Reviewerly/exHarmony huggingface dataset:
Stochastic Gradient Descent in Correlated Settings: A Study on Gaussian Processes

Stochastic gradient descent (SGD) and its variants have established themselves as the go-to algorithms for large-scale machine learning problems with independent samples due to their generalization performance and intrinsic computational advantage. However, the fact that the stochastic gradient is a biased estimator of the full gradient with correlated samples has led to the lack of theoretical understanding of how SGD behaves under correlated settings and hindered its use in such cases. In this paper, we focus on the Gaussian process (GP) and take a step forward towards breaking the barrier by proving minibatch SGD converges to a critical point of the full loss function, and recovers model hyperparameters with rate O(1/K) up to a statistical error term depending on the minibatch size. Numerical studies on both simulated and real datasets demonstrate that minibatch SGD has better generalization over state-of-the-art GP methods while reducing the computational burden and opening a new, previously unexplored, data size regime for GPs.
Cross-lingual sentiment classification in low-resource Bengali language

Sentiment analysis research in low-resource languages such as Bengali is still unexplored due to the scarcity of annotated data and the lack of text processing tools.Therefore, in this work, we focus on generating resources and showing the applicability of the crosslingual sentiment analysis approach in Bengali.For benchmarking, we created and annotated a comprehensive corpus of around 12000 Bengali reviews.To address the lack of standard text-processing tools in Bengali, we leverage resources from English utilizing machine translation.We determine the performance of supervised machine learning (ML) classifiers in machine-translated English corpus and compare it with the original Bengali corpus.Besides, we examine sentiment preservation in the machine-translated corpus utilizing Cohen's Kappa and Gwet's AC1.To circumvent the laborious data labeling process, we explore lexicon-based methods and study the applicability of utilizing cross-domain labeled data from the resource-rich language.We find that supervised ML classifiers show comparable performances in Bengali and machinetranslated English corpus.By utilizing labeled data, they achieve 15%-20% higher F1 scores compared to both lexicon-based and transfer learning-based methods.Besides, we observe that machine translation does not alter the sentiment polarity of the review for most of the cases.Our experimental results demonstrate that the machine translation based crosslingual approach can be an effective way for sentiment classification in Bengali.
Form2Seq : A Framework for Higher-Order Form Structure Extraction

Document structure extraction has been a widely researched area for decades with recent works performing it as a semantic segmentation task over document images using fullyconvolution networks.Such methods are limited by image resolution due to which they fail to disambiguate structures in dense regions which appear commonly in forms.To mitigate this, we propose Form2Seq, a novel sequenceto-sequence (Seq2Seq) inspired framework for structure extraction using text, with a specific focus on forms, which leverages relative spatial arrangement of structures.We discuss two tasks; 1) Classification of low-level constituent elements (TextBlock and empty fillable Widget) into ten types such as field captions, list items, and others; 2) Grouping lower-level elements into higher-order constructs, such as Text Fields, ChoiceFields and ChoiceGroups, used as information collection mechanism in forms.To achieve this, we arrange the constituent elements linearly in natural reading order, feed their spatial and textual representations to Seq2Seq framework, which sequentially outputs prediction of each element depending on the final task.We modify Seq2Seq for grouping task and discuss improvements obtained through cascaded end-to-end training of two tasks versus training in isolation.Experimental results show the effectiveness of our text-based approach achieving an accuracy of 90% on classification task and an F1 of 75.82, 86.01, 61.63 on groups discussed above respectively, outperforming segmentation baselines.Further we show our framework achieves state of the results for table structure recognition on ICDAR 2013 dataset.
Efficient Inference For Neural Machine Translation

Large Transformer models have achieved state-of-the-art results in neural machine translation and have become standard in the field. In this work, we look for the optimal combination of known techniques to optimize inference speed without sacrificing translation quality. We conduct an empirical study that stacks various approaches and demonstrates that combination of replacing decoder self-attention with simplified recurrent units, adopting a deep encoder and a shallow decoder architecture and multi-head attention pruning can achieve up to 109% and 84% speedup on CPU and GPU respectively and reduce the number of parameters by 25% while maintaining the same translation quality in terms of BLEU.
Precise static modeling of Ethereum “memory”

Static analysis of smart contracts as-deployed on the Ethereum blockchain has received much recent attention. However, high-precision analyses currently face significant challenges when dealing with the Ethereum VM (EVM) execution model. A major such challenge is the modeling of low-level, transient “memory” (as opposed to persistent, on-blockchain “storage”) that smart contracts employ. Statically understanding the usage patterns of memory is non-trivial, due to the dynamic allocation nature of in-memory buffers. We offer an analysis that models EVM memory, recovering high-level concepts (e.g., arrays, buffers, call arguments) via deep modeling of the flow of values. Our analysis opens the door to Ethereum static analyses with drastically increased precision. One such analysis detects the extraction of ERC20 tokens by unauthorized users. For another practical vulnerability (redundant calls, possibly used as an attack vector), our memory modeling yields analysis precision of 89%, compared to 16% for a state-of-the-art tool without precise memory modeling. Additionally, precise memory modeling enables the static computation of a contract’s gas cost. This gas-cost analysis has recently been instrumental in the evaluation of the impact of the EIP-1884 repricing (in terms of gas costs) of EVM operations, leading to a reward and significant publicity from the Ethereum Foundation.
A Mask Detection Method for Shoppers Under the Threat of COVID-19 Coronavirus

