OpenRaiser/Intern-Atlas huggingface dataset:
Moving the Network to the Cloud: the Cloud Central Office Revolution and its Implications for the Optical Layer
Venue: arXiv cs.NI 2021
Year: 2021

SDN and NFV have recently changed the way we operate networks. By decoupling control and data plane operations and virtualising their components, they have opened up new frontiers towards reducing network ownership costs and improving usability and efficiency. Recently, their applicability has moved towards public telecommunications networks, with concepts such as the cloud-CO that have pioneered its use in access and metro networks: an idea that has quickly attracted the interest of network operators. By merging mobile, residential and enterprise services into a common framework, built around commoditised data centre types of architectures, future embodiments of this CO virtualisation concept could achieve significant capital and operational cost savings, while providing customised network experience to high-capacity and low-latency future applications. This tutorial provides an overview of the various frameworks and architectures outlining current network disaggregation trends that are leading to the virtualisation/cloudification of central offices. It also provides insight on the virtualisation of the access-metro network, showcasing new software functionalities like the virtual \ac{DBA} mechanisms for \acp{PON}. In addition, we explore how it can bring together different network technologies to enable convergence of mobile and optical access networks and pave the way for the integration of disaggregated ROADM networks. Finally, this paper discusses some of the open challenges towards the realisation of networks capable of delivering guaranteed performance, while sharing resources across multiple operators and services.
Mathematical Models for Local Sensing Hashes
Venue: arXiv cs.LG 2021
Year: 2021

As data volumes continue to grow, searches in data are becoming increasingly time-consuming. Classical index structures for neighbor search are no longer sustainable due to the "curse of dimensionality". Instead, approximated index structures offer a good opportunity to significantly accelerate the neighbor search for clustering and outlier detection and to have the lowest possible error rate in the results of the algorithms. Local sensing hashes is one of those. We indicate directions to mathematically model the properties of it.
On The Number of Different Entries in Involutory MDS Matrices over Finite Fields of Characteristic Two
Venue: arXiv cs.IT 2021
Year: 2021

Two of many criteria of a good MDS matrix are being involutory and having few different elements. This paper investigates the number of different entries in an involutory MDS matrices of order 1, 2, 3, and 4 over finite fields of characteristic two. There are at least three and four different elements in an involutory MDS matrices with, respectively, order three and four, over finite fields of characteristic two.
Spatial machine-learning model diagnostics: a model-agnostic distance-based approach
Venue: arXiv cs.LG 2021
Year: 2021

While significant progress has been made towards explaining black-box machine-learning (ML) models, there is still a distinct lack of diagnostic tools that elucidate the spatial behaviour of ML models in terms of predictive skill and variable importance. This contribution proposes spatial prediction error profiles (SPEPs) and spatial variable importance profiles (SVIPs) as novel model-agnostic assessment and interpretation tools for spatial prediction models with a focus on prediction distance. Their suitability is demonstrated in two case studies representing a regionalization task in an environmental-science context, and a classification task from remotely-sensed land cover classification. In these case studies, the SPEPs and SVIPs of geostatistical methods, linear models, random forest, and hybrid algorithms show striking differences but also relevant similarities. Limitations of related cross-validation techniques are outlined, and the case is made that modelers should focus their model assessment and interpretation on the intended spatial prediction horizon. The range of autocorrelation, in contrast, is not a suitable criterion for defining spatial cross-validation test sets. The novel diagnostic tools enrich the toolkit of spatial data science, and may improve ML model interpretation, selection, and design.
Comparing Cross Correlation-Based Similarities
Venue: arXiv cs.LG 2021
Year: 2021

The real-valued Jaccard and coincidence indices, in addition to their conceptual and computational simplicity, have been verified to be able to provide promising results in tasks such as template matching, tending to yield peaks that are sharper and narrower than those typically obtained by standard cross-correlation, while also attenuating substantially secondary matchings. In this work, the multiset-based correlations based on the real-valued multiset Jaccard and coincidence indices are compared from the perspective of template matching, with encouraging results which have implications for pattern recognition, deep learning, and scientific modeling in general. The multiset-based correlation methods, and especially the coincidence index, presented remarkable performance characterized by sharper and narrower peaks while secondary peaks were attenuated, which was maintained even in presence of intense levels of noise. In particular, the two methods derived from the coincidence index led to particularly interesting results. The cross correlation, however, presented the best robustness to symmetric additive noise, which suggested a new combination of the considered approaches. After a preliminary investigation of the relative performance of the multiset approaches, as well as the classic cross-correlation, a systematic comparison framework is proposed and applied for the study of the aforementioned methods. Several results are reported, including the confirmation, at least for the considered type of data, of the coincidence correlation as providing enhanced performance regarding detection of narrow, sharp peaks while secondary matches are duly attenuated. The combined method also resulted promising for dealing with signals in presence of intense additive noise.
Local News Online and COVID in the U.S.: Relationships among Coverage, Cases, Deaths, and Audience
Venue: arXiv cs.SI 2021
Year: 2021

