Reviewerly/exHarmony huggingface dataset:
Packet-based Network Traffic Classification Using Deep Learning

Recently, the advent of many network applications has led to a tremendous amount of network traffic. A network operator must provide quality of service for each application on the network. To accomplish this goal, various studies have focused on accurately classifying application network traffic. Network management requires technology to classify network traffic without the intervention of the network operator. In this study, we generate packet-based datasets through our own network traffic pre-processing. We train five deep learning models using the convolutional neural network (CNN) and residual network (ResNet) to perform network traffic classification. Finally, we analyze the network traffic classification performance of packet-based datasets using the f1 score of the CNN and ResNet deep learning models, and demonstrate their effectiveness.
A Survey on Neural Architecture Search

The growing interest in both the automation of machine learning and deep learning has inevitably led to the development of a wide variety of automated methods for neural architecture search. The choice of the network architecture has proven to be critical, and many advances in deep learning spring from its immediate improvements. However, deep learning techniques are computationally intensive and their application requires a high level of domain knowledge. Therefore, even partial automation of this process helps to make deep learning more accessible to both researchers and practitioners. With this survey, we provide a formalism which unifies and categorizes the landscape of existing methods along with a detailed analysis that compares and contrasts the different approaches. We achieve this via a comprehensive discussion of the commonly adopted architecture search spaces and architecture optimization algorithms based on principles of reinforcement learning and evolutionary algorithms along with approaches that incorporate surrogate and one-shot models. Additionally, we address the new research directions which include constrained and multi-objective architecture search as well as automated data augmentation, optimizer and activation function search.
A meta-learning recommender system for hyperparameter tuning: Predicting when tuning improves SVM classifiers

For many machine learning algorithms, predictive performance is critically affected by the hyperparameter values used to train them. However, tuning these hyperparameters can come at a high computational cost, especially on larger datasets, while the tuned settings do not always significantly outperform the default values. This paper proposes a recommender system based on meta-learning to identify exactly when it is better to use default values and when to tune hyperparameters for each new dataset. Besides, an in-depth analysis is performed to understand what they take into account for their decisions, providing useful insights. An extensive analysis of different categories of meta-features, meta-learners, and setups across 156 datasets is performed. Results show that it is possible to accurately predict when tuning will significantly improve the performance of the induced models. The proposed system reduces the time spent on optimization processes, without reducing the predictive performance of the induced models (when compared with the ones obtained using tuned hyperparameters). We also explain the decision-making process of the meta-learners in terms of linear separability-based hypotheses. Although this analysis is focused on the tuning of Support Vector Machines, it can also be applied to other algorithms, as shown in experiments performed with decision trees.
Joint Learning of Named Entity Recognition and Entity Linking

Named entity recognition (NER) and entity linking (EL) are two fundamentally related tasks, since in order to perform EL, first the mentions to entities have to be detected.However, most entity linking approaches disregard the mention detection part, assuming that the correct mentions have been previously detected.In this paper, we perform joint learning of NER and EL to leverage their relatedness and obtain a more robust and generalisable system.For that, we introduce a model inspired by the Stack-LSTM approach (Dyer et al., 2015).We observe that, in fact, doing multi-task learning of NER and EL improves the performance in both tasks when comparing with models trained with individual objectives.Furthermore, we achieve results competitive with the state-of-the-art in both NER and EL.
AVDNet: A Small-Sized Vehicle Detection Network for Aerial Visual Data

Detection of small-sized targets in aerial views is a challenging task due to the smallness of vehicle size, complex background, and monotonic object appearances. In this letter, we propose a one-stage vehicle detection network (AVDNet) to robustly detect small-sized vehicles in aerial scenes. In AVDNet, we introduced ConvRes residual blocks at multiple scales to alleviate the problem of vanishing features for smaller objects caused because of the inclusion of deeper convolutional layers. These residual blocks, along with enlarged output feature map, ensure the robust representation of the salient features for small sized objects. Furthermore, we proposed a recurrent-feature aware visualization (RFAV) technique to analyze the network behavior. We also created a new airborne image data set (ABD) by annotating 1396 new objects in 79 aerial images for our experiments. The effectiveness of AVDNet is validated on VEDAI, DLR- 3K, DOTA, and the combined (VEDAI, DLR-3K, DOTA, and ABD) data set. Experimental results demonstrate the significant performance improvement of the proposed method over state-of-the-art detection techniques in terms of mAP, computation, and space complexity.
Controllable Neural Story Plot Generation via Reward Shaping

