Reviewerly/exHarmony huggingface dataset:
FluidMem: Full, Flexible, and Fast Memory Disaggregation for the Cloud

This paper presents a new approach to memory disaggregation called FluidMem that leverages the userfault mechanism in Linux to achieve full memory disaggregation in software. FluidMem enables dynamic and transparent resizing of an unmodified Virtual Machine’s (VM’s) memory footprint in the cloud. As a result, a VM’s memory footprint can seamlessly scale over multiple machines or even be downsized to a near-zero footprint on a given server. FluidMem’s architecture provides flexibility to cloud operators to manage remote memory without requiring guest intervention, while also supporting paging out the entirety of a VM’s pages within its address space. FluidMem integrates with a remote memory backend in a modular way, easily supporting systems such as RAMCloud to harness remote memory. We demonstrate FluidMem outperforms an existing memory disaggregation approach based on network swap. Microbenchmarks are evaluated to characterize the latency of different components of the FluidMem architecture, and two memory-intensive applications are demonstrated using FluidMem, the Graph500 benchmark, and MongoDB. Additionally, we show FluidMem can flexibly and efficiently grow and shrink the memory footprint of a VM as defined by a cloud provider.
One Belt, One Road, One Sentiment? A Hybrid Approach to Gauging Public Opinions on the New Silk Road Initiative

With the rapid adoption of the Internet, fast-moving social media platforms have been able to extract and encapsulate real-time public sentiments on different entities. Real-time sentiment analysis on current dynamic events such as elections, global affairs and sports are essential in the understanding the public's reaction to the states and trajectories of these events. In this paper, we aim to extract the sentiments of the Belt and Road Initiative from Twitter. Using aspect-based sentiment analysis, we were able to obtain the tweet's sentiment polarity on the related aspect category to better understand the topics that were discussed. We have developed an end-to-end sentiment analysis system that collects relevant data from Twitter, processes it and visualizes it on an intuitive display. We employed a hybrid approach of symbolic and sub-symbolic techniques using gated convolutional networks, aspect embeddings and the SenticNet framework to solve the subtasks of aspect category detection and aspect category polarity. A confidence score threshold was used to decide on the results provided by the models from the differing approaches.
Kalman Filter-Based Convolutional Neural Network for Robust Tracking of Froth-Middling Interface in a Primary Separation Vessel in Presence of Occlusions

Bitumen in the oil sands industry is separated from sand using a water-based gravity separation process in a primary separation vessel (PSV). The interface between the froth and the middlings layer is an important parameter to control for optimal operation of the PSV unit. In this paper, a method using computer vision based on convolutional neural networks (CNNs) and Kalman filter (KF) is designed for the detection of the interface in PSV, with the CNN estimating both the interface level and the image quality. The proposed method consists of two parts: an offline and an online stage, wherein the parameters of CNN and KF are trained in the offline stage using the available data. The algorithm is made robust to any new type of occlusions, not present in the training dataset, in the online stage. Experimental results demonstrate that the proposed method is accurate and robust to different abnormalities in the process.
Deep learning for the classification of medical kidney disease: a pilot study for electron microscopy

Artificial intelligence (AI) is a new frontier and often enigmatic for medical professionals. Cloud computing could open up the field of computer vision to a wider medical audience and deep learning on the cloud allows one to design, develop, train and deploy applications with ease. In the field of histopathology, the implementation of various applications in AI has been successful for whole slide images rich in biological diversity. However, the analysis of other tissue medias, including electron microscopy, is yet to be explored. The present study aims to evaluate deep learning for the classification of medical kidney disease on electron microscopy images: amyloidosis, diabetic glomerulosclerosis, membranous nephropathy, membranoproliferative glomerulonephritis (MPGN), and thin basement membrane disease (TBMD). We found good overall classification with the MedKidneyEM-v1 Classifier and when looking at normal and diseased kidneys, the average area under the curve for precision and recall was 0.841. The average area under the curve for precision and recall on the disease only cohort was 0.909. Digital pathology will shape a new era for medical kidney disease and the present study demonstrates the feasibility of deep learning for electron microscopy. Future approaches could be used by renal pathologists to improve diagnostic concordance, determine therapeutic strategies, and optimize patient outcomes in a true clinical environment.
Joint aspect terms extraction and aspect categories detection via multi-task learning

