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This paper focuses on the word disentanglement-based approaches for text style transfer. The related systems first remove the style attribute words in the given texts and then generate the target sentences using the remained neutral templates. Previous work retrieves the style words using intuitive heuristics, which lacks in-depth analysis and can hardly provide a precise word detection. The resulting error will further affect the generation phase and leads to the failure of the style transformation. In this paper, we formalize the style detection task as a dynamic decision process; each word in the given text is classified as a style or neutral content sequentially leveraging the word property, the context, and the decision history. As no background labels indicating the attribute words, reinforcement learning is deployed in our model. An evaluation (reward) function is designed to quantify the style shift and content loss simultaneously, providing an accurate estimation for the decision process. The efficiency of the proposal is verified on two types of style transfer tasks, i.e., sentiment transfer and formality transfer. The experimental results demonstrate the model's competitive performance compared to the state-of-the-art baselines once equipped with an insertion-based generation approach.

Intrusion Detection System (IDS) is an important tool to ensure network security, which can detect and prevent malicious behavior in time. However, the noise and redundancy of data often reduce the detection performance of classifiers. The traditional model of intrusion detection system cannot effectively solve this problem. Therefore, in this paper, autoencoders (AEs) are firstly used to reduce the dimension of the original data, and a hybrid model combining particle swarm optimization (PSO) and gray wolf optimization (GWO) is proposed to optimize the support vector machine (SVM) parameters. This method combines the two optimization algorithms and selects the optimal parameter values according to the locally enhanced particles to train the classifier. In this paper, the NSL-KDD benchmark dataset and UNSW-NB15 dataset are used to evaluate the proposed model, and the model is compared with other classification methods separately. The experimental results show that our hybrid optimization model has better performance in detection accuracy and provides good detection rate and false alarm rate.

In recent years, deep learning has developed rapidly and has shown great performance on many challenging machine learning tasks, such as image classification, natural language processing, and speech recognition. However, researchers have recently discovered that deep learning models have security risks and are easily affected by adversarial examples. The adversarial example is a sample formed by deliberately adding subtle perturbation that is invisible to the human in the dataset. It can make the classification classify incorrectly with a high degree of confidence, which poses more challenges for deep learning research. In this paper, we propose a defense model based on the supervision mechanism. The model adds supervision layers to the original convolutional neural network and improves the robustness and defense ability of the model by improving the loss function. The LeNet-5 and VGG networks are used as the original network models. The experimental results on MNIST and CIFAR-10 confirm that the method proposed in this paper will effectively increase the difficulty of the attackers.

Scientific creativity refers to the development of new and innovative ideas that foster technological growth and advancement. This paper attempts to identify creative individuals in scientific domain by evaluating their visuo-spatial reasoning ability using EEG analysis system. The main objective of the present work is achieved by first procuring EEG signals from the scalp of subjects involved in a mental paper folding task. The acquired signals are pre-processed and transformed into a topological map using correlation analysis to investigate the connectivity patterns amongst different brain lobes pertaining to different degrees of spatial abilities (high and low). The acquired brain networks depict strong interconnections amongst bilateral supramarginal gyrus, right anterior prefrontal and right superior temporal lobes for subject possessing high spatial skills. Since, the correlation based brain network analysis provides a qualitative illustration of the connectivity patterns, centrality induced quantitative analysis have been performed. Such an evaluation has been conducted using two categories of centrality measures viz. degree centrality and betweenness centrality. The results obtained from centrality analysis also infer the active involvement of the aforesaid brain regions during mental paper folding activity. Finally, the centrality based network features are transferred to a novel Capsule Graph Neural Network (CapsGNN) based classifier to categorize the desired class labels. Performance analysis undertaken confirms the superior functionality of the proposed network with respect to the conventional models. Moreover, statistical evaluation conducted also assures the supercilious performance of the proposed classifier. Thus, the present methodology can be utilized for recruiting creative individuals to different sectors of industries associated with innovation such as architecture, game designing and the like.

This paper considers the unsupervised domain adaptation problem, in which we want to find a good prediction function on the unlabeled target domain, by utilizing the information provided in the labeled source domain. A common approach to the domain adaptation problem is to learn a representation space where the distributional discrepancy of the source and target domains is small. Existing methods generally tend to match the marginal distributions of the two domains, while the label information in the source domain is not fully exploited. In this paper, we propose a representation learning approach for domain adaptation, which is addressed as JODAWAT. We aim to adapt the joint distributions of the feature-label pairs in the shared representation space for both domains. In particular, we minimize the Wasserstein distance between the source and target domains, while the prediction performance on the source domain is also guaranteed. The proposed approach results in a minimax adversarial training procedure that incorporates a novel split gradient penalty term. A generalization bound on the target domain is provided to reveal the efficacy of representation learning for joint distribution adaptation. We conduct extensive evaluations on JODAWAT, and test its classification accuracy on multiple synthetic and real datasets. The experimental results justify that our proposed method is able to achieve superior performance compared with various domain adaptation methods.

Aspect-based sentiment analysis analyses the fine-grained sentiment polarity of a particular attribute in a sentence. In addition to the fact that most papers have applied attention mechanisms and neural networks to fine-grained sentiment analysis, some studies have reported outstanding performance from graph-structured neural networks in aspect-based sentiment analysis. In this paper, we propose the use of a graph attention network (GAT) to embed external knowledge and grammatical relations (KD-GAT). First, we employ a GAT to extract the nodes and edges related to sentences in the knowledge graph. Second, we consider the influence of conjunctions and parts of speech based on the aspect-oriented syntactic dependency tree. Finally, we emphasize the effect of aspect position in the mechanism of multiple attention. Experiments on the SemEval 2014 and Twitter datasets show that our model can improve the ability to analyze sentiment and that the graph structure method can better integrate grammatical relations to understand a sentence.

