OpenRaiser/Intern-Atlas huggingface dataset:
Single-channel EOG-based human-machine interface with exploratory assessments using harmonic source separation
Venue: arXiv cs.HC 2022
Year: 2022

There have been many studies on intelligent robotic systems for patients with motor impairments, where different sensor types and different human-machine interface (HMI) methods have been developed. However, these studies fail to achieve complex activity detection at the minimum sensing level. In this paper, exploratory approaches are adopted to investigate ocular activity dynamics and complex activity estimation using a single-channel EOG device. First, the stationarity of ocular activities during a static motion is investigated and some activities are found to be non-stationary. Further, no statistical difference is found between the envelope sequences in the temporal domain. However, when utilized as an alternative to a low-pass filter, high-frequency harmonic components in the frequency domain are found to improve contrasting ocular activities and the performance of the EOG-HMI-based activity detection system substantially. The activities are trained with different classifiers and their prediction success is evaluated with leave-one-session-out cross-validation. Accordingly, the two-dimensional CNN model achieved the highest performance with the accuracy of 72.35\%. Furthermore, the clustering performance is assessed using unsupervised learning and the results are evaluated in terms of how well the feature sets are grouped. The system is further tested in real-time with the graphical user interface and the scores and survey data of the subjects are used to verify the effectiveness.
Untangling Force-Directed Layouts Using Persistent Homology
Venue: arXiv cs.HC 2022
Year: 2022

Force-directed layouts belong to a popular class of methods used to position nodes in a node-link diagram. However, they typically lack direct consideration of global structures, which can result in visual clutter and the overlap of unrelated structures. In this paper, we use the principles of persistent homology to untangle force-directed layouts thus mitigating these issues. First, we devise a new method to use 0-dimensional persistent homology to efficiently generate an initial graph layout. The approach results in faster convergence and better quality graph layouts. Second, we provide a new definition and an efficient algorithm for 1-dimensional persistent homology features (i.e., tunnels/cycles) on graphs. We provide users the ability to interact with the 1-dimensional features by highlighting them and adding cycle-emphasizing forces to the layout. Finally, we evaluate our approach with 32 synthetic and real-world graphs by computing various metrics, e.g., co-ranking, edge crossing, etc., to demonstrate the efficacy of our proposed method.
Targeted Honeyword Generation with Language Models
Venue: arXiv cs.AI 2022
Year: 2022

Honeywords are fictitious passwords inserted into databases in order to identify password breaches. The major difficulty is how to produce honeywords that are difficult to distinguish from real passwords. Although the generation of honeywords has been widely investigated in the past, the majority of existing research assumes attackers have no knowledge of the users. These honeyword generating techniques (HGTs) may utterly fail if attackers exploit users' personally identifiable information (PII) and the real passwords include users' PII. In this paper, we propose to build a more secure and trustworthy authentication system that employs off-the-shelf pre-trained language models which require no further training on real passwords to produce honeywords while retaining the PII of the associated real password, therefore significantly raising the bar for attackers. We conducted a pilot experiment in which individuals are asked to distinguish between authentic passwords and honeywords when the username is provided for GPT-3 and a tweaking technique. Results show that it is extremely difficult to distinguish the real passwords from the artifical ones for both techniques. We speculate that a larger sample size could reveal a significant difference between the two HGT techniques, favouring our proposed approach.
MACE: Multi-Agent Autonomous Collaborative Exploration of Unknown Environments
Venue: arXiv cs.RO 2022
Year: 2022

In this paper, we propose a new framework for multi-agent collaborative exploration of unknown environments. The proposed method combines state-of-the-art algorithms in mapping, safe corridor generation and multi-agent planning. It first takes a volume that we want to explore, then proceeds to give the multiple agents different goals in order to explore a voxel grid of that volume. The exploration ends when all voxels are discovered as free or occupied, or there is no path found for the remaining undiscovered voxels. The state-of-the-art planning algorithm uses time-aware Safe Corridors to guarantee intra-agent collision safety as well safety from static obstacles. The presented approach is tested in a state of the art simulator for up to 4 agents.
Rethinking Graph Neural Networks for the Graph Coloring Problem
Venue: arXiv cs.LG 2022
Year: 2022

