pwc-archive/papers-with-abstracts huggingface dataset:
High Recall, Small Data: The Challenges of Within-System Evaluation in a Live Legal Search System
Authors: Gineke Wiggers, Suzan Verberne, Arjen de Vries, Roel van der Burg
Date: 2024-03-27 00:00:00
Tasks: Document Ranking, Information Retrieval, Retrieval

This paper illustrates some challenges of common ranking evaluation methods for legal information retrieval (IR). We show these challenges with log data from a live legal search system and two user studies. We provide an overview of aspects of legal IR, and the implications of these aspects for the expected challenges of common evaluation methods: test collections based on explicit and implicit feedback, user surveys, and A/B testing. Next, we illustrate the challenges of common evaluation methods using data from a live, commercial, legal search engine. We specifically focus on methods for monitoring the effectiveness of (continuous) changes to document ranking by a single IR system over time. We show how the combination of characteristics in legal IR systems and limited user data can lead to challenges that cause the common evaluation methods discussed to be sub-optimal. In our future work we will therefore focus on less common evaluation methods, such as cost-based evaluation models.
Directed Criteria Citation Recommendation and Ranking Through Link Prediction
Authors: William Watson, Lawrence Yong
Date: 2024-03-18 00:00:00
Tasks: Citation Recommendation, Link Prediction, Prediction

We explore link prediction as a proxy for automatically surfacing documents from existing literature that might be topically or contextually relevant to a new document. Our model uses transformer-based graph embeddings to encode the meaning of each document, presented as a node within a citation network. We show that the semantic representations that our model generates can outperform other content-based methods in recommendation and ranking tasks. This provides a holistic approach to exploring citation graphs in domains where it is critical that these documents properly cite each other, so as to minimize the possibility of any inconsistencies
DeGCN: Deformable Graph Convolutional Networks for Skeleton-Based Action Recognition
Authors: Woomin Myung, Nan Su, Jing-Hao Xue, Guijin Wang
Date: 2024-03-25 00:00:00
Proceeding: IEEE Transactions on Image Processing 2024 3
Tasks: Action Recognition, Skeleton Based Action Recognition

Graph convolutional networks (GCN) have recently been studied to exploit the graph topology of the human body for skeleton-based action recognition. However, most of these methods unfortunately aggregate messages via an inflexible pattern for various action samples, lacking the awareness of intra-class variety and the suitableness for skeleton sequences, which often contain redundant or even detrimental connections. In this paper, we propose a novel Deformable Graph Convolutional Network (DeGCN) to adaptively capture the most informative joints. The proposed DeGCN learns the deformable sampling locations on both spatial and temporal graphs, enabling the model to perceive discriminative receptive fields. Notably, considering human action is inherently continuous, the corresponding temporal features are defined in a continuous latent space. Furthermore, we design an innovative multi-branch framework, which not only strikes a better trade-off between accuracy and model size, but also elevates the effect of ensemble between the joint and bone modalities remarkably. Extensive experiments show that our proposed method achieves state-of-the-art performances on three widely used datasets, NTU RGB+D, NTU RGB+D 120, and NW-UCLA.
Evaluation of Road Transportation System Resilience and Link Importance
Authors: LV Biao1, GAO Zi-qiang1, LIU Yi-liu
Date: 2020-04-01 00:00:00
Proceeding: Journal of Transportation Systems Engineering and Information Technology 2020 4

Resilience comprehensively describes the ability of a roadway system to absorb and recover from a
disruptive event. The existing resilience metrics can be improved to measure the system performance more
accurately and the impact of traffic flow should be considered in the evaluation. This study first examined the
roadway capacity degradation and recovery processes, then used the network efficiency as a measure of system
performance to develop a resilience metric for the entire disruptive event duration period. The study proposed the
resilience achievement worth (RAW) and resilience reduction worth (RRW) indices to reflect the link importance
respectively from the optimistic and pessimistic perspectives, and the heuristic algorithm was used to identify the
importance degrees of roadway links. The results from the examples show that the proposed resilience metric is
able to measure the average cumulative performance of the roadway system during the entire degradation and
recovery processes after a disruptive event, which is more consistent with the function of resilience metric. The
indices of RAW and RRW are also able to effectively identify the link importance degree. The importance degrees
of most of the roadway links change dynamically with time.
V2X Enabled Emergency Vehicle Alert System
Authors: Nitish Kumar, Hershita Shukla, Prof. P. Rajalakhsmi
Date: 2024-03-28 00:00:00
Tasks: Management

