pwc-archive/papers-with-abstracts huggingface dataset:
Exploiting Simulated User Feedback for Conversational Search: Ranking, Rewriting, and Beyond
Authors: Paul Owoicho, Ivan Sekulić, Mohammad Aliannejadi, Jeffrey Dalton, Fabio Crestani
Date: 2023-04-26 00:00:00
Tasks: Conversational Search, Passage Retrieval, Re-Ranking, Retrieval

This research aims to explore various methods for assessing user feedback in mixed-initiative conversational search (CS) systems. While CS systems enjoy profuse advancements across multiple aspects, recent research fails to successfully incorporate feedback from the users. One of the main reasons for that is the lack of system-user conversational interaction data. To this end, we propose a user simulator-based framework for multi-turn interactions with a variety of mixed-initiative CS systems. Specifically, we develop a user simulator, dubbed ConvSim, that, once initialized with an information need description, is capable of providing feedback to a system's responses, as well as answering potential clarifying questions. Our experiments on a wide variety of state-of-the-art passage retrieval and neural re-ranking models show that effective utilization of user feedback can lead to 16% retrieval performance increase in terms of nDCG@3. Moreover, we observe consistent improvements as the number of feedback rounds increases (35% relative improvement in terms of nDCG@3 after three rounds). This points to a research gap in the development of specific feedback processing modules and opens a potential for significant advancements in CS. To support further research in the topic, we release over 30,000 transcripts of system-simulator interactions based on well-established CS datasets.
Window transformer for dialogue document: a joint framework for causal emotion entailment
Authors: Dazhi Jiang, Hao liu, Geng Tu & Runguo Wei
Date: 2023-02-24 00:00:00
Proceeding: International Journal of Machine Learning and Cybernetics 2023 2
Tasks: Causal Emotion Entailment

The Causal Emotion Entailment (CEE) task aims to extract all potential pairs of emotions and corresponding causes from the unannotated emotion document in the conversational context. Most existing methods to solve CEE task follow a two-stage pipeline framework, in which the first stage is to identify emotional clauses and cause clauses and extract clause representation,separately. And in the second stage is to construct the final emotion and cause pairs. However, they ignore the effect of the distance between clauses on emotion-cause pair matching. Here, we construct a joint framework with Window Transformer to handle this problem. The pre-trained BERT and RoBERTa are used as the text encoder to generate a local representation of clauses in a given document. Meanwhile, we feed it into 2D Window Transformer to make the clause representation sensitive to the context within the Window and to obtain the dependencies between clauses. At the same time, the document ranks the candidate clauses to extract causal emotion entailments, which enhances the representation of clause pairs (emotion pairs and cause pairs) by kernel-based relative position embedding. Experimental results indicate that the framework acquires state-of-the-art results on the benchmark dataset.
CSAT‑FTCN: A Fuzzy‑Oriented Model with Contextual Self‑attention Network for Multimodal Emotion Recognition
Authors: Dazhi Jiang, Hao liu, Runguo Wei, Geng Tu
Date: 2023-01-31 00:00:00
Proceeding: Cognitive Computation 2023 1
Tasks: Emotion Recognition, Multimodal Emotion Recognition, Question Answering

Multimodal emotion analysis has become a hot trend because of its wide applications, such as the question-answering
system. However, in a real-world scenario, people usually have mixed or partial emotions about evaluating objects. In this
paper, we introduce a fuzzy temporal convolutional network based on contextual self-attention (CSAT-FTCN) to address
these challenges, which has a membership function modeling various fuzzy emotions for understanding emotions in a more
profound sense. Moreover, the CSAT-FTCN can obtain the dependency relationships of target utterances on internal own
key information and external contextual information to understand emotions in a more profound sense. Additionally, as for
multi-modality data, we introduce an attention fusion (ATF) mechanism to capture the dependency relationship between
different modality information. The experimental results show that our CSAT-FTCN outperforms state-of-the-art models
on tested datasets. The CSAT-FTCN network provides a novel method for multimodal emotion analysis.
Epistemic considerations when AI answers questions for us
Authors: Johan F. Hoorn, Juliet J. -Y. Chen
Date: 2023-04-23 00:00:00

