pwc-archive/papers-with-abstracts huggingface dataset:
The Effect of Third Party Implementations on Reproducibility
Authors: Balázs Hidasi, Ádám Tibor Czapp
Date: 2023-07-27 00:00:00
Tasks: Recommendation Systems

Reproducibility of recommender systems research has come under scrutiny during recent years. Along with works focusing on repeating experiments with certain algorithms, the research community has also started discussing various aspects of evaluation and how these affect reproducibility. We add a novel angle to this discussion by examining how unofficial third-party implementations could benefit or hinder reproducibility. Besides giving a general overview, we thoroughly examine six third-party implementations of a popular recommender algorithm and compare them to the official version on five public datasets. In the light of our alarming findings we aim to draw the attention of the research community to this neglected aspect of reproducibility.
Widespread Flaws in Offline Evaluation of Recommender Systems
Authors: Balázs Hidasi, Ádám Tibor Czapp
Date: 2023-07-27 00:00:00
Tasks: Recommendation Systems

Even though offline evaluation is just an imperfect proxy of online performance -- due to the interactive nature of recommenders -- it will probably remain the primary way of evaluation in recommender systems research for the foreseeable future, since the proprietary nature of production recommenders prevents independent validation of A/B test setups and verification of online results. Therefore, it is imperative that offline evaluation setups are as realistic and as flawless as they can be. Unfortunately, evaluation flaws are quite common in recommender systems research nowadays, due to later works copying flawed evaluation setups from their predecessors without questioning their validity. In the hope of improving the quality of offline evaluation of recommender systems, we discuss four of these widespread flaws and why researchers should avoid them.
Integrating Offline Reinforcement Learning with Transformers for Sequential Recommendation
Authors: Xumei Xi, Yuke Zhao, Quan Liu, Liwen Ouyang, Yang Wu
Date: 2023-07-26 00:00:00
Tasks: Offline RL, reinforcement-learning, Reinforcement Learning, Sequential Recommendation

We consider the problem of sequential recommendation, where the current recommendation is made based on past interactions. This recommendation task requires efficient processing of the sequential data and aims to provide recommendations that maximize the long-term reward. To this end, we train a farsighted recommender by using an offline RL algorithm with the policy network in our model architecture that has been initialized from a pre-trained transformer model. The pre-trained model leverages the superb ability of the transformer to process sequential information. Compared to prior works that rely on online interaction via simulation, we focus on implementing a fully offline RL framework that is able to converge in a fast and stable way. Through extensive experiments on public datasets, we show that our method is robust across various recommendation regimes, including e-commerce and movie suggestions. Compared to state-of-the-art supervised learning algorithms, our algorithm yields recommendations of higher quality, demonstrating the clear advantage of combining RL and transformers.
Measuring Americanization: A Global Quantitative Study of Interest in American Topics on Wikipedia
Authors: Piotr Konieczny, Włodzimierz Lewoniewski
Date: 2023-07-26 00:00:00
Tasks: Articles

We conducted a global comparative analysis of the coverage of American topics in different language versions of Wikipedia, using over 90 million Wikidata items and 40 million Wikipedia articles in 58 languages. Our study aimed to investigate whether Americanization is more or less dominant in different regions and cultures and to determine whether interest in American topics is universal.
Cross-Domain Recommendation via Preference Propagation GraphNet
Authors: Cheng Zhao, Chenliang Li, Cong Fu
Date: 2019-12-01 00:00:00
Proceeding: Conference 2019 12
Tasks: Link Prediction, Transfer Learning

Recommendation can be framed as a graph link prediction task naturally. The user-item interaction graph built within a single domain often suffers from high sparsity. Thus, there has been a surge of approaches to alleviate the sparsity issue via cross-domain mutual augmentation. The SOTA cross-domain recommendation algorithms all try to bridge the gap via knowledge transfer in the latent space. We find there are mainly three problems in their formulations: 1) their knowledge transfer is unaware of the cross-domain graph structure. 2) their framework cannot capture high-order information propagation on the graph. 3) their cross-domain transfer formulations are generally more complicated to be optimized than
the unified methods. In this paper, we propose the Preference Propagation GraphNet (PPGN) to address the above problems. Specifically, we construct a Cross-Domain Preference Matrix (CDPM) to model the interactions of different domains as a whole. Through the propagation layer of PPGN, we try to capture how user preferences propagate in the graph. Consequently, a joint objective for different domains is defined, and we simplify the cross-domain recommendation into a unified multi-task model. Extensive experiments on two pairs of real-world datasets show PPGN outperforms the SOTA
algorithms significantly.
Towards Mitigating the Class-Imbalance Problem for Partial Label Learning
Authors: Jing Wang, Min-Ling Zhang
Date: 2018-07-19 00:00:00
Proceeding: 7 2018 7
Tasks: Partial Label Learning, valid

