pwc-archive/papers-with-abstracts huggingface dataset:
Enhancing search engine precision and user experience through sentiment-based polysemy resolution
Authors: Mike Nkongolo
Date: 2023-11-03 00:00:00
Tasks: Articles, Information Retrieval, Marketing, Sentiment Analysis

With the proliferation of digital content and the need for efficient information retrieval, this study's insights can be applied to various domains, including news services, e-commerce, and digital marketing, to provide users with more meaningful and tailored experiences. The study addresses the common problem of polysemy in search engines, where the same keyword may have multiple meanings. It proposes a solution to this issue by embedding a smart search function into the search engine, which can differentiate between different meanings based on sentiment. The study leverages sentiment analysis, a powerful natural language processing (NLP) technique, to classify and categorize news articles based on their emotional tone. This can provide more insightful and nuanced search results. The article reports an impressive accuracy rate of 85% for the proposed smart search function, which outperforms conventional search engines. This indicates the effectiveness of the sentiment-based approach. The research explores multiple sentiment analysis models, including Sentistrength and Valence Aware Dictionary for Sentiment Reasoning (VADER), to determine the best-performing approach. The findings can be applied to enhance search engines, making them more capable of understanding the context and intent behind users 'queries. This can lead to better search results that are more aligned with what users are looking for. The proposed smart search function can improve the user experience by reducing the need to sift through irrelevant search results. This is particularly important in an age where information overload is common.
Universal Multi-modal Multi-domain Pre-trained Recommendation
Authors: Wenqi Sun, Ruobing Xie, Shuqing Bian, Wayne Xin Zhao, Jie zhou
Date: 2023-11-03 00:00:00
Tasks: Recommendation Systems

There is a rapidly-growing research interest in modeling user preferences via pre-training multi-domain interactions for recommender systems. However, Existing pre-trained multi-domain recommendations mostly select the item texts to be bridges across domains, and simply explore the user behaviors in target domains. Hence, they ignore other informative multi-modal item contents (e.g., visual information), and also lack of thorough consideration of user behaviors from all interactive domains. To address these issues, in this paper, we propose to pre-train universal multi-modal item content presentation for multi-domain recommendation, called UniM^2Rec, which could smoothly learn the multi-modal item content presentations and the multi-modal user preferences from all domains. With the pre-trained multi-domain recommendation model, UniM^2Rec could be efficiently and effectively transferred to new target domains in practice. Extensive experiments conducted on five real-world datasets in target domains demonstrate the superiority of the proposed method over existing competitive methods, especially for the real-world recommendation scenarios that usually struggle with seriously missing or noisy item contents.
Unbiased Offline Evaluation for Learning to Rank with Business Rules
Authors: Matej Jakimov, Alexander Buchholz, Yannik Stein, Thorsten Joachims
Date: 2023-11-03 00:00:00
Tasks: Learning-To-Rank, Off-policy evaluation

For industrial learning-to-rank (LTR) systems, it is common that the output of a ranking model is modified, either as a results of post-processing logic that enforces business requirements, or as a result of unforeseen design flaws or bugs present in real-world production systems. This poses a challenge for deploying off-policy learning and evaluation methods, as these often rely on the assumption that rankings implied by the model's scores coincide with displayed items to the users. Further requirements for reliable offline evaluation are proper randomization and correct estimation of the propensities of displaying each item in any given position of the ranking, which are also impacted by the aforementioned post-processing. We investigate empirically how these scenarios impair off-policy evaluation for learning-to-rank models. We then propose a novel correction method based on the Birkhoff-von-Neumann decomposition that is robust to this type of post-processing. We obtain more accurate off-policy estimates in offline experiments, overcoming the problem of post-processed rankings. To the best of our knowledge this is the first study on the impact of real-world business rules on offline evaluation of LTR models.
BadGPT: Exploring Security Vulnerabilities of ChatGPT via Backdoor Attacks to InstructGPT
Authors: Jiawen Shi, Yixin Liu, Pan Zhou, Lichao Sun
Date: 2023-02-21 00:00:00
Tasks: Backdoor Attack, Language Modeling, Language Modelling, reinforcement-learning, Reinforcement Learning (RL)

Recently, ChatGPT has gained significant attention in research due to its ability to interact with humans effectively. The core idea behind this model is reinforcement learning (RL) fine-tuning, a new paradigm that allows language models to align with human preferences, i.e., InstructGPT. In this study, we propose BadGPT, the first backdoor attack against RL fine-tuning in language models. By injecting a backdoor into the reward model, the language model can be compromised during the fine-tuning stage. Our initial experiments on movie reviews, i.e., IMDB, demonstrate that an attacker can manipulate the generated text through BadGPT.
Closing the Gap to Quadratic Invariance: a Regret Minimization Approach to Optimal Distributed Control
Authors: Daniele Martinelli, Andrea Martin, Giancarlo Ferrari-Trecate, Luca Furieri
Date: 2023-11-03 00:00:00