In this position paper, we argue that careless reliance on AI to answer our questions and to judge our output is a violation of Grice's Maxim of Quality as well as a violation of Lemoine's legal Maxim of Innocence, performing an (unwarranted) authority fallacy, and while lacking assessment signals, committing Type II errors that result from fallacies of the inverse. What is missing in the focus on output and results of AI-generated and AI-evaluated content is, apart from paying proper tribute, the demand to follow a person's thought process (or a machine's decision processes). In deliberately avoiding Neural Networks that cannot explain how they come to their conclusions, we introduce logic-symbolic inference to handle any possible epistemics any human or artificial information processor may have. Our system can deal with various belief systems and shows how decisions may differ for what is true, false, realistic, unrealistic, literal, or anomalous. As is, stota AI such as ChatGPT is a sorcerer's apprentice.
ChatGPT is all you need to decolonize sub-Saharan Vocational Education
Authors: Isidora Tourni, Georgios Grigorakis, Isidoros Marougkas, Konstantinos Dafnis, Vassiliki Tassopoulou
Date: 2023-04-11 00:00:00
Tasks: All, Position

The advances of Generative AI models with interactive capabilities over the past few years offer unique opportunities for socioeconomic mobility. Their potential for scalability, accessibility, affordability, personalizing and convenience sets a first-class opportunity for poverty-stricken countries to adapt and modernize their educational order. As a result, this position paper makes the case for an educational policy framework that would succeed in this transformation by prioritizing vocational and technical training over academic education in sub-Saharan African countries. We highlight substantial applications of Large Language Models, tailor-made to their respective cultural background(s) and needs, that would reinforce their systemic decolonization. Lastly, we provide specific historical examples of diverse states successfully implementing such policies in the elementary steps of their socioeconomic transformation, in order to corroborate our proposal to sub-Saharan African countries to follow their lead.
SamurAI: A Versatile IoT Node With Event-Driven Wake-Up and Embedded ML Acceleration
Authors: Ivan Miro-Panades, Benoit Tain, Jean-Frederic Christmann, David Coriat, Romain Lemaire, Clement Jany, Baudouin Martineau, Fabrice Chaix, Guillaume Waltener, Emmanuel Pluchart, Jean-Philippe Noel, Adam Makosiej, Maxime Montoya, Simone Bacles-Min, David Briand, Jean-Marc Philippe, Yvain Thonnart, Alexandre Valentian, Frederic Heitzmann, Fabien Clermidy
Date: 2023-04-11 00:00:00
Tasks: CPU, image-classification, Image Classification

Increased capabilities such as recognition and self-adaptability are now required from IoT applications. While IoT node power consumption is a major concern for these applications, cloud-based processing is becoming unsustainable due to continuous sensor or image data transmission over the wireless network. Thus optimized ML capabilities and data transfers should be integrated in the IoT node. Moreover, IoT applications are torn between sporadic data-logging and energy-hungry data processing (e.g. image classification). Thus, the versatility of the node is key in addressing this wide diversity of energy and processing needs. This paper presents SamurAI, a versatile IoT node bridging this gap in processing and in energy by leveraging two on-chip sub-systems: a low power, clock-less, event-driven Always-Responsive (AR) part and an energy-efficient On-Demand (OD) part. AR contains a 1.7MOPS event-driven, asynchronous Wake-up Controller (WuC) with a 207ns wake-up time optimized for sporadic computing, while OD combines a deep-sleep RISC-V CPU and 1.3TOPS/W Machine Learning (ML) for more complex tasks up to 36GOPS. This architecture partitioning achieves best in class versatility metrics such as peak performance to idle power ratio. On an applicative classification scenario, it demonstrates system power gains, up to 3.5x compared to cloud-based processing, and thus extended battery lifetime.
Decision Making for Autonomous Vehicles
Authors: Xinchen Li, Levent Guvenc, Bilin Aksun-Guvenc
Date: 2023-04-27 00:00:00
Tasks: Autonomous Vehicles, Decision Making

