Reviewerly/exHarmony huggingface dataset:
HGCN: A Heterogeneous Graph Convolutional Network-Based Deep Learning Model Toward Collective Classification

Collective classification, as an important technique to study networked data, aims to exploit the label autocorrelation for a group of inter-connected entities with complex dependencies. As the emergence of various heterogeneous information networks (HINs), collective classification at present is confronting several severe challenges stemming from the heterogeneity of HINs, such as complex relational hierarchy, potential incompatible semantics and node-context relational semantics. To address the challenges, in this paper, we propose a novel heterogeneous graph convolutional network-based deep learning model, called HGCN, to collectively categorize the entities in HINs. Our work involves three primary contributions: i) HGCN not only learns the latent relations from the relation-sophisticated HINs via multi-layer heterogeneous convolutions, but also captures the semantic incompatibility among relations with properly-learned edge-level filter parameters; ii) to preserve the fine-grained relational semantics of different-type nodes, we propose a heterogeneous graph convolution to directly tackle the original HINs without any in advance transforming the network from heterogeneity to homogeneity; iii) we perform extensive experiments using four real-world datasets to validate our proposed HGCN, the multi-facet results show that our proposed HGCN can significantly improve the performance of collective classification compared with the state-of-the-art baseline methods.
Federated Recommendation System via Differential Privacy

In this paper we are interested in what we term the federated private bandits framework, that combines differential privacy with multi-agent bandit learning. We explore how differential privacy based Upper Confidence Bound (UCB) methods can be applied to multi-agent environments, and in particular to federated learning environments both in ‘master-worker’ and ‘fully decentralized’ settings. We provide theoretical analysis on the privacy and regret performance of the proposed methods and explore the tradeoffs between these two.
Deep Learning-Based Real-Time Multiple-Person Action Recognition System

Action recognition has gained great attention in automatic video analysis, greatly reducing the cost of human resources for smart surveillance. Most methods, however, focus on the detection of only one action event for a single person in a well-segmented video, rather than the recognition of multiple actions performed by more than one person at the same time for an untrimmed video. In this paper, we propose a deep learning-based multiple-person action recognition system for use in various real-time smart surveillance applications. By capturing a video stream of the scene, the proposed system can detect and track multiple people appearing in the scene and subsequently recognize their actions. Thanks to high resolution of the video frames, we establish a zoom-in function to obtain more satisfactory action recognition results when people in the scene become too far from the camera. To further improve the accuracy, recognition results from inflated 3D ConvNet (I3D) with multiple sliding windows are processed by a nonmaximum suppression (NMS) approach to obtain a more robust decision. Experimental results show that the proposed method can perform multiple-person action recognition in real time suitable for applications such as long-term care environments.
A survey on the techniques, applications, and performance of short text semantic similarity

Summary Short text similarity plays an important role in natural language processing (NLP). It has been applied in many fields. Due to the lack of sufficient context in the short text, it is difficult to measure the similarity. The use of semantics similarity to calculate textual similarity has attracted the attention of academia and industry and achieved better results. In this survey, we have conducted a comprehensive and systematic analysis of semantic similarity. We first propose three categories of semantic similarity: corpus‐based, knowledge‐based, and deep learning (DL)‐based. We analyze the pros and cons of representative and novel algorithms in each category. Our analysis also includes the applications of these similarity measurement methods in other areas of NLP. We then evaluate state‐of‐the‐art DL methods on four common datasets, which proved that DL‐based can better solve the challenges of the short text similarity, such as sparsity and complexity. Especially, bidirectional encoder representations from transformer model can fully employ scarce information of short texts and semantic information and obtain higher accuracy and F1 value. We finally put forward some future directions.
INPREM: An Interpretable and Trustworthy Predictive Model for Healthcare

Building a predictive model based on historical Electronic Health Records (EHRs) for personalized healthcare has become an active research area. Benefiting from the powerful ability of feature extraction, deep learning (DL) approaches have achieved promising performance in many clinical prediction tasks. However, due to the lack of interpretability and trustworthiness, it is difficult to apply DL in real clinical cases of decision making. To address this, in this paper, we propose an interpretable and trustworthy predictive model~(INPREM) for healthcare. Firstly, INPREM is designed as a linear model for interpretability while encoding non-linear relationships into the learning weights for modeling the dependencies between and within each visit. This enables us to obtain the contribution matrix of the input variables, which is served as the evidence of the prediction result(s), and help physicians understand why the model gives such a prediction, thereby making the model more interpretable. Secondly, for trustworthiness, we place a random gate (which follows a Bernoulli distribution to turn on or off) over each weight of the model, as well as an additional branch to estimate data noises. With the help of the Monto Carlo sampling and an objective function accounting for data noises, the model can capture the uncertainty of each prediction. The captured uncertainty, in turn, allows physicians to know how confident the model is, thus making the model more trustworthy. We empirically demonstrate that the proposed INPREM outperforms existing approaches with a significant margin. A case study is also presented to show how the contribution matrix and the captured uncertainty are used to assist physicians in making robust decisions.
An efficient general data hiding scheme based on image interpolation