Partial label (PL) learning aims to induce a multi-class classifier from training examples where each of them is associated with a set of candidate labels, among which only one is valid. It is well-known that the problem of class-imbalance stands as a major factor affecting the generalization performance of multi-class classifier, and this problem becomes more pronounced as the ground-truth label of each PL training example is not directly accessible to the learning approach. To mitigate the negative influence of class-imbalance to partial label learning, a novel class-imbalance aware approach named Cimap is proposed by adapting over-sampling techniques for handling PL training examples. Firstly, for each PL training example, Cimap disambiguates its candidate label set by estimating the confidence of each class label being ground-truth one via weighted k-nearest neighbor aggregation. After that, the original PL training set is replenished for model induction by over-sampling existing PL training examples via manipulation of the disambiguation results. Extensive experiments on artificial as well as real-world PL data sets show that Cimap serves as an effective data-level approach to mitigate the class-imbalance problem for partial label learning.
A LLM Assisted Exploitation of AI-Guardian
Authors: Nicholas Carlini
Date: 2023-07-20 00:00:00
Tasks: Computer Security, Language Modeling, Language Modelling

Large language models (LLMs) are now highly capable at a diverse range of tasks. This paper studies whether or not GPT-4, one such LLM, is capable of assisting researchers in the field of adversarial machine learning. As a case study, we evaluate the robustness of AI-Guardian, a recent defense to adversarial examples published at IEEE S&P 2023, a top computer security conference. We completely break this defense: the proposed scheme does not increase robustness compared to an undefended baseline. We write none of the code to attack this model, and instead prompt GPT-4 to implement all attack algorithms following our instructions and guidance. This process was surprisingly effective and efficient, with the language model at times producing code from ambiguous instructions faster than the author of this paper could have done. We conclude by discussing (1) the warning signs present in the evaluation that suggested to us AI-Guardian would be broken, and (2) our experience with designing attacks and performing novel research using the most recent advances in language modeling.
Exploring the Bitcoin Mesoscale
Authors: Nicolò Vallarano, Tiziano Squartini, Claudio J. Tessone
Date: 2023-07-13 00:00:00

The open availability of the entire history of the Bitcoin transactions opens up the possibility to study this system at an unprecedented level of detail. This contribution is devoted to the analysis of the mesoscale structural properties of the Bitcoin User Network (BUN), across its entire history (i.e. from 2009 to 2017). What emerges from our analysis is that the BUN is characterized by a core-periphery structure a deeper analysis of which reveals a certain degree of bow-tieness (i.e. the presence of a Strongly-Connected Component, an IN- and an OUT-component together with some tendrils attached to the IN-component). Interestingly, the evolution of the BUN structural organization experiences fluctuations that seem to be correlated with the presence of bubbles, i.e. periods of price surge and decline observed throughout the entire Bitcoin history: our results, thus, further confirm the interplay between structural quantities and price movements observed in previous analyses.
Practical Implementation of RIS-Aided Spectrum Sensing: A Deep Learning-Based Solution
Authors: Sefa Kayraklık, Ibrahim Yildirim, Ertugrul Basar, Ibrahim Hokelek, Ali Gorcin
Date: 2023-07-27 00:00:00
Tasks: object-detection, Object Detection

This paper presents reconfigurable intelligent surface (RIS)-aided deep learning (DL)-based spectrum sensing for next-generation cognitive radios. To that end, the secondary user (SU) monitors the primary transmitter (PT) signal, where the RIS plays a pivotal role in increasing the strength of the PT signal at the SU. The spectrograms of the synthesized dataset, including the 4G LTE and 5G NR signals, are mapped to images utilized for training the state-of-art object detection approaches, namely Detectron2 and YOLOv7. By conducting extensive experiments using a real RIS prototype, we demonstrate that the RIS can consistently and significantly improve the performance of the DL detectors to identify the PT signal type along with its time and frequency utilization. This study also paves the way for optimizing spectrum utilization through RIS-assisted CR application in next-generation wireless communication systems.
The channel capacity of the ribosome
Authors: Daniel A. Inafuku, Kay L. Kirkpatrick, Onyema Osuagwu, Qier An, David A. Brewster, Mayisha Zeb Nakib
Date: 2023-07-27 00:00:00
Tasks: Translation, Unity