Language-modeling--based approaches to story plot generation attempt to construct a plot by sampling from a language model (LM) to predict the next character, word, or sentence to add to the story. LM techniques lack the ability to receive guidance from the user to achieve a specific goal, resulting in stories that don't have a clear sense of progression and lack coherence. We present a reward-shaping technique that analyzes a story corpus and produces intermediate rewards that are backpropagated into a pre-trained LM in order to guide the model toward a given goal. Automated evaluations show our technique can create a model that generates story plots which consistently achieve a specified goal. Human-subject studies show that the generated stories have more plausible event ordering than baseline plot generation techniques.
Verifying Deep-RL-Driven Systems

Deep reinforcement learning (RL) has recently been successfully applied to networking contexts including routing, flow scheduling, congestion control, packet classification, cloud resource management, and video streaming. Deep-RL-driven systems automate decision making, and have been shown to outperform state-of-the-art handcrafted systems in important domains. However, the (typical) non-explainability of decisions induced by the deep learning machinery employed by these systems renders reasoning about crucial system properties, including correctness and security, extremely difficult. We show that despite the obscurity of decision making in these contexts, verifying that deep-RL-driven systems adhere to desired, designer-specified behavior, is achievable. To this end, we initiate the study of formal verification of deep RL and present Verily, a system for verifying deep-RL-based systems that leverages recent advances in verification of deep neural networks. We employ Verily to verify recently-introduced deep-RL-driven systems for adaptive video streaming, cloud resource management, and Internet congestion control. Our results expose scenarios in which deep-RL-driven decision making yields undesirable behavior. We discuss guidelines for building deep-RL-driven systems that are both safer and easier to verify.
Machine Learning Explainability in Finance: An Application to Default Risk Analysis

We propose a framework for addressing the ‘black box’ problem present in some Machine Learning (ML) applications. We implement our approach by using the Quantitative Input Influence (QII) method of Datta et al (2016) in a real‑world example: a ML model to predict mortgage defaults. This method investigates the inputs and outputs of the model, but not its inner workings. It measures feature influences by intervening on inputs and estimating their Shapley values, representing the features’ average marginal contributions over all possible feature combinations. This method estimates key drivers of mortgage defaults such as the loan‑to‑value ratio and current interest rate, which are in line with the findings of the economics and finance literature. However, given the non‑linearity of ML model, explanations vary significantly for different groups of loans. We use clustering methods to arrive at groups of explanations for different areas of the input space. Finally, we conduct simulations on data that the model has not been trained or tested on. Our main contribution is to develop a systematic analytical framework that could be used for approaching explainability questions in real world financial applications. We conclude though that notable model uncertainties do remain which stakeholders ought to be aware of.
The MyShake Platform: A Global Vision for Earthquake Early Warning

Abstract The MyShake Platform is an operational framework to provide earthquake early warning (EEW) to people in earthquake-prone regions. It is unique among approaches to EEW as it is built on existing smartphone technology to both detect earthquakes and issue warnings. It therefore has the potential to provide EEW wherever there are smartphones, and there are now smartphones wherever there are people. The MyShake framework can also integrate other sources of alerts and deliver them to users, as well and delivering its alerts through other channels as needed. The MyShake Platform builds on experience from the first 3 years of MyShake operation. Over 300,000 people around the globe have downloaded the MyShake app and participated in this citizen science project to detect earthquakes and provide seismic waveforms for research. These operations have shown that earthquakes can be detected, located, and the magnitude estimated ~ 5 to 7 s after the origin time, and alerts can be delivered to smartphones in ~ 1 to 5 s. A human-centered design process produced key insights to the needs of users that have been incorporated into MyShake2.0 which is being release for Android and iOS devices in June 2019. MyShake2.0 will also deliver EEW alerts, initially in California and hopes to expand service to other regions.
A collision avoidance algorithm for ship guidance applications

