OpenRaiser/Intern-Atlas huggingface dataset:
Routing in an Uncertain World: Adaptivity, Efficiency, and Equilibrium
Venue: arXiv cs.GT 2022
Year: 2022

We consider the traffic assignment problem in nonatomic routing games where the players' cost functions may be subject to random fluctuations (e.g., weather disturbances, perturbations in the underlying network, etc.). We tackle this problem from the viewpoint of a control interface that makes routing recommendations based solely on observed costs and without any further knowledge of the system's governing dynamics -- such as the network's cost functions, the distribution of any random events affecting the network, etc. In this online setting, learning methods based on the popular exponential weights algorithm converge to equilibrium at an $\mathcal{O}({1/\sqrt{T}})$ rate: this rate is known to be order-optimal in stochastic networks, but it is otherwise suboptimal in static networks. In the latter case, it is possible to achieve an $\mathcal{O}({1/T^{2}})$ equilibrium convergence rate via the use of finely tuned accelerated algorithms; on the other hand, these accelerated algorithms fail to converge altogether in the presence of persistent randomness, so it is not clear how to achieve the "best of both worlds" in terms of convergence speed. Our paper seeks to fill this gap by proposing an adaptive routing algortihm with the following desirable properties: $(i)$ it seamlessly interpolates between the $\mathcal{O}({1/T^{2}})$ and $\mathcal{O}({1/\sqrt{T}})$ rates for static and stochastic environments respectively; $(ii)$ its convergence speed is polylogarithmic in the number of paths in the network; ${(iii)}$ the method's per-iteration complexity and memory requirements are both linear in the number of nodes and edges in the network; and ${(iv)}$ it does not require any prior knowledge of the problem's parameters.
MaskMTL: Attribute prediction in masked facial images with deep multitask learning
Venue: arXiv cs.CV 2022
Year: 2022

Predicting attributes in the landmark free facial images is itself a challenging task which gets further complicated when the face gets occluded due to the usage of masks. Smart access control gates which utilize identity verification or the secure login to personal electronic gadgets may utilize face as a biometric trait. Particularly, the Covid-19 pandemic increasingly validates the essentiality of hygienic and contactless identity verification. In such cases, the usage of masks become more inevitable and performing attribute prediction helps in segregating the target vulnerable groups from community spread or ensuring social distancing for them in a collaborative environment. We create a masked face dataset by efficiently overlaying masks of different shape, size and textures to effectively model variability generated by wearing mask. This paper presents a deep Multi-Task Learning (MTL) approach to jointly estimate various heterogeneous attributes from a single masked facial image. Experimental results on benchmark face attribute UTKFace dataset demonstrate that the proposed approach supersedes in performance to other competing techniques. The source code is available at https://github.com/ritikajha/Attribute-prediction-in-masked-facial-images-with-deep-multitask-learning
Meta-Generalization for Multiparty Privacy Learning to Identify Anomaly Multimedia Traffic in Graynet
Venue: arXiv cs.CR 2022
Year: 2022

Identifying anomaly multimedia traffic in cyberspace is a big challenge in distributed service systems, multiple generation networks and future internet of everything. This letter explores meta-generalization for a multiparty privacy learning model in graynet to improve the performance of anomaly multimedia traffic identification. The multiparty privacy learning model in graynet is a globally shared model that is partitioned, distributed and trained by exchanging multiparty parameters updates with preserving private data. The meta-generalization refers to discovering the inherent attributes of a learning model to reduce its generalization error. In experiments, three meta-generalization principles are tested as follows. The generalization error of the multiparty privacy learning model in graynet is reduced by changing the dimension of byte-level imbedding. Following that, the error is reduced by adapting the depth for extracting packet-level features. Finally, the error is reduced by adjusting the size of support set for preprocessing traffic-level data. Experimental results demonstrate that the proposal outperforms the state-of-the-art learning models for identifying anomaly multimedia traffic.
The SEED Internet Emulator and Its Applications in Cybersecurity Education
Venue: arXiv cs.CR 2022
Year: 2022

In cybersecurity courses, it is quite challenging to do hands-on activities that involve many components of the Internet, such as bringing down the Internet, attacking a blockchain, etc. To solve this problem, we have developed an open-source Internet Emulator, which is a Python library, consisting of the classes for each essential element of the Internet, including autonomous system, network, host, router, BGP router, Internet exchange, etc. It also includes the classes for a variety of services, including Web server, DNS, Botnet, Darknet, Blockchain, and more are being developed. Using these classes, users can construct a mini-Internet to emulate the real-world Internet. Although it is small, it has all the essential elements of the real Internet. The construction is compiled into Docker container files, and the emulation is executed by Docker on a single machine, or on multiple cloud machines. With this Internet Emulator, we can develop a variety of hands-on activities for cybersecurity courses, including BGP prefix hijacking, attacks on smart contract, using Darknet to achieve anonymity, launching Botnet and ransomware attacks, etc. While the emulator was initially developed for cybersecurity courses, it can also be used for network courses, for students to learn how the Internet technologies work, such as routing, BGP, IP Anycast, and DNS. Many other interesting network technologies can also be deployed on the emulator, such as content delivery network and software-defined network.
Multi-Level Attention for Unsupervised Person Re-Identification
Venue: arXiv cs.CV 2022
Year: 2022

