Reviewerly/exHarmony huggingface dataset:
Random Features Strengthen Graph Neural Networks

Graph neural networks (GNNs) are powerful machine learning models for various graph learning tasks. Recently, the limitations of the expressive power of various GNN models have been revealed. For example, GNNs cannot distinguish some non-isomorphic graphs and they cannot learn efficient graph algorithms. In this paper, we demonstrate that GNNs become powerful just by adding a random feature to each node. We prove that the random features enable GNNs to learn almost optimal polynomial-time approximation algorithms for the minimum dominating set problem and maximum matching problem in terms of approximation ratios. The main advantage of our method is that it can be combined with off-the-shelf GNN models with slight modifications. Through experiments, we show that the addition of random features enables GNNs to solve various problems that normal GNNs, including the graph convolutional networks (GCNs) and graph isomorphism networks (GINs), cannot solve.
Learning-based Practical Smartphone Eavesdropping with Built-in Accelerometer

Motion sensors on current smartphones have been exploited for audio eavesdropping due to their sensitivity to vibrations.However, this threat is considered low-risk because of two widely acknowledged limitations: First, unlike microphones, motion sensors can only pick up speech signals traveling through a solid medium.Thus, the only feasible setup reported previously is to use a smartphone gyroscope to eavesdrop on a loudspeaker placed on the same table.The second limitation comes from a common sense that these sensors can only pick up a narrow band (85-100Hz) of speech signals due to a sampling ceiling of 200Hz.In this paper, we revisit the threat of motion sensors to speech privacy and propose AccelEve, a new side-channel attack that employs a smartphone's accelerometer to eavesdrop on the speaker in the same smartphone.Specifically, it utilizes the accelerometer measurements to recognize the speech emitted by the speaker and to reconstruct the corresponding audio signals.In contrast to previous works, our setup allows the speech signals to always produce strong responses in accelerometer measurements through the shared motherboard, which successfully addresses the first limitation and allows this kind of attacks to penetrate into real-life scenarios.Regarding the sampling rate limitation, contrary to the widely-held belief, we observe up to 500Hz sampling rates in recent smartphones, which almost covers the entire fundamental frequency band (85-255Hz) of adult speech.On top of these pivotal observations, we propose a novel deep learning based system that learns to recognize and reconstruct speech information from the spectrogram representation of acceleration signals.This system employs adaptive optimization on deep neural networks with skip connections using robust and generalizable losses to achieve robust recognition and reconstruction performance.Extensive evaluations demonstrate the effectiveness and high accuracy of our attack under various settings.
Machine Learning and Deep Learning Approaches in Breast Cancer Survival Prediction Using Clinical Data

Breast cancer survival prediction can have an extreme effect on selection of best treatment protocols. Many approaches such as statistical or machine learning models have been employed to predict the survival prospects of patients, but newer algorithms such as deep learning can be tested with the aim of improving the models and prediction accuracy. In this study, we used machine learning and deep learning approaches to predict breast cancer survival in 4,902 patient records from the University of Malaya Medical Centre Breast Cancer Registry. The results indicated that the multilayer perceptron (MLP), random forest (RF) and decision tree (DT) classifiers could predict survivorship, respectively, with 88.2 %, 83.3 % and 82.5 % accuracy in the tested samples. Support vector machine (SVM) came out to be lower with 80.5 %. In this study, tumour size turned out to be the most important feature for breast cancer survivability prediction. Both deep learning and machine learning methods produce desirable prediction accuracy, but other factors such as parameter configurations and data transformations affect the accuracy of the predictive model.
Robust Anomaly Detection in Images Using Adversarial Autoencoders

Reliably detecting anomalies in a given set of images is a task of high practical relevance for visual quality inspection, surveillance, or medical image analysis. Autoencoder neural networks learn to reconstruct normal images, and hence can classify those images as anomalies, where the reconstruction error exceeds some threshold. Here we analyze a fundamental problem of this approach when the training set is contaminated with a small fraction of outliers. We find that continued training of autoencoders inevitably reduces the reconstruction error of outliers, and hence degrades the anomaly detection performance. In order to counteract this effect, an adversarial autoencoder architecture is adapted, which imposes a prior distribution on the latent representation, typically placing anomalies into low likelihood-regions. Utilizing the likelihood model, potential anomalies can be identified and rejected already during training, which results in an anomaly detector that is significantly more robust to the presence of outliers during training.
Segmenting Transparent Objects in the Wild

