pwc-archive/papers-with-abstracts huggingface dataset:
Interaction-level Membership Inference Attack Against Federated Recommender Systems
Authors: Wei Yuan, Chaoqun Yang, Quoc Viet Hung Nguyen, Lizhen Cui, Tieke He, Hongzhi Yin
Date: 2023-01-26 00:00:00
Tasks: Attribute, Federated Learning, Inference Attack, Membership Inference Attack, Recommendation Systems

The marriage of federated learning and recommender system (FedRec) has been widely used to address the growing data privacy concerns in personalized recommendation services. In FedRecs, users' attribute information and behavior data (i.e., user-item interaction data) are kept locally on their personal devices, therefore, it is considered a fairly secure approach to protect user privacy. As a result, the privacy issue of FedRecs is rarely explored. Unfortunately, several recent studies reveal that FedRecs are vulnerable to user attribute inference attacks, highlighting the privacy concerns of FedRecs. In this paper, we further investigate the privacy problem of user behavior data (i.e., user-item interactions) in FedRecs. Specifically, we perform the first systematic study on interaction-level membership inference attacks on FedRecs. An interaction-level membership inference attacker is first designed, and then the classical privacy protection mechanism, Local Differential Privacy (LDP), is adopted to defend against the membership inference attack. Unfortunately, the empirical analysis shows that LDP is not effective against such new attacks unless the recommendation performance is largely compromised. To mitigate the interaction-level membership attack threats, we design a simple yet effective defense method to significantly reduce the attacker's inference accuracy without losing recommendation performance. Extensive experiments are conducted with two widely used FedRecs (Fed-NCF and Fed-LightGCN) on three real-world recommendation datasets (MovieLens-100K, Steam-200K, and Amazon Cell Phone), and the experimental results show the effectiveness of our solutions.
Evolution of Filter Bubbles and Polarization in News Recommendation
Authors: Han Zhang, Ziwei Zhu, James Caverlee
Date: 2023-01-26 00:00:00
Tasks: Articles, News Recommendation, Recommendation Systems

Recent work in news recommendation has demonstrated that recommenders can over-expose users to articles that support their pre-existing opinions. However, most existing work focuses on a static setting or over a short-time window, leaving open questions about the long-term and dynamic impacts of news recommendations. In this paper, we explore these dynamic impacts through a systematic study of three research questions: 1) How do the news reading behaviors of users change after repeated long-term interactions with recommenders? 2) How do the inherent preferences of users change over time in such a dynamic recommender system? 3) Can the existing SOTA static method alleviate the problem in the dynamic environment? Concretely, we conduct a comprehensive data-driven study through simulation experiments of political polarization in news recommendations based on 40,000 annotated news articles. We find that users are rapidly exposed to more extreme content as the recommender evolves. We also find that a calibration-based intervention can slow down this polarization, but leaves open significant opportunities for future improvements
Optimizing Feature Set for Click-Through Rate Prediction
Authors: Fuyuan Lyu, Xing Tang, Dugang Liu, Liang Chen, Xiuqiang He, Xue Liu
Date: 2023-01-26 00:00:00
Tasks: Click-Through Rate Prediction, Prediction

Click-through prediction (CTR) models transform features into latent vectors and enumerate possible feature interactions to improve performance based on the input feature set. Therefore, when selecting an optimal feature set, we should consider the influence of both feature and its interaction. However, most previous works focus on either feature field selection or only select feature interaction based on the fixed feature set to produce the feature set. The former restricts search space to the feature field, which is too coarse to determine subtle features. They also do not filter useless feature interactions, leading to higher computation costs and degraded model performance. The latter identifies useful feature interaction from all available features, resulting in many redundant features in the feature set. In this paper, we propose a novel method named OptFS to address these problems. To unify the selection of feature and its interaction, we decompose the selection of each feature interaction into the selection of two correlated features. Such a decomposition makes the model end-to-end trainable given various feature interaction operations. By adopting feature-level search space, we set a learnable gate to determine whether each feature should be within the feature set. Because of the large-scale search space, we develop a learning-by-continuation training scheme to learn such gates. Hence, OptFS generates the feature set only containing features which improve the final prediction results. Experimentally, we evaluate OptFS on three public datasets, demonstrating OptFS can optimize feature sets which enhance the model performance and further reduce both the storage and computational cost.
mmFlexible: Flexible Directional Frequency Multiplexing for Multi-user mmWave Networks
Authors: Ish Kumar Jain, Rohith Reddy Vennam, Raghav Subbaraman, Dinesh Bharadia
Date: 2023-01-26 00:00:00

