pwc-archive/papers-with-abstracts huggingface dataset:
OpenDMC: An Open-Source Library and Performance Evaluation for Deep-learning-based Multi-frame Compression
Authors: Wei Gao, Shangkun Sun, Huiming Zheng, Yuyang Wu, Hua Ye, Yongchi Zhang
Date: 2023-10-27 00:00:00
Proceeding: journal 2023 10
Tasks: Benchmarking, GPU, Video Compression

Video streaming has become an essential component of our everyday routines. Nevertheless, video data imposes a significant strain on data usage, demanding substantial bandwidth and storage resources for effective transmission. To suit explosively increasing video transmission and storage requirements, deep-learning-based video compression has developed rapidly in the past few years. New methods have mushroomed in order to achieve better Rate-Distortion (RD) performance. However, the absence of an algorithm library that can effectively sort, classify, and conduct extensive benchmark testing on existing algorithms remains a challenge. In this paper, we present an open-source algorithm library called OpenDMC, which integrates a variety of end-to-end video compression methods in cross-platform environments. We provide comprehensive descriptions of the algorithms used in the library, including their contributions and implementation details. We perform a thorough benchmarking test to evaluate the performance of the algorithms. We meticulously compare and analyze each algorithm based on various metrics, including RD performance, running time, and GPU memory usage.
Graph-Enhanced Emotion Neural Decoding
Authors: Zhongyu Huang; Changde Du; Yingheng Wang; Kaicheng Fu; Huiguang He
Date: 2023-02-16 00:00:00
Proceeding: journal 2023 2
Tasks: Emotion Recognition, Representation Learning

Brain signal-based emotion recognition has recently attracted considerable attention since it has powerful potential to be applied in human-computer interaction. To realize the emotional interaction of intelligent systems with humans, researchers have made efforts to decode human emotions from brain imaging data. The majority of current efforts use emotion similarities ( e.g. , emotion graphs) or brain region similarities ( e.g. , brain networks) to learn emotion and brain representations. However, the relationships between emotions and brain regions are not explicitly incorporated into the representation learning process. As a result, the learned representations may not be informative enough to benefit specific tasks, e.g. , emotion decoding. In this work, we propose a novel idea of graph-enhanced emotion neural decoding, which takes advantage of a bipartite graph structure to integrate the relationships between emotions and brain regions into the neural decoding process, thus helping learn better representations. Theoretical analyses conclude that the suggested emotion-brain bipartite graph inherits and generalizes the conventional emotion graphs and brain networks. Comprehensive experiments on visually evoked emotion datasets demonstrate the effectiveness and superiority of our approach.
WEAC: Word embeddings for anomaly classification from event logs
Authors: Amit Pande, Vishal Ahuja
Date: 2018-01-18 00:00:00
Proceeding: International Conference on Big Data (Big Data) 2018 1
Tasks: Anomaly Classification, Anomaly Detection, Word Embeddings

Abstract:
Dramatic progress has been made in the usage of semantic word embeddings for solving word analogy tasks in recent years. Word embeddings or vector representation of words has been the key to many advances in natural language processing. This paper presents a novel application of Word-Embeddings for Anomaly Classification (WEAC), where we detect whether an event log entry is an anomalous one or not. Additionally, WEAC helps us classify the anomaly by identifying the anomalous feature(s) in the event log. For example, an unusual network activity such as a store transaction server logging into dropbox.com would be automatically flagged as anomalous because of the wrong feature associations for entries in the corresponding event log. WEAC works with two training models: Skip-Gram (SG) and Continuous Bag of Words (CBOW). Negative sampling is used to boost the training. The initial results on wikipedia text8 dataset, as well as investigation on enterprise HTTP logs are promising. The model achieved an average detection rate of 65-100% and classification accuracy of 85-100%. The detection rate was superior to state-of-the-art anomaly detection techniques.
ProDCoNN-server: a web server for protein sequence prediction and design from a three-dimensional structure
Authors: Yuan Zhang, Arunima Mandal, Kevin Cui, Xiuwen Liu, Jinfeng Zhang
Date: 2021-04-11 00:00:00
Proceeding: bioRxiv 2021 4
Tasks: Prediction, Protein Design

