OpenRaiser/Intern-Atlas huggingface dataset:
The ACAC_D Model for Mutable Activity Control and Chain of Dependencies in Smart and Collaborative Systems
Venue: arXiv cs.CR 2023
Year: 2023

With the integration of connected devices, artificial intelligence, and heterogeneous networks in IoT-driven cyber-physical systems, our society is evolving as a smart, automated, and connected community. In such dynamic and distributed environments, various operations are carried out considering different contextual factors to support the automation of collaborative devices and systems. These devices often perform long-lived operations or tasks (referred to as activities) to fulfill larger goals in the collaborative environment. These activities are usually mutable (change states) and interdependent. They can influence the execution of other activities in the ecosystem, requiring active and real-time monitoring of the entire connected environment. Recently, a vision for activity-centric access control(ACAC) was proposed to enable security modeling and enforcement from the perspective and abstraction of interdependent activities. The proposed ACAC incorporates four decision parameters: Authorizations(A), oBligations(B), Conditions(C), and activity Dependencies(D) for an object agnostic access control in smart systems. In this paper, we take a step further towards maturing ACAC by focusing on activity dependencies(D) and developing a family of formal mathematically grounded models, referred to as ACAC_D. These formal models consider the real-time mutability of activities in resolving active dependencies among various activities in the ecosystem. Activity dependencies can form a chain where it is possible to have dependencies of dependencies. In ACAC, we also consider the chain of dependencies while handling the mutability of an activity. We highlight the challenges while dealing with chain of dependencies, and provide solutions to resolve these challenges. We also present a proof of concept implementation of with performance analysis for a smart farming use case.
Fundamental Data Structures for Matrix-Free Finite Elements on Hybrid Tetrahedral Grids
Venue: arXiv cs.CE 2023
Year: 2023

This paper presents efficient data structures for the implementation of matrix-free finite element methods on block-structured, hybrid tetrahedral grids. It provides a complete categorization of all geometric sub-objects that emerge from the regular refinement of the unstructured, tetrahedral coarse grid and describes efficient iteration patterns and analytical linearization functions for the mapping of coefficients to memory addresses. This foundation enables the implementation of fast, extreme-scalable, matrix-free, iterative solvers, and in particular geometric multigrid methods by design. Their application to the variable-coefficient Stokes system subject to an enriched Galerkin discretization and to the curl-curl problem discretized with Nédélec edge elements showcases the flexibility of the implementation. Eventually, the solution of a curl-curl problem with $1.6 \cdot 10^{11}$ (more than one hundred billion) unknowns on more than $32000$ processes with a matrix-free full multigrid solver demonstrates its extreme-scalability.
Emotion recognition based on multi-modal electrophysiology multi-head attention Contrastive Learning
Venue: arXiv cs.MM 2023
Year: 2023

Emotion recognition is an important research direction in artificial intelligence, helping machines understand and adapt to human emotional states. Multimodal electrophysiological(ME) signals, such as EEG, GSR, respiration(Resp), and temperature(Temp), are effective biomarkers for reflecting changes in human emotions. However, using electrophysiological signals for emotion recognition faces challenges such as data scarcity, inconsistent labeling, and difficulty in cross-individual generalization. To address these issues, we propose ME-MHACL, a self-supervised contrastive learning-based multimodal emotion recognition method that can learn meaningful feature representations from unlabeled electrophysiological signals and use multi-head attention mechanisms for feature fusion to improve recognition performance. Our method includes two stages: first, we use the Meiosis method to group sample and augment unlabeled electrophysiological signals and design a self-supervised contrastive learning task; second, we apply the trained feature extractor to labeled electrophysiological signals and use multi-head attention mechanisms for feature fusion. We conducted experiments on two public datasets, DEAP and MAHNOB-HCI, and our method outperformed existing benchmark methods in emotion recognition tasks and had good cross-individual generalization ability.
Why Do We Need Neuro-symbolic AI to Model Pragmatic Analogies?
Venue: arXiv cs.AI 2023
Year: 2023

A hallmark of intelligence is the ability to use a familiar domain to make inferences about a less familiar domain, known as analogical reasoning. In this article, we delve into the performance of Large Language Models (LLMs) in dealing with progressively complex analogies expressed in unstructured text. We discuss analogies at four distinct levels of complexity: lexical analogies, syntactic analogies, semantic analogies, and pragmatic analogies. As the analogies become more complex, they require increasingly extensive, diverse knowledge beyond the textual content, unlikely to be found in the lexical co-occurrence statistics that power LLMs. To address this, we discuss the necessity of employing Neuro-symbolic AI techniques that combine statistical and symbolic AI, informing the representation of unstructured text to highlight and augment relevant content, provide abstraction and guide the mapping process. Our knowledge-informed approach maintains the efficiency of LLMs while preserving the ability to explain analogies for pedagogical applications.
TSMD: A Database for Static Color Mesh Quality Assessment Study
Venue: arXiv cs.CV 2023
Year: 2023