Graph coloring, a classical and critical NP-hard problem, is the problem of assigning connected nodes as different colors as possible. However, we observe that state-of-the-art GNNs are less successful in the graph coloring problem. We analyze the reasons from two perspectives. First, most GNNs fail to generalize the task under homophily to heterophily, i.e., graphs where connected nodes are assigned different colors. Second, GNNs are bounded by the network depth, making them possible to be a local method, which has been demonstrated to be non-optimal in Maximum Independent Set (MIS) problem. In this paper, we focus on the aggregation-combine GNNs (AC-GNNs), a popular class of GNNs. We first define the power of AC-GNNs in the coloring problem as the capability to assign nodes different colors. The definition is different with previous one that is based on the assumption of homophily. We identify node pairs that AC-GNNs fail to discriminate. Furthermore, we show that any AC-GNN is a local coloring method, and any local coloring method is non-optimal by exploring the limits of local methods over sparse random graphs, thereby demonstrating the non-optimality of AC-GNNs due to its local property. We then prove the positive correlation between model depth and its coloring power. Moreover, we discuss the color equivariance of graphs to tackle some practical constraints such as the pre-fixing constraints. Following the discussions above, we summarize a series of rules a series of rules that make a GNN color equivariant and powerful in the coloring problem. Then, we propose a simple AC-GNN variation satisfying these rules. We empirically validate our theoretical findings and demonstrate that our simple model substantially outperforms state-of-the-art heuristic algorithms in both quality and runtime.
Energy Savings When Migrating Workloads to the Cloud
Venue: arXiv cs.DC 2022
Year: 2022

In the cloud environment, data centers are efficiently manipulated by cloud service providers (CSPs) in terms of energy consumption. Consequently, migrating workloads to clouds can result in lower energy consumption. This paper demonstrates that the Lift-and-Shift migration with optimal selections of cloud instances can provide significant energy savings, and explains how much and where the energy savings are obtained from. Additionally, the analysis on the variation of energy consumption is given when Auto-Scaling is deployed showing that further energy savings are expected even without refactoring applications. All the conclusions and analyses are based on the real data collected by Cloudamize Inc. from May 2016 to August 2016 over 40,000 machines across approximately 300 data centers.
A Multi-objective Memetic Algorithm for Auto Adversarial Attack Optimization Design
Venue: arXiv cs.CV 2022
Year: 2022

The phenomenon of adversarial examples has been revealed in variant scenarios. Recent studies show that well-designed adversarial defense strategies can improve the robustness of deep learning models against adversarial examples. However, with the rapid development of defense technologies, it also tends to be more difficult to evaluate the robustness of the defensed model due to the weak performance of existing manually designed adversarial attacks. To address the challenge, given the defensed model, the efficient adversarial attack with less computational burden and lower robust accuracy is needed to be further exploited. Therefore, we propose a multi-objective memetic algorithm for auto adversarial attack optimization design, which realizes the automatical search for the near-optimal adversarial attack towards defensed models. Firstly, the more general mathematical model of auto adversarial attack optimization design is constructed, where the search space includes not only the attacker operations, magnitude, iteration number, and loss functions but also the connection ways of multiple adversarial attacks. In addition, we develop a multi-objective memetic algorithm combining NSGA-II and local search to solve the optimization problem. Finally, to decrease the evaluation cost during the search, we propose a representative data selection strategy based on the sorting of cross entropy loss values of each images output by models. Experiments on CIFAR10, CIFAR100, and ImageNet datasets show the effectiveness of our proposed method.
IRL with Partial Observations using the Principle of Uncertain Maximum Entropy
Venue: arXiv cs.LG 2022
Year: 2022

The principle of maximum entropy is a broadly applicable technique for computing a distribution with the least amount of information possible while constrained to match empirically estimated feature expectations. However, in many real-world applications that use noisy sensors computing the feature expectations may be challenging due to partial observation of the relevant model variables. For example, a robot performing apprenticeship learning may lose sight of the agent it is learning from due to environmental occlusion. We show that in generalizing the principle of maximum entropy to these types of scenarios we unavoidably introduce a dependency on the learned model to the empirical feature expectations. We introduce the principle of uncertain maximum entropy and present an expectation-maximization based solution generalized from the principle of latent maximum entropy. Finally, we experimentally demonstrate the improved robustness to noisy data offered by our technique in a maximum causal entropy inverse reinforcement learning domain.
Combining deep learning and crowdsourcing geo-images to predict housing quality in rural China
Venue: arXiv cs.LG 2022
Year: 2022

Housing quality is an essential proxy for regional wealth, security and health. Understanding the distribution of housing quality is crucial for unveiling rural development status and providing political proposals. However,present rural house quality data highly depends on a top-down, time-consuming survey at the national or provincial level but fails to unpack the housing quality at the village level. To fill the gap between accurately depicting rural housing quality conditions and deficient data,we collect massive rural images and invite users to assess their housing quality at scale. Furthermore, a deep learning framework is proposed to automatically and efficiently predict housing quality based on crowd-sourcing rural images.
Pyramidal Predictive Network: A Model for Visual-frame Prediction Based on Predictive Coding Theory
Venue: arXiv cs.CV 2022
Year: 2022