Transparent objects such as windows and bottles made by glass widely exist in the real world. Segmenting transparent objects is challenging because these objects have diverse appearance inherited from the image background, making them had similar appearance with their surroundings. Besides the technical difficulty of this task, only a few previous datasets were specially designed and collected to explore this task and most of the existing datasets have major drawbacks. They either possess limited sample size such as merely a thousand of images without manual annotations, or they generate all images by using computer graphics method (i.e. not real image). To address this important problem, this work proposes a large-scale dataset for transparent object segmentation, named Trans10 K, consisting of 10,428 images of real scenarios with carefully manual annotations, which are 10 times larger than the existing datasets. The transparent objects in Trans10 K are extremely challenging due to high diversity in scale, viewpoint and occlusion. To evaluate the effectiveness of Trans10 K, we propose a novel boundary-aware segmentation method, termed TransLab, which exploits boundary as the clue to improve segmentation of transparent objects. Extensive experiments and ablation studies demonstrate the effectiveness of Trans10 K and validate the practicality of learning object boundary in TransLab. For example, TransLab significantly outperforms 20 recent object segmentation methods based on deep learning, showing that this task is largely unsolved. We believe that both Trans10 K and TransLab have important contributions to both the academia and industry, facilitating future researches and applications. The codes and models will be released at: https://github.com/xieenze/Segment_Transparent_Objects.
Frequency based feature selection method using whale algorithm

Feature selection is the problem of finding the best subset of features which have the most impact in predicting class labels. It is noteworthy that application of feature selection is more valuable in high dimensional datasets. In this paper, a filter feature selection method has been proposed on high dimensional binary medical datasets - Colon, Central Nervous System (CNS), GLI_85, SMK_CAN_187. The proposed method incorporates three sections. First, whale algorithm has been used to discard irrelevant features. Second, the rest of features are ranked based on a frequency based heuristic approach called Mutual Congestion. Third, majority voting has been applied on best feature subsets constructed using forward feature selection with threshold τ = 10. This work provides evidence that Mutual Congestion is solely powerful to predict class labels. Furthermore, applying whale algorithm increases the overall accuracy of Mutual Congestion in most of the cases. The findings also show that the proposed method improves the prediction with selecting the less possible features in comparison with state of the arts. https://github.com/hnematzadeh.
Deep learning only by normal brain PET identify unheralded brain anomalies

Recent deep learning models have shown remarkable accuracy for the diagnostic classification. However, they have limitations in clinical application due to the gap between the training cohorts and real-world data. We aimed to develop a model trained only by normal brain PET data with an unsupervised manner to identify an abnormality in various disorders as imaging data of the clinical routine.Using variational autoencoder, a type of unsupervised learning, Abnormality Score was defined as how far a given brain image is from the normal data. The model was applied to FDG PET data of Alzheimer's disease (AD) and mild cognitive impairment (MCI) and clinical routine FDG PET data for assessing behavioral abnormality and seizures. Accuracy was measured by the area under curve (AUC) of receiver-operating-characteristic (ROC) curve. We investigated whether deep learning has additional benefits with experts' visual interpretation to identify abnormal patterns.The AUC of the ROC curve for differentiating AD was 0.90. The changes in cognitive scores from baseline to 2-year follow-up were significantly correlated with Abnormality Score at baseline. The AUC of the ROC curve for discriminating patients with various disorders from controls was 0.74. Experts' visual interpretation was helped by the deep learning model to identify abnormal patterns in 60% of cases initially not identified without the model.We suggest that deep learning model trained only by normal data was applicable for identifying wide-range of abnormalities in brain diseases, even uncommon ones, proposing its possible use for interpreting real-world clinical data.
A graph-based context-aware requirement elicitation approach in smart product-service systems

The paradigm of Smart product-service systems (Smart PSS) has emerged recently owing to the edge-cutting Information and Communication Technology (ICT) and artificial intelligence (AI) techniques. The unique features of Smart PSS including smartness and connectedness, value co-creation and data-driven design manner, enable the collection and analysis of large volume and heterogeneous contextual data to extract useful knowledge. Therefore, requirement elicitation, as a critical process for new solution (i.e. product-service) design, can be conducted in a rather context-aware manner, assured by those massive user-generated data and product-sensed data during the usage stage. Nevertheless, despite a few works on semantic modelling, scarcely any reports on such mechanism in today's smart, connected environment. Aiming to fill this gap, for the first time, a graph-based context-aware requirement elicitation approach considering contextual information within the Smart PSS is proposed. It leverages the pre-defined product, service, and condition ontologies together with Deepwalk technique, to formulate those concepts as nodes and their relationships as the edge of the proposed requirement graph. Implicit stakeholder requirements within a specific context can be further derived based on such interrelationships in a data-driven manner. To demonstrate its feasibility and effectiveness, an example of smart bike share system is addressed to illustrate the requirement elicitation process. It is hoped that this explorative study can offer valuable insights for the service providers who would like to extract requirements not only from the voice of customers but also from the user-generated data and product-sensed data.
Moving-Horizon Estimation for Linear Dynamic Networks With Binary Encoding Schemes

