pwc-archive/papers-with-abstracts huggingface dataset:
A Conflict-Aware Optimal Goal Assignment Algorithm for Multi-Robot Systems
Authors: Aakash, Indranil Saha
Date: 2024-02-19 00:00:00
Tasks: Multi-Agent Path Finding

The fundamental goal assignment problem for a multi-robot application aims to assign a unique goal to each robot while ensuring collision-free paths, minimizing the total movement cost. A plausible algorithmic solution to this NP-hard problem involves an iterative process that integrates a task planner to compute the goal assignment while ignoring the collision possibilities among the robots and a multi-agent path-finding algorithm to find the collision-free trajectories for a given assignment. This procedure involves a method for computing the next best assignment given the current best assignment. A naive way of computing the next best assignment, as done in the state-of-the-art solutions, becomes a roadblock to achieving scalability in solving the overall problem. To obviate this bottleneck, we propose an efficient conflict-guided method to compute the next best assignment. Additionally, we introduce two more optimizations to the algorithm -- first for avoiding the unconstrained path computations between robot-goal pairs wherever possible, and the second to prevent duplicate constrained path computations for multiple robot-goal pairs. We extensively evaluate our algorithm for up to a hundred robots on several benchmark workspaces. The results demonstrate that the proposed algorithm achieves nearly an order of magnitude speedup over the state-of-the-art algorithm, showcasing its efficacy in real-world scenarios.
Grounding from an AI and Cognitive Science Lens
Authors: Goonmeet Bajaj, Srinivasan Parthasarathy, Valerie L. Shalin, Amit Sheth
Date: 2024-02-19 00:00:00

Grounding is a challenging problem, requiring a formal definition and different levels of abstraction. This article explores grounding from both cognitive science and machine learning perspectives. It identifies the subtleties of grounding, its significance for collaborative agents, and similarities and differences in grounding approaches in both communities. The article examines the potential of neuro-symbolic approaches tailored for grounding tasks, showcasing how they can more comprehensively address grounding. Finally, we discuss areas for further exploration and development in grounding.
Training Table Question Answering via SQL Query Decomposition
Authors: Raphaël Mouravieff, Benjamin Piwowarski, Sylvain Lamprier
Date: 2024-02-19 00:00:00
Tasks: Question Answering, Semantic Parsing

Table Question-Answering involves both understanding the natural language query and grounding it in the context of the input table to extract the relevant information. In this context, many methods have highlighted the benefits of intermediate pre-training from SQL queries. However, while most approaches aim at generating final answers from inputs directly, we claim that there is better to do with SQL queries during training. By learning to imitate a restricted portion of SQL-like algebraic operations, we show that their execution flow provides intermediate supervision steps that allow increased generalization and structural reasoning compared with classical approaches of the field. Our study bridges the gap between semantic parsing and direct answering methods and provides useful insights regarding what types of operations should be predicted by a generative architecture or be preferably executed by an external algorithm.
Manipulating hidden-Markov-model inferences by corrupting batch data
Authors: William N. Caballero, Jose Manuel Camacho, Tahir Ekin, Roi Naveiro
Date: 2024-02-19 00:00:00
Tasks: Time Series

Time-series models typically assume untainted and legitimate streams of data. However, a self-interested adversary may have incentive to corrupt this data, thereby altering a decision maker's inference. Within the broader field of adversarial machine learning, this research provides a novel, probabilistic perspective toward the manipulation of hidden Markov model inferences via corrupted data. In particular, we provision a suite of corruption problems for filtering, smoothing, and decoding inferences leveraging an adversarial risk analysis approach. Multiple stochastic programming models are set forth that incorporate realistic uncertainties and varied attacker objectives. Three general solution methods are developed by alternatively viewing the problem from frequentist and Bayesian perspectives. The efficacy of each method is illustrated via extensive, empirical testing. The developed methods are characterized by their solution quality and computational effort, resulting in a stratification of techniques across varying problem-instance architectures. This research highlights the weaknesses of hidden Markov models under adversarial activity, thereby motivating the need for robustification techniques to ensure their security.
Structure Guided Large Language Model for SQL Generation
Authors: Qinggang Zhang, Hao Chen, Junnan Dong, Shengyuan Chen, Feiran Huang, Xiao Huang
Date: 2024-02-19 00:00:00
Tasks: Language Modeling, Language Modelling, Large Language Model, model, Natural Language Queries, Text to SQL, Text-To-SQL

