pwc-archive/papers-with-abstracts huggingface dataset:
A neural network based heading and position control system of a ship
Authors: Shahroz Unar, Mukhtiar Ali Unar, Zubair Ahmed Memon, Sanam Narejo
Date: 2022-04-02 00:00:00
Tasks: Position

Heading and position control system of ships has remained a challenging control problem. It is a nonlinear multiple input multiple output system. Moreover, the dynamics of the system vary with operating as well as environmental conditions. Conventionally, simple Proportional Integral Derivative controller is used which has well known limitations. Other conventional control techniques have also been investigated but they require an accurate mathematical model of a ship. Unfortunately, accuracy of mathematical models is very difficult to achieve. During the past few decades computational intelligence techniques such as artificial neural networks have been very successful when an accurate mathematical model is not available. Therefore, in this paper, an artificial neural network controller is proposed for heading and position control system. For simulation purposes, a mathematical model with four effective thrusters have been chosen to test the performance of the proposed controller. The final closed loop system has been analyzed and tested through simulation studies. The results are very encouraging.
AutoOpt: A General Framework for Automatically Designing Metaheuristic Optimization Algorithms with Diverse Structures
Authors: Qi Zhao, Bai Yan, Taiwei Hu, Xianglong Chen, Jian Yang, Shi Cheng, Yuhui Shi
Date: 2022-04-03 00:00:00
Tasks: Metaheuristic Optimization

Metaheuristics are widely recognized gradient-free solvers to hard problems that do not meet the rigorous mathematical assumptions of conventional solvers. The automated design of metaheuristic algorithms provides an attractive path to relieve manual design effort and gain enhanced performance beyond human-made algorithms. However, the specific algorithm prototype and linear algorithm representation in the current automated design pipeline restrict the design within a fixed algorithm structure, which hinders discovering novelties and diversity across the metaheuristic family. To address this challenge, this paper proposes a general framework, AutoOpt, for automatically designing metaheuristic algorithms with diverse structures. AutoOpt contains three innovations: (i) A general algorithm prototype dedicated to covering the metaheuristic family as widely as possible. It promotes high-quality automated design on different problems by fully discovering potentials and novelties across the family. (ii) A directed acyclic graph algorithm representation to fit the proposed prototype. Its flexibility and evolvability enable discovering various algorithm structures in a single run of design, thus boosting the possibility of finding high-performance algorithms. (iii) A graph representation embedding method offering an alternative compact form of the graph to be manipulated, which ensures AutoOpt's generality. Experiments on numeral functions and real applications validate AutoOpt's efficiency and practicability.
The Effects of the Environment and Linear Actuators on Robot Morphologies
Authors: Steven Oud, Koen van der Pool
Date: 2022-04-02 00:00:00

The field of evolutionary robotics uses principles of natural evolution to design robots. In this paper, we study the effect of adding a new module inspired by the skeletal muscle to the existing RoboGen framework: the linear actuator. Additionally, we investigate how robots evolved in a plain environment differ from robots evolved in a rough environment. We consider the task of directed locomotion for comparing evolved robot morphologies. The results show that the addition of the linear actuator does not have a significant impact on the performance and morphologies of robots evolved in a plain environment. However, we find significant differences in the morphologies of robots evolved in a plain environment and robots evolved in a rough environment. We find that more complex behavior and morphologies emerge when we change the terrain of the environment.
RFID-Based Indoor Spatial Query Evaluation with Bayesian Filtering Techniques
Authors: Bo Hui, Wenlu Wang, Jiao Yu, Zhitao Gong, Wei-Shinn Ku, Min-Te Sun, Hua Lu
Date: 2022-04-02 00:00:00

People spend a significant amount of time in indoor spaces (e.g., office buildings, subway systems, etc.) in their daily lives. Therefore, it is important to develop efficient indoor spatial query algorithms for supporting various location-based applications. However, indoor spaces differ from outdoor spaces because users have to follow the indoor floor plan for their movements. In addition, positioning in indoor environments is mainly based on sensing devices (e.g., RFID readers) rather than GPS devices. Consequently, we cannot apply existing spatial query evaluation techniques devised for outdoor environments for this new challenge. Because Bayesian filtering techniques can be employed to estimate the state of a system that changes over time using a sequence of noisy measurements made on the system, in this research, we propose the Bayesian filtering-based location inference methods as the basis for evaluating indoor spatial queries with noisy RFID raw data. Furthermore, two novel models, indoor walking graph model and anchor point indexing model, are created for tracking object locations in indoor environments. Based on the inference method and tracking models, we develop innovative indoor range and k nearest neighbor (kNN) query algorithms. We validate our solution through use of both synthetic data and real-world data. Our experimental results show that the proposed algorithms can evaluate indoor spatial queries effectively and efficiently. We open-source the code, data, and floor plan at https://github.com/DataScienceLab18/IndoorToolKit.
Safe Reinforcement Learning via Shielding under Partial Observability
Authors: Steven Carr, Nils Jansen, Sebastian Junges, Ufuk Topcu
Date: 2022-04-02 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Safe Exploration, Safe Reinforcement Learning