Adversarial examples about deep learning models have been paid much attention in recent years, including single-label adversarial examples and multi-label adversarial examples. In this paper, for the first time, an empirical study of generating a multi-label adversarial example to hide all labels in a multi-label example is presented. The objective of hiding all labels in a multi-label example is to make deep learning models know nothing about the environments. That is very worthy of studying because deep learning models will say there is nothing, although the real input has more than one label. In the empirical study, we use five state-of-the-art multi-label attack algorithms, i.e., ML-CW, ML-DP, FGSM, MI-FGSM, MLA-LP, four popular datasets, i.e., VOC2007, VOC2012, NUS-WIDE and COCO, and two typical models ML-GCN and ASL for evaluation. We conduct extensive experiments and report the attack success rates, the amount of perturbations of the adversarial examples generated by state-of-the-art multi-label attack algorithms. We also report the attack performance when a typical defending algorithm based on JPEG compression is used. The work in this paper is beneficial to the future study of generating multi-label adversarial examples as well as defending them.

One of the most important processes in big data analysis is outlier detection, where anomalies of observed data are detected and eliminated to improve the system performance. Many approaches to outlier detection have been proposed so far for classification and prediction purposes. This paper focus on the outlier detection under a practical circumstance such that multiple organizations possess different data sets of a specific task, while they cannot directly share with each other from a privacy point of view; that is, each organization is not allowed to provide their sensitive data to other entities but they are keen to cooperate in data analysis for some reasons. To address this issue, we present a new outlier detection approach to the data analysis for multiple organizations using a decision tree ensemble based on the so-called federated learning scheme. In this paper, we extend an existing outlier detection mode called Isolation Forest to the federated learning concept so that data protection of each entity can be achieved by introducing an additive homomorphic encryption. The experimental results for several benchmark data sets demonstrate that the proposed privacy-preserving Isolation Forest (pp-iForest) achieves stable classification performance, which is almost the same as that under a single-organization setting, even when the number of organization is increased.

Multilingual chat systems are the need of the hour for organizations who render online conversational services to their customers. This can effectively be facilitated using the robust machine translation (MT) systems. Translating user-generated contents is regarded as one of the challenging tasks for MT. As for translating conversations, it is more challenging since the meaning of any particular utterance in a conversation usually depends on its context. Moreover, chats in conversational systems are usually informal, contain code-mixed (mix of more than one language), and other grammatical inconsistencies. In this paper, we use state-of-the-art Transformer models to build our MT systems and to translate conversations between the customer service agents and customers. We propose a novel method which effectively selects contextual information from the source text of conversation to be translated. We also employ a terminology-based pseudo in-domain corpus mining strategy for fine-tuning our translation model. We evaluate our methods on the German-English WMT20 Shared Task on Chat Translation dataset, and obtain 61.1 and 63.9 BLEU points on evaluation test sets for English-to-German and German-to-English, respectively, surpassing the present state-of-the-art MT systems by 0.7 BLEU and 1.5 BLEU points, respectively. In this paper, we use state-of-the-art Transformer models to build our MT systems and to translate conversations between the customer service agents and customers. We propose a novel method which effectively selects contextual information from the source text of conversation to be translated. We also employ a terminology-based pseudo in-domain corpus mining strategy for fine-tuning our translation model. We evaluate our methods on the German-English WMT20 Shared Task on Chat Translation dataset, and obtain 61.1 and 63.9 BLEU points on evaluation test sets for English-to-German and German-to-English, respectively, surpassing the present state-of-the-art MT systems by 0.7 BLEU and 1.5 BLEU points, respectively.

Transfer learning has been widely studied and gained increasing popularity to improve the accuracy of machine learning models by transferring some knowledge acquired in different training. However, no prior work has pointed out that transfer learning can strengthen privacy attacks on machine learning models. In this paper, we propose TransMIA (Transfer learning-based Membership Inference Attacks), which use transfer learning to perform membership inference attacks on the source model when the adversary is able to access the parameters of the transferred model. In particular, we propose a transfer shadow training technique, where an adversary employs the parameters of the transferred model to construct shadow models, to significantly improve the performance of membership inference when a limited amount of shadow training data is available to the adversary. We evaluate our attacks using two real datasets, and show that our attacks outperform the state-of-the-art that does not use our transfer shadow training technique. We also compare four combinations of the learning-based/entropy-based approach and the fine-tuning/freezing approach, all of which employ our transfer shadow training technique. Then we examine the performance of these four approaches based on the distributions of confidence values, and discuss possible countermeasures against our attacks., Comment: IJCNN 2021 conference paper

By using document-level contextual information, document-level neural machine translation can achieve better results than ordinary machine translation, but traditional document-level machine translation is difficult to focus on the contextual sentence articulation relations and deep positional relations within the discourse while utilizing document-level vocabulary, and the model can concentrate only on relatively shallow inter-sentential relations or positional information. In this paper, we consider that most adjacent sentences are connected in document translation, and such links help improve the quality of translation. We propose a document translation model that focuses more on inter-sentential relations based on the previous work, and propose two methods to strengthen the model's positional information input, and combine these two methods to enhance the traditional Transformer positional information input. This paper also proposes a method for inserting paragraph information to allow inter-sentential relations to be learned by the model, and uses the improved Transformer model for Chinese-Mongolian document translation. Experiments show that in the improved Transformer system, the BLEU scores are enhanced on the Chinese-Mongolian machine translation task after fusing positional information and inter-sentential relation information, and the translation achieves better performance.

In recent years, Convolutional Neural Networks (CNN s) have been widely applied in various applications due to its powerful learning capability. However, its lack of explainability hinders its further usage in tasks requiring high reliability. Therefore, interpretability technique is the key to the application and deployment of CNN models. As a typical interpretability technique for CNN, Class Activation Map (CAM) utilizing the gradient based weights and activation map is widely applied to traditional CNN models for offering visual interpretability. However, the activation map adopted by CAM cannot loyally quantify the relevance between input samples and activation values. Hence, in this paper, we propose a new interpretability approach called Salience-CAM employing salience scores to accurately measure the relevance between input samples and activation values. To evaluate the effectiveness of Salience-CAM, comprehensive experiments are conducted on 6 selected time series datasets. By leveraging an evaluation algorithm proposed in this paper, the experimental results show that our proposed Salience-CAM outperforms the baseline by discovering more discriminative features.