Visual-frame prediction is a pixel-dense prediction task that infers future frames from past frames. Lacking of appearance details, low prediction accuracy and high computational overhead are still major problems with current models or methods. In this paper, we propose a novel neural network model inspired by the well-known predictive coding theory to deal with the problems. Predictive coding provides an interesting and reliable computational framework, which will be combined with other theories such as the cerebral cortex at different level oscillates at different frequencies, to design an efficient and reliable predictive network model for visual-frame prediction. Specifically, the model is composed of a series of recurrent and convolutional units forming the top-down and bottom-up streams, respectively. The update frequency of neural units on each of the layer decreases with the increasing of network levels, which results in neurons of higher-level can capture information in longer time dimensions. According to the experimental results, this model shows better compactness and comparable predictive performance with existing works, implying lower computational cost and higher prediction accuracy. Code is available at https://github.com/Ling-CF/PPNet.
A Blockchain-based Decentralised and Dynamic Authorisation Scheme for the Internet of Things
Venue: arXiv cs.CR 2022
Year: 2022

An authorisation has been recognised as an important security measure for preventing unauthorised access to critical resources, such as devices and data, within the Internet of Things (IoT) networks. Existing authorisation methods for the IoT network are based on traditional access control models, which have several drawbacks, including architecture centralisation, policy tampering, access rights validation, malicious third-party policy assignment and control, and network-related overheads. The increasing trend of integrating Blockchain technology with IoT networks demonstrates its importance and potential to address the shortcomings of traditional IoT network authorisation mechanisms. This paper proposes a decentralised, secure, dynamic, and flexible authorisation scheme for IoT networks based on attribute-based access control (ABAC) fine-grained policies stored on a distributed immutable ledger. We design a Blockchain-based ABAC policy management framework divided into Attribute Management Authority (AMA) and Policy Management Authority (PMA) frameworks that use smart contract features to initialise, store, and manage attributes and policies on the Blockchain. To achieve flexibility and dynamicity in the authorisation process, we capture and utilise the environmental-related attributes in conjunction with the subject and object attributes of the ABAC model to define the policies. Furthermore, we designed the Blockchain-based Access Management Framework (AMF) to manage user requests to access IoT devices while maintaining the privacy and auditability of user requests and assigned policies. We implemented a prototype of our proposed scheme and executed it on the local Ethereum Blockchain. Finally, we demonstrated the applicability and flexibility of our proposed scheme for an IoT-based smart home scenario, taking into account deployment, execution and financial costs.
Grasping Core Rules of Time Series through Pure Models
Venue: arXiv cs.LG 2022
Year: 2022

Time series underwent the transition from statistics to deep learning, as did many other machine learning fields. Although it appears that the accuracy has been increasing as the model is updated in a number of publicly available datasets, it typically only increases the scale by several times in exchange for a slight difference in accuracy. Through this experiment, we point out a different line of thinking, time series, especially long-term forecasting, may differ from other fields. It is not necessary to use extensive and complex models to grasp all aspects of time series, but to use pure models to grasp the core rules of time series changes. With this simple but effective idea, we created PureTS, a network with three pure linear layers that achieved state-of-the-art in 80% of the long sequence prediction tasks while being nearly the lightest model and having the fastest running speed. On this basis, we discuss the potential of pure linear layers in both phenomena and essence. The ability to understand the core law contributes to the high precision of long-distance prediction, and reasonable fluctuation prevents it from distorting the curve in multi-step prediction like mainstream deep learning models, which is summarized as a pure linear neural network that avoids over-fluctuating. Finally, we suggest the fundamental design standards for lightweight long-step time series tasks: input and output should try to have the same dimension, and the structure avoids fragmentation and complex operations.
Band-ESS: Streaming Enumerative Coding with Applications to Probabilistic Shaping
Venue: arXiv cs.IT 2022
Year: 2022

Probabilistic amplitude shaping (PAS) is on track to become the de facto coded modulation standard for communication systems aiming to operate close to channel capacity at high transmission rates. The essential component of PAS that breeds this widespread interest is the amplitude shaping block, through which the channel input distribution is controlled. This block is responsible for converting bit strings into amplitude sequences with certain properties, e.g., fixed composition, limited energy, limited energy variation, etc. Recently, band-trellis enumerative sphere shaping (B-ESS) was introduced as an amplitude shaping technique that achieves limited energy variations which is useful in optical communication scenarios. B-ESS operates based on a trellis diagram in which sequences with high energy variations are pruned. In this work, we study the implementation of B-ESS. We first show that thanks to the trellis structure obtained by this pruning, B-ESS can be implemented with very low storage complexity. The trellis computation is shown to be reduced to a set of recursive multiplications with a scalar factor. Then we show that this scalar factor can be adjusted such that the trellis computation is further simplified and realized with only binary shifts. This shift-based B-ESS (1) can be implemented for arbitrarily long blocklengths without incurring an increase in complexity, and (2) can operate in a streaming mode similar to convolutional coding.
Towards Parametric Speech Synthesis Using Gaussian-Markov Model of Spectral Envelope and Wavelet-Based Decomposition of F0
Venue: arXiv cs.SD 2022
Year: 2022

