling to estimate the information of data. We use highly informative data as the initial training dataset. Secondly, this paper uses a deep learning model combining bidirectional encoder representations from Transformers, bidirectional long–short-term memory and conditional random field (BERT-BiLSTM-CRF). BERT extracts the semantic features of data, and BiLSTM predicts the probability distribution of entity labels. We use the CRF for decoding the probability distribution into corresponding entity labels. Finally, we use the initial training dataset for training BERT-BiLSTM-CRF. This model predicts the entity labels of the unlabeled data. Then, we judge if the machine-labeled data is highly reliable and expand the highly reliable data to the initial training dataset. The updated dataset retrains the NER model, so that the trained model has higher precision than the previous model. The results show that our model performs well without a large number of labeled datasets. The model achieves a precision value of 70.31%, recall rate of 74.93% and F1 score of 72.55% in the named entity recognition task, which proves the effectiveness of our model. Besides, the F1 score of BERT-BiLSTM-CRF with uncertainty-based sampling and diversity-based sampling (UD_BBC) is higher than the BiLSTM-CRF based on maximum normalized log-probability (MNLP_BiLSTM-CRF) by 9.00%, when recognizing overall entity categories. It provides a solution to the problem of named entity recognition in educational public opinion.
A survey on machine learning for recurring concept drifting data streams

The problem of concept drift has gained a lot of attention in recent years. This aspect is key in many domains exhibiting non-stationary as well as cyclic patterns and structural breaks affecting their generative processes. In this survey, we review the relevant literature to deal with regime changes in the behaviour of continuous data streams. The study starts with a general introduction to the field of data stream learning, describing recent works on passive or active mechanisms to adapt or detect concept drifts, frequent challenges in this area, and related performance metrics. Then, different supervised and non-supervised approaches such as online ensembles, meta-learning and model-based clustering that can be used to deal with seasonalities in a data stream are covered. The aim is to point out new research trends and give future research directions on the usage of machine learning techniques for data streams which can help in the event of shifts and recurrences in continuous learning scenarios in near real-time.
Investigating photovoltaic solar power output forecasting using machine learning algorithms

Solar power integration in electrical grids is complicated due to dependence on volatile weather conditions. To address this issue, continuous research and development is required to determine the best machine learning (ML) algorithm for PV solar power output forecasting. Existing studies have established the superiority of the artificial neural network (ANN) and random forest (RF) algorithms in this field. However, more recent studies have demonstrated promising PV solar power output forecasting performances by the decision tree (DT), extreme gradient boosting (XGB), and long short-term memory (LSTM) algorithms. Therefore, the present study aims to address a research gap in this field by determining the best performer among these 5 algorithms. A data set from the United States’ National Renewable Energy Laboratory (NREL) consisting of weather parameters and solar power output data for a monocrystalline silicon PV module in Cocoa, Florida was utilized. Comparisons of forecasting scores show that the ANN algorithm is superior as the ANN16 model produces the best mean absolute error (MAE), root mean squared error (RMSE) and coefficient of determination (R2) with values of 0.4693, 0.8816 W, and 0.9988, respectively. It is concluded that ANN is the most reliable and applicable algorithm for PV solar power output forecasting.
Cyber security of railway cyber-physical system (CPS) – A risk management methodology

Along with the increasing application of different cyber-physical systems (CPSs) to connect various components in the rail industry, rising connectivity through communication technologies has also introduced cyber threats against rail-CPSs, causing failures with huge consequences. Implementations of rail-CPSs demand proactive identification, clear-cut definition, and proper handling of their cyber security risks. In this paper, we formulate a risk management methodology for cyber security in rail-CPSs and conduct a retrospective case study on the Advanced Train Control System (ATCS) that has been deployed in many U.S. freight railways. The methodology provides two alternative approaches to fill knowledge gaps in contingency preparation, threat prevention, consequence analysis, and security risk mitigation. In the case study, we demonstrate two cyber threats of ATCS, using attack sequence modeling and consequence analysis, and provide recommendations for risk mitigation. By practicing the methodology with the case study, this work can be used as a general reference to conduct cyber risk management and cyber-robustness evaluations for other existing systems.
Supervised contrastive learning for recommendation

