OpenRaiser/Intern-Atlas huggingface dataset:
Follow the Scent: Defeating IPv6 Prefix Rotation Privacy
Venue: arXiv cs.NI 2021
Year: 2021

IPv6's large address space allows ample freedom for choosing and assigning addresses. To improve client privacy and resist IP-based tracking, standardized techniques leverage this large address space, including privacy extensions and provider prefix rotation. Ephemeral and dynamic IPv6 addresses confound not only tracking and traffic correlation attempts, but also traditional network measurements, logging, and defense mechanisms. We show that the intended anti-tracking capability of these widely deployed mechanisms is unwittingly subverted by edge routers using legacy IPv6 addressing schemes that embed unique identifiers. We develop measurement techniques that exploit these legacy devices to make tracking such moving IPv6 clients feasible by combining intelligent search space reduction with modern high-speed active probing. Via an Internet-wide measurement campaign, we discover more than 9M affected edge routers and approximately 13k /48 prefixes employing prefix rotation in hundreds of ASes worldwide. We mount a six-week campaign to characterize the size and dynamics of these deployed IPv6 rotation pools, and demonstrate via a case study the ability to remotely track client address movements over time. We responsibly disclosed our findings to equipment manufacturers, at least one of which subsequently changed their default addressing logic.
The minimum linear locality of linear codes
Venue: arXiv cs.IT 2021
Year: 2021

Locally recoverable codes (LRCs) were proposed for the recovery of data in distributed and cloud storage systems about nine years ago. A lot of progress on the study of LRCs has been made by now. However, there is a lack of general theory on the minimum linear locality of linear codes. In addition, the minimum linear locality of many known families of linear codes is not studied in the literature. Motivated by these two facts, this paper develops some general theory about the minimum linear locality of linear codes, and investigates the minimum linear locality of a number of families of linear codes, such as $q$-ary Hamming codes, $q$-ary Simplex codes, generalized Reed-Muller codes, ovoid codes, maximum arc codes, the extended hyperoval codes, and near MDS codes. Many classes of both distance-optimal and dimension-optimal LRCs are presented in this paper. The minimum linear locality of many families of linear codes are settled with the general theory developed in this paper.
An Exhaustive Survey on P4 Programmable Data Plane Switches: Taxonomy, Applications, Challenges, and Future Trends
Venue: arXiv cs.NI 2021
Year: 2021

Traditionally, the data plane has been designed with fixed functions to forward packets using a small set of protocols. This closed-design paradigm has limited the capability of the switches to proprietary implementations which are hardcoded by vendors, inducing a lengthy, costly, and inflexible process. Recently, data plane programmability has attracted significant attention from both the research community and the industry, permitting operators and programmers in general to run customized packet processing function. This open-design paradigm is paving the way for an unprecedented wave of innovation and experimentation by reducing the time of designing, testing, and adopting new protocols; enabling a customized, top-down approach to develop network applications; providing granular visibility of packet events defined by the programmer; reducing complexity and enhancing resource utilization of the programmable switches; and drastically improving the performance of applications that are offloaded to the data plane. Despite the impressive advantages of programmable data plane switches and their importance in modern networks, the literature has been missing a comprehensive survey. To this end, this paper provides a background encompassing an overview of the evolution of networks from legacy to programmable, describing the essentials of programmable switches, and summarizing their advantages over Software-defined Networking (SDN) and legacy devices. The paper then presents a unique, comprehensive taxonomy of applications developed with P4 language; surveying, classifying, and analyzing more than 150 articles; discussing challenges and considerations; and presenting future perspectives and open research issues.
Optimizing Video Caching at the Edge: A Hybrid Multi-Point Process Approach
Venue: arXiv cs.MM 2021
Year: 2021

It is always a challenging problem to deliver a huge volume of videos over the Internet. To meet the high bandwidth and stringent playback demand, one feasible solution is to cache video contents on edge servers based on predicted video popularity. Traditional caching algorithms (e.g., LRU, LFU) are too simple to capture the dynamics of video popularity, especially long-tailed videos. Recent learning-driven caching algorithms (e.g., DeepCache) show promising performance, however, such black-box approaches are lack of explainability and interpretability. Moreover, the parameter tuning requires a large number of historical records, which are difficult to obtain for videos with low popularity. In this paper, we optimize video caching at the edge using a white-box approach, which is highly efficient and also completely explainable. To accurately capture the evolution of video popularity, we develop a mathematical model called \emph{HRS} model, which is the combination of multiple point processes, including Hawkes' self-exciting, reactive and self-correcting processes. The key advantage of the HRS model is its explainability, and much less number of model parameters. In addition, all its model parameters can be learned automatically through maximizing the Log-likelihood function constructed by past video request events. Next, we further design an online HRS-based video caching algorithm. To verify its effectiveness, we conduct a series of experiments using real video traces collected from Tencent Video, one of the largest online video providers in China. Experiment results demonstrate that our proposed algorithm outperforms the state-of-the-art algorithms, with 12.3\% improvement on average in terms of cache hit rate under realistic settings.
Probabilistic Learning Vector Quantization on Manifold of Symmetric Positive Definite Matrices
Venue: arXiv cs.LG 2021
Year: 2021