Safe exploration is a common problem in reinforcement learning (RL) that aims to prevent agents from making disastrous decisions while exploring their environment. A family of approaches to this problem assume domain knowledge in the form of a (partial) model of this environment to decide upon the safety of an action. A so-called shield forces the RL agent to select only safe actions. However, for adoption in various applications, one must look beyond enforcing safety and also ensure the applicability of RL with good performance. We extend the applicability of shields via tight integration with state-of-the-art deep RL, and provide an extensive, empirical study in challenging, sparse-reward environments under partial observability. We show that a carefully integrated shield ensures safety and can improve the convergence rate and final performance of RL agents. We furthermore show that a shield can be used to bootstrap state-of-the-art RL agents: they remain safe after initial learning in a shielded setting, allowing us to disable a potentially too conservative shield eventually.
A Machine With Human-Like Memory Systems
Authors: Taewoon Kim, Michael Cochez, Vincent Francois-Lavet, Mark Neerincx, Piek Vossen
Date: 2022-04-04 00:00:00
Tasks: RoomEnv-v0

Inspired by the cognitive science theory, we explicitly model an agent with both semantic and episodic memory systems, and show that it is better than having just one of the two memory systems. In order to show this, we have designed and released our own challenging environment, "the Room", compatible with OpenAI Gym, where an agent has to properly learn how to encode, store, and retrieve memories to maximize its rewards. The Room environment allows for a hybrid intelligence setup where machines and humans can collaborate. We show that two agents collaborating with each other results in better performance than one agent acting alone.
Introduction to the Artificial Intelligence that can be applied to the Network Automation Journey
Authors: Gilbert Moisio, Alexandre Gonzalvez, Noam Zeitoun
Date: 2022-04-02 00:00:00
Tasks: Diversity

The computer network world is changing and the NetDevOps approach has brought the dynamics of applications and systems into the field of communication infrastructure. Businesses are changing and businesses are faced with difficulties related to the diversity of hardware and software that make up those infrastructures. The "Intent-Based Networking - Concepts and Definitions" document describes the different parts of the ecosystem that could be involved in NetDevOps. The recognize, generate intent, translate and refine features need a new way to implement algorithms. This is where artificial intelligence comes in.
SciNoBo : A Hierarchical Multi-Label Classifier of Scientific Publications
Authors: Nikolaos Gialitsis, Sotiris Kotitsas, Haris Papageorgiou
Date: 2022-04-02 00:00:00

Classifying scientific publications according to Field-of-Science (FoS) taxonomies is of crucial importance, allowing funders, publishers, scholars, companies and other stakeholders to organize scientific literature more effectively. Most existing works address classification either at venue level or solely based on the textual content of a research publication. We present SciNoBo, a novel classification system of publications to predefined FoS taxonomies, leveraging the structural properties of a publication and its citations and references organised in a multilayer network. In contrast to other works, our system supports assignments of publications to multiple fields by considering their multidisciplinarity potential. By unifying publications and venues under a common multilayer network structure made up of citing and publishing relationships, classifications at the venue-level can be augmented with publication-level classifications. We evaluate SciNoBo on a publications' dataset extracted from Microsoft Academic Graph and we perform a comparative analysis against a state-of-the-art neural-network baseline. The results reveal that our proposed system is capable of producing high-quality classifications of publications.
Frequency and Multi-Scale Selective Kernel Attention for Speaker Verification
Authors: Sung Hwan Mun, Jee-weon Jung, Min Hyun Han, Nam Soo Kim
Date: 2022-04-03 00:00:00
Tasks: Speaker Verification

The majority of recent state-of-the-art speaker verification architectures adopt multi-scale processing and frequency-channel attention mechanisms. Convolutional layers of these models typically have a fixed kernel size, e.g., 3 or 5. In this study, we further contribute to this line of research utilising a selective kernel attention (SKA) mechanism. The SKA mechanism allows each convolutional layer to adaptively select the kernel size in a data-driven fashion. It is based on an attention mechanism which exploits both frequency and channel domain. We first apply existing SKA module to our baseline. Then we propose two SKA variants where the first variant is applied in front of the ECAPA-TDNN model and the other is combined with the Res2net backbone block. Through extensive experiments, we demonstrate that our two proposed SKA variants consistently improves the performance and are complementary when tested on three different evaluation protocols.
Few Shot Protein Generation
Authors: Soumya Ram, Tristan Bepler
Date: 2022-04-03 00:00:00
Tasks: Multiple Sequence Alignment

