pwc-archive/papers-with-abstracts huggingface dataset:
ProCIS: A Benchmark for Proactive Retrieval in Conversations
Authors: Chris Samarinas, Hamed Zamani
Date: 2024-05-10 00:00:00
Tasks: Retrieval, Text Retrieval

The field of conversational information seeking, which is rapidly gaining interest in both academia and industry, is changing how we interact with search engines through natural language interactions. Existing datasets and methods are mostly evaluating reactive conversational information seeking systems that solely provide response to every query from the user. We identify a gap in building and evaluating proactive conversational information seeking systems that can monitor a multi-party human conversation and proactively engage in the conversation at an opportune moment by retrieving useful resources and suggestions. In this paper, we introduce a large-scale dataset for proactive document retrieval that consists of over 2.8 million conversations. We conduct crowdsourcing experiments to obtain high-quality and relatively complete relevance judgments through depth-k pooling. We also collect annotations related to the parts of the conversation that are related to each document, enabling us to evaluate proactive retrieval systems. We introduce normalized proactive discounted cumulative gain (npDCG) for evaluating these systems, and further provide benchmark results for a wide range of models, including a novel model we developed for this task. We believe that the developed dataset, called ProCIS, paves the path towards developing proactive conversational information seeking systems.
Impedance vs. Power Side-channel Vulnerabilities: A Comparative Study
Authors: Md Sadik Awal, Buddhipriya Gayanath, Md Tauhidur Rahman
Date: 2024-05-10 00:00:00
Tasks: Side Channel Analysis

Physical side channels emerge from the relation between internal computation or data with observable physical parameters of a chip. Previous works mostly focus on properties related to current consumption such as power consumption. The fundamental property behind current consumption occur from the impedance of the chip. Contemporary works have stared using chip impedance as a physical side channel in extracting sensitive information from computing systems. It leverages variations in intrinsic impedance of a chip across different logic states. However, there has been a lack of comparative studies. In this study, we conduct a comparative analysis of the impedance side channel, which has been limitedly explored, and the well-established power side channel. Through experimental evaluation, we investigate the efficacy of these side channels in extracting stored advanced encryption standard (AES) cryptographic key on a memory and analyze their performance. Our findings indicate that impedance analysis demonstrates a higher potential for cryptographic key extraction compared to power side-channel analysis (SCA). Moreover, we identify scenarios where power SCA does not yield satisfactory results, whereas impedance analysis proves to be more robust and effective. This work not only underscores the significance of impedance SCA in enhancing cryptographic security but also emphasizes the necessity for a deeper understanding of its mechanisms and implications.
Seasonality Patterns in 311-Reported Foodborne Illness Cases and Machine Learning-Identified Indications of Foodborne Illnesses from Yelp Reviews, New York City, 2022-2023
Authors: Eden Shaveet, Crystal Su, Daniel Hsu, Luis Gravano
Date: 2024-05-09 00:00:00

Restaurants are critical venues at which to investigate foodborne illness outbreaks due to shared sourcing, preparation, and distribution of foods. Formal channels to report illness after food consumption, such as 311, New York City's non-emergency municipal service platform, are underutilized. Given this, online social media platforms serve as abundant sources of user-generated content that provide critical insights into the needs of individuals and populations. We extracted restaurant reviews and metadata from Yelp to identify potential outbreaks of foodborne illness in connection with consuming food from restaurants. Because the prevalence of foodborne illnesses may increase in warmer months as higher temperatures breed more favorable conditions for bacterial growth, we aimed to identify seasonal patterns in foodborne illness reports from 311 and identify seasonal patterns of foodborne illness from Yelp reviews for New York City restaurants using a Hierarchical Sigmoid Attention Network (HSAN). We found no evidence of significant bivariate associations between any variables of interest. Given the inherent limitations of relying solely on user-generated data for public health insights, it is imperative to complement these sources with other data streams and insights from subject matter experts. Future investigations should involve conducting these analyses at more granular spatial and temporal scales to explore the presence of such differences or associations.
Creating Geospatial Trajectories from Human Trafficking Text Corpora
Authors: Saydeh N. Karabatis, Vandana P. Janeja
Date: 2024-05-09 00:00:00