Today's major concern in traffic management systems includes time-efficient emergency transports. The awareness of environment and vehicle information is necessary for the emergency vehicles as well as the surrounding commercial vehicles that might be driven by inexperienced drivers to act accordingly if they both interact. The information exchange should be quick and accurate along with how much interactive the alerting system is with the drivers. Therefore, technologies like V2X-based alert systems can deal with such emergency situations and hence prevent potential health or social hazards. An alerting system as a part of a smart-connected city is proposed in this paper. The Dedicated Short Range Communication (DSRC) based system has tried to cover the major domain of information about misbehaving vehicles, any pedestrians on the road, and information about the emergency vehicle itself. The commercial vehicle also will have a similar alert system as an application of V2V and V2I. Further in this paper, a realtime monitoring system was developed using grafana dashboard which will be installed in the area's base station to monitor the vehicles in that area.
Disruption management for resource-constrained project scheduling
Authors: G Zhu, JF Bard* and G Yu
Date: 2017-12-21 00:00:00
Proceeding: 21 Dec 2017 12
Tasks: Management, Scheduling

In this paper, we study the problem of how to react when an ongoing project is disrupted. The focus is on the resourceconstrained project scheduling problem with finish–start precedence constraints. We begin by proposing a classification
scheme for the different types of disruptions and then define the constraints and objectives that comprise what we call
the recovery problem. The goal is to get back on track as soon as possible at minimum cost, where cost is now a function
of the deviation from the original schedule. The problem is formulated as an integer linear program and solved with a
hybrid mixed-inter programming/constraint programming procedure that exploits a number of special features in the
constraints. The new model is significantly different from the original one due to the fact that a different set of feasibility
conditions and performance requirements must be considered during the recovery process. The complexity of several
special cases is analysed. To test the hybrid procedure, 554 20-activity instances were solved and the results compared
with those obtained with CPLEX. Computational experiments were also conducted to determine the effects of different
factors related to the recovery process.
Real-time accident detection and physiological signal monitoring to enhance motorbike safety and emergency response
Authors: S. M. Kayser Mehbub Siam, Khadiza Islam Sumaiya, Md Rakib Al-Amin, Tamim Hasan Turjo, Ahsanul Islam, A. H. M. A. Rahim, Md Rakibul Hasan
Date: 2024-03-28 00:00:00

Rapid urbanization and improved living standards have led to a substantial increase in the number of vehicles on the road, consequently resulting in a rise in the frequency of accidents. Among these accidents, motorbike accidents pose a particularly high risk, often resulting in serious injuries or deaths. A significant number of these fatalities occur due to delayed or inadequate medical attention. To this end, we propose a novel automatic detection and notification system specifically designed for motorbike accidents. The proposed system comprises two key components: a detection system and a physiological signal monitoring system. The detection system is integrated into the helmet and consists of a microcontroller, accelerometer, GPS, GSM, and Wi-Fi modules. The physio-monitoring system incorporates a sensor for monitoring pulse rate and SpO$_{2}$ saturation. All collected data are presented on an LCD display and wirelessly transmitted to the detection system through the microcontroller of the physiological signal monitoring system. If the accelerometer readings consistently deviate from the specified threshold decided through extensive experimentation, the system identifies the event as an accident and transmits the victim's information -- including the GPS location, pulse rate, and SpO$_{2}$ saturation rate -- to the designated emergency contacts. Preliminary results demonstrate the efficacy of the proposed system in accurately detecting motorbike accidents and promptly alerting emergency contacts. We firmly believe that the proposed system has the potential to significantly mitigate the risks associated with motorbike accidents and save lives.
Unleashing the Power of AI. A Systematic Review of Cutting-Edge Techniques in AI-Enhanced Scientometrics, Webometrics, and Bibliometrics
Authors: Hamid Reza Saeidnia, Elaheh Hosseini, Shadi Abdoli, Marcel Ausloos
Date: 2024-02-22 00:00:00
Tasks: Articles, Recommendation Systems, Systematic Literature Review

Purpose: The study aims to analyze the synergy of Artificial Intelligence (AI), with scientometrics, webometrics, and bibliometrics to unlock and to emphasize the potential of the applications and benefits of AI algorithms in these fields. Design/methodology/approach: By conducting a systematic literature review, our aim is to explore the potential of AI in revolutionizing the methods used to measure and analyze scholarly communication, identify emerging research trends, and evaluate the impact of scientific publications. To achieve this, we implemented a comprehensive search strategy across reputable databases such as ProQuest, IEEE Explore, EBSCO, Web of Science, and Scopus. Our search encompassed articles published from January 1, 2000, to September 2022, resulting in a thorough review of 61 relevant articles. Findings: (i) Regarding scientometrics, the application of AI yields various distinct advantages, such as conducting analyses of publications, citations, research impact prediction, collaboration, research trend analysis, and knowledge mapping, in a more objective and reliable framework. (ii) In terms of webometrics, AI algorithms are able to enhance web crawling and data collection, web link analysis, web content analysis, social media analysis, web impact analysis, and recommender systems. (iii) Moreover, automation of data collection, analysis of citations, disambiguation of authors, analysis of co-authorship networks, assessment of research impact, text mining, and recommender systems are considered as the potential of AI integration in the field of bibliometrics. Originality/value: This study covers the particularly new benefits and potential of AI-enhanced scientometrics, webometrics, and bibliometrics to highlight the significant prospects of the synergy of this integration through AI.
A Robust UWOC-assisted Multi-hop Topology for Underwater Sensor Network Nodes
Authors: Maaz Salman, Javad Bolboli, Wan-Young Chung
Date: 2024-03-28 00:00:00