Modern mmWave systems have limited scalability due to inflexibility in performing frequency multiplexing. All the frequency components in the signal are beamformed to one direction via pencil beams and cannot be streamed to other user directions. We present a new flexible mmWave system called mmFlexible that enables flexible directional frequency multiplexing, where different frequency components of the mmWave signal are beamformed in multiple arbitrary directions with the same pencil beam. Our system makes two key contributions: (1) We propose a novel mmWave front-end architecture called a delay-phased array that uses a variable delay and variable phase element to create the desired frequency-direction response. (2) We propose a novel algorithm called FSDA (Frequency-space to delay-antenna) to estimate delay and phase values for the real-time operation of the delay-phased array. Through evaluations with mmWave channel traces, we show that mmFlexible provides a 60-150% reduction in worst-case latency compared to baselines
A Framework of Transaction Packaging in High-throughput Blockchains
Authors: Yuxuan Lu, Qian Qi, Xi Chen
Date: 2023-01-26 00:00:00
Tasks: Vocal Bursts Intensity Prediction

We develop a model of coordination and allocation of decentralized multi-sided markets, in which our theoretical analysis is promisingly optimizing the decentralized transaction packaging process at high-throughput blockchains or Web 3.0 platforms. In contrast to the stylized centralized platform, the decentralized platform is powered by blockchain technology, which allows for secure and transparent Peer-to-Peer transactions among users. Traditional single-chain-based blockchains suffer from the well-known blockchain trilemma. Beyond the single-chain-based scheme, decentralized high-throughput blockchains adopt parallel protocols to reconcile the blockchain trilemma, implementing any tasking and desired allocation. However, unneglectable network latency may induce partial observability, resulting in incoordination and misallocation issues for the decentralized transaction packaging process at the current high-throughput blockchain protocols. To address this problem, we consider a strategic coordination mechanism for the decentralized transaction packaging process by using a game-theoretic approach. Under a tractable two-period model, we find a Bayesian Nash equilibrium of the miner's strategic transaction packaging under partial observability. Along with novel algorithms for computing equilibrium payoffs, we show that the decentralized platform can achieve an efficient and stable market outcome. The model also highlights that the proposed mechanism can endogenously offer a base fee per gas without any restructuration of the initial blockchain transaction fee mechanism. The theoretical results that underlie the algorithms also imply bounds on the computational complexity of equilibrium payoffs.
The Hazards and Benefits of Condescension in Social Learning
Authors: Itai Arieli, Yakov Babichenko, Stephan Müller, Farzad Pourbabaee, Omer Tamuz
Date: 2023-01-26 00:00:00

In a misspecified social learning setting, agents are condescending if they perceive their peers as having private information that is of lower quality than it is in reality. Applying this to a standard sequential model, we show that outcomes improve when agents are mildly condescending. In contrast, too much condescension leads to worse outcomes, as does anti-condescension.
Cluster Forming of Multiagent Systems in Rolling Horizon Games with Non-uniform Horizons
Authors: Yurid Nugraha, Ahmet Cetinkaya, Tomohisa Hayakawa, Hideaki Ishii, Quanyan Zhu
Date: 2023-01-26 00:00:00

Consensus and cluster forming of multiagent systems in the face of jamming attacks along with reactive recovery actions by a defender are discussed. The attacker is capable to disable some of the edges of the network with the objective to divide the agents into a smaller size of clusters while, in response, the defender recovers some of the edges by increasing the transmission power. We consider repeated games where the resulting optimal strategies for the two players are derived in a rolling horizon fashion. The attacker and the defender possess different computational abilities to calculate their strategies. This aspect is represented by the non-uniform values of the horizon lengths and the game periods. Theoretical and simulation based results demonstrate the effects of the horizon lengths and the game periods on the agents' states.
Flex-Net: A Graph Neural Network Approach to Resource Management in Flexible Duplex Networks
Authors: Tharaka Perera, Saman Atapattu, Yuting Fang, Prathapasinghe Dharmawansa, Jamie Evans
Date: 2023-01-20 00:00:00
Tasks: Combinatorial Optimization, Graph Neural Network, Management, Scheduling

Flexible duplex networks allow users to dynamically employ uplink and downlink channels without static time scheduling, thereby utilizing the network resources efficiently. This work investigates the sum-rate maximization of flexible duplex networks. In particular, we consider a network with pairwise-fixed communication links. Corresponding combinatorial optimization is a non-deterministic polynomial (NP)-hard without a closed-form solution. In this respect, the existing heuristics entail high computational complexity, raising a scalability issue in large networks. Motivated by the recent success of Graph Neural Networks (GNNs) in solving NP-hard wireless resource management problems, we propose a novel GNN architecture, named Flex-Net, to jointly optimize the communication direction and transmission power. The proposed GNN produces near-optimal performance meanwhile maintaining a low computational complexity compared to the most commonly used techniques. Furthermore, our numerical results shed light on the advantages of using GNNs in terms of sample complexity, scalability, and generalization capability.
A simple model for pink noise from amplitude modulations
Authors: Masahiro Morikawa, Akika Nakamichi
Date: 2023-01-26 00:00:00

