pwc-archive/papers-with-abstracts huggingface dataset:
Evolution of Popularity Bias: Empirical Study and Debiasing
Authors: Ziwei Zhu, Yun He, Xing Zhao, James Caverlee
Date: 2022-07-07 00:00:00
Tasks: Recommendation Systems

Popularity bias is a long-standing challenge in recommender systems. Such a bias exerts detrimental impact on both users and item providers, and many efforts have been dedicated to studying and solving such a bias. However, most existing works situate this problem in a static setting, where the bias is analyzed only for a single round of recommendation with logged data. These works fail to take account of the dynamic nature of real-world recommendation process, leaving several important research questions unanswered: how does the popularity bias evolve in a dynamic scenario? what are the impacts of unique factors in a dynamic recommendation process on the bias? and how to debias in this long-term dynamic process? In this work, we aim to tackle these research gaps. Concretely, we conduct an empirical study by simulation experiments to analyze popularity bias in the dynamic scenario and propose a dynamic debiasing strategy and a novel False Positive Correction method utilizing false positive signals to debias, which show effective performance in extensive experiments.
Star Product PIR Schemes with Colluding Servers over Small Fields
Authors: Hao Chen
Date: 2022-07-07 00:00:00
Tasks: Information Retrieval, Retrieval

Private Information Retrieval (PIR) was first proposed by B. Chor, O. Goldreich, E. Kushilevitz and M. Sudan in their 1995 FOCS paper. For MDS coded distributed storage system private information retrieval was proposed and the capacity of PIR schemes for MDS coded distributed storage was studied. Star product PIR schemes from general coded distributed storage system with colluding servers were constructed over general finite fields. These star product schemes has no restriction on the sizes of fields and can be constructed for coded distributed storage across large number of servers. In this paper we first propose and prove the Singleton type upper bound on the storage rate, ratio of colluding servers and the retrieval rate of the star product PIR schemes. Secondly star product PIR schemes for coded distributed storage from algebraic geometry (AG) codes are analysed. We prove that when the number of the servers goes to the infinity, star product PIR schemes with colluding servers for AG-coded distributed storage have parameters closing to the Singleton type upper bound if the field is large. Comparing with the star product PIR schemes for Reed-Solomon coded and Reed-Muller coded distributed storage we show that PIR schemes with colluding servers for AG coded distributed storage have their performance advantages. AG-code based star product PIR schemes with colluding, Byzantine and unresponsive servers are discussed. $q$-ary cyclic code based star product PIR schemes for replicated data storage are also studied. When the storage code is the Reed-Muller code, the best choice of the retrieval code is not always the Reed-Muller code.
Supervised Contrastive Learning Approach for Contextual Ranking
Authors: Abhijit Anand, Jurek Leonhardt, Koustav Rudra, Avishek Anand
Date: 2022-07-07 00:00:00
Tasks: Contrastive Learning, Data Augmentation, Document Ranking, Fact Checking

Contextual ranking models have delivered impressive performance improvements over classical models in the document ranking task. However, these highly over-parameterized models tend to be data-hungry and require large amounts of data even for fine tuning. This paper proposes a simple yet effective method to improve ranking performance on smaller datasets using supervised contrastive learning for the document ranking problem. We perform data augmentation by creating training data using parts of the relevant documents in the query-document pairs. We then use a supervised contrastive learning objective to learn an effective ranking model from the augmented dataset. Our experiments on subsets of the TREC-DL dataset show that, although data augmentation leads to an increasing the training data sizes, it does not necessarily improve the performance using existing pointwise or pairwise training objectives. However, our proposed supervised contrastive loss objective leads to performance improvements over the standard non-augmented setting showcasing the utility of data augmentation using contrastive losses. Finally, we show the real benefit of using supervised contrastive learning objectives by showing marked improvements in smaller ranking datasets relating to news (Robust04), finance (FiQA), and scientific fact checking (SciFact).
Batch Evaluation Metrics in Information Retrieval: Measures, Scales, and Meaning
Authors: Alistair Moffat
Date: 2022-07-07 00:00:00
Tasks: Information Retrieval, Retrieval

