OpenRaiser/Intern-Atlas huggingface dataset:
Understanding the Complexity and Its Impact on Testing in ML-Enabled Systems
Venue: arXiv cs.SE 2023
Year: 2023

Machine learning (ML) enabled systems are emerging with recent breakthroughs in ML. A model-centric view is widely taken by the literature to focus only on the analysis of ML models. However, only a small body of work takes a system view that looks at how ML components work with the system and how they affect software engineering for MLenabled systems. In this paper, we adopt this system view, and conduct a case study on Rasa 3.0, an industrial dialogue system that has been widely adopted by various companies around the world. Our goal is to characterize the complexity of such a largescale ML-enabled system and to understand the impact of the complexity on testing. Our study reveals practical implications for software engineering for ML-enabled systems.
Discrete Morse Functions and Watersheds
Venue: arXiv cs.DM 2023
Year: 2023

Any watershed, when defined on a stack on a normal pseudomanifold of dimension d, is a pure (d -- 1)-subcomplex that satisfies a drop-of-water principle. In this paper, we introduce Morse stacks, a class of functions that are equivalent to discrete Morse functions. We show that the watershed of a Morse stack on a normal pseudomanifold is uniquely defined, and can be obtained with a linear-time algorithm relying on a sequence of collapses. Last, we prove that such a watershed is the cut of the unique minimum spanning forest, rooted in the minima of the Morse stack, of the facet graph of the pseudomanifold.
A Privacy Preserving Method with a Random Orthogonal Matrix for ConvMixer Models
Venue: arXiv cs.CV 2023
Year: 2023

In this paper, a privacy preserving image classification method is proposed under the use of ConvMixer models. To protect the visual information of test images, a test image is divided into blocks, and then every block is encrypted by using a random orthogonal matrix. Moreover, a ConvMixer model trained with plain images is transformed by the random orthogonal matrix used for encrypting test images, on the basis of the embedding structure of ConvMixer. The proposed method allows us not only to use the same classification accuracy as that of ConvMixer models without considering privacy protection but to also enhance robustness against various attacks compared to conventional privacy-preserving learning.
The use of new technologies to support Public Administration. Sentiment analysis and the case of the app IO
Venue: arXiv cs.SI 2023
Year: 2023

App IO is an app developed for the Italian PA. It is definitely useful for citizens to interact with the PA and to get services that were not digitized yet. Nevertheless, it was not perceived in a good way by the citizens and it has been criticized. As we wanted to find the root that caused all these bad reviews we scraped feedback from mobile app stores using custom-coded automated tools and - after that - we trained two machine learning models to perform both sentiment analysis and emotion detection to understand what caused the bad reviews.
Why People Skip Music? On Predicting Music Skips using Deep Reinforcement Learning
Venue: arXiv cs.IR 2023
Year: 2023

Music recommender systems are an integral part of our daily life. Recent research has seen a significant effort around black-box recommender based approaches such as Deep Reinforcement Learning (DRL). These advances have led, together with the increasing concerns around users' data collection and privacy, to a strong interest in building responsible recommender systems. A key element of a successful music recommender system is modelling how users interact with streamed content. By first understanding these interactions, insights can be drawn to enable the construction of more transparent and responsible systems. An example of these interactions is skipping behaviour, a signal that can measure users' satisfaction, dissatisfaction, or lack of interest. In this paper, we study the utility of users' historical data for the task of sequentially predicting users' skipping behaviour. To this end, we adapt DRL for this classification task, followed by a post-hoc explainability (SHAP) and ablation analysis of the input state representation. Experimental results from a real-world music streaming dataset (Spotify) demonstrate the effectiveness of our approach in this task by outperforming state-of-the-art models. A comprehensive analysis of our approach and of users' historical data reveals a temporal data leakage problem in the dataset. Our findings indicate that, overall, users' behaviour features are the most discriminative in how our proposed DRL model predicts music skips. Content and contextual features have a lesser effect. This suggests that a limited amount of user data should be collected and leveraged to predict skipping behaviour.
From Continual Learning to Causal Discovery in Robotics
Venue: arXiv cs.RO 2023
Year: 2023