The attention mechanism is widely used in deep learning because of its excellent performance in neural networks without introducing additional information. However, in unsupervised person re-identification, the attention module represented by multi-headed self-attention suffers from attention spreading in the condition of non-ground truth. To solve this problem, we design pixel-level attention module to provide constraints for multi-headed self-attention. Meanwhile, for the trait that the identification targets of person re-identification data are all pedestrians in the samples, we design domain-level attention module to provide more comprehensive pedestrian features. We combine head-level, pixel-level and domain-level attention to propose multi-level attention block and validate its performance on for large person re-identification datasets (Market-1501, DukeMTMC-reID and MSMT17 and PersonX).
Supervised Contrastive Learning for Recommendation
Venue: arXiv cs.IR 2022
Year: 2022

In this work, we aim to consider the application of contrastive learning in the scenario of the recommendation system adequately, making it more suitable for recommendation task. We propose a learning paradigm called supervised contrastive learning(SCL) to support the graph convolutional neural network. Specifically, we will calculate the similarity between different nodes in user side and item side respectively during data preprocessing, and then when applying contrastive learning, not only will the augmented views be regarded as the positive samples, but also a certain number of similar samples will be regarded as the positive samples, which is different with SimCLR that treats other samples in a batch as negative samples. We apply SCL on the most advanced LightGCN. In addition, in order to consider the uncertainty of node interaction, we also propose a new data augment method called node replication. Empirical research and ablation study on Gowalla, Yelp2018, Amazon-Book datasets prove the effectiveness of SCL and node replication, which improve the accuracy of recommendations and robustness to interactive noise.
Effective Representation to Capture Collaboration Behaviors between Explainer and User
Venue: arXiv cs.HC 2022
Year: 2022

An explainable AI (XAI) model aims to provide transparency (in the form of justification, explanation, etc) for its predictions or actions made by it. Recently, there has been a lot of focus on building XAI models, especially to provide explanations for understanding and interpreting the predictions made by deep learning models. At UCLA, we propose a generic framework to interact with an XAI model in natural language.
GridTuner: Reinvestigate Grid Size Selection for Spatiotemporal Prediction Models [Technical Report]
Venue: arXiv cs.DB 2022
Year: 2022

With the development of traffic prediction technology, spatiotemporal prediction models have attracted more and more attention from academia communities and industry. However, most existing researches focus on reducing model's prediction error but ignore the error caused by the uneven distribution of spatial events within a region. In this paper, we study a region partitioning problem, namely optimal grid size selection problem (OGSS), which aims to minimize the real error of spatiotemporal prediction models by selecting the optimal grid size. In order to solve OGSS, we analyze the upper bound of real error of spatiotemporal prediction models and minimize the real error by minimizing its upper bound. Through in-depth analysis, we find that the upper bound of real error will decrease then increase when the number of model grids increase from 1 to the maximum allowed value. Then, we propose two algorithms, namely Ternary Search and Iterative Method, to automatically find the optimal grid size. Finally, the experiments verify that the error of prediction has the same trend as its upper bound, and the change trend of the upper bound of real error with respect to the increase of the number of model grids will decrease then increase. Meanwhile, in a case study, by selecting the optimal grid size, the order dispatching results of a state-of-the-art prediction-based algorithm can be improved up to 13.6%, which shows the effectiveness of our methods on tuning the region partition for spatiotemporal prediction models.
An open tool based on lifex for myofibers generation in cardiac computational models
Venue: arXiv cs.MS 2022
Year: 2022