Human trafficking is a crime that affects the lives of millions of people across the globe. Traffickers exploit the victims through forced labor, involuntary sex, or organ harvesting. Migrant smuggling could also be seen as a form of human trafficking when the migrant fails to pay the smuggler and is forced into coerced activities. Several news agencies and anti-trafficking organizations have reported trafficking survivor stories that include the names of locations visited along the trafficking route. Identifying such routes can provide knowledge that is essential to preventing such heinous crimes. In this paper we propose a Narrative to Trajectory (N2T) information extraction system that analyzes reported narratives, extracts relevant information through the use of Natural Language Processing (NLP) techniques, and applies geospatial augmentation in order to automatically plot trajectories of human trafficking routes. We evaluate N2T on human trafficking text corpora and demonstrate that our approach of utilizing data preprocessing and augmenting database techniques with NLP libraries outperforms existing geolocation detection methods.
MM-EMOG: Multi-Label Emotion Graph Representation for Mental Health Classification on Social Media
Authors: Rina Carines Cabral, Soyeon Caren Han, Josiah Poon, Goran Nenadic
Date: 2024-03-18 00:00:00
Proceeding: Robotics 2024 3
Tasks: Classification, Social Media Mental Health Detection

More than 80% of people who commit suicide disclose their intention to do so on social media. The main information we can use in social media is user-generated posts, since personal information is not always available. Identifying all possible emotions in a single textual post is crucial to detecting the user’s mental state; however, human emotions are very complex, and a single text instance likely expresses multiple emotions. This paper proposes a new multi-label emotion graph representation for social media post-based mental health classification. We first construct a word–document graph tensor to describe emotion-based contextual representation using emotion lexicons. Then, it is trained by multi-label emotions and conducts a graph propagation for harmonising heterogeneous emotional information, and is applied to a textual graph mental health classification. We perform extensive experiments on three publicly available social media mental health classification datasets, and the results show clear improvements.
Comparative study of models trained on synthetic data for Ukrainian grammatical error correction
Authors: Maksym Bondarenko, Artem Yushko, Andrii Shportko, Andrii Fedorych
Date: 2024-05-05 00:00:00
Proceeding: EACL 2024 5
Tasks: Grammatical Error Correction, Machine Translation, Synthetic Data Generation

The task of Grammatical Error Correction (GEC) has been extensively studied for the English language. However, its application to low-resource languages, such as Ukrainian, remains an open challenge. In this paper, we develop sequence tagging and neural machine translation models for the Ukrainian language as well as a set of algorithmic correction rules to augment those systems. We also develop synthetic data generation techniques for the Ukrainian language to create high-quality human-like errors. Finally, we determine the best combination of synthetically generated data to augment the existing UA-GEC corpus and achieve the state-of-the-art results of 0.663 F0. 5 score on the newly established UA-GEC benchmark. The code and trained models will be made publicly available on GitHub and HuggingFace.
A Low-Resource Approach to the Grammatical Error Correction of Ukrainian
Authors: Frank Palma Gomez, Alla Rozovskaya, and Dan Roth
Date: 2023-05-05 00:00:00
Proceeding: EACL 2023 5
Tasks: Grammatical Error Correction, Language Modeling, Language Modelling, Machine Translation

We present our system that participated in the shared task on the grammatical error correction of Ukrainian. We have implemented two approaches that make use of large pre-trained language models and synthetic data, that have been used for error correction of English as well as low-resource languages. The first approach is based on fine-tuning a large multilingual language model (mT5) in two stages: first, on synthetic data, and then on gold data. The second approach trains a (smaller) seq2seq Transformer model pre-trained on synthetic data and fine-tuned on gold data. Our mT5-based model scored first in “GEC only” track, and a very close second in the “GEC+Fluency” track. Our two key innovations are (1) finetuning in stages, first on synthetic, and then on gold data; and (2) a high-quality corruption method based on roundtrip machine translation to complement existing noisification approaches.
Scalable physical source-to-field inference with hypernetworks
Authors: Berian James, Stefan Pollok, Ignacio Peis, Jes Frellsen, Rasmus Bjørk
Date: 2024-05-07 00:00:00

