Reviewerly/exHarmony huggingface dataset:
MatchGAN: A Self-supervised Semi-supervised Conditional Generative Adversarial Network

We present a novel self-supervised learning approach for conditional generative adversarial networks (GANs) under a semi-supervised setting. Unlike prior self-supervised approaches which often involve geometric augmentations on the image space such as predicting rotation angles, our pretext task leverages the label space. We perform augmentation by randomly sampling sensible labels from the label space of the few labelled examples available and assigning them as target labels to the abundant unlabelled examples from the same distribution as that of the labelled ones. The images are then translated and grouped into positive and negative pairs by their target labels, acting as training examples for our pretext task which involves optimising an auxiliary match loss on the discriminator’s side. We tested our method on two challenging benchmarks, CelebA and RaFD, and evaluated the results using standard metrics including Fréchet Inception Distance, Inception Score, and Attribute Classification Rate. Extensive empirical evaluation demonstrates the effectiveness of our proposed method over competitive baselines and existing arts. In particular, our method surpasses the baseline with only 20% of the labelled examples used to train the baseline.
Influence of Different Convolutional Neural Network Settings on the Performance of MNIST Handwritten Digits Recognition

Artificial Neural network as a power tool is now working in more and more areas such as recognition of handwritten digits and faces, sentence classification and audio recognition. This project aims at using convolutional neural network (CNN) to recognize handwritten digits provided by MNIST datasets with TensorFlow tool. Several variables such as dropout rate, epoch, fully connected layer with different neuron amount and filter amount were investigated in the experiments to investigate the performance of the model. Both training and validation accuracy were detected and gave us a direct reflection of the influence of different settings. With appropriate 0.2 dropout rate and 50 epochs, the model can accomplish 99.81% training accuracy and 99.38% validation accuracy. By adding fully connected layers with reasonable neuron amount, both training and validation accuracy were improved, and this modification had no extra running time required in our work. The filter number in the convolutional layers was also an important factor in the model performance. More filters can extract more features from the raw images and thus increase the accuracy of 99.95% for training accuracy and 99.47% for validation accuracy. However, this modification was found to have a longer running time.
An Automatic Clustering of Data Points with Alpha and Beta Angles on Apollonius and Subtended Arc Circle based on Computational Geometry

Following the recent rapid increase in the amount of information, local recognition of similar and related sets of data points using neighborhood construction algorithms has gained high significance in various fields. In the area of data mining, a main focus of research has been on locating neighborhood construction algorithms. With their high accuracy level in locating highly similar data points and efficient geometric structures, geometric methods have attracted the attention of the scholars since they both reduce time complexities of neighborhood construction and increase grouping accuracy of similar data sets. Due to the significance of the mentioned challenges in data point analysis, geometric method was used in the present study in which Apollonius circle was used to locally extract the concerned target points on the data. In the proposed method, the researchers have no previous knowledge about the data, and having located the similar data sets by the use of Apollonius circle, the direct/indirect relationships inside the circles are studied to boost accuracy in locating neighborhood among the points. The proposed method using Apollonius geometric and subtended arc methods have higher efficiency than the new algorithms on the majority of real data sets.
RCNN for Region of Interest Detection in Whole Slide Images

Digital pathology has attracted significant attention in recent years. Analysis of Whole Slide Images (WSIs) is challenging because they are very large, i.e., of Giga-pixel resolution. Identifying Regions of Interest (ROIs) is the first step for pathologists to analyse further the regions of diagnostic interest for cancer detection and other anomalies. In this paper, we investigate the use of RCNN, which is a deep machine learning technique, for detecting such ROIs only using a small number of labelled WSIs for training. For experimentation, we used real WSIs from a public hospital pathology service in Western Australia. We used 60 WSIs for training the RCNN model and another 12 WSIs for testing. The model was further tested on a new set of unseen WSIs. The results show that RCNN can be effectively used for ROI detection from WSIs.
Class-Incremental Learning with Rectified Feature-Graph Preservation

In this paper, we address the problem of distillation-based class-incremental learning with a single head. A central theme of this task is to learn new classes that arrive in sequential phases over time while keeping the model’s capability of recognizing seen classes with only limited memory for preserving seen data samples. Many regularization strategies have been proposed to mitigate the phenomenon of catastrophic forgetting. To understand better the essence of these regularizations, we introduce a feature-graph preservation perspective. Insights into their merits and faults motivate our weighted-Euclidean regularization for old knowledge preservation. We further propose rectified cosine normalization and show how it can work with binary cross-entropy to increase class separation for effective learning of new classes. Experimental results on both CIFAR-100 and ImageNet datasets demonstrate that our method outperforms the state-of-the-art approaches in reducing classification error, easing catastrophic forgetting, and encouraging evenly balanced accuracy over different classes. Our project page is at : https://github.com/yhchen12101/FGP-ICL.
Searching for Balanced S-Boxes with High Nonlinearity, Low Differential Uniformity, and Improved DPA-Resistance