Static meshes with texture map are widely used in modern industrial and manufacturing sectors, attracting considerable attention in the mesh compression community due to its huge amount of data. To facilitate the study of static mesh compression algorithm and objective quality metric, we create the Tencent - Static Mesh Dataset (TSMD) containing 42 reference meshes with rich visual characteristics. 210 distorted samples are generated by the lossy compression scheme developed for the Call for Proposals on polygonal static mesh coding, released on June 23 by the Alliance for Open Media Volumetric Visual Media group. Using processed video sequences, a large-scale, crowdsourcing-based, subjective experiment was conducted to collect subjective scores from 74 viewers. The dataset undergoes analysis to validate its sample diversity and Mean Opinion Scores (MOS) accuracy, establishing its heterogeneous nature and reliability. State-of-the-art objective metrics are evaluated on the new dataset. Pearson and Spearman correlations around 0.75 are reported, deviating from results typically observed on less heterogeneous datasets, demonstrating the need for further development of more robust metrics. The TSMD, including meshes, PVSs, bitstreams, and MOS, is made publicly available at the following location: https://multimedia.tencent.com/resources/tsmd.
DCTM: Dilated Convolutional Transformer Model for Multimodal Engagement Estimation in Conversation
Venue: arXiv cs.MM 2023
Year: 2023

Conversational engagement estimation is posed as a regression problem, entailing the identification of the favorable attention and involvement of the participants in the conversation. This task arises as a crucial pursuit to gain insights into human's interaction dynamics and behavior patterns within a conversation. In this research, we introduce a dilated convolutional Transformer for modeling and estimating human engagement in the MULTIMEDIATE 2023 competition. Our proposed system surpasses the baseline models, exhibiting a noteworthy $7$\% improvement on test set and $4$\% on validation set. Moreover, we employ different modality fusion mechanism and show that for this type of data, a simple concatenated method with self-attention fusion gains the best performance.
Federated Representation Learning for Automatic Speech Recognition
Venue: arXiv cs.SD 2023
Year: 2023

Federated Learning (FL) is a privacy-preserving paradigm, allowing edge devices to learn collaboratively without sharing data. Edge devices like Alexa and Siri are prospective sources of unlabeled audio data that can be tapped to learn robust audio representations. In this work, we bring Self-supervised Learning (SSL) and FL together to learn representations for Automatic Speech Recognition respecting data privacy constraints. We use the speaker and chapter information in the unlabeled speech dataset, Libri-Light, to simulate non-IID speaker-siloed data distributions and pre-train an LSTM encoder with the Contrastive Predictive Coding framework with FedSGD. We show that the pre-trained ASR encoder in FL performs as well as a centrally pre-trained model and produces an improvement of 12-15% (WER) compared to no pre-training. We further adapt the federated pre-trained models to a new language, French, and show a 20% (WER) improvement over no pre-training.
Perceptions of the Fourth Industrial Revolution and Artificial Intelligence Impact on Society
Venue: arXiv cs.CY 2023
Year: 2023

The Fourth Industrial Revolution, particularly Artificial Intelligence (AI), has had a profound impact on society, raising concerns about its implications and ethical considerations. The emergence of text generative AI tools like ChatGPT has further intensified concerns regarding ethics, security, privacy, and copyright. This study aims to examine the perceptions of individuals in different information flow categorizations toward AI. The results reveal key themes in participant-supplied definitions of AI and the fourth industrial revolution, emphasizing the replication of human intelligence, machine learning, automation, and the integration of digital technologies. Participants expressed concerns about job replacement, privacy invasion, and inaccurate information provided by AI. However, they also recognized the benefits of AI, such as solving complex problems and increasing convenience. Views on government involvement in shaping the fourth industrial revolution varied, with some advocating for strict regulations and others favoring support and development. The anticipated changes brought by the fourth industrial revolution include automation, potential job impacts, increased social disconnect, and reliance on technology. Understanding these perceptions is crucial for effectively managing the challenges and opportunities associated with AI in the evolving digital landscape.
What Twitter Data Tell Us about the Future?
Venue: arXiv cs.CY 2023
Year: 2023