Object detection, which aims to automatically mark the coordinates of objects of interest in pictures or videos, is an extension of image classification.In recent years, it has been widely used in intelligent traffic management, intelligent monitoring systems, military object detection, and surgical instrument positioning in medical navigation surgery, etc. COVID-19, a novel coronavirus outbreak at the end of 2019, poses a serious threat to public health.Many countries require everyone to wear a mask in public to prevent the spread of coronavirus.To effectively prevent the spread of the coronavirus, we present an object detection method based on single-shot detector (SSD), which focuses on accurate and real-time face masks detection in the supermarket.We make contributions in the following three aspects: 1) presenting a lightweight backbone network for feature extraction, which based on SSD and spatial separable convolution, aiming to improve the detection speed and meet the requirements of real-time detection; 2) proposing a Feature Enhancement Module (FEM) to strengthen the deep features learned from CNN models, aiming to enhance the feature representation of the small objects; 3) constructing COVID-19-Mask, a large-scale dataset to detect whether shoppers are wearing masks, by collecting images in two supermarkets.The experiment results illustrate the high detection precision and realtime performance of the proposed algorithm.
Assessing Technological Innovation on Education in the World of Coronavirus (COVID-19)

Download This Paper Open PDF in Browser Add Paper to My Library Share: Permalink Using these links will ensure access to this page indefinitely Copy URL Copy DOI
NeuroBiometric: An eye blink based biometric authentication system using an event-based neuromorphic vision sensor

The rise of the Internet and identity authentication systems has brought convenience to people's lives but has also introduced the potential risk of privacy leaks. Existing biometric authentication systems based on explicit and static features bear the risk of being attacked by mimicked data. This work proposes a highly efficient biometric authentication system based on transient eye blink signals that are precisely captured by a neuromorphic vision sensor with microsecond-level temporal resolution. The neuromorphic vision sensor only transmits the local pixel-level changes induced by the eye blinks when they occur, which leads to advantageous characteristics such as an ultra-low latency response. We first propose a set of effective biometric features describing the motion, speed, energy and frequency signal of eye blinks based on the microsecond temporal resolution of event densities. We then train the ensemble model and non-ensemble model with our NeuroBiometric dataset for biometrics authentication. The experiments show that our system is able to identify and verify the subjects with the ensemble model at an accuracy of 0.948 and with the non-ensemble model at an accuracy of 0.925. The low false positive rates (about 0.002) and the highly dynamic features are not only hard to reproduce but also avoid recording visible characteristics of a user's appearance. The proposed system sheds light on a new path towards safer authentication using neuromorphic vision sensors.
Allocation Priority Policies for Serverless Function-Execution Scheduling Optimisation

Serverless computing is a Cloud development paradigm where developers write and compose stateless functions, abstracting from their deployment and scaling. In this paper, we address the problem of function-execution scheduling, i.e., how to schedule the execution of Serverless functions to optimise their performance against some user-defined goals. We introduce a declarative language of Allocation Priority Policies (APP) to specify policies that inform the scheduling of function execution. We present a prototypical implementation of APP as an extension of Apache OpenWhisk and we validate it by i) implementing a use case combining IoT, Edge, and Cloud Computing and ii) by comparing its performance to an alternative implementation that uses vanilla OpenWhisk.
From Trustworthy Data to Trustworthy IoT

Internet of Things (IoT) is composed of physical devices, communication networks, and services provided by edge systems and over-the-top applications. IoT connects billions of devices that collect data from the physical environment, which are pre-processed at the edge and then forwarded to processing services at the core of the infrastructure, on top of which cloud-based applications are built and provided to mobile end users. IoT comes with important advantages in terms of applications and added value for its users, making their world smarter and simpler. These advantages, however, are mitigated by the difficulty of guaranteeing IoT trustworthiness, which is still in its infancy. IoT trustworthiness is a must especially in critical domains (e.g., health, transportation) where humans become new components of an IoT system and their life is put at risk by system malfunctioning or breaches. In this article, we put forward the idea that trust in IoT can be boosted if and only if its automation and adaptation processes are based on trustworthy data. We therefore depart from a scenario that considers the quality of a single decision as the main goal of an IoT system and consider the trustworthiness of collected data as a fundamental requirement at the basis of a trustworthy IoT environment. We therefore define a methodology for data collection that filters untrusted data out according to trust rules evaluating the status of the devices collecting data and the collected data themselves. Our approach is based on blockchain and smart contracts and collects data whose trustworthiness and integrity are proven over time. The methodology balances trustworthiness and privacy and is experimentally evaluated in real-world and simulated scenarios using Hyperledger fabric blockchain.
Convolution Inference via Synchronization of a Coupled CMOS Oscillator Array