Aspect Terms Extraction (ATE) and Aspect Categories Detection (ACD) are two fundamental sub-tasks for aspect-based sentiment analysis. Most of the existing works mainly focus on the ATE task or the co-extraction of aspect terms and opinion words, while few attention are paid to the ACD task. In this work, we propose a joint model to seamlessly integrate the ATE and ACD tasks into a multi-task learning framework. Each of the tasks is based on multi-layer Convolutional Neural Networks (CNNs) for computing high-level word representations, and produces a task-specific and a task-share vector. The task-share vector of one task is used to propagate information to the other, and guides the counterpart task to align the informative textual features to produce the task-specific vectors. Finally, a fully-connected layer with a softmax/sigmoid function is applied to the task-specific vectors for the specific information extraction. The rationale underlying the proposed joint model is that, aspect terms and aspect categories are semantically related, and the information propagated between the two tasks can help to capture the semantic alignments between the aspect terms and categories, and produce informative task-specific vectors. Moreover, the ATE task models local semantics at each position of a sentence, while the ACD task extracts global features of the whole sentence. The mutual interactions between local and global features, therefore, can reciprocally capture informative textual features for the information extraction tasks. We validate the effectiveness of the proposed model on two widely used datasets, and show its advantage over the state-of-the-art baselines. We also investigate the effectiveness of the multi-task framework by comparing the proposed model with its variants. Further, we study the robustness of the proposed model by presenting the model performance with respect to different hyperparameters. Finally, we provide visualization examples to gain a better understanding of the advantages the multi-task learning scheme.
Infant brain segmentation based on a combination of VGG‐16 and U‐Net deep neural networks

Medical image segmentation plays a key role in identifying the disease type. In the last decade, various methods have been proposed for medical images segmentation. Despite many efforts made in medical imaging, segmentation of medical images still faces challenges, concerning the variety of shape, location, and texture quality. According to recent studies and magnetic resonance imaging, segmentation of brain images at around 6 months of age is a challenging issue in brain image segmentation due to low tissue contrast between white matter (WM) and grey matter (GM) regions. In this study, using the deep learning model, the convolutional network for the brain fragmentation is presented. First, the image quality is improved using the pre-processing method. The number of layers utilised in the proposed method is less than that of known models. In the pooling layer, instead of using the maximum function, the averaging function is employed. Sixty-four batches are also considered to improve the performance of the proposed method. The method is evaluated on the iSeg-2017 database. The DISC and ASC measures of the proposed method for the three classes of GM, WM, and cerebrovascular fluid are 0.902, 0.594, 0.930, 0.481, 0.971, and 0.231, respectively.
Information Bottleneck Theory on Convolutional Neural Networks

Recent years, many researches attempt to open the black box of deep neural networks and propose a various of theories to understand it. Among them, information bottleneck (IB) theory claims that there are two distinct phases consisting of fitting phase and compression phase in the course of training. This statement attracts many attentions since its success in explaining the inner behavior of feedforward neural networks. In this paper, we employ IB theory to understand the dynamic behavior of convolutional neural networks (CNNs) and investigate how the fundamental features such as convolutional layer width, kernel size, network depth, pooling layers and multi-fully connected layer have impact on the performance of CNNs. In particular, through a series of experimental analysis on benchmark of MNIST and Fashion-MNIST, we demonstrate that the compression phase is not observed in all these cases. This shows us the CNNs have a rather complicated behavior than feedforward neural networks.
DGAZE: Driver Gaze Mapping on Road

Driver gaze mapping is crucial to estimate driver attention and determine which objects the driver is focusing on while driving. We introduce DGAZE, the first large-scale driver gaze mapping dataset. Unlike previous works, our dataset does not require expensive wearable eye-gaze trackers and instead relies on mobile phone cameras for data collection. The data was collected in a lab setting designed to mimic real driving conditions and has point and object-level annotation. It consists of 227,178 road-driver image pairs collected from 20 drivers and contains 103 unique objects on the road belonging to 7 classes: cars, pedestrians, traffic signals, motorbikes, auto-rickshaws, buses and signboards.We also present I-DGAZE, a fused convolutional neural network for predicting driver gaze on the road, which was trained on the DGAZE dataset. Our architecture combines facial features such as face location and head pose along with the image of the left eye to get optimum results. Our model achieves an error of 186.89 pixels on the road view of resolution 1920×1080 pixels. We compare our model with state-of-the-art eye gaze works and present extensive ablation results.
Predicting Stance Polarity and Intensity in Cyber Argumentation With Deep Bidirectional Transformers