Substitution boxes (S-boxes) are one of the most crucial primitives in the field of block ciphers. Recently, differential power analysis (DPA), a very powerful technique which targets implementations of block ciphers, causes the modern block ciphers to be much more vulnerable than ever before. Up to now, the revised transparency order is one of the best metrics to assess the resistance of S-boxes against DPA attacks. In this paper, we present an efficient algorithm to search for cryptographically significant S-boxes with improved DPA-Resistance. Applying our developed algorithm, we generate an $$8\times 8$$ balanced S-box with algebraic degree 7, nonlinearity 112, differential uniformity 4, absolute indicator 32, revised transparency order 6.8820 (whereas the Rijndael S-box has revised transparency order 6.9161) and thereby improved resistance towards DPA attacks. Moreover, many other balanced S-boxes with a much better trade-off of cryptographic characteristics than previous works (e.g. S-boxes given by B. Mazumdar and D. Mukhopadhyay in IEEE Trans. Computers 2017) are also captured. The comparison between ours and previous results manifests that our S-boxes are more secure and robust.
Mobile based augmented reality for flexible human height estimation using touch and motion gesture interaction

Abstract Human height measurement can be achieved by using contact or non-contact techniques. Contact technique is the traditional measuring method which required human resources to perform the measurement. In contrast, for non-contact technique, several kinds of research for measurement have been conducted, mostly with image-processing methods and only a few with the Augmented Reality (AR) approach. The current measuring approaches mostly required external hardware such as laser pointer or artificial fiducial such as 2D markers. In this paper, the world tracking technique and Visual Inertial Odometry is the method used to estimate the human height. The main aim of this paper is to accurately estimate the human height using augmented reality (non-contacted measurements). The methodology used the Apple ARKit plugin, which is the software development tools to build an augmented reality application for IOS device. An algorithm was designed by using Golden Ratio rules to estimate human height from the lower part of human knee; The estimation result is displayed using AR technology to allow the justification of the accuracy of the result. The application is tested with four different measuring methods. The normal full-height measurement result had a 1.13cm (0.73%) bias and a 1.34cm (0.88%) Root Mean Square Error (RMSE); the self-full height measurement had a result of 0.89cm (0.58%) bias and a 1.27cm (0.83%) RMSE; the normal height estimation from the lower part of knee measurement had a result of 0.12cm (0.06%) bias and a 1.34cm (0.89%) RMSE; the self-height estimation from the lower part of knee measurement had a result of 0.15cm (0.09%) bias and a 1.04cm (0.66%) RMSE. The results show that the mobile phone with VIO can be a potential tool for obtaining accurate measurements of human height.
Research on Top-k Association Rules Mining Algorithm Based on Clustering

Abstract Until now, the association rule mining algorithm is still one of the core issues in the field of big data research. At present, there are many research related to association rule mining algorithms, mainly focusing on how to find frequent item sets and how to tailor rules, and based on this direction, there have been many classic algorithms, such as Apriori and FP-growth algorithms. However, most of the above algorithms process and analyze the entire data set, so that although related algorithms can be used to obtain the results, the results are not meticulous. To solve this problem, this paper proposes an algorithm, which first uses the k-means algorithm to perform cluster analysis on the data set to generate a specific number of clusters. Then, in different clusters, the association rules in each cluster are found by the improved Top-k algorithm combined with the correlation coefficient. By integrating clustering and the improved Top-k algorithm, the data set can be analyzed directionally to improve the accuracy and efficiency of the whole algorithm. And the final experiment shows that compared with the original algorithm, the running time is shortened by 14%.
Augmentation Network for Generalised Zero-Shot Learning

