OpenRaiser/Intern-Atlas huggingface dataset:
Generalisation and the Risk--Entropy Curve
Venue: arXiv cs.LG 2022
Year: 2022

In this paper we show that the expected generalisation performance of a learning machine is determined by the distribution of risks or equivalently its logarithm -- a quantity we term the risk entropy -- and the fluctuations in a quantity we call the training ratio. We show that the risk entropy can be empirically inferred for deep neural network models using Markov Chain Monte Carlo techniques. Results are presented for different deep neural networks on a variety of problems. The asymptotic behaviour of the risk entropy acts in an analogous way to the capacity of the learning machine, but the generalisation performance experienced in practical situations is determined by the behaviour of the risk entropy before the asymptotic regime is reached. This performance is strongly dependent on the distribution of the data (features and targets) and not just on the capacity of the learning machine.
textless-lib: a Library for Textless Spoken Language Processing
Venue: arXiv cs.CL 2022
Year: 2022

Textless spoken language processing research aims to extend the applicability of standard NLP toolset onto spoken language and languages with few or no textual resources. In this paper, we introduce textless-lib, a PyTorch-based library aimed to facilitate research in this research area. We describe the building blocks that the library provides and demonstrate its usability by discuss three different use-case examples: (i) speaker probing, (ii) speech resynthesis and compression, and (iii) speech continuation. We believe that textless-lib substantially simplifies research the textless setting and will be handful not only for speech researchers but also for the NLP community at large. The code, documentation, and pre-trained models are available at https://github.com/facebookresearch/textlesslib/ .
Knowledge Graph Reasoning with Logics and Embeddings: Survey and Perspective
Venue: arXiv cs.AI 2022
Year: 2022

Knowledge graph (KG) reasoning is becoming increasingly popular in both academia and industry. Conventional KG reasoning based on symbolic logic is deterministic, with reasoning results being explainable, while modern embedding-based reasoning can deal with uncertainty and predict plausible knowledge, often with high efficiency via vector computation. A promising direction is to integrate both logic-based and embedding-based methods, with the vision to have advantages of both. It has attracted wide research attention with more and more works published in recent years. In this paper, we comprehensively survey these works, focusing on how logics and embeddings are integrated. We first briefly introduce preliminaries, then systematically categorize and discuss works of logic and embedding-aware KG reasoning from different perspectives, and finally conclude and discuss the challenges and further directions.
Characterising Cybercriminals: A Review
Venue: arXiv cs.CY 2022
Year: 2022

This review provides an overview of current research on the known characteristics and motivations of offenders engaging in cyber-dependent crimes. Due to the shifting dynamics of cybercriminal behaviour, and the availability of prior reviews in 2013, this review focuses on original research conducted from 2012 onwards, although some older studies that were not included in prior reviews are also considered. As a basis for interpretation of results, a limited quality assessment was also carried out on included studies through examination of key indicators.
Towards a Unified Pandemic Management Architecture: Survey, Challenges and Future Directions
Venue: arXiv cs.CY 2022
Year: 2022

The pandemic caused by SARS-CoV-2 has left an unprecedented impact on health, economy and society worldwide. Emerging strains are making pandemic management increasingly challenging. There is an urge to collect epidemiological, clinical, and physiological data to make an informed decision on mitigation measures. Advances in the Internet of Things (IoT) and edge computing provide solutions for pandemic management through data collection and intelligent computation. While existing data-driven architectures attempt to automate decision-making, they do not capture the multifaceted interaction among computational models, communication infrastructure, and the generated data. In this paper, we perform a survey of the existing approaches for pandemic management, including online data repositories and contact-tracing applications. We then envision a unified pandemic management architecture that leverages the IoT and edge computing to automate recommendations on vaccine distribution, dynamic lockdown, mobility scheduling and pandemic prediction. We elucidate the flow of data among the layers of the architecture, namely, cloud, edge and end device layers. Moreover, we address the privacy implications, threats, regulations, and existing solutions that may be adapted to optimize the utility of health data with security guarantees. The paper ends with a lowdown on the limitations of the architecture and research directions to enhance its practicality.
Random Walks for Adversarial Meshes
Venue: arXiv cs.CV 2022
Year: 2022