In the recommendation system, collaborative filtering methods based on the graph convolution network can explicitly model the interaction between the nodes of the user–item bipartite graph and effectively use higher-order neighbor information. However, its representations are very susceptible to the noise of interaction. In response to this problem, SGL explored the self-supervised learning on the user–item graph to improve the robustness of GCN. Nevertheless, the contrastive learning framework it applied does not consider the specificity of the recommendation task and the uncertainty of user–item interaction fully. In order to solve the above problems, we propose a learning paradigm called supervised contrastive learning (SCL) based on the graph convolutional neural network. We carefully design SCL guided by the basic idea of the recommendation task that users with similar interaction histories have similar interests and preferences. Specifically, we will calculate the similarity between different nodes on the user side and the item side respectively during data preprocessing firstly. And then when applying contrastive learning, not only will the augmented samples be regarded as the positive samples, but also a certain number of augmented samples of similar nodes will be regarded as the positive samples, which is different with SGL that treats other samples in a batch as negative samples. SCL purposefully makes representations learned by similar nodes close to each other in the feature space. In addition, to address the uncertainty of node interaction, we also propose a new data augment method called node replication. We apply SCL on the most advanced LightGCN. Empirical research and ablation study on Gowalla, Yelp2018, and Amazon-Book datasets prove the effectiveness, accuracy, and robustness of SCL and node replication.
Aspect-based sentiment analysis with attention-assisted graph and variational sentence representation

Aspect-based sentiment analysis (ABSA) is a fine-grained task that detects the sentiment polarities of particular aspect words in a sentence. With the rise of graph convolution networks (GCNs), current ABSA models mostly use graph-based methods. These methods construct a dependency tree for each sentence, and regard each word as a unique node. To be more specific, they conduct classification using aspect representations instead of sentence representations, and update them with GCNs. However, this kind of method relies too much on the quality of the dependency tree and may lose the global sentence information, which is also helpful for classification. To deal with these, we design a new ABSA model AG-VSR. Two kinds of representations are proposed to perform the final classification, Attention-assisted Graph-based Representation (A 2 GR) and Variational Sentence Representation (VSR). A 2 GR is produced by the GCN module, which inputs a dependency tree modified by the attention mechanism. Furthermore, VSR is sampled from a distribution learned by a VAE-like encoder–decoder structure. Extensive experiments show that our model AG-VSR achieves competitive results. Our code and data have been released in https://github.com/wangbing1416/VAGR . • Study aspect-based sentiment analysis tasks using two representations. • Correct the dependency tree with self-attention to learn semantic information. • Fuse more information at graph-level. • Design a Variational encoder–decoder to obtain new sentence representation.
Graph Neural Networks in IoT: A Survey

The Internet of Things (IoT) boom has revolutionized almost every corner of people’s daily lives: healthcare, environment, transportation, manufacturing, supply chain, and so on. With the recent development of sensor and communication technology, IoT artifacts, including smart wearables, cameras, smartwatches, and autonomous systems can accurately measure and perceive their surrounding environment. Continuous sensing generates massive amounts of data and presents challenges for machine learning. Deep learning models (e.g., convolution neural networks and recurrent neural networks) have been extensively employed in solving IoT tasks by learning patterns from multi-modal sensory data. Graph neural networks (GNNs), an emerging and fast-growing family of neural network models, can capture complex interactions within sensor topology and have been demonstrated to achieve state-of-the-art results in numerous IoT learning tasks. In this survey, we present a comprehensive review of recent advances in the application of GNNs to the IoT field, including a deep dive analysis of GNN design in various IoT sensing environments, an overarching list of public data and source codes from the collected publications, and future research directions. To keep track of newly published works, we collect representative papers and their open-source implementations and create a Github repository at GNN4IoT.
Lemurs Optimizer: A New Metaheuristic Algorithm for Global Optimization

The Lemur Optimizer (LO) is a novel nature-inspired algorithm we propose in this paper. This algorithm’s primary inspirations are based on two pillars of lemur behavior: leap up and dance hub. These two principles are mathematically modeled in the optimization context to handle local search, exploitation, and exploration search concepts. The LO is first benchmarked on twenty-three standard optimization functions. Additionally, the LO is used to solve three real-world problems to evaluate its performance and effectiveness. In this direction, LO is compared to six well-known algorithms: Salp Swarm Algorithm (SSA), Artificial Bee Colony (ABC), Sine Cosine Algorithm (SCA), Bat Algorithm (BA), Flower Pollination Algorithm (FPA), and JAYA algorithm. The findings show that the proposed algorithm outperforms these algorithms in fourteen standard optimization functions and proves the LO’s robust performance in managing its exploration and exploitation capabilities, which significantly leads LO towards the global optimum. The real-world experimental findings demonstrate how LO may tackle such challenges competitively.
The Applications of Metaheuristics for Human Activity Recognition and Fall Detection Using Wearable Sensors: A Comprehensive Analysis