Recent advancements in large language models (LLMs) have shown promise in bridging the gap between natural language queries and database management systems, enabling users to interact with databases without the background of SQL. However, LLMs often struggle to comprehend complex database structures and accurately interpret user intentions. Decomposition-based methods have been proposed to enhance the performance of LLMs on complex tasks, but decomposing SQL generation into subtasks is non-trivial due to the declarative structure of SQL syntax and the intricate connections between query concepts and database elements. In this paper, we propose a novel Structure GUided text-to-SQL framework~(SGU-SQL) that incorporates syntax-based prompting to enhance the SQL generation capabilities of LLMs. Specifically, SGU-SQL establishes structure-aware links between user queries and database schema and decomposes the complex generation task using syntax-based prompting to enable more accurate LLM-based SQL generation. Extensive experiments on two benchmark datasets demonstrate that SGU-SQL consistently outperforms state-of-the-art text-to-SQL models.
IGFNet: Illumination-Guided Fusion Network for Semantic Scene Understanding using RGB-Thermal Images
Authors: Haotian Li, Yuxiang Sun
Date: 2023-12-04 00:00:00
Proceeding: IEEE International Conference on Robotics and Biomimetics (ROBIO) 2023 12
Tasks: Autonomous Driving, Scene Understanding, Thermal Image Segmentation

Semantic scene understanding is a fundamental task for autonomous driving. It serves as a build block for many downstream tasks. Under challenging illumination conditions, thermal images can provide complementary information for RGB images. Many multi-modal fusion networks have been proposed using RGB-Thermal data for semantic scene understanding. However, current state-of-the-art methods simply use networks to fuse features on multi-modality inexplicably, rather than designing a fusion method based on the intrinsic characteristics of RGB images and thermal images. To address this issue, we propose IGFNet, an illumination-guided fusion network for RGB-Thermal semantic scene understanding, which utilizes a weight mask generated by the illumination estimation module to weight the RGB and thermal feature maps at different stages. Experimental results show that our network outperforms the state-of-the-art methods on the MFNet dataset. Our code is available at: https://github.com/lab-sun/IGFNet.
Managing Congregations of People by Predicting Likelihood of a Person being Infected by a Contagious Disease like the COVID Virus
Authors: Pranav Gupta, Manish Gupta
Date: 2020-11-06 00:00:00
Proceeding: IEEE Cloud Computing in Emerging Markets 2020 11

Pandemics such as Covid-19 change the status quo. The things we take for granted become no longer true. When the disease is contagious then in-person communication is fraught with danger for all parties. Despite all these dangers people still required to meet in person for various reasons that could range from personal to official to government business. Can we have a way to understand the risk of a person being infected as compared to another person so that we can make decisions of segregating the two people or to decline entry to a person? In this paper we provide an architecture and an approach that caters to estimating a score for each person based on GPS (Global Positioning System) locations. A score is computed by an application running in cloud and receiving as input the GPS trajectory information from each person's device (like a mobile or a smart watch). With the scores of any two people in hand one can predict who is more likely to be infected. We complement this approach with extensive simulation results to validate our approach. Our results show that we can achieve a high accuracy (80% to 90%) of predicting which person (of the two being compared) has an infection.
A novel approach for detecting deep fake videos using graph neural network
Authors: M. M. El-Gayar, Mohamed Abouhawwash, S. S. Askar & Sara Sweidan
Date: 2024-02-01 00:00:00
Proceeding: Journal of Big Data 2024 2
Tasks: DeepFake Detection, Graph Neural Network