This article is concerned with moving-horizon state estimation problems for a class of discrete-time linear dynamic networks. The signals are transmitted via noisy network channels and distortions can be caused by channel noises. As such, the binary encoding schemes, which take advantages of the robustness of the binary data, are exploited during the signal transmission. More specifically, under such schemes, the original signals are encoded into a bit string, transmitted via memoryless binary symmetric channels with certain crossover probabilities, and eventually restored by a decoder at the receiver. Novel centralized and decentralized moving-horizon estimators in the presence of the binary encoding schemes are constructed by solving the respective global and local least-square optimization problems. Sufficient conditions are obtained through intensive stochastic analysis to guarantee the stochastically ultimate boundedness of the estimation errors. A simulation example is presented to verify the effectiveness of the proposed moving-horizon estimators.
Which Tasks Should Be Learned Together in Multi-task Learning?

Many computer vision applications require solving multiple tasks in real-time. A neural network can be trained to solve multiple tasks simultaneously using multi-task learning. This can save computation at inference time as only a single network needs to be evaluated. Unfortunately, this often leads to inferior overall performance as task objectives can compete, which consequently poses the question: which tasks should and should not be learned together in one network when employing multi-task learning? We study task cooperation and competition in several different learning settings and propose a framework for assigning tasks to a few neural networks such that cooperating tasks are computed by the same neural network, while competing tasks are computed by different networks. Our framework offers a time-accuracy trade-off and can produce better accuracy using less inference time than not only a single large multi-task neural network but also many single-task networks.
A Novel Detection and Recognition Method for Continuous Hand Gesture Using FMCW Radar

In this article, a novel method for continuous hand gesture detection and recognition is proposed based on a frequency modulated continuous wave (FMCW) radar. Firstly, we adopt the 2-Dimensional Fast Fourier Transform (2D-FFT) to estimate the range and Doppler parameters of the hand gesture raw data, and construct the range-time map (RTM) and Doppler-time map (DTM). Meanwhile, we apply the Multiple Signal Classification (MUSIC) algorithm to calculate the angle and construct the angle-time map (ATM). Secondly, a hand gesture detection method is proposed to segment the continuous hand gestures using a decision threshold. Thirdly, the central time-frequency trajectory of each hand gesture spectrogram is clustered using the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> -means algorithm, and then the Fusion Dynamic Time Warping (FDTW) algorithm is presented to recognize the hand gestures. Finally, experiments show that the accuracy of the proposed hand gesture detection method can reach 96.17%. The hand gesture average recognition accuracy of the proposed FDTW algorithm is 95.83%, while its time complexity is reduced by more than 50%.
A

Classic methods for clinical temporal relation extraction focus on relational candidates within a sentence. On the other hand, break-through Bidirectional Encoder Representations from Transformers (BERT) are trained on large quantities of arbitrary spans of contiguous text instead of sentences. In this study, we aim to build a sentence-agnostic framework for the task of CONTAINS temporal relation extraction. We establish a new state-of-the-art result for the task, 0.684F for in-domain (0.055-point improvement) and 0.565F for cross-domain (0.018-point improvement), by fine-tuning BERT and pre-training domain-specific BERT models on sentence-agnostic temporal relation instances with WordPiece-compatible encodings, and augmenting the labeled data with automatically generated “silver” instances.
Artistic glyph image synthesis via one-stage few-shot learning