Generalised zero-shot learning (GZSL) is defined by a training process containing a set of visual samples from seen classes and a set of semantic samples from seen and unseen classes, while the testing process consists of the classification of visual samples from the seen and the unseen classes. Current approaches are based on inference processes that rely on the result of a single modality classifier (visual, semantic, or latent joint space) that balances the classification between the seen and unseen classes using gating mechanisms. There are a couple of problems with such approaches: 1) multi-modal classifiers are known to generally be more accurate than single modality classifiers, and 2) gating mechanisms rely on a complex one-class training of an external domain classifier that modulates the seen and unseen classifiers. In this paper, we mitigate these issues by proposing a novel GZSL method – augmentation network that tackles multi-modal and multi-domain inference for generalised zero-shot learning (AN-GZSL). The multi-modal inference combines visual and semantic classification and automatically balances the seen and unseen classification using temperature calibration, without requiring any gating mechanisms or external domain classifiers. Experiments show that our method produces the new state-of-the-art GZSL results for fine-grained benchmark data sets CUB and FLO and for the large-scale data set ImageNet. We also obtain competitive results for coarse-grained data sets SUN and AWA. We show an ablation study that justifies each stage of the proposed AN-GZSL.
A non-algorithmic approach to “programming” quantum computers via machine learning

Major obstacles remain to the implementation of macroscopic quantum computing: hardware problems of noise, decoherence, and scaling; software problems of error correction; and, most important, algorithm construction. Finding truly quantum algorithms is quite difficult, and many of these genuine quantum algorithms, like Shor's prime factoring or phase estimation, require extremely long circuit depth for any practical application, which necessitates error correction. In contrast, we show that machine learning can be used as a systematic method to construct algorithms, that is, to non-algorithmically “program” quantum computers. Quantum machine learning enables us to perform computations without breaking down an algorithm into its gate “building blocks”, eliminating that difficult step and potentially increasing efficiency by simplifying and reducing unnecessary complexity. In addition, our non-algorithmic machine learning approach is robust to both noise and to decoherence, which is ideal for running on inherently noisy NISQ devices which are limited in the number of qubits available for error correction. We demonstrate this using a fundamentally nonclassical calculation: experimentally estimating the entanglement of an unknown quantum state. Results from this have been successfully ported to the IBM hardware and trained using a hybrid reinforcement learning method.
Research on information security of users’ electricity data including electric vehicle based on elliptic curve encryption

In the smart grid and big data environment, accurate and large amount of power load data for users can be obtained with the wide application of non-intrusive load monitoring technology. In the research process of customers’ information, information security protection of users’ electricity data has become a research hotspot urgently. This article proposes a new type of load decomposition method for electric vehicle load information and compares it with hidden Markov model algorithm to verify its accuracy. On this basis, the elliptic curve encryption algorithm is used to encrypt the users’ electricity data, and the function and effectiveness of the encryption algorithm are verified by comparing the load decomposition of the electric vehicle with the unencrypted data.
Research on Character Image Inpainting based on Generative Adversarial Network

Image inpainting technology plays an important role in the process of digitalizing ancient literature. It helps to recover the partially missing or stained characters. Recently the Generative Adversarial Network (GAN) has shown remarkable success in the field of image inpainting. In this paper, we propose a new GAN model using the idea of edge recovery and optimize this model with spatial attenuation mask and conditional labelling to improve performance. Experiments show better results than the previous works in character image inpainting.
Interactive mobile games development for children to learn about money

Abstract Money notes and coins are used for transactions and are essential in daily human life. Children need to be exposed to enough knowledge about the purposes of money notes, and the most important is to identify the money correctly. Children begin to expose to identifying the money at school in standard one. However, depending on formal education is not enough because the children easily get bored, and they did not fully engage with formal education. The use of mobile games for learning purposes is becoming increasingly popular because they are interactive and able to attract students. Therefore, this research aims to develop mobile games to identify and learn about money. Three main phases have been carried out, and the first phase is preliminary research on the learning approach for children and data collection in money Malaysia Ringgit. The second phase is to design and develop mobile games. Three mini-games have been proposed, and the first game is to identify the money notes and coins. The second game is to learn about counting the money and coins, and the third game is to learn to use the money for purchasing purposes. After these three mini-games have been developed in the second phase, the third phase is the evaluation phase. The usability result shows the users agree the mobile games help them to identify money, calculate the value of money, and to perform basic purchasing activities. Based on the user acceptance results show that the mobile game works more effectively with adult or parental guidance. Overall, this research has successfully produced a mobile game that can help the children to learn to identify, count, and use money.
Dense Dual-Path Network for Real-Time Semantic Segmentation

