Reviewerly/exHarmony huggingface dataset:
Detecting Internet of Things attacks using distributed deep learning

The reliability of Internet of Things (IoT) connected devices is heavily dependent on the security model employed to protect user data and prevent devices from engaging in malicious activity. Existing approaches for detecting phishing, distributed denial of service (DDoS), and Botnet attacks often focus on either the device or the back-end. In this paper, we propose a cloud-based distributed deep learning framework for phishing and Botnet attack detection and mitigation. The model comprises two key security mechanisms working cooperatively, namely: (1) a Distributed Convolutional Neural Network (DCNN) model embedded as an IoT device micro-security add-on for detecting phishing and application layer DDoS attacks; and (2) a cloud-based temporal Long-Short Term Memory (LSTM) network model hosted on the back-end for detecting Botnet attacks, and ingest CNN embeddings to detect distributed phishing attacks across multiple IoT devices. The distributed CNN model, embedded into a ML engine in the client's IoT device, allows us to detect and defend the IoT device from phishing attacks at the point of origin. We create a dataset consisting of both phishing and non-phishing URLs to train the proposed CNN add-on security model, and select the N_BaIoT dataset for training the back-end LSTM model. The joint training method minimizes communication and resource requirements for attack detection, and maximizes the usefulness of extracted features. In addition, an aggregation of schemes allows the automatic fusion of multiple requests to improve the overall performance of the system. Our experiments show that the IoT micro-security add-on running the proposed CNN model is capable of detecting phishing attacks with an accuracy of 94.3% and a F-1 score of 93.58%. Using the back-end LSTM model, the model detects Botnet attacks with an accuracy of 94.80% using all malicious data points in the used dataset. Thus, the findings demonstrate that the proposed approach is capable of detecting attacks, both at device and at the back-end level, in a distributed fashion.
Self-Supervised Multi-Channel Hypergraph Convolutional Network for Social Recommendation

Social relations are often used to improve recommendation quality when user-item interaction data is sparse in recommender systems. Most existing social recommendation models exploit pairwise relations to mine potential user preferences. However, real-life interactions among users are very complex and user relations can be high-order. Hypergraph provides a natural way to model high-order relations, while its potentials for improving social recommendation are under-explored. In this paper, we fill this gap and propose a multi-channel hypergraph convolutional network to enhance social recommendation by leveraging high-order user relations. Technically, each channel in the network encodes a hypergraph that depicts a common high-order user relation pattern via hypergraph convolution. By aggregating the embeddings learned through multiple channels, we obtain comprehensive user representations to generate recommendation results. However, the aggregation operation might also obscure the inherent characteristics of different types of high-order connectivity information. To compensate for the aggregating loss, we innovatively integrate self-supervised learning into the training of the hypergraph convolutional network to regain the connectivity information with hierarchical mutual information maximization. Extensive experiments on multiple real-world datasets demonstrate the superiority of the proposed model over the current SOTA methods, and the ablation study verifies the effectiveness and rationale of the multi-channel setting and the self-supervised task. The implementation of our model is available via https://github.com/Coder-Yu/RecQ.
Deep learning based 2D human pose estimation: A survey

Human pose estimation has received significant attention recently due to its various applications in the real world. As the performance of the state-of-the-art human pose estimation methods can be improved by deep learning, this paper presents a comprehensive survey of deep learning based human pose estimation methods and analyzes the methodologies employed. We summarize and discuss recent works with a methodology-based taxonomy. Single-person and multi-person pipelines are first reviewed separately. Then, the deep learning techniques applied in these pipelines are compared and analyzed. The datasets and metrics used in this task are also discussed and compared. The aim of this survey is to make every step in the estimation pipelines interpretable and to provide readers a readily comprehensible explanation. Moreover, the unsolved problems and challenges for future research are discussed.
Inter-sentence Relation Extraction with Document-level Graph Convolutional Neural Network

Inter-sentence relation extraction deals with a number of complex semantic relationships in documents, which require local, non-local, syntactic and semantic dependencies.Existing methods do not fully exploit such dependencies.We present a novel inter-sentence relation extraction model that builds a labelled edge graph convolutional neural network model on a document-level graph.The graph is constructed using various inter-and intra-sentence dependencies to capture local and non-local dependency information.In order to predict the relation of an entity pair, we utilise multi-instance learning with bi-affine pairwise scoring.Experimental results show that our model achieves comparable performance to the state-of-the-art neural models on two biochemistry datasets.Our analysis shows that all the types in the graph are effective for inter-sentence relation extraction.
BAM! Born-Again Multi-Task Networks for Natural Language Understanding

