127 research outputs found
Downlink Resource Scheduling in an LTE System
The problem of allocating resources to multiple users on the downlink of a Long Term Evolution (LTE) cellular communication system is discussed. An optimal (maximum throughput) multiuser scheduler is proposed and its performance is evaluated. Numerical results show that the system performance improves with increasing correlation among OFDMA subcarriers. It is found that a limited amount of feedback information can provide a relatively good performance. A sub-optimal scheduler with a lower computational complexity is also proposed, and shown to provide good performance. The sub-optimal scheme is especially attractive when the number of users is large, as the complexity of the optimal scheme may then be unacceptably high in many practical situations. The performance of a scheduler which addresses fairness among users is also presented
Towards Fairness-Aware Federated Learning
Recent advances in Federated Learning (FL) have brought large-scale
collaborative machine learning opportunities for massively distributed clients
with performance and data privacy guarantees. However, most current works focus
on the interest of the central controller in FL,and overlook the interests of
the FL clients. This may result in unfair treatment of clients which
discourages them from actively participating in the learning process and
damages the sustainability of the FL ecosystem. Therefore, the topic of
ensuring fairness in FL is attracting a great deal of research interest. In
recent years, diverse Fairness-Aware FL (FAFL) approaches have been proposed in
an effort to achieve fairness in FL from different perspectives. However, there
is no comprehensive survey which helps readers gain insight into this
interdisciplinary field. This paper aims to provide such a survey. By examining
the fundamental and simplifying assumptions, as well as the notions of fairness
adopted by existing literature in this field, we propose a taxonomy of FAFL
approaches covering major steps in FL, including client selection,
optimization, contribution evaluation and incentive distribution. In addition,
we discuss the main metrics for experimentally evaluating the performance of
FAFL approaches, and suggest promising future research directions towards
fairness-aware federated learning.Comment: 16 pages, 4 figure
Can ChatGPT Assess Human Personalities? A General Evaluation Framework
Large Language Models (LLMs) especially ChatGPT have produced impressive
results in various areas, but their potential human-like psychology is still
largely unexplored. Existing works study the virtual personalities of LLMs but
rarely explore the possibility of analyzing human personalities via LLMs. This
paper presents a generic evaluation framework for LLMs to assess human
personalities based on Myers Briggs Type Indicator (MBTI) tests. Specifically,
we first devise unbiased prompts by randomly permuting options in MBTI
questions and adopt the average testing result to encourage more impartial
answer generation. Then, we propose to replace the subject in question
statements to enable flexible queries and assessments on different subjects
from LLMs. Finally, we re-formulate the question instructions in a manner of
correctness evaluation to facilitate LLMs to generate clearer responses. The
proposed framework enables LLMs to flexibly assess personalities of different
groups of people. We further propose three evaluation metrics to measure the
consistency, robustness, and fairness of assessment results from
state-of-the-art LLMs including ChatGPT and GPT-4. Our experiments reveal
ChatGPT's ability to assess human personalities, and the average results
demonstrate that it can achieve more consistent and fairer assessments in spite
of lower robustness against prompt biases compared with InstructGPT.Comment: Accepted to EMNLP 2023. Our codes are available at
https://github.com/Kali-Hac/ChatGPT-MBT
Research Letter Performance of Equal Power Allocation in Multiuser OFDM-Based Cognitive Radio Systems
The performance difference between a simple equal subchannel power allocation and the optimal water-filling subchannel power allocation schemes is studied for a multiuser OFDM-based cognitive radio (CR) system. It is shown that this difference depends on the average subchannel gain variations among the CR users as well as the activity levels of the primary users
Inductive Graph Transformer for Delivery Time Estimation
Providing accurate estimated time of package delivery on users' purchasing
pages for e-commerce platforms is of great importance to their purchasing
decisions and post-purchase experiences. Although this problem shares some
common issues with the conventional estimated time of arrival (ETA), it is more
challenging with the following aspects: 1) Inductive inference. Models are
required to predict ETA for orders with unseen retailers and addresses; 2)
High-order interaction of order semantic information. Apart from the
spatio-temporal features, the estimated time also varies greatly with other
factors, such as the packaging efficiency of retailers, as well as the
high-order interaction of these factors. In this paper, we propose an inductive
graph transformer (IGT) that leverages raw feature information and structural
graph data to estimate package delivery time. Different from previous graph
transformer architectures, IGT adopts a decoupled pipeline and trains
transformer as a regression function that can capture the multiplex information
from both raw feature and dense embeddings encoded by a graph neural network
(GNN). In addition, we further simplify the GNN structure by removing its
non-linear activation and the learnable linear transformation matrix. The
reduced parameter search space and linear information propagation in the
simplified GNN enable the IGT to be applied in large-scale industrial
scenarios. Experiments on real-world logistics datasets show that our proposed
model can significantly outperform the state-of-the-art methods on estimation
of delivery time. The source code is available at:
https://github.com/enoche/IGT-WSDM23.Comment: 9 pages, accepted to WSDM 202
Building Ethics into Artificial Intelligence
As artificial intelligence (AI) systems become increasingly ubiquitous, the
topic of AI governance for ethical decision-making by AI has captured public
imagination. Within the AI research community, this topic remains less familiar
to many researchers. In this paper, we complement existing surveys, which
largely focused on the psychological, social and legal discussions of the
topic, with an analysis of recent advances in technical solutions for AI
governance. By reviewing publications in leading AI conferences including AAAI,
AAMAS, ECAI and IJCAI, we propose a taxonomy which divides the field into four
areas: 1) exploring ethical dilemmas; 2) individual ethical decision
frameworks; 3) collective ethical decision frameworks; and 4) ethics in
human-AI interactions. We highlight the intuitions and key techniques used in
each approach, and discuss promising future research directions towards
successful integration of ethical AI systems into human societies
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