Reviewerly/exHarmony huggingface dataset:
Application-Based Online Traffic Classification with Deep Learning Models on SDN Networks

The traffic classification based on the network applications is one important issue for network management. In this paper, we propose an application-based online and offline traffic classification, based on deep learning mechanisms, over software-defined network (SDN) testbed. The designed deep learning model, resigned in the SDN controller, consists of multilayer perceptron (MLP), convolutional neural network (CNN), and Stacked Auto-Encoder (SAE), in the SDN testbed. We employ an open network traffic dataset with seven most popular applications as the deep learning training and testing datasets. By using the TCPreplay tool, the dataset traffic samples are re-produced and analyzed in our SDN testbed to emulate the online traffic service. The performance analyses, in terms of accuracy, precision, recall, and F1 indicators, are conducted and compared with three deep learning models.
Real-time high speed motion prediction using fast aperture-robust event-driven visual flow

Optical flow is a crucial component of the feature space for early visual processing of dynamic scenes especially in new applications such as self-driving vehicles, drones and autonomous robots. The dynamic vision sensors are well suited for such applications because of their asynchronous, sparse and temporally precise representation of the visual dynamics. Many algorithms proposed for computing visual flow for these sensors suffer from the aperture problem as the direction of the estimated flow is governed by the curvature of the object rather than the true motion direction. Some methods that do overcome this problem by temporal windowing under-utilize the true precise temporal nature of the dynamic sensors. In this paper, we propose a novel multi-scale plane fitting based visual flow algorithm that is robust to the aperture problem and also computationally fast and efficient. Our algorithm performs well in many scenarios ranging from fixed camera recording simple geometric shapes to real world scenarios such as camera mounted on a moving car and can successfully perform event-by-event motion estimation of objects in the scene to allow for predictions of upto 500 ms i.e., equivalent to 10 to 25 frames with traditional cameras.
Multi Level Key Exchange and Encryption Protocol for Internet of Things (IoT)

The burgeoning network communications for multiple applications such as commercial, IoT, consumer devices, space, military, and telecommunications are facing many security and privacy challenges. Over the past decade, the Internet of Things (IoT) has been a focus of study. Security and privacy are the most important problems for IoT applications and are still facing huge difficulties. To promote this high-security IoT domain and prevent security attacks from unauthorized users, keys are frequently exchanged through a public key exchange algorithm. This paper introduces a novel algorithm based on Elliptic Curve Cryptography(ECC) for multi-level Public Key Exchange and Encryption Mechanism. It also presents a random number generation technique for secret key generation and a new authentication methodology to enhance the security level. Finally, in terms of security, communication and computational overhead, the performance analysis of the proposed work is compared with the existing protocols.
Quantifying coherence of quantum measurements

Abstract In this work we investigate how to quantify the coherence of quantum measurements. First, we establish a resource theoretical framework to address the coherence of measurement and show that any statistical distance can be adopted to define a coherence monotone of measurement. For instance, the relative entropy fulfills all the required properties as a proper monotone. We specifically introduce a coherence monotone of measurement in terms of off-diagonal elements of positive-operator-valued measure components. This quantification provides a lower bound on the robustness of measurement-coherence that has an operational meaning as the maximal advantage over all incoherent measurements in state discrimination tasks. Finally, we propose an experimental scheme to assess our quantification of measurement-coherence and demonstrate it by performing an experiment using a single qubit on IBM Q processor.
An Improvement of Task Scheduling Algorithms for Green Cloud Computing

The energy consumption generated by servers in task scheduling is an important part of the dynamic energy consumption of cloud computing systems. Saving energy and improving energy efficiency are important foundations for realizing green cloud computing systems. Under green cloud computing, this paper aims to reduce energy consumption and shorten task execution time. This paper combines genetic algorithm and ant colony algorithm to propose a dynamic fusion task-scheduling algorithm. Thereby reducing the energy consumption of cloud computing data centers and computing centers. The simulation results show that the proposed task scheduling algorithm can significantly reduce the time and total energy consumption of cloud computing system tasks.
An Efficient Circuit Compilation Flow for Quantum Approximate Optimization Algorithm

