OpenRaiser/Intern-Atlas huggingface dataset:
Byte Pair Encoding is Suboptimal for Language Model Pretraining
Venue: arXiv cs.CL 2020
Year: 2020

The success of pretrained transformer language models (LMs) in natural language processing has led to a wide range of pretraining setups. In particular, these models employ a variety of subword tokenization methods, most notably byte-pair encoding (BPE) (Sennrich et al., 2016; Gage, 1994), the WordPiece method (Schuster and Nakajima, 2012), and unigram language modeling (Kudo, 2018), to segment text. However, to the best of our knowledge, the literature does not contain a direct evaluation of the impact of tokenization on language model pretraining. We analyze differences between BPE and unigram LM tokenization, finding that the latter method recovers subword units that align more closely with morphology and avoids problems stemming from BPE's greedy construction procedure. We then compare the fine-tuned task performance of identical transformer masked language models pretrained with these tokenizations. Across downstream tasks and two languages (English and Japanese), we find that the unigram LM tokenization method matches or outperforms BPE. We hope that developers of future pretrained LMs will consider adopting the unigram LM method over the more prevalent BPE.
Challenges in Vessel Behavior and Anomaly Detection: From Classical Machine Learning to Deep Learning
Venue: arXiv cs.LG 2020
Year: 2020

The global expansion of maritime activities and the development of the Automatic Identification System (AIS) have driven the advances in maritime monitoring systems in the last decade. Monitoring vessel behavior is fundamental to safeguard maritime operations, protecting other vessels sailing the ocean and the marine fauna and flora. Given the enormous volume of vessel data continually being generated, real-time analysis of vessel behaviors is only possible because of decision support systems provided with event and anomaly detection methods. However, current works on vessel event detection are ad-hoc methods able to handle only a single or a few predefined types of vessel behavior. Most of the existing approaches do not learn from the data and require the definition of queries and rules for describing each behavior. In this paper, we discuss challenges and opportunities in classical machine learning and deep learning for vessel event and anomaly detection. We hope to motivate the research of novel methods and tools, since addressing these challenges is an essential step towards actual intelligent maritime monitoring systems.
Location-Enabled IoT (LE-IoT): A Survey of Positioning Techniques, Error Sources, and Mitigation
Venue: arXiv cs.NI 2020
Year: 2020

The Internet of Things (IoT) has started to empower the future of many industrial and mass-market applications. Localization techniques are becoming key to add location context to IoT data without human perception and intervention. Meanwhile, the newly-emerged Low-Power Wide-Area Network (LPWAN) technologies have advantages such as long-range, low power consumption, low cost, massive connections, and the capability for communication in both indoor and outdoor areas. These features make LPWAN signals strong candidates for mass-market localization applications. However, there are various error sources that have limited localization performance by using such IoT signals. This paper reviews the IoT localization system through the following sequence: IoT localization system review -- localization data sources -- localization algorithms -- localization error sources and mitigation -- localization performance evaluation. Compared to the related surveys, this paper has a more comprehensive and state-of-the-art review on IoT localization methods, an original review on IoT localization error sources and mitigation, an original review on IoT localization performance evaluation, and a more comprehensive review of IoT localization applications, opportunities, and challenges. Thus, this survey provides comprehensive guidance for peers who are interested in enabling localization ability in the existing IoT systems, using IoT systems for localization, or integrating IoT signals with the existing localization sensors.
Weighted Aggregating Stochastic Gradient Descent for Parallel Deep Learning
Venue: arXiv cs.LG 2020
Year: 2020

This paper investigates the stochastic optimization problem with a focus on developing scalable parallel algorithms for deep learning tasks. Our solution involves a reformation of the objective function for stochastic optimization in neural network models, along with a novel parallel strategy, coined weighted aggregating stochastic gradient descent (WASGD). Following a theoretical analysis on the characteristics of the new objective function, WASGD introduces a decentralized weighted aggregating scheme based on the performance of local workers. Without any center variable, the new method automatically assesses the importance of local workers and accepts them according to their contributions. Furthermore, we have developed an enhanced version of the method, WASGD+, by (1) considering a designed sample order and (2) applying a more advanced weight evaluating function. To validate the new method, we benchmark our schemes against several popular algorithms including the state-of-the-art techniques (e.g., elastic averaging SGD) in training deep neural networks for classification tasks. Comprehensive experiments have been conducted on four classic datasets, including the CIFAR-100, CIFAR-10, Fashion-MNIST, and MNIST. The subsequent results suggest the superiority of the WASGD scheme in accelerating the training of deep architecture. Better still, the enhanced version, WASGD+, has been shown to be a significant improvement over its basic version.
Generating Narrative Text in a Switching Dynamical System
Venue: arXiv cs.CL 2020
Year: 2020