This paper is on decision making of autonomous vehicles for handling roundabouts. The round intersection is introduced first followed by the Markov Decision Processes (MDP), the Partially Observable Markov Decision Processes (POMDP) and the Object Oriented Partially Observable Markov Decision Process (OOPOMDP). The Partially Observable Monte-Carlo Planning algorihtm (POMCP) algorithm is introduced and OOPOMDP is applied to decision making for autonomous vehicles in round intersections. Decision making is formulated as a POMDP problem and the penalty function is formulated and set followed by improvement of decision making with policy prediction. The augmented objective state and policy based state transition is introduced simulations are used to demonstrate the effectiveness of the proposed method.
Controlled density transport using Perron Frobenius generators
Authors: Jake Buzhardt, Phanindra Tallapragada
Date: 2023-04-26 00:00:00

We consider the problem of the transport of a density of states from an initial state distribution to a desired final state distribution through a dynamical system with actuation. In particular, we consider the case where the control signal is a function of time, but not space; that is, the same actuation is applied at every point in the state space. This is motivated by several problems in fluid mechanics, such as mixing and manipulation of a collection of particles by a global control input such as a uniform magnetic field, as well as by more general control problems where a density function describes an uncertainty distribution or a distribution of agents in a multi-agent system. We formulate this problem using the generators of the Perron-Frobenius operator associated with the drift and control vector fields of the system. By considering finite-dimensional approximations of these operators, the density transport problem can be expressed as a control problem for a bilinear system in a high-dimensional, lifted state. With this system, we frame the density control problem as a problem of driving moments of the density function to the moments of a desired density function, where the moments of the density can be expressed as an output which is linear in the lifted state. This output tracking problem for the lifted bilinear system is then solved using differential dynamic programming, an iterative trajectory optimization scheme.
When Do Graph Neural Networks Help with Node Classification? Investigating the Impact of Homophily Principle on Node Distinguishability
Authors: Sitao Luan, Chenqing Hua, Minkai Xu, Qincheng Lu, Jiaqi Zhu, Xiao-Wen Chang, Jie Fu, Jure Leskovec, Doina Precup
Date: 2023-04-25 00:00:00
Tasks: Node Classification, Stochastic Block Model

Homophily principle, i.e., nodes with the same labels are more likely to be connected, has been believed to be the main reason for the performance superiority of Graph Neural Networks (GNNs) over Neural Networks on node classification tasks. Recent research suggests that, even in the absence of homophily, the advantage of GNNs still exists as long as nodes from the same class share similar neighborhood patterns. However, this argument only considers intra-class Node Distinguishability (ND) but neglects inter-class ND, which provides incomplete understanding of homophily on GNNs. In this paper, we first demonstrate such deficiency with examples and argue that an ideal situation for ND is to have smaller intra-class ND than inter-class ND. To formulate this idea and study ND deeply, we propose Contextual Stochastic Block Model for Homophily (CSBM-H) and define two metrics, Probabilistic Bayes Error (PBE) and negative generalized Jeffreys divergence, to quantify ND. With the metrics, we visualize and analyze how graph filters, node degree distributions and class variances influence ND, and investigate the combined effect of intra- and inter-class ND. Besides, we discovered the mid-homophily pitfall, which occurs widely in graph datasets. Furthermore, we verified that, in real-work tasks, the superiority of GNNs is indeed closely related to both intra- and inter-class ND regardless of homophily levels. Grounded in this observation, we propose a new hypothesis-testing based performance metric beyond homophily, which is non-linear, feature-based and can provide statistical threshold value for GNNs' the superiority. Experiments indicate that it is significantly more effective than the existing homophily metrics on revealing the advantage and disadvantage of graph-aware modes on both synthetic and benchmark real-world datasets.
Fault Tolerant Super Twisting Sliding Mode Control of a Quadrotor UAV Using Control Allocation
Authors: Mehmet Karahan, Mertcan Inal, Cosku Kasnakoglu
Date: 2023-04-27 00:00:00