Quantum approximate optimization algorithm (QAOA) is a promising quantum-classical hybrid algorithm to solve hard combinatorial optimization problems. The two-qubits gates used in quantum circuit for QAOA are commutative i.e., the order of gates can be altered without changing the logical output. This re-ordering leads to execution of more gates in parallel and a smaller number of additional gates to compile the QAOA circuit resulting in lower circuit depth and gate-count which is beneficial for circuit run-time and noise. A lower number of gates means a lower accumulation of gate errors, and a lower circuit depth means the quantum bits will have a lower time to decohere (lose state). However, finding the best re-ordered circuit is a difficult problem and does not scale well with circuit size. This paper presents a compilation flow with 3 approaches to find an optimal re-ordered circuit with reduced depth and gate count. Our approaches can reduce gate count up to 23.21% and circuit depth up to 53.65%. Our approaches are compiler agnostic, can be integrated with existing compilers, and scalable.
Reverse-Engineering Deep Neural Networks Using Floating-Point Timing Side-Channels

Trained Deep Neural Network (DNN) models have become valuable intellectual property. A new attack surface has emerged for DNNs: model reverse engineering. Several recent attempts have utilized various common side channels. However, recovering DNN parameters, weights and biases, remains a challenge. In this paper, we present a novel attack that utilizes a floating-point timing side channel to reverse-engineer parameters of multi-layer perceptron (MLP) models in software implementation, entirely and precisely. To the best of our knowledge, this is the first work that leverages a floating-point timing side-channel for effective DNN model recovery.
Atmospheric effects on satellite-mediated continuous-variable quantum key distribution

Abstract Satellite-mediated quantum key distribution (QKD) has received considerable attention due to its potential to reach a global scale. Associated with this application is the atmospheric effects that set a notably restrict to the secret key rate. Here we demonstrate the atmospheric effects on satellite-mediated continuous-variable QKD with respect to the transmittance, fiber coupling, and excess noise. We consider the effects on both downlink and uplink while illustrating the discrepancy between them. Numerical analysis shows that downlink has more reliable transmittance than uplink while enduring a severe fiber coupling issue. It reveals that the discrepancy in transmittance is mainly due to the energy redistribution of the arriving beam. In addition, the time-of-arrival fluctuation notably reduces transmittance when the transmitted pulse half-width is sufficiently narrow (less than 10 −13 ), and it introduces a controllable excess noise if the signal has a strong correlation with the local oscillator.
Functional Verification of Smart Contracts via Strong Data Integrity

We present an invariant-based specification and verification methodology that allows us to conveniently specify and verify strong data integrity properties for Solidity smart contracts. Our approach is able to reason precisely about arbitrary usage of the contracts, which may include re-entrance, a common security pitfall in smart contracts. We implemented the approach in a prototype verification tool, called SolidiKeY, and applied it successfully to a number of smart contracts.
Cultural Emperor Penguin Optimizer and Its Application for Face Recognition

Face recognition is an important technology with practical application prospect. One of the most popular classifiers for face recognition is support vector machine (SVM). However, selection of penalty parameter and kernel parameter determines the performance of SVM, which is the major challenge for SVM to solve classification problems. In this paper, with a view to obtaining the optimal SVM model for face recognition, a new hybrid intelligent algorithm is proposed for multiparameter optimization problem of SVM, which is a fusion of cultural algorithm (CA) and emperor penguin optimizer (EPO), namely, cultural emperor penguin optimizer (CEPO). The key aim of CEPO is to enhance the exploitation capability of EPO with the help of cultural algorithm basic framework. The performance of CEPO is evaluated by six well-known benchmark test functions compared with eight state-of-the-art algorithms. To verify the performance of CEPO-SVM, particle swarm optimization-based SVM (PSO-SVM), genetic algorithm-based SVM (GA-SVM), CA-SVM, and EPO-SVM, moth-flame optimization-based SVM (MFO-SVM), grey wolf optimizer-based SVM (GWO-SVM), cultural firework algorithm-based SVM (CFA-SVM), and emperor penguin and social engineering optimizer-based SVM (EPSEO-SVM) are used for the comparison experiments. The experimental results confirm that the parameters optimized by CEPO are more instructive to make the classification performance of SVM better in terms of accuracy, convergence rate, stability, robustness, and run time.
A survey on Recognizing Textual Entailment as an NLP Evaluation

Recognizing Textual Entailment (RTE) was proposed as a unified evaluation framework to compare semantic understanding of different NLP systems. In this survey paper, we provide an overview of different approaches for evaluating and understanding the reasoning capabilities of NLP systems. We then focus our discussion on RTE by highlighting prominent RTE datasets as well as advances in RTE dataset that focus on specific linguistic phenomena that can be used to evaluate NLP systems on a fine-grained level. We conclude by arguing that when evaluating NLP systems, the community should utilize newly introduced RTE datasets that focus on specific linguistic phenomena.
The Hateful Memes Challenge: Detecting Hate Speech in Multimodal Memes