Neural network-based Text-to-Speech has significantly improved the quality of synthesized speech. Prominent methods (e.g., Tacotron2, FastSpeech, FastPitch) usually generate Mel-spectrogram from text and then synthesize speech using vocoder (e.g., WaveNet, WaveGlow, HiFiGAN). Compared with traditional parametric approaches (e.g., STRAIGHT and WORLD), neural vocoder based end-to-end models suffer from slow inference speed, and the synthesized speech is usually not robust and lack of controllability. In this work, we propose a novel updated vocoder, which is a simple signal model to train and easy to generate waveforms. We use the Gaussian-Markov model toward robust learning of spectral envelope and wavelet-based statistical signal processing to characterize and decompose F0 features. It can retain the fine spectral envelope and achieve high controllability of natural speech. The experimental results demonstrate that our proposed vocoder achieves better naturalness of reconstructed speech than the conventional STRAIGHT vocoder, slightly better than WaveNet, and somewhat worse than the WaveRNN.
ECI: a Customizable Cache Coherency Stack for Hybrid FPGA-CPU Architectures
Venue: arXiv cs.AR 2022
Year: 2022

Unlike other accelerators, FPGAs are capable of supporting cache coherency, thereby turning them into a more powerful architectural option than just a peripheral accelerator. However, most existing deployments of FPGAs are either non-cache coherent or support only an asymmetric design where cache coherency is controlled from the CPU. Taking advantage of a recently released two socket CPU-FPGA architecture, in this paper we describe ECI, a flexible implementation of cache coherency on the FPGA capable of supporting both symmetric and asymmetric protocols. ECI is open and customizable, given applications the opportunity to fully interact with the cache coherency protocol, thereby opening up many interesting system design and research opportunities not available in existing designs. Through extensive microbenchmarks we show that ECI exhibits highly competitive performance and discuss in detail one use-case illustrating the benefits of having an open cache coherency stack on the FPGA.
Applying Regularized Schrödinger-Bridge-Based Stochastic Process in Generative Modeling
Venue: arXiv cs.LG 2022
Year: 2022

Compared to the existing function-based models in deep generative modeling, the recently proposed diffusion models have achieved outstanding performance with a stochastic-process-based approach. But a long sampling time is required for this approach due to many timesteps for discretization. Schrödinger bridge (SB)-based models attempt to tackle this problem by training bidirectional stochastic processes between distributions. However, they still have a slow sampling speed compared to generative models such as generative adversarial networks. And due to the training of the bidirectional stochastic processes, they require a relatively long training time. Therefore, this study tried to reduce the number of timesteps and training time required and proposed regularization terms to the existing SB models to make the bidirectional stochastic processes consistent and stable with a reduced number of timesteps. Each regularization term was integrated into a single term to enable more efficient training in computation time and memory usage. Applying this regularized stochastic process to various generation tasks, the desired translations between different distributions were obtained, and accordingly, the possibility of generative modeling based on a stochastic process with faster sampling speed could be confirmed. The code is available at https://github.com/KiUngSong/RSB.
Cognitive Radio-Inspired Rate-Splitting Multiple Access for Semi-Grant-Free Transmissions
Venue: arXiv cs.IT 2022
Year: 2022

In this paper, we propose a cognitive radio-inspired rate-splitting multiple access (CR-RSMA) scheme to assist semi-grant-free (SGF) transmissions in which a grant-based user (GBU) and multiple grant-free users (GFUs) access the base-station (BS) by sharing the same resource block. Using the cognitive radio principle, the GBU and admitted GFU are treated as the primary and secondary users, respectively, and rate-splitting is applied at the admitted GFU to realize SGF transmissions. The admitted GFU's transmit power allocation, target rate allocation, and successive interference cancellation decoding order at the BS are jointly optimized to attain the maximum achievable rate for the admitted GFU without deteriorating the GBU's outage performance compared to orthogonal multiple access. Due to the extended non-outage zone, CR-RSMA-assised SGF (CR-RSMA-SGF) transmissions achieve a lower outage probability than SGF transmissions assisted by cognitive radio-inspired non-orthogonal multiple access. Exact expressions and asymptotic analysis for the admitted GFU's outage probability are derived to evaluate the system performance achieved by CR-RSMA-SGF transmissions. The superior outage performance and full multiuser diversity gain achieved by CR-RSMA-SGF transmissions are verified by the analytical and simulation results.