This paper concerns the cooperative spectrum sensing (CSS) for cognitive radio (CR) networks, where the secondary users (SUs) collaborate to detect the presence of the primary users (PUs). With CSS, the information from different SUs is first fused, then, the detection of the PU signal is implemented based on the fused information. Most of the previous works focus on the design of a mapping function that calculates the probability of the existence of the PU signal based on the fused information. In this paper, we study the fusion process which combines the information from all SUs based on the local property of each SU. A graph-based multi-view learning framework for CSS (GMCSS) is designed to better fuse the information from different SUs. In the proposed framework, the information from each SU is considered as a view of the state of the target wireless channel and is fused with the information from other SUs through a graph-based learning process. Simulation results demonstrate the effectiveness of our method.

Script event prediction is a significant task to extract event evolution rules from text. It requires to capture the intra-rationality and inter-relationship of events. The previous methods have made great progress, but they can not fully capture the inner features of events. This paper proposes an event representation layer with argument-level attention to capture the relationship between predicate and arguments, which benefit to assess the rationality of the event. Besides, we integrate the enhanced event representation with the multi-level event similarity scores to obtain more robust performance. The multi-level similarity consists of scene similarity and semantic consistency. We utilize the similarity between candidates and individual events to evaluate the scene similarity. Semantic consistency is evaluated by the hidden state of Gate Recurrent Unit (GRU). Unlike the previous work, we only use the last hidden state of GRU instead of all hidden states to reduce training time. In addition, we adopt adversarial training to improve the robustness and generalization of the model. The experimental results verify the effectiveness of our model compared to these baseline methods. This paper also demonstrates what the attention mechanism has learned to enhance the interpretability of our model.

Online anomalous taxi trajectory detection, which identifies anomalies from ongoing taxi trajectories, has become an important and fundamental concern in many real-world applications. Most of the existing studies define the anomalous trajectories as the ones deviating from the majority of routes or showing abnormal driving time and distance at the same time. However, due to the complexity of road conditions and the variety of passenger preferences, those methods have large false-positive rates, i.e., reporting many normal routes as anomalies. A high false-positive rate is harmful since false alarms will 1) bring unnecessary panic to passengers, 2) cause fiscally punishments to normal drivers, and 3) wastes human resource to deal with drivers' complaints. To this end, this paper proposes an online anomalous trajectory detection method, namely multidimensional criteria based anomalous trajectory (MCAT), to identify anomalous trajectories online. It judges anomalies by considering multidimensional criteria (similarity, time, distance) at the same time, reducing the false positives without sacrificing false negative rates. We evaluate the proposed method based on the real-world taxi data collected from Shanghai, China. The experimental results demonstrate that our method can outperform state-of-the-art methods in terms of accuracy, false-negative rate, and false-positive rate.

The task of reasoning over knowledge graph for factoid questions has received significant interest from the research community of natural language processing. Performing this task inevitably faces the issues of question complexity and reasoning efficiency. In this paper, we investigate modern reasoning approaches over knowledge graph to tackle complex factoid questions of diverse reasoning schemas with attractive speedup in computational efficiency. To this end, we propose two evidence retrieval strategies to generate concise and informative evidence graph of high semantic-relevance and factual coverage to the question. Then, we adopt DELFT, a graph neural networks based framework that takes the linguistic structure representation of a question and the evidence graph as input, to predict the answer by reasoning over the evidence graph. We evaluate the performance across several baselines in terms of effectiveness and efficiency on two real-world datasets, MOOCQA and MetaQA. The results show the superiority of message passing paradigm in delivering a robust reasoner with better answer quality and significantly improved computational efficiency.

Existing image captioning approaches fail to generate fine-grained captions due to the lack of rich encoding representation of an image. In this paper, we present an attentive contextual network (ACN) to learn the spatially transformed image features and dense multi-scale contextual information of an image to generate semantically meaningful captions. At first, we construct deformable network on intermediate layers of convolutional neural network (CNN) to cultivate spatial invariant features. And the multi-scale contextual features are produced by employing contextual network on top of last layers of CNN. Then, we exploit attention mechanism on contextual network to extract dense contextual features. Further, the extracted spatial and contextual features are combined to encode the holistic representation of an image. Finally, a multi-stage caption decoder with visual attention module is incorporated to generate fine-grained captions. The performance of the proposed approach is demonstrated on COCO dataset, the largest dataset for image captioning.

Spiking neural networks (SNNs) are promising to replicate the efficiency of the brain by utilizing a paradigm of spike-based computation. Training a deep SNN is of great importance for solving practical tasks as well as discovering the fascinating capability of spike-based computation. The biologically plausible scheme of local learning motivates many approaches that enable training deep networks in an efficient parallel way. However, most of the existing spike-based local learning approaches show relatively low performances on challenging tasks. In this paper, we propose a new spike-based temporal dependent local learning (TDLL) algorithm, where each hidden layer of a deep SNN is independently trained with an auxiliary trainable spiking projection layer, and temporal dependency is fully employed to construct local errors for adjusting parameters. We examine the performance of the proposed TDLL with various networks on the MNIST, Fashion-MNIST, SVHN and CIFAR-10 datasets. Experimental results highlight that our method can scale up to larger networks, and more importantly, achieves relatively high accuracies on all benchmarks, which are even competitive with the ones obtained by global backpropagation-based methods. This work therefore contributes to providing an effective and efficient local learning method for deep SNNs, which could greatly benefit the developments of distributed neuromorphic computing.

