Search by Subject - Artificial Intelligence, Machine Learning, Computer Vision, Natural language processing (2024)

Building Detection-Resistant Reconnaissance Attacks Based on Adversarial Explainability

• Mohammed M. Alani,
• Atefeh Mashatan,
• Ali Miri

The growing popularity of Internet-of-Things devices makes them a desired target for malicious actors. Most attacks start with a reconnaissance phase where the attacker gathers information about the services running on the device, the open ports, and any ...

SAMANTHA: A chatbot to assist users in training tasks to prevent workplace hazards

• David Contreras Aguilar,
• Fernando Medina,
• Mauricio Oyanedel,
• Maria Salamó,
• Miquel Sànchez-Marrè

In businesses, preventing workplace hazards becomes crucial. In order to limit negative effects on people, society, and the economy, it is crucial for both the organization and its employees to reduce accidents and occupational illnesses. Staff training ...

System Optimizations for Enabling Training of Extreme Long Sequence Transformer Models

• Sam Ade Jacobs,
• Masahiro Tanaka,
• Chengming Zhang,
• Minjia Zhang,
• Reza Yazdani Aminadabi,
• Shuaiwen Leon Song,
• Samyam Rajbhandari,
• Yuxiong He

Computation in a typical Transformer-based large language model (LLM) can be characterized by batch size, hidden dimension, number of layers, and sequence length. Until now, system works for accelerating LLM training have focused on the first three ...

Brief Announcement: A Case for Byzantine Machine Learning

• Sadegh Farhadkhani,
• Rachid Guerraoui,
• Nirupam Gupta,
• Rafael Pinot

The success of machine learning (ML) has been intimately linked with the availability of large amounts of data, typically collected from heterogeneous sources and processed on vast networks of computing devices (also called workers). Beyond accuracy, the ...

Parallel Best Arm Identification in Heterogeneous Environments

• Nikolai Karpov,
• Qin Zhang

In this paper, we study the tradeoffs between the time and the number of communication rounds of the best arm identification problem in the heterogeneous collaborative learning model, where multiple agents interact with possibly different environments ...

Incorporating Citizen-Generated Data into Large Language Models

• Jagadeesh Vadapalli,
• Srishti Gupta,
• Bishwa Karki,
• Chun-Hua Tsai

This study investigates the use of citizen-generated data to optimize a large language model (LLM) chatbot that gives nutrition advice. By actively participating in the data collection and annotation process from FDA-approved websites, citizens provided ...

Examining Public Sector AI Adoption: Mechanisms for AI adoption in the absence of authoritative strategic direction

• Antonio Molin

Artificial Intelligence (AI) is recognized to bring great benefits to the organizations that can successfully adopt this emerging technological domain into their operations. This paper examines the impact of governance and strategic direction on AI ...

Leveraging Large Language Models for Effective Organizational Navigation

• Haresh Chandrasekar,
• Srishti Gupta,
• Chun-Tzu Liu,
• Chun-Hua Tsai

The advent of the internet has significantly enhanced accessibility to information, facilitating the engagement of diverse communities with online resources. Despite the abundance of information available, navigating the structures of large ...

Who evaluates the algorithms? An overview of the algorithmic accountability ecosystem

• J. Ignacio Criado,
• Ariana Guevara-Gomez

Algorithmic accountability is a concept that has been gaining interest in academic and professional circles, especially when considering the potential negative impacts of Artificial Intelligence in diverse scenarios. In this article, we present the ...

A Framework for Assessing Country Reputation: Case Study of China during the COVID-19 Pandemic

• Xiaoqun Zhang,
• Miyoung Chong,
• Loni Hagen,
• Haihua Chen

This study investigates China’s reputation on Twitter in the beginning of the COVID-19 period. Drawing from a four-dimensional framework of country reputation, this study examined the dimensions and sentiments of the public discourse. Twitterverse ...

AI Impact on Health Equity for Marginalized, Racial, and Ethnic Minorities

• Nchebe-Jah Iloanusi,
• Soon Ae Chun

Predictive analytics technologies like machine learning, AI and Generative AI models like Large Language Models (LLMs), have garnered enthusiasm for their potential to improve healthcare services in smart cities. However, these rapidly developing ...

Leadership and Transformation in the Public Sector: An Empirical Exploration of AI Adoption and Efficiency during the Fourth Industrial Revolution

• David Valle-Cruz,
• Rigoberto Garcia-Contreras,
• J. Patricia Munoz-Chávez

The fourth industrial revolution (4IR) demands transformative leadership, as leaders grapple with gaps and questions, particularly in optimizing Artificial Intelligence (AI). This paper seeks to comprehend public sector leaders' perceptions, identifying ...

From GenAI to Political Profiling Avatars: A Data-Driven Approach to Crafting Virtual Experts for Voting Advice Applications

• José Alberto Mancera Andrade,
• Luis Terán

Voting advice applications (VAAs) are pivotal web-based tools that guide citizens to align with political parties and candidates that match their preferences. Traditional methods for creating candidate profiles predominantly rely on questionnaire ...

Shrinking VOD Traffic via Rényi-Entropic Optimal Transport

• Chi-Jen (Roger) Lo,
• Mahesh K. Marina,
• Nishanth Sastry,
• Kai Xu,
• Saeed Fadaei,
• Yong Li

In response to the exponential surge in Video on Demand (VOD) traffic, numerous research endeavors have concentrated on optimizing and enhancing infrastructure efficiency. In contrast, this paper explores whether users' demand patterns can be shaped to ...

NetDiffusion: Network Data Augmentation Through Protocol-Constrained Traffic Generation

• Xi Jiang,
• Shinan Liu,
• Aaron Gember-Jacobson,
• Arjun Nitin Bhagoji,
• Paul Schmitt,
• Francesco Bronzino,
• Nick Feamster

Datasets of labeled network traces are essential for a multitude of machine learning (ML) tasks in networking, yet their availability is hindered by privacy and maintenance concerns, such as data staleness. To overcome this limitation, synthetic network ...

FedQV: Leveraging Quadratic Voting in Federated Learning

• Tianyue Chu,
• Nikolaos Laoutaris

Automated Backend Allocation for Multi-Model, On-Device AI Inference

• Venkatraman Iyer,
• Sungho Lee,
• Semun Lee,
• Juitem Joonwoo Kim,
• Hyunjun Kim,
• Youngjae Shin

On-Device Artificial Intelligence (AI) services such as face recognition, object tracking and voice recognition are rapidly scaling up deployments on embedded, memory-constrained hardware devices. These services typically delegate AI inference models for ...

Agents of Autonomy: A Systematic Study of Robotics on Modern Hardware

• Mohammad Bakhshalipour,
• Phillip B. Gibbons

From Open-Source to Primetime: The Making of an AI News Anchor and its Role in the New Landscape of Disinformation

• Matyas Bohacek

In the summer of 2023, the Writers Guild of America embarked on what would become one of its longest strikes in history. Concurrently, the early stirrings of the presidential campaign saw several ads circulating with convincingly altered video and audio ...

Multimedia AI vs Information Disorder: A Journey of Discovery

• Duc Tien Dang Nguyen

In this talk, Dang-Nguyen discusses case studies conducted in collaboration with fact-checkers and journalists in the Nordic countries, with a specific focus on the user needs of fact-checkers, their workflows, and tools employed for verifying visual ...