OpenRaiser/Intern-Atlas huggingface dataset:
A System for Interactive Examination of Learned Security Policies
Venue: arXiv cs.CR 2022
Year: 2022

We present a system for interactive examination of learned security policies. It allows a user to traverse episodes of Markov decision processes in a controlled manner and to track the actions triggered by security policies. Similar to a software debugger, a user can continue or or halt an episode at any time step and inspect parameters and probability distributions of interest. The system enables insight into the structure of a given policy and in the behavior of a policy in edge cases. We demonstrate the system with a network intrusion use case. We examine the evolution of an IT infrastructure's state and the actions prescribed by security policies while an attack occurs. The policies for the demonstration have been obtained through a reinforcement learning approach that includes a simulation system where policies are incrementally learned and an emulation system that produces statistics that drive the simulation runs.
Proactive Anomaly Detection for Robot Navigation with Multi-Sensor Fusion
Venue: arXiv cs.RO 2022
Year: 2022

Despite the rapid advancement of navigation algorithms, mobile robots often produce anomalous behaviors that can lead to navigation failures. The ability to detect such anomalous behaviors is a key component in modern robots to achieve high-levels of autonomy. Reactive anomaly detection methods identify anomalous task executions based on the current robot state and thus lack the ability to alert the robot before an actual failure occurs. Such an alert delay is undesirable due to the potential damage to both the robot and the surrounding objects. We propose a proactive anomaly detection network (PAAD) for robot navigation in unstructured and uncertain environments. PAAD predicts the probability of future failure based on the planned motions from the predictive controller and the current observation from the perception module. Multi-sensor signals are fused effectively to provide robust anomaly detection in the presence of sensor occlusion as seen in field environments. Our experiments on field robot data demonstrates superior failure identification performance than previous methods, and that our model can capture anomalous behaviors in real-time while maintaining a low false detection rate in cluttered fields. Code, dataset, and video are available at https://github.com/tianchenji/PAAD
Byzantine-Robust Federated Linear Bandits
Venue: arXiv cs.LG 2022
Year: 2022

In this paper, we study a linear bandit optimization problem in a federated setting where a large collection of distributed agents collaboratively learn a common linear bandit model. Standard federated learning algorithms applied to this setting are vulnerable to Byzantine attacks on even a small fraction of agents. We propose a novel algorithm with a robust aggregation oracle that utilizes the geometric median. We prove that our proposed algorithm is robust to Byzantine attacks on fewer than half of agents and achieves a sublinear $\tilde{\mathcal{O}}({T^{3/4}})$ regret with $\mathcal{O}(\sqrt{T})$ steps of communication in $T$ steps. Moreover, we make our algorithm differentially private via a tree-based mechanism. Finally, if the level of corruption is known to be small, we show that using the geometric median of mean oracle for robust aggregation further improves the regret bound.
Best-Response Bayesian Reinforcement Learning with Bayes-adaptive POMDPs for Centaurs
Venue: arXiv cs.AI 2022
Year: 2022

Centaurs are half-human, half-AI decision-makers where the AI's goal is to complement the human. To do so, the AI must be able to recognize the goals and constraints of the human and have the means to help them. We present a novel formulation of the interaction between the human and the AI as a sequential game where the agents are modelled using Bayesian best-response models. We show that in this case the AI's problem of helping bounded-rational humans make better decisions reduces to a Bayes-adaptive POMDP. In our simulated experiments, we consider an instantiation of our framework for humans who are subjectively optimistic about the AI's future behaviour. Our results show that when equipped with a model of the human, the AI can infer the human's bounds and nudge them towards better decisions. We discuss ways in which the machine can learn to improve upon its own limitations as well with the help of the human. We identify a novel trade-off for centaurs in partially observable tasks: for the AI's actions to be acceptable to the human, the machine must make sure their beliefs are sufficiently aligned, but aligning beliefs might be costly. We present a preliminary theoretical analysis of this trade-off and its dependence on task structure.
Clues in Tweets: Twitter-Guided Discovery and Analysis of SMS Spam
Venue: arXiv cs.CR 2022
Year: 2022

