OpenRaiser/Intern-Atlas huggingface dataset:
Combining Generators of Adversarial Malware Examples to Increase Evasion Rate
Venue: arXiv cs.CR 2023
Year: 2023

Antivirus developers are increasingly embracing machine learning as a key component of malware defense. While machine learning achieves cutting-edge outcomes in many fields, it also has weaknesses that are exploited by several adversarial attack techniques. Many authors have presented both white-box and black-box generators of adversarial malware examples capable of bypassing malware detectors with varying success. We propose to combine contemporary generators in order to increase their potential. Combining different generators can create more sophisticated adversarial examples that are more likely to evade anti-malware tools. We demonstrated this technique on five well-known generators and recorded promising results. The best-performing combination of AMG-random and MAB-Malware generators achieved an average evasion rate of 15.9% against top-tier antivirus products. This represents an average improvement of more than 36% and 627% over using only the AMG-random and MAB-Malware generators, respectively. The generator that benefited the most from having another generator follow its procedure was the FGSM injection attack, which improved the evasion rate on average between 91.97% and 1,304.73%, depending on the second generator used. These results demonstrate that combining different generators can significantly improve their effectiveness against leading antivirus programs.
Revenue Management without Demand Forecasting: A Data-Driven Approach for Bid Price Generation
Venue: arXiv cs.LG 2023
Year: 2023

Traditional revenue management relies on long and stable historical data and predictable demand patterns. However, meeting those requirements is not always possible. Many industries face demand volatility on an ongoing basis, an example would be air cargo which has much shorter booking horizon with highly variable batch arrivals. Even for passenger airlines where revenue management (RM) is well-established, reacting to external shocks is a well-known challenge that requires user monitoring and manual intervention. Moreover, traditional RM comes with strict data requirements including historical bookings and pricing even in the absence of any bookings, spanning multiple years. For companies that have not established a practice in RM, that type of extensive data is usually not available. We present a data-driven approach to RM which eliminates the need for demand forecasting and optimization techniques. We develop a methodology to generate bid prices using historical booking data only. Our approach is an ex-post greedy heuristic to estimate proxies for marginal opportunity costs as a function of remaining capacity and time-to-departure solely based on historical booking data. We utilize a neural network algorithm to project bid price estimations into the future. We conduct an extensive simulation study where we measure performance of our methodology compared to that of an optimally generated bid price using dynamic programming (DP). We also extend our simulations to measure performance of both data-driven and DP generated bid prices under the presence of demand misspecification. Our results show that our data-driven methodology stays near a theoretical optimum (<1% revenue gap) for a wide-range of settings, whereas DP deviates more significantly from the optimal as the magnitude of misspecification is increased. This highlights the robustness of our data-driven approach.
Investigation of ensemble methods for the detection of deepfake face manipulations
Venue: arXiv cs.CV 2023
Year: 2023

The recent wave of AI research has enabled a new brand of synthetic media, called deepfakes. Deepfakes have impressive photorealism, which has generated exciting new use cases but also raised serious threats to our increasingly digital world. To mitigate these threats, researchers have tried to come up with new methods for deepfake detection that are more effective than traditional forensics and heavily rely on deep AI technology. In this paper, following up on encouraging prior work for deepfake detection with attribution and ensemble techniques, we explore and compare multiple designs for ensemble detectors. The goal is to achieve robustness and good generalization ability by leveraging ensembles of models that specialize in different manipulation categories. Our results corroborate that ensembles can achieve higher accuracy than individual models when properly tuned, while the generalization ability relies on access to a large number of training data for a diverse set of known manipulations.
Efficient Quality-Diversity Optimization through Diverse Quality Species
Venue: arXiv cs.LG 2023
Year: 2023