In this paper, we study the applications of metaheuristics (MH) optimization algorithms in human activity recognition (HAR) and fall detection based on sensor data. It is known that MH algorithms have been utilized in complex engineering and optimization problems, including feature selection (FS). Thus, in this regard, this paper used nine MH algorithms as FS methods to boost the classification accuracy of the HAR and fall detection applications. The applied MH were the Aquila optimizer (AO), arithmetic optimization algorithm (AOA), marine predators algorithm (MPA), artificial bee colony (ABC) algorithm, genetic algorithm (GA), slime mold algorithm (SMA), grey wolf optimizer (GWO), whale optimization algorithm (WOA), and particle swarm optimization algorithm (PSO). First, we applied efficient prepossessing and segmentation methods to reveal the motion patterns and reduce the time complexities. Second, we developed a light feature extraction technique using advanced deep learning approaches. The developed model was ResRNN and was composed of several building blocks from deep learning networks including convolution neural networks (CNN), residual networks, and bidirectional recurrent neural networks (BiRNN). Third, we applied the mentioned MH algorithms to select the optimal features and boost classification accuracy. Finally, the support vector machine and random forest classifiers were employed to classify each activity in the case of multi-classification and to detect fall and non-fall actions in the case of binary classification. We used seven different and complex datasets for the multi-classification case: the PAMMP2, Sis-Fall, UniMiB SHAR, OPPORTUNITY, WISDM, UCI-HAR, and KU-HAR datasets. In addition, we used the Sis-Fall dataset for the binary classification (fall detection). We compared the results of the nine MH optimization methods using different performance indicators. We concluded that MH optimization algorithms had promising performance in HAR and fall detection applications.
Learning Modality-Specific and -Agnostic Representations for Asynchronous Multimodal Language Sequences

Understanding human behaviors and intents from videos is a challenging task. Video flows usually involve time-series data from different modalities, such as natural language, facial gestures, and acoustic information. Due to the variable receiving frequency for sequences from each modality, the collected multimodal streams are usually unaligned. For multimodal fusion of asynchronous sequences, the existing methods focus on projecting multiple modalities into a common latent space and learning the hybrid representations, which neglects the diversity of each modality and the commonality across different modalities. Motivated by this observation, we propose a Multimodal Fusion approach for learning modality-Specific and modality-Agnostic representations (MFSA) to refine multimodal representations and leverage the complementarity across different modalities. Specifically, a predictive self-attention module is used to capture reliable contextual dependencies and enhance the unique features over the modality-specific spaces. Meanwhile, we propose a hierarchical cross-modal attention module to explore the correlations between cross-modal elements over the modality-agnostic space. In this case, a double-discriminator strategy is presented to ensure the production of distinct representations in an adversarial manner. Eventually, the modality-specific and -agnostic multimodal representations are used together for downstream tasks. Comprehensive experiments on three multimodal datasets clearly demonstrate the superiority of our approach.
A novel two-phase clustering-based under-sampling method for imbalanced classification problems

Classification problems with imbalanced data are challenging because traditional classifiers tend to misclassify minority samples. This paper introduces a novel two-phase method in the categories of under-sampling data-level and ensemble-based approaches to tackle these problems. Compared to classic k-means clustering, the main property of our method is that the majority class is partitioned into clusters so that there are no minority samples in the convex-hull of the majority samples of each cluster, and at the same time, the size of each cluster is controlled. Thus, it is expected that by using our method, the general pattern of data in the feature space is kept, and the possibility of changing the data distribution is reduced. Computational results over a variety of imbalanced classification datasets confirm the superiority of our method over the existing methods from different metrics.
TFAD

Time series anomaly detection is a challenging problem due to the complex temporal dependencies and the limited label data. Although some algorithms including both traditional and deep models have been proposed, most of them mainly focus on time-domain modeling, and do not fully utilize the information in the frequency domain of the time series data. In this paper, we propose a Time-Frequency analysis based time series Anomaly Detection model, or TFAD for short, to exploit both time and frequency domains for performance improvement. Besides, we incorporate time series decomposition and data augmentation mechanisms in the designed time-frequency architecture to further boost the abilities of performance and interpretability. Empirical studies on widely used benchmark datasets show that our approach obtains state-of-the-art performance in univariate and multivariate time series anomaly detection tasks. Code is provided at https://github.com/DAMO-DI-ML/CIKM22-TFAD.