Modulation Recognition Algorithm Based On Truncated Migration And Parallel Resnet,
2022
1.School of Electronic and Information Engineering, Nanjing University of Information Science & Technology, Nanjing 210044, China;2.Jiangsu Collaborative Innovation Center on Atmospheric Environment and Equipment, Nanjing University of Information Science & Technology, Nanjing 210044, China;
Modulation Recognition Algorithm Based On Truncated Migration And Parallel Resnet, Yecai Guo, Qingwei Wang
Journal of System Simulation
Abstract: A truncated migration data preprocessing algorithm is proposed for the problem of limited time series characteristics of the signal extracted by convolutional neural network. The distance unit at one end of the sampling matrix is truncated, migrated to the other end to form a new matrix, allowing the convolutional neural network to extract more sampling points and compare more symbolic information.An improved parallel ResNet is proposed, which focuses on features in both horizontal and vertical directions simultaneously by two parallel branches. The results show that the algorithm has an accuracy rate of about 10% higher than that of ordinary …
A Machine Learning Framework For Automatic Speech Recognition In Air Traffic Control Using Word Level Binary Classification And Transcription,
2022
Rowan University
A Machine Learning Framework For Automatic Speech Recognition In Air Traffic Control Using Word Level Binary Classification And Transcription, Fowad Shahid Sohail
Theses and Dissertations
Advances in Artificial Intelligence and Machine learning have enabled a variety of new technologies. One such technology is Automatic Speech Recognition (ASR), where a machine is given audio and transcribes the words that were spoken. ASR can be applied in a variety of domains to improve general usability and safety. One such domain is Air Traffic Control (ATC). ASR in ATC promises to improve safety in a mission critical environment. ASR models have historically required a large amount of clean training data. ATC environments are noisy and acquiring labeled data is a difficult, expertise dependent task. This thesis attempts to …
An Enterprise Risk Management Framework To Design Pro-Ethical Ai Solutions,
2022
University of South Florida
An Enterprise Risk Management Framework To Design Pro-Ethical Ai Solutions, Quintin P. Mcgrath
USF Tampa Graduate Theses and Dissertations
The effective use of Artificial Intelligence (AI) has immediate business benefits for an organization and its stakeholders through efficiency and quality gains, and the potential to explore and implement new business models. However, there are risks of unintended ethical consequences. Enterprise Risk Management (ERM) focuses on managing risk while maximizing business value from exploiting opportunities. Using applied ethics as a basis and the perspective that ethics includes both enabling human flourishing and not violating accepted norms, I argue that greater business value is achieved when an organization simultaneously targets the maximization of benefits and the minimization of harms for the …
Learning Hierarchical Metrical Structure Beyond Measures,
2022
Music X Lab, NYU Shanghai, China & Mohamed bin Zayed University of Artificial Intelligence
Learning Hierarchical Metrical Structure Beyond Measures, Junyan Jiang, Daniel Chin, Yixiao Zhang, Gus Xia
Machine Learning Faculty Publications
Music contains hierarchical structures beyond beats and measures. While hierarchical structure annotations are helpful for music information retrieval and computer musicology, such annotations are scarce in current digital music databases. In this paper, we explore a data-driven approach to automatically extract hierarchical metrical structures from scores. We propose a new model with a Temporal Convolutional Network-Conditional Random Field (TCN-CRF) architecture. Given a symbolic music score, our model takes in an arbitrary number of voices in a beat-quantized form, and predicts a 4-level hierarchical metrical structure from downbeat-level to section-level. We also annotate a dataset using RWC-POP MIDI files to facilitate …
Led Down The Rabbit Hole: Exploring The Potential Of Global Attention For Biomedical Multi-Document Summarisation,
2022
The University of Melbourne, Australia
Led Down The Rabbit Hole: Exploring The Potential Of Global Attention For Biomedical Multi-Document Summarisation, Yulia Otmakhova, Hung Thinh Truong, Timothy Baldwin, Trevor Cohn, Karin Verspoor, Jey Han Lau
Natural Language Processing Faculty Publications
In this paper we report on our submission to the Multidocument Summarisation for Literature Review (MSLR) shared task. Specifically, we adapt PRIMERA (Xiao et al., 2022) to the biomedical domain by placing global attention on important biomedical entities in several ways. We analyse the outputs of the 23 resulting models, and report patterns in the results related to the presence of additional global attention, number of training steps, and the input configuration. © 2022, CC BY-SA.
