OpenRaiser/Intern-Atlas huggingface dataset:
A Survey of Mobile Edge Computing in the Industrial Internet
Venue: arXiv cs.NI 2019
Year: 2019

With the advent of a new round of the Industrial Revolution, the Industrial Internet will carry the convergence of heterogeneous network and the dynamic reconfiguration of industrial equipment. In order to further provide higher performance of network capabilities, the Industrial Internet has experienced unprecedented growth while facing enormous challenges from the actual needs of industrial networks. The typical scenarios in industrial applications, combined with the technical advantages of mobile edge computing, are described in view of the low latency, high bandwidth and high reliability demanded by the Industrial Internet in the new era. The key technologies of mobile edge computing for the Industrial Internet have been outlined in this treatise, whose feasibility and importance are demonstrated by typical industrial applications that have been deployed. As combined with the development trend of the Industrial Internet, this paper summarizes the existing work and discusses the future research direction of key technologies of mobile edge computing for the Industrial Internet.
A selected review on reinforcement learning based control for autonomous underwater vehicles
Venue: arXiv cs.RO 2019
Year: 2019

Recently, reinforcement learning (RL) has been extensively studied and achieved promising results in a wide range of control tasks. Meanwhile, autonomous underwater vehicle (AUV) is an important tool for executing complex and challenging underwater tasks. The advances in RL offers ample opportunities for developing intelligent AUVs. This paper provides a selected review on RL based control for AUVs with the focus on applications of RL to low-level control tasks for underwater regulation and tracking. To this end, we first present a concise introduction to the RL based control framework. Then, we provide an overview of RL methods for AUVs control problems, where the main challenges and recent progresses are discussed. Finally, two representative cases of RL-based controllers are given in detail for the model-free RL methods on AUVs.
Monetizing Mobile Data via Data Rewards
Venue: arXiv cs.GT 2019
Year: 2019

Most mobile network operators generate revenues by directly charging users for data plan subscriptions. Some operators now also offer users data rewards to incentivize them to watch mobile ads, which enables the operators to collect payments from advertisers and create new revenue streams. In this work, we analyze and compare two data rewarding schemes: a Subscription-Aware Rewarding (SAR) scheme and a Subscription-Unaware Rewarding (SUR) scheme. Under the SAR scheme, only the subscribers of the operators' data plans are eligible for the rewards; under the SUR scheme, all users are eligible for the rewards (e.g., the users who do not subscribe to the data plans can still get SIM cards and receive data rewards by watching ads). We model the interactions among an operator, users, and advertisers by a two-stage Stackelberg game, and characterize their equilibrium strategies under both the SAR and SUR schemes. We show that the SAR scheme can lead to more subscriptions and a higher operator revenue from the data market, while the SUR scheme can lead to better ad viewership and a higher operator revenue from the ad market. We further show that the operator's optimal choice between the two schemes is sensitive to the users' data consumption utility function and the operator's network capacity. We provide some counter-intuitive insights. For example, when each user has a logarithmic utility function, the operator should apply the SUR scheme (i.e., reward both subscribers and non-subscribers) if and only if it has a small network capacity.
PacketCGAN: Exploratory Study of Class Imbalance for Encrypted Traffic Classification Using CGAN
Venue: arXiv cs.CR 2019
Year: 2019

With more and more adoption of Deep Learning (DL) in the field of image processing, computer vision and NLP, researchers have begun to apply DL to tackle with encrypted traffic classification problems. Although these methods can automatically extract traffic features to overcome the difficulty of traditional classification methods like DPI in terms of feature engineering, a large amount of data is needed to learn the characteristics of various types of traffic. Therefore, the performance of classification model always significantly depends on the quality of datasets. Nevertheless, the building of datasets is a time-consuming and costly task, especially encrypted traffic data. Apparently, it is often more difficult to collect a large amount of traffic samples of those unpopular encrypted applications than well-known, which leads to the problem of class imbalance between major and minor encrypted applications in datasets. In this paper, we proposed a novel traffic data augmenting method called PacketCGAN using Conditional GAN. As a generative model, PacketCGAN exploit the benefit of CGAN to generate specified traffic to address the problem of the datasets' imbalance. As a proof of concept, three classical DL models like Convolutional Neural Network (CNN) were adopted and designed to classify four encrypted traffic datasets augmented by Random Over Sampling (ROS), SMOTE(Synthetic Minority Over-sampling Techinique) , vanilla GAN and PacketCGAN respectively based on two public datasets: ISCX2012 and USTC-TFC2016. The experimental evaluation results demonstrate that DL based encrypted traffic classifier over dataset augmented by PacketCGAN can achieve better performance than the others.
Reviewing and Improving the Gaussian Mechanism for Differential Privacy
Venue: arXiv cs.CR 2019
Year: 2019