Information exchange through public networks has the risk of information disclosure, making data hiding an important technique to protect privacy. Image interpolation is also a common technique in image processing. Recent years, research on data hiding based on image interpolation have matured. In this paper, we propose an efficient data hiding scheme based on image interpolation that can be adopted into different image interpolation techniques. Different from other schemes, the converted decimal secret data is embedded into the cover image by adding or subtracting the cover pixels according to whether the current pixel value is lower or higher than the original pixel value and is embedded following the block complexity from high to low. Therefore, our proposed scheme keeps a relative balance between hiding capacity and image quality that can increase the stego-image quality and obtain a relatively higher image quality when the embedded bits are lower. The experimental results show that our proposed scheme has a relatively higher hiding capacity and can achieve almost a 1.6 dB increase on average compared with other schemes. The visual effect is also superior under the different image interpolation techniques.
Coupled adversarial learning for semi-supervised heterogeneous face recognition

Visible-near infrared (VIS-NIR) face matching is a challenging issue in heterogeneous face recognition due to the large spectrum domain discrepancy as well as the over-fitting on insufficient pairwise VIS and NIR images during training. This paper proposes a coupled adversarial learning (CAL) approach for the VIS-NIR face matching by performing adversarial learning on both image and feature levels. On the image level, we learn a transformation network from unpaired NIR-VIS images to transform a NIR image to VIS domain. Cycle loss, global intensity loss and local texture loss are employed to better capture the discrepancy between NIR and VIS domains. The synthesized NIR or VIS images can be further used to alleviate the over-fitting problem in a semi-supervised way. On the feature level, we seek a shared feature space in which the heterogeneous face matching problem can be approximately treated as a homogeneous face matching problem. An adversarial loss and an orthogonal constraint are employed to reduce the spectrum domain discrepancy and the over-fitting problem, respectively. Experimental results show that CAL not only synthesizes high-quality VIS or NIR images, but also obtains state-of-the-art recognition results.
MP2ML

Privacy-preserving machine learning (PPML) has many applications, from medical image classification and anomaly detection to financial analysis. nGraph-HE enables data scientists to perform private inference of deep learning (DL) models trained using popular frameworks such as TensorFlow. nGraph-HE computes linear layers using the CKKS homomorphic encryption (HE) scheme. The non-polynomial activation functions, such as MaxPool and ReLU, are evaluated in the clear by the data owner who obtains the intermediate feature maps. This leaks the feature maps to the data owner from which it may be possible to deduce the DL model weights. As a result, such protocols may not be suitable for deployment, especially when the DL model is intellectual property.
IoT based Smart Agriculture using Machine Learning

Agriculture balances both food requirement for mankind and supplies indispensable raw materials for many industries, and it is the most significant and fundamental occupation in India. The advancement in inventive farming techniques is gradually enhancing the crop yield making it more profitable and reduce irrigation wastages. The proposed model is a smart irrigation system which predicts the water requirement for a crop, using machine learning algorithm. Moisture, temperature and humidity are the three most essential parameters to determine the quantity of water required in any agriculture field. This system comprises of temperature, humidity and moisture sensor, deployed in an agricultural field, sends data through a microprocessor, developing an IoT device with cloud. Decision tree algorithm, an efficient machine learning algorithm is applied on the data sensed from the field in to predict results efficiently. The results obtained through decision tree algorithm is sent through a mail alert to the farmers, which helps in decision making regarding water supply in advance.
Classifier ensemble methods in feature selection

