OpenRaiser/Intern-Atlas huggingface dataset:
Single-source Domain Expansion Network for Cross-Scene Hyperspectral Image Classification
Venue: arXiv cs.CV 2022
Year: 2022

Currently, cross-scene hyperspectral image (HSI) classification has drawn increasing attention. It is necessary to train a model only on source domain (SD) and directly transferring the model to target domain (TD), when TD needs to be processed in real time and cannot be reused for training. Based on the idea of domain generalization, a Single-source Domain Expansion Network (SDEnet) is developed to ensure the reliability and effectiveness of domain extension. The method uses generative adversarial learning to train in SD and test in TD. A generator including semantic encoder and morph encoder is designed to generate the extended domain (ED) based on encoder-randomization-decoder architecture, where spatial and spectral randomization are specifically used to generate variable spatial and spectral information, and the morphological knowledge is implicitly applied as domain invariant information during domain expansion. Furthermore, the supervised contrastive learning is employed in the discriminator to learn class-wise domain invariant representation, which drives intra-class samples of SD and ED. Meanwhile, adversarial training is designed to optimize the generator to drive intra-class samples of SD and ED to be separated. Extensive experiments on two public HSI datasets and one additional multispectral image (MSI) dataset demonstrate the superiority of the proposed method when compared with state-of-the-art techniques.
Joint Linear and Nonlinear Computation across Functions for Efficient Privacy-Preserving Neural Network Inference
Venue: arXiv cs.CR 2022
Year: 2022

While it is encouraging to witness the recent development in privacy-preserving Machine Learning as a Service (MLaaS), there still exists a significant performance gap for its deployment in real-world applications. We observe the state-of-the-art frameworks follow a compute-and-share principle for every function output where the summing in linear functions, which is the last of two steps for function output, involves all rotations (which is the most expensive HE operation), and the multiplexing in nonlinear functions, which is also the last of two steps for function output, introduces noticeable communication rounds. Therefore, we challenge the conventional compute-and-share logic and introduce the first joint linear and nonlinear computation across functions that features by 1) the PHE triplet for computing the nonlinear function, with which the multiplexing is eliminated; 2) the matrix encoding to calculate the linear function, with which all rotations for summing is removed; and 3) the network adaptation to reassemble the model structure, with which the joint computation module is utilized as much as possible. The boosted efficiency is verified by the numerical complexity, and the experiments demonstrate up to 13x speedup for various functions used in the state-of-the-art models and up to 5x speedup over mainstream neural networks.
Every picture tells a story: Image-grounded controllable stylistic story generation
Venue: arXiv cs.CL 2022
Year: 2022

Generating a short story out of an image is arduous. Unlike image captioning, story generation from an image poses multiple challenges: preserving the story coherence, appropriately assessing the quality of the story, steering the generated story into a certain style, and addressing the scarcity of image-story pair reference datasets limiting supervision during training. In this work, we introduce Plug-and-Play Story Teller (PPST) and improve image-to-story generation by: 1) alleviating the data scarcity problem by incorporating large pre-trained models, namely CLIP and GPT-2, to facilitate a fluent image-to-text generation with minimal supervision, and 2) enabling a more style-relevant generation by incorporating stylistic adapters to control the story generation. We conduct image-to-story generation experiments with non-styled, romance-styled, and action-styled PPST approaches and compare our generated stories with those of previous work over three aspects, i.e., story coherence, image-story relevance, and style fitness, using both automatic and human evaluation. The results show that PPST improves story coherence and has better image-story relevance, but has yet to be adequately stylistic.
Fraud Detection Using Optimized Machine Learning Tools Under Imbalance Classes
Venue: arXiv cs.LG 2022
Year: 2022

Fraud detection is a challenging task due to the changing nature of fraud patterns over time and the limited availability of fraud examples to learn such sophisticated patterns. Thus, fraud detection with the aid of smart versions of machine learning (ML) tools is essential to assure safety. Fraud detection is a primary ML classification task; however, the optimum performance of the corresponding ML tool relies on the usage of the best hyperparameter values. Moreover, classification under imbalanced classes is quite challenging as it causes poor performance in minority classes, which most ML classification techniques ignore. Thus, we investigate four state-of-the-art ML techniques, namely, logistic regression, decision trees, random forest, and extreme gradient boost, that are suitable for handling imbalance classes to maximize precision and simultaneously reduce false positives. First, these classifiers are trained on two original benchmark unbalanced fraud detection datasets, namely, phishing website URLs and fraudulent credit card transactions. Then, three synthetically balanced datasets are produced for each original data set by implementing the sampling frameworks, namely, RandomUnderSampler, SMOTE, and SMOTEENN. The optimum hyperparameters for all the 16 experiments are revealed using the method RandomzedSearchCV. The validity of the 16 approaches in the context of fraud detection is compared using two benchmark performance metrics, namely, area under the curve of receiver operating characteristics (AUC ROC) and area under the curve of precision and recall (AUC PR). For both phishing website URLs and credit card fraud transaction datasets, the results indicate that extreme gradient boost trained on the original data shows trustworthy performance in the imbalanced dataset and manages to outperform the other three methods in terms of both AUC ROC and AUC PR.
ASTra: A Novel Algorithm-Level Approach to Imbalanced Classification
Venue: arXiv cs.LG 2022
Year: 2022