Anticipation is a fundamental human cognitive ability that involves thinking about and living towards the future. While language markers reflect anticipatory thinking, research on anticipation from the perspective of natural language processing is limited. This study aims to investigate the futures projected by futurists on Twitter and explore the impact of language cues on anticipatory thinking among social media users. We address the research questions of what futures Twitter's futurists anticipate and share, and how these anticipated futures can be modeled from social data. To investigate this, we review related works on anticipation, discuss the influence of language markers and prestigious individuals on anticipatory thinking, and present a taxonomy system categorizing futures into "present futures" and "future present". This research presents a compiled dataset of over 1 million publicly shared tweets by future influencers and develops a scalable NLP pipeline using SOTA models. The study identifies 15 topics from the LDA approach and 100 distinct topics from the BERTopic approach within the futurists' tweets. These findings contribute to the research on topic modelling and provide insights into the futures anticipated by Twitter's futurists. The research demonstrates the futurists' language cues signals futures-in-the-making that enhance social media users to anticipate their own scenarios and respond to them in present. The fully open-sourced dataset, interactive analysis, and reproducible source code are available for further exploration.
Proposing a conceptual framework: social media listening for public health behavior
Venue: arXiv cs.CY 2023
Year: 2023

Existing communications and behavioral theories have been adopted to address health misinformation. Although various theories and models have been used to investigate the COVID-19 pandemic, there is no framework specially designed for social listening or misinformation studies using social media data and natural language processing techniques. This study aimed to propose a novel yet theory-based conceptual framework for misinformation research. We collected theories and models used in COVID-19 related studies published in peer-reviewed journals. The theories and models ranged from health behaviors, communications, to misinformation. They are analyzed and critiqued for their components, followed by proposing a conceptual framework with a demonstration. We reviewed Health Belief Model, Theory of Planned Behavior/Reasoned Action, Communication for Behavioral Impact, Transtheoretical Model, Uses and Gratifications Theory, Social Judgment Theory, Risk Information Seeking and Processing Model, Behavioral and Social Drivers, and Hype Loop. Accordingly, we proposed the Social Media Listening for Public Health Behavior Conceptual Framework by not only integrating important attributes of existing theories, but also adding new attributes. The proposed conceptual framework was demonstrated in the Freedom Convoy social media listening. The proposed conceptual framework can be used to better understand public discourse on social media, and it can be integrated with other data analyses to gather a more comprehensive picture. The framework will continue to be revised and adopted as health misinformation evolves.
Harnessing Web3 on Carbon Offset Market for Sustainability: Framework and A Case Study
Venue: arXiv cs.CY 2023
Year: 2023

Blockchain, pivotal in shaping the metaverse and Web3, often draws criticism for high energy consumption and carbon emission. The rise of sustainability-focused blockchains, especially when intersecting with innovative wireless technologies, revises this predicament. To understand blockchain's role in sustainability, we propose a three-layers structure encapsulating four green utilities: Recording and Tracking, Wide Verification, Value Trading, and Concept Disseminating. Nori, a decentralized voluntary carbon offset project, serves as our case, illuminating these utilities. Our research unveils unique insights into the on-chain carbon market participants, affect factors of the market, value propositions of NFT-based carbon credits, and the role of social media to spread the concept of carbon offset. We argue that blockchain's contribution to sustainability is significant, with carbon offsetting potentially evolving as a new standard within the blockchain sector.
A short review of the main concerns in A.I. development and application within the public sector supported by NLP and TM
Venue: arXiv cs.CY 2023
Year: 2023

Artificial Intelligence is not a new subject, and business, industry and public sectors have used it in different ways and contexts and considering multiple concerns. This work reviewed research papers published in ACM Digital Library and IEEE Xplore conference proceedings in the last two years supported by fundamental concepts of Natural Language Processing (NLP) and Text Mining (TM). The objective was to capture insights regarding data privacy, ethics, interpretability, explainability, trustworthiness, and fairness in the public sector. The methodology has saved analysis time and could retrieve papers containing relevant information. The results showed that fairness was the most frequent concern. The least prominent topic was data privacy (although embedded in most articles), while the most prominent was trustworthiness. Finally, gathering helpful insights about those concerns regarding A.I. applications in the public sector was also possible.
The Role of ChatGPT in Democratizing Data Science: An Exploration of AI-facilitated Data Analysis in Telematics
Venue: arXiv cs.CY 2023
Year: 2023

The realm of data science, once reserved for specialists, is undergoing a revolution with the rapid emergence of generative AI, particularly through tools like ChatGPT. This paper posits ChatGPT as a pivotal bridge, drastically lowering the steep learning curve traditionally associated with complex data analysis. By generating intuitive data narratives and offering real-time assistance, ChatGPT democratizes the field, enabling a wider audience to glean insights from intricate datasets. A notable illustration of this transformative potential is provided through the examination of a synthetically generated telematics dataset, wherein ChatGPT aids in distilling complex patterns and insights. However, the journey to democratization is not without its hurdles. The paper delves into challenges presented by such AI, from potential biases in analysis to ChatGPT's limited reasoning capabilities. While the promise of a democratized data science landscape beckons, it is imperative to approach this transition with caution, cognizance, and an ever-evolving understanding of the tool's capabilities and constraints.
Seasonality Based Reranking of E-commerce Autocomplete Using Natural Language Queries
Venue: arXiv cs.IR 2023
Year: 2023