We present ProDCoNN-server, a web server for protein sequence design and prediction from a given protein structure. The server is based on a previously developed deep learning model for protein design, ProDCoNN, which achieved state-of-the-art performance when tested on large numbers of test proteins and benchmark datasets. The prediction is very fast compared with other protein sequence prediction servers - it takes only a few minutes for a query protein on average. Two models could be selected for different purposes: BBO for full sequence prediction, extendable for multiple sequence generation, and BBS for single position prediction with the type of other residues known. ProDCoNN-server outputs the predicted sequence and the probability matrix for each amino acid at each predicted residue. The probability matrix can also be visualized as a sequence logos figure (BBO) or probability distribution plot (BBS). The server is available at: https://prodconn.stat.fsu.edu/.
TRANSFER :- DEEP INDUCTIVE NETWORK FOR FACIAL EMOTION RECOGNITION
Authors: Arpita Gupta, Nandhini Swaminathan, Ramadoss Balakrishnan
Date: 2020-07-01 00:00:00
Proceeding: JOURNAL OF MECHANICS OF CONTINUA AND MATHEMATICAL SCIENCES 2020 7
Tasks: Emotion Recognition, Facial Emotion Recognition, Inductive Learning, Transfer Learning

The image-based Facial Emotion Recognition (FER) aims to classify the image into basic emotions being communicated by it. FER is one of the most prominent research areas in computer vision. Most of the existing works are aimed at high-quality images which are collected in the lab environment. These images are very different from the real-life facial emotion that leads to a lack of wild labeled data. Deep learning using transfer learning has shown promising results in computer vision in solving the problem of lack of labeled data. In the recent system, there is a great focus to overcome the lack of data issue in FER. Our paper has utilized the deep residual networks with inductive learning and self-attention module to overcome this problem. We have experimented different pre training settings and datasets for the model, which are Image Net and VGG face dataset (source datasets). The selfattention block is applied for better visual perspective to the model. Our target dataset is FER-2013, a benchmark dataset in FER. Trans FER is a deep residual network based on inductive learning and attention module. Our proposed approach has achieved superior performance than the existing state of the art models in the FER application using transfer learning.
Probability Tools for Sequential Random Projection
Authors: Yingru Li
Date: 2024-02-16 00:00:00
Tasks: Decision Making, Decision Making Under Uncertainty, LEMMA, Sequential Decision Making

We introduce the first probabilistic framework tailored for sequential random projection, an approach rooted in the challenges of sequential decision-making under uncertainty. The analysis is complicated by the sequential dependence and high-dimensional nature of random variables, a byproduct of the adaptive mechanisms inherent in sequential decision processes. Our work features a novel construction of a stopped process, facilitating the analysis of a sequence of concentration events that are interconnected in a sequential manner. By employing the method of mixtures within a self-normalized process, derived from the stopped process, we achieve a desired non-asymptotic probability bound. This bound represents a non-trivial martingale extension of the Johnson-Lindenstrauss (JL) lemma, marking a pioneering contribution to the literature on random projection and sequential analysis.
Non-Contact Acquisition of PPG Signal using Chest Movement-Modulated Radio Signals
Authors: Israel Jesus Santos Filho, Muhammad Mahboob Ur Rahman, Taous-Meriem Laleg-Kirati, Tareq Al-Naffouri
Date: 2024-02-22 00:00:00
Tasks: Dimensionality Reduction, Photoplethysmography (PPG), regression

