OpenRaiser/Intern-Atlas huggingface dataset:
Emotion in Cognitive Architecture: Emergent Properties from Interactions with Human Emotion
Venue: arXiv cs.HC 2022
Year: 2022

This document presents endeavors to represent emotion in a computational cognitive architecture. The first part introduces research organizing with two axes of emotional affect: pleasantness and arousal. Following this basic of emotional components, the document discusses an aspect of emergent properties of emotion, showing interaction studies with human users. With these past author's studies, the document concludes that the advantage of the cognitive human-agent interaction approach is in representing human internal states and processes.
Intrinsic Motivation in Dynamical Control Systems
Venue: arXiv cs.AI 2022
Year: 2022

Biological systems often choose actions without an explicit reward signal, a phenomenon known as intrinsic motivation. The computational principles underlying this behavior remain poorly understood. In this study, we investigate an information-theoretic approach to intrinsic motivation, based on maximizing an agent's empowerment (the mutual information between its past actions and future states). We show that this approach generalizes previous attempts to formalize intrinsic motivation, and we provide a computationally efficient algorithm for computing the necessary quantities. We test our approach on several benchmark control problems, and we explain its success in guiding intrinsically motivated behaviors by relating our information-theoretic control function to fundamental properties of the dynamical system representing the combined agent-environment system. This opens the door for designing practical artificial, intrinsically motivated controllers and for linking animal behaviors to their dynamical properties.
An Analysis of Honeypots and their Impact as a Cyber Deception Tactic
Venue: arXiv cs.CR 2022
Year: 2022

This paper explores deploying a cyber honeypot system to learn how cyber defenders can use a honeypot system as a deception mechanism to gather intelligence. Defenders can gather intelligence about an attacker such as the autonomous system that the IP of the attacker is allocated from, the way the attacker is trying to penetrate the system, what different types of attacks are being used, the commands the attacker is running once they are inside the honeypot, and what malware the attacker is downloading to the deployed system. We demonstrate an experiment to implement a honeypot system that can lure in attackers and gather all the information mentioned above. The data collected is then thoroughly analyzed and explained to understand all this information. This experiment can be recreated and makes use of many open-source tools to successfully create a honeypot system.
Memory Augmented Lookup Dictionary based Language Modeling for Automatic Speech Recognition
Venue: arXiv cs.CL 2022
Year: 2022

Recent studies have shown that using an external Language Model (LM) benefits the end-to-end Automatic Speech Recognition (ASR). However, predicting tokens that appear less frequently in the training set is still quite challenging. The long-tail prediction problems have been widely studied in many applications, but only been addressed by a few studies for ASR and LMs. In this paper, we propose a new memory augmented lookup dictionary based Transformer architecture for LM. The newly introduced lookup dictionary incorporates rich contextual information in training set, which is vital to correctly predict long-tail tokens. With intensive experiments on Chinese and English data sets, our proposed method is proved to outperform the baseline Transformer LM by a great margin on both word/character error rate and tail tokens error rate. This is achieved without impact on the decoding efficiency. Overall, we demonstrate the effectiveness of our proposed method in boosting the ASR decoding performance, especially for long-tail tokens.
Autonomous Driving Simulator based on Neurorobotics Platform
Venue: arXiv cs.RO 2022
Year: 2022

There are many artificial intelligence algorithms for autonomous driving, but directly installing these algorithms on vehicles is unrealistic and expensive. At the same time, many of these algorithms need an environment to train and optimize. Simulation is a valuable and meaningful solution with training and testing functions, and it can say that simulation is a critical link in the autonomous driving world. There are also many different applications or systems of simulation from companies or academies such as SVL and Carla. These simulators flaunt that they have the closest real-world simulation, but their environment objects, such as pedestrians and other vehicles around the agent-vehicle, are already fixed programmed. They can only move along the pre-setting trajectory, or random numbers determine their movements. What is the situation when all environmental objects are also installed by Artificial Intelligence, or their behaviors are like real people or natural reactions of other drivers? This problem is a blind spot for most of the simulation applications, or these applications cannot be easy to solve this problem. The Neurorobotics Platform from the TUM team of Prof. Alois Knoll has the idea about "Engines" and "Transceiver Functions" to solve the multi-agents problem. This report will start with a little research on the Neurorobotics Platform and analyze the potential and possibility of developing a new simulator to achieve the true real-world simulation goal. Then based on the NRP-Core Platform, this initial development aims to construct an initial demo experiment. The consist of this report starts with the basic knowledge of NRP-Core and its installation, then focus on the explanation of the necessary components for a simulation experiment, at last, about the details of constructions for the autonomous driving system, which is integrated object detection and autonomous control.
Targeted k-node Collapse Problem: Towards Understanding the Robustness of Local k-core Structure
Venue: arXiv cs.SI 2022
Year: 2022