We present the MSA-to-protein transformer, a generative model of protein sequences conditioned on protein families represented by multiple sequence alignments (MSAs). Unlike existing approaches to learning generative models of protein families, the MSA-to-protein transformer conditions sequence generation directly on a learned encoding of the multiple sequence alignment, circumventing the need for fitting dedicated family models. By training on a large set of well-curated multiple sequence alignments in Pfam, our MSA-to-protein transformer generalizes well to protein families not observed during training and outperforms conventional family modeling approaches, especially when MSAs are small. Our generative approach accurately models epistasis and indels and allows for exact inference and efficient sampling unlike other approaches. We demonstrate the protein sequence modeling capabilities of our MSA-to-protein transformer and compare it with alternative sequence modeling approaches in comprehensive benchmark experiments.
Monte Carlo Physarum Machine: Characteristics of Pattern Formation in Continuous Stochastic Transport Networks
Authors: Oskar Elek, Joseph N. Burchett, J. Xavier Prochaska, Angus G. Forbes
Date: 2022-04-04 00:00:00

We present Monte Carlo Physarum Machine: a computational model suitable for reconstructing continuous transport networks from sparse 2D and 3D data. MCPM is a probabilistic generalization of Jones's 2010 agent-based model for simulating the growth of Physarum polycephalum slime mold. We compare MCPM to Jones's work on theoretical grounds, and describe a task-specific variant designed for reconstructing the large-scale distribution of gas and dark matter in the Universe known as the Cosmic web. To analyze the new model, we first explore MCPM's self-patterning behavior, showing a wide range of continuous network-like morphologies -- called "polyphorms" -- that the model produces from geometrically intuitive parameters. Applying MCPM to both simulated and observational cosmological datasets, we then evaluate its ability to produce consistent 3D density maps of the Cosmic web. Finally, we examine other possible tasks where MCPM could be useful, along with several examples of fitting to domain-specific data as proofs of concept.
Towards a New Science of Disinformation
Authors: Claudio S. Pinhanez, German H. Flores, Marisa A. Vasconcelos, Mu Qiao, Nick Linck, Rogério de Paula, Yuya J. Ong
Date: 2022-03-17 00:00:00
Tasks: Face Swapping

How can we best address the dangerous impact that deep learning-generated fake audios, photographs, and videos (a.k.a. deepfakes) may have in personal and societal life? We foresee that the availability of cheap deepfake technology will create a second wave of disinformation where people will receive specific, personalized disinformation through different channels, making the current approaches to fight disinformation obsolete. We argue that fake media has to be seen as an upcoming cybersecurity problem, and we have to shift from combating its spread to a prevention and cure framework where users have available ways to verify, challenge, and argue against the veracity of each piece of media they are exposed to. To create the technologies behind this framework, we propose that a new Science of Disinformation is needed, one which creates a theoretical framework both for the processes of communication and consumption of false content. Key scientific and technological challenges facing this research agenda are listed and discussed in the light of state-of-art technologies for fake media generation and detection, argument finding and construction, and how to effectively engage users in the prevention and cure processes.
Artificial Intelligence: Framework of driving triggers to past, present and future applications and influencers of industry sector adoption
Authors: Richard Fulton, Diane Fulton, Susan Kaplan
Date: 2022-03-30 00:00:00
Tasks: Diversity

To gain a sense of the development of Artificial Intelligence (AI), this research analyzes what has been done in the past, presently in the last decade and what is predicted for the next several decades. The paper will highlight the biggest changes in AI and give examples of how these technologies are applied in several key industry sectors along with influencers that can affect adoption speed. Lastly, the research examines the driving triggers such as cost, speed, accuracy, diversity/inclusion and interdisciplinary research/collaboration that propel AI into an essential transformative technology.
SecureSense: Defending Adversarial Attack for Secure Device-Free Human Activity Recognition
Authors: Jianfei Yang, Han Zou, Lihua Xie
Date: 2022-04-04 00:00:00
Tasks: Activity Recognition, Adversarial Attack, Human Activity Recognition, Person Identification

Deep neural networks have empowered accurate device-free human activity recognition, which has wide applications. Deep models can extract robust features from various sensors and generalize well even in challenging situations such as data-insufficient cases. However, these systems could be vulnerable to input perturbations, i.e. adversarial attacks. We empirically demonstrate that both black-box Gaussian attacks and modern adversarial white-box attacks can render their accuracies to plummet. In this paper, we firstly point out that such phenomenon can bring severe safety hazards to device-free sensing systems, and then propose a novel learning framework, SecureSense, to defend common attacks. SecureSense aims to achieve consistent predictions regardless of whether there exists an attack on its input or not, alleviating the negative effect of distribution perturbation caused by adversarial attacks. Extensive experiments demonstrate that our proposed method can significantly enhance the model robustness of existing deep models, overcoming possible attacks. The results validate that our method works well on wireless human activity recognition and person identification systems. To the best of our knowledge, this is the first work to investigate adversarial attacks and further develop a novel defense framework for wireless human activity recognition in mobile computing research.
Exosoul: ethical profiling in the digital world
Authors: Costanza Alfieri, Paola Inverardi, Patrizio Migliarini, Massimiliano Palmiero
Date: 2022-03-30 00:00:00
Tasks: Clustering, Ethics

