OpenRaiser/Intern-Atlas huggingface dataset:
Translate your gibberish: black-box adversarial attack on machine translation systems
Venue: arXiv cs.CL 2023
Year: 2023

Neural networks are deployed widely in natural language processing tasks on the industrial scale, and perhaps the most often they are used as compounds of automatic machine translation systems. In this work, we present a simple approach to fool state-of-the-art machine translation tools in the task of translation from Russian to English and vice versa. Using a novel black-box gradient-free tensor-based optimizer, we show that many online translation tools, such as Google, DeepL, and Yandex, may both produce wrong or offensive translations for nonsensical adversarial input queries and refuse to translate seemingly benign input phrases. This vulnerability may interfere with understanding a new language and simply worsen the user's experience while using machine translation systems, and, hence, additional improvements of these tools are required to establish better translation.
Less is More: Towards Lightweight Cost Estimator for Database Systems
Venue: arXiv cs.DB 2023
Year: 2023

We present FasCo, a simple yet effective learning-based estimator for the cost of executing a database query plan. FasCo uses significantly shorter training time and a lower inference cost than the state-of-the-art approaches, while achieving higher estimation accuracy. The effectiveness of FasCo comes from embedding abundant explicit execution-plan-based features and incorporating a novel technique called cardinality calibration. Extensive experimental results show that FasCo achieves orders of magnitude higher efficiency than the state-of-the-art methods: on the JOB-M benchmark dataset, it cuts off training plans by 98\%, reducing training time from more than two days to about eight minutes while entailing better accuracy. Furthermore, in dynamic environments, FasCo can maintain satisfactory accuracy even without retraining, narrowing the gap between learning-based estimators and real systems.
A Survey on Oversmoothing in Graph Neural Networks
Venue: arXiv cs.LG 2023
Year: 2023

Node features of graph neural networks (GNNs) tend to become more similar with the increase of the network depth. This effect is known as over-smoothing, which we axiomatically define as the exponential convergence of suitable similarity measures on the node features. Our definition unifies previous approaches and gives rise to new quantitative measures of over-smoothing. Moreover, we empirically demonstrate this behavior for several over-smoothing measures on different graphs (small-, medium-, and large-scale). We also review several approaches for mitigating over-smoothing and empirically test their effectiveness on real-world graph datasets. Through illustrative examples, we demonstrate that mitigating over-smoothing is a necessary but not sufficient condition for building deep GNNs that are expressive on a wide range of graph learning tasks. Finally, we extend our definition of over-smoothing to the rapidly emerging field of continuous-time GNNs.
Real-Time Parallel Programming: State of Play and Open Issues
Venue: arXiv cs.DC 2023
Year: 2023

Real-time systems applications usually consist of a set of concurrent activities with timing-related properties. Developing these applications requires programming paradigms that can effectively handle the specification of concurrent activities and timing constraints, as well as controlling their execution on a particular platform. The prevailing trend for high-performance, and the use of fine-grained parallel execution makes this an even more challenging task. This paper provides an overview of the state-of-the-art and challenges for the development of real-time parallel applications, focusing on two current research directions, one from the high-performance arena (based on OpenMP) and another from the real-time and critical systems domain (based on Ada). The paper provides a review of the supported features of each one of the approaches, identifying the still open issues and the current research directions.
Differentially Private Algorithms for Synthetic Power System Datasets
Venue: arXiv cs.CR 2023
Year: 2023

While power systems research relies on the availability of real-world network datasets, data owners (e.g., system operators) are hesitant to share data due to security and privacy risks. To control these risks, we develop privacy-preserving algorithms for the synthetic generation of optimization and machine learning datasets. Taking a real-world dataset as input, the algorithms output its noisy, synthetic version, which preserves the accuracy of the real data on a specific downstream model or even a large population of those. We control the privacy loss using Laplace and Exponential mechanisms of differential privacy and preserve data accuracy using a post-processing convex optimization. We apply the algorithms to generate synthetic network parameters and wind power data.
Deep-Learning Aided Channel Training and Precoding in FDD Massive MIMO with Channel Statistics Knowledge
Venue: arXiv cs.IT 2023
Year: 2023

We propose a method for channel training and precoding in FDD massive MIMO based on deep neural networks (DNNs), exploiting Downlink (DL) channel covariance knowledge. The DNN is optimized to maximize the DL multi-user sum-rate, by producing a pre-beamforming matrix based on user channel covariances that maps the original channel vectors to effective channels. Measurements of these effective channels are received at the users via common pilot transmission and sent back to the base station (BS) through analog feedback without further processing. The BS estimates the effective channels from received feedback and constructs a linear precoder by concatenating the optimized pre-beamforming matrix with a zero-forcing precoder over the effective channels. We show that the proposed method yields significantly higher sum-rates than the state-of-the-art DNN-based channel training and precoding scheme, especially in scenarios with small pilot and feedback size relative to the channel coherence block length. Unlike many works in the literature, our proposition does not involve deployment of a DNN at the user side, which typically comes at a high computational cost and parameter-transmission overhead on the system, and is therefore considerably more practical.
Towards Sustainable DevOps: A Decision Making Framework
Venue: arXiv cs.SE 2023
Year: 2023