We propose a novel output layer activation function, which we name ASTra (Asymmetric Sigmoid Transfer function), which makes the classification of minority examples, in scenarios of high imbalance, more tractable. We combine this with a loss function that helps to effectively target minority misclassification. These two methods can be used together or separately, with their combination recommended for the most severely imbalanced cases. The proposed approach is tested on datasets with IRs from 588.24 to 4000 and very few minority examples (in some datasets, as few as five). Results using neural networks with from two to 12 hidden units are demonstrated to be comparable to, or better than, equivalent results obtained in a recent study that deployed a wide range of complex, hybrid data-level ensemble classifiers.
Variational Inference for Model-Free and Model-Based Reinforcement Learning
Venue: arXiv cs.LG 2022
Year: 2022

Variational inference (VI) is a specific type of approximate Bayesian inference that approximates an intractable posterior distribution with a tractable one. VI casts the inference problem as an optimization problem, more specifically, the goal is to maximize a lower bound of the logarithm of the marginal likelihood with respect to the parameters of the approximate posterior. Reinforcement learning (RL) on the other hand deals with autonomous agents and how to make them act optimally such as to maximize some notion of expected future cumulative reward. In the non-sequential setting where agents' actions do not have an impact on future states of the environment, RL is covered by contextual bandits and Bayesian optimization. In a proper sequential scenario, however, where agents' actions affect future states, instantaneous rewards need to be carefully traded off against potential long-term rewards. This manuscript shows how the apparently different subjects of VI and RL are linked in two fundamental ways. First, the optimization objective of RL to maximize future cumulative rewards can be recovered via a VI objective under a soft policy constraint in both the non-sequential and the sequential setting. This policy constraint is not just merely artificial but has proven as a useful regularizer in many RL tasks yielding significant improvements in agent performance. And second, in model-based RL where agents aim to learn about the environment they are operating in, the model-learning part can be naturally phrased as an inference problem over the process that governs environment dynamics. We are going to distinguish between two scenarios for the latter: VI when environment states are fully observable by the agent and VI when they are only partially observable through an observation distribution.
An Adaptive Black-box Defense against Trojan Attacks (TrojDef)
Venue: arXiv cs.CR 2022
Year: 2022

Trojan backdoor is a poisoning attack against Neural Network (NN) classifiers in which adversaries try to exploit the (highly desirable) model reuse property to implant Trojans into model parameters for backdoor breaches through a poisoned training process. Most of the proposed defenses against Trojan attacks assume a white-box setup, in which the defender either has access to the inner state of NN or is able to run back-propagation through it. In this work, we propose a more practical black-box defense, dubbed TrojDef, which can only run forward-pass of the NN. TrojDef tries to identify and filter out Trojan inputs (i.e., inputs augmented with the Trojan trigger) by monitoring the changes in the prediction confidence when the input is repeatedly perturbed by random noise. We derive a function based on the prediction outputs which is called the prediction confidence bound to decide whether the input example is Trojan or not. The intuition is that Trojan inputs are more stable as the misclassification only depends on the trigger, while benign inputs will suffer when augmented with noise due to the perturbation of the classification features. Through mathematical analysis, we show that if the attacker is perfect in injecting the backdoor, the Trojan infected model will be trained to learn the appropriate prediction confidence bound, which is used to distinguish Trojan and benign inputs under arbitrary perturbations. However, because the attacker might not be perfect in injecting the backdoor, we introduce a nonlinear transform to the prediction confidence bound to improve the detection accuracy in practical settings. Extensive empirical evaluations show that TrojDef significantly outperforms the-state-of-the-art defenses and is highly stable under different settings, even when the classifier architecture, the training process, or the hyper-parameters change.
Boost Decentralized Federated Learning in Vehicular Networks by Diversifying Data Sources
Venue: arXiv cs.LG 2022
Year: 2022

