Reviewerly/exHarmony huggingface dataset:
Feature Alignment and Aggregation Siamese Networks for Fast Visual Tracking

Siamese networks have been successfully introduced into visual tracking, which match the best candidate and a target template via a couple of networks with shared parameters. However, most Siamese network-based trackers (SNTs) are tailored to best match the canonical posture of the template and the search-region images, resulting in inferior performance when the target objects have large-scale pose variations. Besides, SNTs fail to discriminate distractors well because they only leverage high-level semantic features as target representations that cannot well tell from different targets of the same category. To address these issues, this paper presents an efficient and effective SNT that is based on feature alignment and aggregation networks. Specifically, we first design an effective feature alignment network module to calibrate the search-region image. This module results in a more reliable matching response that is robust to severe target pose variations. Then, we develop an effective shallow-level and high-level feature aggregation network module to complement the feature characteristics, making the learned feature representation not only well differentiate the target from distractors, but also robust to target appearance variations. Afterwards, we employ a channel-attention mechanism to further strengthen the discriminative capability of the aggregated feature representation. Finally, both the alignment and the aggregation modules are seamlessly integrated into the Siamese networks for robust tracking. Meanwhile, we offline learn the network parameters end-to-end without time-consuming fine-tuning. Extensive evaluations on a variety of benchmarks including VOT-2017, OTB-100, UAV123 and GOT-10k demonstrate favorable performance of our tracker against state-of-the-art ones with a speed of 60 fps.
MR Image Enhancement using Adaptive Weighted Mean Filtering and Homomorphic Filtering

Magnetic resonance image enhancement plays crucial role in numerous bio-medical applications. In this paper, the noisy magnetic resonance (MR) brain images were enhanced using Adaptive Weighted Mean Filtering (AWMF) and homomorphic filtering. The MR images always suffer from low contrast. Homomorphic filtering is popular technique to enhance the image contrast. Homomorphic filtering works based on illumination-reflectance model. It improves the image quality by doing contrast enhancement and dynamic range compression simultaneously. In general, MR images are affected by Rician noise, salt and pepper noise and Gaussian noise. Salt and pepper noise (SPN) considerably reduce the quality of the MR images. Contrast ratio and image quality is significantly degraded in the presence of SPN. Pre-processing is required for noisy MR images before applying to homomorphic filter. Many techniques have been proposed to de-noise the salt and pepper noise such as mean, median and adaptive filters. These filters are used to eliminate low level of SPN. High level of SPN can be eliminated by AWMF. In pre-processing, the AWMF is used to denoising the noisy images. Then de-noised image is enhanced using homomorphic filter. The efficiency of the proposed method is compared with median filter (MF) and based on pixel density filter (BPDF). The simulation results show that our proposed algorithm is more efficient than existing algorithms.
Improving Classification Performance of Softmax Loss Function Based on Scalable Batch-Normalization

Convolutional neural networks (CNNs) have made great achievements on computer vision tasks, especially the image classification. With the improvement of network structure and loss functions, the performance of image classification is getting higher and higher. The classic Softmax + cross-entropy loss has been the norm for training neural networks for years, which is calculated from the output probability of the ground-truth class. Then the network’s weight is updated by gradient calculation of the loss. However, after several epochs of training, the back-propagation errors usually become almost negligible. For the above considerations, we proposed that batch normalization with adjustable scale could be added after network output to alleviate the problem of vanishing gradient problem in deep learning. The experimental results show that our method can significantly improve the final classification accuracy on different network structures, and is also better than many other improved classification Loss.
Document-level multi-topic sentiment classification of Email data with BiLSTM and data augmentation

Email data has unique characteristics, involving multiple topics, lengthy replies, formal language, high variance in length, high duplication, anomalies, and indirect relationships that distinguish it from other social media data. In order to better model Email documents and to capture complex sentiment structures in the content, we develop a framework for document-level multi-topic sentiment classification of Email data. Note that, a large volume of labeled Email data is rarely publicly available. We introduce an optional data augmentation process to increase the size of datasets with synthetically labeled data to reduce the probability of overfitting and underfitting during the training process. To generate segments with topic embeddings and topic weighting vectors as inputs for our proposed model, we apply both latent Dirichlet allocation topic modeling and semantic text segmentation to post-process Email documents. Empirical results obtained with multiple sets of experiments, including performance comparison against various state-of-the-art algorithms with and without data augmentation and diverse parameter settings, are analyzed to demonstrate the effectiveness of our proposed framework.
A hybrid scheme-based one-vs-all decision trees for multi-class classification tasks