Oscillator neural networks (ONNs) are a promising hardware option for artificial intelligence. With an abundance of theoretical treatments of ONNs, few experimental implementations exist to date. In contrast to prior publications of only building block functionality, we report a practical experimental demonstration of neural computing using an ONN. The arrays contain 26 CMOS ring oscillators in the GHz range of frequencies are tuned by image data and convolution kernels. Synchronization of oscillators results in an analog output voltage approximating convolution neural network operation.
RecipeNLG: A Cooking Recipes Dataset for Semi-Structured Text Generation

Semi-structured text generation is a non-trivial problem. Although last years have brought lots of improvements in natural language generation, thanks to the development of neural models trained on large scale datasets, these approaches still struggle with producing structured, context- and commonsense-aware texts. Moreover, it is not clear how to evaluate the quality of generated texts. To address these problems, we introduce RecipeNLG – a novel dataset of cooking recipes. We discuss the data collection process and the relation between the semi-structured texts and cooking recipes. We use the dataset to approach the problem of generating recipes. Finally, we make use of multiple metrics to evaluate the generated recipes.
Machine Learning for Better Understanding of Autistics

This paper presents a neural system that assists in understanding the autistics; in particular children who cannot make eye contact with other people and seem aloof. This neural system may help parents and doctors understand their autistics that may have difficulties in understanding and communicating. Thus, such system can identify the emotional and facial status of an autistic who may not capable of showing or saying it. A backpropagation neural network is used in this work to identify the emotional expressions of autistics by reading their faces. The system is image based work, means that it uses face images in order to recognize their emotional expressions and classify them into seven different emotions: Sad, Happy, Nervous, Surprised, Disgust, Fear, Angry. The classification phase is where the images are classified based on their facial emotional expressions and here a feedforward backpropagation neural network is used. This network is trained using backpropagation learning algorithm, which uses gradient descent algorithm to minimize the error and learn.
Mapping Hydrothermally Altered Minerals and Gossans using Hyperspectral data in Eastern Kumaon Himalaya, India

Identification and delineation of alteration minerals and regolith play an important role in the discrimination of metallic and non-metallic minerals and are also significant for metallic and non-metallic mineralization zone identification. In this work, altered minerals and gossans have been identified in and around the Askot area in eastern Kumaon Himalaya using hyperspectral data. Analysis of the Hyperion data indicates alteration minerals in the alteration zones surrounding the Askot sulfide orebody. These minerals include alunite, axinite, illite, and pyrite. Besides, iron bearing minerals such as goethite and hematite are found in the gossans. These gossans are formed by surface weathering of sulfide orebodies and found in a northeast direction along the strike of the Askot orebody. Field surveys in two target prospective mineralized zones confirm the presence of gossan and altered minerals identified via the Hyperion hyperspectral data. This study demonstrates the ability of hyperspectral data and field investigations to identify hydrothermal alteration minerals, gossans, and potential mineralized zones in the Himalaya.
COVID-19 Machine Learning Based Survival Analysis and Discharge Time Likelihood Prediction Using Clinical Data

Download This Paper Open PDF in Browser Add Paper to My Library Share: Permalink Using these links will ensure access to this page indefinitely Copy URL Copy DOI
Marble

State-of-the-art deep learning (DL) systems are vulnerable to adversarial examples, which hinders their potential adoption in safety-and security-critical scenarios. While some recent progress has been made in analyzing the robustness of feed-forward neural networks, the robustness analysis for stateful DL systems, such as recurrent neural networks (RNNs), still remains largely uncharted. In this paper, we propose Marble, a model-based approach for quantitative robustness analysis of real-world RNN-based DL systems. Marble builds a probabilistic model to compactly characterize the robustness of RNNs through abstraction. Furthermore, we propose an iterative refinement algorithm to derive a precise abstraction, which enables accurate quantification of the robustness measurement. We evaluate the effectiveness of Marble on both LSTM and GRU models trained separately with three popular natural language datasets. The results demonstrate that (1) our refinement algorithm is more efficient in deriving an accurate abstraction than the random strategy, and (2) Marble enables quantitative robustness analysis, in rendering better efficiency, accuracy, and scalability than the state-of-the-art techniques.
Neural Attention Distillation: Erasing Backdoor Triggers from Deep  Neural Networks