Recently, federated learning (FL) has received intensive research because of its ability in preserving data privacy for scattered clients to collaboratively train machine learning models. Commonly, a parameter server (PS) is deployed for aggregating model parameters contributed by different clients. Decentralized federated learning (DFL) is upgraded from FL which allows clients to aggregate model parameters with their neighbours directly. DFL is particularly feasible for vehicular networks as vehicles communicate with each other in a vehicle-to-vehicle (V2V) manner. However, due to the restrictions of vehicle routes and communication distances, it is hard for individual vehicles to sufficiently exchange models with others. Data sources contributing to models on individual vehicles may not diversified enough resulting in poor model accuracy. To address this problem, we propose the DFL-DDS (DFL with diversified Data Sources) algorithm to diversify data sources in DFL. Specifically, each vehicle maintains a state vector to record the contribution weight of each data source to its model. The Kullback-Leibler (KL) divergence is adopted to measure the diversity of a state vector. To boost the convergence of DFL, a vehicle tunes the aggregation weight of each data source by minimizing the KL divergence of its state vector, and its effectiveness in diversifying data sources can be theoretically proved. Finally, the superiority of DFL-DDS is evaluated by extensive experiments (with MNIST and CIFAR-10 datasets) which demonstrate that DFL-DDS can accelerate the convergence of DFL and improve the model accuracy significantly compared with state-of-the-art baselines.
Indoor Path Planning for Multiple Unmanned Aerial Vehicles via Curriculum Learning
Venue: arXiv cs.RO 2022
Year: 2022

Multi-agent reinforcement learning was performed in this study for indoor path planning of two unmanned aerial vehicles (UAVs). Each UAV performed the task of moving as fast as possible from a randomly paired initial position to a goal position in an environment with obstacles. To minimize training time and prevent the damage of UAVs, learning was performed by simulation. Considering the non-stationary characteristics of the multi-agent environment wherein the optimal behavior varies based on the actions of other agents, the action of the other UAV was also included in the state space of each UAV. Curriculum learning was performed in two stages to increase learning efficiency. A goal rate of 89.0% was obtained compared with other learning strategies that obtained goal rates of 73.6% and 79.9%.
"Is your explanation stable?": A Robustness Evaluation Framework for Feature Attribution
Venue: arXiv cs.AI 2022
Year: 2022

Understanding the decision process of neural networks is hard. One vital method for explanation is to attribute its decision to pivotal features. Although many algorithms are proposed, most of them solely improve the faithfulness to the model. However, the real environment contains many random noises, which may leads to great fluctuations in the explanations. More seriously, recent works show that explanation algorithms are vulnerable to adversarial attacks. All of these make the explanation hard to trust in real scenarios. To bridge this gap, we propose a model-agnostic method \emph{Median Test for Feature Attribution} (MeTFA) to quantify the uncertainty and increase the stability of explanation algorithms with theoretical guarantees. MeTFA has the following two functions: (1) examine whether one feature is significantly important or unimportant and generate a MeTFA-significant map to visualize the results; (2) compute the confidence interval of a feature attribution score and generate a MeTFA-smoothed map to increase the stability of the explanation. Experiments show that MeTFA improves the visual quality of explanations and significantly reduces the instability while maintaining the faithfulness. To quantitatively evaluate the faithfulness of an explanation under different noise settings, we further propose several robust faithfulness metrics. Experiment results show that the MeTFA-smoothed explanation can significantly increase the robust faithfulness. In addition, we use two scenarios to show MeTFA's potential in the applications. First, when applied to the SOTA explanation method to locate context bias for semantic segmentation models, MeTFA-significant explanations use far smaller regions to maintain 99\%+ faithfulness. Second, when tested with different explanation-oriented attacks, MeTFA can help defend vanilla, as well as adaptive, adversarial attacks against explanations.
Towards Zero Touch Networks: From the Perspective of Hierarchical Language Systems
Venue: arXiv cs.NI 2022
Year: 2022

With ever-increasing complexity and dynamicity of communication networks, intelligent network operation and maintenance has become more important to network operators. With the fast development of artificial intelligence, concepts such as "Zero Touch", "Intent-based", "Knowledge-defined" and "Self-driving" networks have become well-known in networking community for a great vision of making networks automatically manageable and responsive to user demands. This article discusses how to achieve Zero Touch Networks from the perspective of language-like systems. We propose a novel hierarchical `language' framework dedicated for networks to enable the Zero Touch Network, which covers from symbolizing network components, a unified framework for understanding network systems, to the logical description with network semantics. A case study based on the proposed language framework is provided. Finally, we discuss the challenges and open issues of intelligence models for zero touch networks.
An Exploratory Study on the Predominant Programming Paradigms in Python Code
Venue: arXiv cs.SE 2022
Year: 2022