Decision tree algorithms have been proved to be a powerful and popular approach in classification tasks. However, they do not have reasonable classification performance in multi-class scenarios. In the present study, decision tree algorithms are combined with the one-vs-all (OVA) binarization technique to improve the generalization capabilities of the scheme. However, unlike previous literature that has focused on aggregation strategies, the present study is focused on the process of building base classifiers over the OVA scheme. A novel split criterion, entitled by the splitting point correction matrix (SPCM), is proposed in this regards, which can effectively deal with the unbalance problem caused by the OVA scheme. The SPCM is a kind of hybrid scheme, which integrates distribution and permutation information from the training data at each splitting point. Therefore, compared to other classical split criteria, such as the C4.5, the proposed method can make the right choice about the optimal splitting point at the root or internal nodes from multi-angle. In order to evaluate the effectiveness of the SPCM approach, extensive experiments are carried out compared to the classical and state-of-the-art methods. The experiments are performed on sixteen datasets, where the effectiveness and the accuracy of the proposed method is verified. It is concluded that the SPCM method not only has excellent classification performance but also produces a more compact decision tree. Moreover, it is found that the SPCM method has especially a considerable improvement in the depth of tree.
A Face Expression Recognition Using CNN &amp; LBP

Visual interaction is an effective means of communication for human beings as social beings. Even a simple change in facial expression signifies happiness, sorrow, surprise and anxiety. The facial expressions of every person should vary in various contexts such as lighting, posture and even background. All these factors still remain an issue while recognizing facial expressions. This paper hopes to bring out a fair comparison between two of the most commonly used face expression recognition [FER] techniques and to shed some light on their precision. The methods being used here are Local binary patterns [LBP] and Convolution neural networks [CNN]. The LBP is meant as a method only for the purpose of extracting features so the Support vector machine [SVM] classifier is being utilized for classifying the extracted features from LBP. The dataset used for the purpose of testing and training in this paper are CK+, JAFFE and YALE FACE. About 70% is being utilized for training and the rest 30% is used in the testing. CNN gives better precision than any other dataset with 97.32% of recognition rate on CK+ dataset and also shows 31.82% of the YALE FACE dataset that is the least accurate we have.
Towards understanding cybersecurity capability in Australian healthcare organisations: a systematic review of recent trends, threats and mitigation

Cybersecurity threats in the Health sector are increasing globally due to the rising value of sensitive health information and availability of digitalised personal health records. This systematic review compares international and Australian health system cybersecurity landscapes in relation to the introduction of universal electronic health records. It examines recent trends in healthcare cybersecurity breaches that can disrupt essential services if patient safety and privacy are compromised. Often health systems and health mangers are ill-equipped to mitigate such threats. Recommendations are provided to proactively mature the cybersecurity culture within healthcare organisations, thus increasing their resilience to cyber threat.
3DFaceGAN: Adversarial Nets for 3D Face Representation, Generation, and Translation

Abstract Over the past few years, Generative Adversarial Networks (GANs) have garnered increased interest among researchers in Computer Vision, with applications including, but not limited to, image generation, translation, imputation, and super-resolution. Nevertheless, no GAN-based method has been proposed in the literature that can successfully represent, generate or translate 3D facial shapes (meshes). This can be primarily attributed to two facts, namely that (a) publicly available 3D face databases are scarce as well as limited in terms of sample size and variability (e.g., few subjects, little diversity in race and gender), and (b) mesh convolutions for deep networks present several challenges that are not entirely tackled in the literature, leading to operator approximations and model instability, often failing to preserve high-frequency components of the distribution. As a result, linear methods such as Principal Component Analysis (PCA) have been mainly utilized towards 3D shape analysis, despite being unable to capture non-linearities and high frequency details of the 3D face—such as eyelid and lip variations. In this work, we present 3DFaceGAN, the first GAN tailored towards modeling the distribution of 3D facial surfaces, while retaining the high frequency details of 3D face shapes. We conduct an extensive series of both qualitative and quantitative experiments, where the merits of 3DFaceGAN are clearly demonstrated against other, state-of-the-art methods in tasks such as 3D shape representation, generation, and translation.
Indirect Steganographic Embedding Method Based on Modifications of the Basis of the Polyadic System

Develloped a new approach of indirect steganographic embedding of data based on modifications of the basis of the polyadic system. In comparison with other methods indirect embedding is performed by modification of the bases of image fragment elements with the subsequent forming code on the basis of the modified basis for adaptive position numbers.
Blockchain-Based Decentralized Reverse Bidding in Fog Computing