Early work on narrative modeling used explicit plans and goals to generate stories, but the language generation itself was restricted and inflexible. Modern methods use language models for more robust generation, but often lack an explicit representation of the scaffolding and dynamics that guide a coherent narrative. This paper introduces a new model that integrates explicit narrative structure with neural language models, formalizing narrative modeling as a Switching Linear Dynamical System (SLDS). A SLDS is a dynamical system in which the latent dynamics of the system (i.e. how the state vector transforms over time) is controlled by top-level discrete switching variables. The switching variables represent narrative structure (e.g., sentiment or discourse states), while the latent state vector encodes information on the current state of the narrative. This probabilistic formulation allows us to control generation, and can be learned in a semi-supervised fashion using both labeled and unlabeled data. Additionally, we derive a Gibbs sampler for our model that can fill in arbitrary parts of the narrative, guided by the switching variables. Our filled-in (English language) narratives outperform several baselines on both automatic and human evaluations.
Governance of the Internet of Things (IoT)
Venue: arXiv cs.CY 2020
Year: 2020

Today's increasing rate of technological change results from the rapid growth in computer processing speed, when combined with the cost decline of processing capacity, and is of historical import. The daily life of billions of individuals worldwide has been forever changed by technology in just the last few years. Costly data breaches continue at an alarming rate. The challenge facing humans as they attempt to govern the process of artificial intelligence, machine learning, and the impact of billions of sensory devices connected to the Internet is the subject of this Article. We proceed in nine sections. First, we define the Internet of Things (IoT), comment on the explosive growth in sensory devices connected to the Internet, provide examples of IoT devices, and speak to the promise of the IoT. Second, we discuss legal requirements for corporate governance as a foundation for considering the challenge of governing the IoT. Third, we look at potential IoT threats. Fourth, we discuss the Mirai botnet. Fifth, is a look at the IoT threat vector vulnerabilities during times of crisis. Sixth, we discuss the Manufactured Usage Description (MUD) methodology. Seventh, is a discussion of recent regulatory developments. Next, we look at a few recommendations. And finally, we conclude. We believe this Article contributes to our understanding of the widespread exposure to malware associated with IoT and adds to the nascent but emerging literature on governance of enterprise risk, a subject of vital societal importance.
Downstream Model Design of Pre-trained Language Model for Relation Extraction Task
Venue: arXiv cs.CL 2020
Year: 2020

Supervised relation extraction methods based on deep neural network play an important role in the recent information extraction field. However, at present, their performance still fails to reach a good level due to the existence of complicated relations. On the other hand, recently proposed pre-trained language models (PLMs) have achieved great success in multiple tasks of natural language processing through fine-tuning when combined with the model of downstream tasks. However, original standard tasks of PLM do not include the relation extraction task yet. We believe that PLMs can also be used to solve the relation extraction problem, but it is necessary to establish a specially designed downstream task model or even loss function for dealing with complicated relations. In this paper, a new network architecture with a special loss function is designed to serve as a downstream model of PLMs for supervised relation extraction. Experiments have shown that our method significantly exceeded the current optimal baseline models across multiple public datasets of relation extraction.
Mixture Density Conditional Generative Adversarial Network Models (MD-CGAN)
Venue: arXiv cs.LG 2020
Year: 2020

Generative Adversarial Networks (GANs) have gained significant attention in recent years, with impressive applications highlighted in computer vision in particular. Compared to such examples, however, there have been more limited applications of GANs to time series modelling, including forecasting. In this work, we present the Mixture Density Conditional Generative Adversarial Model (MD-CGAN), with a focus on time series forecasting. We show that our model is capable of estimating a probabilistic posterior distribution over forecasts and that, in comparison to a set of benchmark methods, the MD-CGAN model performs well, particularly in situations where noise is a significant component of the observed time series. Further, by using a Gaussian mixture model as the output distribution, MD-CGAN offers posterior predictions that are non-Gaussian.
Canopy: A Verifiable Privacy-Preserving Token Ring based Communication Protocol for Smart Homes
Venue: arXiv cs.CR 2020
Year: 2020

This paper focuses on the new privacy challenges that arise in smart homes. Specifically, the paper focuses on inferring the user's activities -- which may, in turn, lead to the user's privacy -- via inferences through device activities and network traffic analysis. We develop techniques that are based on a cryptographically secure token circulation in a ring network consisting of smart home devices to prevent inferences from device activities, via device workflow, i.e., inferences from a coordinated sequence of devices' actuation. The solution hides the device activity and corresponding channel activities, and thus, preserve the individual's activities. We also extend our solution to deal with a large number of devices and devices that produce large-sized data by implementing parallel rings. Our experiments also evaluate the performance in terms of communication overheads of the proposed approach and the obtained privacy.
ShanghaiTech at MRP 2019: Sequence-to-Graph Transduction with Second-Order Edge Inference for Cross-Framework Meaning Representation Parsing
Venue: arXiv cs.CL 2020
Year: 2020