The concept of k-core, which indicates the largest induced subgraph where each node has k or more neighbors, plays a significant role in measuring the cohesiveness and the engagement of a network, and it is exploited in diverse applications, e.g., network analysis, anomaly detection, community detection, etc. Recent works have demonstrated the vulnerability of k-core under malicious perturbations which focuses on removing the minimal number of edges to make a whole k-core structure collapse. However, to the best of our knowledge, there is no existing research concentrating on how many edges should be removed at least to make an arbitrary node in k-core collapse. Therefore, in this paper, we make the first attempt to study the Targeted k-node Collapse Problem (TNCP) with four novel contributions. Firstly, we offer the general definition of TNCP problem with the proof of its NP-hardness. Secondly, in order to address the TNCP problem, we propose a heuristic algorithm named TNC and its improved version named ATNC for implementations on large-scale networks. After that, the experiments on 16 real-world networks across various domains verify the superiority of our proposed algorithms over 4 baseline methods along with detailed comparisons and analyses. Finally, the significance of TNCP problem for precisely evaluating the resilience of k-core structures in networks is validated.
Efficient Methods for Approximating the Shapley Value for Asset Sharing in Energy Communities
Venue: arXiv cs.GT 2022
Year: 2022

With the emergence of energy communities, where a number of prosumers invest in shared generation and storage, the issue of fair allocation of benefits is increasingly important. The Shapley value has attracted increasing interest for redistribution in energy settings - however, computing it exactly is intractable beyond a few dozen prosumers. In this paper, we first conduct a systematic review of the literature on the use of Shapley value in energy-related applications, as well as efforts to compute or approximate it. Next, we formalise the main methods for approximating the Shapley value in community energy settings, and propose a new one, which we call the stratified expected value approximation. To compare the performance of these methods, we design a novel method for exact Shapley value computation, which can be applied to communities of up to several hundred agents by clustering the prosumers into a smaller number of demand profiles. We perform a large-scale experimental comparison of the proposed methods, for communities of up to 200 prosumers, using large-scale, publicly available data from two large-scale energy trials in the UK (UKERC Energy Data Centre, 2017, UK Power Networks Innovation, 2021). Our analysis shows that, as the number of agents in the community increases, the relative difference to the exact Shapley value converges to under 1% for all the approximation methods considered. In particular, for most experimental scenarios, we show that there is no statistical difference between the newly proposed stratified expected value method and the existing state-of-the-art method that uses adaptive sampling (O'Brien et al., 2015), although the cost of computation for large communities is an order of magnitude lower.
An Integrated Visual System for Unmanned Aerial Vehicles Tracking and Landing on the Ground Vehicles
Venue: arXiv cs.RO 2022
Year: 2022

The vision of unmanned aerial vehicles is very significant for UAV-related applications such as search and rescue, landing on a moving platform, etc. In this work, we have developed an integrated system for the UAV landing on the moving platform, and the UAV object detection with tracking in the complicated environment. Firstly, we have proposed a robust LoG-based deep neural network for object detection and tracking, which has great advantages in robustness to object scale and illuminations compared with typical deep network-based approaches. Then, we have also improved based on the original Kalman filter and designed an iterative multi-model-based filter to tackle the problem of unknown dynamics in real circumstances of motion estimations. Next, we implemented the whole system and do ROS Gazebo-based testing in two complicated circumstances to verify the effectiveness of our design. Finally, we have deployed the proposed detection, tracking, and motion estimation strategies into real applications to do UAV tracking of a pillar and obstacle avoidance. It is demonstrated that our system shows great accuracy and robustness in real applications.
Lightmorphic Signatures Analysis Toolkit
Venue: arXiv cs.LG 2022
Year: 2022

In this paper we discuss the theory used in the design of an open source lightmorphic signatures analysis toolkit (LSAT). In addition to providing a core functionality, the software package enables specific optimizations with its modular and customizable design. To promote its usage and inspire future contributions, LSAT is publicly available. By using a self-supervised neural network and augmented machine learning algorithms, LSAT provides an easy-to-use interface with ample documentation. The experiments demonstrate that LSAT improves the otherwise tedious and error-prone tasks of translating lightmorphic associated data into usable spectrograms, enhanced with parameter tuning and performance analysis. With the provided mathematical functions, LSAT validates the nonlinearity encountered in the data conversion process while ensuring suitability of the forecasting algorithms.
ENGNN: A General Edge-Update Empowered GNN Architecture for Radio Resource Management in Wireless Networks
Venue: arXiv cs.NI 2022
Year: 2022

