pwc-archive/papers-with-abstracts huggingface dataset:
Multimodal Matching-aware Co-attention Networks with Mutual Knowledge Distillation for Fake News Detection
Authors: Linmei Hu, Ziwang Zhao, Weijian Qi, Xuemeng Song, Liqiang Nie
Date: 2022-12-12 00:00:00
Tasks: Fake News Detection, Image-text matching, Knowledge Distillation, Text Matching

Fake news often involves multimedia information such as text and image to mislead readers, proliferating and expanding its influence. Most existing fake news detection methods apply the co-attention mechanism to fuse multimodal features while ignoring the consistency of image and text in co-attention. In this paper, we propose multimodal matching-aware co-attention networks with mutual knowledge distillation for improving fake news detection. Specifically, we design an image-text matching-aware co-attention mechanism which captures the alignment of image and text for better multimodal fusion. The image-text matching representation can be obtained via a vision-language pre-trained model. Additionally, based on the designed image-text matching-aware co-attention mechanism, we propose to build two co-attention networks respectively centered on text and image for mutual knowledge distillation to improve fake news detection. Extensive experiments on three benchmark datasets demonstrate that our proposed model achieves state-of-the-art performance on multimodal fake news detection.
Untargeted Attack against Federated Recommendation Systems via Poisonous Item Embeddings and the Defense
Authors: Yang Yu, Qi Liu, Likang Wu, Runlong Yu, Sanshi Lei Yu, Zaixi Zhang
Date: 2022-12-11 00:00:00
Tasks: Contrastive Learning, Recommendation Systems

Federated recommendation (FedRec) can train personalized recommenders without collecting user data, but the decentralized nature makes it susceptible to poisoning attacks. Most previous studies focus on the targeted attack to promote certain items, while the untargeted attack that aims to degrade the overall performance of the FedRec system remains less explored. In fact, untargeted attacks can disrupt the user experience and bring severe financial loss to the service provider. However, existing untargeted attack methods are either inapplicable or ineffective against FedRec systems. In this paper, we delve into the untargeted attack and its defense for FedRec systems. (i) We propose ClusterAttack, a novel untargeted attack method. It uploads poisonous gradients that converge the item embeddings into several dense clusters, which make the recommender generate similar scores for these items in the same cluster and perturb the ranking order. (ii) We propose a uniformity-based defense mechanism (UNION) to protect FedRec systems from such attacks. We design a contrastive learning task that regularizes the item embeddings toward a uniform distribution. Then the server filters out these malicious gradients by estimating the uniformity of updated item embeddings. Experiments on two public datasets show that ClusterAttack can effectively degrade the performance of FedRec systems while circumventing many defense methods, and UNION can improve the resistance of the system against various untargeted attacks, including our ClusterAttack.
A Comparison of Audio Preprocessing Techniques and Deep Learning Algorithms for Raga Recognition
Authors: Devayani Hebbar, Vandana Jagtap
Date: 2022-12-10 00:00:00
Tasks: Audio Signal Processing, Information Retrieval, Music Information Retrieval, Retrieval

Ragas form the foundation for Indian Classical Music. The task of Raga Recognition has gained traction in the Music Information Retrieval community in the recent past, which can be attributed to the nuances of Indian Classical Music that have resulted in a plethora of research problems in Computing. In this work, we used two different digital audio signal processing techniques to preprocess audio samples of Carnatic classical ragas that were then processed by various Deep Learning models. Their results were compared in order to infer which DASP technique is better suited to the task of raga recognition. We obtained state of the art results, with our best model reaching a testing accuracy of 98.1%. We also compared each model ability to distinguish between similar ragas.
Learning from Students: Online Contrastive Distillation Network for General Continual Learning
Authors: Jin Li, Zhong Ji, Gang Wang, Qiang Wang, Feng Gao
Date: 2022-09-24 00:00:00
Proceeding: Conference 2022 9
Tasks: Continual Learning