This paper presents the system used in our submission to the \textit{CoNLL 2019 shared task: Cross-Framework Meaning Representation Parsing}. Our system is a graph-based parser which combines an extended pointer-generator network that generates nodes and a second-order mean field variational inference module that predicts edges. Our system achieved \nth{1} and \nth{2} place for the DM and PSD frameworks respectively on the in-framework ranks and achieved \nth{3} place for the DM framework on the cross-framework ranks.
Fast-SCAN decoding of Polar Codes
Venue: arXiv cs.IT 2020
Year: 2020

Polar codes are able to achieve the capacity of memoryless channels under successive cancellation (SC) decoding. Soft Cancellation (SCAN) is a soft-output decoder based on the SC schedule, useful in iterative decoding and concatenation of polar codes. However, the sequential nature of this decoder leads to high decoding latency compared to state-of-the-art codes. To reduce the latency of SCAN, in this paper we identify special nodes in the decoding tree, corresponding to specific frozen-bit sequences, and propose dedicated low-latency decoding approaches for each of them. The resulting fast-SCAN decoder does not alter the soft-output compared to the standard SCAN while dramatically reducing the decoding latency and yielding the same error-correction performance.
A Simple Capacity Outer Bound for Two-Way Channels and Capacity Approximation Results
Venue: arXiv cs.IT 2020
Year: 2020

Channel symmetry properties that imply the tightness of Shannon's random coding inner bound have recently been used to determine the capacity region of discrete-memoryless two-way channels (DM-TWCs). For channels without such symmetry properties, outer bounds are often needed to estimate the capacity region. However, validating symmetry conditions and/or evaluating non-trivial outer bounds are computationally demanding, especially for channels with large input and output alphabets. In this paper, three easy-to-check conditions that identify DM-TWCs with no such symmetry properties as well as an easy-to-compute outer bound are derived. The bound is obtained from Shannon's inner bound computation but is non-trivial. Using this outer bound, approximate capacity results can be established for certain DM-TWCs. The results are illustrated by two examples.
Fluid Intelligence Doesn't Matter! Effects of Code Examples on the Usability of Crypto APIs
Venue: arXiv cs.SE 2020
Year: 2020

Context: Programmers frequently look for the code of previously solved problems that they can adapt for their own problem. Despite existing example code on the web, on sites like Stack Overflow, cryptographic Application Programming Interfaces (APIs) are co monly misused. There is little known about what makes examples helpful for developers in using crypto APIs. Analogical problem solving is a psychological theory that investigates how people use known solutions to solve new problems. There is evidence that the capacity to reason and solve novel problems a.k.a Fluid Intelligence (Gf ) and structurally and procedurally similar solutions support problem solving. Aim: Our goal is to understand whether similarity and Gf also have an effect in the context of using cryptographic APIs with the help of code examples. Method: We conducted a controlled experiment with 76 student participants developing with or without procedurally similar examples, one of two Java crypto libraries and measured the Gf of the participants as well as the effect on usability (effectiveness, efficiency, satisfaction) and security bugs. Results: We observed a strong effect of code examples with a high procedural similarity on all dependent variables. Fluid intelligence Gf had no effect. It also made no difference which library the participants used. Conclusions: Example code must be more highly similar to a concrete solution, not very abstract and generic to have a positive effect in a development task.
Earned Benefit Maximization in Social Networks Under Budget Constraint
Venue: arXiv cs.SI 2020
Year: 2020

Given a social network with nonuniform selection cost of the users, the problem of \textit{Budgeted Influence Maximization} (BIM in short) asks for selecting a subset of the nodes within an allocated budget for initial activation, such that due to the cascading effect, influence in the network is maximized. In this paper, we study this problem with a variation, where a set of nodes are designated as target nodes, each of them is assigned with a benefit value, that can be earned by influencing them, and our goal is to maximize the earned benefit by initially activating a set of nodes within the budget. We call this problem as the \textsc{Earned Benefit Maximization Problem}. First, we show that this problem is NP\mbox{-}Hard and the benefit function is \textit{monotone}, \textit{sub\mbox{-}modular} under the \textit{Independent Cascade Model} of diffusion. We propose an incremental greedy strategy for this problem and show, with minor modification it gives $(1-\frac{1}{\sqrt{e}})$\mbox{-}factor approximation guarantee on the earned benefit. Next, by exploiting the sub\mbox{-}modularity property of the benefit function, we improve the efficiency of the proposed greedy algorithm. Then, we propose a hop\mbox{-}based heuristic method, which works based on the computation of the `expected earned benefit' of the effective neighbors corresponding to the target nodes. Finally, we perform a series of extensive experiments with four real\mbox{-}life, publicly available social network datasets. From the experiments, we observe that the seed sets selected by the proposed algorithms can achieve more benefit compared to many existing methods. Particularly, the hop\mbox{-}based approach is found to be more efficient than the other ones for solving this problem.
A Deep Learning Approach for Determining Effects of Tuta Absoluta in Tomato Plants
Venue: arXiv cs.CV 2020
Year: 2020