Feature selection has become an indispensable preprocessing step in an expert system. Improving the feature selection performance could guide such a system to make better decisions. Classifier ensembles are known to improve performance when compared to the use of a single classifier. In this study, we aim to perform a formal comparison of different classifier ensemble methods on the feature selection domain. For this purpose, we compare the performances of six classifier ensemble methods: a greedy approach, two average-based approaches, two majority voting approaches, and a meta-classifier approach. In our study, the classifier ensemble involves five machine learning techniques: Logistic Regression, Support Vector Machines, Extreme Learning Machine, Naïve Bayes, and Decision Tree. Experiments are carried on 12 well-known datasets, and results with statistical tests are provided. The results indicate that ensemble methods perform better than single classifiers, yet, they require a longer execution time. Moreover, they can minimize the number of features better than existing ensemble algorithms, namely Random Forest, AdaBoost, and Gradient Boosting, in a less amount of time. Among ensemble methods, the greedy based method performs well in terms of both classification accuracy and execution time.
Mammographic image classification with deep fusion learning

Abstract To better address the recognition of abnormalities among mammographic images, in this study we apply the deep fusion learning approach based on Pre-trained models to discover the discriminative patterns between Normal and Tumor categories. We designed a deep fusion learning framework for mammographic image classification. This framework works in two main steps. After obtaining the regions of interest (ROIs) from original dataset, the first step is to train our proposed deep fusion models on those ROI patches which are randomly collected from all ROIs. We proposed the deep fusion model (Model1) to directly fuse the deep features to classify the Normal and Tumor ROI patches. To explore the association among channels of the same block, we propose another deep fusion model (Model2) to integrate the cross-channel deep features using 1 × 1 convolution. The second step is to obtain the final prediction by performing the majority voting on all patches' prediction of one ROI. The experimental results show that Model1 achieves the whole accuracy of 0.8906, recall rate of 0.913, and precision rate of 0.8077 for Tumor class. Accordingly, Model2 achieves the whole accuracy of 0.875, recall rate of 0.9565, and precision rate 0.7,586 for Tumor class. Finally, we open source our Python code at https://github.com/yxchspring/MIAS in order to share our tool with the research community.
Smartphone Based Human Activity Recognition with Feature Selection and Dense Neural Network

For the past few years, smartphone based human activity recognition (HAR) has gained much popularity due to its embedded sensors which have found various applications in healthcare, surveillance, human-device interaction, pattern recognition etc. In this paper, we propose a neural network model to classify human activities, which uses activity-driven hand-crafted features. First, the neighborhood component analysis derived feature selection is used to choose a subset of important features from the available time and frequency domain parameters. Next, a dense neural network consisting of four hidden layers is modeled to classify the input features into different categories. The model is evaluated on publicly available UCI HAR data set consisting of six daily activities; our approach achieved 95.79% classification accuracy. When compared with existing state-of-the-art methods, our proposed model outperformed most other methods while using fewer features, thus showing the importance of proper feature selection.
SNARGs for bounded depth computations and PPAD hardness from sub-exponential LWE

We construct a succinct non-interactive publicly-verifiable delegation scheme for any log-space uniform circuit under the sub-exponential Learning With Errors (LWE) assumption. For a circuit C:{0,1}N→{0,1} of size S and depth D, the prover runs in time poly(S), the communication complexity is D · polylog(S), and the verifier runs in time (D+N) ·polylog(S). To obtain this result, we introduce a new cryptographic primitive: a lossy correlation-intractable hash function family. We use this primitive to soundly instantiate the Fiat-Shamir transform for a large class of interactive proofs, including the interactive sum-check protocol and the GKR protocol, assuming the sub-exponential hardness of LWE.
Bipartite Graph based Multi-view Clustering

For graph-based multi-view clustering, a critical issue is to capture consensus cluster structures via a two-stage learning scheme. Specifically, first learn similarity graph matrices of multiple views and then fuse them into a unified superior graph matrix. Most current methods learn pairwise similarities between data points for each view independently, which is widely used in single view. However, the consensus information contained in multiple views are ignored, and the involved biases lead to an undesirable unified graph matrix. To this end, we propose a bipartite graph based multi-view clustering (BIGMC) approach. The consensus information can be represented by a small number of representative uniform anchor points for different views. A bipartite graph is constructed between data points and the anchor points. BIGMC constructs the bipartite graph matrices of all views and fuses them to produce a unified bipartite graph matrix. The unified bipartite graph matrix in turn improves the bipartite graph similarity matrix of each view and updates the anchor points. The final unified graph matrix forms the final clusters directly. In BIGMC, an adaptive weight is added for each view to avoid outlier views. A low-rank constraint is imposed on the Laplacian matrix of the unified matrix to construct a multi-component unified bipartite graph, where the component number corresponds to the required cluster number. The objective function is optimized in an alternating optimization fashion. Experimental results on synthetic and real-world data sets demonstrate its effectiveness and superiority compared with the state-of-the-art baselines.
Color Image Compression-Encryption Using Fractional-Order Hyperchaotic System and DNA Coding