Reconstructing accurate causal models of dynamic systems from time-series of sensor data is a key problem in many real-world scenarios. In this paper, we present an overview based on our experience about practical challenges that the causal analysis encounters when applied to autonomous robots and how Continual Learning~(CL) could help to overcome them. We propose a possible way to leverage the CL paradigm to make causal discovery feasible for robotics applications where the computational resources are limited, while at the same time exploiting the robot as an active agent that helps to increase the quality of the reconstructed causal models.
Rethinking Smart Contract Fuzzing: Fuzzing With Invocation Ordering and Important Branch Revisiting
Venue: arXiv cs.CR 2023
Year: 2023

Blockchain smart contracts have given rise to a variety of interesting and compelling applications and emerged as a revolutionary force for the Internet. Quite a few practitioners have devoted themselves to developing tools for detecting bugs in smart contracts. One line of efforts revolve around static analysis techniques, which heavily suffer from high false-positive rates. Another line of works concentrate on fuzzing techniques. Unfortunately, current fuzzing approaches for smart contracts tend to conduct fuzzing starting from the initial state of the contract, which expends too much energy revolving around the initial state and thus is usually unable to unearth bugs triggered by other states. Moreover, most existing methods treat each branch equally, failing to take care of the branches that are rare or more likely to possess bugs. This might lead to resources wasted on normal branches. In this paper, we try to tackle these challenges from three aspects: (1) In generating function invocation sequences, we explicitly consider data dependencies between functions to facilitate exploring richer states. We further prolong a function invocation sequence S1 by appending a new sequence S2, so that S2 can start fuzzing from states that are different from the initial state. (2) We incorporate a branch distance-based measure to evolve test cases iteratively towards a target branch. (3) We engage a branch search algorithm to discover rare and vulnerable branches, and design an energy allocation mechanism to take care of exercising these crucial branches. We implement IR-Fuzz and extensively evaluate it over 12K real-world contracts. Empirical results show that: (i) IR-Fuzz achieves 28% higher branch coverage than state-of-the-art fuzzing approaches, and (ii) IR-Fuzz detects more vulnerabilities and increases the average accuracy of vulnerability detection by 7% over current methods.
The Democratic Illusion through the Technological Illusion: a Case Study of the Implementation of a Blockchain to Support an E-voting Platform in Moscow (Active Citizen)
Venue: arXiv cs.CY 2023
Year: 2023

This paper presents an ongoing analyze of the Active Citizen e-voting system proposed by the Moscow city hall. This research points out that the main objective of the platform is not to enhance the democratic power of the Muscovites, but to strengthen the position of Moscow as a modern city at a world scale and the position of the city hall in the Russian political system.
Performance of RIS-empowered NOMA-based D2D Communication under Nakagami-m Fading
Venue: arXiv cs.IT 2023
Year: 2023

Reconfigurable intelligent surfaces (RISs) have sparked a renewed interest in the research community envisioning future wireless communication networks. In this study, we analyzed the performance of RIS-enabled non-orthogonal multiple access (NOMA) based device-to-device (D2D) wireless communication system, where the RIS is partitioned to serve a pair of D2D users. Specifically, closed-form expressions are derived for the upper and lower limits of spectral efficiency (SE) and energy efficiency (EE). In addition, the performance of the proposed NOMA-based system is also compared with its orthogonal counterpart. Extensive simulation is done to corroborate the analytical findings. The results demonstrate that RIS highly enhances the performance of a NOMA-based D2D network.
Change Propagation Without Joins
Venue: arXiv cs.DB 2023
Year: 2023