In this work, we focus on the design of optimal controllers that must comply with an information structure. State-of-the-art approaches do so based on the H2 or Hinfty norm to minimize the expected or worst-case cost in the presence of stochastic or adversarial disturbances. Large-scale systems often experience a combination of stochastic and deterministic disruptions (e.g., sensor failures, environmental fluctuations) that spread across the system and are difficult to model precisely, leading to sub-optimal closed-loop behaviors. Hence, we propose improving performance for these scenarios by minimizing the regret with respect to an ideal policy that complies with less stringent sensor-information constraints. This endows our controller with the ability to approach the improved behavior of a more informed policy, which would detect and counteract heterogeneous and localized disturbances more promptly. Specifically, we derive convex relaxations of the resulting regret minimization problem that are compatible with any desired controller sparsity, while we reveal a renewed role of the Quadratic Invariance (QI) condition in designing informative benchmarks to measure regret. Last, we validate our proposed method through numerical simulations on controlling a multi-agent distributed system, comparing its performance with traditional H2 and Hinfty policies.
Neural Machine Translation Models with Attention-Based Dropout Layer
Authors: Huma Israr, Safdar Abbas Khan, Muhammad Ali Tahir, Muhammad Khuram Shahzad, Muneer Ahmad, Jasni Mohamad Zain
Date: 2023-05-01 00:00:00
Proceeding: Computers, Materials & Continua 2023 5
Tasks: Machine Translation, NMT, Translation

Abstract: In bilingual translation, attention-based Neural Machine Translation (NMT) models are used to achieve synchrony between input and output sequences and the notion of alignment. NMT model has obtained state-of-the-art performance for several language pairs. However, there has been little work exploring useful architectures for Urdu-to-English machine translation. We conducted extensive Urdu-to-English translation experiments using Long short-term memory (LSTM)/Bidirectional recurrent neural networks (Bi-RNN)/Statistical recurrent unit (SRU)/Gated recurrent unit (GRU)/Convolutional neural network (CNN) and Transformer. Experimental results show that Bi-RNN and LSTM with attention mechanism trained iteratively, with a scalable data set, make precise predictions on unseen data. The trained models yielded competitive results by achieving 62.6% and 61% accuracy and 49.67 and 47.14 BLEU scores, respectively. From a qualitative perspective, the translation of the test sets was examined manually, and it was observed that trained models tend to produce repetitive output more frequently. The attention score produced by Bi-RNN and LSTM produced clear alignment, while GRU showed incorrect translation for words, poor alignment and lack of a clear structure. Therefore, we considered refining the attention-based models by defining an additional attention-based dropout layer. Attention dropout fixes alignment errors and minimizes translation errors at the word level. After empirical demonstration and comparison with their counterparts, we found improvement in the quality of the resulting translation system and a decrease in the perplexity and over-translation score. The ability of the proposed model was evaluated using Arabic-English and Persian-English datasets as well. We empirically concluded that adding an attention-based dropout layer helps improve GRU, SRU, and Transformer translation and is considerably more efficient in translation quality and speed.
MetaTrinity: Enabling Fast Metagenomic Classification via Seed Counting and Edit Distance Approximation
Authors: Arvid E. Gollwitzer, Mohammed Alser, Joel Bergtholdt, Joel Lindegger, Maximilian-David Rumpf, Can Firtina, Serghei Mangul, Onur Mutlu
Date: 2023-11-03 00:00:00

Metagenomics, the study of genome sequences of diverse organisms cohabiting in a shared environment, has experienced significant advancements across various medical and biological fields. Metagenomic analysis is crucial, for instance, in clinical applications such as infectious disease screening and the diagnosis and early detection of diseases such as cancer. A key task in metagenomics is to determine the species present in a sample and their relative abundances. Currently, the field is dominated by either alignment-based tools, which offer high accuracy but are computationally expensive, or alignment-free tools, which are fast but lack the needed accuracy for many applications. In response to this dichotomy, we introduce MetaTrinity, a tool based on heuristics, to achieve a fundamental improvement in accuracy-runtime tradeoff over existing methods. We benchmark MetaTrinity against two leading metagenomic classifiers, each representing different ends of the performance-accuracy spectrum. On one end, Kraken2, a tool optimized for performance, shows modest accuracy yet a rapid runtime. The other end of the spectrum is governed by Metalign, a tool optimized for accuracy. Our evaluations show that MetaTrinity achieves an accuracy comparable to Metalign while gaining a 4x speedup without any loss in accuracy. This directly equates to a fourfold improvement in runtime-accuracy tradeoff. Compared to Kraken2, MetaTrinity requires a 5x longer runtime yet delivers a 17x improvement in accuracy. This demonstrates a 3.4x enhancement in the accuracy-runtime tradeoff for MetaTrinity. This dual comparison positions MetaTrinity as a broadly applicable solution for metagenomic classification, combining advantages of both ends of the spectrum: speed and accuracy. MetaTrinity is publicly available at https://github.com/CMU-SAFARI/MetaTrinity.
A Highly-Compact Direct-Injection Universal Power Flow and Quality Control Circuit
Authors: Mowei Lu, Mengjie Qin, Jan Kacetl, Eeshta Suresh, Teng Long, Stefan M. Goetz
Date: 2023-11-03 00:00:00