Adversarial attacks/perturbations are becoming important for NLP research, as it has been shown recently that text-attacking adversaries can degrade an NLP model's performance without the victim's knowledge. This has far-reaching implications, especially when an NLP system is deployed in critical applications such as health or finance. In fact, the robustness of state-of-the-art models such as NLP transformers have increasingly been scrutinised due to their vulnerability against adversarial perturbations such as TextFooler and BERT-Attack. These methods, however, focus on changing words, which at times, ruins the readability and semantics of the sample. This paper introduces Special Symbol Text Attacks ‘SSTA’, a technique to improve the performance of language adversarial perturbations using special symbols that have downstream task information associated with them even though that should not have been the case. Our tests show that introducing such symbols which are meaningless to a human within a sentence, can perturb the sample in a particular direction. When this technique is used with TextFooler, which is a recent benchmark for the creation of NLP adversaries, through the TextAttack framework, it can improve all main evaluation metrics on three sentiment classification tasks and fake news detection. A simple, novel and symbol-specific adversarial learning technique is then introduced to reduce the influence of such special symbols.

Generating text with high quality and sufficient diversity is a fundamental task in natural language generation. Although generative adversarial networks (GANs) achieve promising results in text generation, GAN-based language models suffer from mode collapse, i.e., the generator tends to sacrifice diversity and focus on limited text patterns with high quality. By contrast, maximum likelihood estimation (MLE) based language models could cover various text patterns and generate diversified samples with poor quality. This paper proposes a cooperative framework with GANs and entropic auto-encoders (EAEs), named GAN-EAE, to synthesize their advantages for text generation, where EAEs are powerful MLE-based generative models based on deterministic auto-encoders. By imitating the output distribution of EAEs, the generator shapes its output distribution closer to the real data distribution against mode collapse. Meanwhile, by learning the samples from the generator of GANs, EAEs subtly distribute probability mass on high quality patterns for improving generation quality. The similar samples obtained from the generator may raise mode collapse and should be downplayed during adversarial training. Thus, a sample re-weighting mechanism is adopted to improve diversity by measuring the inner distance of generated samples. Experimental results demonstrate that GAN-EAE could improve both GANs and EAEs to achieve state-of-the-art performance.

Prognostication of vehicle trajectories in unknown environments is intrinsically a challenging and difficult problem to solve. The behavior of such vehicles is highly influenced by surrounding traffic, road conditions, and rogue participants present in the environment. Moreover, the presence of pedestrians, traffic lights, stop signs, etc., makes it much harder to infer the behavior of various traffic agents. This paper attempts to solve the problem of spatio-temporal look-ahead trajectory prediction using a novel recurrent neural network called the Memory Neuron Network. The Memory Neuron Network (MNN) attempts to capture the input-output relationship between the past positions and the future positions of the traffic agents. The proposed prediction model is computationally less intensive and has a simple architecture as compared to other deep learning models that utilize LSTMs and GRUs. It is then evaluated on the publicly available NGSIM dataset and its performance is compared with several state-of-art algorithms. Additionally, the performance is also evaluated on a custom synthetic dataset generated from the CARLA simulator. It is seen that the proposed model outperforms the existing state-of-art algorithms. Finally, the model is integrated with the CARLA simulator to test its robustness in real-time traffic scenarios.

It is necessary for the development of self-driving to fulfill the requirements for safety, stability, and intelligence, especially in high-speed conditions. Therefore, the detection of pedestrians that may occur in highway scenarios during driving and understanding the meaning of their behaviors in advance are significantly important for the self-driving vehicle to make correct decisions. However, no existing datasets are available for behavior recognition in self-driving scenarios. In order to advance the task of interactive cognition between the vehicle and pedestrians, in this paper, we present a new dataset, called THU-IntrudBehavior, that collects lane intrusion behaviors of pedestrians that can be applied in real world highway scenarios. The dataset contains diverse behaviors of single or multiple pedestrians/cyclists that are simulated in different urban roads under various weather conditions. We describe annotations of each video and report several experimental results of baseline methods on our self-collected dataset. Our THU-IntrudBehavior dataset provides new support for behavior recognition in high-speed conditions for self-driving.

With the increase of data centers, the number of disks also grows rapidly. Therefore, the prediction of disk failures has become an important task for both academia and industry. Existing prediction schemes predict disk failure in the short prediction horizon or with a short time window. However, these schemes cannot achieve ideal performance for a long prediction horizon with a long time window. In this paper, we proposed a deep learning method that can effectively solve the above problems. We refine the Self-Monitoring, Analysis and Reporting Technology (SMART) attributes by using information entropy to select the most related attributes for prediction. Moreover, we proposed the Multiple Channel Convolutional Neural Network based LSTM (MCCNN-LSTM) model to predict whether disk failures will occur in a given disk in next few days. We further evaluate the MCCNN-LSTM model by comparing it with the state-of-the-art works. Extensive experiments show that our model can improve FDR (Fault Detection Rate) to 99.8% and reduce FAR (False Alarm Rate) to 0.2%.

Deep CNNs, though have achieved the state of the art performance in image classification tasks, remain a black-box to a human using them. There is a growing interest in explaining the working of these deep models to improve their trustworthiness. In this paper, we introduce a Posthoc Architecture-agnostic Concept Extractor (PACE) that automatically extracts smaller sub-regions of the image called concepts relevant to the black-box prediction. PACE tightly integrates the faithfulness of the explanatory framework to the black-box model. To the best of our knowledge, this is the first work that extracts class-specific discriminative concepts in a posthoc manner automatically. The PACE framework is used to generate explanations for two different CNN architectures trained for classifying the AWA2 and Imagenet-Birds datasets. Extensive human subject experiments are conducted to validate the human interpretability and consistency of the explanations extracted by PACE. The results from these experiments suggest that over 72% of the concepts extracted by PACE are human interpretable., Accepted at International Joint Conference on Neural Networks (IJCNN 2021)

Sequential recommendation that aims to predict user preference with historical user interactions becomes one of the most popular tasks in the recommendation area. The existing methods concentrated on user's sequential features among exposed items have achieved good performance. However, they only rely on single item prediction optimization to learn data representation, which ignores the association between context data and sequence data. In this paper, we propose a novel self-supervised learning based sequential recommendation network (SSLRN), which contrastively learns data correlation to promote data representation of users and items. We design two auxiliary contrastive learning tasks to regularize user and item representation based on mutual information maximization (MIM). In particular, the item contrastive learning captures sequential contrast feature with sequence-item MIM, and the user contrastive learning regularizes user latent representation with user-item MIM. We evaluate our model on five real-world datasets and the experimental results show that the proposed framework significantly and consistently outperforms state-of-the-art sequential recommendation techniques.