We revisit the classical change propagation framework for query evaluation under updates. The standard framework takes a query plan and materializes the intermediate views, which incurs high polynomial costs in both space and time, with the join operator being the culprit. In this paper, we propose a new change propagation framework without joins, thus naturally avoiding this polynomial blowup. Meanwhile, we show that the new framework still supports constant-delay enumeration of both the deltas and the full query results, the same as in the standard framework. Furthermore, we provide a quantitative analysis of its update cost, which not only recovers many recent theoretical results on the problem, but also yields an effective approach to optimizing the query plan. The new framework is also easy to be integrated into an existing streaming database system. Experimental results show that our system prototype, implemented using Flink DataStream API, significantly outperforms other systems in terms of space, time, and latency.
Reconstructing Individual Data Points in Federated Learning Hardened with Differential Privacy and Secure Aggregation
Venue: arXiv cs.CR 2023
Year: 2023

Federated learning (FL) is a framework for users to jointly train a machine learning model. FL is promoted as a privacy-enhancing technology (PET) that provides data minimization: data never "leaves" personal devices and users share only model updates with a server (e.g., a company) coordinating the distributed training. While prior work showed that in vanilla FL a malicious server can extract users' private data from the model updates, in this work we take it further and demonstrate that a malicious server can reconstruct user data even in hardened versions of the protocol. More precisely, we propose an attack against FL protected with distributed differential privacy (DDP) and secure aggregation (SA). Our attack method is based on the introduction of sybil devices that deviate from the protocol to expose individual users' data for reconstruction by the server. The underlying root cause for the vulnerability to our attack is a power imbalance: the server orchestrates the whole protocol and users are given little guarantees about the selection of other users participating in the protocol. Moving forward, we discuss requirements for privacy guarantees in FL. We conclude that users should only participate in the protocol when they trust the server or they apply local primitives such as local DP, shifting power away from the server. Yet, the latter approaches come at significant overhead in terms of performance degradation of the trained model, making them less likely to be deployed in practice.
FGAHOI: Fine-Grained Anchors for Human-Object Interaction Detection
Venue: arXiv cs.CV 2023
Year: 2023

Human-Object Interaction (HOI), as an important problem in computer vision, requires locating the human-object pair and identifying the interactive relationships between them. The HOI instance has a greater span in spatial, scale, and task than the individual object instance, making its detection more susceptible to noisy backgrounds. To alleviate the disturbance of noisy backgrounds on HOI detection, it is necessary to consider the input image information to generate fine-grained anchors which are then leveraged to guide the detection of HOI instances. However, it is challenging for the following reasons. i) how to extract pivotal features from the images with complex background information is still an open question. ii) how to semantically align the extracted features and query embeddings is also a difficult issue. In this paper, a novel end-to-end transformer-based framework (FGAHOI) is proposed to alleviate the above problems. FGAHOI comprises three dedicated components namely, multi-scale sampling (MSS), hierarchical spatial-aware merging (HSAM) and task-aware merging mechanism (TAM). MSS extracts features of humans, objects and interaction areas from noisy backgrounds for HOI instances of various scales. HSAM and TAM semantically align and merge the extracted features and query embeddings in the hierarchical spatial and task perspectives in turn. In the meanwhile, a novel training strategy Stage-wise Training Strategy is designed to reduce the training pressure caused by overly complex tasks done by FGAHOI. In addition, we propose two ways to measure the difficulty of HOI detection and a novel dataset, i.e., HOI-SDC for the two challenges (Uneven Distributed Area in Human-Object Pairs and Long Distance Visual Modeling of Human-Object Pairs) of HOI instances detection.
ROBUSfT: Robust Real-Time Shape-from-Template, a C++ Library
Venue: arXiv cs.CV 2023
Year: 2023

Tracking the 3D shape of a deforming object using only monocular 2D vision is a challenging problem. This is because one should (i) infer the 3D shape from a 2D image, which is a severely underconstrained problem, and (ii) implement the whole solution pipeline in real-time. The pipeline typically requires feature detection and matching, mismatch filtering, 3D shape inference and feature tracking algorithms. We propose ROBUSfT, a conventional pipeline based on a template containing the object's rest shape, texturemap and deformation law. ROBUSfT is ready-to-use, wide-baseline, capable of handling large deformations, fast up to 30 fps, free of training, and robust against partial occlusions and discontinuity in video frames. It outperforms the state-of-the-art methods in challenging datasets. ROBUSfT is implemented as a publicly available C++ library and we provide a tutorial on how to use it in https://github.com/mrshetab/ROBUSfT
Design, Modeling and Control of a Quadruped Robot SPIDAR: Spherically Vectorable and Distributed Rotors Assisted Air-Ground Amphibious Quadruped Robot
Venue: arXiv cs.RO 2023
Year: 2023