In software industry, the DevOps is an increasingly adopting software development paradigm. Towards the sustainable DevOps adoption, there is a need to transform the organization Culture, Automation, Measurement and Sharing (CAMS) aspects concerning to core theme of continues development and operations. The software organizations face several complexities while implementing the DevOps principles. The sustainable DevOps implementation assist the software organizations to develop the quality projects with good return on investment. This evidence-based study aims to explore the guidelines of sustainable DevOps implementation, reported in literature and industry practices. Using systematic literature review and questionnaire survey, we identified the 48 guidelines for sustainable DevOps implementation. We further develop a decision-making framework aiming to assist the practitioners to consider the most significant set of guidelines on priority. The results show that out of CAMS, culture is the most important principle for sustainable DevOps implementation. Moreover, (i) enterprises should focus on building a collaborative culture with shared goals, (ii) assess your organization readiness to utilize a microservices architecture and (iii) educate executives at your company about the benefits of DevOps to gain resource and budget support are the highest priority guidelines for sustainable DevOps implementation. We believe that this in-depth study helps the practitioners to understand the core principles and guidelines for sustainable DevOps implementation.
Randomized Rumor Spreading Revisited (Long Version)
Venue: arXiv cs.DC 2023
Year: 2023

We develop a simple and generic method to analyze randomized rumor spreading processes in fully connected networks. In contrast to all previous works, which heavily exploit the precise definition of the process under investigation, we only need to understand the probability and the covariance of the events that uninformed nodes become informed. This universality allows us to easily analyze the classic push, pull, and push-pull protocols both in their pure version and in several variations such as messages failing with constant probability or nodes calling a random number of others each round. Some dynamic models can be analyzed as well, e.g., when the network is a $G(n,p)$ random graph sampled independently each round [Clementi et al. (ESA 2013)]. Despite this generality, our method determines the expected rumor spreading time precisely apart from additive constants, which is more precise than almost all previous works. We also prove tail bounds showing that a deviation from the expectation by more than an additive number of $r$ rounds occurs with probability at most $\exp(-Ω(r))$. We further use our method to discuss the common assumption that nodes can answer any number of incoming calls. We observe that the restriction that only one call can be answered leads to a significant increase of the runtime of the push-pull protocol. In particular, the double logarithmic end phase of the process now takes logarithmic time. This also increases the message complexity from the asymptotically optimal $Θ(n \log\log n)$ [Karp, Shenker, Schindelhauer, Vöcking (FOCS 2000)] to $Θ(n \log n)$. We propose a simple variation of the push-pull protocol that reverts back to the double logarithmic end phase and thus to the $Θ(n \log\log n)$ message complexity.
Explaining the Performance of Collaborative Filtering Methods With Optimal Data Characteristics
Venue: arXiv cs.IR 2023
Year: 2023

The performance of a Collaborative Filtering (CF) method is based on the properties of a User-Item Rating Matrix (URM). And the properties or Rating Data Characteristics (RDC) of a URM are constantly changing. Recent studies significantly explained the variation in the performances of CF methods resulted due to the change in URM using six or more RDC. Here, we found that the significant proportion of variation in the performances of different CF techniques can be accounted to two RDC only. The two RDC are the number of ratings per user or Information per User (IpU) and the number of ratings per item or Information per Item (IpI). And the performances of CF algorithms are quadratic to IpU (or IpI) for a square URM. The findings of this study are based on seven well-established CF methods and three popular public recommender datasets: 1M MovieLens, 25M MovieLens, and Yahoo! Music Rating datasets
Shedding Light on Static Partitioning Hypervisors for Arm-based Mixed-Criticality Systems
Venue: arXiv cs.OS 2023
Year: 2023

In this paper, we aim to understand the properties and guarantees of static partitioning hypervisors (SPH) for Arm-based mixed-criticality systems (MCS). To this end, we performed a comprehensive empirical evaluation of popular open-source SPH, i.e., Jailhouse, Xen (Dom0-less), Bao, and seL4 CAmkES VMM, focusing on two key requirements of modern MCS: real-time and safety. The goal of this study is twofold. Firstly, to empower industrial practitioners with hard data to reason about the different trade-offs of SPH. Secondly, we aim to raise awareness of the research and open-source communities to the still open problems in SPH by unveiling new insights regarding lingering weaknesses. All artifacts will be open-sourced to enable independent validation of results and encourage further exploration on SPH.
A Unified Framework of Policy Learning for Contextual Bandit with Confounding Bias and Missing Observations
Venue: arXiv cs.LG 2023
Year: 2023