A polygonal mesh is the most-commonly used representation of surfaces in computer graphics. Therefore, it is not surprising that a number of mesh classification networks have recently been proposed. However, while adversarial attacks are wildly researched in 2D, the field of adversarial meshes is under explored. This paper proposes a novel, unified, and general adversarial attack, which leads to misclassification of several state-of-the-art mesh classification neural networks. Our attack approach is black-box, i.e. it has access only to the network's predictions, but not to the network's full architecture or gradients. The key idea is to train a network to imitate a given classification network. This is done by utilizing random walks along the mesh surface, which gather geometric information. These walks provide insight onto the regions of the mesh that are important for the correct prediction of the given classification network. These mesh regions are then modified more than other regions in order to attack the network in a manner that is barely visible to the naked eye.
Perspectives on risk prioritization of data center vulnerabilities using rank aggregation and multi-objective optimization
Venue: arXiv cs.CR 2022
Year: 2022

Nowadays, data has become an invaluable asset to entities and companies, and keeping it secure represents a major challenge. Data centers are responsible for storing data provided by software applications. Nevertheless, the number of vulnerabilities has been increasing every day. Managing such vulnerabilities is essential for building a reliable and secure network environment. Releasing patches to fix security flaws in software is a common practice to handle these vulnerabilities. However, prioritization becomes crucial for organizations with an increasing number of vulnerabilities since time and resources to fix them are usually limited. This review intends to present a survey of vulnerability ranking techniques and promote a discussion on how multi-objective optimization could benefit the management of vulnerabilities risk prioritization. The state-of-the-art approaches for risk prioritization were reviewed, intending to develop an effective model for ranking vulnerabilities in data centers. The main contribution of this work is to point out multi-objective optimization as a not commonly explored but promising strategy to prioritize vulnerabilities, enabling better time management and increasing security.
The Perception of Filipinos on the Advent of Cryptocurrency and Non-Fungible Token (NFT) Games
Venue: arXiv cs.CY 2022
Year: 2022

This study aims to shed light on the rise of play-to-earn games in the Philippines alongside cryptocurrency. The lack of research and public understanding of its benefits and drawbacks prompted the researchers to investigate its market. As such, the study tried to look into the risks and benefits of crypto gaming if it would be regulated by the government, and how market volatility influences the churn rate of crypto games. The research used a descriptive study to determine the perception of people who are engaged in playing a crypto-game named as Axie Infinity. The results showed that most players spend their time playing Axie Infinity for about 1 to 4 hours a day. Predominantly, the return of investments for playing the game will take about 1 to 3 months. It also showed that these players agreed that there is a possible financial instability in a volatile market. With this, they have a high trust issue in terms of price manipulation, privacy and security, and its design and usability. Understanding the cryptocurrency market requires comprehending the perspective of the people who are engaged in a play-to-earn game, and their concerns are critical for any government actions aimed at regulating self-employed income earners playing (Non-fungible Tokens) NFT games in the Philippines.
Do Lessons from Metric Learning Generalize to Image-Caption Retrieval?
Venue: arXiv cs.CV 2022
Year: 2022

The triplet loss with semi-hard negatives has become the de facto choice for image-caption retrieval (ICR) methods that are optimized from scratch. Recent progress in metric learning has given rise to new loss functions that outperform the triplet loss on tasks such as image retrieval and representation learning. We ask whether these findings generalize to the setting of ICR by comparing three loss functions on two ICR methods. We answer this question negatively: the triplet loss with semi-hard negative mining still outperforms newly introduced loss functions from metric learning on the ICR task. To gain a better understanding of these outcomes, we introduce an analysis method to compare loss functions by counting how many samples contribute to the gradient w.r.t. the query representation during optimization. We find that loss functions that result in lower evaluation scores on the ICR task, in general, take too many (non-informative) samples into account when computing a gradient w.r.t. the query representation, which results in sub-optimal performance. The triplet loss with semi-hard negatives is shown to outperform the other loss functions, as it only takes one (hard) negative into account when computing the gradient.
Texture Aware Autoencoder Pre-training And Pairwise Learning Refinement For Improved Iris Recognition
Venue: arXiv cs.CV 2022
Year: 2022

This paper presents a texture aware end-to-end trainable iris recognition system, specifically designed for datasets like iris having limited training data. We build upon our previous stagewise learning framework with certain key optimization and architectural innovations. First, we pretrain a Stage-1 encoder network with an unsupervised autoencoder learning optimized with an additional data relation loss on top of usual reconstruction loss. The data relation loss enables learning better texture representation which is pivotal for a texture rich dataset such as iris. Robustness of Stage-1 feature representation is further enhanced with an auxiliary denoising task. Such pre-training proves beneficial for effectively training deep networks on data constrained iris datasets. Next, in Stage-2 supervised refinement, we design a pairwise learning architecture for an end-to-end trainable iris recognition system. The pairwise learning includes the task of iris matching inside the training pipeline itself and results in significant improvement in recognition performance compared to usual offline matching. We validate our model across three publicly available iris datasets and the proposed model consistently outperforms both traditional and deep learning baselines for both Within-Dataset and Cross-Dataset configurations
Damped Online Newton Step for Portfolio Selection
Venue: arXiv cs.LG 2022
Year: 2022