Differential privacy provides a rigorous framework to quantify data privacy, and has received considerable interest recently. A randomized mechanism satisfying $(ε, δ)$-differential privacy (DP) roughly means that, except with a small probability $δ$, altering a record in a dataset cannot change the probability that an output is seen by more than a multiplicative factor $e^ε $. A well-known solution to $(ε, δ)$-DP is the Gaussian mechanism initiated by Dwork et al. [1] in 2006 with an improvement by Dwork and Roth [2] in 2014, where a Gaussian noise amount $\sqrt{2\ln \frac{2}δ} \times \fracΔε$ of [1] or $\sqrt{2\ln \frac{1.25}δ} \times \fracΔε$ of [2] is added independently to each dimension of the query result, for a query with $\ell_2$-sensitivity $Δ$. Although both classical Gaussian mechanisms [1,2] assume $0 < ε\leq 1$, our review finds that many studies in the literature have used the classical Gaussian mechanisms under values of $ε$ and $δ$ where the added noise amounts of [1,2] do not achieve $(ε,δ)$-DP. We obtain such result by analyzing the optimal noise amount $σ_{DP-OPT}$ for $(ε,δ)$-DP and identifying $ε$ and $δ$ where the noise amounts of classical mechanisms are even less than $σ_{DP-OPT}$. Since $σ_{DP-OPT}$ has no closed-form expression and needs to be approximated in an iterative manner, we propose Gaussian mechanisms by deriving closed-form upper bounds for $σ_{DP-OPT}$. Our mechanisms achieve $(ε,δ)$-DP for any $ε$, while the classical mechanisms [1,2] do not achieve $(ε,δ)$-DP for large $ε$ given $δ$. Moreover, the utilities of our mechanisms improve those of [1,2] and are close to that of the optimal yet more computationally expensive Gaussian mechanism.
Token Bucket-based Throughput Constraining in Cross-layer Schedulers
Venue: arXiv cs.NI 2019
Year: 2019

In this paper we consider upper and lower constraining users' service rates in a slotted, cross-layer scheduler context. Such schedulers often cannot guarantee these bounds, despite the usefulness in adhering to Quality of Service (QoS) requirements, aiding the admission control system or providing different levels of service to users. We approach this problem with a low-complexity algorithm that is easily integrated in any utility function-based cross-layer scheduler. The algorithm modifies the weights of the associated Network Utility Maximization problem, rather than for example applying a token bucket to the scheduler's output or adding constraints in the physical layer. We study the efficacy of the algorithm through simulations with various schedulers from literature and mixes of traffic. The metrics we consider show that we can bound the average service rate within about five slots, for most schedulers. Schedulers whose weight is very volatile are more difficult to constrain.
Adaptive Initialization Method for K-means Algorithm
Venue: arXiv cs.LG 2019
Year: 2019