We present a generative model that amortises computation for the field around e.g. gravitational or magnetic sources. Exact numerical calculation has either computational complexity $\mathcal{O}(M\times{}N)$ in the number of sources and field evaluation points, or requires a fixed evaluation grid to exploit fast Fourier transforms. Using an architecture where a hypernetwork produces an implicit representation of the field around a source collection, our model instead performs as $\mathcal{O}(M + N)$, achieves accuracy of $\sim\!4\%-6\%$, and allows evaluation at arbitrary locations for arbitrary numbers of sources, greatly increasing the speed of e.g. physics simulations. We also examine a model relating to the physical properties of the output field and develop two-dimensional examples to demonstrate its application. The code for these models and experiments is available at https://github.com/cmt-dtu-energy/hypermagnetics.
Composition and Merging of Assume-Guarantee Contracts Are Tensor Products
Authors: Inigo Incer
Date: 2024-05-09 00:00:00

We show that the operations of composition and merging of contracts are part of the tensor product structure of the algebra of contracts.
Switched Vector Field-based Guidance for General Reference Path Following in Planar Environment
Authors: Subham Basak, Satadal Ghosh
Date: 2024-05-10 00:00:00
Tasks: Autonomous Vehicles

Reference path following is a key component in the functioning of almost all engineered autonomous agents. Among several path following guidance methods in existing literature, vector-field-based guidance approach has got wide attention because of its simplicity and guarantee of stability under a broad class of scenarios. However, the usage of same cross-track-error-dependent structure of desired vector field in most of the existing literature irrespective of instantaneous cross-track error and course angle of unmanned vehicle makes it quite restrictive in attaining faster convergence and also leads to infeasibly high turn rate command for many scenarios. To this end, this paper presents a novel switched vector field-based guidance for following a general reference path, in which the structure of the desired vector field depends on instantaneous cross-track-error and vehicle's course angle. While the developed method ensures faster convergence, it also ensures that the guidance command always stays within a realistic threshold satisfying its curvature constraint, thus making it more real-life implementable for autonomous vehicles with kino-dynamic constraints. Theoretical analysis for convergence of the developed guidance scheme is presented. Possibilities of undesirable chattering at phase transitions are also eliminated. Numerical simulation studies are presented to validate the satisfactory performance of the developed algorithm.
Is the panel fair? Evaluating panel compositions through network analysis. The case of research assessments in Italy
Authors: Alberto Baccini, Cristina Re
Date: 2024-05-10 00:00:00
Tasks: Fairness

Research evaluation is usually governed by panels of peers. Procedural fairness refers to the principles that ensures decisions are made through a fair and transparent process. It requires that the composition of panels is fair. A fair panel is usually defined in terms of observable characteristics of scholars such as gender or affiliations. The formal adherence to these criteria is not sufficient to guarantee a fair composition in terms of scholarly thinking, background, or policy orientation. An empirical strategy for exploring the fairness in the intellectual composition of panels is proposed, based on the observation of links between panellists. The case study regards the three panels selected to evaluate research in economics, statistics and business during the Italian research assessment exercises. The first two panels were appointed directly by the governmental agency responsible for the evaluation, while the third was randomly selected. Hence the third panel can be considered as a control for evaluating about the fairness of the others. The fair representation is explored by comparing the networks of panellists based on their co-authorship relations, the networks based on journals in which they published and the networks based on their affiliated institutions (universities, research centres and newspapers). The results show that the members of the first two panels had connections much higher than the members of the control group. Hence the composition of the first two panels should be considered as unfair, as the results of the research assessments.
Dual-band bandpass filter derived from the transformation of a single-band bandpass filter
Authors: Chandramouli H. Mahadevaswamy, Anup Raju Vasistha, Augustine O. Nwajana
Date: 2024-05-10 00:00:00
Tasks: Dielectric Constant