In online deliberation, participants argue in support or opposition to one another's arguments and ideas to advocate their position. Often their stance expressed in their posts is implicit and must be derived from the post's text. Existing stance detection models predict the polarity of the user's stance from the text, but do not consider the stance's intensity. We introduce a new research problem, stance polarity, and intensity prediction in response relationships between posts. This problem seeks to predict both the stance polarity and intensity of a replying post toward its parent post in online deliberation. Using our cyber argumentation platform, we have collected an empirical data set with explicitly labeled stance polarity and intensity relationships. In this work, we create six models: five are adapted from top-performing stance detection models and another novel model that fine-tunes the deep bidirectional transformer model BERT. We train and test these six models on our empirical data set to compare their performance for stance polarity and intensity prediction and stance detection. Our results demonstrate that our method of encoding the stance polarity and intensity labels allows the models to predict stance polarity and intensity without compromising their accuracy for stance detection, making these models more versatile. Our results reveal that a novel split architecture for fine-tuning the BERT model outperforms the other models for stance polarity and intensity prediction by 5% accuracy. This work is the first to train models for predicting both the stance polarity and intensity in one combined task while maintaining good accuracy.
Improving Laplace Mechanism of Differential Privacy by Personalized Sampling

The differential privacy is the state-of-the-art conception for privacy preservation due to its strong privacy guarantees, however it suffers from low accuracy. In this paper, we propose a personalized sample Laplace mechanism by combining the Laplace mechanism with sampling technology. In order to improve the accuracy, the proposed mechanism assigns personalized sampling probability to each record. Based on the personalized sampling probability, we prove that the proposed mechanism satisfies ε differential privacy. Then we compare the proposed mechanism with other mechanisms in term of the accuracy. Through extensive experiments on synthetic data set and real world data set, we demonstrate that the performance of proposed mechanism is better.
FIDOnuous: A FIDO2/WebAuthn Extension to Support Continuous Web Authentication

For many years username and password are the common solution to protect sensitive web services despite its various drawbacks. While many alternatives were proposed to improve security, passwords are still included in any authentication procedure today. With the proposal of FIDO and FIDO2/WebAuthn a very promising approach was presented in the last years that may replace passwords at some time since it also enables password-less authentication in addition to work as a second factor for any web authentication. Although FIDO2/WebAuthn solves many problems of passwords using public/private key cryptography and the possibility to use strong authentication mechanisms like biometrics, it is still not capable of detecting an attacker once a successful login has happened. In this paper we evaluate the extension of FIDO2/WebAuthn to enable continuous authentication in the web. While this extension would enable the many proposals of continuous authentication for system or device protection to be used for web authentication, it allows the exchange of the relying parties' security requirements on the one hand and the authenticator's capabilities on the other hand, too. We evaluate our extension using an Android-based roaming authenticator communicating via Bluetooth Low Energy and show that the FIDO2/WebAuthn extension mechanism is suitable. While a real world deployment would require modifications in the different browser implementations, we further point out the challenges resulting from the different implementation levels and the high dynamics in the standard development such as different notification windows or parallelism issues.
A Blockchain-Based Vehicle-Trust Management Framework Under a Crowdsourcing Environment

Vehicular crowdsourcing networks (VCNs) enable vehicles to provide or obtain traffic-related services in a costefficient and flexible manner. Therefore, it is crucial to provide trusted management in VCNs for high reliability towards both service producers and consumers. However, most recent VCN platforms rely on a third party to manage crowdsourcing services which might be not fully trusted by users. For the issue, this paper proposes a blockchain-based trust management scheme for VCNs to provide a decentralized and trusted service management. A comprehensive trust evaluation model (TEM) is designed to quantify the trust degree of each vehicular node, and a vehicle-trust blockchain framework called VTchain is proposed to preserve the trust values of nodes while guaranteeing transparency and trustworthiness. Particularly, we leverage a trusted execution environment (TEE) to provide secure trust evaluation to tackle possible untrusted road-side units. In addition, we introduce TEM-based Proof of Trust to support blockchain maintenance, which works together with an efficient consensus algorithm Zyzzyva for improved scalability. Finally, extensive experiments are conducted by developing a testbed deployed on cloud servers for measurements.
Multi-class imbalanced semi-supervised learning from streams through online ensembles

Multi-class imbalance, in which the rates of instances in the various classes differ substantially, poses a major challenge when learning from evolving streams. In this setting, minority class instances may arrive infrequently and in bursts, making accurate model construction problematic. Further, skewed streams are not only susceptible to concept drifts, but class labels may also be absent, expensive to obtain, or only arrive after some delay. The combined effects of multi-class skew, concept drift and semi-supervised learning have received limited attention in the online learning community. In this paper, we introduce a multi-class online ensemble algorithm that is suitable for learning in such settings. Specifically, our algorithm uses sampling with replacement while dynamically increasing the weights of underrepresented classes based on recall in order to produce models that benefit all classes. Our approach addresses the potential lack of labels by incorporating a self-training semi-supervised learning method for labeling instances. Our experimental results show that our online ensemble performs well against multi-class imbalanced data containing concept drifts. In addition, our algorithm produces accurate predictions, even in the presence of unlabeled data.
A tri‐attention enhanced graph convolutional network for skeleton‐based action recognition

