Resolving Class Imbalance for LiDAR-based Object Detector by Dynamic Weight Average and Contextual Ground Truth Sampling

An autonomous driving system requires a 3D object detector, which must perceive all present road agents reliably to navigate an environment safely. However, real-world driving datasets often suffer from the problem of data imbalance, which causes difficulties in training a model that works well across all classes, resulting in an undesired imbalanced sub-optimal performance. In this work, we propose a method to address this data imbalance problem. Our method consists of two main components: (i) a LiDAR-based 3D object detector with per-class multiple detection heads where losses from each head are modified by dynamic weight average to be balanced. (ii) Contextual ground truth (GT) sampling, where we improve conventional GT sampling techniques by leveraging semantic information to augment point cloud with sampled ground truth GT objects. Our experiment with KITTI and nuScenes datasets confirms our proposed method's effectiveness in dealing with the data imbalance problem, producing better detection accuracy compared to existing approaches.Comment: 10 page

Convex optimization framework for intermediate deadline assignment in soft and hard real-time distributed systems

It is generally challenging to determine end-to-end delays of applications for maximizing the aggregate system utility subject to timing constraints. Many practical approaches suggest the use of intermediate deadline of tasks in order to control and upper-bound their end-to-end delays. This paper proposes a unified framework for different time-sensitive, global optimization problems, and solves them in a distributed manner using Lagrangian duality. The framework uses global viewpoints to assign intermediate deadlines, taking resource contention among tasks into consideration. For soft real-time tasks, the proposed framework effectively addresses the deadline assignment problem while maximizing the aggregate quality of service. For hard real-time tasks, we show that existing heuristic solutions to the deadline assignment problem can be incorporated into the proposed framework, enriching their mathematical interpretation

Unsupervised Dialogue Topic Segmentation in Hyperdimensional Space

We present HyperSeg, a hyperdimensional computing (HDC) approach to unsupervised dialogue topic segmentation. HDC is a class of vector symbolic architectures that leverages the probabilistic orthogonality of randomly drawn vectors at extremely high dimensions (typically over 10,000). HDC generates rich token representations through its low-cost initialization of many unrelated vectors. This is especially beneficial in topic segmentation, which often operates as a resource-constrained pre-processing step for downstream transcript understanding tasks. HyperSeg outperforms the current state-of-the-art in 4 out of 5 segmentation benchmarks -- even when baselines are given partial access to the ground truth -- and is 10 times faster on average. We show that HyperSeg also improves downstream summarization accuracy. With HyperSeg, we demonstrate the viability of HDC in a major language task. We open-source HyperSeg to provide a strong baseline for unsupervised topic segmentation.Comment: Interspeech 202

Multiprocessor Real-Time Scheduling Considering Concurrency and Urgency

It has been widely studied how to schedule real-time tasks on multiprocessor platforms. Several studies find optimal scheduling policies for implicit deadline task systems, but it is hard to understand how each policy utilizes the two important aspects of scheduling real-time tasks on multiprocessors: inter-job concurrency and job urgency. In this paper, we introduce a new scheduling policy that considers these two properties. We prove that the policy is optimal for the special case when the execution time of all tasks are equally one and deadlines are implicit, and observe that the policy is a new concept in that it is not an instance of Pfair or ERfair. It remains open to find a scheduliability condition for general task systems under our scheduling policy

Channel and timeslot co-scheduling with minimal channel switching for data aggregation in MWSNs.

Collision-free transmission and efficient data transfer between nodes can be achieved through a set of channels in multichannel wireless sensor networks (MWSNs). While using multiple channels, we have to carefully consider channel interference, channel and time slot (resources) optimization, channel switching delay, and energy consumption. Since sensor nodes operate on low battery power, the energy consumed in channel switching becomes an important challenge. In this paper, we propose channel and time slot scheduling for minimal channel switching in MWSNs, while achieving efficient and collision-free transmission between nodes. The proposed scheme constructs a duty-cycled tree while reducing the amount of channel switching. As a next step, collision-free time slots are assigned to every node based on the minimal data collection delay. The experimental results demonstrate that the validity of our scheme reduces the amount of channel switching by 17.5%, reduces energy consumption for channel switching by 28%, and reduces the schedule length by 46%, as compared to the existing schemes.N/

An Examination of Trust Effects and Pre-Existing Relational Risks in eGovernment Services

This study brings to attention distinct characteristics of government-citizen relationships that should be addressed when modeling citizens’ G2C service usage behavior and calls for an e-government user acceptance model. Earlier studies on G2C service adoption have shown that private sector-oriented models can result in inconsistent findings when applied to different service types or circumstances. This paper argues that government-citizen relationships often go beyond underlying assumption of user acceptance models applied across different areas, and that a more generalizable model for various egovernment services is essential for understanding and improving citizens’ e-government service acceptance. This argument is further developed by an empirical examination of a government-citizen relationship where use of an e-government service requires citizens to transmit highly sensitive information, but trustworthiness of the authority does not affect citizens’ use of the service

Information Sharing: A Study of Information Attributes and their Relative Significance During Catastrophic Events

We live in a digital era where the global community relies on Information Systems to conduct all kinds of operations, including averting or responding to unanticipated risks and disasters. This can only happen when there is a robust information exchange facilitation mechanism in place, which can help in taking quick and legitimate steps in dealing with any kind of emergent situation. Prior literature in the field of information assurance has focused on building defense mechanisms to protect assets and reduce vulnerability to foreign attacks. Nevertheless, information assurance does not simply mean building an impermeable membrane and safeguarding information, but also implies letting information be securely shared, if required, among a set of related groups or organizations that serve a common purpose. This chapter will revolve around the central pivot of Information Sharing. Further, to study the relative significance of various information dimensions in different disaster situations, content analyses are conducted. The results hence obtained can be used to develop a prioritization framework for different disaster response activities, thus to increase the mitigation efficiency. We will also explore roles played by few existing organizations and technologies across the globe that are actively involved in Information Sharing to mitigate the impact of disasters and extreme events