Author: Ella Lane

Authors: Hanjun Choi、Hyunsung Kim、Minho Lee、Chang-Jo Kim、Jinsung Yoon、Sang-Ki Ko Paper: https://arxiv.org/abs/2408.10878 Introduction In various spatiotemporal domains such as transportation, robotics, surveillance, and sports, handling multi-agent trajectory data is crucial. However, acquiring complete trajectory data is challenging due to practical issues like signal loss in wearable devices or occlusions in computer vision systems. This problem is particularly pronounced in sports, where players can disappear from the camera view. To address the prevalence of missing values in multi-agent trajectory data, this paper proposes a Derivative-Based Hybrid Prediction (DBHP) framework. This framework aims to impute missing trajectories accurately by leveraging physical constraints and derivative…

Authors: Gassyrbek Kosherbay、Nurgissa Apbaz Paper: https://arxiv.org/abs/2408.10549 AI-Based IVR: Enhancing Call Center Efficiency with AI Technologies Introduction In the modern era, traditional Interactive Voice Response (IVR) systems in call centers, which rely on voice menus and operator scripts, often fall short of meeting the growing demands of customers. Clients expect more personalized and efficient service, while operators face high workloads due to the diverse range of inquiries. Artificial Intelligence (AI) has emerged as a crucial tool for addressing these challenges by automating and optimizing processes in call centers. AI can analyze large volumes of data, identify hidden patterns, and offer solutions…

Authors: Jiao Chen、Suyan Dai、Fangfang Chen、Zuohong Lv、Jianhua Tang Paper: https://arxiv.org/abs/2408.09972 Introduction Background The rapid advancement of intelligent transportation and autonomous driving technologies has brought about significant challenges in motion planning systems. Traditional methods often rely on fixed algorithms and models, which struggle to adapt to dynamic traffic conditions and personalized driver needs. Integrating large language models (LLMs) into autonomous vehicles can enhance system personalization and adaptability, enabling better handling of complex and dynamic open-world scenarios. Problem Statement Despite the potential of LLMs, traditional edge computing models face significant challenges in processing complex driving data in real-time. This study introduces EC-Drive, a…

Authors: Eduardo Jr Piedad、Zherish Galvin Mayordo、Eduardo Prieto-Araujo、Oriol Gomis-Bellmunt Paper: https://arxiv.org/abs/2408.09649 Introduction Machine fault detection is a critical aspect of industrial operations, as it helps in minimizing downtime and preventing operational interruptions. Traditional methods of fault detection often rely on vibration-based sensor data, which can be expensive and invasive. Recent advancements in artificial intelligence, particularly in machine learning (ML) and deep learning (DL), have shown promise in diagnosing motor conditions using motor phase current signals. This study focuses on converting time-series motor current signals into time-frequency 2D plots using Short-time Fourier Transform (STFT) methods and employing Convolutional Neural Networks (CNNs) to…

Authors: Omar Ghazal、Tian Lan、Shalman Ojukwu、Komal Krishnamurthy、Alex Yakovlev、Rishad Shafik Paper: https://arxiv.org/abs/2408.09456 Introduction In the realm of modern computing, the von Neumann bottleneck has emerged as a significant challenge. This bottleneck arises from the frequent data transfer between memory and processing units, leading to substantial data throughput and energy costs. Traditional computing architectures, which rely on this constant data movement, struggle to keep up with the demands of big data and machine learning (ML) applications. In-memory computing (IMC) offers a promising solution by processing data directly within the memory array, thereby eliminating the need for constant data movement. This approach leverages the…

Authors: Po-Hsuan Huang、Hsuan-Lei Shao Paper: https://arxiv.org/abs/2408.09404 Introduction Word co-occurrence networks (WCN) have garnered significant interest due to their potential applications in various linguistic and computational fields, such as semantic similarity, keyword extraction, and text summarization. Understanding the structure of these networks is crucial for leveraging their full potential. Previous studies have shown that WCNs built from well-formed texts exhibit certain properties, including being small-world, following a two-regime power law distribution, and being generally disassortative. Conversely, WCNs constructed from ill-formed texts, such as microblog posts, display different characteristics, such as being scale-free and following a power law distribution. However, these observations…

Authors: Zhiwei Li、Guodong Long、Tianyi Zhou、Jing Jiang、Chengqi Zhang Paper: https://arxiv.org/abs/2408.08931 Introduction In the digital age, recommendation systems have become indispensable tools for filtering online information and helping users discover products, content, and services that match their preferences. Collaborative Filtering (CF) is widely recognized for its ability to generate personalized recommendations by analyzing the relationships between users and items based on user interaction data. However, with the enforcement of data privacy laws like GDPR, safeguarding privacy has become increasingly critical. Traditional CF methods typically require centralizing user data on servers for processing, a practice that is no longer viable in today’s privacy-conscious…

Authors: Jerry Huang、Prasanna Parthasarathi、Mehdi Rezagholizadeh、Sarath Chandar Paper: https://arxiv.org/abs/2408.08470 Introduction The advent of large language models (LLMs) has revolutionized natural language processing, enabling models to perform tasks with human-like proficiency. However, the computational demands of these models pose significant challenges, particularly in resource-constrained environments. One major bottleneck is the high latency associated with auto-regressive generation, where each token generation requires a full inference pass through the model. This paper explores a novel approach to mitigate this issue through context-aware assistant selection, leveraging multiple draft models to accelerate inference without compromising performance. Methodology Motivation The primary goal is to reduce the latency…

Authors: Pranav Venkatesh、Kami Vinton、Dhiraj Murthy、Kellen Sharp、Akaash Kolluri Paper: https://arxiv.org/abs/2408.06900 Introduction Social bots are automated accounts on social media platforms designed to mimic human behavior and engage with users. These bots can manipulate public perception, spread disinformation, and amplify fringe agendas, leading to significant societal harm. While mainstream platforms like Twitter have developed robust bot detection tools, niche and fringe platforms such as Parler, Gab, and Gettr remain vulnerable. To address this gap, the authors introduce Entendre, an open-access, scalable, and platform-agnostic bot detection framework. Previous Work Botometer is a notable example of a successful bot detection service for Twitter. It…