We present analyses from a real-time information monitoring system of online local news in the U.S. We study relationships among online local news coverage of COVID, cases and deaths in an area, and properties of local news outlets and their audiences. Our analysis relies on a unique dataset of the online content of over 300 local news outlets, encompassing over 750,000 articles over a period of 10 months spanning April 2020 to February 2021. We find that the rate of COVID coverage over time by local news outlets was primarily associated with death rates at the national level, but that this effect dissipated over the course of the pandemic as news about COVID was steadily displaced by sociopolitical events, like the 2020 U.S. elections. We also find that both the volume and content of COVID coverage differed depending on local politics, and outlet audience size, as well as evidence that more vulnerable populations received less pandemic-related news.
WikiContradiction: Detecting Self-Contradiction Articles on Wikipedia
Venue: arXiv cs.CL 2021
Year: 2021

While Wikipedia has been utilized for fact-checking and claim verification to debunk misinformation and disinformation, it is essential to either improve article quality and rule out noisy articles. Self-contradiction is one of the low-quality article types in Wikipedia. In this work, we propose a task of detecting self-contradiction articles in Wikipedia. Based on the "self-contradictory" template, we create a novel dataset for the self-contradiction detection task. Conventional contradiction detection focuses on comparing pairs of sentences or claims, but self-contradiction detection needs to further reason the semantics of an article and simultaneously learn the contradiction-aware comparison from all pairs of sentences. Therefore, we present the first model, Pairwise Contradiction Neural Network (PCNN), to not only effectively identify self-contradiction articles, but also highlight the most contradiction pairs of contradiction sentences. The main idea of PCNN is two-fold. First, to mitigate the effect of data scarcity on self-contradiction articles, we pre-train the module of pairwise contradiction learning using SNLI and MNLI benchmarks. Second, we select top-K sentence pairs with the highest contradiction probability values and model their correlation to determine whether the corresponding article belongs to self-contradiction. Experiments conducted on the proposed WikiContradiction dataset exhibit that PCNN can generate promising performance and comprehensively highlight the sentence pairs the contradiction locates.
Remote Memory-Deduplication Attacks
Venue: arXiv cs.CR 2021
Year: 2021

Memory utilization can be reduced by merging identical memory blocks into copy-on-write mappings. Previous work showed that this so-called memory deduplication can be exploited in local attacks to break ASLR, spy on other programs,and determine the presence of data, i.e., website images. All these attacks exploit memory deduplication across security domains, which in turn was disabled. However, within a security domain or on an isolated system with no untrusted local access, memory deduplication is still not considered a security risk and was recently re-enabled on Windows by default. In this paper, we present the first fully remote memorydeduplication attacks. Unlike previous attacks, our attacks require no local code execution. Consequently, we can disclose memory contents from a remote server merely by sending and timing HTTP/1 and HTTP/2 network requests. We demonstrate our attacks on deduplication both on Windows and Linux and attack widely used server software such as Memcached and InnoDB. Our side channel leaks up to 34.41 B/h over the internet, making it faster than comparable remote memory-disclosure channels. We showcase our remote memory-deduplication attack in three case studies: First, we show that an attacker can disclose the presence of data in memory on a server running Memcached. We show that this information disclosure channel can also be used for fingerprinting and detect the correct libc version over the internet in 166.51 s. Second, in combination with InnoDB, we present an information disclosure attack to leak MariaDB database records. Third, we demonstrate a fully remote KASLR break in less than 4 minutes allowing to derandomize the kernel image of a virtual machine over the Internet, i.e., 14 network hops away. We conclude that memory deduplication must also be considered a security risk if only applied within a single security domain.
Joint Learning of Visual-Audio Saliency Prediction and Sound Source Localization on Multi-face Videos
Venue: arXiv cs.CV 2021
Year: 2021