In this study, a fault-tolerant super-twisting sliding mode controller with a control allocation system for a quadrotor aircraft is proposed. Super twisting sliding mode control is a robust control technique that handles a system with a relative degree equal to one. A super-twisting sliding mode controller is proposed because of its robustness to uncertainties and perturbations. It increases accuracy and reduces chattering. A control allocation algorithm is developed to cope with the actuator fault. Firstly, a nonlinear model of the quadrotor unmanned aerial vehicle (UAV) is presented. Then, the controller design and type of the actuator fault are explained. The control allocation algorithm is used to optimize the trajectory tracking performance of the quadrotor in the presence of an actuator fault. A control allocation algorithm is an effective approach to implementing fault-tolerant control. When actuator faults are identified, they can be modeled as changes in the B matrix of constraints. Various simulations have been made for situations with and without actuator failure. In normal conditions, the quadrotor can accurately track altitude, roll, pitch and yaw references. In faulty conditions, the quadrotor can follow the references with a small error. Simulations prove the effectiveness of the control allocation algorithm, which stabilizes the quadrotor in case of an actuator fault. Overall, this paper presents a novel fault-tolerant controller design for quadrotor aircraft that effectively addresses actuator faults using a super-twisting sliding mode controller and control allocation algorithm.
Proceedings of the 3rd International Workshop on Autonomous Systems Safety
Authors: Christoph Thieme, Marilia Ramos, Ingrid B. Utne, Ali Mosleh
Date: 2023-04-27 00:00:00

The International Workshop for Autonomous System Safety (IWASS) is a joint effort by the B. John Garrick Institute for the Risk Sciences at the University of California Los Angeles (UCLA-GIRS) and the Norwegian University of Science and Technology (NTNU). IWASS is an invitation-only event designed to be a platform for cross-industrial and interdisciplinary effort and knowledge exchange on autonomous systems' Safety, Reliability, and Security (SRS). The workshop gathers experts from academia, regulatory agencies, and industry to discuss challenges and potential solutions for SRS of autonomous systems from different perspectives. It complements existing events organized around specific types of autonomous systems (e.g., cars, ships, aviation) or particular safety or security-related aspects of such systems (e.g., cyber risk, software reliability, etc.). IWASS distinguishes itself from these events by addressing these topics together and proposing solutions for SRS challenges common to different types of autonomous systems. IWASS 2022 was held on August 28th in Dublin, Ireland, and gathered 30 participants from 20 organizations from around the globe. In addition, a panel session at the European Safety and Reliability Conference (ESREL 2023) discussed the workshop's main conclusions and additional points with a larger audience. This report summarizes IWASS 2022 discussions. It provides an overview of the main points raised by a community of experts on the current status of autonomous systems SRS. It also outlines research directions for safer, more reliable and secure future autonomous systems.
Empirical Individual State Observability
Authors: Benjamin Cellini, Burak Boyacıoğlu, Floris van Breugel
Date: 2023-04-27 00:00:00

A dynamical system is observable if there is a one-to-one mapping from the system's measured outputs and inputs to all of the system's states. Analytical and empirical tools exist for quantifying the (full state) observability of linear and nonlinear systems; however, empirical tools for evaluating the observability of individual state variables are lacking. Here, a new empirical approach termed Empirical Individual State Observability (E-ISO) is developed to quantify the level of observability of individual state variables. E-ISO first builds an empirical observability matrix via simulation, then applies convex optimization to efficiently determine the subset of its rows required to estimate each state variable individually. Finally, (un)observability measures for these subsets are calculated to provide independent estimates of the observability of each state variable. Multiple example applications of E-ISO on linear and nonlinear systems are shown to be consistent with analytical results. Broadly, E-ISO will be an invaluable tool both for designing active sensing control laws or optimizing sensor placement to increase the observability of individual state variables for engineered systems, and analyzing the trajectory decisions made by organisms.
Design of Reconfigurable Intelligent Surfaces for Wireless Communication: A Review
Authors: Rujing Xiong, Jianan Zhang, Fuhai Wang, Zhengyu Wang, Xiang Ren, Junshuo Liu, Jialong Lu, Kai Wan, Tiebin Mi, Robert Caiming Qiu
Date: 2023-04-27 00:00:00
Tasks: Image Reconstruction