We study the offline contextual bandit problem, where we aim to acquire an optimal policy using observational data. However, this data usually contains two deficiencies: (i) some variables that confound actions are not observed, and (ii) missing observations exist in the collected data. Unobserved confounders lead to a confounding bias and missing observations cause bias and inefficiency problems. To overcome these challenges and learn the optimal policy from the observed dataset, we present a new algorithm called Causal-Adjusted Pessimistic (CAP) policy learning, which forms the reward function as the solution of an integral equation system, builds a confidence set, and greedily takes action with pessimism. With mild assumptions on the data, we develop an upper bound to the suboptimality of CAP for the offline contextual bandit problem.
On new infinite families of completely regular and completely transitive codes
Venue: arXiv cs.IT 2023
Year: 2023

In two previous papers we constructed new families of completely regular codes by concatenation methods. Here we determine cases in which the new codes are completely transitive. For these cases we also find the automorphism groups of such codes. For the remaining cases, we show that the codes are not completely transitive assuming an upper bound on the order of the monomial automorphism groups, according to computational results.
A set of semantic data flow diagrams and its security analysis based on ontologies and knowledge graphs
Venue: arXiv cs.CR 2023
Year: 2023

For a long time threat modeling was treated as a manual, complicated process. However modern agile development methodologies and cloud computing technologies require adding automatic threat modeling approaches. This work considers two challenges: creating a set of machine-readable data flow diagrams that represent real cloud based applications; and usage domain specific knowledge for automatic analysis of the security aspects of such applications. The set of 180 semantic diagrams (ontologies and knowledge graphs) is created based on cloud configurations (Docker Compose); the set includes a manual taxonomy that allows to define the design and functional aspects of the web based and data processing applications; the set can be used for various research in the threat modeling field. This work also evaluates how ontologies and knowledge graphs can be used to automatically recognize patterns (mapped to security threats) in diagrams. A pattern represents features of a diagram in form of a request to a knowledge base, what enables its recognition in a semantic representation of a diagram. In an experiment four groups of the patterns are created (web applications, data processing, network, and docker specific), and the diagrams are examined by the patterns. Automatic results, received for the web applications and data processing patterns, are compared with the manual taxonomy in order to study challenges of automatic threat modeling.
Smartphones with UWB: Evaluating the Accuracy and Reliability of UWB Ranging
Venue: arXiv cs.CR 2023
Year: 2023

More and more consumer devices implement the IEEE Ultra-Wide Band (UWB) standard to perform distance measurements for sensitive tasks such as keyless entry and startup of modern cars, to find lost items using coin-sized trackers, and for smart payments. While UWB promises the ability to perform time-of-flight centimeter-accurate distance measurements between two devices, the accuracy and reliability of the implementation in up-to-date consumer devices have not been evaluated so far. In this paper, we present the first evaluation of UWB smartphones from Apple, Google, and Samsung, focusing on accuracy and reliability in passive keyless entry and smart home automation scenarios. To perform the measurements for our analysis, we build a custom-designed testbed based on a Gimbal-based platform for Wireless Evaluation (GWEn), which allows us to create reproducible measurements. All our results, including all measurement data and a manual to reconstruct a GWEn are published online. We find that the evaluated devices can measure the distance with an error of less than 20cm, but fail in producing reliable measurements in all scenarios. Finally, we give recommendations on how to handle measurement results when implementing a passive keyless entry system.
Training Invertible Neural Networks as Autoencoders
Venue: arXiv cs.LG 2023
Year: 2023

Autoencoders are able to learn useful data representations in an unsupervised matter and have been widely used in various machine learning and computer vision tasks. In this work, we present methods to train Invertible Neural Networks (INNs) as (variational) autoencoders which we call INN (variational) autoencoders. Our experiments on MNIST, CIFAR and CelebA show that for low bottleneck sizes our INN autoencoder achieves results similar to the classical autoencoder. However, for large bottleneck sizes our INN autoencoder outperforms its classical counterpart. Based on the empirical results, we hypothesize that INN autoencoders might not have any intrinsic information loss and thereby are not bounded to a maximal number of layers (depth) after which only suboptimal results can be achieved.
Distributed Timed Elastic Band (DTEB) Planner: Trajectory Sharing and Collision Prediction for Multi-Robot Systems
Venue: arXiv cs.RO 2023
Year: 2023