This work proposes a new challenge set for multimodal classification, focusing on detecting hate speech in multimodal memes. It is constructed such that unimodal models struggle and only multimodal models can succeed: difficult examples (benign confounders) are added to the dataset to make it hard to rely on unimodal signals. The task requires subtle reasoning, yet is straightforward to evaluate as a binary classification problem. We provide baseline performance numbers for unimodal models, as well as for multimodal models with various degrees of sophistication. We find that state-of-the-art methods perform poorly compared to humans (64.73% vs. 84.7% accuracy), illustrating the difficulty of the task and highlighting the challenge that this important problem poses to the community.
Attributed Collaboration Network Embedding for Academic Relationship Mining

Finding both efficient and effective quantitative representations for scholars in scientific digital libraries has been a focal point of research. The unprecedented amounts of scholarly datasets, combined with contemporary machine learning and big data techniques, have enabled intelligent and automatic profiling of scholars from this vast and ever-increasing pool of scholarly data. Meanwhile, recent advance in network embedding techniques enables us to mitigate the challenges of large scale and sparsity of academic collaboration networks. In real-world academic social networks, scholars are accompanied with various attributes or features, such as co-authorship and publication records, which result in attributed collaboration networks. It has been observed that both network topology and scholar attributes are important in academic relationship mining. However, previous studies mainly focus on network topology, whereas scholar attributes are overlooked. Moreover, the influence of different scholar attributes are unclear. To bridge this gap, in this work, we present a novel framework of Attributed Collaboration Network Embedding (ACNE) for academic relationship mining. ACNE extracts four types of scholar attributes based on the proposed scholar profiling model, including demographics, research, influence, and sociability. ACNE can learn a low-dimensional representation of scholars considering both scholar attributes and network topology simultaneously. We demonstrate the effectiveness and potentials of ACNE in academic relationship mining by performing collaborator recommendation on two real-world datasets and the contribution and importance of each scholar attribute on scientific collaborator recommendation is investigated. Our work may shed light on academic relationship mining by taking advantage of attributed collaboration network embedding.
FREE: A Fast and Robust End-to-End Video Text Spotter

Currently, video text spotting tasks usually fall into the four-staged pipeline: detecting text regions in individual images, recognizing localized text regions frame-wisely, tracking text streams and post-processing to generate final results. However, they may suffer from the huge computational cost as well as sub-optimal results due to the interferences of low-quality text and the none-trainable pipeline strategy. In this article, we propose a fast and robust end-to-end video text spotting framework named FREE by only recognizing the localized text stream one-time instead of frame-wise recognition. Specifically, FREE first employs a well-designed spatial-temporal detector that learns text locations among video frames. Then a novel text recommender is developed to select the highest-quality text from text streams for recognizing. Here, the recommender is implemented by assembling text tracking, quality scoring and recognition into a trainable module. It not only avoids the interferences from the low-quality text but also dramatically speeds up the video text spotting. FREE unites the detector and recommender into a whole framework, and helps achieve global optimization. Besides, we collect a large scale video text dataset for promoting the video text spotting community, containing 100 videos from 21 real-life scenarios. Extensive experiments on public benchmarks show our method greatly speeds up the text spotting process, and also achieves the remarkable state-of-the-art.
Summarize before Aggregate: A Global-to-local Heterogeneous Graph Inference Network for Conversational Emotion Recognition

Conversational Emotion Recognition (CER) is a crucial task in Natural Language Processing (NLP) with wide applications.Prior works in CER generally focus on modeling emotion influences solely with utterance-level features, with little attention paid on phrase-level semantic connection between utterances.Phrases carry sentiments when they are referred to emotional events under certain topics, providing a global semantic connection between utterances throughout the entire conversation.In this work, we propose a two-stage Summarization and Aggregation Graph Inference Network (SumAggGIN), which seamlessly integrates inference for topic-related emotional phrases and local dependency reasoning over neighbouring utterances in a global-to-local fashion.Topic-related emotional phrases, which constitutes the global topic-related emotional connections, are recognized by our proposed heterogeneous Summarization Graph.Local dependencies, which captures short-term emotional effects between neighbouring utterances, are further injected via an Aggregation Graph to distinguish the subtle differences between utterances containing emotional phrases.The two steps of graph inference are tightly-coupled for a comprehensively understanding of emotional fluctuation.Experimental results on three CER benchmark datasets verify the effectiveness of our proposed model, which outperforms the stateof-the-art approaches.
Loss-Based Attention for Interpreting Image-Level Prediction of Convolutional Neural Networks