Fog computing systems are designed to provide localized computation, storage, and communication services in close proximity to the endpoint mobile and IoT devices. Fog service providers typically monetize their service usage via centralized payment mechanisms in unverifiable and non-transparent manner. Therefore, there exists a need for a trust-enabling payment mechanism whereby fog service providers should be incentivized or penalized based upon the continuous feedback from endpoint devices. We propose a decentralized reverse-bidding scheme developed using the key features of blockchain and smart contracts. We develop a solution that allows the users or devices to initiate the bidding process by making a request for services to be provided by nearby public fog nodes, and these fog nodes to make bid offers in return. The proposed scheme ensures that all fog nodes on the network can equally and fairly make offers to win the bid. The bidding process incorporates the automated payments at the end of the service. Our solution is implemented using Ethereum smart contracts. It also integrates a reputation system for fog nodes and imposes a penalty for misbehaving nodes. Our solution is fully decentralized and provides a high level of trust, transparency, and security. In the paper, we present the system architecture, implementation details, and show the correct functionality of the overall proposed solution. In addition, we provide performance, cost, and security analyses of the smart contract code to demonstrate its effectiveness and robustness against major security concerns. The results show that the cost of running the smart contract remained less than three cents with the current Ethereum price (i.e., 183.22 USD/Eth). We have also made our smart contract code publicly available on Github.
SQL, NewSQL, and NOSQL Databases: A Comparative Survey

Non-relational databases have proved to be a viable alternative to the traditional relational database management systems, especially with the ever-increasing volume of data. Also known as "NoSQL" database systems, a scalable management system enabling handling such volumes of data. This work aims at providing an overview of the different NoSQL management systems, classifying them into four main categories: key-value stores, column-oriented, document-oriented, and graph oriented, comparing each of the categories using multiple criteria including the CAP theorem and BASE properties.
Early temporal prediction of Type 2 Diabetes Risk Condition from a General Practitioner Electronic Health Record: A Multiple Instance Boosting Approach

Early prediction of target patients at high risk of developing Type 2 diabetes (T2D) plays a significant role in preventing the onset of overt disease and its associated comorbidities. Although fundamental in early phases of T2D natural history, insulin resistance is not usually quantified by General Practitioners (GPs). Triglyceride-glucose (TyG) index has been proven useful in clinical studies for quantifying insulin resistance and for the early identification of individuals at T2D risk but still not applied by GPs for diagnostic purposes. The aim of this study is to propose a multiple instance learning boosting algorithm (MIL-Boost) for creating a predictive model capable of early prediction of worsening insulin resistance (low vs high T2D risk) in terms of TyG index. The MIL-Boost is applied to past electronic health record (EHR) patients’ information stored by a single GP. The proposed MIL-Boost algorithm proved to be effective in dealing with this task, by performing better than the other state-of-the-art ML competitors (Recall from 0.70 and up to 0.83). The proposed MIL-based approach is able to extract hidden patterns from past EHR temporal data, even not directly exploiting triglycerides and glucose measurements. The major advantages of our method can be found in its ability to model the temporal evolution of longitudinal EHR data while dealing with small sample size and variability in the observations (e.g., a small variable number of prescriptions for non-hospitalized patients). The proposed algorithm may represent the main core of a clinical decision support system.
A Distributed Oracle Using Intel SGX for Blockchain-Based IoT Applications

A blockchain oracle problem is a problem that defines a mechanism for how to safely bring external data to the blockchain. Although there have been various research efforts to solve this problem, existing solutions are limited in that they do not support either data availability or data integrity. Furthermore, no solution has been proposed to minimize the response time when an oracle server is malicious or overloaded. This paper proposes a distributed oracle using Intel Software Guard Extensions (SGX). The proposed approach uses multiple oracle servers to support data availability. It also supports data integrity using Intel SGX and Transport Layer Security (TLS) communication. The reputation system, which favors oracle servers with short response times, minimizes the average response time even if some of the oracle servers are malicious. The benchmarking results show that the response time of the proposed approach with 3 oracle servers is only 14% slower than a centralized oracle called Town-crier and scales well even if the number of oracle servers is increased up to 9. The reputation system is also evaluated, and its feasibility is analyzed using various experiments.
Privacy-aware offloading for training tasks of generative adversarial network in edge computing

