OpenRaiser/Intern-Atlas huggingface dataset:
Recent Trends on Privacy-Preserving Technologies under Standardization at the IETF
Venue: arXiv cs.CR 2023
Year: 2023

End-users are concerned about protecting the privacy of their sensitive personal data that are generated while working on information systems. This extends to both the data they actively provide including personal identification in exchange for products and services as well as its related metadata such as unnecessary access to their location. This is when certain privacy-preserving technologies come into a place where Internet Engineering Task Force (IETF) plays a major role in incorporating such technologies at the fundamental level. Thus, this paper offers an overview of the privacy-preserving mechanisms for layer 3 (i.e. IP) and above that are currently under standardization at the IETF. This includes encrypted DNS at layer 5 classified as DNS-over-TLS (DoT), DNS-over-HTTPS (DoH), and DNS-over-QUIC (DoQ) where the underlying technologies like QUIC belong to layer 4. Followed by that, we discuss Privacy Pass Protocol and its application in generating Private Access Tokens and Passkeys to replace passwords for authentication at the application layer (i.e. end-user devices). Lastly, to protect user privacy at the IP level, Private Relays and MASQUE are discussed. This aims to make designers, implementers, and users of the Internet aware of privacy-related design choices.
Translating Video Recordings of Complex Mobile App UI Gestures into Replayable Scenarios
Venue: arXiv cs.SE 2023
Year: 2023

Screen recordings of mobile applications are easy to obtain and capture a wealth of information pertinent to software developers (e.g., bugs or feature requests), making them a popular mechanism for crowdsourced app feedback. Thus, these videos are becoming a common artifact that developers must manage. In light of unique mobile development constraints, including swift release cycles and rapidly evolving platforms, automated techniques for analyzing all types of rich software artifacts provide benefit to mobile developers. Unfortunately, automatically analyzing screen recordings presents serious challenges, due to their graphical nature, compared to other types of (textual) artifacts. To address these challenges, this paper introduces V2S+, an automated approach for translating video recordings of Android app usages into replayable scenarios. V2S+ is based primarily on computer vision techniques and adapts recent solutions for object detection and image classification to detect and classify user gestures captured in a video, and convert these into a replayable test scenario. Given that V2S+ takes a computer vision-based approach, it is applicable to both hybrid and native Android applications. We performed an extensive evaluation of V2S+ involving 243 videos depicting 4,028 GUI-based actions collected from users exercising features and reproducing bugs from a collection of over 90 popular native and hybrid Android apps. Our results illustrate that V2S+ can accurately replay scenarios from screen recordings, and is capable of reproducing $\approx$ 90.2% of sequential actions recorded in native application scenarios on physical devices, and $\approx$ 83% of sequential actions recorded in hybrid application scenarios on emulators, both with low overhead. A case study with three industrial partners illustrates the potential usefulness of V2S+ from the viewpoint of developers.
Generative appearance replay for continual unsupervised domain adaptation
Venue: arXiv cs.CV 2023
Year: 2023

Deep learning models can achieve high accuracy when trained on large amounts of labeled data. However, real-world scenarios often involve several challenges: Training data may become available in installments, may originate from multiple different domains, and may not contain labels for training. Certain settings, for instance medical applications, often involve further restrictions that prohibit retention of previously seen data due to privacy regulations. In this work, to address such challenges, we study unsupervised segmentation in continual learning scenarios that involve domain shift. To that end, we introduce GarDA (Generative Appearance Replay for continual Domain Adaptation), a generative-replay based approach that can adapt a segmentation model sequentially to new domains with unlabeled data. In contrast to single-step unsupervised domain adaptation (UDA), continual adaptation to a sequence of domains enables leveraging and consolidation of information from multiple domains. Unlike previous approaches in incremental UDA, our method does not require access to previously seen data, making it applicable in many practical scenarios. We evaluate GarDA on two datasets with different organs and modalities, where it substantially outperforms existing techniques.
Comparison of tree-based ensemble algorithms for merging satellite and earth-observed precipitation data at the daily time scale
Venue: arXiv cs.LG 2023
Year: 2023