A prevalent limitation of optimizing over a single objective is that it can be misguided, becoming trapped in local optimum. This can be rectified by Quality-Diversity (QD) algorithms, where a population of high-quality and diverse solutions to a problem is preferred. Most conventional QD approaches, for example, MAP-Elites, explicitly manage a behavioral archive where solutions are broken down into predefined niches. In this work, we show that a diverse population of solutions can be found without the limitation of needing an archive or defining the range of behaviors in advance. Instead, we break down solutions into independently evolving species and use unsupervised skill discovery to learn diverse, high-performing solutions. We show that this can be done through gradient-based mutations that take on an information theoretic perspective of jointly maximizing mutual information and performance. We propose Diverse Quality Species (DQS) as an alternative to archive-based QD algorithms. We evaluate it over several simulated robotic environments and show that it can learn a diverse set of solutions from varying species. Furthermore, our results show that DQS is more sample-efficient and performant when compared to other QD algorithms. Relevant code and hyper-parameters are available at: https://github.com/rwickman/NEAT_RL.
A framework for fully autonomous design of materials via multiobjective optimization and active learning: challenges and next steps
Venue: arXiv cs.LG 2023
Year: 2023

In order to deploy machine learning in a real-world self-driving laboratory where data acquisition is costly and there are multiple competing design criteria, systems need to be able to intelligently sample while balancing performance trade-offs and constraints. For these reasons, we present an active learning process based on multiobjective black-box optimization with continuously updated machine learning models. This workflow is built on open-source technologies for real-time data streaming and modular multiobjective optimization software development. We demonstrate a proof of concept for this workflow through the autonomous operation of a continuous-flow chemistry laboratory, which identifies ideal manufacturing conditions for the electrolyte 2,2,2-trifluoroethyl methyl carbonate.
Documentation Practices in Agile Software Development: A Systematic Literature Review
Venue: arXiv cs.SE 2023
Year: 2023

Context: Agile development methodologies in the software industry have increased significantly over the past decade. Although one of the main aspects of agile software development (ASD) is less documentation, there have always been conflicting opinions about what to document in ASD. Objective: This study aims to systematically identify what to document in ASD, which documentation tools and methods are in use, and how those tools can overcome documentation challenges. Method: We performed a systematic literature review of the studies published between 2010 and June 2021 that discusses agile documentation. Then, we systematically selected a pool of 74 studies using particular inclusion and exclusion criteria. After that, we conducted a quantitative and qualitative analysis using the data extracted from these studies. Results: We found nine primary vital factors to add to agile documentation from our pool of studies. Our analysis shows that agile practitioners have primarily developed their documentation tools and methods focusing on these factors. The results suggest that the tools and techniques in agile documentation are not in sync, and they separately solve different challenges. Conclusions: Based on our results and discussion, researchers and practitioners will better understand how current agile documentation tools and practices perform. In addition, investigation of the synchronization of these tools will be helpful in future research and development.
Pilgrimage to Pureland: Art, Perception and the Wutai Mural VR Reconstruction
Venue: arXiv cs.HC 2023
Year: 2023

Virtual reality (VR) supports audiences to engage with cultural heritage proactively. We designed an easy-to-access and guided Pilgrimage To Pureland VR reconstruction of Dunhuang Mogao Grottoes to offer the general public an accessible and engaging way to explore the Dunhuang murals. We put forward an immersive VR reconstruction paradigm that can efficiently convert complex 2D artwork into a VR environment. We reconstructed the Mt. Wutai pilgrimage mural in Cave 61, Mogao Grottoes, Dunhuang, into an immersive VR environment and created a plot-based and interactive experience that offers users a more accessible solution to visit, understand and appreciate the complex religious, historical, and artistic value of Dunhuang murals. \textcolor{black}{Our system remarkably smoothed users' approaches to those elusive cultural heritages. Appropriate adaptation of plots and 3D VR transfer consistent with the original art style could enhance the accessibility of cultural heritages.
Surveillance Face Presentation Attack Detection Challenge
Venue: arXiv cs.CV 2023
Year: 2023