The development and the spread of increasingly autonomous digital technologies in our society pose new ethical challenges beyond data protection and privacy violation. Users are unprotected in their interactions with digital technologies and at the same time autonomous systems are free to occupy the space of decisions that is prerogative of each human being. In this context the multidisciplinary project Exosoul aims at developing a personalized software exoskeleton which mediates actions in the digital world according to the moral preferences of the user. The exoskeleton relies on the ethical profiling of a user, similar in purpose to the privacy profiling proposed in the literature, but aiming at reflecting and predicting general moral preferences. Our approach is hybrid, first based on the identification of profiles in a top-down manner, and then on the refinement of profiles by a personalized data-driven approach. In this work we report our initial experiment on building such top-down profiles. We consider the correlations between ethics positions (idealism and relativism) personality traits (honesty/humility, conscientiousness, Machiavellianism and narcissism) and worldview (normativism), and then we use a clustering approach to create ethical profiles predictive of user's digital behaviors concerning privacy violation, copy-right infringements, caution and protection. Data were collected by administering a questionnaire to 317 young individuals. In the paper we discuss two clustering solutions, one data-driven and one model-driven, in terms of validity and predictive power of digital behavior.
T*$\varepsilon$ -- Bounded-Suboptimal Efficient Motion Planning for Minimum-Time Planar Curvature-Constrained Systems
Authors: Doron Pinsky, Petr Váňa, Jan Faigl, Oren Salzman
Date: 2022-04-04 00:00:00
Tasks: Motion Planning

We consider the problem of finding collision-free paths for curvature-constrained systems in the presence of obstacles while minimizing execution time. Specifically, we focus on the setting where a planar system can travel at some range of speeds with unbounded acceleration. This setting can model many systems, such as fixed-wing drones. Unfortunately, planning for such systems might require evaluating many (local) time-optimal transitions connecting two close-by configurations, which is computationally expensive. Existing methods either pre-compute all such transitions in a preprocessing stage or use heuristics to speed up the search, thus foregoing any guarantees on solution quality. Our key insight is that computing all the time-optimal transitions is both~(i)~computationally expensive and~(ii)~unnecessary for many problem instances. We show that by finding bounded-suboptimal solutions (solutions whose cost is bounded by $1+\varepsilon$ times the cost of the optimal solution for any user-provided $\varepsilon$) and not time-optimal solutions, one can dramatically reduce the number of time-optimal transitions used. We demonstrate using empirical evaluation that our planning framework can reduce the runtime by several orders of magnitude compared to the state-of-the-art while still providing guarantees on the quality of the solution.
Deep Anomaly Detection on Attributed Networks
Authors: Kaize Ding, Jundong Li, Rohit Bhanushali, Huan Liu
Date: 2019-05-02 00:00:00
Proceeding: 2019 SIAM International Conference on Data Mining (SDM) 2019 5
Tasks: Anomaly Detection, Attribute

Attributed networks are ubiquitous and form a critical component of modern information infrastructure, where additional node attributes complement the raw network structure in knowledge discovery. Recently, detecting anomalous nodes on attributed networks has attracted an increasing amount of research attention, with broad applications in various high-impact domains, such as cybersecurity, finance, and healthcare. Most of the existing attempts, however, tackle the problem with shallow learning mechanisms by ego-network or community analysis, or through subspace selection. Undoubtedly, these models cannot fully address the computational challenges on attributed networks. For example, they often suffer from the network sparsity and data nonlinearity issues, and fail to capture the complex interactions between different information modalities, thus negatively impact the performance of anomaly detection. To tackle the aforementioned problems, in this paper, we study the anomaly detection problem on attributed networks by developing a novel deep model. In particular, our proposed deep model: (1) explicitly models the topological structure and nodal attributes seamlessly for node embedding learning with the prevalent graph convolutional network (GCN); and (2) is customized to address the anomaly detection problem by virtue of deep autoencoder that leverages the learned embeddings to reconstruct the original data. The synergy between GCN and autoencoder enables us to spot anomalies by measuring the reconstruction errors of nodes from both the structure and the attribute perspectives. Extensive experiments on real-world attributed network datasets demonstrate the efficacy of our proposed algorithm.