Merging satellite products and ground-based measurements is often required for obtaining precipitation datasets that simultaneously cover large regions with high density and are more accurate than pure satellite precipitation products. Machine and statistical learning regression algorithms are regularly utilized in this endeavour. At the same time, tree-based ensemble algorithms are adopted in various fields for solving regression problems with high accuracy and low computational cost. Still, information on which tree-based ensemble algorithm to select for correcting satellite precipitation products for the contiguous United States (US) at the daily time scale is missing from the literature. In this study, we worked towards filling this methodological gap by conducting an extensive comparison between three algorithms of the category of interest, specifically between random forests, gradient boosting machines (gbm) and extreme gradient boosting (XGBoost). We used daily data from the PERSIANN (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks) and the IMERG (Integrated Multi-satellitE Retrievals for GPM) gridded datasets. We also used earth-observed precipitation data from the Global Historical Climatology Network daily (GHCNd) database. The experiments referred to the entire contiguous US and additionally included the application of the linear regression algorithm for benchmarking purposes. The results suggest that XGBoost is the best-performing tree-based ensemble algorithm among those compared...
Unlocking Metaverse-as-a-Service The three pillars to watch: Privacy and Security, Edge Computing, and Blockchain
Venue: arXiv cs.CR 2023
Year: 2023

In this article, the authors provide a comprehensive overview on three core pillars of metaverse-as-a-service (MaaS) platforms; privacy and security, edge computing, and blockchain technology. The article starts by investigating security aspects for the wireless access to the metaverse. Then it goes through the privacy and security issues inside the metaverse from data-centric, learning-centric, and human-centric points-of-view. The authors address private and secure mechanisms for privatizing sensitive data attributes and securing machine learning algorithms running in a distributed manner within the metaverse platforms. Novel visions and less-investigated methods are reviewed to help mobile network operators and metaverse service providers facilitate the realization of secure and private MaaS through different layers of the metaverse, ranging from the access layer to the social interactions among clients. Later in the article, it has been explained how the paradigm of edge computing can strengthen different aspects of the metaverse. Along with that, the challenges of using edge computing in the metaverse have been comprehensively investigated. Additionally, the paper has comprehensively investigated and analyzed 10 main challenges of MaaS platforms and thoroughly discussed how blockchain technology provides solutions for these constraints. At the final, future vision and directions, such as content-centric security and zero-trust metaverse, some blockchain's unsolved challenges are also discussed to bring further insights for the network designers in the metaverse era.
Semantic Units: Organizing knowledge graphs into semantically meaningful units of representation
Venue: arXiv cs.DB 2023
Year: 2023

Knowledge graphs and ontologies are becoming increasingly important as technical solutions for Findable, Accessible, Interoperable, and Reusable data and metadata (FAIR Guiding Principles). We discuss four challenges that impede the use of FAIR knowledge graphs and propose semantic units as their potential solution. Semantic units structure a knowledge graph into identifiable and semantically meaningful subgraphs. Each unit is represented by its own resource, instantiates a corresponding semantic unit class, and can be implemented as a FAIR Digital Object and a nanopublication in RDF/OWL and property graphs. We distinguish statement and compound units as basic categories of semantic units. Statement units represent smallest, independent propositions that are semantically meaningful for a human reader. They consist of one or more triples and mathematically partition a knowledge graph. We distinguish assertional, contingent (prototypical), and universal statement units as basic types of statement units and propose representational schemes and formal semantics for them (including for absence statements, negations, and cardinality restrictions) that do not involve blank nodes and that translate back to OWL. Compound units, on the other hand, represent semantically meaningful collections of semantic units and we distinguish various types of compound units, representing different levels of representational granularity, different types of granularity trees, and different frames of reference. Semantic units support making statements about statements, can be used for graph-alignment, subgraph-matching, knowledge graph profiling, and for managing access restrictions to sensitive data. Organizing the graph into semantic units supports the separation of ontological, diagnostic (i.e., referential), and discursive information, and it also supports the differentiation of multiple frames of reference.
Automated Black-box Testing of Mass Assignment Vulnerabilities in RESTful APIs
Venue: arXiv cs.CR 2023
Year: 2023

Mass assignment is one of the most prominent vulnerabilities in RESTful APIs. This vulnerability originates from a misconfiguration in common web frameworks, such that naming convention and automatic binding can be exploited by an attacker to craft malicious requests writing confidential resources and (massively) overriding data, that should be read-only and/or confidential. In this paper, we adopt a black-box testing perspective to automatically detect mass assignment vulnerabilities in RESTful APIs. Execution scenarios are generated purely based on the OpenAPI specification, that lists the available operations and their message format. Clustering is used to group similar operations and reveal read-only fields, the latter are candidate for mass assignment. Then, interaction sequences are automatically generated by instantiating abstract testing templates, trying to exploit the potential vulnerabilities. Finally, test cases are run, and their execution is assessed by a specific oracle, in order to reveal whether the vulnerability could be successfully exploited. The proposed novel approach has been implemented and evaluated on a set of case studies written in different programming languages. The evaluation highlights that the approach is quite effective in detecting seeded vulnerabilities, with a remarkably high accuracy.
Average Is Not Enough: Caveats of Multilingual Evaluation
Venue: arXiv cs.CL 2023
Year: 2023