The goal of General Continual Learning (GCL) is to preserve learned knowledge and learn new knowledge with constant memory from an infinite data stream where task boundaries are blurry. Distilling the model’s response of reserved samples between the old and the new models is an effective way to achieve promising performance on GCL. However, it accumulates the inherent old model’s response bias and is not robust to model changes. To this end, we propose an Online Contrastive Distillation Network (OCD-Net) to tackle these problems, which explores the merit of the student model in each time step to guide the training process of the teacher model. Concretely, the teacher model is devised to help the student model to consolidate the learned knowledge, which is trained online via integrating the parameters of the student model to accumulate the new knowledge. Moreover, our OCD-Net incorporates both relation and adaptive response to help the student model alleviate the catastrophic forgetting, which is also beneficial for the teacher model to preserve the learned knowledge. Extensive experiments on six benchmark datasets demonstrate that our OCD-Net significantly outperforms
state-of-the-art approaches in 3.03% ∼ 8.71% with various buffer sizes.
Towards Scene Understanding for Autonomous Operations on Airport Aprons
Authors: Daniel Steininger, Andreas Kriegler, Wolfgang Pointner, Verena Widhalm, Julia Simon, Oliver Zendel
Date: 2022-12-04 00:00:00
Proceeding: Asian Conference on Computer Vision (ACCV) Workshops 2022 12
Tasks: Autonomous Driving, Benchmarking, Domain Adaptation, Fine-Grained Image Classification, Object Detection, Robust Object Detection, Scene Understanding, Small Object Detection

Enhancing logistics vehicles on airport aprons with assistant and autonomous capabilities offers the potential to significantly increase
safety and efficiency of operations. However, this research area is still underrepresented compared to other automotive domains, especially regarding available image data, which is essential for training and benchmarking AI-based approaches. To mitigate this gap, we introduce a novel dataset specialized on static and dynamic objects commonly encountered while navigating apron areas. We propose an efficient approach for image acquisition as well as annotation of object instances and environmental
parameters. Furthermore, we derive multiple dataset variants on which we conduct baseline classification and detection experiments. The resulting models are evaluated with respect to their overall performance and robustness against specific environmental conditions. The results are quite promising for future applications and provide essential insights regarding the selection of aggregation strategies as well as current potentials and limitations of similar approaches in this research domain.
Blockchain Network Analysis: A Comparative Study of Decentralized Banks
Authors: Yufan Zhang, Zichao Chen, Yutong Sun, Yulin Liu, Luyao Zhang
Date: 2022-12-11 00:00:00

Decentralized finance (DeFi) is known for its unique mechanism design, which applies smart contracts to facilitate peer-to-peer transactions. The decentralized bank is a typical DeFi application. Ideally, a decentralized bank should be decentralized in the transaction. However, many recent studies have found that decentralized banks have not achieved a significant degree of decentralization. This research conducts a comparative study among mainstream decentralized banks. We apply core-periphery network features analysis using the transaction data from four decentralized banks, Liquity, Aave, MakerDao, and Compound. We extract six features and compare the banks' levels of decentralization cross-sectionally. According to the analysis results, we find that: 1) MakerDao and Compound are more decentralized in the transactions than Aave and Liquity. 2) Although decentralized banking transactions are supposed to be decentralized, the data show that four banks have primary external transaction core addresses such as Huobi, Coinbase, and Binance, etc. We also discuss four design features that might affect network decentralization. Our research contributes to the literature at the interface of decentralized finance, financial technology (Fintech), and social network analysis and inspires future protocol designs to live up to the promise of decentralized finance for a truly peer-to-peer transaction network.
On Blockchain We Cooperate: An Evolutionary Game Perspective
Authors: Luyao Zhang, Xinyu Tian
Date: 2022-12-10 00:00:00

