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IEEE India Conference (INDICON)

作者： Manali Patel Krupa Jariwala Chiranjoy Chattopadhyay Department of CSE SVNIT Surat India School of Computing and Data Science FLAME University Pune India

Investors and financial analysts require accurate predictions of stock prices to make informed decisions. We present Momentum Spillover Network (MSNet), a deep learning model that predicts stock prices on the National Stock Exchange of India by considering interdependencies existing between stocks in the same sector. Traditional approaches have focused solely on capturing hidden temporal dependencies, ignoring the impact of interconnectedness between stocks. To evaluate MSNet, we experimented with data from companies with the largest market capitalization from various sectors that covers over 70% of the NIFTY-50 index. MSNet effectively identifies the most impactful peer companies and predicts the future price movement of a target company with higher accuracy than existing deep learning approaches, highlighting the importance of incorporating relational dependency between stocks along with temporal dependency.

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Deepfakes: A Review on Technologies, Applications and Strategies

Deepfakes: A Review on Technologies, Applications and Strate...

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2023 International Conference on Power Energy, Environment and Intelligent Control, PEEIC 2023

作者： Kaswan, Kuldeep Singh Malik, Kiran Dhatterwal, Jagjit Singh Naruka, Mahaveer Singh Govardhan, D. School of Computing Science and Engineering Galgotias University UP Greater Noida India Mriem Department of Computer Science and Engineering Rohtak India Koneru Lakshmaiah Education Foundation Department of Artificial Intelligence and Data Science Andhra Pradesh India G L Bajaj Institute of Technology and Management Greater Noida India Institute of Aeronautical Engineering Department of Aeronautical Engineering Hyderabad India

ISBN: (纸本)9798350357769

Deepfakes are artificial intelligence-generated multimedia, primarily videos or audio, that effectively edit or invent information by superimposing one person's likeness or voice onto that of another. They use deep learning techniques such as GAN s to generate hyper-realistic simulations that blur the line between reality and fiction. Deepfakes encompass everything from political satire to possible concerns in the fields of disinformation, privacy invasion, and cybersecurity. Understanding deepfakes is critical in an age when manipulated media may influence public opinion and undermine confidence. The primary objective of this paper is to provide a holistic overview of deepfake technology, shedding light on both its creative potential and inherent risks. Through a systematic exploration of the evolution, applications, techniques, and detection strategies, we aim to equip readers with an informed perspective on this ever-evolving field. By synthesizing existing knowledge and insights, this paper intends to contribute to the ongoing discourse surrounding deepfakes, enabling stakeholders to make informed decisions, develop effective countermeasures, and anticipate the future trajectory of this technology. Ultimately, our work strives to foster awareness and preparedness in an age where discerning the truth has never been more vital. © 2023 IEEE.

关键词： AI CNN Deepfakes GAN Machine Learning RNN

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Self Supervised Vision for Climate Downscaling

arXiv

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arXiv 2024年

作者： Singh, Karandeep Park, Sungwon Jeong, Chaeyoon Nellikkattil, Arjun Cha, Meeyoung Shidqi, Naufal Zeller, Elke Data Science Group IBS Korea Republic of School of Computing KAIST Korea Republic of Department of Climate System PNU Korea Republic of IBS & School of Computing KAIST Korea Republic of

Climate change is one of the most critical challenges that our planet is facing today. Rising global temperatures are already bringing noticeable changes to Earth’s weather and climate patterns with an increased frequency of unpredictable and extreme weather events. Future projections for climate change research are based on Earth System Models (ESMs), the computer models that simulate the Earth’s climate system. ESMs provide a framework to integrate various physical systems, but their output is bound by the enormous computational resources required for running and archiving higher-resolution simulations. For a given resource budget, the ESMs are generally run on a coarser grid, followed by a computationally lighter "downscaling" process to obtain a finer-resolution output. In this work, we present a deep-learning model for downscaling ESM simulation data that does not require high-resolution ground truth data for model optimization. This is realized by leveraging salient data distribution patterns and the hidden dependencies between weather variables for an individual data point at runtime. Extensive evaluation with 2x, 3x, and 4x scaling factors demonstrates that the proposed model consistently obtains superior performance over that of various baselines. The improved downscaling performance and no dependence on high-resolution ground truth data make the proposed method a valuable tool for climate research and mark it as a promising direction for future research. Copyright © 2024, The Authors. All rights reserved.

关键词： Earth system models

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Structured multi-track accompaniment arrangement via style prior modelling 24

Structured multi-track accompaniment arrangement via style p...