It can be challenging to train multi-task neural networks that outperform or even match their single-task counterparts.To help address this, we propose using knowledge distillation where single-task models teach a multi-task model.We enhance this training with teacher annealing, a novel method that gradually transitions the model from distillation to supervised learning, helping the multi-task model surpass its single-task teachers.We evaluate our approach by multi-task fine-tuning BERT on the GLUE benchmark.Our method consistently improves over standard single-task and multi-task training.
CNN-Based Chinese NER with Lexicon Rethinking

Character-level Chinese named entity recognition (NER) that applies long short-term memory (LSTM) to incorporate lexicons has achieved great success. However, this method fails to fully exploit GPU parallelism and candidate lexicons can conflict. In this work, we propose a faster alternative to Chinese NER: a convolutional neural network (CNN)-based method that incorporates lexicons using a rethinking mechanism. The proposed method can model all the characters and potential words that match the sentence in parallel. In addition, the rethinking mechanism can address the word conflict by feeding back the high-level features to refine the networks. Experimental results on four datasets show that the proposed method can achieve better performance than both word-level and character-level baseline methods. In addition, the proposed method performs up to 3.21 times faster than state-of-the-art methods, while realizing better performance.
Breast tumor segmentation and shape classification in mammograms using generative adversarial and convolutional neural network

Mammogram inspection in search of breast tumors is a tough assignment that radiologists must carry out frequently. Therefore, image analysis methods are needed for the detection and delineation of breast tumors, which portray crucial morphological information that will support reliable diagnosis. In this paper, we proposed a conditional Generative Adversarial Network (cGAN) devised to segment a breast tumor within a region of interest (ROI) in a mammogram. The generative network learns to recognize the tumor area and to create the binary mask that outlines it. In turn, the adversarial network learns to distinguish between real (ground truth) and synthetic segmentations, thus enforcing the generative network to create binary masks as realistic as possible. The cGAN works well even when the number of training samples are limited. As a consequence, the proposed method outperforms several state-of-the-art approaches. Our working hypothesis is corroborated by diverse segmentation experiments performed on INbreast and a private in-house dataset. The proposed segmentation model, working on an image crop containing the tumor as well as a significant surrounding area of healthy tissue (loose frame ROI), provides a high Dice coefficient and Intersection over Union (IoU) of 94% and 87%, respectively. In addition, a shape descriptor based on a Convolutional Neural Network (CNN) is proposed to classify the generated masks into four tumor shapes: irregular, lobular, oval and round. The proposed shape descriptor was trained on DDSM, since it provides shape ground truth (while the other two datasets does not), yielding an overall accuracy of 80%, which outperforms the current state-of-the-art.
On the Convergence of FedAvg on Non-IID Data

Federated learning enables a large amount of edge computing devices to jointly learn a model without data sharing. As a leading algorithm in this setting, Federated Averaging (\texttt{FedAvg}) runs Stochastic Gradient Descent (SGD) in parallel on a small subset of the total devices and averages the sequences only once in a while. Despite its simplicity, it lacks theoretical guarantees under realistic settings. In this paper, we analyze the convergence of \texttt{FedAvg} on non-iid data and establish a convergence rate of $\mathcal{O}(\frac{1}{T})$ for strongly convex and smooth problems, where $T$ is the number of SGDs. Importantly, our bound demonstrates a trade-off between communication-efficiency and convergence rate. As user devices may be disconnected from the server, we relax the assumption of full device participation to partial device participation and study different averaging schemes; low device participation rate can be achieved without severely slowing down the learning. Our results indicate that heterogeneity of data slows down the convergence, which matches empirical observations. Furthermore, we provide a necessary condition for \texttt{FedAvg} on non-iid data: the learning rate $\eta$ must decay, even if full-gradient is used; otherwise, the solution will be $\Omega (\eta)$ away from the optimal.
Online Knowledge Distillation with Diverse Peers

Distillation is an effective knowledge-transfer technique that uses predicted distributions of a powerful teacher model as soft targets to train a less-parameterized student model. A pre-trained high capacity teacher, however, is not always available. Recently proposed online variants use the aggregated intermediate predictions of multiple student models as targets to train each student model. Although group-derived targets give a good recipe for teacher-free distillation, group members are homogenized quickly with simple aggregation functions, leading to early saturated solutions. In this work, we propose Online Knowledge Distillation with Diverse peers (OKDDip), which performs two-level distillation during training with multiple auxiliary peers and one group leader. In the first-level distillation, each auxiliary peer holds an individual set of aggregation weights generated with an attention-based mechanism to derive its own targets from predictions of other auxiliary peers. Learning from distinct target distributions helps to boost peer diversity for effectiveness of group-based distillation. The second-level distillation is performed to transfer the knowledge in the ensemble of auxiliary peers further to the group leader, i.e., the model used for inference. Experimental results show that the proposed framework consistently gives better performance than state-of-the-art approaches without sacrificing training or inference complexity, demonstrating the effectiveness of the proposed two-level distillation framework.
A Novel Mixed Control Approach for Fuzzy Systems via Membership Functions Online Learning Policy