Query autocomplete (QAC) also known as typeahead, suggests list of complete queries as user types prefix in the search box. It is one of the key features of modern search engines specially in e-commerce. One of the goals of typeahead is to suggest relevant queries to users which are seasonally important. In this paper we propose a neural network based natural language processing (NLP) algorithm to incorporate seasonality as a signal and present end to end evaluation of the QAC ranking model. Incorporating seasonality into autocomplete ranking model can improve autocomplete relevance and business metric.
BEAM: The Modeling Framework for Behavior, Energy, Autonomy & Mobility
Venue: arXiv cs.MA 2023
Year: 2023

This report outlines the concepts, mechanisms and inner dynamics of the BEAM (Behavior, Energy, Autonomy, and Mobility) modeling framework. BEAM is an open-source large-scale high-resolution transportation model that harnesses the principles of the actor model of computation to build a powerful and efficient agent-based model of travel behavior. It allows a detailed microscopic view of how people make travel choices and interact with the transportation system, enabling more accurate simulations of human mobility and urban transport networks. It also allows the analysis of numerous spatially defined but interacting layers, and integrates them into a cohesive representation of a regional transportation system. This integrated picture provides invaluable insights to policy makers and other stakeholders about how changes to the transportation system result in changes to traffic congestion, mode share, energy use, and emissions throughout a modeled region. These capabilities are demonstrated with a case study of New York City that showcase BEAM's application in a very large and intricate urban transportation system, without relying on existing travel demand models. The unique ability of BEAM to simulate individual behaviors, integrate with other models, and adapt to different real-world scenarios underscores its importance in the rapidly evolving field of transportation and emphasizes its potential as a valuable proof-of-concept tool to contribute to more informed and effective policy and planning decisions.
Efficient Model Adaptation for Continual Learning at the Edge
Venue: arXiv cs.LG 2023
Year: 2023

Most machine learning (ML) systems assume stationary and matching data distributions during training and deployment. This is often a false assumption. When ML models are deployed on real devices, data distributions often shift over time due to changes in environmental factors, sensor characteristics, and task-of-interest. While it is possible to have a human-in-the-loop to monitor for distribution shifts and engineer new architectures in response to these shifts, such a setup is not cost-effective. Instead, non-stationary automated ML (AutoML) models are needed. This paper presents the Encoder-Adaptor-Reconfigurator (EAR) framework for efficient continual learning under domain shifts. The EAR framework uses a fixed deep neural network (DNN) feature encoder and trains shallow networks on top of the encoder to handle novel data. The EAR framework is capable of 1) detecting when new data is out-of-distribution (OOD) by combining DNNs with hyperdimensional computing (HDC), 2) identifying low-parameter neural adaptors to adapt the model to the OOD data using zero-shot neural architecture search (ZS-NAS), and 3) minimizing catastrophic forgetting on previous tasks by progressively growing the neural architecture as needed and dynamically routing data through the appropriate adaptors and reconfigurators for handling domain-incremental and class-incremental continual learning. We systematically evaluate our approach on several benchmark datasets for domain adaptation and demonstrate strong performance compared to state-of-the-art algorithms for OOD detection and few-/zero-shot NAS.
Avatar Fusion Karaoke: Research and development on multi-user music play VR experience in the metaverse
Venue: arXiv cs.HC 2023
Year: 2023

This paper contributes to building a standard process of research and development (R&D) for new user experiences (UX) in metaverse services. We tested this R&D process on a new UX proof of concept (PoC) for Meta Quest head-mounted display (HMDs) consisting of a school-life karaoke experience with the hypothesis that it is possible to design the avatars with only the necessary functions and rendering costs. The school life metaverse is a relevant subject for discovering issues and problems in this type of simultaneous connection. To qualitatively evaluate the potential of a multi-person metaverse experience, this study investigated subjects where each avatar requires expressive skills. While avatar play experiences feature artistic expressions, such as dancing, playing musical instruments, and drawing, and these can be used to evaluate their operability and expressive capabilities qualitatively, the Quest's tracking capabilities are insufficient for full-body performance and graphical art expression. Considering such hardware limitations, this study evaluated the Quest, focusing primarily on UX simplicity using AI Fusion techniques and expressiveness in instrumental scenes played by approximately four avatars. This research reported methods for multiuser metaverse communication and its supporting technologies, such as head-mounted devices and their graphics performance, special interaction techniques, and complementary tools and the importance of PoC development, its evaluation, and its iterations. The result is remarkable for further research; these expressive technologies in a multi-user context are directly related to the quality of communication within the metaverse and the value of the user-generated content (UGC) produced there.