Aspect-based sentiment analIsis (ABSA) aims to detect the sentiment polaritI of a specific aspect in an opinionated sentence. Current work focuses on exploiting the sIntactic tree to shorten the distance between the aspect term and context words. However, the “hard-pruning” strategI on the sIntactic tree maI lead to the reduction of importa nt sIntactic information. In this paper, we propose a novel sInt actical distance attention guided graph convolutional network (SDGCN) for ABSA. Our model is capable of fullI exploiting the sIntactic knowledge with a “soft pruning” strategI and learning crucial fine-grain sIntactic distance info rmation. AdditionallI, an effective denselI connected graph convolutional laIer is applied to avoid the over-sm oothing problem of standard GCN. Experiments conducted on three benchmark datasets show that our model achieves promising results comparing to the baseline models.

Existing lexically constrained machine translation employs data augmentation and incorporates lexical constraints during decoding period, which requires a bilingual dictionary or costs much decoding time. In this paper, we propose a simple but effective method to leverage lexical constraints. We use separate encoders to encode source sentence and lexical constraints, with self-attention layer mask to disentangle the two encoding subtasks. Our method does not require bilingual dictionaries or modify decoding process. Experiments on WMT 2016 English-German (En-De) and IWSLT 2017 English-Chinese (En-Zh) datasets show that our method gains improvement compared to baseline models.

The lack of high-quality expert labeled data is a common shortfall for medical image segmentation, which promotes semi-supervised learning scheme to an active research topic. The pseudo-labeling technique has been demonstrated to be a powerful module in semi-supervised segmentation framework for leveraging unlabeled data. However, simple generated pseudo labels are inevitably noisy and limited by the introduced confirmation biases, for the reason that the prediction errors of these pseudo labels would enhance the misleading to the segmentation network. In this paper, we propose to estimate the prediction confidence during the training process and further utilize the confidence to calibrate the pseudo label with the purpose to mitigate the confirmation bias problem. To emphasize, the pixel-wise confidence of the prediction results are learned through an adversarial network, while untrustworthy areas could be determined based on the prediction confidence. Rectified pseudo labels on untrustworthy areas are modified and further be utilized for medical image segmentation. The effectiveness on segmentation performance and noisy pseudo label calibration are proved by comparing several supervised or semi-supervised methods on BraTS2015 dataset and other two 3D medical image datasets.

With the ever increasing complexity of the user queries in a multi-domain based task-oriented dialogue system, it is imperative to facilitate robust Spoken Language Understanding (SLU) modules that perform multiple tasks in an unified way. In this paper, we present a novel multi-task approach for the joint modelling of three tasks together, namely, Domain Classification, Intent Detection and Slot-Filling. We hypothesize with the intuition that the cross dependencies of all these three tasks mutually help each other towards their representations and classifications which further simplify the SLU module in a multi-domain scenario. Towards this end, we propose a BERT language model based multi-task framework utilizing capsule networks and conditional random fields for addressing the classification and sequence labeling problems, respectively, for different tasks. Experimental results indicate that the proposed multi-task model outperformed several strong baselines and its single task counterparts on three benchmark datasets of different domains and attained state-of-the-art results on different tasks.

Rumor detection is a task of identifying information that spread among people whose truth value is false or unverified, and it has been a great challenge due to the rapid development of social media. The traditional machine learning based detection methods can make full use of informative features but cannot extract high-level representations. Other methods involved deep learning neural networks exploit propagation structural information to achieve high accuracy, for example, Bi-Directional Graph Convolution Networks(BiGCN) achieved the best performance on rumor detection by operating on bottom-up and top-down structures. However, those deep learning methods ignore other useful features like content-based features. In this paper, we not only make full use of three aspects of features based on a new concept: kernel subtree, which focus more on informative features of influential nodes of an event, but also propose a new model, which consists of Separation Convolution blocks, Long Short Term Memory(LSTM) and Squeeze and Excitation Networks(SENet), to make comprehensive use of features extracted on the basis of kernel subtree. First, we utilize Separation Convolutions to learn more local information with different kernel size, then LSTM can learn high-level interactions among features and find more global information. After that, SENet applies attention mechanism to put more weights on informative channels of feature maps. Meanwhile, on test set, Gradient Boosting Decision Tree(GBDT) is used to assist our model with few events. The experiments on the PHEME dataset show that our approach can identify rumors with accuracy 95% which outperforms BiGCN by 10% at least.

Text embedding has recently emerged as a very useful and successful method for semantic representation. Following initial word-level embedding methods such as Latent Semantic Analysis (LSA) and topic-based bag-of-words approaches like Latent Dirichlet Allocation (LDA), the focus has turned to language models and text encoders implemented as neural networks - ranging from word-level models to those embedding whole documents. The distinctive feature of these models is their ability to infer semantic spaces at all levels based purely on data, with no need for complexities such as syntactic analysis or ontology building. Many of these models are available pre-trained on enormous amounts of data, providing downstream applications with general-purpose semantic spaces. In particular, embedding models at the sentence level or higher are most useful in applications because the meaning of text only becomes clear at that level. Most text embedding methods produce text embeddings in high-dimensional spaces, with a dimensionality ranging from a few hundred to thousands. However, it is often useful to visualize semantic spaces in very low dimension, which requires the use of dimensionality reduction methods. It is not clear what language models and what method of dimensionality reduction would work well in these cases. In this paper, we compare four text embedding methods in combination with three methods of dimensionality reduction to map three related real-world datasets comprising textual descriptions of items in a particular domain (sports) to a 2-dimensional semantic visualization space. The results provide several insights into the utility of these methods for data of this type.