This article focuses on the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mathcal {L}_{2}-\mathcal {L} _{\infty}/ \mathcal {H}_{\infty}$</tex-math></inline-formula> optimization control issue for a family of nonlinear plants by Takagi–Sugeno (T–S) fuzzy approach with actuator failure. First, considering unmeasurable system states, sufficient criteria for devising fuzzy imperfect premise matching dynamic output feedback controller to maintain asymptotic stability while guaranteeing a mixed performance for T–S fuzzy systems are provided. Therewith, in the light of feasible areas of dynamic output feedback controller membership functions (MFs), a new MFs online learning policy using gradient descent algorithm is proposed to learn the real-time values of MFs to acquire a better <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mathcal {L}_{2}-\mathcal {L}_{\infty}/ \mathcal {H}_{\infty}$</tex-math></inline-formula> control effect. Different from the traditional method using an imperfect premise matching scheme, under the proposed optimization algorithm, the trajectory of mixed performance index is lowered effectively. Afterward, a sufficient criterion is presented for assuring the convergence of the error of the cost function. Finally, the superiority of this online optimization learning policy is confirmed via simulations.
Multisource Data Reconstruction-Based Deep Unsupervised Hashing for Unisource Remote Sensing Image Retrieval

Unsupervised hashing for remote sensing (RS) image retrieval first extracts image features and then use these features to construct supervised information (e.g., pseudo-labels) to train hashing networks. Existing methods usually regard RS images as natural images to extract unisource features. However, these features only contain partial information about ground objects and cannot produce reliable pseudo-labels. In addition, existing methods only generate a pseudo single-label to annotate each RS image, which cannot accurately represent multiple scenes in a RS image. To address these drawbacks, this paper proposes a new Multisource data reconstruction-based deep unsupervised Hashing method, called MrHash, which explores the characteristics of RS images to construct reliable pseudo-labels. In particular, we first use geographic coordinates to obtain different satellite images and develop a novel autoencoder network to extract multisource features from these images. Then pseudo multi-labels are designed to deal with the coexistence of multiple scenes in a single image. These labels are generated by a custom probability function with extracted multisource features. Finally, we propose a novel multi-semantic hash loss by using the Kull-back–Leibler (KL) divergence to preserve the semantic similarity of these pseudo multi-labels in Hamming space. Our newly developed MrHash only uses multisource images to construct supervised information, and hash code generation still relies on a unisource input image. Experiments on benchmark datasets clearly show the superiority of the proposed method over state-of-the-art baselines. https://github.com/sunyuxi/MrHash.
A Streaming On-Device End-To-End Model Surpassing Server-Side Conventional Model Quality and Latency

Thus far, end-to-end (E2E) models have not been shown to outperform state-of-the-art conventional models with respect to both quality, i.e., word error rate (WER), and latency, i.e., the time the hypothesis is finalized after the user stops speaking. In this paper, we develop a first-pass Recurrent Neural Network Transducer (RNN-T) model and a second-pass Listen, Attend, Spell (LAS) rescorer that surpasses a conventional model in both quality and latency. On the quality side, we incorporate a large number of utterances across varied domains [1] to increase acoustic diversity and the vocabulary seen by the model. We also train with accented English speech to make the model more robust to different pronunciations. In addition, given the increased amount of training data, we explore a varied learning rate schedule. On the latency front, we explore using the end-of-sentence decision emitted by the RNN-T model to close the microphone, and also introduce various optimizations to improve the speed of LAS rescoring. Overall, we find that RNN-T+LAS offers a better WER and latency tradeoff compared to a conventional model. For example, for the same latency, RNN-T+LAS obtains a 8% relative improvement in WER, while being more than 400-times smaller in model size.
Comparison of Distance Measures in Cluster Analysis with Dichotomous Data

The current study examines the performance of cluster analysis with dichotomous data using distance measures based on response pattern similarity.In many contexts, such as educational and psychological testing, cluster analysis is a useful means for exploring datasets and identifying underlying groups among individuals.However, standard approaches to cluster analysis assume that the variables used to group observations are continuous in nature.This paper focuses on four methods for calculating distance between individuals using dichotomous data, and the subsequent introduction of these distances to a clustering algorithm such as Ward's.The four methods in question, are potentially useful for practitioners because they are relatively easy to carry out using standard statistical software such as SAS and SPSS, and have been shown to have potential for correctly grouping observations based on dichotomous data.Results of both a simulation study and application to a set of binary survey responses show that three of the four measures behave similarly, and can yield correct cluster recovery rates of between 60% and 90%.Furthermore, these methods were found to work better, in nearly all cases, than using the raw data with Ward's clustering algorithm.
Cross-lingual Transfer Learning for Multilingual Task Oriented Dialog