Multimodal locomotion capability is an emerging topic in robotics field, and various novel mobile robots have been developed to enable the maneuvering in both terrestrial and aerial domains. Among these hybrid robots, several state-of-the-art bipedal \robots enable the complex walking motion which is interlaced with flying. These robots are also desired to have the manipulation ability; however, it is difficult for the current forms to keep stability with the joint motion in midair due to the central\ized rotor arrangement. Therefore, in this work, we develop a novel air-ground amphibious quadruped robot called SPIDAR which is assisted by spherically vectorable rotors distributed in each link to enable both walking motion and transformable flight. F\irst, we present a unique mechanical design for quadruped robot that enables terrestrial and aerial locomotion. We then reveal the modeling method for this hybrid robot platform, and further develop an integrated control strategy for both walking and fl\ying with joint motion. Finally, we demonstrate the feasibility of the proposed hybrid quadruped robot by performing a seamless motion that involves static walking and subsequent flight. To the best of our knowledge, this work is the first to achieve a \quadruped robot with multimodal locomotion capability, which also shows the potential of manipulation in multiple domains.
A Formal Disproof of the Hirsch Conjecture
Venue: arXiv cs.LO 2023
Year: 2023

The purpose of this paper is the formal verification of a counterexample of Santos et al. to the so-called Hirsch Conjecture on the diameter of polytopes (bounded convex polyhedra). In contrast with the pen-and-paper proof, our approach is entirely computational: we implement in Coq and prove correct an algorithm that explicitly computes, within the proof assistant, vertex-edge graphs of polytopes as well as their diameter. The originality of this certificate-based algorithm is to achieve a tradeoff between simplicity and efficiency. Simplicity is crucial in obtaining the proof of correctness of the algorithm. This proof splits into the correctness of an abstract algorithm stated over proof-oriented data types and the correspondence with a low-level implementation over computation-oriented data types. A special effort has been made to reduce the algorithm to a small sequence of elementary operations (e.g., matrix multiplications, basic routines on sets and graphs), in order to make the derivation of the correctness of the low-level implementation more transparent. Efficiency allows us to scale up to polytopes with a challenging combinatorics. For instance, we formally check the two counterexamples of Matschke, Santos and Weibel to the Hirsch conjecture, respectively 20- and 23-dimensional polytopes with 36 425 and 73 224 vertices involving rational coefficients with up to 40 digits in their numerator and denominator. We also illustrate the performance of the method by computing the list of vertices or the diameter of well-known classes of polytopes, such as (polars of) cyclic polytopes involved in McMullen's Upper Bound Theorem.
On the Comparison of Discounted-Sum Automata with Multiple Discount Factors
Venue: arXiv cs.FL 2023
Year: 2023

We look into the problems of comparing nondeterministic discounted-sum automata on finite and infinite words. That is, the problems of checking for automata $A$ and $B$ whether or not it holds that for all words $w$, $A(w)=B(w), A(w) \leq B(w)$, or $A(w)<B(w)$. These problems are known to be decidable when both automata have the same single integral discount factor, while decidability is open in all other settings: when the single discount factor is a non-integral rational; when each automaton can have multiple discount factors; and even when each has a single integral discount factor, but the two are different. We show that it is undecidable to compare discounted-sum automata with multiple discount factors, even if all are integrals, while it is decidable to compare them if each has a single, possibly different, integral discount factor. To this end, we also provide algorithms to check for given nondeterministic automaton $N$ and deterministic automaton $D$, each with a single, possibly different, rational discount factor, whether or not $N(w) = D(w)$, $N(w) \geq D(w)$, or $N(w) > D(w)$ for all words $w$.
Illegal Intelligent Reflecting Surface Based Active Channel Aging: When Jammer Can Attack Without Power and CSI
Venue: arXiv cs.IT 2023
Year: 2023

