pwc-archive/papers-with-abstracts huggingface dataset:
KuaiRand: An Unbiased Sequential Recommendation Dataset with Randomly Exposed Videos
Authors: Chongming Gao, Shijun Li, Yuan Zhang, Jiawei Chen, Biao Li, Wenqiang Lei, Peng Jiang, Xiangnan He
Date: 2022-08-18 00:00:00
Tasks: Interactive Recommendation, Multi-Task Learning, Recommendation Systems, Sequential Recommendation

Recommender systems deployed in real-world applications can have inherent exposure bias, which leads to the biased logged data plaguing the researchers. A fundamental way to address this thorny problem is to collect users' interactions on randomly expose items, i.e., the missing-at-random data. A few works have asked certain users to rate or select randomly recommended items, e.g., Yahoo!, Coat, and OpenBandit. However, these datasets are either too small in size or lack key information, such as unique user ID or the features of users/items. In this work, we present KuaiRand, an unbiased sequential recommendation dataset containing millions of intervened interactions on randomly exposed videos, collected from the video-sharing mobile App, Kuaishou. Different from existing datasets, KuaiRand records 12 kinds of user feedback signals (e.g., click, like, and view time) on randomly exposed videos inserted in the recommendation feeds in two weeks. To facilitate model learning, we further collect rich features of users and items as well as users' behavior history. By releasing this dataset, we enable the research of advanced debiasing large-scale recommendation scenarios for the first time. Also, with its distinctive features, KuaiRand can support various other research directions such as interactive recommendation, long sequential behavior modeling, and multi-task learning. The dataset and its news will be available at https://kuairand.com.
Incidental Data: Observation of Privacy Compromising Data on Social Media Platforms
Authors: Stefan Kutschera
Date: 2022-08-18 00:00:00

Social media plays an important role for a vast majority in one's internet life. Likewise, sharing, publishing and posting content through social media became nearly effortless. This unleashes new threats as unintentionally shared information may be used against oneself or beloved ones. With open source intelligence data and methods, we show how unindented published data can be revealed and further analyze possibilities that can potentially compromise one's privacy. This is backed up by a popular view from interviewed experts from various fields of expertise. We were able to show that only 2 hours of manually fetching data are sufficient in order to unveil private personal information that was not intended to be published by the person. Two distinctive methods are described with several approaches. From our results, we were able to describe a thirteen-step awareness guideline and proposed a change of law within Austrian legislation. Our work has shown that awareness among persons on social media needs to be raised. Critically reflecting on our work has revealed several ethical implications that made countermeasures necessary; however, it can be assumed that criminals do not do that.
Modeling Two-Way Selection Preference for Person-Job Fit
Authors: Chen Yang, Yupeng Hou, Yang song, Tao Zhang, Ji-Rong Wen, Wayne Xin Zhao
Date: 2022-08-18 00:00:00
Tasks: Contrastive Learning, Graph Representation Learning, Representation Learning, Vocal Bursts Valence Prediction

Person-job fit is the core technique of online recruitment platforms, which can improve the efficiency of recruitment by accurately matching the job positions with the job seekers. Existing works mainly focus on modeling the unidirectional process or overall matching. However, recruitment is a two-way selection process, which means that both candidate and employer involved in the interaction should meet the expectation of each other, instead of unilateral satisfaction. In this paper, we propose a dual-perspective graph representation learning approach to model directed interactions between candidates and jobs. To model the two-way selection preference from the dual-perspective of job seekers and employers, we incorporate two different nodes for each candidate (or job) and characterize both successful matching and failed matching via a unified dual-perspective interaction graph. To learn dual-perspective node representations effectively, we design an effective optimization algorithm, which involves a quadruple-based loss and a dual-perspective contrastive learning loss. Extensive experiments on three large real-world recruitment datasets have shown the effectiveness of our approach.
Visual Pursuit Control based on Gaussian Processes with Switched Motion Trajectories
Authors: Marco Omainska, Junya Yamauchi, Masayuki Fujita
Date: 2022-08-18 00:00:00
Tasks: 3D Pose Estimation, drone-based object tracking, Gaussian Processes, Motion Estimation, Real-Time Visual Tracking