This position paper discusses the problem of multilingual evaluation. Using simple statistics, such as average language performance, might inject linguistic biases in favor of dominant language families into evaluation methodology. We argue that a qualitative analysis informed by comparative linguistics is needed for multilingual results to detect this kind of bias. We show in our case study that results in published works can indeed be linguistically biased and we demonstrate that visualization based on URIEL typological database can detect it.
Recent Advances on Federated Learning: A Systematic Survey
Venue: arXiv cs.LG 2023
Year: 2023

Federated learning has emerged as an effective paradigm to achieve privacy-preserving collaborative learning among different parties. Compared to traditional centralized learning that requires collecting data from each party, in federated learning, only the locally trained models or computed gradients are exchanged, without exposing any data information. As a result, it is able to protect privacy to some extent. In recent years, federated learning has become more and more prevalent and there have been many surveys for summarizing related methods in this hot research topic. However, most of them focus on a specific perspective or lack the latest research progress. In this paper, we provide a systematic survey on federated learning, aiming to review the recent advanced federated methods and applications from different aspects. Specifically, this paper includes four major contributions. First, we present a new taxonomy of federated learning in terms of the pipeline and challenges in federated scenarios. Second, we summarize federated learning methods into several categories and briefly introduce the state-of-the-art methods under these categories. Third, we overview some prevalent federated learning frameworks and introduce their features. Finally, some potential deficiencies of current methods and several future directions are discussed.
Towards Deployable RL -- What's Broken with RL Research and a Potential Fix
Venue: arXiv cs.LG 2023
Year: 2023

Reinforcement learning (RL) has demonstrated great potential, but is currently full of overhyping and pipe dreams. We point to some difficulties with current research which we feel are endemic to the direction taken by the community. To us, the current direction is not likely to lead to "deployable" RL: RL that works in practice and can work in practical situations yet still is economically viable. We also propose a potential fix to some of the difficulties of the field.
Optimal Decoy Resource Allocation for Proactive Defense in Probabilistic Attack Graphs
Venue: arXiv cs.MA 2023
Year: 2023

This paper investigates the problem of synthesizing proactive defense systems in which the defender can allocate deceptive targets and modify the cost of actions for the attacker who aims to compromise security assets in this system. We model the interaction of the attacker and the system using a formal security model -- a probabilistic attack graph. By allocating fake targets/decoys, the defender aims to distract the attacker from compromising true targets. By increasing the cost of some attack actions, the defender aims to discourage the attacker from committing to certain policies and thereby improve the defense. To optimize the defense given limited decoy resources and operational constraints, we formulate the synthesis problem as a bi-level optimization problem, while the defender designs the system, in anticipation of the attacker's best response given that the attacker has disinformation about the system due to the use of deception. Though the general formulation with bi-level optimization is NP-hard, we show that under certain assumptions, the problem can be transformed into a constrained optimization problem. We proposed an algorithm to approximately solve this constrained optimization problem using a novel incentive-design method for projected gradient ascent. We demonstrate the effectiveness of the proposed method using extensive numerical experiments.
An ensemble-based framework for mispronunciation detection of Arabic phonemes
Venue: arXiv cs.SD 2023
Year: 2023

Determination of mispronunciations and ensuring feedback to users are maintained by computer-assisted language learning (CALL) systems. In this work, we introduce an ensemble model that defines the mispronunciation of Arabic phonemes and assists learning of Arabic, effectively. To the best of our knowledge, this is the very first attempt to determine the mispronunciations of Arabic phonemes employing ensemble learning techniques and conventional machine learning models, comprehensively. In order to observe the effect of feature extraction techniques, mel-frequency cepstrum coefficients (MFCC), and Mel spectrogram are blended with each learning algorithm. To show the success of proposed model, 29 letters in the Arabic phonemes, 8 of which are hafiz, are voiced by a total of 11 different person. The amount of data set has been enhanced employing the methods of adding noise, time shifting, time stretching, pitch shifting. Extensive experiment results demonstrate that the utilization of voting classifier as an ensemble algorithm with Mel spectrogram feature extraction technique exhibits remarkable classification result with 95.9% of accuracy.
How to get the most out of Twinned Regression Methods
Venue: arXiv cs.LG 2023
Year: 2023

Twinned regression methods are designed to solve the dual problem to the original regression problem, predicting differences between regression targets rather then the targets themselves. A solution to the original regression problem can be obtained by ensembling predicted differences between the targets of an unknown data point and multiple known anchor data points. We explore different aspects of twinned regression methods: (1) We decompose different steps in twinned regression algorithms and examine their contributions to the final performance, (2) We examine the intrinsic ensemble quality, (3) We combine twin neural network regression with k-nearest neighbor regression to design a more accurate and efficient regression method, and (4) we develop a simplified semi-supervised regression scheme.
Kernel Subspace and Feature Extraction
Venue: arXiv cs.LG 2023
Year: 2023