The recent proliferation of personal wireless communication devices is driving the need for multi-band frequency selective components including multiplexers and dual-band filters. This paper presents a simple technique for transforming a single-band bandpass filter (BPF) into a dual-band BPF. A second order (two-pole) single-band bandpass filter was chosen for this research, giving rise to a fourth order (four-pole) dual-band bandpass filter after the proposed filter transformation. Both filters were then implemented using the compact U-shaped microstrip resonator for improved device miniaturization. The proposed work features a centre frequency of 1.4 GHz for the single-band bandpass filter, with a span of 3.4% fractional bandwidth. The dual-band bandpass filter operates at 1.35 and 1.45 GHz. The design implementation employs the commercially available Rogers RT/Duroid 6010LM substrate, having a dissipation factor of 0.0023, dielectric constant of 10.7, diel thickness (h) of 1.27 mm, and top/bottom cladding of 35 microns. The results reported for the theoretical and practical designs show good agreement and improved performance when compared to similar research works in literature. The practical responses of the prototype dual-band BPF indicate a good return loss of better than 18 dB across both bands, and an insertion loss of better than 0.1 dB.
Model-Based Adaptive Control of Modular Multilevel Converters
Authors: Davide Tebaldi, Roberto Zanasi
Date: 2024-05-10 00:00:00
Tasks: model

Electrical power conversions are common in a large variety of engineering applications. With reference to AC/DC and DC/AC power conversions, a strong research interest resides in multilevel converters, thanks to the many advantages they provide over standard two-level converters. In this paper, we first provide a power-oriented model of Modular Multilevel Converters (MMCs), followed by a detailed harmonic analysis. The model is given in the form of a block scheme that can be directly implemented in the Matlab/Simulink environment. The performed harmonic analysis gives a deep and exact understanding of the different terms affecting the evolution of the voltage trajectories in the upper and lower arms of the converter. Next, we propose a new model-based adaptive control scheme for MMCs. The proposed control allows to determine the optimal average capacitor voltages reference in real-time, thus allowing to properly track the desired load current while minimizing the harmonic content in the generated load current itself.
Systematic interval observer design for linear systems
Authors: Thach Ngoc Dinh, Gia Quoc Bao Tran
Date: 2024-05-10 00:00:00

We first develop systematic and comprehensive interval observer designs for linear time-invariant (LTI) systems, under standard assumptions of observability and interval bounds on the initial condition and uncertainties. Traditionally, such designs rely on specific transformations into Metzler (in continuous time) or non-negative (in discrete time) forms, which may impose limitations. We demonstrate that these can be effectively replaced by an LTI transformation that is straightforward to compute offline. Subsequently, we extend the framework to time-varying systems, overcoming the limitations of conventional approaches that offer no guarantees. Our method utilizes dynamic transformations into higher-dimensional target systems, for which interval observers can always be constructed. These transformations become left-invertible after a finite time, provided the system is observable and the target dynamics are sufficiently high-dimensional and fast, thereby enabling the finite-time recovery of interval bounds in the original coordinates. Academic examples are provided to illustrate the proposed methodology.
Industrial Application of the Shapley value-based Redispatch Cost Allocation to Large-Scale Power Grids requires AC Optimal Power Flow
Authors: Rebecca Bauer, Xinliang Dai, Veit Hagenmeyer
Date: 2024-05-10 00:00:00
Tasks: Management