Deep fake technology has emerged as a double-edged sword in the digital world. While it holds potential for legitimate uses, it can also be exploited to manipulate video content, causing severe social and security concerns. The research gap lies in the fact that traditional deep fake detection methods, such as visual quality analysis or inconsistency detection, need help to keep up with the rapidly advancing technology used to create deep fakes. That means there's a need for more sophisticated detection techniques. This paper introduces an enhanced approach for detecting deep fake videos using graph neural network (GNN). The proposed method splits the detection process into two phases: a mini-batch graph convolution network stream four-block CNN stream comprising Convolution, Batch Normalization, and Activation function. The final step is a flattening operation, which is essential for connecting the convolutional layers to the dense layer. The fusion of these two phases is performed using three different fusion networks: FuNet-A (additive fusion), FuNet-M (element-wise multiplicative fusion), and FuNet-C (concatenation fusion). The paper further evaluates the proposed model on different datasets, where it achieved an impressive training and validation accuracy of 99.3% after 30 epochs.
DeepCode AI Fix: Fixing Security Vulnerabilities with Large Language Models
Authors: Berkay Berabi, Alexey Gronskiy, Veselin Raychev, Gishor Sivanrupan, Victor Chibotaru, Martin Vechev
Date: 2024-02-19 00:00:00
Tasks: Code Repair, Few-Shot Learning, Program Repair

The automated program repair field has attracted substantial interest over the years, but despite significant research efforts, creating a system that works well for complex semantic bugs such as security vulnerabilities has proven difficult. A promising direction to solve this challenge is by leveraging large language models (LLMs), which are increasingly used to solve various programming tasks. In this paper, we investigate the effectiveness of LLMs for solving code-repair task. We show that the task is difficult as it requires the model to learn long-range code relationships, a task that inherently relies on extensive amounts of training data. At the same time, creating a large, clean dataset for complex program bugs and their corresponding fixes is non-trivial. We propose a technique to address these challenges with a new approach for querying and fine-tuning LLMs. The idea is to use program analysis to limit the LLM's attention mechanism on the portions of code needed to perform the fix, drastically reducing the amount of required training data. Concretely, for training and inference, rather than feeding the entire program to the LLM, we reduce its code to a much shorter snippet that contains the reported defect together with the necessary context - and use that instead. Our evaluation shows that this code reduction approach substantially improves available models such as GPT-4 using few-shot learning, as well as fine-tuning models. To train and evaluate our system, we created a comprehensive code fixing dataset by extensively labeling 156 bug patterns (including 40 security rules), requiring complex interprocedural dataflow to discover. Our best system with Mixtral-8x7B can remove more than 80% of the reported defects while exactly matching the human fix in between 10 and 50% of cases, outperforming baselines based on GPT-3.5 and GPT-4, or based on window-based models like TFix.
MLSTL-WSN: Machine Learning-based Intrusion Detection using SMOTETomek in WSNs
Authors: Md. Alamin Talukder, Selina Sharmin, Md Ashraf Uddin, Md Manowarul Islam, Sunil Aryal
Date: 2024-02-17 00:00:00
Tasks: Intrusion Detection

Wireless Sensor Networks (WSNs) play a pivotal role as infrastructures, encompassing both stationary and mobile sensors. These sensors self-organize and establish multi-hop connections for communication, collectively sensing, gathering, processing, and transmitting data about their surroundings. Despite their significance, WSNs face rapid and detrimental attacks that can disrupt functionality. Existing intrusion detection methods for WSNs encounter challenges such as low detection rates, computational overhead, and false alarms. These issues stem from sensor node resource constraints, data redundancy, and high correlation within the network. To address these challenges, we propose an innovative intrusion detection approach that integrates Machine Learning (ML) techniques with the Synthetic Minority Oversampling Technique Tomek Link (SMOTE-TomekLink) algorithm. This blend synthesizes minority instances and eliminates Tomek links, resulting in a balanced dataset that significantly enhances detection accuracy in WSNs. Additionally, we incorporate feature scaling through standardization to render input features consistent and scalable, facilitating more precise training and detection. To counteract imbalanced WSN datasets, we employ the SMOTE-Tomek resampling technique, mitigating overfitting and underfitting issues. Our comprehensive evaluation, using the WSN Dataset (WSN-DS) containing 374,661 records, identifies the optimal model for intrusion detection in WSNs. The standout outcome of our research is the remarkable performance of our model. In binary, it achieves an accuracy rate of 99.78% and in multiclass, it attains an exceptional accuracy rate of 99.92%. These findings underscore the efficiency and superiority of our proposal in the context of WSN intrusion detection, showcasing its effectiveness in detecting and mitigating intrusions in WSNs.
Stable matching as transport
Authors: Federico Echenique, Joseph Root, Fedor Sandomirskiy
Date: 2024-02-20 00:00:00
Tasks: Fairness