Python is a multi-paradigm programming language that fully supports object-oriented (OO) programming. The language allows writing code in a non-procedural imperative manner, using procedures, using classes, or in a functional style. To date, no one has studied what paradigm(s), if any, are predominant in Python code and projects. In this work, we first define a technique to classify Python files into predominant paradigm(s). We then automate our approach and evaluate it against human judgements, showing over 80% agreement. We then analyze over 100k open-source Python projects, automatically classifying each source file and investigating the paradigm distributions. The results indicate Python developers tend to heavily favor OO features. We also observed a positive correlation between OO and procedural paradigms and the size of the project. And despite few files or projects being predominantly functional, we still found many functional feature uses.
Detecting Unjustified Assumptions in Subclasses via Elegant Objects Representation
Venue: arXiv cs.PL 2022
Year: 2022

Elegant Objects (EO) is a programming language based on ideas of pure objects and the Decorator pattern. Bugayenko has suggested it as an intermediate representation for object-oriented programs. This paper presents a version of dynamic dispatch modelled in EO and formulates a problem of unjustified assumptions in decorator objects, which parallels similar problem in subclasses. Then, we introduce an approach to detect such problems in EO programs via method inlining and limited property inference. Finally, we discuss prototype implementation of this approach in Scala programming language.
Dynamics of Fourier Modes in Torus Generative Adversarial Networks
Venue: arXiv cs.LG 2022
Year: 2022

Generative Adversarial Networks (GANs) are powerful Machine Learning models capable of generating fully synthetic samples of a desired phenomenon with a high resolution. Despite their success, the training process of a GAN is highly unstable and typically it is necessary to implement several accessory heuristics to the networks to reach an acceptable convergence of the model. In this paper, we introduce a novel method to analyze the convergence and stability in the training of Generative Adversarial Networks. For this purpose, we propose to decompose the objective function of the adversary min-max game defining a periodic GAN into its Fourier series. By studying the dynamics of the truncated Fourier series for the continuous Alternating Gradient Descend algorithm, we are able to approximate the real flow and to identify the main features of the convergence of the GAN. This approach is confirmed empirically by studying the training flow in a $2$-parametric GAN aiming to generate an unknown exponential distribution. As byproduct, we show that convergent orbits in GANs are small perturbations of periodic orbits so the Nash equillibria are spiral attractors. This theoretically justifies the slow and unstable training observed in GANs.
A Principled Evaluation Protocol for Comparative Investigation of the Effectiveness of DNN Classification Models on Similar-but-non-identical Datasets
Venue: arXiv cs.LG 2022
Year: 2022

Deep Neural Network (DNN) models are increasingly evaluated using new replication test datasets, which have been carefully created to be similar to older and popular benchmark datasets. However, running counter to expectations, DNN classification models show significant, consistent, and largely unexplained degradation in accuracy on these replication test datasets. While the popular evaluation approach is to assess the accuracy of a model by making use of all the datapoints available in the respective test datasets, we argue that doing so hinders us from adequately capturing the behavior of DNN models and from having realistic expectations about their accuracy. Therefore, we propose a principled evaluation protocol that is suitable for performing comparative investigations of the accuracy of a DNN model on multiple test datasets, leveraging subsets of datapoints that can be selected using different criteria, including uncertainty-related information. By making use of this new evaluation protocol, we determined the accuracy of $564$ DNN models on both (1) the CIFAR-10 and ImageNet datasets and (2) their replication datasets. Our experimental results indicate that the observed accuracy degradation between established benchmark datasets and their replications is consistently lower (that is, models do perform better on the replication test datasets) than the accuracy degradation reported in published works, with these published works relying on conventional evaluation approaches that do not utilize uncertainty-related information.
Private Simultaneous Messages Based on Quadratic Residues
Venue: arXiv cs.CR 2022
Year: 2022

Private Simultaneous Messages (PSM) model is a minimal model for secure multiparty computation. Feige, Kilian, and Naor (STOC 1994) and Ishai (Cryptology and Information Security Series 2013) constructed PSM protocols based on quadratic residues. In this paper, we define QR-PSM protocols as a generalization of these protocols. A QR-PSM protocol is a PSM protocol whose decoding function outputs the quadratic residuosity of what is computed from messages. We design a QR-PSM protocol for any symmetric function $f: \{0,1\}^n \rightarrow \{0,1\}$ of communication complexity $O(n^2)$. As far as we know, it is the most efficient PSM protocol since the previously known best PSM protocol was of $O(n^2\log n)$ (Beimel et al., CRYPTO 2014). We also study the sizes of the underlying finite fields $\mathbb{F}_p$ in the protocols since the communication complexity of a QR-PSM protocol is proportional to the bit length of the prime $p$. In particular, we show that the $N$-th Peralta prime $P_N$, which is used for general QR-PSM protocols, can be taken as at most $(1+o(1))N^2 2^{2N-2}$, which improves the Peralta's known result (Mathematics of Computation 1992) by a constant factor $(1+\sqrt{2})^2$.