Neural networks (NNs) have been applied in numerous real-life applications and services. These include the applications in disease and healthcare analytics, such as identification and predictive analytics of coronavirus disease 2019 (COVID-19). However, many existing NN-based solutions train the models based on data (e.g., computed tomography (CT) scan images, viral/molecular test results) that can be expensive to produce and/or not easily accessible. They also require large volumes of these data for training. However, partially due to privacy concerns and other factors, the volume of available COVID-19 data can be limited. Hence, in this paper, we present a solution for predictive analytics of COVID-19 with NNs. Our solution consists of three algorithms, which make good use of autoencoder and few-shot learning, to train the prediction model with only a few samples of more accessible and less expensive types of data (e.g., serology/antibody test results from blood samples). Evaluation results on a real-life Brazilian COVID-19 dataset demonstrate the effectiveness of our solution in predictive analytics of COVD-19 with NNs.

While various neural machine translation (NMT) methods have integrated multiple prior knowledge to guide the translation, no research is available on combining with source-target bilingual translation template. In this paper, we firstly propose a maximal-length noun phrase template (MNP-Template), which constructs a novel translation template focusing on the constituency syntactic structure. Secondly, building on the multi-source transformer framework, we design a template-based machine translation (TBMT) model to integrate the syntactic knowledge of the retrieved target template similar to the ground-truth translation in the NMT decoder. Experiment results show the effectiveness of MNP- Template and TBMT on test subsets filtered by the fuzzy match score. Moreover, our method achieves significant improvement in out-of-domain test sets, which well-validated the university across diverse domains.

Bilingual lexicons and parallel phrases have a great effect on certain tasks of natural language processing (NLP). Recent researches have proved that the high-quality bilingual lexicons can hence the performance of the machine translation. When it comes to some special tasks of NLP, the incorporation of bilingual lexicons can bring about obvious effectiveness. The bilingual lexicons and parallel phrases can be easily extracted from parallel corpora, but in contrast to the monolingual corpora, the number of parallel corpora is still scarce. Actually, the monolingual corpora also have the potential to mine a large amount of parallel word and phrase pairs. In this paper, we propose two strategies to extract parallel words and phrases from monolingual corpora. On one hand, we present the indirect mining strategy, Anchored Mining (AM), which injects the anchoring point into each mining procedure to improve the accuracy. On the other hand, inspired by the process of humans learning a foreign language, we further propose another novel, direct algorithm named Bootstrapping Mining (BM), which mimics the human learning process and aims to learn parallel phrases automatically in a self-iterative way. Additionally, we propose a novel metric, phrase probability-sub item average probability (PP-SAP), which is applied to quantitatively evaluate the rationality of each extracted parallel phrase pair in the monolingual corpora. We conduct the experiments on large-scale English-Chinese, English-Russia, and English-France monolingual corpora, and the results show that our methods can mine high-quality bilingual lexicons and parallel phrases. We also evaluate our algorithms on low-resource monolingual corpora and get good results as well.

In this paper, we study the problem of blood glucose forecasting and provide a deep personalized solution. Predicting blood glucose level in people with diabetes has significant value because health complications of abnormal glucose level are serious, sometimes even leading to death. Therefore, having a model that can accurately and quickly warn patients of potential problems is essential. To develop a better deep model for blood glucose forecasting, we analyze the data and detect important patterns. These observations helped us to propose a method that has several key advantages over existing methods: 1- it learns a personalized model for each patient as well as a global model; 2- it uses an attention mechanism and extracted time features to better learn long-term dependencies in the data; 3- it introduces a new, robust training procedure for time series data. We empirically show the efficacy of our model on a real dataset., 8 pages, accepted to IJCNN 2021

Demand forecasting is a fundamental aspect of inventory and supply chain management. Due to the sporadic nature of the demand, demand forecasting involves dealing with intermittent time series in domains such as retail, manufacturing. Conventional forecasting methods do not work well for intermittent time series due to inherent sparsity in such series. Researchers have proposed multiple methods to deal with intermittent time series such as Croston and its variants. Our work aims to provide an insight into the various forecasting methods traditionally known to work well for forecasting intermittent series. We have also explored deep learning methods that have been proposed in recent literature. These methods are thoroughly reviewed and explained in this survey paper. Additionally, experiments are done on two publicly available datasets to compare the performance of the traditional methods with deep learning models. Furthermore, a hybrid model made of independent classification and regression trees has been implemented and studied as well. We provide a comprehensive evaluation that aims at selecting the appropriate method, given the dataset, context, and objectives that have to be met by the forecasting practitioner/researcher.

The existing Stochastic Petri Net (SPN) analysis methods are based on a series of steps, including generating the reachable graph, solving the state equation, etc. Unfortunately, these methods cannot perform performance analysis when the state equation has no unique solution. The end-to-end deep learning methods can build a mapping relationship from SPN to performance indicators, which avoids solving the state equation. However, there is a lack of benchmark datasets for SPN learning and training. This paper proposes an automatic generation method of SPN datasets, including SPN random generation, data labeling, data enhancement, and filtering. To relieve the local aggregation problem of random-based organization, a grid-based data organization method is proposed to ensure the diversity of the datasets. In the experimental section, the generated datasets are trained and tested on three types of neural networks. The results show that the generated benchmark datasets are useful, and the increase of dataset size will significantly improve the learning performance.