The paper presents a collision avoidance algorithm for ship open sea navigation, based on an ad hoc modified version of the Rapidly-exploring Random Tree (RRT*) algorithm. The proposed approach is designed to act as the high level layer of the navigation control structure for an autonomous ship. Collision and grounding still represent the primary source of sea accidents, thus an automatic system able to detect static and moving obstacles and plan an evasive route could significantly improve safety during navigation, especially in crowded areas. Focusing on the maritime field, a review of the scientific literature dealing with collision avoidance is presented, showing potential benefits and weaknesses of the different approaches. Among the several methods, details about the RRT and RRT* algorithms are given. The ship path planning problem is introduced and discussed, formulating suitable cost functions and taking into account both topological and kinematic constraints. The algorithm is able to manage multiple moving obstacles with variable speed and course. Eventually, a time-domain ship simulator is used to test the effectiveness of the proposed algorithm over a number of realistic operation scenarios. The obtained results are presented and critically discussed.
A Neural Network for Detailed Human Depth Estimation From a Single Image

This paper presents a neural network to estimate a detailed depth map of the foreground human in a single RGB image. The result captures geometry details such as cloth wrinkles, which are important in visualization applications. To achieve this goal, we separate the depth map into a smooth base shape and a residual detail shape and design a network with two branches to regress them respectively. We design a training strategy to ensure both base and detail shapes can be faithfully learned by the corresponding network branches. Furthermore, we introduce a novel network layer to fuse a rough depth map and surface normals to further improve the final result. Quantitative comparison with fused `ground truth' captured by real depth cameras and qualitative examples on unconstrained Internet images demonstrate the strength of the proposed method.
Demonstrating delay-based reservoir computing using a compact photonic integrated chip

Photonic delay-based reservoir computing (RC) has gained considerable attention lately, as it allows for simple technological implementations of the RC concept that can operate at high speed. In this paper, we discuss a practical, compact and robust implementation of photonic delay-based RC, by integrating a laser and a 5.4cm delay line on an InP photonic integrated circuit. We demonstrate the operation of this chip with 23 nodes at a speed of 0.87GSa/s, showing performances that are similar to previous non-integrated delay-based setups. We also investigate two other post-processing methods to obtain more nodes in the output layer. We show that these methods improve the performance drastically, without compromising the computation speed.
Generate, Segment, and Refine: Towards Generic Manipulation Segmentation

Detecting manipulated images has become a significant emerging challenge. The advent of image sharing platforms and the easy availability of advanced photo editing software have resulted in a large quantities of manipulated images being shared on the internet. While the intent behind such manipulations varies widely, concerns on the spread of false news and misinformation is growing. Current state of the art methods for detecting these manipulated images suffers from the lack of training data due to the laborious labeling process. We address this problem in this paper, for which we introduce a manipulated image generation process that creates true positives using currently available datasets. Drawing from traditional work on image blending, we propose a novel generator for creating such examples. In addition, we also propose to further create examples that force the algorithm to focus on boundary artifacts during training. Strong experimental results validate our proposal.
Using machine learning approach for microseismic events recognition in underground excavations: Comparison of ten frequently-used models

Correctly distinguishing microseismic and blasting events in underground excavations is fundamental to subsequent geophysical analysis activities such as rock burst early warning and tunnel deformation monitoring. Conventional distinguishing approaches such as waveform analysis require operating personnel strong physical and signal processing knowledge for predetermining man-crafted identification criteria, which may not achieve decent recognition accuracy since the collected signals are always varied. In addition, increasingly growing monitoring data cause conventional methods to be time-consuming and inefficient. Based on these considerations, machine learning approach was introduced to efficiently and accurately distinguishing microseismic/blasting events without developing explicit identification instructions for signals. This study investigated the performances of ten frequently-used machine learning models for microseismic/blasting events recognition. A complete identification process encompassing feature selection, model training, hyperparameter tuning was demonstrated to achieve ten models' precedence measured by five selected performance indicators. Then, models' performances were comprehensively considered by a fuzzy evaluation model to generate final conclusions: The Logistic regression model yielded the best performance for microseismic/blasting events identification while the Gaussian process model did the worst. The results gained from this study should serve to encourage machine learning in microseismic/blasting events identification, as well as give geological engineers reliable prior knowledge in model selection.
VR content creation and exploration with deep learning: A survey

Virtual reality (VR) offers an artificial, computer generated simulation of a real life environment. It originated in the 1960s and has evolved to provide increasing immersion, interactivity, imagination, and intelligence. Because deep learning systems are able to represent and compose information at various levels in a deep hierarchical fashion, they can build very powerful models which leverage large quantities of visual media data. Intelligence of VR methods and applications has been significantly boosted by the recent developments in deep learning techniques. VR content creation and exploration relates to image and video analysis, synthesis and editing, so deep learning methods such as fully convolutional networks and general adversarial networks are widely employed, designed specifically to handle panoramic images and video and virtual 3D scenes. This article surveys recent research that uses such deep learning methods for VR content creation and exploration. It considers the problems involved, and discusses possible future directions in this active and emerging research area.
Do consumers really trust cryptocurrencies?