Recently, many encryption algorithms based on fractional-order chaotic system have been proposed to solve the problem of image encryption. In this paper, we propose a novel color image compression-encryption algorithm based on fractional-order hyperchaotic system and DNA coding. In the data compression stage, the data of R, G, and B channels of the color image are converted to the frequency domain by two-dimensional discrete cosine transform (DCT), and then the amount of encrypted data is reduced by the quantization process. We design a data processing algorithm to ensure that the data after DCT is compatible with DNA coding data format. In the encryption stage, the processes of DNA encoding and decoding, DNA operation, and pixel scrambling are all controlled by the corresponding chaotic sequences, which are generated by the chaotic system. The original image is used to calculate the initial state of the chaotic system, which improves the performance of the algorithm against the chosen-plaintext attack significantly. Experimental results and security analysis illustrate that the proposed algorithm has excellent compression and security performance. It can not only reconstruct the original image well under the condition of low compression ratio, but also provide high security to resist various attacks. Besides, experimental results also indicate that the algorithm proposed can be applied to the fields of color image compression, encryption, and transmission.
Interactions Guided Generative Adversarial Network for unsupervised image captioning

Most of the current image captioning models that have achieved great successes heavily depend on manually labeled image-caption pairs. However, it is expensive and time-consuming to acquire large scale paired data. In this paper, we propose the Interactions Guided Generative Adversarial Network (IGGAN) for unsupervised image captioning, which joints multi-scale feature representation and object-object interactions. To get robust feature representation, the image is encoded by ResNet with a new Multi-scale module and adaptive Channel attention (RMCNet). Moreover, the information on object-object interactions is extracted by our Mutual Attention Network (MAN) and then adopted in the process of adversarial generation, which enhances the rationality of generated sentences. To encourage the sentence to be semantically consistent with the image, we utilize the image and generated sentence to reconstruct each other by cycle consistency in IGGAN. Our proposed model can generate sentences without any manually labeled image-caption pairs. Experimental results show that our proposed model achieves quite promising performance on the MSCOCO image captioning dataset. The ablation studies validate the effectiveness of our proposed modules.
ActivityGAN

Label Scarcity and Data Augmentation have long been challenging problems in the research of Human-oriented Artificial Intelligence. Following the trends of Deep Learning, Human Activity Recognition (HAR) tasks have been significantly optimized in the recent decade with handful of industrial applications on health evaluation and security monitoring. Nevertheless, data acquisition on human activities has been increasingly problematic considering the limited sensor resources and high cost of investment on labor of human volunteers. In this paper, we propose a pioneering unified architecture of convolutional generative adversarial networks, namely ActivityGAN, to effectively generate sensor-based data simulating human physical activities. This architecture comprises of a generation model which is a stack of one-dimensional convolution (1D-convolution) and transposed convolution (1D-transposed convolution) layers, and a discrimination model which employs two-dimensional convolution networks (2D-convolution) with reshaped input of time series. We train the proposed architecture on a collection of activity data and evaluate the generator's output, namely synthetic data, with three approaches of visualization. We then assess the usability of synthetic data by evaluating the test accuracy of models trained with mixed real and synthetic data or with synthetic data that substitutes real data. The study's results show that our proposed architecture is able to generate sufficient synthetic data which are distinguishable by visualization techniques and trainable for HAR machine learning models.
A Summary of the COLIEE 2019 Competition

We summarize the evaluation of the 6th Competition on Legal Information Extraction/Entailment (COLIEE 2019). The competition consists of four tasks: two on case law and two on statute law. The case law component includes an information retrieval task (Task 1), and the confirmation of an entailment relation between an existing case and an unseen case (Task 2). The statute law component also includes an information retrieval task (Task 3) and an entailment/question answering task (Task 4), which attempts to confirm whether a particular statute applies to a yes/no question. Participation was open to any group in the world, based on any approach. Eleven different teams participated in the case law competition tasks, some of them in more than one task. We received results from 7 teams for Task 1 (15 runs) and 7 teams for Task 2 (18 runs). For the statute law tasks, 8 different teams participated, some in more than one task. Seven teams submitted a total of 13 runs for Task 3, and 7 teams submitted a total of 15 runs for Task 4. Here we summarize each team’s approaches, our official evaluation, and some analysis of the variety of methods that produced the evaluation results.