In this paper, we develop a new classification method for manifold-valued data in the framework of probabilistic learning vector quantization. In many classification scenarios, the data can be naturally represented by symmetric positive definite matrices, which are inherently points that live on a curved Riemannian manifold. Due to the non-Euclidean geometry of Riemannian manifolds, traditional Euclidean machine learning algorithms yield poor results on such data. In this paper, we generalize the probabilistic learning vector quantization algorithm for data points living on the manifold of symmetric positive definite matrices equipped with Riemannian natural metric (affine-invariant metric). By exploiting the induced Riemannian distance, we derive the probabilistic learning Riemannian space quantization algorithm, obtaining the learning rule through Riemannian gradient descent. Empirical investigations on synthetic data, image data , and motor imagery EEG data demonstrate the superior performance of the proposed method.
Autonomous Navigation through intersections with Graph ConvolutionalNetworks and Conditional Imitation Learning for Self-driving Cars
Venue: arXiv cs.RO 2021
Year: 2021

In autonomous driving, navigation through unsignaled intersections with many traffic participants moving around is a challenging task. To provide a solution to this problem, we propose a novel branched network G-CIL for the navigation policy learning. Specifically, we firstly represent such dynamic environments as graph-structured data and propose an effective strategy for edge definition to aggregate surrounding information for the ego-vehicle. Then graph convolutional neural networks are used as the perception module to capture global and geometric features from the environment. To generate safe and efficient navigation policy, we further incorporate it with conditional imitation learning algorithm, to learn driving behaviors directly from expert demonstrations. Our proposed network is capable of handling a varying number of surrounding vehicles and generating optimal control actions (e.g., steering angle and throttle) according to the given high-level commands (e.g., turn left towards the global goal). Evaluations on unsignaled intersections with various traffic densities demonstrate that our end-to-end trainable neural network outperforms the baselines with higher success rate and shorter navigation time.
A NIR-to-VIS face recognition via part adaptive and relation attention module
Venue: arXiv cs.CV 2021
Year: 2021

In the face recognition application scenario, we need to process facial images captured in various conditions, such as at night by near-infrared (NIR) surveillance cameras. The illumination difference between NIR and visible-light (VIS) causes a domain gap between facial images, and the variations in pose and emotion also make facial matching more difficult. Heterogeneous face recognition (HFR) has difficulties in domain discrepancy, and many studies have focused on extracting domain-invariant features, such as facial part relational information. However, when pose variation occurs, the facial component position changes, and a different part relation is extracted. In this paper, we propose a part relation attention module that crops facial parts obtained through a semantic mask and performs relational modeling using each of these representative features. Furthermore, we suggest component adaptive triplet loss function using adaptive weights for each part to reduce the intra-class identity regardless of the domain as well as pose. Finally, our method exhibits a performance improvement in the CASIA NIR-VIS 2.0 and achieves superior result in the BUAA-VisNir with large pose and emotion variations.
New Formulation for Coloring Circle Graphs and its Application to Capacitated Stowage Stack Minimization
Venue: arXiv cs.DM 2021
Year: 2021

A circle graph is a graph in which the adjacency of vertices can be represented as the intersection of chords of a circle. The problem of calculating the chromatic number is known to be NP-complete, even on circle graphs. In this paper, we propose a new integer linear programming formulation for a coloring problem on circle graphs. We also show that the linear relaxation problem of our formulation finds the fractional chromatic number of a given circle graph. As a byproduct, our formulation gives a polynomial-sized linear programming formulation for calculating the fractional chromatic number of a circle graph. We also extend our result to a formulation for a capacitated stowage stack minimization problem.
Zero-Error Sum Modulo Two with a Common Observation
Venue: arXiv cs.IT 2021
Year: 2021