We present for the first time a novel method that utilizes the chest movement-modulated radio signals for non-contact acquisition of the photoplethysmography (PPG) signal. Under the proposed method, a software-defined radio (SDR) exposes the chest of a subject sitting nearby to an orthogonal frequency division multiplexing signal with 64 sub-carriers at a center frequency 5.24 GHz, while another SDR in the close vicinity collects the modulated radio signal reflected off the chest. This way, we construct a custom dataset by collecting 160 minutes of labeled data (both raw radio data as well as the reference PPG signal) from 16 healthy young subjects. With this, we first utilize principal component analysis for dimensionality reduction of the radio data. Next, we denoise the radio signal and reference PPG signal using wavelet technique, followed by segmentation and Z-score normalization. We then synchronize the radio and PPG segments using cross-correlation method. Finally, we proceed to the waveform translation (regression) task, whereby we first convert the radio and PPG segments into frequency domain using discrete cosine transform (DCT), and then learn the non-linear regression between them. Eventually, we reconstruct the synthetic PPG signal by taking inverse DCT of the output of regression block, with a mean absolute error of 8.1294. The synthetic PPG waveform has a great clinical significance as it could be used for non-contact performance assessment of cardiovascular and respiratory systems of patients suffering from infectious diseases, e.g., covid19.
Bispectrum Unbiasing for Dilation-Invariant Multi-reference Alignment
Authors: Liping Yin, Anna Little, Matthew Hirn
Date: 2024-02-22 00:00:00

Motivated by modern data applications such as cryo-electron microscopy, the goal of classic multi-reference alignment (MRA) is to recover an unknown signal $f: \mathbb{R} \to \mathbb{R}$ from many observations that have been randomly translated and corrupted by additive noise. We consider a generalization of classic MRA where signals are also corrupted by a random scale change, i.e. dilation. We propose a novel data-driven unbiasing procedure which can recover an unbiased estimator of the bispectrum of the unknown signal, given knowledge of the dilation distribution. Lastly, we invert the recovered bispectrum to achieve full signal recovery, and validate our methodology on a set of synthetic signals.
Attention-Enhanced Prioritized Proximal Policy Optimization for Adaptive Edge Caching
Authors: Farnaz Niknia, Ping Wang, Zixu Wang, Aakash Agarwal, Adib S. Rezaei
Date: 2024-02-08 00:00:00
Tasks: Deep Reinforcement Learning, Q-Learning, reinforcement-learning, Reinforcement Learning, Transfer Learning

This paper tackles the growing issue of excessive data transmission in networks. With increasing traffic, backhaul links and core networks are under significant traffic, leading to the investigation of caching solutions at edge routers. Many existing studies utilize Markov Decision Processes (MDP) to tackle caching problems, often assuming decision points at fixed intervals; however, real-world environments are characterized by random request arrivals. Additionally, critical file attributes such as lifetime, size, and priority significantly impact the effectiveness of caching policies, yet existing research fails to integrate all these attributes in policy design. In this work, we model the caching problem using a Semi-Markov Decision Process (SMDP) to better capture the continuous-time nature of real-world applications, enabling caching decisions to be triggered by random file requests. We then introduce a Proximal Policy Optimization (PPO)--based caching strategy that fully considers file attributes like lifetime, size, and priority. Simulations show that our method outperforms a recent Deep Reinforcement Learning-based technique. To further advance our research, we improved the convergence rate of PPO by prioritizing transitions within the replay buffer through an attention mechanism. This mechanism evaluates the similarity between the current state and all stored transitions, assigning higher priorities to transitions that exhibit greater similarity.
Enhancement of High-definition Map Update Service Through Coverage-aware and Reinforcement Learning
Authors: Jeffrey Redondo, Zhenhui Yuan, Nauman Aslam
Date: 2024-02-08 00:00:00
Tasks: Autonomous Driving, Autonomous Vehicles, Q-Learning

High-definition (HD) Map systems will play a pivotal role in advancing autonomous driving to a higher level, thanks to the significant improvement over traditional two-dimensional (2D) maps. Creating an HD Map requires a huge amount of on-road and off-road data. Typically, these raw datasets are collected and uploaded to cloud-based HD map service providers through vehicular networks. Nevertheless, there are challenges in transmitting the raw data over vehicular wireless channels due to the dynamic topology. As the number of vehicles increases, there is a detrimental impact on service quality, which acts as a barrier to a real-time HD Map system for collaborative driving in Autonomous Vehicles (AV). In this paper, to overcome network congestion, a Q-learning coverage-time-awareness algorithm is presented to optimize the quality of service for vehicular networks and HD map updates. The algorithm is evaluated in an environment that imitates a dynamic scenario where vehicles enter and leave. Results showed an improvement in latency for HD map data of $75\%$, $73\%$, and $10\%$ compared with IEEE802.11p without Quality of Service (QoS), IEEE802.11 with QoS, and IEEE802.11p with new access category (AC) for HD map, respectively.
Exploring the Influence of Driving Context on Lateral Driving Style Preferences: A Simulator-Based Study
Authors: Johann Haselberger, Maximilian Böhle, Bernhard Schick, Steffen Müller
Date: 2024-02-22 00:00:00
Tasks: Autonomous Vehicles