This paper considers a scenario of pursuing a moving target that may switch behaviors due to external factors in a dynamic environment by motion estimation using visual sensors. First, we present an improved Visual Motion Observer with switched Gaussian Process models for an extended class of target motion profiles. We then propose a pursuit control law with an online method to estimate the switching behavior of the target by the GP model uncertainty. Next, we prove ultimate boundedness of the control and estimation errors for the switch in target behavior with high probability. Finally, a Digital Twin simulation demonstrates the effectiveness of the proposed switching estimation and control law to prove applicability to real world scenarios.
IoT based Smart Water Quality Prediction for Biofloc Aquaculture
Authors: Md. Mamunur Rashid, Al-Akhir Nayan, Md. Obaidur Rahman, Sabrina Afrin Simi, Joyeta Saha, Muhammad Golam Kibria
Date: 2022-07-27 00:00:00
Tasks: Prediction

Traditional fish farming faces several challenges, including water pollution, temperature imbalance, feed, space, cost, etc. Biofloc technology in aquaculture transforms the manual into an advanced system that allows the reuse of unused feed by converting them into microbial protein. The objective of the research is to propose an IoT-based solution to aquaculture that increases efficiency and productivity. The article presented a system that collects data using sensors, analyzes them using a machine learning model, generates decisions with the help of Artificial Intelligence (AI), and sends notifications to the user. The proposed system has been implemented and tested to validate and achieve a satisfactory result.
Deep Billboards towards Lossless Real2Sim in Virtual Reality
Authors: Naruya Kondo, So Kuroki, Ryosuke Hyakuta, Yutaka Matsuo, Shixiang Shane Gu, Yoichi Ochiai
Date: 2022-08-08 00:00:00
Tasks: Diversity, Neural Rendering

An aspirational goal for virtual reality (VR) is to bring in a rich diversity of real world objects losslessly. Existing VR applications often convert objects into explicit 3D models with meshes or point clouds, which allow fast interactive rendering but also severely limit its quality and the types of supported objects, fundamentally upper-bounding the "realism" of VR. Inspired by the classic "billboards" technique in gaming, we develop Deep Billboards that model 3D objects implicitly using neural networks, where only 2D image is rendered at a time based on the user's viewing direction. Our system, connecting a commercial VR headset with a server running neural rendering, allows real-time high-resolution simulation of detailed rigid objects, hairy objects, actuated dynamic objects and more in an interactive VR world, drastically narrowing the existing real-to-simulation (real2sim) gap. Additionally, we augment Deep Billboards with physical interaction capability, adapting classic billboards from screen-based games to immersive VR. At our pavilion, the visitors can use our off-the-shelf setup for quickly capturing their favorite objects, and within minutes, experience them in an immersive and interactive VR world with minimal loss of reality. Our project page: https://sites.google.com/view/deepbillboards/
Optimal Energy Management in Autonomous Power Systems with Probabilistic Security Constraints and Adaptive Frequency Control
Authors: Spyridon Chapaloglou, Erick Alves, Vincenzo Trovato, Elisabetta Tedeschi
Date: 2022-08-18 00:00:00
Tasks: energy management, Management, Scheduling, Uncertainty Quantification

The decarbonization of many heavy power-consuming industries is dependent on the integration of renewable energy sources and energy storage systems in isolated autonomous power systems. The optimal energy management in such schemes becomes harder due to the increased complexity and stability requirements, the rapidly varying operating conditions and uncertainty of renewable sources, the conflicting objectives across different timescales, the limited amount of reliable power sources and energy storage. The state of charge management when energy storage is used for multiple services, such as optimal scheduling and frequency support, is one of the most notorious problems in this context. To address this issue, an optimal energy management system is proposed in this paper. It co-optimizes the primary frequency control layer and the dispatch schedule of conventional generators and energy storage by taking advantage of an algorithm that provides adaptive active power demand uncertainty quantification, theoretical guarantees for frequency stability, and bounds for the reserves for frequency support assigned to the energy storage system. A convex reformulation is derived enabling the efficient solution of the involved optimization problem, being a test case of an isolated offshore oil and gas platform presented for validation.
Safe Control of Partially-Observed Linear Time-Varying Systems with Minimal Worst-Case Dynamic Regret
Authors: HongYu Zhou, Vasileios Tzoumas
Date: 2022-08-18 00:00:00