This paper investigates the classical modulo two sum problem in source coding, but with a common observation: a transmitter observes $(X,Z)$, the other transmitter observes $(Y,Z)$, and the receiver wants to compute $X \oplus Y$ without error. Through a coupling argument, this paper establishes a new lower bound on the sum-rate when $X-Z-Y$ forms a Markov chain.
Towards Explainable Exploratory Landscape Analysis: Extreme Feature Selection for Classifying BBOB Functions
Venue: arXiv cs.NE 2021
Year: 2021

Facilitated by the recent advances of Machine Learning (ML), the automated design of optimization heuristics is currently shaking up evolutionary computation (EC). Where the design of hand-picked guidelines for choosing a most suitable heuristic has long dominated research activities in the field, automatically trained heuristics are now seen to outperform human-derived choices even for well-researched optimization tasks. ML-based EC is therefore not any more a futuristic vision, but has become an integral part of our community. A key criticism that ML-based heuristics are often faced with is their potential lack of explainability, which may hinder future developments. This applies in particular to supervised learning techniques which extrapolate algorithms' performance based on exploratory landscape analysis (ELA). In such applications, it is not uncommon to use dozens of problem features to build the models underlying the specific algorithm selection or configuration task. Our goal in this work is to analyze whether this many features are indeed needed. Using the classification of the BBOB test functions as testbed, we show that a surprisingly small number of features -- often less than four -- can suffice to achieve a 98\% accuracy. Interestingly, the number of features required to meet this threshold is found to decrease with the problem dimension. We show that the classification accuracy transfers to settings in which several instances are involved in training and testing. In the leave-one-instance-out setting, however, classification accuracy drops significantly, and the transformation-invariance of the features becomes a decisive success factor.
Quantum Fair Machine Learning
Venue: arXiv cs.LG 2021
Year: 2021

In this paper, we inaugurate the field of quantum fair machine learning. We undertake a comparative analysis of differences and similarities between classical and quantum fair machine learning algorithms, specifying how the unique features of quantum computation alter measures, metrics and remediation strategies when quantum algorithms are subject to fairness constraints. We present the first results in quantum fair machine learning by demonstrating the use of Grover's search algorithm to satisfy statistical parity constraints imposed on quantum algorithms. We provide lower-bounds on iterations needed to achieve such statistical parity within $ε$-tolerance. We extend canonical Lipschitz-conditioned individual fairness criteria to the quantum setting using quantum metrics. We examine the consequences for typical measures of fairness in machine learning context when quantum information processing and quantum data are involved. Finally, we propose open questions and research programmes for this new field of interest to researchers in computer science, ethics and quantum computation.
Joint Beamforming and Phase Optimization in a Multi-user Communication System Composed of Dual Reconfigurable Intelligent Surfaces
Venue: arXiv cs.IT 2021
Year: 2021

Reconfigurable Intelligent Surface (RIS) has becoming a useful tool in future wireless communication systems for close-distance communication network. This paper we use Reconfigurable Intelligent Surface (RIS) for downlink multi-user communication designed to improve energy collection performance while satisfying wireless information and Power Transfer (WIPT). The designed system consists of an IRS-assisted system consists of a multi-antenna assisted base station (BS) and two opposite multi-antenna assisted information receiver cooperated (RIS) as energy receiver (ERs) that meets energy collection requirements. Based on the electromagnetic property of Reconfigurable Intelligent Surface (RIS), like two mirrors that are opposite each other, setting two Reconfigurable Intelligent Surface (RIS) attached to the city buildings to reflect the sending signals. The transmitting precoding of the Multi-antenna Auxiliary Base Station (BS) and the angular phase transfer matrix of the multi-antenna Auxiliary Information Receiver (IRs) need to be optimized together to maximize the energy harvesting of IoT devices for energy efficiency (EE) of the IRs system and to provide users with the efficiency of the received signal. In order to solve the joint optimization problem effectively, we turn the non-convex maximize problem into the equivalent formal error method based on the mean square, and finally use the iterative algorithm for optimization. As for algorithm, we respectively use MSE method, semidefinite relaxation techniques to simplify transmitting beamforming matrix and the matrix phase shift. Through the observation of simulation data, it can be concluded that the performance optimization method of SDR based on RIS is effective.
Segmenting Microcalcifications in Mammograms and its Applications
Venue: arXiv cs.CV 2021
Year: 2021