The K-means algorithm is a widely used clustering algorithm that offers simplicity and efficiency. However, the traditional K-means algorithm uses the random method to determine the initial cluster centers, which make clustering results prone to local optima and then result in worse clustering performance. Many initialization methods have been proposed, but none of them can dynamically adapt to datasets with various characteristics. In our previous research, an initialization method for K-means based on hybrid distance was proposed, and this algorithm can adapt to datasets with different characteristics. However, it has the following drawbacks: (a) When calculating density, the threshold cannot be uniquely determined, resulting in unstable results. (b) Heavily depending on adjusting the parameter, the parameter must be adjusted five times to obtain better clustering results. (c) The time complexity of the algorithm is quadratic, which is difficult to apply to large datasets. In the current paper, we proposed an adaptive initialization method for the K-means algorithm (AIMK) to improve our previous work. AIMK can not only adapt to datasets with various characteristics but also obtain better clustering results within two interactions. In addition, we then leverage random sampling in AIMK, which is named as AIMK-RS, to reduce the time complexity. AIMK-RS is easily applied to large and high-dimensional datasets. We compared AIMK and AIMK-RS with 10 different algorithms on 16 normal and six extra-large datasets. The experimental results show that AIMK and AIMK-RS outperform the current initialization methods and several well-known clustering algorithms. Furthermore, AIMK-RS can significantly reduce the complexity of applying it to extra-large datasets with high dimensions. The time complexity of AIMK-RS is O(n).
AdaSample: Adaptive Sampling of Hard Positives for Descriptor Learning
Venue: arXiv cs.CV 2019
Year: 2019

Triplet loss has been widely employed in a wide range of computer vision tasks, including local descriptor learning. The effectiveness of the triplet loss heavily relies on the triplet selection, in which a common practice is to first sample intra-class patches (positives) from the dataset for batch construction and then mine in-batch negatives to form triplets. For high-informativeness triplet collection, researchers mostly focus on mining hard negatives in the second stage, while paying relatively less attention to constructing informative batches. To alleviate this issue, we propose AdaSample, an adaptive online batch sampler, in this paper. Specifically, hard positives are sampled based on their informativeness. In this way, we formulate a hardness-aware positive mining pipeline within a novel maximum loss minimization training protocol. The efficacy of the proposed method is evaluated on several standard benchmarks, where it demonstrates a significant and consistent performance gain on top of the existing strong baselines.
Online Hashing with Efficient Updating of Binary Codes
Venue: arXiv cs.DS 2019
Year: 2019

Online hashing methods are efficient in learning the hash functions from the streaming data. However, when the hash functions change, the binary codes for the database have to be recomputed to guarantee the retrieval accuracy. Recomputing the binary codes by accumulating the whole database brings a timeliness challenge to the online retrieval process. In this paper, we propose a novel online hashing framework to update the binary codes efficiently without accumulating the whole database. In our framework, the hash functions are fixed and the projection functions are introduced to learn online from the streaming data. Therefore, inefficient updating of the binary codes by accumulating the whole database can be transformed to efficient updating of the binary codes by projecting the binary codes into another binary space. The queries and the binary code database are projected asymmetrically to further improve the retrieval accuracy. The experiments on two multi-label image databases demonstrate the effectiveness and the efficiency of our method for multi-label image retrieval.
Moving Fast and Breaking Things: How to stop crashing more than twice
Venue: arXiv cs.CR 2019
Year: 2019

"Moving fast, and breaking things", instead of "being safe and secure", is the credo of the IT industry. In this paper, we take a look at how we keep falling for the same security issues, and what we can learn from aviation safety to learn building and operating IT systems securely. We find that computer security should adopt the idea of safety. This entails not only building systems that are operating as desired in the presence of an active attacker, but also building them in a way that they remain secure and operational in the presence of any failure. Furthermore, we propose a 'clean slate policy design' to counter the current state of verbose, hardly followed best practices, together with an incident handling and reporting structure similar to that found in aviation safety.
ConCare: Personalized Clinical Feature Embedding via Capturing the Healthcare Context
Venue: arXiv cs.LG 2019
Year: 2019