We present safe control of partially-observed linear time-varying systems in the presence of unknown and unpredictable process and measurement noise. We introduce a control algorithm that minimizes dynamic regret, i.e., that minimizes the suboptimality against an optimal clairvoyant controller that knows the unpredictable future a priori. Specifically, our algorithm minimizes the worst-case dynamic regret among all possible noise realizations given a worst-case total noise magnitude. To this end, the control algorithm accounts for three key challenges: safety constraints; partially-observed time-varying systems; and unpredictable process and measurement noise. We are motivated by the future of autonomy where robots will autonomously perform complex tasks despite unknown and unpredictable disturbances leveraging their on-board control and sensing capabilities. To synthesize our minimal-regret controller, we formulate a constrained semi-definite program based on a System Level Synthesis approach for partially-observed time-varying systems. We validate our algorithm in simulated scenarios, including trajectory tracking scenarios of a hovering quadrotor collecting GPS and IMU measurements. Our algorithm is observed to have better performance than either or both the $\mathcal{H}_2$ and $\mathcal{H}_\infty$ controllers, demonstrating a Best of Both Worlds performance.
Using Active Distribution Network Flexibility to Increase Transmission System Voltage Stability Margins
Authors: Giorgos Prionistis, Costas Vournas
Date: 2022-08-18 00:00:00

The increasing penetration of Distributed Energy Resources (DER) in the distribution network creates new challenges in the operation of both the transmission and the distribution network. However, the controllability of the converter interfaced devices (CIG), also unveils opportunities for flexible operation and provision of ancillary services with or without economic incentives. The main scope of this work is to create a framework in order to calculate the operational flexibility of an Active Distribution Network and use it to address a centralized Optimal Power Flow Problem by the Transmission System Operator, and in particular the Voltage Stability Margin maximization. Two different approaches are proposed to calculate the Flexibility Region (FR) in the PQ plane, and the centralized optimization is applied to simple and more complex transmission test systems and feeder configurations.
Dynamic State Estimation-Based Protection for Induction Motor Loads
Authors: Arthur K. Barnes, Sarbajit Basu, Adam Mate
Date: 2022-08-18 00:00:00
Tasks: State Estimation

Ensuring protective device coordination is critical to maintain the resilience and improve the reliability of large microgrids. Inverter-interfaced generation, however, poses significant challenges when designing protection systems. Traditional time-overcurrent protective devices are unsuitable on account of the lack of fault current. Present industry practice is to force all inverters to shut down during faults, which prevents large microgrids from operating in a resilient and reliable manner. Dynamic state estimation (DSE) has been proposed for line protection, and more recently for the protection of load buses or downstream radial portions of microgrids. However, only passive loads with series resistive-inductive loads have been tested with DSE, even though the behavior of dynamic loads -- such as induction motors or power electronics -- may differ significantly during faults. This paper considers the case of applying DSE to protecting a load bus serving a three-phase induction motor.
Neuro-Adaptive Boundary Force Control of Dual One-Link Flexible Arms with Unmodeled Dynamics and Input Constraints
Authors: Mahdi Hejrati
Date: 2022-08-18 00:00:00