Deep neural networks (DNNs) are known vulnerable to backdoor attacks, a training time attack that injects a trigger pattern into a small proportion of training data so as to control the model's prediction at the test time. Backdoor attacks are notably dangerous since they do not affect the model's performance on clean examples, yet can fool the model to make incorrect prediction whenever the trigger pattern appears during testing. In this paper, we propose a novel defense framework Neural Attention Distillation (NAD) to erase backdoor triggers from backdoored DNNs. NAD utilizes a teacher network to guide the finetuning of the backdoored student network on a small clean subset of data such that the intermediate-layer attention of the student network aligns with that of the teacher network. The teacher network can be obtained by an independent finetuning process on the same clean subset. We empirically show, against 6 state-of-the-art backdoor attacks, NAD can effectively erase the backdoor triggers using only 5\% clean training data without causing obvious performance degradation on clean examples. Code is available in https://github.com/bboylyg/NAD.
Intuitionistic Fuzzy C-Means Algorithm Based on Membership Information Transfer-Ring and Similarity Measurement

The fuzzy C-means clustering (FCM) algorithm is used widely in medical image segmentation and suitable for segmenting brain tumors. Therefore, an intuitionistic fuzzy C-means algorithm based on membership information transferring and similarity measurements (IFCM-MS) is proposed to segment brain tumor magnetic resonance images (MRI) in this paper. The original FCM lacks spatial information, which leads to a high noise sensitivity. To address this issue, the membership information transfer model is adopted to the IFCM-MS. Specifically, neighborhood information and the similarity of adjacent iterations are incorporated into the clustering process. Besides, FCM uses simple distance measurements to calculate the membership degree, which causes an unsatisfactory result. So, a similarity measurement method is designed in the IFCM-MS to improve the membership calculation, in which gray information and distance information are fused adaptively. In addition, the complex structure of the brain results in MRIs with uncertainty boundary tissues. To overcome this problem, an intuitive fuzzy attribute is embedded into the IFCM-MS. Experiments performed on real brain tumor images demonstrate that our IFCM-MS has low noise sensitivity and high segmentation accuracy.
Imbalanced Data Classification via Cooperative Interaction Between Classifier and Generator

Learning classifiers with imbalanced data can be strongly biased toward the majority class. To address this issue, several methods have been proposed using generative adversarial networks (GANs). Existing GAN-based methods, however, do not effectively utilize the relationship between a classifier and a generator. This article proposes a novel three-player structure consisting of a discriminator, a generator, and a classifier, along with decision boundary regularization. Our method is distinctive in which the generator is trained in cooperation with the classifier to provide minority samples that gradually expand the minority decision region, improving performance for imbalanced data classification. The proposed method outperforms the existing methods on real data sets as well as synthetic imbalanced data sets.
EnCNN-UPMWS: Waste Classification by a CNN Ensemble Using the UPM Weighting Strategy

The accurate and effective classification of household solid waste (HSW) is an indispensable component in the current procedure of waste disposal. In this paper, a novel ensemble learning model called EnCNN-UPMWS, which is based on convolutional neural networks (CNNs) and an unequal precision measurement weighting strategy (UPMWS), is proposed for the classification of HSW via waste images. First, three state-of-the-art CNNs, namely GoogLeNet, ResNet-50, and MobileNetV2, are used as ingredient classifiers to separately predict and obtain three predicted probability vectors, which are significant elements that affect the prediction performance by providing complementary information about the patterns to be classified. Then, the UPMWS is introduced to determine the weight coefficients of the ensemble models. The actual one-hot encoding labels of the validation set and the predicted probability vectors from the CNN ensemble are creatively used to calculate the weights for each classifier during the training phase, which can bring the aggregated prediction vector closer to the target label and improve the performance of the ensemble model. The proposed model was applied to two datasets, namely TrashNet (an open-access dataset) and FourTrash, which was constructed by collecting a total of 47,332 common HSW images containing four types of waste (wet waste, recyclables, harmful waste, and dry waste). The experimental results demonstrate the effectiveness of the proposed method in terms of its accuracy and F1-scores. Moreover, it was found that the UPMWS can simply and effectively enhance the performance of the ensemble learning model, and has potential applications in similar tasks of classification via ensemble learning.
Beyond federated learning: On confidentiality-critical machine learning applications in industry

Federated machine learning frameworks, which take into account confidentiality of distributed data sources are of increasing interest in smart manufacturing. However, the scope of applicability of most such frameworks is restricted in industrial settings due to limitations in the assumptions on the data sources involved. In this work, first, we shed light on the nature of this arising gap between current federated learning and requirements in industrial settings. Our discussion aims at clarifying related notions in emerging sub-disciplines of machine learning, which are partially overlapping. Second, we envision a new confidentiality-preserving approach for smart manufacturing applications based on the more general setting of transfer learning, and envision its implementation in a module-based platform.