We propose a simple model for the origin of pink noise (or 1/f fluctuation) based on the beat of cooperative waves. These cooperative waves arise spontaneously in a system with synchronization, resonance, and infrared divergence. Many cooperative waves with close frequencies can produce signals of arbitrary small frequencies from a system of small size. This beat mechanism can be understood as amplitude modulation. The pink noise can appear after the demodulation process, which produces a variety of pink noise in many fields. The pink noise thus formed from the beat has nothing to do with dissipation or long-time memory. We also suggest new ways of looking at pink noise in shallow earthquakes, solar flares, and stellar activities.
Classification of vertices on social networks by multiple approaches
Authors: Hacı İsmail Aslan, Chang Choi, Hoon Ko
Date: 2023-01-13 00:00:00
Tasks: Classification, Graph Neural Network

Due to the advent of the expressions of data other than tabular formats, the topological compositions which make samples interrelated came into prominence. Analogically, those networks can be interpreted as social connections, dataflow maps, citation influence graphs, protein bindings, etc. However, in the case of social networks, it is highly crucial to evaluate the labels of discrete communities. The reason underneath for such a study is the non-negligible importance of analyzing graph networks to partition the vertices by using the topological features of network graphs, solely. For each of these interaction-based entities, a social graph, a mailing dataset, and two citation sets are selected as the testbench repositories. This paper, it was not only assessed the most valuable method but also determined how graph neural networks work and the need to improve against non-neural network approaches which are faster and computationally cost-effective. Also, this paper showed a limit to be excesses by prospective graph neural network variations by using the topological features of networks trialed.
Cost-benefit Analysis And Comparisons For Different Offshore Wind Energy Transmission Systems
Authors: Jesus Silva-Rodriguez, Jin Lu, Xingpeng Li
Date: 2023-01-26 00:00:00

This paper investigates how to efficiently and economically deliver offshore energy generated by offshore wind turbines to onshore. Both power cables and hydrogen energy facilities are analyzed. Each method is examined with the associated proposed optimal sizing model. A cost-benefit analysis is conducted, and these methods are compared under different scenarios. Three long-distance energy transmission methods are proposed to transmit offshore energy onshore, considering the capital/operation costs for related energy conversion and transmission facilities. The first method that deploys power cables only is a benchmark method. The other two methods utilize a hydrogen supercenter (HSC) and transmit offshore energy to and onshore substation through hydrogen pipelines. For the second method, electrolyzers are placed at offshore wind farms connected to HSC with low-pressure hydrogen pipelines. For the third method, offshore wind power is distributed to the HSC and then converted into hydrogen using electrolyzers placed at the HSC. An offshore scenario including three wind farms located in the Gulf of Mexico and one onshore substation is used as the base test case. Numerical simulations are conducted over a planning period of 30 years using the proposed optimization models for each method separately. Simulation results show that the proposed hydrogen-based methods can effectively transmit offshore energy to the onshore substation. The distances between the wind farms can influence the selection and configuration of the offshore wind energy system. Moreover, the proposed study analyzes the cost and benefits for various systems with different energy carriers. It can also suggest which method could be the best candidate for offshore energy delivery system under which scenario and demonstrate a promising future for offshore wind power generation.
On the Downlink SINR Meta Distribution of UAV-assisted Wireless Networks
Authors: Yujie Qin, Mustafa A. Kishk, Mohamed-Slim Alouini
Date: 2023-01-26 00:00:00

The meta distribution of the signal-to-interference-plus-noise ratio (SINR) provides fine-grained information about each link's performance in a wireless system and the reliability of the whole network. While the UAV-enabled network has been studied extensively, most of the works focus on the spatial average performance, such as coverage probability, while SINR meta distribution has received less attention. In this paper, we use the SINR meta distribution for the first time to systematically analyze the improvement and the influence of deploying UAVs on the reliability of a wireless network. We first derive the $b$-th moments of the conditional success probability of the UAV-enabled network and give the approximated expressions derived by Gil-Pelaez theorem and the beta approximation of the meta distribution. Our numerical results show that deploying UAVs in wireless networks in most cases can greatly improve the system reliability, which denotes the fraction of users achieving cellular coverage, especially for the spatially-clustered users. In addition, establishing LoS links is not always beneficial since it also increases the interference. For instance, with the increase of the SINR threshold, the system reliability of a high LoS probability environment decreases dramatically and it is even lower than a low LoS probability environment. We also show that in highrise urban areas, UAVs can help in establishing extremely reliable (very high SINR) links.
How flexible electrification can integrate fluctuating renewables
Authors: Leonard Göke, Jens Weibezahn, Mario Kendziorski
Date: 2023-01-26 00:00:00