The primary purpose of this article is to accomplish safe grasping task by means of dual one-link flexible manipulators. In order to design a force-sensor-less force control, the direct force control problem is reduced to common motion control problem, in a way that by satisfying new control objectives the grasping task is established. Afterwards, for the first time in the field of dual one-link flexible manipulators, intelligent control methods are combined with robust control approaches in an effort to; i) accomplish motion control objectives, ii) handle uncertainties in the system, and iii) consider unknown, mixed input constraints, resulting in NABFC (Neuro-Adaptive Boundary Force Control). Moreover, to deal with unknown model uncertainties as well as unknown input saturation and dead zones, Radial Basic Function Neural-Networks (RBFNNs) are used. In the same way, adaptive control is utilized to estimate unknown parameters. By exploiting Lyapunov's direct method, proper Lyapunov functional and Energy multiplier method are defined to express well-known yet strong stability procedure, which compensates a complex stability procedure proposed in the previous works. In the presence of the designed controller, the presented stability procedure resulted in a uniform ultimate boundedness (UUB) stability for the system. Finally, for comparison aim between the designed controller with other controllers, numerical analysis is used to demonstrate both the excellent performance of the proposed controller and the correctness of the stability analysis outcomes.
An Enhanced Gradient Based Optimized Controller for Load Frequency Control of a Two Area Automatic Generation Control System
Authors: Nabil Anan Orka, Sheikh Samit Muhaimin, Md. Nazmush Shakib Shahi, Ashik Ahmed
Date: 2022-08-18 00:00:00

This work proposes the adoption of Enhanced Gradient-Based Optimizer (EGBO) as a new approach to the Load Frequency Control (LFC) problem in a two-area interconnected power system. The importance of determining the optimal parameters for the controllers for the LFC problem cannot be overstated, and the fact that estimating these parameters require complex and nonlinear computations makes the optimization procedure even more unique and challenging. Consequently, application of an efficient optimization algorithm to successfully attain optimal controller parameters is critical. To accomplish this task, the proposed EGBO algorithm is compared to the fundamental Gradient-Based Optimizer (GBO), Chimp Optimization Algorithm (ChOA), Sine Cosine Algorithm (SCA), Grey Wolf Optimization (GWO), and Particle Swarm Optimization (PSO) for optimizing an Integral-Time-multiplied-Absolute-Error (ITAE) based objective function. The relevant findings show that the EGBO algorithm is competitively superior in terms of resilience, precision, and latency when compared to other optimization methods. Lastly, the statistical comparison further strengthens the outcome of the study.
Quickest Detection for Human-Sensor Systems using Quantum Decision Theory
Authors: Luke Snow, Vikram Krishnamurthy, Brian M. Sadler
Date: 2022-08-18 00:00:00
Tasks: Decision Making

In mathematical psychology, recent models for human decision-making use Quantum Decision Theory to capture important human-centric features such as order effects and violation of the sure-thing principle (total probability law). We construct and analyze a human-sensor system where a quickest detector aims to detect a change in an underlying state by observing human decisions that are influenced by the state. Apart from providing an analytical framework for such human-sensor systems, we also analyze the structure of the quickest detection policy. We show that the quickest detection policy has a single threshold and the optimal cost incurred is lower bounded by that of the classical quickest detector. This indicates that intermediate human decisions strictly hinder detection performance. We also analyze the sensitivity of the quickest detection cost with respect to the quantum decision parameters of the human decision maker, revealing that the performance is robust to inaccurate knowledge of the decision-making process. Numerical results are provided which suggest that observing the decisions of more rational decision makers will improve the quickest detection performance. Finally, we illustrate a numerical implementation of this quickest detector in the context of the Prisoner's Dilemma problem, in which it has been observed that Quantum Decision Theory can uniquely model empirically tested violations of the sure-thing principle.
On the Observability of Gaussian Models using Discrete Density Approximations
Authors: Ariane Hanebeck, Claudia Czado
Date: 2022-08-17 00:00:00

This paper proposes a novel method for testing observability in Gaussian models using discrete density approximations (deterministic samples) of (multivariate) Gaussians. Our notion of observability is defined by the existence of the maximum a posteriori estimator. In the first step of the proposed algorithm, the discrete density approximations are used to generate a single representative design observation vector to test for observability. In the second step, a number of carefully chosen design observation vectors are used to obtain information on the properties of the estimator. By using measures like the variance and the so-called local variance, we do not only obtain a binary answer to the question of observability but also provide a quantitative measure.
Synergistic Integration of Techniques of VC, Communication Technologies and Unities of Calculation Transportable for Generate a System Embedded That Monitors Pyroclastic Flows in Real Time
Authors: Kevin Barrera Llanga, Cruz Christian, Viteri Xavier, Mendoza Dario
Date: 2022-08-15 00:00:00