Underwater environment is substantially less explored territory as compared to earth surface due to lack of robust underwater communication infrastructure. For Internet of Underwater things connectivity, underwater wireless optical communication can play a vital role, compared to conventional radio frequency communication, due to longer range, high data rate, low latency, and unregulated bandwidth. This study proposes underwater wireless optical communication driven local area network UWOC LAN, comprised of multiple network nodes with optical transceivers. Moreover, the temperature sensor data is encapsulated with individual authentication identity to enhance the security of the framework at the user end. The proposed system is evaluated in a specially designed water tank of 4 meters. The proposed system evaluation analysis shows that the system can transmit underwater temperature data reliably in real time. The proposed secure UWOC LAN is tested within a communication range of 16 meters by incorporating multi hop connectivity to monitor the underwater environment.
Optimal Pilot Design for OTFS in Linear Time-Varying Channels
Authors: Ids van der Werf, Richard Heusdens, Richard C. Hendriks, Geert Leus
Date: 2024-03-28 00:00:00

This paper investigates the positioning of the pilot symbols, as well as the power distribution between the pilot and the communication symbols in the orthogonal time frequency space (OTFS) modulation scheme. We analyze the pilot placements that minimize the mean squared error (MSE) in estimating the channel taps. This allows us to identify two new pilot allocations for OTFS. In addition, we optimize the average channel capacity by adjusting the power balance. We show that this leads to a significant increase in average capacity. The results provide valuable guidance for designing the OTFS parameters to achieve maximum capacity. Numerical simulations are performed to validate the findings.
Decentralizing Coherent Joint Transmission Precoding via Fast ADMM with Deterministic Equivalents
Authors: Xinyu Bian, Yuhao Liu, Yizhou Xu, Tianqi Hou, Wenjie Wang, Yuyi Mao, Jun Zhang
Date: 2024-03-28 00:00:00

Inter-cell interference (ICI) suppression is critical for multi-cell multi-user networks. In this paper, we investigate advanced precoding techniques for coordinated multi-point (CoMP) with downlink coherent joint transmission, an effective approach for ICI suppression. Different from the centralized precoding schemes that require frequent information exchange among the cooperating base stations, we propose a decentralized scheme to minimize the total power consumption. In particular, based on the covariance matrices of global channel state information, we estimate the ICI bounds via the deterministic equivalents and decouple the original design problem into sub-problems, each of which can be solved in a decentralized manner. To solve the sub-problems at each base station, we develop a low-complexity solver based on the alternating direction method of multipliers (ADMM) in conjunction with the convex-concave procedure (CCCP). Simulation results demonstrate the effectiveness of our proposed decentralized precoding scheme, which achieves performance similar to the optimal centralized precoding scheme. Besides, our proposed ADMM solver can substantially reduce the computational complexity, while maintaining outstanding performance.
GENESIS-RL: GEnerating Natural Edge-cases with Systematic Integration of Safety considerations and Reinforcement Learning
Authors: Hsin-Jung Yang, Joe Beck, Md Zahid Hasan, Ekin Beyazit, Subhadeep Chakraborty, Tichakorn Wongpiromsarn, Soumik Sarkar
Date: 2024-03-27 00:00:00
Tasks: Autonomous Driving

In the rapidly evolving field of autonomous systems, the safety and reliability of the system components are fundamental requirements. These components are often vulnerable to complex and unforeseen environments, making natural edge-case generation essential for enhancing system resilience. This paper presents GENESIS-RL, a novel framework that leverages system-level safety considerations and reinforcement learning techniques to systematically generate naturalistic edge cases. By simulating challenging conditions that mimic the real-world situations, our framework aims to rigorously test entire system's safety and reliability. Although demonstrated within the autonomous driving application, our methodology is adaptable across diverse autonomous systems. Our experimental validation, conducted on high-fidelity simulator underscores the overall effectiveness of this framework.
Graph Bayesian Optimization for Multiplex Influence Maximization
Authors: Zirui Yuan, Minglai Shao, Zhiqian Chen
Date: 2024-03-25 00:00:00
Tasks: Bayesian Optimization