Visual and audio events simultaneously occur and both attract attention. However, most existing saliency prediction works ignore the influence of audio and only consider vision modality. In this paper, we propose a multitask learning method for visual-audio saliency prediction and sound source localization on multi-face video by leveraging visual, audio and face information. Specifically, we first introduce a large-scale database of multi-face video in visual-audio condition (MVVA), containing eye-tracking data and sound source annotations. Using this database, we find that sound influences human attention, and conversly attention offers a cue to determine sound source on multi-face video. Guided by these findings, a visual-audio multi-task network (VAM-Net) is introduced to predict saliency and locate sound source. VAM-Net consists of three branches corresponding to visual, audio and face modalities. Visual branch has a two-stream architecture to capture spatial and temporal information. Face and audio branches encode audio signals and faces, respectively. Finally, a spatio-temporal multi-modal graph (STMG) is constructed to model the interaction among multiple faces. With joint optimization of these branches, the intrinsic correlation of the tasks of saliency prediction and sound source localization is utilized and their performance is boosted by each other. Experiments show that the proposed method outperforms 12 state-of-the-art saliency prediction methods, and achieves competitive results in sound source localization.
Engineering Edge-Cloud Offloading of Big Data for Channel Modelling in THz-range Communications
Venue: arXiv cs.NI 2021
Year: 2021

Channel estimation in mmWave and THz-range wireless communications (producing Gb/Tb-range of data) is critical to configuring system parameters related to transmission signal quality, and yet it remains a daunting challenge both in software and hardware. Current methods of channel estimations, be it modeling- or data-based (machine learning (ML)), - use and create big data. This in turn requires a large amount of computational resources, read operations to prove if there is some predefined channel configurations, e.g., QoS requirements, in the database, as well as write operations to store the new combinations of QoS parameters in the database. Especially the ML-based approach requires high computational and storage resources, low latency and a higher hardware flexibility. In this paper, we engineer and study the offloading of the above operations to edge and cloud computing systems to understand the suitability of edge and cloud computing to provide rapid response with channel and link configuration parameters on the example of THz channel modeling. We evaluate the performance of the engineered system when the computational and storage resources are orchestrated based on: 1) monolithic architecture, 2) microservices architectures, both in edge-cloud based approach. For microservices approach, we engineer both Docker Swarm and Kubernetes systems. The measurements show a great promise of edge computing and microservices that can quickly respond to properly configure parameters and improve transmission distance and signal quality with ultra-high speed wireless communications.
Quality Measures in Biometric Systems
Venue: arXiv cs.CV 2021
Year: 2021

Biometric technology has been increasingly deployed in the past decade, offering greater security and convenience than traditional methods of personal recognition. Although biometric signals' quality heavily affects a biometric system's performance, prior research on evaluating quality is limited. Quality is a critical issue in security, especially in adverse scenarios involving surveillance cameras, forensics, portable devices, or remote access through the Internet. This article analyzes what factors negatively impact biometric quality, how to overcome them, and how to incorporate quality measures into biometric systems. A review of the state of the art in these matters gives an overall framework for the challenges of biometric quality.
Learning Provably Robust Motion Planners Using Funnel Libraries
Venue: arXiv cs.RO 2021
Year: 2021

This paper presents an approach for learning motion planners that are accompanied with probabilistic guarantees of success on new environments that hold uniformly for any disturbance to the robot's dynamics within an admissible set. We achieve this by bringing together tools from generalization theory and robust control. First, we curate a library of motion primitives where the robustness of each primitive is characterized by an over-approximation of the forward reachable set, i.e., a "funnel". Then, we optimize probably approximately correct (PAC)-Bayes generalization bounds for training our planner to compose these primitives such that the entire funnels respect the problem specification. We demonstrate the ability of our approach to provide strong guarantees on two simulated examples: (i) navigation of an autonomous vehicle under external disturbances on a five-lane highway with multiple vehicles, and (ii) navigation of a drone across an obstacle field in the presence of wind disturbances.
Computer Vision for Supporting Image Search
Venue: arXiv cs.CV 2021
Year: 2021

Computer vision and multimedia information processing have made extreme progress within the last decade and many tasks can be done with a level of accuracy as if done by humans, or better. This is because we leverage the benefits of huge amounts of data available for training, we have enormous computer processing available and we have seen the evolution of machine learning as a suite of techniques to process data and deliver accurate vision-based systems. What kind of applications do we use this processing for ? We use this in autonomous vehicle navigation or in security applications, searching CCTV for example, and in medical image analysis for healthcare diagnostics. One application which is not widespread is image or video search directly by users. In this paper we present the need for such image finding or re-finding by examining human memory and when it fails, thus motivating the need for a different approach to image search which is outlined, along with the requirements of computer vision to support it.
Compressive Features in Offline Reinforcement Learning for Recommender Systems
Venue: arXiv cs.AI 2021
Year: 2021