Text generation is one of the essential yet challenging tasks in natural language processing. However, the input text alone is usually hard to provide enough information to generate the desired output. Previous work attempts to incorporate syntactic information into the generative models based on variational autoencoder(VAE). But these methods have difficulty in adequately modeling the tree structure of syntactic data. In this paper, we formulate the syntactic structure as a graph and introduce a syntax encoder based on graph neural network(GNN) to model the syntactic information of sentences. Based on the syntax encoder, we propose a novel syntax-enhanced variational autoencoder(SEVAE) with two variants. The variant SEVAE-m merges sentence information and syntactic information into one latent space to enrich the fine-grained syntactic information of latent representations. And the variant SEVAE-s with two separate latent spaces allows the sentence decoder to dynamically attend to semantic and syntactic information from two latent variables. Experiments on two benchmark datasets show that our methods achieve significant and consistent improvements compared with previous work.

Meta-learning classification is about training a classifier by remembering some common knowledge from limited labeled examples in a support set. It has attracted more and more attention in machine learning community. Despite the success, existing Meta-learning algorithms usually ignore the relationship between the samples of the support set and the samples of the query set in a task after feature extraction and thus fail to obtain the most discriminative features, which leads to an unreliable class prototype and a low distinguishability of feature representations. In this paper, we propose a novel metric-based meta-learning algorithm called Prototype Enhancement Network (PEN) to tackle the above problems. The proposed algorithm uses: (1) a Task Context-Related (TCR) Module to obtain a more discriminative and context-related feature representation of samples within a task; and (2) a Prototype Reset (PR) module to expands the support set with partially unlabeled query set samples to mitigate the low-data problem and obtain a more representative class prototype. The cooperation of the TCR module and the PR module effectively enhance the reliability and the accuracy of the class prototype. Experiments are conducted on two classification benchmarks (mini-ImageNet and tiered-ImageN et) and the results confirm the effectiveness of PEN compared with other state-of-the-art methods.

In view of the vulnerability of multi-view deep models to adversarial perturbation, this paper designs two kinds of multi-view adversarial autoencoders (MAAEs). We first propose the MAAE1 defense, in which each view is used to train the corresponding single-view adversarial autoencoders separately, and then the reconstructed output is fed into the target model for classification. Based on the consistency and complementary principle, we further integrate the scheme of adjusting the weights of different views adaptively into the design of multi-view defense, referred to it as MAAE2. Experimental results verify that the proposed multi-view adversarial defense effectively enhances the adversarial robustness of the deep multi-view model without negatively affecting the classification accuracy in the original examples. Besides, MAAEs show superior adversarial robustness to unseen perturbations.

While machine learning brings convenience, it also faces the issue of data privacy. For privacy issues, most researches focus on implementing homomorphic encryption or differential privacy to protect data, while ignoring the potential threats caused by the leakage of model parameters. However, a malicious attacker can still recover sensitive data information through model parameters. On the one hand, traditional methods cannot take both high accuracy and low computation time into account. On the other hand, they cannot resist the reconstruction attack from the model's parameter. In order to address this problem, this paper designs a privacy protection framework named FLI, which is inspired by public key infrastructure. In FLI, all participants and the server are trained and aggregated under one framework based on federated learning, which includes key exchange and shares with the idea of homomorphic encryption. Under the algorithm we design, the malicious adversary cannot recover effective information after obtaining the transformed parameters, while the server can still perform effective parameter aggregation. To evaluate the performance of FLI, we conduct extensive experiments. The experimental results show that the computation time is within an acceptable range while ensuring high accuracy.

In conventional unsupervised multi-view clustering (MVC), learning of representations from heterogeneous multiview data and its subsequent clustering are often separately optimized. The disparate optimization would lead to suboptimal performance because multi-view representation learning is not goal-directed. In this paper, we unify unsupervised multi-view learning and deep clustering in a novel discriminative Self-supervised Deep Correlational Multi-view Clustering (SDC-MVC) network. A new unified loss function is proposed to incorporate consensus information into discriminative representations, in which, the former is learnt by maximizing the canonical correlation among multi-view representations projected by neural networks, and the later is achieved through using confident clustering assignments as supervision. Further, multi-view representations are harnessed by our proposed Deep Serial Feature-level (DSF) Fusion layer. Experiments on three public datasets demonstrated that our method outperforms six state-of-the-art correlation-based MVC algorithms in terms of three evaluation metrics.

In addition to biological plausibility, spiking neural networks (SNNs) are drawing significant attention recently due to their promising advantages in computational efficiency, which could potentially help to overcome the consumption obstacle in deep learning. Training deep SNNs is of great importance for solving practical tasks. In this paper, we propose a new deep spike learning rule to train deep SNNs to associate input spike patterns with desired output spike numbers. Our proposed rule is able to construct error signals based on a critical time point that is likely close to change the neuron's response toward its desired. We evaluate the performance of our method with both static and dynamic vision datasets. Experimental results show that the proposed rule can effectively learn spike patterns encoded with both rate and temporal codes, and more importantly, achieves impressive accuracies on all benchmark datasets. We further provide a comprehensive analysis of both codes with respect to efficiency and robustness. Our study thus provides an effective rule that is generalized to process information under a broad range of coding schemes, which would be of great merit for spike-based learning and processing.

Malware poses a threat to computing systems worldwide, and security experts work tirelessly to detect and classify malware as accurately and quickly as possible. Since malware can use evasion techniques to bypass static analysis and security mechanisms, dynamic analysis methods are more useful for accurately analyzing the behavioral patterns of malware. Previous studies showed that malware behavior can be represented by sequences of executed system calls and that machine learning algorithms can leverage such sequences for the task of malware classification (a.k.a. malware categorization). Accurate malware classification is helpful for malware signature generation and is thus beneficial to antivirus vendors; this capability is also valuable to organizational security experts, enabling them to mitigate malware attacks and respond to security incidents. In this paper, we propose an improved methodology for malware classification, based on analyzing sequences of system calls invoked by malware in a dynamic analysis environment. We show that adding an attention mechanism to a LSTM model improves accuracy for the task of malware classification, thus outperforming the state-of-the-art algorithm by up to 6%. We also show that the transformer architecture can be used to analyze very long sequences with significantly lower time complexity for training and prediction. Our proposed method can serve as the basis for a decision support system for security experts, for the task of malware categorization.