Influence maximization (IM) is the problem of identifying a limited number of initial influential users within a social network to maximize the number of influenced users. However, previous research has mostly focused on individual information propagation, neglecting the simultaneous and interactive dissemination of multiple information items. In reality, when users encounter a piece of information, such as a smartphone product, they often associate it with related products in their minds, such as earphones or computers from the same brand. Additionally, information platforms frequently recommend related content to users, amplifying this cascading effect and leading to multiplex influence diffusion. This paper first formulates the Multiplex Influence Maximization (Multi-IM) problem using multiplex diffusion models with an information association mechanism. In this problem, the seed set is a combination of influential users and information. To effectively manage the combinatorial complexity, we propose Graph Bayesian Optimization for Multi-IM (GBIM). The multiplex diffusion process is thoroughly investigated using a highly effective global kernelized attention message-passing module. This module, in conjunction with Bayesian linear regression (BLR), produces a scalable surrogate model. A data acquisition module incorporating the exploration-exploitation trade-off is developed to optimize the seed set further. Extensive experiments on synthetic and real-world datasets have proven our proposed framework effective. The code is available at https://github.com/zirui-yuan/GBIM.
Lane-Change in Dense Traffic with Model Predictive Control and Neural Networks
Authors: Sangjae Bae, David Isele, Alireza Nakhaei, Peng Xu, Alexandre Miranda Anon, Chiho Choi, Kikuo Fujimura, Scott Moura
Date: 2024-03-28 00:00:00
Tasks: Model Predictive Control

This paper presents an online smooth-path lane-change control framework. We focus on dense traffic where inter-vehicle space gaps are narrow, and cooperation with surrounding drivers is essential to achieve the lane-change maneuver. We propose a two-stage control framework that harmonizes Model Predictive Control (MPC) with Generative Adversarial Networks (GAN) by utilizing driving intentions to generate smooth lane-change maneuvers. To improve performance in practice, the system is augmented with an adaptive safety boundary and a Kalman Filter to mitigate sensor noise. Simulation studies are investigated in different levels of traffic density and cooperativeness of other drivers. The simulation results support the effectiveness, driving comfort, and safety of the proposed method.
Feedback Optimization of Incentives for Distribution Grid Services
Authors: Guido Cavraro, Joshua Comden, Andrey Bernstein
Date: 2024-03-28 00:00:00

Energy prices and net power injection limitations regulate the operations in distribution grids and typically ensure that operational constraints are met. Nevertheless, unexpected or prolonged abnormal events could undermine the grid's functioning. During contingencies, customers could contribute effectively to sustaining the network by providing services. This paper proposes an incentive mechanism that promotes users' active participation by essentially altering the energy pricing rule. The incentives are modeled via a linear function whose parameters can be computed by the system operator (SO) by solving an optimization problem. Feedback-based optimization algorithms are then proposed to seek optimal incentives by leveraging measurements from the grid, even in the case when the SO does not have a full grid and customer information. Numerical simulations on a standard testbed validate the proposed approach.
Expectation Maximization Aided Modified Weighted Sequential Energy Detector for Distributed Cooperative Spectrum Sensing
Authors: Mohammed Rashid, Jeffrey A. Nanzer
Date: 2024-03-28 00:00:00

Energy detector (ED) is a popular choice for distributed cooperative spectrum sensing because it does not need to be cognizant of the primary user (PU) signal characteristics. However, the conventional ED-based sensing usually requires large number of observed samples per energy statistic, particularly at low signal-to-noise ratios (SNRs), for improved detection capability. This is due to the fact that it uses the energy only from the present sensing interval for the PU detection. Previous studies have shown that even with fewer observed samples per energy statistics, improved detection capabilities can be achieved by aggregating both present and past ED samples in a test statistic. Thus, a weighted sequential energy detector (WSED) has been proposed, but it is based on aggregating all the collected ED samples over an observation window. For a highly dynamic PU over the consecutive sensing intervals, that involves also combining the outdated samples in the test statistic that do not correspond to the present state of the PU. In this paper, we propose a modified WSED (mWSED) that uses the primary user states information over the window to aggregate only the highly correlated ED samples in its test statistic. In practice, since the PU states are a priori unknown, we also develop a joint expectation-maximization and Viterbi (EM-Viterbi) algorithm based scheme to iteratively estimate the states by using the ED samples collected over the window. The estimated states are then used in mWSED to compute its test statistics, and the algorithm is referred to here as the EM-mWSED algorithm. Simulation results show that EM-mWSED outperforms other schemes and its performance improves by increasing the average number of neighbors per SU in the network, and by increasing the SNR or the number of samples per energy statistic.