Cooperation is fundamental for human prosperity. Blockchain, as a trust machine, is a cooperative institution in cyberspace that supports cooperation through distributed trust with consensus protocols. While studies in computer science focus on fault tolerance problems with consensus algorithms, economic research utilizes incentive designs to analyze agent behaviors. To achieve cooperation on blockchains, emerging interdisciplinary research introduces rationality and game-theoretical solution concepts to study the equilibrium outcomes of various consensus protocols. However, existing studies do not consider the possibility for agents to learn from historical observations. Therefore, we abstract a general consensus protocol as a dynamic game environment, apply a solution concept of bounded rationality to model agent behavior, and resolve the initial conditions for three different stable equilibria. In our game, agents imitatively learn the global history in an evolutionary process toward equilibria, for which we evaluate the outcomes from both computing and economic perspectives in terms of safety, liveness, validity, and social welfare. Our research contributes to the literature across disciplines, including distributed consensus in computer science, game theory in economics on blockchain consensus, evolutionary game theory at the intersection of biology and economics, bounded rationality at the interplay between psychology and economics, and cooperative AI with joint insights into computing and social science. Finally, we discuss that future protocol design can better achieve the most desired outcomes of our honest stable equilibria by increasing the reward-punishment ratio and lowering both the cost-punishment ratio and the pivotality rate.
Changes in Power and Information Flow in Resting-state EEG by Working Memory Process
Authors: Gi-Hwan Shin, Young-Seok Kweon, Heon-Gyu Kwak
Date: 2022-12-12 00:00:00
Tasks: EEG, Electroencephalogram (EEG), Retrieval

Many studies have analyzed working memory (WM) from electroencephalogram (EEG). However, little is known about changes in the brain neurodynamics among resting-state (RS) according to the WM process. Here, we identified frequency-specific power and information flow patterns among three RS EEG before and after WM encoding and WM retrieval. Our results demonstrated the difference in power and information flow among RS EEG in delta (1-3.5 Hz), alpha (8-13.5 Hz), and beta (14-29.5 Hz) bands. In particular, there was a marked increase in the alpha band after WM retrieval. In addition, we calculated the association between significant characteristics of RS EEG and WM performance, and interestingly, correlations were found only in the alpha band. These results suggest that RS EEG according to the WM process has a significant impact on the variability and WM performance of brain mechanisms in relation to cognitive function.
Throughput analysis of RIS-assisted UAV wireless systems under disorientation and misalignment
Authors: Alexandros-Apostolos A. Boulogeorgos, Angeliki Alexiou, Marco Di Renzo
Date: 2022-12-10 00:00:00

Reconfigurable intelligent surface (RIS)-assisted unmanned areal vehicles (UAV) communications have been identified as a key enabler of a number of next-generation applications. However, to the best of our knowledge, there is no generalized framework for the quantification of the throughput performance of RIS-assisted UAV systems. Motivated by this, in this paper, we present a comprehensive system model that accounts for the impact of multipath fading, which is modeled by means of mixture gamma, transceiver hardware imperfections, and stochastic beam disorientation and misalignment in order to examine the throughput performance of a RIS-assisted UAV wireless system. In this direction, we present a novel closed-form expression for the system's throughput for two scenarios: i) in the presence and ii) in the absence of disorientation and misalignment. Interestingly, our results reveal the importance of accurate modeling of the aforementioned phenomena as well as the existence of an optimal transmission spectral efficiency.
GPU-accelerated Guided Source Separation for Meeting Transcription
Authors: Desh Raj, Daniel Povey, Sanjeev Khudanpur
Date: 2022-12-10 00:00:00
Tasks: blind source separation, CPU, GPU, Speech Recognition, Target Speaker Extraction