Currently, the generative adversarial network (GAN), with complex training processes in the physical machine (PM), has achieved great priority in image generation, audio conversion, image translation, etc. To improve the training efficiency of GAN, the edge computing paradigm is accepted as an alternative of the PMs to accommodate the training tasks, that is, the training tasks are migrated to the edge nodes (ENs) for hosting. However, it is still a key challenge to keep the overall network performance (i.e., load balance, transmission time) and privacy protection of training tasks at the same time. To address this challenge, a privacy-aware task offloading method, named POM, is developed accordingly in this paper. First, improving the strength pareto evolutionary algorithm (SPEA2) is fully investigated to obtain the offloading strategies for collaboratively improving the training performance and privacy preservation. Then, the most balanced offloading strategy is acquired for training GAN. Eventually, systematic experiments indicate that POM achieves an optimal performance efficiently among the other representative benchmark methods.
Enhancing Data-Driven Algorithms for Human Pose Estimation and Action Recognition Through Simulation

Recognizing human actions, reliably inferring their meaning and being able to potentially exchange mutual social information are core challenges for autonomous systems when they directly share the same space with humans. Intelligent transport systems in particular face this challenge, as interactions with people are often required. The development and testing of technical perception solutions is done mostly on standard vision benchmark datasets for which manual labelling of sensory ground truth has been a tedious but necessary task. Furthermore, rarely occurring human activities are underrepresented in these datasets, leading to algorithms not recognizing such activities. For this purpose, we introduce a modular simulation framework, which offers to train and validate algorithms on various human-centred scenarios. We describe the usage of simulation data to train a state-of-the-art human pose estimation algorithm to recognize unusual human activities in urban areas. Since the recognition of human actions can be an important component of intelligent transport systems, we investigated how simulations can be applied for his purpose. Laboratory experiments show that we can train a recurrent neural network with only simulated data based on motion capture data and 3D avatars, which achieves an almost perfect performance in the classification of those human actions on real data.
Hadamard Matrix Guided Online Hashing

Online image hashing has attracted increasing research attention recently, which receives large-scale data in a streaming manner to update the hash functions on-the-fly. Its key challenge lies in the difficulty of balancing the learning timeliness and model accuracy. To this end, most works follow a supervised setting, i.e., using class labels to boost the hashing performance, which defects in two aspects: first, strong constraints, e.g., orthogonal or similarity preserving, are used, which however are typically relaxed and lead to large accuracy drops. Second, large amounts of training batches are required to learn the up-to-date hash functions, which largely increase the learning complexity. To handle the above challenges, a novel supervised online hashing scheme termed Hadamard Matrix Guided Online Hashing (HMOH) is proposed in this paper. Our key innovation lies in introducing Hadamard matrix, which is an orthogonal binary matrix built via Sylvester method. In particular, to release the need of strong constraints, we regard each column of Hadamard matrix as the target code for each class label, which by nature satisfies several desired properties of hashing codes. To accelerate the online training, LSH is first adopted to align the lengths of target code and to-be-learned binary code. We then treat the learning of hash functions as a set of binary classification problems to fit the assigned target code. Finally, extensive experiments on four widely-used benchmarks demonstrate the superior accuracy and efficiency of HMOH over various state-of-the-art methods. Codes can be available at https://github.com/lmbxmu/mycode .
Deep Learning-Based Sentiment Classification: A Comparative Survey

Recently, Deep Learning (DL) approaches have been applied to solve the Sentiment Classification (SC) problem, which is a core task in reviews mining or Sentiment Analysis (SA). The performances of these approaches are affected by different factors. This paper addresses these factors and classifies them into three categories: data preparation based factors, feature representation based factors and the classification techniques based factors. The paper is a comprehensive literature-based survey that compares the performance of more than 100 DL-based SC approaches by using 21 public datasets of reviews given by customers within three specific application domains (products, movies and restaurants). These 21 datasets have different characteristics (balanced/imbalanced, size, etc.) to give a global vision for our study. The comparison explains how the proposed factors quantitatively affect the performance of the studied DL-based SC approaches.
The Design of Pseudo-Participation

Participation is key to building an equitable, realistic and democratic future. Yet a lack of agency in decision making and agenda-setting is a growing phenomenon in the design of digital public services. We call this pseudo-participation by and in design. The configuration of digital artifacts and/or processes can provide an illusion of participation but lack supportive processes and affordances to allow meaningful participation to happen. This exploratory paper examines the realm of pseudo-participation in the design of public digital services through two concepts: 1) pseudo-participation by design, digital interfaces, and tools that provide the illusion of participation to the people, 2) pseudo-participation in design, processes in which those affected by the design decisions are marginalized and not given any agency. We contribute to the re-imagination of participatory design in modern societies where the role of politics has become ubiquitous and is yet to be critically scrutinized by designers.