A sequence of recent papers has considered the role of measurement scales in information retrieval (IR) experimentation, and presented the argument that (only) uniform-step interval scales should be used, and hence that well-known metrics such as reciprocal rank, expected reciprocal rank, normalized discounted cumulative gain, and average precision, should be either discarded as measurement tools, or adapted so that their metric values lie at uniformly-spaced points on the number line. These papers paint a rather bleak picture of past decades of IR evaluation, at odds with the community's overall emphasis on practical experimentation and measurable improvement. Our purpose in this work is to challenge that position. In particular, we argue that mappings from categorical and ordinal data to sets of points on the number line are valid provided there is an external reason for each target point to have been selected. We first consider the general role of measurement scales, and of categorical, ordinal, interval, ratio, and absolute data collections. In connection with the first two of those categories we also provide examples of the knowledge that is captured and represented by numeric mappings to the real number line. Focusing then on information retrieval, we argue that document rankings are categorical data, and that the role of an effectiveness metric is to provide a single value that represents the usefulness to a user or population of users of any given ranking, with usefulness able to be represented as a continuous variable on a ratio scale. That is, we argue that current IR metrics are well-founded, and, moreover, that those metrics are more meaningful in their current form than in the proposed "intervalized" versions.
Contrastive Information Transfer for Pre-Ranking Systems
Authors: Yue Cao, Xiaojiang Zhou, Peihao Huang, Yao Xiao, Dayao Chen, Sheng Chen
Date: 2022-07-07 00:00:00
Tasks: Recommendation Systems, Re-Ranking, Selection bias

Real-word search and recommender systems usually adopt a multi-stage ranking architecture, including matching, pre-ranking, ranking, and re-ranking. Previous works mainly focus on the ranking stage while very few focus on the pre-ranking stage. In this paper, we focus on the information transfer from ranking to pre-ranking stage. We propose a new Contrastive Information Transfer (CIT) framework to transfer useful information from ranking model to pre-ranking model. We train the pre-ranking model to distinguish the positive pair of representation from a set of positive and negative pairs with a contrastive objective. As a consequence, the pre-ranking model can make full use of rich information in ranking model's representations. The CIT framework also has the advantage of alleviating selection bias and improving the performance of recall metrics, which is crucial for pre-ranking models. We conduct extensive experiments including offline datasets and online A/B testing. Experimental results show that CIT achieves superior results than competitive models. In addition, a strict online A/B testing at one of the world's largest E-commercial platforms shows that the proposed model achieves 0.63\% improvements on CTR and 1.64\% improvements on VBR. The proposed model now has been deployed online and serves the main traffic of this system, contributing a remarkable business growth.
Comparing the Utility and Disclosure Risk of Synthetic Data with Samples of Microdata
Authors: Claire Little, Mark Elliot, Richard Allmendinger
Date: 2022-07-02 00:00:00

Most statistical agencies release randomly selected samples of Census microdata, usually with sample fractions under 10% and with other forms of statistical disclosure control (SDC) applied. An alternative to SDC is data synthesis, which has been attracting growing interest, yet there is no clear consensus on how to measure the associated utility and disclosure risk of the data. The ability to produce synthetic Census microdata, where the utility and associated risks are clearly understood, could mean that more timely and wider-ranging access to microdata would be possible. This paper follows on from previous work by the authors which mapped synthetic Census data on a risk-utility (R-U) map. The paper presents a framework to measure the utility and disclosure risk of synthetic data by comparing it to samples of the original data of varying sample fractions, thereby identifying the sample fraction which has equivalent utility and risk to the synthetic data. Three commonly used data synthesis packages are compared with some interesting results. Further work is needed in several directions but the methodology looks very promising.
ISAC from the Sky: UAV Trajectory Design for Joint Communication and Target Localization
Authors: Xiaoye Jing, Fan Liu, Christos Masouros, Yong Zeng
Date: 2022-07-06 00:00:00
Tasks: ISAC

Unmanned aerial vehicles (UAVs) as aerial base stations (BSs) are able to provide not only the communication service to ground users, but also the sensing functionality to localize targets of interests. In this paper, we consider an airborne integrated sensing and communications (ISAC) system where a UAV, which acts both as a communication BS and a mono-static radar, flies over a given area to transmit downlink signal to a ground communication user. In the meantime, the same transmitted signal is also exploited for mono-static radar sensing. We aim to optimize the UAV trajectory, such that the performance for both communication and sensing (C$\&$S) is explicitly considered. In particular, we first formulate the trajectory design problem into a weighted optimization problem, where a flexible performance trade-off between C$\&$S is achieved. As a step forward, a multi-stage trajectory design approach is proposed to improve the target estimation accuracy. While the resultant optimization problem is difficult to solve directly, we develop an iterative algorithm to obtain a locally optimal solution. Finally, numerical results show that the target estimation error obtained by the trade-off approach is about an order of magnitude better than a communication-only approach with a slight decrease on communication performance.
5G for Railways: the Next Generation Railway Dedicated Communications
Authors: Ruisi He, Bo Ai, Zhangdui Zhong, Mi Yang, Ruifeng Chen, Jianwen Ding, Zhangfeng Ma, Guiqi Sun, Changzhu Liu
Date: 2022-07-07 00:00:00