Automatic generation of artistic glyph images is a challenging task that attracts many research interests. Previous methods either are specifically designed for shape synthesis or focus on texture transfer. In this paper, we propose a novel model, AGIS-Net, to transfer both shape and texture styles in one-stage with only a few stylized samples. To achieve this goal, we first disentangle the representations for content and style by using two encoders, ensuring the multi-content and multi-style generation. Then we utilize two collaboratively working decoders to generate the glyph shape image and its texture image simultaneously. In addition, we introduce a local texture refinement loss to further improve the quality of the synthesized textures. In this manner, our one-stage model is much more efficient and effective than other multi-stage stacked methods. We also propose a large-scale dataset with Chinese glyph images in various shape and texture styles, rendered from 35 professional-designed artistic fonts with 7,326 characters and 2,460 synthetic artistic fonts with 639 characters, to validate the effectiveness and extendability of our method. Extensive experiments on both English and Chinese artistic glyph image datasets demonstrate the superiority of our model in generating high-quality stylized glyph images against other state-of-the-art methods.
Blockchain technology in corporate governance: disrupting chain reaction or not?

Purpose The purpose of this paper is to determine the stakeholders’ acceptance on blockchain and to investigate the model fit by using “Technology Acceptance Model” with special reference to corporate governance through cryptography to resolve the decades-old problems of financial record-keeping. Design/methodology/approach The whole analysis has been performed in the two steps, i.e. confirmatory factors analysis and structural equation modeling, to prove model fit between behavioral intention and actual behavior for using blockchain technology. Total 223 respondents have been selected, and the selection of the respondent is primarily on the basis of their previous experience with trading corporate equities. Findings The study determines empirically all the mentioned relationships of attitude, perceived ease of use and perceived usefulness with the behavioral intention as per the conceptual model to prove the relationship. The results of the manuscript shows the model fit indexes for various constructs are prove the model fit as per the theorized model. The values of the various indexes are found to be under the permissible range which explains the relationship of various constructs based on the theorized model. Research limitations/implications Despite, the limitations in terms of selection of sampling methods, outcome and the interpretation, the results proves the fit with the theoretical framework. The major implication is to understand the real-time use of blockchain technology for the transfer of shares from one party to other. Practical implications Stakeholders in corporate governance namely customers, creditors, suppliers, community, employees, owners, investors, trade unions and social activists could benefit in different ways. Investors could benefit from being able to purchase equity at low price and to sell them into a market with greater liquidity, but they would found it difficult to camouflage their trades. Social implications The study opines that virtually all aspects of the corporate governance can be improved through the adoption of this technology resulting in greater transparency, improved liquidity and lowering costs. Originality/value This study will be a reference for global players in the financial industry that have started investing in this innovative technology vis-à-vis recent announcement of adoption of blockchain by global exchanges including NASDAQ, NYSE and Deutsche Borse, as a new method for trading, tracking ownership and monitoring systemic risk for strengthening corporate governance mechanism. This study will have a significant index for future reference where the technology adoption will be tested to have better corporate governance which will be useful for academics and professionals.
An Extended Idea about Decision Trees

Decision trees have been widely recognized as a data mining and machine learning methodology that receives a set of attribute values as the input and generates a Boolean decision as the output. In this paper, I tried two experiments to demonstrate that the fundamental theory of decision trees can be extended to go beyond Boolean decisions. In the first experiment, the decision tree algorithm is extended to be capable of making three-way decisions. Likewise, in the second experiment, the decision tree algorithm is extended to be capable of making four-way decisions. As a result, a conclusion is reached that this extended idea about decision trees can be generalized to make n-way decisions.
ERNIE-GEN: An Enhanced Multi-Flow Pre-training and Fine-tuning Framework for Natural Language Generation

Current pre-training works in natural language generation pay little attention to the problem of exposure bias on downstream tasks. To address this issue, we propose an enhanced multi-flow sequence to sequence pre-training and fine-tuning framework named ERNIE-GEN, which bridges the discrepancy between training and inference with an infilling generation mechanism and a noise-aware generation method. To make generation closer to human writing patterns, this framework introduces a span-by-span generation flow that trains the model to predict semantically-complete spans consecutively rather than predicting word by word. Unlike existing pre-training methods, ERNIE-GEN incorporates multi-granularity target sampling to construct pre-training data, which enhances the correlation between encoder and decoder. Experimental results demonstrate that ERNIE-GEN achieves state-of-the-art results with a much smaller amount of pre-training data and parameters on a range of language generation tasks, including abstractive summarization (Gigaword and CNN/DailyMail), question generation (SQuAD), dialogue generation (Persona-Chat) and generative question answering (CoQA). The source codes and pre-trained models have been released at https://github.com/PaddlePaddle/ERNIE/ernie-gen.
Continuous-Variable Quantum Key Distribution with Rateless Reconciliation Protocol

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