In this paper, we develop a recommender system for a game that suggests potential items to players based on their interactive behaviors to maximize revenue for the game provider. Our approach is built on a reinforcement learning-based technique and is trained on an offline data set that is publicly available on an IEEE Big Data Cup challenge. The limitation of the offline data set and the curse of high dimensionality pose significant obstacles to solving this problem. Our proposed method focuses on improving the total rewards and performance by tackling these main difficulties. More specifically, we utilized sparse PCA to extract important features of user behaviors. Our Q-learning-based system is then trained from the processed offline data set. To exploit all possible information from the provided data set, we cluster user features to different groups and build an independent Q-table for each group. Furthermore, to tackle the challenge of unknown formula for evaluation metrics, we design a metric to self-evaluate our system's performance based on the potential value the game provider might achieve and a small collection of actual evaluation metrics that we obtain from the live scoring environment. Our experiments show that our proposed metric is consistent with the results published by the challenge organizers. We have implemented the proposed training pipeline, and the results show that our method outperforms current state-of-the-art methods in terms of both total rewards and training speed. By addressing the main challenges and leveraging the state-of-the-art techniques, we have achieved the best public leaderboard result in the challenge. Furthermore, our proposed method achieved an estimated score of approximately 20% better and can be trained faster by 30 times than the best of the current state-of-the-art methods.
A Benchmark for Modeling Violation-of-Expectation in Physical Reasoning Across Event Categories
Venue: arXiv cs.CV 2021
Year: 2021

Recent work in computer vision and cognitive reasoning has given rise to an increasing adoption of the Violation-of-Expectation (VoE) paradigm in synthetic datasets. Inspired by infant psychology, researchers are now evaluating a model's ability to label scenes as either expected or surprising with knowledge of only expected scenes. However, existing VoE-based 3D datasets in physical reasoning provide mainly vision data with little to no heuristics or inductive biases. Cognitive models of physical reasoning reveal infants create high-level abstract representations of objects and interactions. Capitalizing on this knowledge, we established a benchmark to study physical reasoning by curating a novel large-scale synthetic 3D VoE dataset armed with ground-truth heuristic labels of causally relevant features and rules. To validate our dataset in five event categories of physical reasoning, we benchmarked and analyzed human performance. We also proposed the Object File Physical Reasoning Network (OFPR-Net) which exploits the dataset's novel heuristics to outperform our baseline and ablation models. The OFPR-Net is also flexible in learning an alternate physical reality, showcasing its ability to learn universal causal relationships in physical reasoning to create systems with better interpretability.
Privacy Guarantees of BLE Contact Tracing: A Case Study on COVIDWISE
Venue: arXiv cs.CR 2021
Year: 2021

Google and Apple jointly introduced a digital contact tracing technology and an API called "exposure notification," to help health organizations and governments with contact tracing. The technology and its interplay with security and privacy constraints require investigation. In this study, we examine and analyze the security, privacy, and reliability of the technology with actual and typical scenarios (and expected typical adversary in mind), and quite realistic use cases. We do it in the context of Virginia's COVIDWISE app. This experimental analysis validates the properties of the system under the above conditions, a result that seems crucial for the peace of mind of the exposure notification technology adopting authorities, and may also help with the system's transparency and overall user trust.
CONFAIR: Configurable and Interpretable Algorithmic Fairness
Venue: arXiv cs.LG 2021
Year: 2021

The rapid growth of data in the recent years has led to the development of complex learning algorithms that are often used to make decisions in real world. While the positive impact of the algorithms has been tremendous, there is a need to mitigate any bias arising from either training samples or implicit assumptions made about the data samples. This need becomes critical when algorithms are used in automated decision making systems that can hugely impact people's lives. Many approaches have been proposed to make learning algorithms fair by detecting and mitigating bias in different stages of optimization. However, due to a lack of a universal definition of fairness, these algorithms optimize for a particular interpretation of fairness which makes them limited for real world use. Moreover, an underlying assumption that is common to all algorithms is the apparent equivalence of achieving fairness and removing bias. In other words, there is no user defined criteria that can be incorporated into the optimization procedure for producing a fair algorithm. Motivated by these shortcomings of existing methods, we propose the CONFAIR procedure that produces a fair algorithm by incorporating user constraints into the optimization procedure. Furthermore, we make the process interpretable by estimating the most predictive features from data. We demonstrate the efficacy of our approach on several real world datasets using different fairness criteria.