Illegal intelligent reflecting surfaces (I-IRSs), i.e., the illegal deployment and utilization of IRSs, impose serious harmful impacts on wireless networks. The existing I-IRS-based illegal jammer (IJ) requires channel state information (CSI) or extra power or both, and therefore, the I-IRS-based IJ seems to be difficult to implement in practical wireless networks. To raise concerns about significant potential threats posed by I-IRSs, we propose an alternative method to jam legitimate users (LUs) without relying on the CSI. By using an I-IRS to actively change wireless channels, the orthogonality of multi-user beamforming vectors and the co-user channels is destroyed, and significant inter-user interference is then caused, which is referred to as active channel aging. Such a fully-passive jammer (FPJ) can launch jamming attacks on multi-user multiple-input single-output (MU-MISO) systems via inter-user interference caused by active channel aging, where the IJ requires no additional transmit power and instantaneous CSI. The simulation results show the effectiveness of the proposed FPJ scheme. Moreover, we also investigate how the transmit power and the number of quantization phase shift bits influence the jamming performance.
LENet: Lightweight And Efficient LiDAR Semantic Segmentation Using Multi-Scale Convolution Attention
Venue: arXiv cs.CV 2023
Year: 2023

LiDAR-based semantic segmentation is critical in the fields of robotics and autonomous driving as it provides a comprehensive understanding of the scene. This paper proposes a lightweight and efficient projection-based semantic segmentation network called LENet with an encoder-decoder structure for LiDAR-based semantic segmentation. The encoder is composed of a novel multi-scale convolutional attention (MSCA) module with varying receptive field sizes to capture features. The decoder employs an Interpolation And Convolution (IAC) mechanism utilizing bilinear interpolation for upsampling multi-resolution feature maps and integrating previous and current dimensional features through a single convolution layer. This approach significantly reduces the network's complexity while also improving its accuracy. Additionally, we introduce multiple auxiliary segmentation heads to further refine the network's accuracy. Extensive evaluations on publicly available datasets, including SemanticKITTI, SemanticPOSS, and nuScenes, show that our proposed method is lighter, more efficient, and robust compared to state-of-the-art semantic segmentation methods. Full implementation is available at https://github.com/fengluodb/LENet.
SoK: Adversarial Machine Learning Attacks and Defences in Multi-Agent Reinforcement Learning
Venue: arXiv cs.LG 2023
Year: 2023

Multi-Agent Reinforcement Learning (MARL) is vulnerable to Adversarial Machine Learning (AML) attacks and needs adequate defences before it can be used in real world applications. We have conducted a survey into the use of execution-time AML attacks against MARL and the defences against those attacks. We surveyed related work in the application of AML in Deep Reinforcement Learning (DRL) and Multi-Agent Learning (MAL) to inform our analysis of AML for MARL. We propose a novel perspective to understand the manner of perpetrating an AML attack, by defining Attack Vectors. We develop two new frameworks to address a gap in current modelling frameworks, focusing on the means and tempo of an AML attack against MARL, and identify knowledge gaps and future avenues of research.
Active-IRS-Aided Wireless Communication: Fundamentals, Designs and Open Issues
Venue: arXiv cs.IT 2023
Year: 2023

Intelligent reflecting surface (IRS) has emerged as a promising technology to realize smart radio environment for future wireless communication systems. Existing works in this line of research have mainly considered the conventional passive IRS that reflects wireless signals without power amplification, while in this article, we give an overview of a new type of IRS, called active IRS, which enables simultaneous signal reflection and amplification, thus significantly extending the signal coverage of passive IRS. We first present the fundamentals of active IRS, including its hardware architecture, signal and channel models, as well as practical constraints, in comparison with those of passive IRS. Then, we discuss new considerations and open issues in designing active-IRS-aided wireless communications, such as the reflection optimization, channel estimation, and deployment for active IRS, as well as its integrated design with passive IRS. Finally, numerical results are provided to show the potential performance gains of active IRS as compared to passive IRS and traditional active relay.