This paper links matching markets with aligned preferences to optimal transport theory. We show that stability, efficiency, and fairness emerge as solutions to a parametric family of optimal transport problems. The parameter reflects society's preferences for inequality. This link offers insights into structural properties of matchings and trade-offs between objectives; showing how stability can lead to welfare inequalities, even among similar agents. Our model captures supply-demand imbalances in contexts like spatial markets, school choice, and ride-sharing. We also show that large markets with idiosyncratic preferences can be well approximated by aligned preferences, expanding the applicability of our results.
Operational Collective Intelligence of Humans and Machines
Authors: Nikolos Gurney, Fred Morstatter, David V. Pynadath, Adam Russell, Gleb Satyukov
Date: 2024-02-16 00:00:00
Tasks: Decision Making

We explore the use of aggregative crowdsourced forecasting (ACF) as a mechanism to help operationalize ``collective intelligence'' of human-machine teams for coordinated actions. We adopt the definition for Collective Intelligence as: ``A property of groups that emerges from synergies among data-information-knowledge, software-hardware, and individuals (those with new insights as well as recognized authorities) that enables just-in-time knowledge for better decisions than these three elements acting alone.'' Collective Intelligence emerges from new ways of connecting humans and AI to enable decision-advantage, in part by creating and leveraging additional sources of information that might otherwise not be included. Aggregative crowdsourced forecasting (ACF) is a recent key advancement towards Collective Intelligence wherein predictions (X\% probability that Y will happen) and rationales (why I believe it is this probability that X will happen) are elicited independently from a diverse crowd, aggregated, and then used to inform higher-level decision-making. This research asks whether ACF, as a key way to enable Operational Collective Intelligence, could be brought to bear on operational scenarios (i.e., sequences of events with defined agents, components, and interactions) and decision-making, and considers whether such a capability could provide novel operational capabilities to enable new forms of decision-advantage.
Spontaneous Theory of Mind for Artificial Intelligence
Authors: Nikolos Gurney, David V. Pynadath, Volkan Ustun
Date: 2024-02-16 00:00:00

Existing approaches to Theory of Mind (ToM) in Artificial Intelligence (AI) overemphasize prompted, or cue-based, ToM, which may limit our collective ability to develop Artificial Social Intelligence (ASI). Drawing from research in computer science, cognitive science, and related disciplines, we contrast prompted ToM with what we call spontaneous ToM -- reasoning about others' mental states that is grounded in unintentional, possibly uncontrollable cognitive functions. We argue for a principled approach to studying and developing AI ToM and suggest that a robust, or general, ASI will respond to prompts \textit{and} spontaneously engage in social reasoning.
When LLMs Meets Acoustic Landmarks: An Efficient Approach to Integrate Speech into Large Language Models for Depression Detection
Authors: Xiangyu Zhang, Hexin Liu, Kaishuai Xu, Qiquan Zhang, Daijiao Liu, Beena Ahmed, Julien Epps
Date: 2024-02-17 00:00:00
Tasks: Depression Detection