This paper presents a novel direct-injection modular universal power flow and quality control topology exclusively using lower power components. In addition to conventional high-voltage applications, it is particularly attractive for the distribution and secondary grids, e.g., in soft open points, down to low voltage as it can exploit the latest developments in low-voltage high-current semiconductors. In contrast to other concepts that do not interface the grid through transformers, it does not need to convert the entire line power but only the injected or extracted power difference. The proposed power flow and quality (f/q) controller comprises a shunt active front end, together with high-frequency links serving as a power supply for a series floating module per phase. Each of the floating modules is in series with one phase of the line, floating with the electric potential of that particular phase, avoiding any ground connection. Omitting bulky and dynamically limited line transformers of conventional universal power flow controllers, the presented direct-injection f/q controller enables exceptionally small size and volume, high power density, high frequency content, and fast response. In contrast to direct-injection concepts with full back-to-back converters, it only needs to handle a fraction of the power. The circuit combines grid-voltage low-current electronics in the shunt unit and low-voltage high-current modules in the floating series injection units. Simulations and experiments demonstrate and validate the concept.
From Discrete to Continuous Binary Best-Response Dynamics: Discrete Fluctuations Almost Surely Vanish with Population Size
Authors: Azadeh Aghaeeyan, Pouria Ramazi
Date: 2023-11-03 00:00:00

In binary decision-making, individuals often choose either the rare or the common action. In the framework of evolutionary game theory, the best-response update rule can be used to model this dichotomy. Those who prefer the common action are called \emph{coordinators}, and those who prefer the rare one are called \emph{anticoordinators}. A finite mixed population of the two types may undergo perpetual fluctuations, the characterization of which appears to be challenging. It is particularly unknown whether the fluctuations persist as population size grows. To fill this gap, we approximate the discrete finite population dynamics of coordinators and anticoordinators with the associated mean dynamics in the form of differential inclusions. We show that the family of the state sequences of the discrete dynamics for increasing population sizes forms a generalized stochastic approximation process for the differential inclusion. On the other hand, we show that the differential inclusions always converge to an equilibrium. This implies that the reported perpetual fluctuations in the finite discrete dynamics of coordinators and anticoordinators almost surely vanish with population size. The results encourage to first analyze the often simpler {continuous-time} mean dynamics of the discrete population dynamics as the continuous-time dynamics partly reveal the asymptotic behavior of the discrete dynamics.
Active Exploration in Iterative Gaussian Process Regression for Uncertainty Modeling in Autonomous Racing
Authors: Tommaso Benciolini, Chen Tang, Marion Leibold, Catherine Weaver, Masayoshi Tomizuka, Wei Zhan
Date: 2023-11-03 00:00:00
Tasks: Autonomous Racing, Model Predictive Control, regression

Autonomous racing creates challenging control problems, but Model Predictive Control (MPC) has made promising steps toward solving both the minimum lap-time problem and head-to-head racing. Yet, accurate models of the system are necessary for model-based control, including models of vehicle dynamics and opponent behavior. Both dynamics model error and opponent behavior can be modeled with Gaussian Process (GP) regression. GP models can be updated iteratively from data collected using the controller, but the strength of the GP model depends on the diversity of the training data. We propose a novel active exploration mechanism for iterative GP regression that purposefully collects additional data at regions of higher uncertainty in the GP model. In the exploration, a MPC collects diverse data by balancing the racing objectives and the exploration criterion; then the GP is re-trained. The process is repeated iteratively; in later iterations, the exploration is deactivated, and only the racing objectives are optimized. Thus, the MPC can achieve better performance by leveraging the improved GP model. We validate our approach in the highly realistic racing simulation platform Gran Turismo Sport of Sony Interactive Entertainment Inc for a minimum lap time challenge, and in numerical simulation of head-to-head. Our active exploration mechanism yields a significant improvement in the GP prediction accuracy compared to previous approaches and, thus, an improved racing performance.
Control Design for Trajectory Tracking and Stabilization of Sensor LOS in an Inertially Stabilized Platform
Authors: Abinash Agasti, Angana Hazarika, Bharath Bhikkaji
Date: 2023-11-03 00:00:00