To overcome increasing traffic, provide various new services, further ensure safety and security, significantly improve travel comfort, a new communication system for railways is required. Since 2019, public networks have been evolving to the fifth generation communication (5G) worldwide, whereas the main communication system of railway is still based on the second generation communication (2G). It is thus necessary for railways to replace the current 2G-based technology with the next generation railway dedicated communication system with improved capacity and capability, and the 5G for railways (5G-R) technology is a promising solution for further intelligent railways. This article gives a review of the current developments of the next generation railway communications, followed by a discussion of the typical services that the 5G-R can provide to intelligent railways. Then, main application scenarios of 5G-R are summarized and system configurations are compared. Some key technologies of 5G-R such as network architecture, massive MIMO, millimeter-wave, multiple access scheme, ultra-reliable low latency communication, and advanced video processing are presented and analyzed. Finally, some challenges of 5G-R are highlighted.
Data-driven State of Risk Prediction and Mitigation in Support of the Net-zero Carbon Electric Grid
Authors: Mladen Kezunovic, Rashid Baembitov, Mohammad Khoshjahan
Date: 2022-07-07 00:00:00

An approach for reaching the net-zero carbon electricity grid is to intensify the deployment of distributed renewable generation resources such as photovoltaic (PV) solar and wind generation, complemented with stationary and mobile (electric vehicle) battery energy storage systems (BESS). This paper assumes a scenario where the PV renewable generation and BESS are integrated into a nano-Grid (n-Grid), a prosumer-owned virtual power plant installed at the residential or commercial sites. To be profitable, this distributed energy resource (DER) needs to be managed effectively to support its own load and the wholesale and retail market services. Our paper introduces a risk-based, data-driven approach focused on predicting the State of Risk (SoR) of the utility grid outages. The SoR prediction enables the development of optimal mitigation strategies aiming at reducing the impact of the grid outages by harvesting the n-Grid flexibility. Several data analytics tools to assist the n-Grid operators and aggregators for n-Grid participation in the wholesale market ancillary service products are introduced, and some preliminary implementation results are demonstrated.
Linearized Physics-Based Lithium-Ion Battery Model for Power System Economic Studies
Authors: Anton V. Vykhodtsev, Darren Jang, Qianpu Wang, William Rosehart, Hamidreza Zareipour
Date: 2022-07-07 00:00:00

This paper proposes the linearized physics-based model of a lithium-ion battery that can be incorporated into the optimization framework for power system economic studies. The proposed model is a linear approximation of the single particle model and it allows to characterize dynamics of the physical processes inside the battery that impact the battery operation. There is a need for such model as a simplistic power-energy model that is widely employed in operation and planning studies with the lithium-ion battery energy storage system (LIBESS) results in infeasible operation and misleading economic assessment. The proposed linearized model is computationally beneficial compared with a recently used nonlinear physics-based model. The energy arbitrage application is used to assess the advantages of the proposed model over a simple power-energy model.
On Aggregation Performance in Privacy Conscious Hierarchical Flexibility Coordination Schemes
Authors: Thomas Offergeld, Nils Mattus, Florian Schmidtke, Andreas Ulbig
Date: 2022-07-07 00:00:00
Tasks: Privacy Preserving

In this paper we introduce a method for performance quantification of flexibility aggregation in flexibility coordination schemes (FCS), with a focus on privacy preserving hierarchical FCS. The quantification is based on two performance metrics: The aggregation error and the aggregation efficiency. We present the simulation framework and the modelling of one complex type of flexibility providing units (FPUs), namely energy storage systems (ESS). ESS cause intertemporal constraints for flexibility coordination that lead to aggregation errors in case flexibility is aggregated from heterogeneous groups of FPUs. We identify one parameter responsible for the aggregation error to be the power-to-energy ratio of the ESS. A grouping of FPUs using similarity in their power-to-energy ratios is shown to improve the coordination performance. Additionally, we describe the influence of flexibility demand timeseries on the aggregation error, concluding that future assessments of aggregation errors should consider multiple representative demand timeseries, which is a non-trivial task. Finally, we discuss the applicability of the developed method to scenarios of larger system size.
Inertia Adequacy in Transient Stability Models for Synthetic Electric Grids
Authors: Adam B. Birchfield
Date: 2022-07-07 00:00:00