At the end of an extensive investigation of the volcanic eruptions in the world, we determined patterns that coincide in this process, this data can be analyzed by artificial vision, obtaining the largest amount of information from images in an embedded system, using monitoring algorithms for compare continuous matrices, control camera positioning and link this information with mass communication technologies. The present work shows the development of a viable early warning technology solution that allows to analyze the behavior of volcanic flows automatically in a rash in real time, with a very high level of efficiency in the analysis of possible trajectories, direction and quantity of the lava flows as well as the massive mass media directed to the affected people.
Transformer Networks for Predictive Group Elevator Control
Authors: Jing Zhang, Athanasios Tsiligkaridis, Hiroshi Taguchi, Arvind Raghunathan, Daniel Nikovski
Date: 2022-08-15 00:00:00
Tasks: regression

We propose a Predictive Group Elevator Scheduler by using predictive information of passengers arrivals from a Transformer based destination predictor and a linear regression model that predicts remaining time to destinations. Through extensive empirical evaluation, we find that the savings of Average Waiting Time (AWT) could be as high as above 50% for light arrival streams and around 15% for medium arrival streams in afternoon down-peak traffic regimes. Such results can be obtained after carefully setting the Predicted Probability of Going to Elevator (PPGE) threshold, thus avoiding a majority of false predictions for people heading to the elevator, while achieving as high as 80% of true predictive elevator landings as early as after having seen only 60% of the whole trajectory of a passenger.
An intertwined neural network model for EEG classification in brain-computer interfaces
Authors: Andrea Duggento, Mario De Lorenzo, Stefano Bargione, Allegra Conti, Vincenzo Catrambone, Gaetano Valenza, Nicola Toschi
Date: 2022-08-04 00:00:00
Tasks: Brain Computer Interface, Denoising, EEG, Electroencephalogram (EEG), Motor Imagery

The brain computer interface (BCI) is a nonstimulatory direct and occasionally bidirectional communication link between the brain and a computer or an external device. Classically, EEG-based BCI algorithms have relied on models such as support vector machines and linear discriminant analysis or multiclass common spatial patterns. During the last decade, however, more sophisticated machine learning architectures, such as convolutional neural networks, recurrent neural networks, long short-term memory networks and gated recurrent unit networks, have been extensively used to enhance discriminability in multiclass BCI tasks. Additionally, preprocessing and denoising of EEG signals has always been key in the successful decoding of brain activity, and the determination of an optimal and standardized EEG preprocessing activity is an active area of research. In this paper, we present a deep neural network architecture specifically engineered to a) provide state-of-the-art performance in multiclass motor imagery classification and b) remain robust to preprocessing to enable real-time processing of raw data as it streams from EEG and BCI equipment. It is based on the intertwined use of time-distributed fully connected (tdFC) and space-distributed 1D temporal convolutional layers (sdConv) and explicitly addresses the possibility that interaction of spatial and temporal features of the EEG signal occurs at all levels of complexity. Numerical experiments demonstrate that our architecture provides superior performance compared baselines based on a combination of 3D convolutions and recurrent neural networks in a six-class motor imagery network, with a subjectwise accuracy that reaches 99%. Importantly, these results remain unchanged when minimal or extensive preprocessing is applied, possibly paving the way for a more transversal and real-time use of deep learning architectures in EEG classification.
An Alternative Derivation of the Gaussian Noise model
Authors: Alberto Bononi, Paolo Serena
Date: 2022-08-14 00:00:00
Tasks: model

By extending the results in Bononi 2012, we provide here a complete alternative derivation of Turin Gaussian Noise (GN) model for dual-polarization dispersion uncompensated coherent optical links. This paper contains the lecture notes used by the authors first on July 19, 2012, and then again on june 6, 2016 at the University of Parma.