Named entity recognition (NER) is an important and challenging task in natural language processing. In this paper, we investigate thoroughly about the advances of Chinese NER in recent years. We explore the validity of a wide range of approaches in the literature of NLP that may benefit NER. We further employ the effective ones, such as data augmentation, adversarial learning, cross-sentence context and cost-sensitive learning to improve the performance of our BERT-based backbone model. Empirical results show that our model with this bag of tricks outperforms previous state-of-the-art on Weibo and achieves competitive performance on MSRA. Our code is publicly available11https://github.com/ccoay/bag-ner.

This paper presents a benchmark of supervised Automated Machine Learning (AutoML) tools. Firstly, we an- alyze the characteristics of eight recent open-source AutoML tools (Auto-Keras, Auto-PyTorch, Auto-Sklearn, AutoGluon, H2O AutoML, rminer, TPOT and TransmogrifAI) and describe twelve popular OpenML datasets that were used in the benchmark (divided into regression, binary and multi-class classification tasks). Then, we perform a comparison study with hundreds of computational experiments based on three scenarios: General Machine Learning (GML), Deep Learning (DL) and XGBoost (XGB). To select the best tool, we used a lexicographic approach, considering first the average prediction score for each task and then the computational effort. The best predictive results were achieved for GML, which were further compared with the best OpenML public results. Overall, the best GML AutoML tools obtained competitive results, outperforming the best OpenML models in five datasets. These results confirm the potential of the general-purpose AutoML tools to fully automate the Machine Learning (ML) algorithm selection and tuning., Opti-Edge: 5G Digital Services Optimization at the Edge, Individual Project, NUP: POCI-01-0247-FEDER-045220, co-funded by the Incentive System for Research and Technological Development, from the Thematic Operational Program Competitiveness of the national framework program - Portugal2020

Data imputation is an important and widely researched problem in data analysis with applications to a variety of problem domains. Existing literature explores different methods to estimate missing values in a dataset. In this paper, we analyze the task of imputing multiple incomplete numerical datasets that arrive sequentially, each with an overlapping yet distinguishable set of features. We propose a novel procedure based on the Generative Adversarial Network (GAN) architecture, called EvoGAN, which uses transfer learning to build a unified model to perform imputation for all datasets in the sequence. Our methods exploit overlap between datasets and utilize the knowledge gathered from previous rounds. We validate our model on several sequences of datasets and find compelling results for compression rate and acceleration which outperform the state-of-the-art imputation models that are based on an input of a single dataset.

Instance segmentation with neural networks is an essential task in environment perception. In many works, it has been observed that neural networks can predict false positive instances with high confidence values and true positives with low ones. Thus, it is important to accurately model the uncertainties of neural networks in order to prevent safety issues and foster interpretability. In applications such as automated driving, the reliability of neural networks is of highest interest. In this paper, we present a time-dynamic approach to model uncertainties of instance segmentation networks and apply this to the detection of false positives as well as the estimation of prediction quality. The availability of image sequences in online applications allows for tracking instances over multiple frames. Based on an instances history of shape and uncertainty information, we construct temporal instance-wise aggregated metrics. The latter are used as input to post-processing models that estimate the prediction quality in terms of instance-wise intersection over union. The proposed method only requires a readily trained neural network (that may operate on single frames) and video sequence input. In our experiments, we further demonstrate the use of the proposed method by replacing the traditional score value from object detection and thereby improving the overall performance of the instance segmentation network.

Clickbait headlines have become a nudge in social media and news websites. The methods to identify clickbaits are largely being developed for English. There is a need for the same in other languages as well with the increase in the usage of social media platforms in different languages. In this work, we present an annotated clickbait dataset of 112,657 headlines that can be used for building an automated clickbait detection system for Telugu, a resource-poor language. Our contribution in this paper includes (i) generation of the latest pre-trained language models, including RoBERTa, ALBERT, and ELECTRA trained on a large Telugu corpora of 8,015,588 sentences that we had collected, (ii) data analysis and benchmarking the performance of different approaches ranging from hand-crafted features to state-of-the-art models. We show that the pre-trained language models trained on Telugu outperform the existing pre-trained models viz. BERT-Mulingual-Case [1], XLM-MLM [2], and XLM-R [3] on clickbait task. On a large Telugu clickbait dataset of 112,657 samples, the Light Gradient Boosted Machines (LGBM) model achieves an F1-score of 0.94 for clickbait headlines. For Non-Clickbait headlines, F1-score of 0.93 is obtained which is similar to that of Clickbait class. We open-source our dataset, pre-trained models, and code ‘ We show that the pre-trained language models trained on Telugu outperform the existing pre-trained models viz. BERT-Mulingual-Case [1], XLM-MLM [2], and XLM-R [3] on clickbait task. On a large Telugu clickbait dataset of 112,657 samples, the Light Gradient Boosted Machines (LGBM) model achieves an F1-score of 0.94 for clickbait headlines. For Non-Clickbait headlines, F1-score of 0.93 is obtained which is similar to that of Clickbait class. We open-source our dataset, pre-trained models, and code11https://github.com/subbareddy248/Clickbait-Resources

Chatbots have received significant attention in the last years. These systems have improved operational efficiency by reducing the cost of customer service and more and more are used in customer service channels. More recently, with the addition of speech capabilities added to those systems, bot language modifications have to be done in order to improve user experience. In this paper, we analyze the language used in those systems using datasets collected from real chatbots. For that, we first propose models that are able to identify the language style (writing, speech, and computer-mediated) using as linguist features syntactic (Biber's dimensions) and sentence embeddings as well as state-of-art datasets. Our results show that our models were able to distinguish among the three classes with an accuracy of up to 66%. Finally, we evaluate real chatbots systems, either speech- and text-based ones, using our proposed models. We found that these real chatbots are generally using a computer-mediated style, but there is indication that their developers tend to adapt the language style to be more speech-like when text-to-speech is used.