If a disturbance rocks a low-inertia power system, the frequency decline may be too rapid to arrest before it triggers undesirable responses from generators and loads. In the worst case, this instability could lead to blackout and major equipment damage. Electric utilities, to combat this, study inertia adequacy in systems that are particularly vulnerable. This process, involving detailed transient simulations, usually leads to a notion of a system-wide inertia floor. Ongoing questions in this analysis are in how to set the inertial floor and to what extent the location of frequency control resources matters. This paper proposes a new analysis technique that quantifies theoretical locational rate of change of frequency (ROCOF) as a computationally efficient screening algorithm scalable to large systems. An additional challenge in moving this area forward is the lack of high-quality, public benchmark dynamics cases. This paper presents a synthetic case for such purposes and a methodology for validation, to ensure that it is well suited to inertia adequacy studies to improve electric grid performance.
Benefits and Challenges of Dynamic Modelling of Cascading Failures in Power Systems
Authors: Yitian Dai, Robin Preece, Mathaios Panteli
Date: 2022-07-07 00:00:00
Tasks: Benchmarking

Time-based dynamic models of cascading failures have been recognized as one of the most comprehensive methods of representing detailed cascading information and are often used for benchmarking and validation. This paper provides an overview of the progress in the field of dynamic analysis of cascading failures in power systems and outlines the benefits and challenges of dynamic simulations in future grids. The benefits include the ability to capture temporal characteristics of system dynamics and provide timing information to facilitate control actions for blackout mitigation. The greatest barriers to dynamic modelling of cascading failures are the computational burden, and the extensive but often unavailable data requirements for dynamic representation of a power system. These factors are discussed in detail in this paper and the need for in-depth research into dynamic modelling of cascading failures is highlighted. Furthermore, case studies of dynamic cascading simulation of 200-bus and 2000-bus benchmark systems provide initial guidance for the selection of critical parameters to enhance simulation efficiency. Finally, cross-validation and comparison against a quasi-steady state DC power flow model is performed, with various metrics compared.
Reinforcement Learning for Distributed Transient Frequency Control with Stability and Safety Guarantees
Authors: Zhenyi Yuan, Changhong Zhao, Jorge Cortes
Date: 2022-07-07 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

This paper proposes a reinforcement learning-based approach for optimal transient frequency control in power systems with stability and safety guarantees. Building on Lyapunov stability theory and safety-critical control, we derive sufficient conditions on the distributed controller design that ensure the stability and transient frequency safety of the closed-loop system. Our idea of distributed dynamic budget assignment makes these conditions less conservative than those in recent literature, so that they can impose less stringent restrictions on the search space of control policies. We construct neural network controllers that parameterize such control policies and use reinforcement learning to train an optimal one. Simulations on the IEEE 39-bus network illustrate the guaranteed stability and safety properties of the controller along with its significantly improved optimality.
State Prediction of Human-in-the-Loop Multi-rotor System with Stochastic Human Behavior Model
Authors: Joonwon Choi, Sooyung Byeon, Inseok Hwang
Date: 2022-07-07 00:00:00

Reachability analysis is a widely used method to analyze the safety of a Human-in-the-Loop Cyber Physical System (HiLCPS). This strategy allows the HiLCPS to respond against an imminent threat in advance by predicting reachable states of the system. However, it could lead to an unnecessarily conservative reachable set if the prediction only relies on the system dynamics without explicitly considering human behavior, and thus the risk might be overestimated. To reduce the conservativeness of the reachability analysis, we present a state prediction method which takes into account a stochastic human behavior model represented as a Gaussian Mixture Model (GMM). In this paper, we focus on the multi-rotor in a near-collision situation. The stochastic human behavior model is trained using experimental data to represent human operators' evasive maneuver. Then, we can retrieve a human control input probability distribution from the trained stochastic human behavior model using the Gaussian Mixture Regression (GMR). The proposed algorithm predicts the probability distribution of the multi-rotor's future state based on the given dynamics and the retrieved human control input probability distribution. Besides, the proposed state prediction method considers the uncertainty of the initial state modeled as a GMM, which yields more robust performance. Human subject experiment results are provided to demonstrate the effectiveness of the proposed algorithm.
Whole-System First-Swing Stability of Inverter-Based Inertia-Free Power Systems
Authors: Yitong Li, Yunjie Gu
Date: 2022-07-07 00:00:00

The emphasis on inertia for system stability has been a long-held tradition in conventional grids. The fast and flexible controllability of inverters opens up new possibilities. This paper investigates the {first-swing stability} of inverter-based inertia-free power systems. We illustrate that by replacing inertia with fast primary control (i.e. inertia-free), the first-swing stability region is greatly extended compared to the classic equal area criterion of inertia-rich systems. The extended stability region is fully decentralised and ensures whole-system stability for all swing modes across all timescales and interaction boundaries in a complex grid. The removal of inertia has impacts on frequency stability but these impacts can be well mitigated. The findings of the paper are proved mathematically and verified by simulation on the IEEE 68-bus system.