AI ARTIFICIAL INTELLIGENCE 2019


Artificial intelligence (AI) is the simulation of human intelligence processes by machines, especially computer systems. These processes include learning (the acquisition of information and rules for using the information), reasoning (using rules to reach approximate or definite conclusions) and self-correction.

Detecting AI -Synthesized Speech Using Bispectral Analysis
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From speech to images, and videos, advances in machine learning have led to dramatic improvements in the quality and realism of so-called AI -synthesized content. While there are many exciting and interesting applications, this type of content can also be used to create

AI for Smart Consumer Electronics: At the Edge or in the Cloud
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I welcome the readers to the September 2019 issue of the IEEE Consumer Electronics magazine. This becomes our first issue after Consumer Electronic Society decided to move from the previous editorial service to new one called Common Design Service for its

Motion Gaming AI using Time Series Forecasting and Dynamic Difficulty Adjustment for Improving Exercise Balance and Enjoyment
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This paper proposes a motion gaming AI that encourages players to use their body parts in a well-balanced manner while promoting their enjoyment. The proposed AI uses time series forecasting to predict what actions its opponent human player will perform with respect to a

Building Explainable AI Evaluation for Autonomous Perception
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The development of the robust visual intelligence is one of the long-term challenging problems. From the perspective of artificial intelligence evaluation, the need to discover and explain the potential shortness of the evaluated intelligent algorithms/systems as well as the

Explainable AI as Collaborative Task Solving
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We present a new framework for explainable AI systems (XAI) aimed at increasing human trust in the systems performance through explanations. Based on the Theory of Mind, our framework X-ToM explicitly models machines mind (pg M), humans mind as inferred by the

Revisiting online scheduling for AI -driven internet of things
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ABSTRACT AI -driven Internet of Things (IoT) use AI Inference to characterize data processed from various sensors. Together, AI and IoT support smart buildings, cities, cars and drones. However, AI Inference requires updates when executed in new contexts

Edge Intelligence: The Convergence of Humans, Things, and AI
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Edge AI and Human Augmentation are two major technology trends, driven by recent advancements in edge computing, IoT, and AI accelerators. As humans, things, and AI continue to grow closer together, systems engineers and researchers are faced with new

AI City Challenge 2019 City-scale video analytics for smart transportation
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Understanding large-scale video traffic big data is the new frontier of todays AI smart transportation advancement. The AI City Challenge 2019 is the third sequel of a yearly event that draws significantly growing attention and participation. This paper presents works

The 2019 AI City Challenge
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Abstract The AI City Challenge has been created to accelerate intelligent video analysis that helps make cities smarter and safer. With millions of traffic video cameras acting as sensors around the world, there is a significant opportunity for real-time and batch analysis of these

Dataset Modeling for Data-Driven AI -Based Personalized Wireless Networks
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Current wireless networks are over-provisioned in order to maintain an average acceptable user experience for most users on the network. Over-provisioned networks suffer from several issues, however, including network inefficiency and the inability to maintain a certain

VPULab participation at AI City Challenge 2019
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In this paper, we present an approach for Multi-target and Multi-Camera Vehicle Tracking and another approach for Vehicle Re-Identification (ReID) across multiple cameras. We evaluate both approaches over CityFlow: A City-Scale Benchmark participating in Track 1

Multi-camera Vehicle Tracking and Re-identification on AI City Challenge 2019
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In this work, we present our solutions to the image-based vehicle re-identification (ReID) track and multi-camera vehicle tracking (MVT) tracks on AI City Challenge 2019 (AIC2019). For the ReID track, we propose an enhanced multi-granularity network with multiple