Guided source separation (GSS) is a type of target-speaker extraction method that relies on pre-computed speaker activities and blind source separation to perform front-end enhancement of overlapped speech signals. It was first proposed during the CHiME-5 challenge and provided significant improvements over the delay-and-sum beamforming baseline. Despite its strengths, however, the method has seen limited adoption for meeting transcription benchmarks primarily due to its high computation time. In this paper, we describe our improved implementation of GSS that leverages the power of modern GPU-based pipelines, including batched processing of frequencies and segments, to provide 300x speed-up over CPU-based inference. The improved inference time allows us to perform detailed ablation studies over several parameters of the GSS algorithm -- such as context duration, number of channels, and noise class, to name a few. We provide end-to-end reproducible pipelines for speaker-attributed transcription of popular meeting benchmarks: LibriCSS, AMI, and AliMeeting. Our code and recipes are publicly available: https://github.com/desh2608/gss.
A machine learning approach to support decision in insider trading detection
Authors: Piero Mazzarisi, Adele Ravagnani, Paola Deriu, Fabrizio Lillo, Francesca Medda, Antonio Russo
Date: 2022-12-06 00:00:00

Identifying market abuse activity from data on investors' trading activity is very challenging both for the data volume and for the low signal to noise ratio. Here we propose two complementary unsupervised machine learning methods to support market surveillance aimed at identifying potential insider trading activities. The first one uses clustering to identify, in the vicinity of a price sensitive event such as a takeover bid, discontinuities in the trading activity of an investor with respect to his/her own past trading history and on the present trading activity of his/her peers. The second unsupervised approach aims at identifying (small) groups of investors that act coherently around price sensitive events, pointing to potential insider rings, i.e. a group of synchronised traders displaying strong directional trading in rewarding position in a period before the price sensitive event. As a case study, we apply our methods to investor resolved data of Italian stocks around takeover bids.
On the Age-Optimality of Relax-then-Truncate Approach under Partial Battery Knowledge in Energy Harvesting IoT Networks
Authors: Mohammad Hatami, Markus Leinonen, Marian Codreanu
Date: 2022-12-12 00:00:00

We consider an energy harvesting (EH) IoT network, where users make on-demand requests to a cache-enabled edge node to send status updates about various random processes, each monitored by an EH sensor. The edge node serves users' requests by either commanding the corresponding sensor to send a fresh status update or retrieving the most recently received measurement from the cache. We aim to find a control policy at the edge node that minimizes the average on-demand AoI over all sensors subject to per-slot transmission and energy constraints under partial battery knowledge at the edge node. Namely, the limited radio resources (e.g., bandwidth) causes that only a limited number of sensors can send status updates at each time slot (i.e., per-slot transmission constraint) and the scarcity of energy for the EH sensors imposes an energy constraint. Besides, the edge node is informed of the sensors' battery levels only via received status update packets, leading to uncertainty about the battery levels for the decision-making.We develop a low-complexity algorithm -- termed relax-then-truncate -- and prove that it is asymptotically optimal as the number of sensors goes to infinity. Numerical results illustrate that the proposed method achieves significant gains over a request-aware greedy policy and show that it has near-optimal performance even for moderate numbers of sensors.
Energy dissipation and hysteresis cycles in pre-sliding transients of kinetic friction
Authors: Michael Ruderman
Date: 2022-12-12 00:00:00
Tasks: Friction

The problem of transient hysteresis cycles induced by the pre-sliding kinetic friction is relevant for analyzing the system dynamics e.g. of micro- and nano-positioning instruments and devices and their controlled operation. The associated energy dissipation and consequent convergence of the state trajectories occur due to the structural hysteresis damping of contact surface asperities during reversals, and it is neither exponential (i.e. viscous type) nor finite-time (i.e. Coulomb type). In this paper, we discuss the energy dissipation and convergence during the pre-sliding cycles and show how a piecewise smooth force-displacement hysteresis map enters into the energy balance of an unforced system of the second order. An existing friction modeling approach with a low number of the free parameters, the Dahl model, is then exemplified alongside the developed analysis.
Process Optimization of Black Soldier Fly Egg Production via Model Based Control
Authors: Alexander Kobelski, Arne-Jens Hempel, Murali Padmanabha, Luiz-Carlos Wille, Stefan Streif
Date: 2022-12-12 00:00:00