Face Anti-spoofing (FAS) is essential to secure face recognition systems from various physical attacks. However, most of the studies lacked consideration of long-distance scenarios. Specifically, compared with FAS in traditional scenes such as phone unlocking, face payment, and self-service security inspection, FAS in long-distance such as station squares, parks, and self-service supermarkets are equally important, but it has not been sufficiently explored yet. In order to fill this gap in the FAS community, we collect a large-scale Surveillance High-Fidelity Mask (SuHiFiMask). SuHiFiMask contains $10,195$ videos from $101$ subjects of different age groups, which are collected by $7$ mainstream surveillance cameras. Based on this dataset and protocol-$3$ for evaluating the robustness of the algorithm under quality changes, we organized a face presentation attack detection challenge in surveillance scenarios. It attracted 180 teams for the development phase with a total of 37 teams qualifying for the final round. The organization team re-verified and re-ran the submitted code and used the results as the final ranking. In this paper, we present an overview of the challenge, including an introduction to the dataset used, the definition of the protocol, the evaluation metrics, and the announcement of the competition results. Finally, we present the top-ranked algorithms and the research ideas provided by the competition for attack detection in long-range surveillance scenarios.
Can SAM Segment Polyps?
Venue: arXiv cs.CV 2023
Year: 2023

Recently, Meta AI Research releases a general Segment Anything Model (SAM), which has demonstrated promising performance in several segmentation tasks. As we know, polyp segmentation is a fundamental task in the medical imaging field, which plays a critical role in the diagnosis and cure of colorectal cancer. In particular, applying SAM to the polyp segmentation task is interesting. In this report, we evaluate the performance of SAM in segmenting polyps, in which SAM is under unprompted settings. We hope this report will provide insights to advance this polyp segmentation field and promote more interesting works in the future. This project is publicly at https://github.com/taozh2017/SAMPolyp.
Herder Ants: Ant Colony Optimization with Aphids for Discrete Event-Triggered Dynamic Optimization Problems
Venue: arXiv cs.NE 2023
Year: 2023

Currently available dynamic optimization strategies for Ant Colony Optimization (ACO) algorithm offer a trade-off of slower algorithm convergence or significant penalty to solution quality after each dynamic change occurs. This paper proposes a discrete dynamic optimization strategy called Ant Colony Optimization (ACO) with Aphids, modelled after a real-world symbiotic relationship between ants and aphids. ACO with Aphids strategy is designed to improve solution quality of discrete domain Dynamic Optimization Problems (DOPs) with event-triggered discrete dynamism. The proposed strategy aims to improve the inter-state convergence rate throughout the entire dynamic optimization. It does so by minimizing the fitness penalty and maximizing the convergence speed that occurs after the dynamic change. This strategy is tested against Full-Restart and Pheromone-Sharing strategies implemented on the same ACO core algorithm solving Dynamic Multidimensional Knapsack Problem (DMKP) benchmarks. ACO with Aphids has demonstrated superior performance over the Pheromone-Sharing strategy in every test on average gap reduced by 29.2%. Also, ACO with Aphids has outperformed the Full-Restart strategy for large datasets groups, and the overall average gap is reduced by 52.5%.
Optimal Self-Dual Inequalities to Order Polarized BECs
Venue: arXiv cs.IT 2023
Year: 2023

$1 - (1-x^M) ^ {2^M} > (1 - (1-x)^M) ^{2^M}$ is proved for all $x \in [0,1]$ and all $M > 1$. This confirms a conjecture about polar code, made by Wu and Siegel in 2019, that $W^{0^m 1^M}$ is more reliable than $W^{1^m 0^M}$, where $W$ is any binary erasure channel and $M = 2^m$. The proof relies on a remarkable relaxation that $m$ needs not be an integer, a cleverly crafted hexavariate ordinary differential equation, and a genius generalization of Green's theorem that concerns function composition. The resulting inequality is optimal, $M$ cannot be $2^m - 1$, witnessing how far polar code deviates from Reed--Muller code.
Density Devolution for Ordering Synthetic Channels
Venue: arXiv cs.IT 2023
Year: 2023