Early quantification of Tuta absoluta pest's effects in tomato plants is a very important factor in controlling and preventing serious damages of the pest. The invasion of Tuta absoluta is considered a major threat to tomato production causing heavy loss ranging from 80 to 100 percent when not properly managed. Therefore, real-time and early quantification of tomato leaf miner Tuta absoluta, can play an important role in addressing the issue of pest management and enhance farmers' decisions. In this study, we propose a Convolutional Neural Network (CNN) approach in determining the effects of Tuta absoluta in tomato plants. Four CNN pre-trained architectures (VGG16, VGG19, ResNet and Inception-V3) were used in training classifiers on a dataset containing health and infested tomato leaves collected from real field experiments. Among the pre-trained architectures, experimental results showed that Inception-V3 yielded the best results with an average accuracy of 87.2 percent in estimating the severity status of Tuta absoluta in tomato plants. The pre-trained models could also easily identify High Tuta severity status compared to other severity status (Low tuta and No tuta)
Neural Networks Model for Travel Time Prediction Based on ODTravel Time Matrix
Venue: arXiv cs.LG 2020
Year: 2020

Public transportation system commuters are often interested in getting accurate travel time information to plan their daily activities. However, this information is often difficult to predict accurately due to the irregularities of road traffic, caused by factors such as weather conditions, road accidents, and traffic jams. In this study, two neural network models namely multi-layer(MLP) perceptron and long short-term model(LSTM) are developed for predicting link travel time of a busy route with input generated using Origin-Destination travel time matrix derived from a historical GPS dataset. The experiment result showed that both models can make near-accurate predictions however, LSTM is more susceptible to noise as time step increases.
Skin Diseases Detection using LBP and WLD- An Ensembling Approach
Venue: arXiv cs.CV 2020
Year: 2020

In all developing and developed countries in the world, skin diseases are becoming a very frequent health problem for the humans of all age groups. Skin problems affect mental health, develop addiction to alcohol and drugs and sometimes causes social isolation. Considering the importance, we propose an automatic technique to detect three popular skin diseases- Leprosy, Tinea versicolor and Vitiligofrom the images of skin lesions. The proposed technique involves Weber local descriptor and Local binary pattern to represent texture pattern of the affected skin regions. This ensemble technique achieved 91.38% accuracy using multi-level support vector machine classifier, where features are extracted from different regions that are based on center of gravity. We have also applied some popular deep learn-ing networks such as MobileNet, ResNet_152, GoogLeNet,DenseNet_121, and ResNet_101. We get 89% accuracy using ResNet_101. The ensemble approach clearly outperform all of the used deep learning networks. This imaging tool will be useful for early skin disease screening.
Empirical Perspectives on One-Shot Semi-supervised Learning
Venue: arXiv cs.LG 2020
Year: 2020

One of the greatest obstacles in the adoption of deep neural networks for new applications is that training the network typically requires a large number of manually labeled training samples. We empirically investigate the scenario where one has access to large amounts of unlabeled data but require labeling only a single prototypical sample per class in order to train a deep network (i.e., one-shot semi-supervised learning). Specifically, we investigate the recent results reported in FixMatch for one-shot semi-supervised learning to understand the factors that affect and impede high accuracies and reliability for one-shot semi-supervised learning of Cifar-10. For example, we discover that one barrier to one-shot semi-supervised learning for high-performance image classification is the unevenness of class accuracy during the training. These results point to solutions that might enable more widespread adoption of one-shot semi-supervised training methods for new applications.
How Crisp is the Crease? A Subjective Study on Web Browsing Perception of Above-The-Fold
Venue: arXiv cs.HC 2020
Year: 2020

Quality of Experience (QoE) for various types of websites has gained significant attention in recent years. In order to design and evaluate websites, a metric that can estimate a user's experienced quality robustly for diverse content is necessary. SpeedIndex (SI) has been widely adopted to estimate perceived web page loading progress. It measures the speed of rendering pixels for the webpage that is visible in the browser window. This is termed Above-The-Fold (ATF). The influence of animated content on the perception of ATF has been less comprehensively explored. In this paper, we present an experimental design and methodology to measure ATF perception for websites with and without animated elements for various page content categories. We found that pages with animated elements caused people to have more varied perceptions of ATF under different network conditions. Animated content also impacts the page load estimation accuracy of SI for websites. We discuss how the difference in the perception of ATF will impact the QoE management of web applications. We explain the necessity of revisiting the visual assessment of ATF to include the animated contents and improve the robustness of metrics like SI.