With its critical role in business and service delivery through mobile devices, SMS (Short Message Service) has long been abused for spamming, which is still on the rise today possibly due to the emergence of A2P bulk messaging. The effort to control SMS spam has been hampered by the lack of up-to-date information about illicit activities. In our research, we proposed a novel solution to collect recent SMS spam data, at a large scale, from Twitter, where users voluntarily report the spam messages they receive. For this purpose, we designed and implemented SpamHunter, an automated pipeline to discover SMS spam reporting tweets and extract message content from the attached screenshots. Leveraging SpamHunter, we collected from Twitter a dataset of 21,918 SMS spam messages in 75 languages, spanning over four years. To our best knowledge, this is the largest SMS spam dataset ever made public. More importantly, SpamHunter enables us to continuously monitor emerging SMS spam messages, which facilitates the ongoing effort to mitigate SMS spamming. We also performed an in-depth measurement study that sheds light on the new trends in the spammer's strategies, infrastructure and spam campaigns. We also utilized our spam SMS data to evaluate the robustness of the spam countermeasures put in place by the SMS ecosystem, including anti-spam services, bulk SMS services, and text messaging apps. Our evaluation shows that such protection cannot effectively handle those spam samples: either introducing significant false positives or missing a large number of newly reported spam messages.
Multi-modality Associative Bridging through Memory: Speech Sound Recollected from Face Video
Venue: arXiv cs.CV 2022
Year: 2022

In this paper, we introduce a novel audio-visual multi-modal bridging framework that can utilize both audio and visual information, even with uni-modal inputs. We exploit a memory network that stores source (i.e., visual) and target (i.e., audio) modal representations, where source modal representation is what we are given, and target modal representations are what we want to obtain from the memory network. We then construct an associative bridge between source and target memories that considers the interrelationship between the two memories. By learning the interrelationship through the associative bridge, the proposed bridging framework is able to obtain the target modal representations inside the memory network, even with the source modal input only, and it provides rich information for its downstream tasks. We apply the proposed framework to two tasks: lip reading and speech reconstruction from silent video. Through the proposed associative bridge and modality-specific memories, each task knowledge is enriched with the recalled audio context, achieving state-of-the-art performance. We also verify that the associative bridge properly relates the source and target memories.
Empirical Analysis of Lifelog Data using Optimal Feature Selection based Unsupervised Logistic Regression (OFS-ULR) Model with Spark Streaming
Venue: arXiv cs.LG 2022
Year: 2022

Recent advancement in the field of pervasive healthcare monitoring systems causes the generation of a huge amount of lifelog data in real-time. Chronic diseases are one of the most serious health challenges in developing and developed countries. According to WHO, this accounts for 73% of all deaths and 60% of the global burden of diseases. Chronic disease classification models are now harnessing the potential of lifelog data to explore better healthcare practices. This paper is to construct an optimal feature selection-based unsupervised logistic regression model (OFS-ULR) to classify chronic diseases. Since lifelog data analysis is crucial due to its sensitive nature; thus the conventional classification models show limited performance. Therefore, designing new classifiers for the classification of chronic diseases using lifelog data is the need of the age. The vital part of building a good model depends on pre-processing of the dataset, identifying important features, and then training a learning algorithm with suitable hyper parameters for better performance. The proposed approach improves the performance of existing methods using a series of steps such as (i) removing redundant or invalid instances, (ii) making the data labelled using clustering and partitioning the data into classes, (iii) identifying the suitable subset of features by applying either some domain knowledge or selection algorithm, (iv) hyper parameter tuning for models to get best results, and (v) performance evaluation using Spark streaming environment. For this purpose, two-time series datasets are used in the experiment to compute the accuracy, recall, precision, and f1-score. The experimental analysis proves the suitability of the proposed approach as compared to the conventional classifiers and our newly constructed model achieved highest accuracy and reduced training complexity among all among all.
Multivariate Microaggregation of Set-Valued Data
Venue: arXiv cs.CR 2022
Year: 2022