Unsupervised Lexical Substitution With Decontextualised Embeddings,
2022
School of Computing and Information Systems, The University of Melbourne, Australia & RIKEN Center for Advanced Intelligence Project (AIP), Japan
Unsupervised Lexical Substitution With Decontextualised Embeddings, Takashi Wada, Timothy Baldwin, Yuji Matsumoto, Jey Han Lau
Natural Language Processing Faculty Publications
We propose a new unsupervised method for lexical substitution using pre-trained language models. Compared to previous approaches that use the generative capability of language models to predict substitutes, our method retrieves substitutes based on the similarity of contextualised and decontextualised word embeddings, i.e. the average contextual representation of a word in multiple contexts. We conduct experiments in English and Italian, and show that our method substantially outperforms strong baselines and establishes a new state-of-the-art without any explicit supervision or fine-tuning. We further show that our method performs particularly well at predicting low-frequency substitutes, and also generates a diverse list of …
Artificial Intelligence-Driven Design Of Fuel Mixtures,
2022
Clean Combustion Research Center (CCRC), Physical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia
Artificial Intelligence-Driven Design Of Fuel Mixtures, Nursulu Kuzhagaliyeva, Samuel Horváth, John Williams, Andre Nicolle, S. Mani Sarathy
Machine Learning Faculty Publications
High-performance fuel design is imperative to achieve cleaner burning and high-efficiency engine systems. We introduce a data-driven artificial intelligence (AI) framework to design liquid fuels exhibiting tailor-made properties for combustion engine applications to improve efficiency and lower carbon emissions. The fuel design approach is a constrained optimization task integrating two parts: (i) a deep learning (DL) model to predict the properties of pure components and mixtures and (ii) search algorithms to efficiently navigate in the chemical space. Our approach presents the mixture-hidden vector as a linear combination of each single component’s vectors in each blend and incorporates it into the …
Beat Transformer: Demixed Beat And Downbeat Tracking With Dilated Self-Attention,
2022
Institute of Data Science, NUS, Singapore & Integrative Sciences and Engineering Programme, NUS Graduate School, Singapore
Beat Transformer: Demixed Beat And Downbeat Tracking With Dilated Self-Attention, Jingwei Zhao, Gus Xia, Ye Wang
Machine Learning Faculty Publications
We propose Beat Transformer, a novel Transformer encoder architecture for joint beat and downbeat tracking. Different from previous models that track beats solely based on the spectrogram of an audio mixture, our model deals with demixed spectrograms with multiple instrument channels. This is inspired by the fact that humans perceive metrical structures from richer musical contexts, such as chord progression and instrumentation. To this end, we develop a Transformer model with both time-wise attention and instrument-wise attention to capture deep-buried metrical cues. Moreover, our model adopts a novel dilated self-attention mechanism, which achieves powerful hierarchical modelling with only linear complexity. …
Domain Adversarial Training On Conditional Variational Auto-Encoder For Controllable Music Generation,
2022
Institute of Data Science, NUS, Singapore & Integrative Sciences and Engineering Programme, NUS Graduate School, Singapore
Domain Adversarial Training On Conditional Variational Auto-Encoder For Controllable Music Generation, Jingwei Zhao, Gus Xia, Ye Wang
Machine Learning Faculty Publications
The variational auto-encoder has become a leading framework for symbolic music generation, and a popular research direction is to study how to effectively control the generation process. A straightforward way is to control a model using different conditions during inference. However, in music practice, conditions are usually sequential (rather than simple categorical labels), involving rich information that overlaps with the learned representation. Consequently, the decoder gets confused about whether to “listen to” the latent representation or the condition, and sometimes just ignores the condition. To solve this problem, we leverage domain adversarial training to disentangle the representation from condition cues …
Secure File Storage On Cloud Using Hybrid Cryptography,
2022
Multimedia University, Malaysia
Secure File Storage On Cloud Using Hybrid Cryptography, Jian-Foo Lai, Swee-Huay Heng
Journal of Informatics and Web Engineering
As technology today is moving forward exponentially, data exchange over the Internet has become a daily routine. Furthermore, businesses are growing internationally and offices are being established in a variety of different places throughout the world. This has resulted in the necessity to make data accessible and practical from any place. As a result, information sent via an may lead to critical security problems involving the breach of secrecy, authentication, and data integrity. This paper introduces a cloud storage system by utilising hybrid cryptography approach that leverages both advantages of symmetric key and asymmetric key cryptographic techniques. In our proposed …