Technological advancements focus on developing comfortable and acceptable driving characteristics in autonomous vehicles. Present driving functions predominantly possess predefined parameters, and there is no universally accepted driving style for autonomous vehicles. While driving may be technically safe and the likelihood of road accidents is reduced, passengers may still feel insecure due to a mismatch in driving styles between the human and the autonomous system. Incorporating driving style preferences into automated vehicles enhances acceptance, reduces uncertainty, and poses the opportunity to expedite their adoption. Despite the increased research focus on driving styles, there remains a need for comprehensive studies investigating how variations in the driving context impact the assessment of automated driving functions. Therefore, this work evaluates lateral driving style preferences for autonomous vehicles on rural roads, considering different weather and traffic situations. A controlled study was conducted with a variety of German participants utilizing a high-fidelity driving simulator. The subjects experienced four different driving styles, including mimicking of their own driving behavior under two weather conditions. A notable preference for a more passive driving style became evident based on statistical analyses of participants' responses during and after the drives. This study could not confirm the hypothesis that subjects prefer to be driven by mimicking their own driving behavior. Furthermore, the study illustrated that weather conditions and oncoming traffic substantially influence the perceived comfort during autonomous rides. The gathered dataset is openly accessible at https://www.kaggle.com/datasets/jhaselberger/idcld-subject-study-on-driving-style-preferences.
SICRN: Advancing Speech Enhancement through State Space Model and Inplace Convolution Techniques
Authors: Changjiang Zhao, Shulin He, Xueliang Zhang
Date: 2024-02-22 00:00:00
Tasks: Speech Enhancement

Speech enhancement aims to improve speech quality and intelligibility, especially in noisy environments where background noise degrades speech signals. Currently, deep learning methods achieve great success in speech enhancement, e.g. the representative convolutional recurrent neural network (CRN) and its variants. However, CRN typically employs consecutive downsampling and upsampling convolution for frequency modeling, which destroys the inherent structure of the signal over frequency. Additionally, convolutional layers lacks of temporal modelling abilities. To address these issues, we propose an innovative module combing a State space model and Inplace Convolution (SIC), and to replace the conventional convolution in CRN, called SICRN. Specifically, a dual-path multidimensional State space model captures the global frequencies dependency and long-term temporal dependencies. Meanwhile, the 2D-inplace convolution is used to capture the local structure, which abandons the downsampling and upsampling. Systematic evaluations on the public INTERSPEECH 2020 DNS challenge dataset demonstrate SICRN's efficacy. Compared to strong baselines, SICRN achieves performance close to state-of-the-art while having advantages in model parameters, computations, and algorithmic delay. The proposed SICRN shows great promise for improved speech enhancement.
Avoiding an AI-imposed Taylor's Version of all music history
Authors: Nick Collins, Mick Grierson
Date: 2024-02-05 00:00:00
Tasks: All, Diversity

As future musical AIs adhere closely to human music, they may form their own attachments to particular human artists in their databases, and these biases may in the worst case lead to potential existential threats to all musical history. AI super fans may act to corrupt the historical record and extant recordings in favour of their own preferences, and preservation of the diversity of world music culture may become even more of a pressing issue than the imposition of 12 tone equal temperament or other Western homogenisations. We discuss the technical capability of AI cover software and produce Taylor's Versions of famous tracks from Western pop history as provocative examples; the quality of these productions does not affect the overall argument (which might even see a future AI try to impose the sound of paperclips onto all existing audio files, let alone Taylor Swift). We discuss some potential defenses against the danger of future musical monopolies, whilst analysing the feasibility of a maximal 'Taylor Swiftication' of the complete musical record.
Using Hybrid System Dynamics and Discrete Event Simulations to Identify High Leverage Targets for Process Improvement in a Skill-based Organizational Structure
Authors: Eric Enos, Daniel Herber
Date: 2024-02-22 00:00:00