Purpose In this study, we focus on consumer perceptions of cryptocurrencies. We hypothesize that knowledge of cryptocurrencies, trust in government, and the speed of transactions are the main factors contributing to consumers' trust in cryptocurrencies. Design/methodology/approach 451 MTurk workers, a convenient sample incentivized with a small monetary payment, participated in a cross-sectional online study with cryptocurrencies serving as the focal product category. Findings We obtained support for our hypothesized notion that knowledge of cryptocurrencies, trust in government, and the speed of transactions are the main factors contributing to consumers' trust in cryptocurrencies. Our research makes several important theoretical contributions. First, we demonstrate that consumers who understand and know how cryptocurrencies work are more likely to trust and invest in the currency. Next, we demonstrate that consumers are more likely to trust cryptocurrencies and their peer-to-peer transactions if, preferably, they take place via a central issuer and are regulated by their respective governments. Originality/value This study is the first known paper to focus on cryptocurrencies from the consumers' perspective. Next, we identify key antecedents of trust towards cryptocurrencies. Second, we reveal the role of government concerning cryptocurrencies. Finally, FinTech firms and banks (should they choose to enter the cryptocurrency market) need not spend time and money on marketing, advertising, and promotions in order to try to allay consumers' anxiety when it comes to their uptake in the different digital currencies. Rather, this would allow the FinTech firms and banks to allocate resources to focus their attention on marketing, advertising and promoting the factors (i.e. knowledge, trust in government, and speed of transaction) that drive intent to invest in cryptocurrencies.
Motion-encoded particle swarm optimization for moving target search using UAVs

This paper presents a novel algorithm named the motion-encoded particle swarm optimization (MPSO) for finding a moving target with unmanned aerial vehicles (UAVs). From the Bayesian theory, the search problem can be converted to the optimization of a cost function that represents the probability of detecting the target. Here, the proposed MPSO is developed to solve that problem by encoding the search trajectory as a series of UAV motion paths evolving over the generation of particles in a PSO algorithm. This motion-encoded approach allows for preserving important properties of the swarm including the cognitive and social coherence, and thus resulting in better solutions. Results from extensive simulations with existing methods show that the proposed MPSO improves the detection performance by 24% and time performance by 4.71 times compared to the original PSO, and moreover, also outperforms other state-of-the-art metaheuristic optimization algorithms including the artificial bee colony (ABC), ant colony optimization (ACO), genetic algorithm (GA), differential evolution (DE), and tree-seed algorithm (TSA) in most search scenarios. Experiments have been conducted with real UAVs in searching for a dynamic target in different scenarios to demonstrate MPSO merits in a practical application.
Wavelet Based Digital Watermarking Scheme for Medical Images

The transfer of medical images between hospitals is become a normal exercise for better decision making. Digital watermarking has played a very significant role in medical sciences for diagnosis and medication to protect reliability, availability, and confidentiality. Several methods are proposed for medical images using the spatial and transform domain. However, the data falsification ratio is high in the existing mechanisms while exchanging medical images. Hence, we suggest a wavelet based digital watermarking scheme for medical images using three level DWT and BCH coding, which can be used to the medical practitioners for accurate decisions. Security analysis of our proposed work has been done for security assessment, and performance results are discussed to confirm its implementation feasibility in medical sector.
Parallelized quantum error correction with fracton topological codes

Fracton topological phases have a large number of materialized symmetries that enforce a rigid structure on their excitations. Remarkably, we find that the symmetries of a quantum error-correcting code based on a fracton phase enable us to design decoding algorithms. Here we propose and implement decoding algorithms for the three-dimensional X-cube model. In our example, decoding is parallelized into a series of two-dimensional matching problems, thus significantly simplifying the most time consuming component of the decoder. We also find that the rigid structure of its point excitations enable us to obtain high threshold error rates. Our decoding algorithms bring to light some key ideas that we expect to be useful in the design of decoders for general topological stabilizer codes. Moreover, the notion of parallelization unifies several concepts in quantum error correction. We conclude by discussing the broad applicability of our methods, and we explain the connection between parallelizable codes and other methods of quantum error correction. In particular we propose that the new concept represents a generalization of single-shot error correction.