A Systematic Review On Non-Functional Requirements Documentation In Agile Methodology,
2022
Multimedia University, Malaysia
A Systematic Review On Non-Functional Requirements Documentation In Agile Methodology, Steven Loh Mun Keong, Zarina Che Embi
Journal of Informatics and Web Engineering
This systematic literature review studies and summarizes findings of requirements documentation of non-functional requirements practiced by agile software development teams. It identifies current practices and existing gaps when agile software teams discuss and implement non-functional requirements, and the current methods used to document non-functional requirements. Our aim is to identify available evidence on current practices and gaps in documenting non-functional requirements. The review was conducted by searching major databases for publications between 2018 and 2022. The inclusion and exclusion criteria as well as quality assessment scoring criteria were subsequently established. Results show that common themes in the practices and gaps …
Learning Experience With Learnwithemma,
2022
Multimedia University, Malaysia
Learning Experience With Learnwithemma, Clara Susaie, Choo-Kim Tan, Pey-Yun Goh
Journal of Informatics and Web Engineering
The presence of Covid-19 was a game-changer in all the sectors that traditional learning, working, selling even living methods have changed from basic methods to something else to curb Covid-19. The impact was huge on most sectors due to the lack of experience in overcoming pandemics. One of the sectors that face the most struggle was educational institutions. For many years, face-to-face learning and teaching method have been in practice. While during Covid-19, everyone was forced to attend online classes as a precautious measure. Online learning is completely based on digital study without the physical presence of students or lecturers. …
Emotion Recognition By Facial Expression And Voice: Review And Analysis,
2022
Multimedia University, Malaysia
Emotion Recognition By Facial Expression And Voice: Review And Analysis, Yixen Lim Lim, Kok-Why Ng, Palanichamy Naveen, Su-Cheng Haw
Journal of Informatics and Web Engineering
Emotion is a scorching topic in the recent years due to the critical unseen stress incurred during the pandemic and post-pandemic. This is worsening with the recent economy’s inflation and increase of living cost, many employees are seriously affected and drawn forth many families saddened cases and tremendous drop of working performance. The increasing stress brings a lot of harm not only to the individual but to the company’s and country’s growth. To recognize emotion through a single model is less accurate, however, recruiting multiple-models may lead to latency in data processing and possibly misleading results if the input models …
Cmr3d: Contextualized Multi-Stage Refinement For 3d Object Detection,
2022
Mohamed bin Zayed University of Artificial Intelligence
Cmr3d: Contextualized Multi-Stage Refinement For 3d Object Detection, Dhanalaxmi Gaddam, Jean Lahoud, Fahad Shahbaz Khan, Rao Anwer, Hisham Cholakkal
Computer Vision Faculty Publications
Existing deep learning-based 3D object detectors typically rely on the appearance of individual objects and do not explicitly pay attention to the rich contextual information of the scene. In this work, we propose Contextualized Multi-Stage Refinement for 3D Object Detection (CMR3D) framework, which takes a 3D scene as input and strives to explicitly integrate useful contextual information of the scene at multiple levels to predict a set of object bounding-boxes along with their corresponding semantic labels. To this end, we propose to utilize a context enhancement network that captures the contextual information at different levels of granularity followed by a …
Decentralized Personalized Federated Learning: Lower Bounds And Optimal Algorithm For All Personalization Modes,
2022
Moscow Institute of Physics and Technology (MIPT), Russian Federation & Mohamed bin Zayed University of Artificial Intelligence
Decentralized Personalized Federated Learning: Lower Bounds And Optimal Algorithm For All Personalization Modes, Abdurakhmon Sadiev, Ekaterina Borodich, Aleksandr Beznosikov, Darina Dvinskikh, Saveliy Chezhegov, Rachael Tappenden, Martin Takac, Alexander Gasnikov
Machine Learning Faculty Publications
This paper considers the problem of decentralized, personalized federated learning. For centralized personalized federated learning, a penalty that measures the deviation from the local model and its average, is often added to the objective function. However, in a decentralized setting this penalty is expensive in terms of communication costs, so here, a different penalty — one that is built to respect the structure of the underlying computational network — is used instead. We present lower bounds on the communication and local computation costs for this problem formulation and we also present provably optimal methods for decentralized personalized federated learning. Numerical …