To phase-out fossil fuels, energy systems must shift to renewable electricity as the main source of primary energy. In this paper, we analyze how electrification can support the integration of fluctuating renewables, like wind and PV, and mitigate the need for storage and thermal backup plants. Using a cost minimizing model for system planning, we find substantial benefits of electricity demand in heating, transport, and industry adapting to supply. In Germany, flexible demand halves the residual peak-load and the residual demand and reduces excess generation by 80%. Flexible operation of electrolyzers has the most significant impact accounting for 42% of the reduction in residual peak-load and 59% in residual demand. District heating networks and BEVs also provide substantial flexibility, while the contribution of space and process heating is negligible. The results are robust to restrictions on the expansion of the transmission grid.
A Multiclass Simulation-Based Dynamic Traffic Assignment Model for Mixed Traffic Flow of Connected and Autonomous Vehicles and Human-Driven Vehicles
Authors: Behzad Bamdad Mehrabani, Jakob Erdmann, Luca Sgambi, Seyedehsan Seyedabrishami, Maaike Snelder
Date: 2023-01-26 00:00:00
Tasks: Autonomous Vehicles

One of the potential capabilities of Connected and Autonomous Vehicles (CAVs) is that they can have different route choice behavior and driving behavior compared to human Driven Vehicles (HDVs). This will lead to mixed traffic flow with multiple classes of route choice behavior. Therefore, it is crucial to solve the multiclass Traffic Assignment Problem (TAP) in mixed traffic of CAVs and HDVs. Few studies have tried to solve this problem; however, most used analytical solutions, which are challenging to implement in real and large networks (especially in dynamic cases). Also, studies in implementing simulation-based methods have not considered all of CAVs' potential capabilities. On the other hand, several different (conflicting) assumptions are made about the CAV's route choice behavior in these studies. So, providing a tool that can solve the multiclass TAP of mixed traffic under different assumptions can help researchers to understand the impacts of CAVs better. To fill these gaps, this study provides an open-source solution framework of the multiclass simulation-based traffic assignment problem for mixed traffic of CAVs and HDVs. This model assumes that CAVs follow system optimal principles with rerouting capability, while HDVs follow user equilibrium principles. Moreover, this model can capture the impacts of CAVs on road capacity by considering distinct driving behavioral models in both micro and meso scales traffic simulation. This proposed model is tested in two case studies which shows that as the penetration rate of CAVs increases, the total travel time of all vehicles decreases.
Time-delay based output feedback control of fourth-order oscillatory systems
Authors: Michael Ruderman
Date: 2023-01-26 00:00:00

We consider a robust stabilization of the fourth-order oscillatory systems with non-collocated output sensing. Worth recalling is that the fourth-order systems are relatively common in mechatronics as soon as there are two-mass or more generally two-inertia dynamics with significant elasticities in the link. A novel yet simple control method is introduced based on the time-delayed output feedback. The delayed output feedback requires only the oscillation frequency to be known and allows for a robust control design that leads to cancelation of the resonance peak. We use the stability margins to justify the transfer characteristics and robustness of the time-delay control in frequency domain. The main advantage of the proposed method over the other possible lead-based loop-shaping strategies is that neither time derivatives of the noisy output nor the implementation of transfer functions with a numerator degree greater than zero are required to deploy the controller. This comes in favor of practical applications. An otherwise inherently instable proportional-integral (PI) feedback of the non-collocated output is shown to be stabilized by the proposed method. The control developed and associated analysis are also confirmed by the experimental results shown for the low damped two-mass oscillator system with uncertainties.
Structures of M-Invariant Dual Subspaces with Respect to a Boolean Network
Authors: Dongyao Bi, Lijun Zhang, Kuize Zhang, Shenggui Zhang
Date: 2023-01-26 00:00:00

This paper presents the following research findings on Boolean networks (BNs) and their dual subspaces.First, we establish a bijection between the dual subspaces of a BN and the partitions of its state set. Furthermore, we demonstrate that a dual subspace is $M$-invariant if and only if the associated partition is equitable (i.e., for every two cells of the partition, every two states in the former have the same number of out-neighbors in the latter) for the BN's state-transition graph (STG). Here $M$ represents the structure matrix of the BN.Based on the equitable graphic representation, we provide, for the first time, a complete structural characterization of the smallest $M$-invariant dual subspaces generated by a set of Boolean functions. Given a set of output functions, we prove that a BN is observable if and only if the partition corresponding to the smallest $M$-invariant dual subspace generated by this set of functions is trivial (i.e., all partition cells are singletons). Building upon our structural characterization, we also present a method for constructing output functions that render the BN observable.