Microcalcifications are small deposits of calcium that appear in mammograms as bright white specks on the soft tissue background of the breast. Microcalcifications may be a unique indication for Ductal Carcinoma in Situ breast cancer, and therefore their accurate detection is crucial for diagnosis and screening. Manual detection of these tiny calcium residues in mammograms is both time-consuming and error-prone, even for expert radiologists, since these microcalcifications are small and can be easily missed. Existing computerized algorithms for detecting and segmenting microcalcifications tend to suffer from a high false-positive rate, hindering their widespread use. In this paper, we propose an accurate calcification segmentation method using deep learning. We specifically address the challenge of keeping the false positive rate low by suggesting a strategy for focusing the hard pixels in the training phase. Furthermore, our accurate segmentation enables extracting meaningful statistics on clusters of microcalcifications.
Automated Query Reformulation for Efficient Search based on Query Logs From Stack Overflow
Venue: arXiv cs.SE 2021
Year: 2021

As a popular Q&A site for programming, Stack Overflow is a treasure for developers. However, the amount of questions and answers on Stack Overflow make it difficult for developers to efficiently locate the information they are looking for. There are two gaps leading to poor search results: the gap between the user's intention and the textual query, and the semantic gap between the query and the post content. Therefore, developers have to constantly reformulate their queries by correcting misspelled words, adding limitations to certain programming languages or platforms, etc. As query reformulation is tedious for developers, especially for novices, we propose an automated software-specific query reformulation approach based on deep learning. With query logs provided by Stack Overflow, we construct a large-scale query reformulation corpus, including the original queries and corresponding reformulated ones. Our approach trains a Transformer model that can automatically generate candidate reformulated queries when given the user's original query. The evaluation results show that our approach outperforms five state-of-the-art baselines, and achieves a 5.6% to 33.5% boost in terms of $\mathit{ExactMatch}$ and a 4.8% to 14.4% boost in terms of $\mathit{GLEU}$.
Counterfactual Planning in AGI Systems
Venue: arXiv cs.AI 2021
Year: 2021

We present counterfactual planning as a design approach for creating a range of safety mechanisms that can be applied in hypothetical future AI systems which have Artificial General Intelligence. The key step in counterfactual planning is to use an AGI machine learning system to construct a counterfactual world model, designed to be different from the real world the system is in. A counterfactual planning agent determines the action that best maximizes expected utility in this counterfactual planning world, and then performs the same action in the real world. We use counterfactual planning to construct an AGI agent emergency stop button, and a safety interlock that will automatically stop the agent before it undergoes an intelligence explosion. We also construct an agent with an input terminal that can be used by humans to iteratively improve the agent's reward function, where the incentive for the agent to manipulate this improvement process is suppressed. As an example of counterfactual planning in a non-agent AGI system, we construct a counterfactual oracle. As a design approach, counterfactual planning is built around the use of a graphical notation for defining mathematical counterfactuals. This two-diagram notation also provides a compact and readable language for reasoning about the complex types of self-referencing and indirect representation which are typically present inside machine learning agents.
Crawling political communities in Twitter and extracting political affiliations
Venue: arXiv cs.SI 2021
Year: 2021

In theory, a major advantage to the big data approach in studying online communities is that it should be possible to collect a representative random sample from a broadly defined population. However, in practice, data collection processes are not formalized, even for famous social media platforms such as Twitter and Facebook. As a result, there is ambiguity left on questions such as "how much data is enough?" and how representative are the samples of the broader population being studied in online social networks. In this paper, I propose a focused back-and-forth crawl approach and a validated seed choice method for collecting network-level data from Twitter. The proposed crawl method can extract community structures without needing a complete network graph for the Twitter network and validate its size using "reference score". It also takes care of the sampling size problem in Twitter by tracking the percentage of known nodes that have been included in the data. Thus, solving most major problems in Twitter data collection procedures and moving a step further to formalizing data collection methods for the platform. Once the communities are crawled, and the network graph is clean and complete; it is then possible to train Machine Learning classifiers using communities as features to predict political affiliations of users on a larger scale. As a case, I used the proposed method for separating French political communities on Twitter from the global Twitter community and knowing the political affiliations of users on a continuous scale.
Hierarchical Variational Autoencoder for Visual Counterfactuals
Venue: arXiv cs.LG 2021
Year: 2021

Conditional Variational Auto Encoders (VAE) are gathering significant attention as an Explainable Artificial Intelligence (XAI) tool. The codes in the latent space provide a theoretically sound way to produce counterfactuals, i.e. alterations resulting from an intervention on a targeted semantic feature. To be applied on real images more complex models are needed, such as Hierarchical CVAE. This comes with a challenge as the naive conditioning is no longer effective. In this paper we show how relaxing the effect of the posterior leads to successful counterfactuals and we introduce VAEX an Hierarchical VAE designed for this approach that can visually audit a classifier in applications.