Modeling the whole cardiac function involves the solution of several complex multi-physics and multi-scale models that are highly computationally demanding, which call for simpler yet accurate, high-performance computational tools. Despite the efforts made by several research groups, no software for whole-heart fully-coupled cardiac simulations in the scientific community has reached full maturity yet. In this work we present the first publicly released package of lifex, a high-performance Finite Element solver for multi-physics and multi-scale problems developed in the framework of the iHEART project. The goal of lifex is twofold. On the one side, it aims at making in silico experiments easily reproducible and accessible to a wide community of users, including those with a background in medicine or bio-engineering. On the other hand, as an academic research library lifex can be exploited by scientific computing experts to explore new mathematical models and numerical methods within a robust development framework. lifex has been developed with a modular structure and will be released bundled in different modules. The tool presented here proposes an innovative generator for myocardial fibers based on Laplace-Dirichlet Rule-Based Methods, which are the essential building blocks for modeling the electrophysiological, mechanical and electromechanical cardiac function, from single-chamber to whole-heart simulations. This report comes with an extensive technical and mathematical documentation to welcome new users to the core structure of a prototypical lifex application and to provide them with a possible approach to include the generated cardiac fibers into more sophisticated computational pipelines.
Social Media Networks Attacks and their Preventive Mechanisms: A Review
Venue: arXiv cs.SI 2022
Year: 2022

We live in a virtual world where actual lifestyles are replicated. The growing reliance on the use of social media networks worldwide has resulted in great concern for information security. One of the factors popularizing the social media platforms is how they connect people worldwide to interact, share content, and engage in mutual interactions of common interest that cut across geographical boundaries. Behind all these incredible gains are digital crime equivalence that threatens the physical socialization. Criminal-minded elements and hackers are exploiting Social Media Platforms (SMP) for many nefarious activities to harm others. As detection tools are developed to control these crimes, hackers' tactics and techniques are constantly evolving. Hackers are constantly developing new attacking tools and hacking strategies to gain malicious access to systems and attack social media network thereby making it difficult for security administrators and organizations to develop and implement the proper policies and procedures necessary to prevent the hackers' attacks. The increase in cyber-attacks on the social media platforms calls for urgent and more intelligent security measures to enhance the effectiveness of social media platforms. This paper explores the mode and tactics of hackers' mode of attacks on social media and ways of preventing their activities against users to ensure secure social cyberspace and enhance virtual socialization. Social media platforms are briefly categorized, the various types of attacks are also highlighted with current state-of-the-art preventive mechanisms to overcome the attacks as proposed in research works, finally, social media intrusion detection mechanism is suggested as a second line of defense to combat cybercrime on social media networks
Fiuncho: a program for any-order epistasis detection in CPU clusters
Venue: arXiv cs.DC 2022
Year: 2022

Epistasis can be defined as the statistical interaction of genes during the expression of a phenotype. It is believed that it plays a fundamental role in gene expression, as individual genetic variants have reported a very small increase in disease risk in previous Genome-Wide Association Studies. The most successful approach to epistasis detection is the exhaustive method, although its exponential time complexity requires a highly parallel implementation in order to be used. This work presents Fiuncho, a program that exploits all levels of parallelism present in \textit{x86\_64} CPU clusters in order to mitigate the complexity of this approach. It supports epistasis interactions of any order, and when compared with other exhaustive methods, it is on average 358, 7 and 3 times faster than MDR, MPI3SNP and BitEpi, respectively.
Morphological Analysis of Japanese Hiragana Sentences using the BI-LSTM CRF Model
Venue: arXiv cs.CL 2022
Year: 2022

This study proposes a method to develop neural models of the morphological analyzer for Japanese Hiragana sentences using the Bi-LSTM CRF model. Morphological analysis is a technique that divides text data into words and assigns information such as parts of speech. This technique plays an essential role in downstream applications in Japanese natural language processing systems because the Japanese language does not have word delimiters between words. Hiragana is a type of Japanese phonogramic characters, which is used for texts for children or people who cannot read Chinese characters. Morphological analysis of Hiragana sentences is more difficult than that of ordinary Japanese sentences because there is less information for dividing. For morphological analysis of Hiragana sentences, we demonstrated the effectiveness of fine-tuning using a model based on ordinary Japanese text and examined the influence of training data on texts of various genres.
QoS and Resource aware Security Orchestration System
Venue: arXiv cs.NI 2022
Year: 2022

Network Function Virtualization (NFV) and Software Distributed Networking (SDN) technologies play a crucial role in enabling 5G system and beyond. A synergy between these both technologies has been identified for enabling a new concept dubbed service function chains (SFC) that aims to reduce both the capital expenditures (CAPEX) and operating expenses (OPEX). The SFC paradigm considers different constraints and key performance indicators (KPIs), that includes QoS and different resources, for enabling network slice services. However, the large-scale, complexity and security issues brought by these technologies create an extra overhead for ensuring secure network slicing. To cope with these challenges, this paper proposes a cost-efficient optimized SFC management system that enables the creation of SFCs for enabling efficient and secure network slices. The proposed system considers the network and computational resources and current network security levels to ensure trusted deployments. The simulation results demonstrated the efficiency of the proposed solution for achieving its designed objectives. The proposed solution efficiently manages the SFCs by optimizing deployment costs and reducing overall end-to-end delay
Spectrum Sharing for Secrecy Performance Enhancement in D2D-enabled UAV Networks
Venue: arXiv cs.NI 2022
Year: 2022

