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37,175 results found
Publication

On the Convergence of Typical Decomposition Approaches in MOEA/D for Many-objective Optimization Problems 

by Zhengxin Huang; et. al.

Decomposition approach is an important strategy in multi-objective evolutionary algorithm based on decomposition (MOEA/D), which is a popular method for handing many-objective optimization problems (MaOPs). This paper presents a theoretical analysis on the convergence ability of using the typical weighted sum (WS), Tchebycheff (TCH) or penalty-based boundary intersection (PBI) approach in a basic MOEA/D for solving two benchmark MaOPs. The results show that using WS is the most efficient case, and the algorithm can find…

Publication

Predictive Job Scheduling under Uncertain Constraints in Cloud Computing 

by Hang Dong; et. al.

Capacity management has always been a great challenge for cloud platforms due to massive, heterogeneous on-demand instances running at different times. To better plan the capacity for the whole platform, a class of cloud computing instances have been released to collect computing demands beforehand. To use such instances, users are allowed to submit jobs to run for a pre-specified uninterrupted duration in a flexible range of time in the future with a discount compared to…

Publication

Belonging There: VROOM-ing into the Uncanny Valley of XR Telepresence 

by Brennan Jones; et. al.

The world is entering a new normal of hybrid organisations, in which it will be common that some members are co-located and others are remote. Hybridity is rife with asymmetries that affect our sense of belonging in an organisational space. This paper reports a study of an XR Telepresence technology probe to explore how remote workers might present themselves and be perceived as an equal and unique embodied being in a workplace. VROOM (Virtual Robot…

Publication

AsyMo: Scalable and Efficient Deep-Learning Inference on Asymmetric Mobile CPUs 

by Manni Wang; et. al.

On-device deep learning (DL) inference has attracted vast interest. Mobile CPUs are the most common hardware for on-device inference and many inference frameworks have been developed for them. Yet, due to the hardware complexity, DL inference on mobile CPUs suffers from two common issues: the poor performance scalability on the asymmetric multiprocessor, and energy inefficiency. We identify the root causes are improper task partitioning and unbalanced task distribution for the poor scalability, and unawareness of…

Publication

Formation of Social Ties Predicts Food Choice: A Campus-wide Longitudinal Study 

by Kristina Gligorić; et. al.

Nutrition is a key determinant of long-term health, and social influence has long been theorized to be a key determinant of nutrition. It has been difficult to quantify the postulated role of social influence on nutrition using traditional methods such as surveys, due to the typically small scale and short duration of studies. To overcome these limitations, we leverage a novel source of data: logs of 38 million food purchases made over an 8-year period…

Publication

PECAM: Privacy-Enhanced Video Streaming and Analytics via Securely-Reversible Transformation 

by Hao Wu; et. al.

As Video Streaming and Analytics (VSA) systems become increasingly popular, serious privacy concerns have risen on exposing too much unnecessary private information to the VSA providers. Yet, it is challenging to protect privacy while still preserving desired VSA features, i.e., effective analytics, forensic support, resource efficiency, and real-time execution. In this paper, we present a VSA privacy enhancement system (PECAM), which addresses the above challenge with no change in the VSA back-end. PECAM leverages a…

Publication

HALO: Hierarchy-aware Fault Localization for Cloud Systems 

by Xu Zhang; et. al.

A typical cloud system has a large amount of telemetry data collected by pervasive software monitors that keep tracking the health status of the system. The telemetry data is essentially multi-dimensional data, which contains attributes and failure/success status of the system being monitored. By identifying the attribute value combinations where the failures are mostly concentrated (which we call fault-indicating combination), we can localize the cause of system failures into a smaller scope, thus facilitating fault…

Publication

Authenticating Drivers Using Automotive Batteries 

by Liang He; et. al.

Automakers have been improving, or even trying to replace, key-based driver authentication solutions, owing to their vulnerability to cyber attacks and single-point-of-failures, as well as their inability of driver identification. In line with this effort, we design a novel driver authentication system using automotive batteries, called Batteries-as-Authenticators (BAuth), to mitigate the limitations of key-based solutions by providing a second-factor authentication. BAuth is an add-on module installed between vehicles and their batteries, which uses the batteries…

Publication

Data Augmentation for Spoken Language Understanding via Pretrained Models 

by Baolin Peng; et. al.

The training of spoken language understanding (SLU) models often faces the problem of data scarcity. In this paper, we put forward a data augmentation method with pretrained language models to boost the variability and accuracy of generated utterances. Furthermore, we investigate and propose solutions to two previously overlooked scenarios of data scarcity in SLU: i) Rich-in-Ontology: ontology information with numerous valid dialogue acts are given; ii) Rich-in-Utterance: a large number of unlabelled utterances are available.…