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Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Jingwei Zhao Gus Xia Ziyu Wang Ye Wang Institute of Data Science NUS and Integrative Sciences and Engineering Programme NUS Graduate School Machine Learning Department MBZUAI and Computer Science Department NYU Shanghai Computer Science Department NYU Shanghai and Machine Learning Department MBZUAI School of Computing NUS and Institute of Data Science NUS and Integrative Sciences and Engineering Programme NUS Graduate School

ISBN: (纸本)9798331314385

In the realm of music AI, arranging rich and structured multi-track accompaniments from a simple lead sheet presents significant challenges. Such challenges include maintaining track cohesion, ensuring long-term coherence, and optimizing computational efficiency. In this paper, we introduce a novel system that leverages prior modelling over disentangled style factors to address these challenges. Our method presents a two-stage process: initially, a piano arrangement is derived from the lead sheet by retrieving piano texture styles; subsequently, a multi-track orchestration is generated by infusing orchestral function styles into the piano arrangement. Our key design is the use of vector quantization and a unique multi-stream Transformer to model the long-term flow of the orchestration style, which enables flexible, controllable, and structured music generation. Experiments show that by factorizing the arrangement task into interpretable sub-stages, our approach enhances generative capacity while improving efficiency. Additionally, our system supports a variety of music genres and provides style control at different composition hierarchies. We further show that our system achieves superior coherence, structure, and overall arrangement quality compared to existing baselines.

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Improving Audience Ratings Prediction of Japanese TV Dramas Using Knowledge-Based Embeddings

Improving Audience Ratings Prediction of Japanese TV Dramas ...

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International Conference on Machine Learning and Applications (ICMLA)

作者： Jerry Bonnell Stefan Wuchty Mitsunori Ogihara Frost Institute for Data Science and Computing University of Miami Coral Gables FL USA Department of Computer Science University of Miami Coral Gables FL USA

ISBN: (数字)9798350374889

ISBN: (纸本)9798350374896

Accurate prediction of television (TV) audience ratings is a crucial technology for advertisers and broadcast-ers, which influences advertising costs and indicates program popularity. As for Japanese TV dramas, previous studies have examined factors available prior to broadcast, such as cast com-position, metadata variables (e.g., scheduled time slot, broadcasting station, episode duration), and indicators of actor popularity. However, such studies have been limited in their evaluation of contextual information related to these factors and have not considered additional modalities such as text data. Here, we propose the use of knowledge-based embeddings derived from Japanese Wikipedia to improve audience ratings prediction. The proposed bag-of-entities method is applied to represent dramas in terms of cast, production team, and plot synopsis elements, and is evaluated against conventional metadata features. In an ablation study, supervised forecasting models using Support Vector Machine (SVM) are trained on combinations of the feature groups. These are devised and tested on viewership data collected from the Audience Rating TV (ARTV) database for 574 Japanese TV dramas aired between 2003 and 2021. Our experimental results indicate that a SVM model conditioned on the full feature set outperforms baseline methods, achieving a 77.4 % F1-score. After stratifying predictions based on time slot popularity, the approach is found to be effective for predicting audience ratings in challenging, high-popularity time slots, with improvements in recognition rate up to 19.1 %.

关键词： Support vector machines Humanities TV Atmospheric modeling Production Encyclopedias Predictive models Metadata Internet Online services

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Topology and density control of satellite-defined photonic quantum networks

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Physical Review Research 2025年第1期7卷 013314-013314页

作者： Xiaojuan Ma Indranil Gupta Yan Wang Siebel School of Computing and Data Science University of Illinois Urbana-Champaign Champaign Illinois 61820 USA Department of Mathematics Wayne State University Detroit Michigan 48202 USA

Creating photonic quantum networks by distributing entangled photon pairs through low Earth orbit satellites is a promising technological advance. A recent work has studied a model of such networks and reported the presence of a heavy-tailed degree distribution. This heterogeneous structure is highly undesirable when it comes to the quantum memory utilization efficiency and network robustness under malicious attack. In this work, however, we show that such a topology is not necessarily inherent to satellite-based quantum networks. We theoretically analyze factors that determine the connection probability between two nodes and propose a principled design methodology to control both the topology and density of the resulting network. We present numerical evidence that our method can continuously transform the heterogeneous heavy-tailed network into a more homogeneous Erdős-Rényi-like random network with the prescribed level of density as characterized by the average or maximal degree. Such results are in good agreement with our theoretical analysis, which not only predicts the qualitative structural transition but also provides a quantitative way to estimate important network features during the process. Under our control strategies, the resulting network can achieve various desirable properties, such as a short path length and diameter, high quantum memory utilization efficiency, and enhanced robustness against attack. We believe the design principle proposed in this work represents an important step towards building and controlling functionally efficient satellite-photonic quantum networks in the future.

关键词： Quantum entanglement

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Characterizing Complex Network Properties of Knowledge Graphs 15

Characterizing Complex Network Properties of Knowledge Graph...