Predicting the patient's clinical outcome from the historical electronic medical records (EMR) is a fundamental research problem in medical informatics. Most deep learning-based solutions for EMR analysis concentrate on learning the clinical visit embedding and exploring the relations between visits. Although those works have shown superior performances in healthcare prediction, they fail to explore the personal characteristics during the clinical visits thoroughly. Moreover, existing works usually assume that the more recent record weights more in the prediction, but this assumption is not suitable for all conditions. In this paper, we propose ConCare to handle the irregular EMR data and extract feature interrelationship to perform individualized healthcare prediction. Our solution can embed the feature sequences separately by modeling the time-aware distribution. ConCare further improves the multi-head self-attention via the cross-head decorrelation, so that the inter-dependencies among dynamic features and static baseline information can be effectively captured to form the personal health context. Experimental results on two real-world EMR datasets demonstrate the effectiveness of ConCare. The medical findings extracted by ConCare are also empirically confirmed by human experts and medical literature.
SAMSum Corpus: A Human-annotated Dialogue Dataset for Abstractive Summarization
Venue: arXiv cs.CL 2019
Year: 2019

This paper introduces the SAMSum Corpus, a new dataset with abstractive dialogue summaries. We investigate the challenges it poses for automated summarization by testing several models and comparing their results with those obtained on a corpus of news articles. We show that model-generated summaries of dialogues achieve higher ROUGE scores than the model-generated summaries of news -- in contrast with human evaluators' judgement. This suggests that a challenging task of abstractive dialogue summarization requires dedicated models and non-standard quality measures. To our knowledge, our study is the first attempt to introduce a high-quality chat-dialogues corpus, manually annotated with abstractive summarizations, which can be used by the research community for further studies.
Fooling with facts: Quantifying anchoring bias through a large-scale online experiment
Venue: arXiv cs.CY 2019
Year: 2019

Living in the 'Information Age' means that not only access to information has become easier but also that the distribution of information is more dynamic than ever. Through a large-scale online field experiment, we provide new empirical evidence for the presence of the anchoring bias in people's judgment due to irrational reliance on a piece of information that they are initially given. The comparison of the anchoring stimuli and respective responses across different tasks reveals a positive, yet complex relationship between the anchors and the bias in participants' predictions of the outcomes of events in the future. Participants in the treatment group were equally susceptible to the anchors regardless of their level of engagement, previous performance, or gender. Given the strong and ubiquitous influence of anchors quantified here, we should take great care to closely monitor and regulate the distribution of information online to facilitate less biased decision making.
ComHapDet: A Spatial Community Detection Algorithm for Haplotype Assembly
Venue: arXiv cs.SI 2019
Year: 2019

Background: Haplotypes, the ordered lists of single nucleotide variations that distinguish chromosomal sequences from their homologous pairs, may reveal an individual's susceptibility to hereditary and complex diseases and affect how our bodies respond to therapeutic drugs. Reconstructing haplotypes of an individual from short sequencing reads is an NP-hard problem that becomes even more challenging in the case of polyploids. While increasing lengths of sequencing reads and insert sizes {\color{black} helps improve accuracy of reconstruction}, it also exacerbates computational complexity of the haplotype assembly task. This has motivated the pursuit of algorithmic frameworks capable of accurate yet efficient assembly of haplotypes from high-throughput sequencing data. Results: We propose a novel graphical representation of sequencing reads and pose the haplotype assembly problem as an instance of community detection on a spatial random graph. To this end, we construct a graph where each read is a node with an unknown community label associating the read with the haplotype it samples. Haplotype reconstruction can then be thought of as a two-step procedure: first, one recovers the community labels on the nodes (i.e., the reads), and then uses the estimated labels to assemble the haplotypes. Based on this observation, we propose ComHapDet - a novel assembly algorithm for diploid and ployploid haplotypes which allows both bialleleic and multi-allelic variants. Conclusions: Performance of the proposed algorithm is benchmarked on simulated as well as experimental data obtained by sequencing Chromosome $5$ of tetraploid biallelic \emph{Solanum-Tuberosum} (Potato). The results demonstrate the efficacy of the proposed method and that it compares favorably with the existing techniques.
XSS Vulnerabilities in Cloud-Application Add-Ons
Venue: arXiv cs.CR 2019
Year: 2019