Black soldier fly (BSF) larvae (\textit{Hermetia illucens}) are a valuable protein source for manufacturing animal feed. To maximize their production, both the quantity and quality of their reproductive cycle, i.e. egg production during oviposition, must be increased. In artificial environments, flies often sit idle in cages without mating, depleting their energy reserves and resulting in lower egg production per female. By controlling environmental conditions such as temperature and light inside breeding cages, the flies may be stimulated in a way that improves egg output. However, this stimulation increases the energy demand of the process and may stress the flies, resulting in reduced egg production. Therefore, control must be applied in a careful way, which requires knowledge of the egg production cycle. In this work, a mathematical model describing the various fly life stages and their transition to the egg production process is developed. Relevant factors are identified and their effect on the fly life and egg production is mathematically described. Parameters are identified using data from literature and goodness of fit is evaluated. Using the model, an optimal control problem is formulated with the goal of minimizing the energy costs and increasing the egg production quantity. In Simulation, our approach showed 13\% higher output in shorter time at reduced energy costs compared to a (standard) constant setpoint approach. Optimal control could reach same amount of egg in 60~\% of time compared to standard scenario.
Integrated optimization of train timetables rescheduling and response vehicles on a disrupted metro line
Authors: Hui Wang, Jialin Liu, Feng Li, Hao Ji, Bin Jia, Ziyou Gao
Date: 2022-12-12 00:00:00

When an unexpected metro disruption occurs, metro managers need to reschedule timetables to avoid trains going into the disruption area, and transport passengers stranded at disruption stations as quickly as possible. This paper proposes a two-stage optimization model to jointly make decisions for two tasks. In the first stage, the timetable rescheduling problem with cancellation and short-turning strategies is formulated as a mixed integer linear programming (MILP). In particular, the instantaneous parameters and variables are used to describe the accumulation of time-varying passenger flow. In the second one, a system-optimal dynamic traffic assignment (SODTA) model is employed to dynamically schedule response vehicles, which is able to capture the dynamic traffic and congestion. Numerical cases of Beijing Metro Line 9 verify the efficiency and effectiveness of our proposed model, and results show that: (1) when occurring a disruption event during peak hours, the impact on the normal timetable is greater, and passengers in the direction with fewer train services are more affected; (2) if passengers stranded at the terminal stations of disruption area are not transported in time, they will rapidly increase at a speed of more than 300 passengers per minute; (3) compared with the fixed shortest path, using the response vehicles reduces the total travel time about 7%. However, it results in increased travel time for some passengers.
Multi-Objective Vector Control of a Three-Phase Vibratory Energy Harvester
Authors: Connor H. Ligeikis, Jeffrey T. Scruggs
Date: 2022-12-12 00:00:00

In vibration energy harvesting technologies, feedback control is required to maximize the average power generated from stochastic disturbances. In large-scale applications it is often advantageous to use three-phase conversion technologies for transduction. In such situations, vector control techniques can be used to optimally control the transducer currents in the direct-quadrature reference frame, as dynamic functions of feedback measurements. In this paradigm, converted energy is optimally controlled via the quadrature current. The direct current is only used to maintain control of the quadrature current when the machine's internal back-EMF exceeds the voltage of the power bus, a technique called field weakening. Due to increased dissipation in the stator coil, the use of field weakening results in a reduction in power conversion, relative to what would theoretically be possible with a larger bus voltage. This over-voltage issue can be alternatively addressed by imposing a competing objective in the optimization of the quadrature current controller, such that the frequency and duration of these over-voltage events are reduced. However, this also results in reduced generated power, due to the need to satisfy the competing constraint. This paper examines the tradeoff between these two approaches to over-voltage compensation, and illustrates a methodology for determining the optimum balance between the two approaches.