We revisit the classic online portfolio selection problem, where at each round a learner selects a distribution over a set of portfolios to allocate its wealth. It is known that for this problem a logarithmic regret with respect to Cover's loss is achievable using the Universal Portfolio Selection algorithm, for example. However, all existing algorithms that achieve a logarithmic regret for this problem have per-round time and space complexities that scale polynomially with the total number of rounds, making them impractical. In this paper, we build on the recent work by Haipeng et al. 2018 and present the first practical online portfolio selection algorithm with a logarithmic regret and whose per-round time and space complexities depend only logarithmically on the horizon. Behind our approach are two key technical novelties of independent interest. We first show that the Damped Online Newton steps can approximate mirror descent iterates well, even when dealing with time-varying regularizers. Second, we present a new meta-algorithm that achieves an adaptive logarithmic regret (i.e. a logarithmic regret on any sub-interval) for mixable losses.
Enhancing Healthcare System Using Blockchain Smart Contracts
Venue: arXiv cs.DC 2022
Year: 2022

The concept of blockchain has emerged as an effective solution for data-sensitive domains, such as healthcare, financial services, etc., due to its various attributes like immutability, non-repudiation, and availability. Thus, implementation of this technology in various domains rose exponentially; one of such fields is the healthcare supply chain. Managing healthcare supply chain processes effectively is very crucial for the healthcare system. Despite various innovations in the method of treatment methodologies, the healthcare supply chain management system is not up to the mark and lacks efficiency. The traditional healthcare supply chain system is time-consuming and lacks the work synergy among the various stakeholders of the supply chain. Thus, In this paper, we propose a framework based on blockchain smart contracts and decentralized storage to connect all the supply chain stakeholders. Smart contracts in the framework enforce and depict various interactions and transactions among the stakeholders, thus helping to automate these processes, promote transparency, improve efficiency, and minimize service time. The preliminary results show that the proposed framework is more efficient, secure, and economically feasible.
Judging a socially assistive robot (SAR) by its cover; The effect of body structure, outline, and color on users' perception
Venue: arXiv cs.HC 2022
Year: 2022

Socially assistive robots (SARs) aim to provide assistance through social interaction. Previous studies contributed to understanding users` perceptions and preferences regarding existing commercially available SARs. Yet, very few studies regarding SARs' appearance used designated SAR designs, and even fewer evaluated isolated visual qualities (VQ). In this work, we aim to assess the effect of isolated VQs systematically. To achieve this, we first conducted market research and deconstructed the VQs attributed to SARs. Then, a reconstruction of body structure, outline, and color scheme was done, resulting in the creation of 30 new SAR models that differ in their VQs, allowing us to isolate one character at a time. We used these new designs to evaluate users' preferences and perceptions in two empirical studies. Our empirical findings link VQs with perceptions of SAR characteristics. These can lead to forming guidelines for the industrial design processes of new SARs to match user expectations.
Defending against Reconstruction Attacks with Rényi Differential Privacy
Venue: arXiv cs.LG 2022
Year: 2022

Reconstruction attacks allow an adversary to regenerate data samples of the training set using access to only a trained model. It has been recently shown that simple heuristics can reconstruct data samples from language models, making this threat scenario an important aspect of model release. Differential privacy is a known solution to such attacks, but is often used with a relatively large privacy budget (epsilon > 8) which does not translate to meaningful guarantees. In this paper we show that, for a same mechanism, we can derive privacy guarantees for reconstruction attacks that are better than the traditional ones from the literature. In particular, we show that larger privacy budgets do not protect against membership inference, but can still protect extraction of rare secrets. We show experimentally that our guarantees hold against various language models, including GPT-2 finetuned on Wikitext-103.
One Configuration to Rule Them All? Towards Hyperparameter Transfer in Topic Models using Multi-Objective Bayesian Optimization
Venue: arXiv cs.CL 2022
Year: 2022

Topic models are statistical methods that extract underlying topics from document collections. When performing topic modeling, a user usually desires topics that are coherent, diverse between each other, and that constitute good document representations for downstream tasks (e.g. document classification). In this paper, we conduct a multi-objective hyperparameter optimization of three well-known topic models. The obtained results reveal the conflicting nature of different objectives and that the training corpus characteristics are crucial for the hyperparameter selection, suggesting that it is possible to transfer the optimal hyperparameter configurations between datasets.
On Dynamic Lifting and Effect Typing in Circuit Description Languages (Extended Version)
Venue: arXiv cs.PL 2022
Year: 2022