We study kernel methods in machine learning from the perspective of feature subspace. We establish a one-to-one correspondence between feature subspaces and kernels and propose an information-theoretic measure for kernels. In particular, we construct a kernel from Hirschfeld--Gebelein--Rényi maximal correlation functions, coined the maximal correlation kernel, and demonstrate its information-theoretic optimality. We use the support vector machine (SVM) as an example to illustrate a connection between kernel methods and feature extraction approaches. We show that the kernel SVM on maximal correlation kernel achieves minimum prediction error. Finally, we interpret the Fisher kernel as a special maximal correlation kernel and establish its optimality.
A Pulse-Shape Binary Multiplex Modulation
Venue: arXiv cs.IT 2023
Year: 2023

The root raised-cosine pulse commonly used in linear digital modulations yields exactly two intersymbol interference components from the preceding and the subsequent data symbols, provided that the roll-off factor is $100\%$ and the modulation packing factor is set to $50\%$. This can be exploited to symmetrically multiplex two data streams of transmitted symbols. Hence, the proposed scheme is referred to as pulse-shape binary multiplex modulation. The demodulation of the two multiplexed data streams at the receiver can be aided by making the streams mutually orthogonal. It can be achieved by superposition modulation with symbol-by-symbol interference cancellation, proper design of transmission sequences interleaving pilot and data symbols in order to also enable channel estimation, and using orthogonal spreading sequences. The presented numerical results indicate that the proposed modulation scheme can outperform Nyquist signaling in terms of transmission reliability or the time required for transmitting the whole sequence of data symbols. For instance, differentially encoded modulation symbols can be transmitted twice as fast by the proposed modulation scheme with a 3 dB penalty in signal-to-noise ratio over additive white Gaussian noise channels.
Beckman Defense
Venue: arXiv cs.LG 2023
Year: 2023

Optimal transport (OT) based distributional robust optimisation (DRO) has received some traction in the recent past. However, it is at a nascent stage but has a sound potential in robustifying the deep learning models. Interestingly, OT barycenters demonstrate a good robustness against adversarial attacks. Owing to the computationally expensive nature of OT barycenters, they have not been investigated under DRO framework. In this work, we propose a new barycenter, namely Beckman barycenter, which can be computed efficiently and used for training the network to defend against adversarial attacks in conjunction with adversarial training. We propose a novel formulation of Beckman barycenter and analytically obtain the barycenter using the marginals of the input image. We show that the Beckman barycenter can be used to train adversarially trained networks to improve the robustness. Our training is extremely efficient as it requires only a single epoch of training. Elaborate experiments on CIFAR-10, CIFAR-100 and Tiny ImageNet demonstrate that training an adversarially robust network with Beckman barycenter can significantly increase the performance. Under auto attack, we get a a maximum boost of 10\% in CIFAR-10, 8.34\% in CIFAR-100 and 11.51\% in Tiny ImageNet. Our code is available at https://github.com/Visual-Conception-Group/test-barycentric-defense.
Envy-free dynamic pricing schemes
Venue: arXiv cs.GT 2023
Year: 2023

A combinatorial market consists of a set of indivisible items and a set of agents, where each agent has a valuation function that specifies for each subset of items its value for the given agent. From an optimization point of view, the goal is usually to determine a pair of pricing and allocation of the items that provides an efficient distribution of the resources, i.e., maximizes the social welfare, or is as profitable as possible for the seller, i.e., maximizes the revenue. To overcome the weaknesses of mechanisms operating with static prices, a recent line of research has concentrated on dynamic pricing schemes. In this model, agents arrive in an unspecified sequential order, and the prices can be updated between two agent-arrivals. Though the dynamic setting is capable of maximizing social welfare in various scenarios, the assumption that the agents arrive one after the other eliminates the standard concept of fairness. In this paper, we study the existence of optimal dynamic prices under fairness constraints in unit-demand markets. We propose four possible notions of envy-freeness of different strength depending on the time period over which agents compare themselves to others: the entire time horizon, only the past, only the future, or only the present. For social welfare maximization, while the first definition leads to Walrasian equilibria, we give polynomial-time algorithms that always find envy-free optimal dynamic prices in the remaining three cases. In contrast, for revenue maximization, we show that the corresponding problems are APX-hard if the ordering of the agents is fixed. On the positive side, we give polynomial-time algorithms for the setting when the seller can choose the order in which agents arrive.