Semantic segmentation has achieved remarkable results with high computational cost and a large number of parameters. However, real-world applications require efficient inference speed on embedded devices. Most previous works address the challenge by reducing depth, width and layer capacity of network, which leads to poor performance. In this paper, we introduce a novel Dense Dual-Path Network (DDPNet) for real-time semantic segmentation under resource constraints. We design a light-weight and powerful backbone with dense connectivity to facilitate feature reuse throughout the whole network and the proposed Dual-Path module (DPM) to sufficiently aggregate multi-scale contexts. Meanwhile, a simple and effective framework is built with a skip architecture utilizing the high-resolution feature maps to refine the segmentation output and an upsampling module leveraging context information from the feature maps to refine the heatmaps. The proposed DDPNet shows an obvious advantage in balancing accuracy and speed. Specifically, on Cityscapes test dataset, DDPNet achieves 75.3% mIoU with 52.6 FPS for an input of \(1024\times 2048\) resolution on a single GTX 1080Ti card. Compared with other state-of-the-art methods, DDPNet achieves a significant better accuracy with a comparable speed and fewer parameters.
Intelligent Electricity Purchase and Sale Trading Platform Based on Block Chain

With the power reform and the opening of the electricity market, the traditional centralized system is unable to meet the demand of power transaction among P2P users due to the complexity of transaction users and the diversity of transaction data. Intelligent power purchase and automated sale trading platform uses the bottom technology of block chain to build a decentralized trusted trading platform for multi-users. The platform uses terminal acquisition equipment with edge computing function to pre-process and upload massive data to distributed block nodes, and traces the source of each kilowatt-hour power. According to the characteristics of current power sources and users' electricity consumption habits, it intelligently recommends power consumption scheme. At the same time, intelligent contracts provide open, transparent, tamper-proof trading mode and self-purchase and sale operation to ensure data reliability and user flexibility. With the advantages of advanced payment method, shorter transaction time and zero default rate, the platform attracts power users to trade actively, promotes the competition and activity of the power market, and has guiding significance for the capital flow and market health of the industry.
Practical Verifiable Computation by Using a Hardware-Based Correct Execution Environment

The verifiable computation paradigm has been studied extensively as a means to verifying the result of outsourced computation. In said scheme, the verifier requests computation from the prover and verifies the result by checking the output and proof received from the prover. Although they have great potential for various critical applications, verifiable computations have not been widely used in practice, because of their significant performance overhead. Existing cryptography-based approaches incur significant overhead, because a cryptography-based mathematical frame needs to be constructed, which prevents deviation from the correct computation. The proposed approach is to reduce the overhead by trusting the computing hardware platform where the computation is outsourced. If one trusts the hardware to do the computation, the hardware can take the place of the cryptographic computing frame, thereby guaranteeing correct computation. The key challenge of this approach is to define what exactly the hardware should guarantee for verifiable computation. For this, we introduce the concept of Correct Execution Environment (CEE), which guarantees instruction correctness and state preservation. We prove that these two requirements are satisfactory conditions for a correct output. By employing a CEE, the verifiable computation scheme can be simplified, and its overhead can be reduced drastically. The presented experimental results demonstrate that the execution time is approximately 1.7 million times faster and the verification time over 50 times faster than a state-of-the-art cryptographic approach.
A fault attack on KCipher-2

For the stream cipher KCipher-2 of the Japanese e-Government Recommended Ciphers List, we present fault attacks requiring only the possibility to reset the cipher and to inject byte faults into certain registers. For the polynomial systems resulting from an injection sequence into one register, we develop a dedicated algebraic solving technique. Using the Marking Algorithm of Horn Logic and the computation of certain Gröbner bases, we optimize the number and locations of fault injection sequences which allow the recovery of the full state of the cipher. The optimal attack requires only about 21 fault injections and 100 seconds for solving the polynomial systems and finding the internal state.
Simultaneous Multi‐Attribute Image‐to‐Image Translation Using Parallel Latent Transform Networks

Abstract Image‐to‐image translation has been widely studied. Since real‐world images can often be described by multiple attributes, it is useful to manipulate them at the same time. However, most methods focus on transforming between two domains, and when they chain multiple single attribute transform networks together, the results are affected by the order of chaining, and the performance drops with the out‐of‐domain issue for intermediate results. Existing multi‐domain transfer methods mostly manipulate multiple attributes by adding a list of attribute labels to the network feature, but they also suffer from interference of different attributes, and perform worse when multiple attributes are manipulated. We propose a novel approach to multi‐attribute image‐to‐image translation using several parallel latent transform networks, where multiple attributes are manipulated in parallel and simultaneously, which eliminates both issues. To avoid the interference of different attributes, we introduce a novel soft independence constraint for the changes caused by different attributes. Extensive experiments show that our method outperforms state‐of‐the‐art methods.
Blockchains, Cybercrime, and Forensics

Auditors are often asked to opine on technical security and encryption technologies, as well as providing admissible evidence in legal cases. This chapter reviews the most commonly encountered security and encryption technologies, with guides to how auditors should assess and manage them.