In order to achieve high data rate and ubiquitous connectivity in future wireless networks, a key task is to efficiently manage the radio resource by judicious beamforming and power allocation. Unfortunately, the iterative nature of the commonly applied optimization-based algorithms cannot meet the low latency requirements due to the high computational complexity. For real-time implementations, deep learning-based approaches, especially the graph neural networks (GNNs), have been demonstrated with good scalability and generalization performance due to the permutation equivariance (PE) property. However, the current architectures are only equipped with the node-update mechanism, which prohibits the applications to a more general setup, where the unknown variables are also defined on the graph edges. To fill this gap, we propose an edge-update mechanism, which enables GNNs to handle both node and edge variables and prove its PE property with respect to both transmitters and receivers. Simulation results on typical radio resource management problems demonstrate that the proposed method achieves higher sum rate but with much shorter computation time than state-of-the-art methods and generalizes well on different numbers of base stations and users, different noise variances, interference levels, and transmit power budgets.
Speed up the inference of diffusion models via shortcut MCMC sampling
Venue: arXiv cs.CV 2022
Year: 2022

Diffusion probabilistic models have generated high quality image synthesis recently. However, one pain point is the notorious inference to gradually obtain clear images with thousands of steps, which is time consuming compared to other generative models. In this paper, we present a shortcut MCMC sampling algorithm, which balances training and inference, while keeping the generated data's quality. In particular, we add the global fidelity constraint with shortcut MCMC sampling to combat the local fitting from diffusion models. We do some initial experiments and show very promising results. Our implementation is available at https://github.com//vividitytech/diffusion-mcmc.git.
Refining Network Message Segmentation with Principal Component Analysis
Venue: arXiv cs.CR 2022
Year: 2022

Reverse engineering of undocumented protocols is a common task in security analyses of networked services. The communication itself, captured in traffic traces, contains much of the necessary information to perform such a protocol reverse engineering. The comprehension of the format of unknown messages is of particular interest for binary protocols that are not human-readable. One major challenge is to discover probable fields in a message as the basis for further analyses. Given a set of messages, split into segments of bytes by an existing segmenter, we propose a method to refine the approximation of the field inference. We use principle component analysis (PCA) to discover linearly correlated variance between sets of message segments. We relocate the boundaries of the initial coarse segmentation to more accurately match with the true fields. We perform different evaluations of our method to show its benefit for the message format inference and subsequent analysis tasks from literature that depend on the message format. We can achieve a median improvement of the message format accuracy across different real-world protocols by up to 100 %.
Fast, Cheap and Good: Lightweight Methods Are Undervalued
Venue: arXiv cs.CR 2022
Year: 2022

Engineering techniques to address the endless parade of security issues are an important area of research. Properties of practices in industrial use are rarely studied. Security workers satisfice. There is a widespread perception that security work must be cumbersome, and thus there's no value to assessing levels of effort. This is complemented by a belief that the nth day of work will produce value equal to the first. These perceptions impact both practice and research. This paper expands the acceptable paradigms for security analysis to include the fast, cheap and good enough. "Nothing" is often enough for industry. This paper makes a case for valuing lightweight ("fast and cheap") methods, presents a set of case studies and evaluation criteria for such tools, including card decks and role playing games.
RingAuth: Wearable Authentication using a Smart Ring
Venue: arXiv cs.CR 2022
Year: 2022

In this paper, we show that by using inertial sensor data generated by a smart ring, worn on the finger, the user can be authenticated when making mobile payments or when knocking on a door (for access control). The proposed system can be deployed purely in software and does not require updates to existing payment terminals or infrastructure. We also demonstrate that smart ring data can authenticate smartwatch gestures, and vice versa, allowing either device to act as an implicit second factor for the other. To validate the system, we conduct a user study (n=21) to collect inertial sensor data from users as they perform gestures, and we evaluate the system against an active impersonation attacker. Based on this data, we develop payment and access control authentication models for which we achieve EERs of 0.04 and 0.02, respectively.
An efficient hybrid classification approach for COVID-19 based on Harris Hawks Optimization and Salp Swarm Optimization
Venue: arXiv cs.NE 2022
Year: 2022

Feature selection can be defined as one of the pre-processing steps that decrease the dimensionality of a dataset by identifying the most significant attributes while also boosting the accuracy of classification. For solving feature selection problems, this study presents a hybrid binary version of the Harris Hawks Optimization algorithm (HHO) and Salp Swarm Optimization (SSA) (HHOSSA) for Covid-19 classification. The proposed (HHOSSA) presents a strategy for improving the basic HHO's performance using the Salp algorithm's power to select the best fitness values. The HHOSSA was tested against two well-known optimization algorithms, the Whale Optimization Algorithm (WOA) and the Grey wolf optimizer (GWO), utilizing a total of 800 chest X-ray images. A total of four performance metrics (Accuracy, Recall, Precision, F1) were employed in the studies using three classifiers (Support vector machines (SVMs), k-Nearest Neighbor (KNN), and Extreme Gradient Boosting (XGBoost)). The proposed algorithm (HHOSSA) achieved 96% accuracy with the SVM classifier, and 98% accuracy with two classifiers, XGboost and KNN.
Site-specific weed management in corn using UAS imagery analysis and computer vision techniques
Venue: arXiv cs.CV 2022
Year: 2022