This paper addresses the hardware structure of Reconfigurable Intelligent Surfaces (RIS) and presents a comprehensive overview of RIS design, considering both unit design and prototype systems. It commences by tracing the evolutionary trajectory of RIS, originating from static cell-structured hypersurfaces. The article conducts a meticulous examination from the standpoint of adaptability, elucidating the diverse array of unit structures and design philosophies that underlie existing RIS frameworks. Following this, the study systematically categorizes and synthesizes channel modeling research for RIS-facilitated wireless communication, leveraging both physical insights and statistical data. Additionally, the article provides a detailed exposition of current RIS experimental setups and their corresponding empirical findings, delving into the attributes of prototype design and system functionalities. Moreover, this work introduces an in-house developed RIS prototype. The prototype undergoes rigorous empirical evaluation, encompassing multi-hop RIS signal amplification, image reconstruction, and real-world indoor signal coverage experiments. The empirical results robustly affirm the efficacy of RIS in effectively mitigating signal coverage blind spots and enabling radio wave imaging. With RIS-enhanced augmentation, the average indoor signal gain surpasses 8 dB.
Distributed Multi-Horizon Model Predictive Control for Network of Energy Hubs
Authors: Varsha Behrunani, Hanmin Cai, Philipp Heer, Roy S. Smith, John Lygeros
Date: 2023-04-27 00:00:00
Tasks: energy trading, Model Predictive Control

The increasing penetration of renewable energy resources has transformed the energy system from traditional hierarchical energy delivery paradigm to a distributed structure. Such development is accompanied with continuous liberalization in the energy sector, giving rise to possible energy trading among networked local energy hub. Joint operation of such hubs can improve energy efficiency and support the integration of renewable energy resource. Acknowledging peer-to-peer trading between hubs, their optimal operation within the network can maximize consumption of locally produced energy. However, for such complex systems involving multiple stakeholders, both computational tractability and privacy concerns need to be accounted for. We investigate both decentralized and centralized model predictive control (MPC) approaches for a network of energy hubs. While the centralized control strategy offers superior performance to the decentralized method, its implementation is computationally prohibitive and raises privacy concerns, as the information of each hub has to be shared extensively. On the other hand, a classical decentralized control approach can ease the implementation at the expense of sub-optimal performance of the overall system. In this work, a distributed scheme based on a consensus alternating direction method of multipliers (ADMM) algorithm is proposed. It combines the performance of the centralized approach with the privacy preservation of decentralized approach. A novel multi-horizon MPC framework is also introduced to increase the prediction horizon without compromising the time discretization or making the problem computationally intractable. A benchmark three-hub network is used to compare the performance of the mentioned methods. The results show superior performance in terms of total cost, computational time, robustness to demand and prices variations.
Metal Inter-layer Via Keep-out-zone in M3D IC: A Critical Process-aware Design Consideration
Authors: Madhava Sarma Vemuri, Umamaheswara Rao Tida
Date: 2023-04-26 00:00:00

Metal inter-layer via (MIV) in Monolithic three-dimensional integrated circuits (M3D-IC) is used to connect inter-layer devices and provide power and clock signals across multiple layers. The size of MIV is comparable to logic gates because of the significant reduction in substrate layers due to sequential integration. Despite MIV's small size, the impact of MIV on the performance of adjacent devices should be considered to implement IC designs in M3D-IC technology. In this work, we systematically study the changes in performance of transistors when they are placed near MIV to understand the effect of MIV on adjacent devices when MIV passes through the substrate. Simulation results suggest that the keep-out-zone (KOZ) for MIV should be considered to ensure the reliability of M3D-IC technology and this KOZ is highly dependent on the M3D-IC process. In this paper, we show that the transistor placed near MIV considering the M1 metal pitch as the separation will have up to $68,668\times$ increase in leakage current, when the channel doping is $10^{15} cm^{-3}$, source/drain doping of $10^{18}cm^{-3}$ and substrate layer height of $100\ nm$. We also show that, this increase in leakage current can also be reduced significantly by having KOZ around MIV, which is dependent on the process.