Depression is a critical concern in global mental health, prompting extensive research into AI-based detection methods. Among various AI technologies, Large Language Models (LLMs) stand out for their versatility in mental healthcare applications. However, their primary limitation arises from their exclusive dependence on textual input, which constrains their overall capabilities. Furthermore, the utilization of LLMs in identifying and analyzing depressive states is still relatively untapped. In this paper, we present an innovative approach to integrating acoustic speech information into the LLMs framework for multimodal depression detection. We investigate an efficient method for depression detection by integrating speech signals into LLMs utilizing Acoustic Landmarks. By incorporating acoustic landmarks, which are specific to the pronunciation of spoken words, our method adds critical dimensions to text transcripts. This integration also provides insights into the unique speech patterns of individuals, revealing the potential mental states of individuals. Evaluations of the proposed approach on the DAIC-WOZ dataset reveal state-of-the-art results when compared with existing Audio-Text baselines. In addition, this approach is not only valuable for the detection of depression but also represents a new perspective in enhancing the ability of LLMs to comprehend and process speech signals.
AFPR-CIM: An Analog-Domain Floating-Point RRAM-based Compute-In-Memory Architecture with Dynamic Range Adaptive FP-ADC
Authors: Haobo Liu, Zhengyang Qian, Wei Wu, Hongwei Ren, Zhiwei Liu, Leibin Ni
Date: 2024-02-21 00:00:00

Power consumption has become the major concern in neural network accelerators for edge devices. The novel non-volatile-memory (NVM) based computing-in-memory (CIM) architecture has shown great potential for better energy efficiency. However, most of the recent NVM-CIM solutions mainly focus on fixed-point calculation and are not applicable to floating-point (FP) processing. In this paper, we propose an analog-domain floating-point CIM architecture (AFPR-CIM) based on resistive random-access memory (RRAM). A novel adaptive dynamic-range FP-ADC is designed to convert the analog computation results into FP codes. Output current with high dynamic range is converted to a normalized voltage range for readout, to prevent precision loss at low power consumption. Moreover, a novel FP-DAC is also implemented which reconstructs FP digital codes into analog values to perform analog computation. The proposed AFPR-CIM architecture enables neural network acceleration with FP8 (E2M5) activation for better accuracy and energy efficiency. Evaluation results show that AFPR-CIM can achieve 19.89 TFLOPS/W energy efficiency and 1474.56 GOPS throughput. Compared to traditional FP8 accelerator, digital FP-CIM, and analog INT8-CIM, this work achieves 4.135x, 5.376x, and 2.841x energy efficiency enhancement, respectively.
Parameter identification algorithm for a LTV system with partially unknown state matrix
Authors: Olga Kozachek, Nikolay Nikolaev, Olga Slita, Alexey Bobtsov
Date: 2024-02-21 00:00:00

In this paper an adaptive state observer and parameter identification algorithm for a linear time-varying system are developed under condition that the state matrix of the system contains unknown time-varying parameters of a known form. The state vector is observed using only output and input measurements without identification of the unknown parameters. When the state vector estimate is obtained, the identification algorithm is applied to find unknown parameters of the system.
A Monolithic Cybersecurity Architecture for Power Electronic Systems
Authors: Kirti Gupta, Subham Sahoo, Bijaya Ketan Panigrahi
Date: 2024-02-21 00:00:00

Power electronic systems (PES) face significant threats from various data availability and integrity attacks, significantly affecting the performance of communication networks and power system operation. As a result, several attack detection and reconstruction techniques are deployed, which makes it a costly \& complex cybersecurity operational platform with significant room for incremental extensions for mitigation against future threats. Unlike the said traditional arrangements, our paper introduces a foundational approach by establishing a monolithic cybersecurity architecture (MCA) via incorporating semantic principles into the sampling process for distributed energy resources (DERs). This unified approach concurrently compensates for the intrusion challenges posed by cyber attacks by reconstructing signals using the dynamics of the inner control layer. This reconstruction considers essential semantic attributes, like Priority, Freshness, and Relevance to ensure resilient dynamic performance. Hence, the proposed scheme promises a generalized route to concurrently tackle a global set of cyber attacks in elevating the resilience of PES. Finally, rigorous validation on a modified IEEE 69-bus distribution system and a real-world South California Edison (SCE) 47-bus network, using OPAL-RT under diverse operating conditions, underscores its robustness, model-free design capability, scalability, and adaptability to dynamic cyber graphs and system reconfiguration.