Currently, weed control in commercial corn production is performed without considering weed distribution information in the field. This kind of weed management practice leads to excessive amounts of chemical herbicides being applied in a given field. The objective of this study was to perform site-specific weed control (SSWC) in a corn field by 1) using an unmanned aerial system (UAS) to map the spatial distribution information of weeds in the field; 2) creating a prescription map based on the weed distribution map, and 3) spraying the field using the prescription map and a commercial size sprayer. In this study, we are proposing a Crop Row Identification (CRI) algorithm, a computer vision algorithm that identifies corn rows on UAS imagery. After being identified, the corn rows were then removed from the imagery and the remaining vegetation fraction was classified as weeds. Based on that information, a grid-based weed prescription map was created and the weed control application was implemented through a commercial-size sprayer. The decision of spraying herbicides on a particular grid was based on the presence of weeds in that grid cell. All the grids that contained at least one weed were sprayed, while the grids free of weeds were not. Using our SSWC approach, we were able to save 26.23\% of the land (1.97 acres) from being sprayed with chemical herbicides compared to the existing method. This study presents a full workflow from UAS image collection to field weed control implementation using a commercial-size sprayer, and it shows that some level of savings can potentially be obtained even in a situation with high weed infestation, which might provide an opportunity to reduce chemical usage in corn production systems.
Proceedings of the 1st International Workshop on Reading Music Systems
Venue: arXiv cs.CV 2022
Year: 2022

The International Workshop on Reading Music Systems (WoRMS) is a workshop that tries to connect researchers who develop systems for reading music, such as in the field of Optical Music Recognition, with other researchers and practitioners that could benefit from such systems, like librarians or musicologists. The relevant topics of interest for the workshop include, but are not limited to: Music reading systems; Optical music recognition; Datasets and performance evaluation; Image processing on music scores; Writer identification; Authoring, editing, storing and presentation systems for music scores; Multi-modal systems; Novel input-methods for music to produce written music; Web-based Music Information Retrieval services; Applications and projects; Use-cases related to written music. These are the proceedings of the 1st International Workshop on Reading Music Systems, held in Paris on the 20th of September 2018.
EuclidNet: Deep Visual Reasoning for Constructible Problems in Geometry
Venue: arXiv cs.CV 2022
Year: 2022

In this paper, we present a deep learning-based framework for solving geometric construction problems through visual reasoning, which is useful for automated geometry theorem proving. Constructible problems in geometry often ask for the sequence of straightedge-and-compass constructions to construct a given goal given some initial setup. Our EuclidNet framework leverages the neural network architecture Mask R-CNN to extract the visual features from the initial setup and goal configuration with extra points of intersection, and then generate possible construction steps as intermediary data models that are used as feedback in the training process for further refinement of the construction step sequence. This process is repeated recursively until either a solution is found, in which case we backtrack the path for a step-by-step construction guide, or the problem is identified as unsolvable. Our EuclidNet framework is validated on complex Japanese Sangaku geometry problems, demonstrating its capacity to leverage backtracking for deep visual reasoning of challenging problems.
Comparative Study of Parameter Selection for Enhanced Edge Inference for a Multi-Output Regression model for Head Pose Estimation
Venue: arXiv cs.CV 2022
Year: 2022

Magnitude-based pruning is a technique used to optimise deep learning models for edge inference. We have achieved over 75% model size reduction with a higher accuracy than the original multi-output regression model for head-pose estimation.
Transformers in Action Recognition: A Review on Temporal Modeling
Venue: arXiv cs.CV 2022
Year: 2022

In vision-based action recognition, spatio-temporal features from different modalities are used for recognizing activities. Temporal modeling is a long challenge of action recognition. However, there are limited methods such as pre-computed motion features, three-dimensional (3D) filters, and recurrent neural networks (RNN) for modeling motion information in deep-based approaches. Recently, transformers success in modeling long-range dependencies in natural language processing (NLP) tasks has gotten great attention from other domains; including speech, image, and video, to rely entirely on self-attention without using sequence-aligned RNNs or convolutions. Although the application of transformers to action recognition is relatively new, the amount of research proposed on this topic within the last few years is astounding. This paper especially reviews recent progress in deep learning methods for modeling temporal variations. It focuses on action recognition methods that use transformers for temporal modeling, discussing their main features, used modalities, and identifying opportunities and challenges for future research.