Sebastian Schuster, Sonal Gupta, Rushin Shah, Mike Lewis. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). 2019.
Algorand: A secure and efficient distributed ledger

A distributed ledger is a tamperproof sequence of data that can be publicly accessed and augmented by everyone, without being maintained by a centralized party. Distributed ledgers stand to revolutionize the way a modern society operates. They can secure all kinds of traditional transactions, such as payments, asset transfers and titles, in the exact order in which the transactions occur; and enable totally new transactions, such as cryptocurrencies and smart contracts. They can remove intermediaries and usher in a new paradigm for trust. As currently implemented, however, distributed ledgers scale poorly and cannot achieve their enormous potential. In this paper we propose Algorand, an alternative, secure and efficient distributed ledger. Algorand is permissionless and works in a highly asynchronous environment. Unlike prior implementations of distributed ledgers based on “proof of work,” Algorand dispenses with “miners” and requires only a negligible amount of computation. Moreover, its transaction history “forks” only with negligible probability: that is, Algorand guarantees the finality of a transaction the moment the transaction enters the ledger.
JW300: A Wide-Coverage Parallel Corpus for Low-Resource Languages

Viable cross-lingual transfer critically depends on the availability of parallel texts.Shortage of such resources imposes a development and evaluation bottleneck in multilingual processing.We introduce JW300, a parallel corpus of over 300 languages with around 100 thousand parallel sentences per language pair on average.In this paper, we present the resource and showcase its utility in experiments with crosslingual word embedding induction and multisource part-of-speech projection.
Cross-Modality Paired-Images Generation for RGB-Infrared Person Re-Identification

RGB-Infrared (IR) person re-identification is very challenging due to the large cross-modality variations between RGB and IR images. The key solution is to learn aligned features to the bridge RGB and IR modalities. However, due to the lack of correspondence labels between every pair of RGB and IR images, most methods try to alleviate the variations with set-level alignment by reducing the distance between the entire RGB and IR sets. However, this set-level alignment may lead to misalignment of some instances, which limits the performance for RGB-IR Re-ID. Different from existing methods, in this paper, we propose to generate cross-modality paired-images and perform both global set-level and fine-grained instance-level alignments. Our proposed method enjoys several merits. First, our method can perform set-level alignment by disentangling modality-specific and modality-invariant features. Compared with conventional methods, ours can explicitly remove the modality-specific features and the modality variation can be better reduced. Second, given cross-modality unpaired-images of a person, our method can generate cross-modality paired images from exchanged images. With them, we can directly perform instance-level alignment by minimizing distances of every pair of images. Extensive experimental results on two standard benchmarks demonstrate that the proposed model favourably against state-of-the-art methods. Especially, on SYSU-MM01 dataset, our model can achieve a gain of 9.2% and 7.7% in terms of Rank-1 and mAP. Code is available at https://github.com/wangguanan/JSIA-ReID.
Predicting the direction, maximum, minimum and closing prices of daily Bitcoin exchange rate using machine learning techniques

Bitcoin is the most accepted cryptocurrency in the world, which makes it attractive for investors and traders. However, the challenge in predicting the Bitcoin exchange rate is its high volatility. Therefore, the prediction of its behavior is of great importance for financial markets. In this way, recent studies have been carried out on what internal and/or external Bitcoin information is relevant to its prediction. The increased use of machine learning techniques to predict time series and the acceptance of cryptocurrencies as financial instruments motivated the present study to seek more accurate predictions for the Bitcoin exchange rate. In this way, in a first stage of the proposed methodology, different feature selection techniques were evaluated in order to obtain the most relevant attributes for the predictions. In the sequence, it was analyzed the behavior of Artificial Neural Networks (ANN), Support Vector Machines (SVM) and Ensemble algorithms (based on Recurrent Neural Networks and the k-Means clustering method) for price direction predictions. Likewise, the ANN and SVM were employed for regression of the maximum, minimum and closing prices of the Bitcoin. Moreover, the regression results were also used as inputs to try to improve the price direction predictions. The results showed that the selected attributes and the best machine learning model achieved an improvement of more than 10%, in accuracy, for the price direction predictions, with respect to the state-of-the-art papers, using the same period of information. In relation to the maximum, minimum and closing Bitcoin prices regressions, it was possible to obtain Mean Absolute Percentage Errors between 1% and 2%. Based on these results, it was possible to demonstrate the efficacy of the proposed methodology when compared to other studies.