Data controllers manage immense data, and occasionally, it is released publically to help the researchers to conduct their studies. However, this publically shared data may hold personally identifiable information (PII) that can be collected to re-identify a person. Therefore, an effective anonymization mechanism is required to anonymize such data before it is released publically. Microaggregation is one of the Statistical Disclosure Control (SDC) methods that are widely used by many researchers. This method adapts the k-anonymity principle to generate k-indistinguishable records in the same clusters to preserve the privacy of the individuals. However, in these methods, the size of the clusters is fixed (i.e., k records), and the clusters generated through these methods may hold non-homogeneous records. By considering these issues, we propose an adaptive size clustering technique that aggregates homogeneous records in similar clusters, and the size of the clusters is determined after the semantic analysis of the records. To achieve this, we extend the MDAV microaggregation algorithm to semantically analyze the unstructured records by relying on the taxonomic databases (i.e., WordNet), and then aggregating them in homogeneous clusters. Furthermore, we propose a distance measure that determines the extent to which the records differ from each other, and based on this, homogeneous adaptive clusters are constructed. In experiments, we measured the cohesiveness of the clusters in order to gauge the homogeneity of records. In addition, a method is proposed to measure information loss caused by the redaction method. In experiments, the results show that the proposed mechanism outperforms the existing state-of-the-art solutions.
PRADA: Practical Black-Box Adversarial Attacks against Neural Ranking Models
Venue: arXiv cs.IR 2022
Year: 2022

Neural ranking models (NRMs) have shown remarkable success in recent years, especially with pre-trained language models. However, deep neural models are notorious for their vulnerability to adversarial examples. Adversarial attacks may become a new type of web spamming technique given our increased reliance on neural information retrieval models. Therefore, it is important to study potential adversarial attacks to identify vulnerabilities of NRMs before they are deployed. In this paper, we introduce the Word Substitution Ranking Attack (WSRA) task against NRMs, which aims to promote a target document in rankings by adding adversarial perturbations to its text. We focus on the decision-based black-box attack setting, where the attackers cannot directly get access to the model information, but can only query the target model to obtain the rank positions of the partial retrieved list. This attack setting is realistic in real-world search engines. We propose a novel Pseudo Relevance-based ADversarial ranking Attack method (PRADA) that learns a surrogate model based on Pseudo Relevance Feedback (PRF) to generate gradients for finding the adversarial perturbations. Experiments on two web search benchmark datasets show that PRADA can outperform existing attack strategies and successfully fool the NRM with small indiscernible perturbations of text.
Detection of Dangerous Events on Social Media: A Perspective Review
Venue: arXiv cs.SI 2022
Year: 2022

Social media is an essential gateway of information and communication for people worldwide. The amount of time spent and reliance of people on social media makes it a vital resource for detecting events happening in real life. Thousands of significant events are posted by users every hour in the form of multimedia. Some individuals and groups target the audience to promote their agenda among these users. Their cause can threaten other groups and individuals who do not share the same views or have specific differences. Any group with a definitive cause cannot survive without the support which acts as a catalyst for their agenda. A phenomenon occurs where people are fed information that motivates them to act on their behalf and carry out their agenda. One is benefit results in the loss of the others by putting their lives, assets, physical and emotional health in danger. This paper introduces a concept of dangerous events to approach this problem and their three main types based on their characteristics: action, scenarios, and sentiment-based dangerous events.
Learning the Proximity Operator in Unfolded ADMM for Phase Retrieval
Venue: arXiv cs.SD 2022
Year: 2022