Optical sensors are often mounted on moving platforms to aid in a variety of tasks like data collection, surveillance and navigation. This necessitates the precise control of the inertial orientation of the optical sensor line-of-sight (LOS) towards a desired stationary or mobile target. A two-axes gimbal assembly is considered to achieve this control objective which can be decomposed into two parts - stabilization and tracking. A novel state space model is proposed based on the dynamics of a two-axes gimbal system. Using a suitable change of variables, this state space model is transformed into an LTI system. Feedback linearization based control laws are proposed that achieve the desired objectives of stabilization and tracking. The effectiveness of these control laws are demonstrated via simulation in MATLAB based on a typical model of a two-axes gimbal system.
Method development for lowering supply temperatures in existing buildings using minimal building information and demand measurement data
Authors: Jan stock, Philipp Althaus, Sascha Johnen, André Xhonneux, Dirk Müller
Date: 2023-11-03 00:00:00

Regarding climate change, the need to reduce greenhouse gas emissions is well-known. As building heating contributes to a high share of total energy consumption, which relies mainly on fossil energy sources, improving heating efficiency is promising to consider. Lowering supply temperatures of the heating systems in buildings offers a huge potential for efficiency improvements since different heat supply technologies, such as heat pumps or district heating, benefit from low supply temperatures. However, most estimations of possible temperature reductions in existing buildings are based on available measurement data on room level or detailed building information about the building's physics to develop simulation models. To reveal the potential of temperature reduction for several buildings and strive for a wide applicability, the presented method focuses on estimations for temperature reduction in existing buildings with limited input data. By evaluating historic heat demand data on the building level, outdoor temperatures and information about installed heaters, the minimal actual necessary supply temperature is calculated for each heater in the building using the LMTD approach. Based on the calculated required supply temperatures for each room at different outdoor temperatures, the overall necessary supply temperatures to be provided to the building are chosen. Thus, the minimal heatcurve possible for an existing building is deduced. The method described is applied to multiple existing office buildings at the campus of Forschungszentrum Juelich, Germany, demonstrating the fast application for several buildings with limited expenditure. Furthermore, a developed adapted heatcurve is implemented in one real building and evaluated in relation to the previously applied heatcurve of the heating system.
Passive Handwriting Tracking via Weak mmWave Communication Signals
Authors: Chao Yu, Yan Luo, Renqi Chen, Rui Wang
Date: 2023-11-03 00:00:00

In this letter, a cooperative sensing framework based on millimeter wave (mmWave) communication systems is proposed to detect tiny motions with a millimeter-level resolution. Particularly, the cooperative sensing framework is facilitated with one transmitter and two receivers. There are two radio frequency (RF) chains at each receiver. Hence, the Doppler effect due to the tiny motions can be detected via passive sensing respectively at the receivers, and the velocities of the motions can be estimated by integrating the Doppler frequencies. It is demonstrated that the proposed cooperative sensing system is able to track the handwriting with 90% error below 6 mm. Moreover, the proposed cooperative sensing is robust to the strength of received signal. For example, it works even without the line-of-sight paths from the transmitter to the receivers or the sensing target, where the received signal strength is not sufficient for timing synchronization or demodulation.
Low Overhead Beam Alignment for Mobile Millimeter Channel Based on Continuous-Time Prediction
Authors: Huang-Chou Lin, Kuang-Hao, Liu
Date: 2023-11-03 00:00:00

In millimeter-wave (mmWave) communications, directional transmission based on beamforming is important to compensate for high pathloss. To maintain the desired direction transmission gain, beam scanning that involves the transmitter sending the pilot signal over all available beam directions to find the optimal beam is often considered. Alternatively, beam tracking using partial beams can save the beam training overhead through algorithms such as statistical analysis models and kalman filter (KF). Unfortunately, existing beam tracking solutions are limited to a fixed beam variation pattern. In this work, we propose an adaptive online beam alignment (AOBA) scheme, which aims to reduce training overhead and achieve accurate beam alignment for any movement profile. The proposed AOBA periodically performs beam tracking using a small amount but carefully selected candidate beams and switches to beam scanning using all available beams based on a given switching rule. During the interval without the pilot signal, the optimal beam at an arbitrary time instant is predicted with the aid of the recently proposed ordinary differential equation (ODE)-long short-term memory (LSTM) model. Extensive simulations are conducted to evaluate the performance of the proposed AOBA in comparison with several existing beam alignment schemes.