A burgeoning topic in the current energy transition are the huge costs of redispatch congestion management (CM) in large transmission systems. One of the German transmission system operators (TSOs) raised the critical inquiry of how to allocate the redispatch costs amongst TSOs in an equitable and beneficial way. Previously, a Shapley value-based approach has been introduced on small test grids, using the linear DC approximation of optimal power flow (OPF). However, within the application of CM, its feasibility and accuracy for large-scale power grids and its impact on the computed congestions remain uncertain. Therefore, this study investigates the applicability of the DC OPF compared to the exact AC OPF with regard to the Shapley values, for both small and large-scale grids. Numerical simulation shows significant differences in the congested lines, the overall redispatch costs, and the Shapley values. These findings suggest that for future CM, the TSOs should further investigate AC OPF solutions.
Joint Uplink and Downlink Rate Splitting for Fog Computing-Enabled Internet of Medical Things
Authors: Jiasi Zhou, Yan Chen, Cong Zhou, Yanjing Sun
Date: 2024-05-10 00:00:00

The Internet of Medical Things (IoMT) facilitates in-home electronic healthcare, transforming traditional hospital-based medical examination approaches. This paper proposes a novel transmit scheme for fog computing-enabled IoMT that leverages uplink and downlink rate splitting (RS). Fog computing allows offloading partial computation tasks to the edge server and processing the remainder of the tasks locally. The uplink RS and downlink RS utilize their flexible interference management capabilities to suppress offloading and feedback delay. Our overarching goal is to minimize the total time cost for task offloading, data processing, and result feedback. The resulting problem requires the joint design of task offloading, computing resource allocation, uplink beamforming, downlink beamforming, and common rate allocation. To solve the formulated non-convex problem, we introduce several auxiliary variables and then construct accurate surrogates to smooth the achievable rate. Moreover, we derive the optimal computation resource allocation per user with closed-form expressions. On this basis, we recast the computing resource allocation and energy consumption at the base station to a convex constraint set. We finally develop an alternating optimization algorithm to update the auxiliary variable and inherent variable alternately. Simulation results show that our transmit scheme and algorithm exhibit considerable performance enhancements over several benchmarks.
Resilient control of networked switched systems subject to deception attack and DoS attack
Authors: Rui Zhao, Zhiqiang Zuo, Ying Tan, Yijing Wang, Wentao Zhang
Date: 2024-05-10 00:00:00

In this paper, the resilient control for switched systems in the presence of deception attack and denial-of-service (DoS) attack is addressed. Due to the interaction of two kinds of attacks and the asynchronous phenomenon of controller mode and subsystem mode, the system dynamics becomes much more complex. A criterion is derived to ensure the mean square security level of the closed-loop system. This in turn reveals the balance of system resilience and control performance. Furthermore, a mixed-switching control strategy is put forward to make the system globally asymptotically stable. It is shown that the system will still converge to the equilibrium even if the deception attack occurs. Finally, simulations are carried out to verify the effectiveness of the theoretical results.
Cooperative Route Guidance and Flow Control for Mixed Road Networks Comprising Expressway and Arterial Network
Authors: Yunran Di, Haotian Shi, Weihua Zhang, Heng Ding, Xiaoyan Zheng, Bin Ran
Date: 2024-05-09 00:00:00
Tasks: Model Predictive Control

Facing the congestion challenges of mixed road networks comprising expressways and arterial road networks, traditional control solutions fall short. To effectively alleviate traffic congestion in mixed road networks, it is crucial to clear the interaction between expressways and arterial networks and achieve orderly coordination between them. This study employs the multi-class cell transmission model (CTM) combined with the macroscopic fundamental diagram (MFD) to model the traffic dynamics of expressway systems and arterial subregions, enabling vehicle path tracking across these two systems. Consequently, a comprehensive traffic transmission model suitable for mixed road networks has been integrated. Utilizing the SUMO software, a simulation platform for the mixed road network is established, and the average trip lengths within the model have been calibrated. Based on the proposed traffic model, this study constructs a route guidance model for mixed road networks and develops an integrated model predictive control (MPC) strategy that merges route guidance, perimeter control, and ramp metering to address the challenges of mixed road networks' traffic flow control. A case study of a scenario in which a bidirectional expressway connects two subregions is conducted, and the results validate the effectiveness of the proposed cooperative guidance and control (CGC) method in reducing overall congestion in mixed road networks.