With the assistance of device-to-device (D2D) communications, unmanned aerial vehicle (UAV) networks are anticipated to support widespread applications in the fifth generation (5G) and beyond wireless systems, by providing seamless coverage, flexible deployment, and high channel rate. However, the networks face significant security threats from malicious eavesdroppers due to the inherent broadcast and openness nature of wireless channels. To ensure secure communications of such networks, physical layer security is a promising technique, which utilizes the randomness and noise of wireless channels to enhance secrecy performance. This article investigates physical layer security performance via spectrum sharing in D2D-enabled UAV networks. We first present two typical network architectures where each UAV serves as either a flying base station or an aerial user equipment. Then, we propose a spectrum sharing strategy to fully exploit interference incurred by spectrum reuse for improving secrecy performance. We further conduct two case studies to evaluate the spectrum sharing strategy in these two typical network architectures, and also show secrecy performance gains compared to traditional spectrum sharing strategy. Finally, we discuss some future research directions in D2D-enabled UAV networks.
Enhancing Selective Encryption for H.264/AVC Using Advanced Encryption Standard
Venue: arXiv cs.CR 2022
Year: 2022

Multimedia information availability has increased dramatically with the advent of mobile devices. but with this availability comes problems of maintaining the security of information that is displayed in public. Many approaches have been used or proposed for providing security for information dissemination over networks, protection system classified with more specific as encryption information, and combination between video compression and encryption to increase information security. The strength of the combination between Video compression and encryption science is due to the nonexistence of standard algorithms to be used in video compression and encrypting secret video stream. Also there are many ways could be used in video encryption methods such as combining several different encryption methods with video compression to pass a secret video streaming. Furthermore, there is no formal method to be followed to discover an encrypted video. For this reason, the task of this paper becomes difficult. In this paper proposed a new system of video encryption is presented. The proposed system aim to gain a deep understanding of video data security on multimedia technologies, to investigate how encryption and decryption could be implemented for real time video applications, and to enhance the selective encryption for H.264/AVC. The system includes two main functions; first is the encoding/encryption of video stream, through the execution of two processes (the input sequences of video is first compressed by the H.264/AVC encoder, and the encoded bit stream (I-frame) is partially encrypted using AES block cipher). And the second function is the decryption/decoding of the encrypted video through two process (specify the encrypted I-frame stream, decryption of the I-frame, and decoding with H.264/AVC decoder). The system is implemented by using Matlab.
Multiplayer Performative Prediction: Learning in Decision-Dependent Games
Venue: arXiv cs.GT 2022
Year: 2022

Learning problems commonly exhibit an interesting feedback mechanism wherein the population data reacts to competing decision makers' actions. This paper formulates a new game theoretic framework for this phenomenon, called "multi-player performative prediction". We focus on two distinct solution concepts, namely (i) performatively stable equilibria and (ii) Nash equilibria of the game. The latter equilibria are arguably more informative, but can be found efficiently only when the game is monotone. We show that under mild assumptions, the performatively stable equilibria can be found efficiently by a variety of algorithms, including repeated retraining and the repeated (stochastic) gradient method. We then establish transparent sufficient conditions for strong monotonicity of the game and use them to develop algorithms for finding Nash equilibria. We investigate derivative free methods and adaptive gradient algorithms wherein each player alternates between learning a parametric description of their distribution and gradient steps on the empirical risk. Synthetic and semi-synthetic numerical experiments illustrate the results.
Optimizing Diffusion Rate and Label Reliability in a Graph-Based Semi-supervised Classifier
Venue: arXiv cs.LG 2022
Year: 2022

Semi-supervised learning has received attention from researchers, as it allows one to exploit the structure of unlabeled data to achieve competitive classification results with much fewer labels than supervised approaches. The Local and Global Consistency (LGC) algorithm is one of the most well-known graph-based semi-supervised (GSSL) classifiers. Notably, its solution can be written as a linear combination of the known labels. The coefficients of this linear combination depend on a parameter $α$, determining the decay of the reward over time when reaching labeled vertices in a random walk. In this work, we discuss how removing the self-influence of a labeled instance may be beneficial, and how it relates to leave-one-out error. Moreover, we propose to minimize this leave-one-out loss with automatic differentiation. Within this framework, we propose methods to estimate label reliability and diffusion rate. Optimizing the diffusion rate is more efficiently accomplished with a spectral representation. Results show that the label reliability approach competes with robust L1-norm methods and that removing diagonal entries reduces the risk of overfitting and leads to suitable criteria for parameter selection.