Cloud-application add-ons are microservices that extend the functionality of the core applications. Many application vendors have opened their APIs for third-party developers and created marketplaces for add-ons (also add-ins or apps). This is a relatively new phenomenon, and its effects on the application security have not been widely studied. It seems likely that some of the add-ons have lower code quality than the core applications themselves and, thus, may bring in security vulnerabilities. We found that many such add-ons are vulnerable to cross-site scripting (XSS). The attacker can take advantage of the document-sharing and messaging features of the cloud applications to send malicious input to them. The vulnerable add-ons then execute client-side JavaScript from the carefully crafted malicious input. In a major analysis effort, we systematically studied 300 add-ons for three popular application suites, namely Microsoft Office Online, G Suite and Shopify, and discovered a significant percentage of vulnerable add-ons in each marketplace. We present the results of this study, as well as analyze the add-on architectures to understand how the XSS vulnerabilities can be exploited and how the threat can be mitigated.
Single Machine Batch Scheduling to Minimize the Weighted Number of Tardy Jobs
Venue: arXiv cs.DS 2019
Year: 2019

The $1|B,r_j|\sum w_jU_j$ scheduling problem takes as input a batch setup time $Δ$ and a set of $n$ jobs, each having a processing time, a release date, a weight, and a due date; the task is to find a sequence of batches that minimizes the weighted number of tardy jobs. This problem was introduced by Hochbaum and Landy in 1994; as a wide generalization of {\sc Knapsack}, it is $\mathsf{NP}$-hard. In this work we provide a multivariate complexity analysis of the $1|B,r_j|\sum w_jU_j$ problem with respect to several natural parameters. That is, we establish a thorough classification into fixed-parameter tractable and $\mathsf{W}[1]$-hard problems, for parameter combinations of (i) $\#p$ = distinct number of processing times, (ii) $\#w$ = number of distinct weights, (iii) $\#d$ = number of distinct due dates, (iv) $\#r$ = number of distinct release dates, and (v) $b$ = batch sizes. Thereby, we significantly extend the work of Hermelin et al. (2018) who analyzed the parameterized complexity of the non-batch variant of this problem without release dates. As one of our key results, we prove that $1|B,r_j|\sum w_jU_j$ is $\mathsf{W}[1]$-hard parameterized by the number of distinct processing times and distinct due dates. To the best of our knowledge, these are the first parameterized intractability results for scheduling problems with few distinct processing times. Further, we show that $1|B,r_j|\sum w_jU_j$ is fixed-parameter tractable with respect to parameter $\#p+\#d+\#r$ and with respect to parameter $\#w+\#d$ if there is just a single release date. Both results hold even if the number of jobs per batch is limited by some integer $b$.
SimpleBooks: Long-term dependency book dataset with simplified English vocabulary for word-level language modeling
Venue: arXiv cs.CL 2019
Year: 2019

With language modeling becoming the popular base task for unsupervised representation learning in Natural Language Processing, it is important to come up with new architectures and techniques for faster and better training of language models. However, due to a peculiarity of languages -- the larger the dataset, the higher the average number of times a word appears in that dataset -- datasets of different sizes have very different properties. Architectures performing well on small datasets might not perform well on larger ones. For example, LSTM models perform well on WikiText-2 but poorly on WikiText-103, while Transformer models perform well on WikiText-103 but not on WikiText-2. For setups like architectural search, this is a challenge since it is prohibitively costly to run a search on the full dataset but it is not indicative to experiment on smaller ones. In this paper, we introduce SimpleBooks, a small dataset with the average word frequency as high as that of much larger ones. Created from 1,573 Gutenberg books with the highest ratio of word-level book length to vocabulary size, SimpleBooks contains 92M word-level tokens, on par with WikiText-103 (103M tokens), but has the vocabulary of 98K, a third of WikiText-103's. SimpleBooks can be downloaded from https://dldata-public.s3.us-east-2.amazonaws.com/simplebooks.zip.
Tree search algorithms for the Sequential Ordering Problem
Venue: arXiv cs.DM 2019
Year: 2019

We present a study of several generic tree search techniques applied to the Sequential Ordering Problem. This study enables us to propose a simple and competitive tree search algorithm. It consists of an iterative Beam Search algorithm that favors search over inference and integrates dynamic programming inspired cuts. It proves optimality on half of the SOPLIB instances and finds new best known solutions on 6 among 7 open instances of the benchmark in a small amount of time.