In the realm of quantum computing, circuit description languages represent a valid alternative to traditional QRAM-style languages. They indeed allow for finer control over the output circuit, without sacrificing flexibility nor modularity. We introduce a generalization of the paradigmatic lambda-calculus Proto-Quipper-M, itself modeling the core features of the quantum circuit description language Quipper. The extension, called Proto-Quipper-K, is meant to capture a very general form of dynamic lifting. This is made possible by the introduction of a rich type and effect system in which not only computations, but also the very types are effectful. The main results we give for the introduced language are the classic type soundness results, namely subject reduction and progress.
Active Uncertainty Reduction for Human-Robot Interaction: An Implicit Dual Control Approach
Venue: arXiv cs.RO 2022
Year: 2022

The ability to accurately predict human behavior is central to the safety and efficiency of robot autonomy in interactive settings. Unfortunately, robots often lack access to key information on which these predictions may hinge, such as people's goals, attention, and willingness to cooperate. Dual control theory addresses this challenge by treating unknown parameters of a predictive model as stochastic hidden states and inferring their values at runtime using information gathered during system operation. While able to optimally and automatically trade off exploration and exploitation, dual control is computationally intractable for general interactive motion planning, mainly due to the fundamental coupling between robot trajectory optimization and human intent inference. In this paper, we present a novel algorithmic approach to enable active uncertainty reduction for interactive motion planning based on the implicit dual control paradigm. Our approach relies on sampling-based approximation of stochastic dynamic programming, leading to a model predictive control problem that can be readily solved by real-time gradient-based optimization methods. The resulting policy is shown to preserve the dual control effect for a broad class of predictive human models with both continuous and categorical uncertainty. The efficacy of our approach is demonstrated with simulated driving examples.
Multi-Modal Data Fusion in Enhancing Human-Machine Interaction for Robotic Applications: A Survey
Venue: arXiv cs.HC 2022
Year: 2022

Human-machine interaction has been around for several decades now, with new applications emerging every day. One of the major goals that remain to be achieved is designing an interaction similar to how a human interacts with another human. Therefore, there is a need to develop interactive systems that could replicate a more realistic and easier human-machine interaction. On the other hand, developers and researchers need to be aware of state-of-the-art methodologies being used to achieve this goal. We present this survey to provide researchers with state-of-the-art data fusion technologies implemented using multiple inputs to accomplish a task in the robotic application domain. Moreover, the input data modalities are broadly classified into uni-modal and multi-modal systems and their application in myriad industries, including the health care industry, which contributes to the medical industry's future development. It will help the professionals to examine patients using different modalities. The multi-modal systems are differentiated by a combination of inputs used as a single input, e.g., gestures, voice, sensor, and haptic feedback. All these inputs may or may not be fused, which provides another classification of multi-modal systems. The survey concludes with a summary of technologies in use for multi-modal systems.
Further Collapses in TFNP
Venue: arXiv cs.CC 2022
Year: 2022

We show $\textsf{EOPL}=\textsf{PLS}\cap\textsf{PPAD}$. Here the class $\textsf{EOPL}$ consists of all total search problems that reduce to the End-of-Potential-Line problem, which was introduced in the works by Hubacek and Yogev (SICOMP 2020) and Fearnley et al. (JCSS 2020). In particular, our result yields a new simpler proof of the breakthrough collapse $\textsf{CLS}=\textsf{PLS}\cap\textsf{PPAD}$ by Fearnley et al. (STOC 2021). We also prove a companion result $\textsf{SOPL}=\textsf{PLS}\cap\textsf{PPADS}$, where $\textsf{SOPL}$ is the class associated with the Sink-of-Potential-Line problem.
Beyond Deterministic Translation for Unsupervised Domain Adaptation
Venue: arXiv cs.CV 2022
Year: 2022

In this work we challenge the common approach of using a one-to-one mapping ('translation') between the source and target domains in unsupervised domain adaptation (UDA). Instead, we rely on stochastic translation to capture inherent translation ambiguities. This allows us to (i) train more accurate target networks by generating multiple outputs conditioned on the same source image, leveraging both accurate translation and data augmentation for appearance variability, (ii) impute robust pseudo-labels for the target data by averaging the predictions of a source network on multiple translated versions of a single target image and (iii) train and ensemble diverse networks in the target domain by modulating the degree of stochasticity in the translations. We report improvements over strong recent baselines, leading to state-of-the-art UDA results on two challenging semantic segmentation benchmarks. Our code is available at https://github.com/elchiou/Beyond-deterministic-translation-for-UDA.