Constructing a polar code is all about selecting a subset of rows from a Kronecker power of $[^1_1{}^0_1]$. It is known that, under successive cancellation decoder, some rows are Pareto-better than the other. For instance, whenever a user sees a substring $01$ in the binary expansion of a row index and replaces it with $10$, the user obtains a row index that is always more welcomed. We call this a "rule" and denote it by $10 \succcurlyeq 01$. In present work, we first enumerate some rules over binary erasure channels such as $1001 \succcurlyeq 0110$ and $10001 \succcurlyeq 01010$ and $10101 \succcurlyeq 01110$. We then summarize them using a "rule of rules": if $10a \succcurlyeq 01b$ is a rule, where $a$ and $b$ are arbitrary binary strings, then $100a \succcurlyeq 010b$ and $101a \succcurlyeq 011b$ are rules. This work's main contribution is using field theory, Galois theory, and numerical analysis to develop an algorithm that decides if a rule of rules is mathematically sound. We apply the algorithm to enumerate some rules of rules. Each rule of rule is capable of generating an infinite family of rules. For instance, $10c01 \succcurlyeq 01c10$ for arbitrary binary string $c$ can be generated. We found an application of $10c01 \succcurlyeq 01c10$ that is related to integer partition and the dominance order therein.
FedBlockHealth: A Synergistic Approach to Privacy and Security in IoT-Enabled Healthcare through Federated Learning and Blockchain
Venue: arXiv cs.CR 2023
Year: 2023

The rapid adoption of Internet of Things (IoT) devices in healthcare has introduced new challenges in preserving data privacy, security and patient safety. Traditional approaches need to ensure security and privacy while maintaining computational efficiency, particularly for resource-constrained IoT devices. This paper proposes a novel hybrid approach combining federated learning and blockchain technology to provide a secure and privacy-preserved solution for IoT-enabled healthcare applications. Our approach leverages a public-key cryptosystem that provides semantic security for local model updates, while blockchain technology ensures the integrity of these updates and enforces access control and accountability. The federated learning process enables a secure model aggregation without sharing sensitive patient data. We implement and evaluate our proposed framework using EMNIST datasets, demonstrating its effectiveness in preserving data privacy and security while maintaining computational efficiency. The results suggest that our hybrid approach can significantly enhance the development of secure and privacy-preserved IoT-enabled healthcare applications, offering a promising direction for future research in this field.
Privacy-Enhanced Living: A Local Differential Privacy Approach to Secure Smart Home Data
Venue: arXiv cs.CR 2023
Year: 2023

The rapid expansion of Internet of Things (IoT) devices in smart homes has significantly improved the quality of life, offering enhanced convenience, automation, and energy efficiency. However, this proliferation of connected devices raises critical concerns regarding security and privacy of the user data. In this paper, we propose a differential privacy-based system to ensure comprehensive security for data generated by smart homes. We employ the randomized response technique for the data and utilize Local Differential Privacy (LDP) to achieve data privacy. The data is then transmitted to an aggregator, where an obfuscation method is applied to ensure individual anonymity. Furthermore, we implement the Hidden Markov Model (HMM) technique at the aggregator level and apply differential privacy to the private data received from smart homes. Consequently, our approach achieves a dual layer of privacy protection, addressing the security concerns associated with IoT devices in smart cities.
Autoencoders with Intrinsic Dimension Constraints for Learning Low Dimensional Image Representations
Venue: arXiv cs.CV 2023
Year: 2023