Self-Supervised Learning For Invariant Representations From Multi-Spectral And Sar Images,
2022
Technological University Dublin
Self-Supervised Learning For Invariant Representations From Multi-Spectral And Sar Images, Pallavi Jain, Bianca Schoen Phelan, Robert J. Ross
Articles
Self-Supervised learning (SSL) has become the new state of the art in several domain classification and segmentation tasks. One popular category of SSL are distillation networks such as Bootstrap Your Own Latent (BYOL). This work proposes RS-BYOL, which builds on BYOL in the remote sensing (RS) domain where data are non-trivially different from natural RGB images. Since multi-spectral (MS) and synthetic aperture radar (SAR) sensors provide varied spectral and spatial resolution information, we utilise them as an implicit augmentation to learn invariant feature embeddings. In order to learn RS based invariant features with SSL, we trained RS-BYOL in two ways, …
Transformers In Remote Sensing: A Survey,
2022
Mohamed bin Zayed University of Artificial Intelligence
Transformers In Remote Sensing: A Survey, Abdulaziz Amer Aleissaee, Amandeep Kumar, Rao Anwer, Salman Khan, Hisham Cholakkal, Gui-Song Xia, Fahad Shahbaz Khan
Computer Vision Faculty Publications
Deep learning-based algorithms have seen a massive popularity in different areas of remote sensing image analysis over the past decade. Recently, transformers-based architectures, originally introduced in natural language processing, have pervaded computer vision field where the self-attention mechanism has been utilized as a replacement to the popular convolution operator for capturing long-range dependencies. Inspired by recent advances in computer vision, remote sensing community has also witnessed an increased exploration of vision transformers for a diverse set of tasks. Although a number of surveys have focused on transformers in computer vision in general, to the best of our knowledge we are …
Accomontage2: A Complete Harmonization And Accompaniment Arrangement System,
2022
Music X Lab, NYU Shanghai, China & Mohamed bin Zayed University of Artificial Intelligence
Accomontage2: A Complete Harmonization And Accompaniment Arrangement System, Li Yi, Haochen Hu, Jingwei Zhao, Gus Xia
Machine Learning Faculty Publications
We propose AccoMontage2, a system capable of doing full-length song harmonization and accompaniment arrangement based on a lead melody. Following AccoMontage, this study focuses on generating piano arrangements for popular/folk songs and it carries on the generalized template-based retrieval method. The novelties of this study are twofold. First, we invent a harmonization module (which AccoMontage does not have). This module generates structured and coherent full-length chord progression by optimizing and balancing three loss terms: a micro-level loss for note-wise dissonance, a meso-level loss for phrase-template matching, and a macro-level loss for full piece coherency. Second, we develop a graphical user …
Overview Of The Clef-2022 Checkthat! Lab Task 2 On Detecting Previously Fact-Checked Claims,
2022
Mohamed bin Zayed University of Artificial Intelligence
Overview Of The Clef-2022 Checkthat! Lab Task 2 On Detecting Previously Fact-Checked Claims, Preslav Nakov, Giovanni Da San Martino, Firoj Alam, Shaden Shaar, Hamdy Mubarak, Nikolay Babulkov
Natural Language Processing Faculty Publications
We describe the fourth edition of the CheckThat! Lab, part of the 2022 Conference and Labs of the Evaluation Forum (CLEF). The lab evaluates technology supporting three tasks related to factuality, and it covers seven languages such as Arabic, Bulgarian, Dutch, English, German, Spanish, and Turkish. Here, we present the task 2, which asks to detect previously fact-checked claims (in two languages). A total of six teams participated in this task, submitted a total of 37 runs, and most submissions managed to achieve sizable improvements over the baselines using transformer based models such as BERT, RoBERTa. In this paper, we …
Negational Symmetry Of Quantum Neural Networks For Binary Pattern Classification,
2022
University of Oxford, United Kingdom
Negational Symmetry Of Quantum Neural Networks For Binary Pattern Classification, Nanqing Dong, Michael Kampffmeyer, Irina Voiculescu, Eric P. Xing
Machine Learning Faculty Publications
Although quantum neural networks (QNNs) have shown promising results in solving simple machine learning tasks recently, the behavior of QNNs in binary pattern classification is still underexplored. In this work, we find that QNNs have an Achilles’ heel in binary pattern classification. To illustrate this point, we provide a theoretical insight into the properties of QNNs by presenting and analyzing a new form of symmetry embedded in a family of QNNs with full entanglement, which we term negational symmetry. Due to negational symmetry, QNNs can not differentiate between a quantum binary signal and its negational counterpart. We empirically evaluate the …