This paper considers the phase retrieval (PR) problem, which aims to reconstruct a signal from phaseless measurements such as magnitude or power spectrograms. PR is generally handled as a minimization problem involving a quadratic loss. Recent works have considered alternative discrepancy measures, such as the Bregman divergences, but it is still challenging to tailor the optimal loss for a given setting. In this paper we propose a novel strategy to automatically learn the optimal metric for PR. We unfold a recently introduced ADMM algorithm into a neural network, and we emphasize that the information about the loss used to formulate the PR problem is conveyed by the proximity operator involved in the ADMM updates. Therefore, we replace this proximity operator with trainable activation functions: learning these in a supervised setting is then equivalent to learning an optimal metric for PR. Experiments conducted with speech signals show that our approach outperforms the baseline ADMM, using a light and interpretable neural architecture.
Synthetic Graph Generation to Benchmark Graph Learning
Venue: arXiv cs.LG 2022
Year: 2022

Graph learning algorithms have attained state-of-the-art performance on many graph analysis tasks such as node classification, link prediction, and clustering. It has, however, become hard to track the field's burgeoning progress. One reason is due to the very small number of datasets used in practice to benchmark the performance of graph learning algorithms. This shockingly small sample size (~10) allows for only limited scientific insight into the problem. In this work, we aim to address this deficiency. We propose to generate synthetic graphs, and study the behaviour of graph learning algorithms in a controlled scenario. We develop a fully-featured synthetic graph generator that allows deep inspection of different models. We argue that synthetic graph generations allows for thorough investigation of algorithms and provides more insights than overfitting on three citation datasets. In the case study, we show how our framework provides insight into unsupervised and supervised graph neural network models.
The Parking Problem: A Game-Theoretic Solution
Venue: arXiv cs.MA 2022
Year: 2022

In this paper, we propose a game-theoretic solution to the parking problem, by exploiting a strategic-reasoning approach for multi-agent systems. Precisely, cars are modeled by agents interacting among them in a multi-player game setting, whose aim is to get a free slot parking-place satisfying their own constraints. The overall assignment is then given as a Nash equilibrium solution. We come up with an algorithm (and its implementation in a tool) that works in quadratic time. We give evidence of the benefits of our approach by running our tool on a large hospital parking space.
Order Selection Prophet Inequality: From Threshold Optimization to Arrival Time Design
Venue: arXiv cs.DS 2022
Year: 2022

In the classical prophet inequality, a gambler faces a sequence of items, whose values are drawn independently from known distributions. Upon the arrival of each item, its value is realized and the gambler either accepts it and the game ends, or irrevocably rejects it and continues to the next item. The goal is to maximize the value of the selected item and compete against the expected maximum value of all items. A tight competitive ratio of $\frac{1}{2}$ is established in the classical setting and various relaxations have been proposed to surpass the barrier, including the i.i.d. model, the order selection model, and the random order model. In this paper, we advance the study of the order selection prophet inequality, in which the gambler is given the extra power for selecting the arrival order of the items. Our main result is a $0.725$-competitive algorithm, that substantially improves the state-of-the-art $0.669$ ratio by Correa, Saona and Ziliotto~(Math. Program. 2021), achieved in the harder random order model. Recently, Agrawal, Sethuraman and Zhang~(EC 2021) proved that the task of selecting the optimal order is NP-hard. Despite this fact, we introduce a novel algorithm design framework that translates the discrete order selection problem into a continuous arrival time design problem. From this perspective, we can focus on the arrival time design without worrying about the threshold optimization afterwards. As a side result, we achieve the optimal $0.745$ competitive ratio by applying our algorithm to the i.i.d. model.
Satellite Monitoring of Terrestrial Plastic Waste
Venue: arXiv cs.CY 2022
Year: 2022