Although deep neural networks have achieved great success on numerous large-scale tasks, poor interpretability is still a notorious obstacle for practical applications. In this paper, we propose a novel and general attention mechanism, loss-based attention, upon which we modify deep neural networks to mine significant image patches for explaining which parts determine the image decision-making. This is inspired by the fact that some patches contain significant objects or their parts for image-level decision. Unlike previous attention mechanisms that adopt different layers and parameters to learn weights and image prediction, the proposed loss-based attention mechanism mines significant patches by utilizing the same parameters to learn patch weights and logits (class vectors), and image prediction simultaneously, so as to connect the attention mechanism with the loss function for boosting the patch precision and recall. Additionally, different from previous popular networks that utilize max-pooling or stride operations in convolutional layers without considering the spatial relationship of features, the modified deep architectures first remove them to preserve the spatial relationship of image patches and greatly reduce their dependencies, and then add two convolutional or capsule layers to extract their features. With the learned patch weights, the image-level decision of the modified deep architectures is the weighted sum on patches. Extensive experiments on large-scale benchmark databases demonstrate that the proposed architectures can obtain better or competitive performance to state-of-the-art baseline networks with better interpretability. The source codes are available on: https://github.com/xsshi2015/Loss-based-Attention-for-Interpreting-Image-level-Prediction-of-Convolutional-Neural-Networks.
Efficient and effective training of sparse recurrent neural networks

Abstract Recurrent neural networks (RNNs) have achieved state-of-the-art performances on various applications. However, RNNs are prone to be memory-bandwidth limited in practical applications and need both long periods of training and inference time. The aforementioned problems are at odds with training and deploying RNNs on resource-limited devices where the memory and floating-point operations (FLOPs) budget are strictly constrained. To address this problem, conventional model compression techniques usually focus on reducing inference costs, operating on a costly pre-trained model. Recently, dynamic sparse training has been proposed to accelerate the training process by directly training sparse neural networks from scratch. However, previous sparse training techniques are mainly designed for convolutional neural networks and multi-layer perceptron. In this paper, we introduce a method to train intrinsically sparse RNN models with a fixed number of parameters and floating-point operations (FLOPs) during training. We demonstrate state-of-the-art sparse performance with long short-term memory and recurrent highway networks on widely used tasks, language modeling, and text classification. We simply use the results to advocate that, contrary to the general belief that training a sparse neural network from scratch leads to worse performance than dense networks, sparse training with adaptive connectivity can usually achieve better performance than dense models for RNNs.
A Framework of Joint Low-Rank and Sparse Regression for Image Memorability Prediction

Image memorability is to measure the degree to which an image is remembered. Generally image memorability prediction involves two steps: feature representation and prediction. Most previous work just focused on addressing the first step by investigating the factors of making an image memorable. They not only lack the use of a learning mechanism in feature representation, but also often neglect the second step. In this paper, we first propose a joint low-rank and sparse regression (JLRSR) framework to address this problem. JLRSR aims to jointly learn: 1) a low-rank projection matrix that enables us to decompose the original data into a component part and an error part and 2) a sparse regression coefficient vector for image memorability prediction. The projection matrix and the regression coefficients are bound by a sparse constraint to make our approach invariant to training samples. Moreover, a graph regularizer is constructed to improve the generalization performance and prevent overfitting. We then extend JLRSR to a multi-view version called Mv-JLRSR by imposing the block-wise constraint to ensure the group effect and the view correlation constraint to eliminate the heterogeneity among views. Experiment results validate the effectiveness of our proposed approaches.
Adversarial Decomposition of Text Representation

Alexey Romanov, Anna Rumshisky, Anna Rogers, David Donahue. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). 2019.
Real-Time Semantic Segmentation for Fisheye Urban Driving Images Based on ERFNet

The interest in fisheye cameras has recently risen in the autonomous vehicles field, as they are able to reduce the complexity of perception systems while improving the management of dangerous driving situations. However, the strong distortion inherent to these cameras makes the usage of conventional computer vision algorithms difficult and has prevented the development of these devices. This paper presents a methodology that provides real-time semantic segmentation on fisheye cameras leveraging only synthetic images. Furthermore, we propose some Convolutional Neural Networks(CNN) architectures based on Efficient Residual Factorized Network(ERFNet) that demonstrate notable skills handling distortion and a new training strategy that improves the segmentation on the image borders. Our proposals are compared to similar state-of-the-art works showing an outstanding performance and tested in an unknown real world scenario using a fisheye camera integrated in an open-source autonomous electric car, showing a high domain adaptation capability.