Skeleton-based action recognition has recently attracted a lot of research interests due to its advantage in computational efficiency. Some recent work building upon Graph Convolutional Networks (GCNs) has shown promising performance in this task by modelling intrinsic spatial correlations between skeleton joints. However, these methods only consider local properties of action sequences in the spatial-temporal domain, and consequently, are limited in distinguishing complex actions with similar local movements. To address this problem, a novel tri-attention module (TAM) is proposed to guide GCNs to perceive significant variations across local movements. Specifically, the devised TAM is implemented in three steps: i) A dimension permuting unit is proposed to characterise skeleton action sequences in three different domains: body poses, joint trajectories, and evolving projections. ii) A global statistical modelling unit is introduced to aggregate the first-order and second-order properties of global contexts to perceive the significant movement variations of each domain. iii) A fusion unit is presented to integrate the features of these three domains together and leverage as orientation for graph convolution at each layer. Through these three steps, significant-variation frames, joints, and channels can be enhanced. We conduct extensive experiments on two large-scale benchmark datasets, NTU RGB-D and Kinetics-Skeleton. Experimental results demonstrate that the proposed TAM can be easily plugged into existing GCNs and achieve comparable performance with the state-of-the-art methods.
An android based language translator application

Abstract A mobile translator is a mobile application that can be utilised for translating from English to any other dialect, and vice versa. The problem of language difference has hindered effective information communication over the years. There have been difficulties in information communication amid countries over the years. In modern times, language interpreters must understand and speak both the language been translated to and verse-visa. This traditional approach used for solving the problem of language differences has not been productive and favourable. Also, the teaching of different languages can be difficult due to language difference problems. The individual will also have to be taught by a tutor who will incur extra expenses and may not be the most efficient and favourable method. Therefore, the study develops an android phone language converter app in other to make learning and language translation easy and facilitates stress-free communication. The proposed language translation uses Google’s real-time translation API natural language processing with Java programming language to develop the application. The most used languages globally (i.e., English, Spanish, Arabic, Hindi, French, and Chinese) were used for the android application translation. This application can be useful for Tourists for communication purposes, thus allowing them to integrate with the local people and access the right information. The system will also be able to evaluate language translation to determine their suitability for everyday conversation; given the fact that it is an android application, one will always be willing to use their phone to learn, compared to having them on a computer or learn from a physical tutor when your phone can be your tutor. The application was evaluated based on the classification time the memory usage, and the battery life all through distinctive use.
Liver-Tumor Detection Using CNN ResUNet

Liver tumor is the fifth most occurring type of tumor in men and the ninth most occurring type of tumor in women according to recent reports of Global cancer statistics 2018. There are several imaging tests like Computed Tomography (CT), Magnetic Resonance Imaging (MRI), and ultrasound that can diagnose the liver tumor after taking the sample from the tissue of the liver. These tests are costly and time-consuming. This paper proposed that image processing through deep learning Convolutional Neural Network (CNNs) ResUNet model that can be helpful for the early diagnose of tumor instead of conventional methods. The existing studies have mainly used the two Cascaded CNNs for liver segmentation and evaluation of Region Of Interest (ROI). This study uses ResUNet, an updated version of U-Net and ResNet Models that utilize the service of Residential blocks. We apply over method on the 3D-IRCADb01 dataset that is based on CT slices of liver tumor affected patients. The results showed the True Value Accuracy around 99% and F1 score performance around 95%. This method will be helpful for early and accurate diagnose of the Liver tumor to save the lives of many patients in the field of Biotechnology.
Smart Markers in Smart Contracts: Enabling Multiway Branching and Merging in Blockchain for Decentralized Runtime Verification

The increasing complexity of modern hardware and software platform along with the imperative assurance on stability deems runtime verification of task fulfillment necessary in distributed systems. Distributing the burden of a central verification monitor to individual devices could improve the efficiency. Our previous work shows the possibility of achieving decentralized runtime verification by incorporating some mechanisms of the blockchain technology for locating the accountability when error occurs. However, traditional blockchain technology disallows branching and hence does not support verification of tasks which involves multiway dependencies. In this paper, we introduce a novel approach of smart marker that can be included in a blockchain to enable multiway branching and merging in order to verify the fulfillment of tasks that involve one-to-many and many-to-one dependencies. The design of smart marker satisfies three requirements of recognizability, compatibility, and authenticability. We implement a prototype of the smart marker scheme and analyze its performance.