Autoencoders have achieved great success in various computer vision applications. The autoencoder learns appropriate low dimensional image representations through the self-supervised paradigm, i.e., reconstruction. Existing studies mainly focus on the minimizing the reconstruction error on pixel level of image, while ignoring the preservation of Intrinsic Dimension (ID), which is a fundamental geometric property of data representations in Deep Neural Networks (DNNs). Motivated by the important role of ID, in this paper, we propose a novel deep representation learning approach with autoencoder, which incorporates regularization of the global and local ID constraints into the reconstruction of data representations. This approach not only preserves the global manifold structure of the whole dataset, but also maintains the local manifold structure of the feature maps of each point, which makes the learned low-dimensional features more discriminant and improves the performance of the downstream algorithms. To our best knowledge, existing works are rare and limited on exploiting both global and local ID invariant properties on the regularization of autoencoders. Numerical experimental results on benchmark datasets (Extended Yale B, Caltech101 and ImageNet) show that the resulting regularized learning models achieve better discriminative representations for downstream tasks including image classification and clustering.
Handling Heavy Occlusion in Dense Crowd Tracking by Focusing on the Heads
Venue: arXiv cs.CV 2023
Year: 2023

With the rapid development of deep learning, object detection and tracking play a vital role in today's society. Being able to identify and track all the pedestrians in the dense crowd scene with computer vision approaches is a typical challenge in this field, also known as the Multiple Object Tracking (MOT) challenge. Modern trackers are required to operate on more and more complicated scenes. According to the MOT20 challenge result, the pedestrian is 4 times denser than the MOT17 challenge. Hence, improving the ability to detect and track in extremely crowded scenes is the aim of this work. In light of the occlusion issue with the human body, the heads are usually easier to identify. In this work, we have designed a joint head and body detector in an anchor-free style to boost the detection recall and precision performance of pedestrians in both small and medium sizes. Innovatively, our model does not require information on the statistical head-body ratio for common pedestrians detection for training. Instead, the proposed model learns the ratio dynamically. To verify the effectiveness of the proposed model, we evaluate the model with extensive experiments on different datasets, including MOT20, Crowdhuman, and HT21 datasets. As a result, our proposed method significantly improves both the recall and precision rate on small & medium sized pedestrians and achieves state-of-the-art results in these challenging datasets.
Pointwise Maximal Leakage on General Alphabets
Venue: arXiv cs.IT 2023
Year: 2023

Pointwise maximal leakage (PML) is an operationally meaningful privacy measure that quantifies the amount of information leaking about a secret $X$ to a single outcome of a related random variable $Y$. In this paper, we extend the notion of PML to random variables on arbitrary probability spaces. We develop two new definitions: First, we extend PML to countably infinite random variables by considering adversaries who aim to guess the value of discrete (finite or countably infinite) functions of $X$. Then, we consider adversaries who construct estimates of $X$ that maximize the expected value of their corresponding gain functions. We use this latter setup to introduce a highly versatile form of PML that captures many scenarios of practical interest whose definition requires no assumptions about the underlying probability spaces.
Towards a Switching-Algebraic Theory of Weighted Monotone Voting Systems: The Case of Banzhaf Voting Indices
Venue: arXiv cs.GT 2023
Year: 2023

This paper provides a serious attempt towards constructing a switching-algebraic theory for weighted monotone voting systems, whether they are scalar-weighted or vector-weighted. The paper concentrates on the computation of a prominent index of voting powers, viz., the Banzhaf voting index. This computation involves two distinct operations: (a) either Boolean differencing (Boolean differentiation) or Boolean quotient construction (Boolean restriction), and (b) computation of the weight (the number of true vectors or minterms) of a switching function. We introduce novel Boolean-based symmetry-aware techniques for computing the Banzhaf index by way of four voting systems. The paper finally outlines further steps needed towards the establishment of a full-fledged switching-algebraic theory of weighted monotone voting systems. Througout the paper, a tutorial flavour is retained, multiple solutions of consistent results are given, and a liasion is established among game-theoretic voting theory, switching algebra, and sytem reliability analysis.