Autonomous navigation of mobile robots is a well studied problem in robotics. However, the navigation task becomes challenging when multi-robot systems have to cooperatively navigate dynamic environments with deadlock-prone layouts. We present a Distributed Timed Elastic Band (DTEB) Planner that combines Prioritized Planning with the online TEB trajectory Planner, in order to extend the capabilities of the latter to multi-robot systems. The proposed planner is able to reactively avoid imminent collisions as well as predictively resolve potential deadlocks among a team of robots, while navigating in a complex environment. The results of our simulation demonstrate the reliable performance and the versatility of the planner in different environment settings. The code and tests for our approach are available online.
A Comparative Analysis of Port Scanning Tool Efficacy
Venue: arXiv cs.CR 2023
Year: 2023

Port scanning refers to the systematic exploration of networked computing systems. The goal of port scanning is to identify active services and associated information. Although this technique is often employed by malicious actors to locate vulnerable systems within a network, port scanning is also a legitimate method employed by IT professionals to troubleshoot network issues and maintain system security. In the case of the latter, cybersecurity practitioners use port scanning catalog exposed systems, identify potential misconfigurations, or test controls that may be running on a system. Existing literature has thoroughly established a taxonomy for port scanning. The taxonomy maps the types of scans as well as techniques. In fact, there are several tools mentioned repeatedly in the literature. Those are Nmap, Zmap, and masscan. Further, the presence of multiple tools signals that how a port scanner interacts with target systems impacts the output of the tool. In other words, the various tools may not behave identically or produce identical output. Yet, no work has been done to quantify the efficacy for these popular tools in a uniform, rigorous manner. Accordingly, we used a comparative experimental protocol to measure the accuracy, false positive, false negative, and efficiency of Nmap, Zmap, and masscan. The results show no difference between port scanners in general performance. However, the results revealed a statistically significant difference in efficiency. This information can be used to guide the selection of port scanning tools based on specific needs and requirements. As well, for researchers, the outcomes may also suggest areas for future work in the development novel port scanning tools.
EVA-02: A Visual Representation for Neon Genesis
Venue: arXiv cs.CV 2023
Year: 2023

We launch EVA-02, a next-generation Transformer-based visual representation pre-trained to reconstruct strong and robust language-aligned vision features via masked image modeling. With an updated plain Transformer architecture as well as extensive pre-training from an open & accessible giant CLIP vision encoder, EVA-02 demonstrates superior performance compared to prior state-of-the-art approaches across various representative vision tasks, while utilizing significantly fewer parameters and compute budgets. Notably, using exclusively publicly accessible training data, EVA-02 with only 304M parameters achieves a phenomenal 90.0 fine-tuning top-1 accuracy on ImageNet-1K val set. Additionally, our EVA-02-CLIP can reach up to 80.4 zero-shot top-1 on ImageNet-1K, outperforming the previous largest & best open-sourced CLIP with only ~1/6 parameters and ~1/6 image-text training data. We offer four EVA-02 variants in various model sizes, ranging from 6M to 304M parameters, all with impressive performance. To facilitate open access and open research, we release the complete suite of EVA-02 to the community at https://github.com/baaivision/EVA/tree/master/EVA-02.
Recursive Euclidean Distance Based Robust Aggregation Technique For Federated Learning
Venue: arXiv cs.LG 2023
Year: 2023

Federated learning has gained popularity as a solution to data availability and privacy challenges in machine learning. However, the aggregation process of local model updates to obtain a global model in federated learning is susceptible to malicious attacks, such as backdoor poisoning, label-flipping, and membership inference. Malicious users aim to sabotage the collaborative learning process by training the local model with malicious data. In this paper, we propose a novel robust aggregation approach based on recursive Euclidean distance calculation. Our approach measures the distance of the local models from the previous global model and assigns weights accordingly. Local models far away from the global model are assigned smaller weights to minimize the data poisoning effect during aggregation. Our experiments demonstrate that the proposed algorithm outperforms state-of-the-art algorithms by at least $5\%$ in accuracy while reducing time complexity by less than $55\%$. Our contribution is significant as it addresses the critical issue of malicious attacks in federated learning while improving the accuracy of the global model.
Optimized preprocessing and Tiny ML for Attention State Classification
Venue: arXiv cs.LG 2023
Year: 2023

In this paper, we present a new approach to mental state classification from EEG signals by combining signal processing techniques and machine learning (ML) algorithms. We evaluate the performance of the proposed method on a dataset of EEG recordings collected during a cognitive load task and compared it to other state-of-the-art methods. The results show that the proposed method achieves high accuracy in classifying mental states and outperforms state-of-the-art methods in terms of classification accuracy and computational efficiency.