Plastic waste is a significant environmental pollutant that is difficult to monitor. We created a system of neural networks to analyze spectral, spatial, and temporal components of Sentinel-2 satellite data to identify terrestrial aggregations of waste. The system works at continental scale. We evaluated performance in Indonesia and detected 374 waste aggregations, more than double the number of sites found in public databases. The same system deployed across twelve countries in Southeast Asia identifies 996 subsequently confirmed waste sites. For each detected site, we algorithmically monitor waste site footprints through time and cross-reference other datasets to generate physical and social metadata. 19% of detected waste sites are located within 200 m of a waterway. Numerous sites sit directly on riverbanks, with high risk of ocean leakage.
The Group Loss++: A deeper look into group loss for deep metric learning
Venue: arXiv cs.CV 2022
Year: 2022

Deep metric learning has yielded impressive results in tasks such as clustering and image retrieval by leveraging neural networks to obtain highly discriminative feature embeddings, which can be used to group samples into different classes. Much research has been devoted to the design of smart loss functions or data mining strategies for training such networks. Most methods consider only pairs or triplets of samples within a mini-batch to compute the loss function, which is commonly based on the distance between embeddings. We propose Group Loss, a loss function based on a differentiable label-propagation method that enforces embedding similarity across all samples of a group while promoting, at the same time, low-density regions amongst data points belonging to different groups. Guided by the smoothness assumption that "similar objects should belong to the same group", the proposed loss trains the neural network for a classification task, enforcing a consistent labelling amongst samples within a class. We design a set of inference strategies tailored towards our algorithm, named Group Loss++ that further improve the results of our model. We show state-of-the-art results on clustering and image retrieval on four retrieval datasets, and present competitive results on two person re-identification datasets, providing a unified framework for retrieval and re-identification.
Coverage hole detection in WSN with force-directed algorithm and transfer learning
Venue: arXiv cs.NI 2022
Year: 2022

Coverage hole detection is an important research problem in wireless sensor network research community. However, distributed approaches proposed in recent years for coverage hole detection problem have high computational complexity. In this paper, we propose a novel approach for coverage hole detection in wireless sensor networks called FD-TL (Force-directed and Transfer-learning) which is based on layout generation capability of Force-directed Algorithms and image recognition power of Convolutional Neural Network with transfer learning. In contrast to existing approaches, the proposed approach is a pure topology-based approach since FD-TL can detect both triangular and non-triangular coverage holes from a wireless sensor network based on the input network topology without relying on the physical locations of the anchor nodes. In FD-TL, a Force-directed Algorithm is used to generate a series of possible layouts from a given input topology. Next, a Convolutional Neural Network is used to recognize potential coverage holes from the generated layouts. During the training phase, a transfer learning method is used to aid the recognition process. Experimental results show that FD-TL method can achieve 90% sensitivity and 96% specificity for coverage hole detection in wireless sensor networks.
A Performance Evaluation of Nomon: A Flexible Interface for Noisy Single-Switch Users
Venue: arXiv cs.HC 2022
Year: 2022

Some individuals with motor impairments communicate using a single switch -- such as a button click, air puff, or blink. Row-column scanning provides a method for choosing items arranged in a grid using a single switch. An alternative, Nomon, allows potential selections to be arranged arbitrarily rather than requiring a grid (as desired for gaming, drawing, etc.) -- and provides an alternative probabilistic selection method. While past results suggest that Nomon may be faster and easier to use than row-column scanning, no work has yet quantified performance of the two methods over longer time periods or in tasks beyond writing. In this paper, we also develop and validate a webcam-based switch that allows a user without a motor impairment to approximate the response times of a motor-impaired single switch user; although the approximation is not a replacement for testing with single-switch users, it allows us to better initialize, calibrate, and evaluate our method. Over 10 sessions with the webcam switch, we found users typed faster and more easily with Nomon than with row-column scanning. The benefits of Nomon were even more pronounced in a picture-selection task. Evaluation and feedback from a motor-impaired switch user further supports the promise of Nomon.