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15th International Conference on Knowledge Engineering and Ontology Development, KEOD 2023 as part of the 15th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management, IC3K 2023

作者： Rossanez, Anderson da Silva Torres, Ricardo dos Reis, Julio Cesar Institute of Computing University of Campinas SP Campinas Brazil Agricultural Biosystems Engineering Wageningen Data Competence Center Wageningen University & Research Wageningen Netherlands Department of ICT and Natural Sciences Norwegian University of Science and Technology Ålesund Norway

ISBN: (纸本)9789897586712

Knowledge Graphs have been established as one the most relevant representations to encode knowledge, with relevant applications in the public and private sectors. One common research direction concerning the analysis of created knowledge graphs relies on the assumption that their intrinsic properties and structure are similar to what is observed in complex networks. However, studies concerning identifying typical complex network structures in knowledge graphs are lacking in the literature. This paper bridges this gap by analyzing commonly and recently used knowledge graphs in the semantic web field, seeking to demonstrate their complex network properties. Evaluation involving DBpedia and Wikidata data confirms the occurrence of intrinsic complex network structures in their respective knowledge graphs. Copyright © 2023 by SCITEPRESS – science and Technology Publications, Lda. Under CC license (CC BY-NC-ND 4.0).

关键词： Complex networks

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Human Brain Exhibits Distinct Patterns When Listening to Fake Versus Real Audio: Preliminary Evidence

arXiv

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arXiv 2024年

作者： Salehi, Mahsa Stefanov, Kalin Shareghi, Ehsan Department of Data Science and Artificial Intelligence Monash University MelbourneVIC Australia Department of Human-Centred Computing Monash University MelbourneVIC Australia

In this paper we study the variations in human brain activity when listening to real and fake audio. Our preliminary results suggest that the representations learned by a state-of-the-art deepfake audio detection algorithm, do not exhibit clear distinct patterns between real and fake audio. In contrast, human brain activity, as measured by EEG, displays distinct patterns when individuals are exposed to fake versus real audio. This preliminary evidence enables future research directions in areas such as deepfake audio detection. © 2024, CC BY.

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SM2PNet: A Deep Learning Approach Towards Indian Stock Market Movement Prediction

SM2PNet: A Deep Learning Approach Towards Indian Stock Marke...

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International Conference for Convergence of Technology (I2CT)

作者： Manali Patel Krupa Jariwala Chiranjoy Chattopadhyay Department of CSE SVNIT Surat India School of Computing and Data Science FLAME University Pune India

The objective of this study is to forecast the future price movements of equities listed on the National Stock Exchange (NSE) of India. To anticipate the future price movement, all prior statistical and machine learning techniques have focused on identifying latent temporal dependencies. In actuality, stock prices reflect all available information and are influenced by several external variables, making forecasting difficult. One such major element is the interdependence that exists between stock pairings from the same industry. By incorporating this relational dependence combined with the temporal dependency, we may attain improved prediction performance. In order to demonstrate this, we have created a model, SM2PNet (Stock Market Movement Prediction Network), which takes into consideration the characteristics of highly correlated firms in order to examine their impact on the stock price of the selected target company. We’ve analysed predefined sector-industry linkages in order to identify the most relevant peer firms. Sequential model long short-term memory (LSTM) is used to represent temporal dependence. The investigation confirms that SM2PNet beats all other available deep learning methods.

关键词： Deep learning Industries Couplings Correlation Companies Predictive models Stock markets

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UAV-Enabled Relaying and Edge computing: Hybrid Network Optimization

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IEEE Transactions on Vehicular Technology 2025年

作者： Ma, Bo Ye, Jiawei Feng, Shaohan Li, Chuanhuang Niyato, Dusit Kim, Dong In Hangzhou310018 China Nanyang Technological University College of Computing and Data Science 639798 Singapore Sungkyunkwan University Department of Electrical and Computer Engineering Suwon16419 Korea Republic of

This paper exploits an Unmanned Aerial Vehicle assisted Mobile Edge computing (UAV-MEC) system to meet the computation demand of User Equipments (UEs). However, in conventional UAV-MEC architecture, a UAV is dedicated only to one single service, which will inevitably limit overall system performance. To address the above issue of the conventional UAVMEC architecture, we propose a hybrid UAV-MEC model. In this model, a UAV is treated as an agent to provide either relay or computing services. Moreover, we propose an MEC Multiple Agent UAV Trajectory and User Association Policy Gradient (MEC-MAUTUA-PG) algorithm to optimize offloading strategies of the UEs as well as the trajectory design of UAV. We show the superiority of the proposed algorithm in terms of delay and the safety of trajectory design of UAV in the environment. © 1967-2012 IEEE.

关键词： Mobile edge computing

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