This paper is based on a case study of an IT organization in a large, US-based healthcare provider, and develops simluation models to identify areas for performance improvement. These organizations are often grouped into departments by technical skill and support both operational work (tickets) and project work (tasks) of various priorities. From a practical standpoint, resource managers and staff regularly manage all work as queued and assign / complete it based on the priorities of the day. Using project and operational metrics from the case study organization, the hybrid model using both system dynamics and discrete event simulation developed through this research depicts the flow of work through a skill-based team as well as many of the key factors that influence that workflow, both positive and negative. Experience indicates that the interaction between project and operational work -- as well as between teams with differing skills -- entangles work queues and wait times within those queues in a way that rapidly scales in complexity as the number of interacting individuals and teams increases. Results from model simulation bear out this intuition. Scaling the models to accommodate multiple teams is a topic of future research.
Run Time Assurance for Simultaneous Constraint Satisfaction During Spacecraft Attitude Maneuvering
Authors: Cassie-Kay McQuinn, Kyle Dunlap, Nathaniel Hamilton, Jabari Wilson, Kerianne L. Hobbs
Date: 2024-02-22 00:00:00

A fundamental capability for On-orbit Servicing, Assembly, and Manufacturing (OSAM) is inspection of the vehicle to be serviced, or the structure being assembled. This research assumes autonomous slewing to maintain situational awareness of multiple vehicles operating in close proximity where several safety constraints must be satisfied. A variety of techniques may be used as the primary controller. The focus of this research is developing Run Time Assurance (RTA) filters that monitor system behavior and the output of the primary controller to enforce safety constraint satisfaction. Specifically, this research explores combining a subset of the constraints into an Active Set Invariance Filter (ASIF) RTA defined using control barrier functions. This method is minimally invasive to the primary control by minimizing deviation from the desired control output of the primary controller, while simultaneously enforcing all safety constraints. The RTA is designed to ensure the spacecraft maintains attitude requirements for communication and data transfer with a ground station during scheduled communication windows, adheres to conical attitude keep out zones, limits thermally unfavorable attitude duration, maintains attitude requirements for sufficient power generation, ensures maneuvers are below threshold to cause structural damage, ensures maximum angular velocity is below limits to maintain ability to respond quickly to new slewing commands, and conserves actuator use to prevent wear when possible. Slack variables are introduced into the ASIF controller to prioritize safety constraints when a solution to all safety constraints is infeasible. Monte Carlo simulation results as well as plots of example cases are shown and evaluated for a three degree of freedom spacecraft with reaction wheel attitude control.
Observability for Nonlinear Systems: Connecting Variational Dynamics, Lyapunov Exponents, and Empirical Gramians
Authors: Mohamad H. Kazma, Ahmad F. Taha
Date: 2024-02-22 00:00:00

Observability quantification is a key problem in dynamic network sciences. While it has been thoroughly studied for linear systems, observability quantification for nonlinear networks is less intuitive and more cumbersome. One common approach to quantify observability for nonlinear systems is via the Empirical Gramian (Empr-Gram) -- a generalized form of the Gramian of linear systems. In this paper, we produce three new results. First, we establish that a variational form of discrete-time autonomous nonlinear systems (computed via perturbing initial conditions) yields a so-called Variational Gramian (Var-Gram) that is equivalent to the classic Empr-Gram; the former being easier to compute than the latter. Via Lyapunov exponents derived from Lyapunov's direct method, the paper's second result derives connections between existing observability measures and Var-Gram. The third result demonstrates the applicability of these new notions for sensor selection/placement in nonlinear systems. Numerical case studies demonstrate these three developments and their merits.