Translation is one of the most fundamental processes in the biological cell. Because of the central role that translation plays across all domains of life, the enzyme that carries out this process, the ribosome, is required to process information with high accuracy. This accuracy often approaches values near unity experimentally. In this paper, we model the ribosome as an information channel and demonstrate mathematically that this biological machine has information-processing capabilities that have not been recognized previously. In particular, we calculate bounds on the ribosome's theoretical Shannon capacity and numerically approximate this capacity. Finally, by incorporating estimates on the ribosome's operation time, we show that the ribosome operates at speeds safely below its capacity, allowing the ribosome to process information with an arbitrary degree of error. Our results show that the ribosome achieves a high accuracy in line with purely information-theoretic means.
Modeling Interference for the Coexistence of 6G Networks and Passive Sensing Systems
Authors: Paolo Testolina, Michele Polese, Josep M. Jornet, Tommaso Melodia, Michele Zorzi
Date: 2023-07-27 00:00:00
Tasks: Astronomy

Future wireless networks and sensing systems will benefit from access to large chunks of spectrum above 100 GHz, to achieve terabit-per-second data rates in 6th Generation (6G) cellular systems and improve accuracy and reach of Earth exploration and sensing and radio astronomy applications. These are extremely sensitive to interference from artificial signals, thus the spectrum above 100 GHz features several bands which are protected from active transmissions under current spectrum regulations. To provide more agile access to the spectrum for both services, active and passive users will have to coexist without harming passive sensing operations. In this paper, we provide the first, fundamental analysis of Radio Frequency Interference (RFI) that large-scale terrestrial deployments introduce in different satellite sensing systems now orbiting the Earth. We develop a geometry-based analysis and extend it into a data-driven model which accounts for realistic propagation, building obstruction, ground reflection, for network topology with up to $10^5$ nodes in more than $85$ km$^2$. We show that the presence of harmful RFI depends on several factors, including network load, density and topology, satellite orientation, and building density. The results and methodology provide the foundation for the development of coexistence solutions and spectrum policy towards 6G.
Multi-objective Deep Reinforcement Learning for Mobile Edge Computing
Authors: Ning Yang, Junrui Wen, Meng Zhang, Ming Tang
Date: 2023-07-05 00:00:00
Tasks: Deep Reinforcement Learning, Edge-computing, reinforcement-learning, Reinforcement Learning, Scheduling

Mobile edge computing (MEC) is essential for next-generation mobile network applications that prioritize various performance metrics, including delays and energy consumption. However, conventional single-objective scheduling solutions cannot be directly applied to practical systems in which the preferences of these applications (i.e., the weights of different objectives) are often unknown or challenging to specify in advance. In this study, we address this issue by formulating a multi-objective offloading problem for MEC with multiple edges to minimize expected long-term energy consumption and transmission delay while considering unknown preferences as parameters. To address the challenge of unknown preferences, we design a multi-objective (deep) reinforcement learning (MORL)-based resource scheduling scheme with proximal policy optimization (PPO). In addition, we introduce a well-designed state encoding method for constructing features for multiple edges in MEC systems, a sophisticated reward function for accurately computing the utilities of delay and energy consumption. Simulation results demonstrate that our proposed MORL scheme enhances the hypervolume of the Pareto front by up to 233.1% compared to benchmarks. Our full framework is available at https://github.com/gracefulning/mec_morl_multipolicy.
Mitigating Cross-Database Differences for Learning Unified HRTF Representation
Authors: Yutong Wen, You Zhang, Zhiyao Duan
Date: 2023-07-27 00:00:00

Individualized head-related transfer functions (HRTFs) are crucial for accurate sound positioning in virtual auditory displays. As the acoustic measurement of HRTFs is resource-intensive, predicting individualized HRTFs using machine learning models is a promising approach at scale. Training such models require a unified HRTF representation across multiple databases to utilize their respectively limited samples. However, in addition to differences on the spatial sampling locations, recent studies have shown that, even for the common location, HRTFs across databases manifest consistent differences that make it trivial to tell which databases they come from. This poses a significant challenge for learning a unified HRTF representation across databases. In this work, we first identify the possible causes of these cross-database differences, attributing them to variations in the measurement setup. Then, we propose a novel approach to normalize the frequency responses of HRTFs across databases. We show that HRTFs from different databases cannot be classified by their database after normalization. We further show that these normalized HRTFs can be used to learn a more unified HRTF representation across databases than the prior art. We believe that this normalization approach paves the road to many data-intensive tasks on HRTF modeling.
Copilot for Xcode: Exploring AI-Assisted Programming by Prompting Cloud-based Large Language Models
Authors: Chee Wei Tan, Shangxin Guo, Man Fai Wong, Ching Nam Hang
Date: 2023-07-08 00:00:00
Tasks: Code Generation, Prompt Engineering

This paper presents an AI-assisted programming tool called Copilot for Xcode for program composition and design to support human software developers. By seamlessly integrating cloud-based Large Language Models (LLM) with Apple's local development environment, Xcode, this tool enhances productivity and unleashes creativity for software development in Apple software ecosystem (e.g., iOS apps, macOS). Leveraging advanced natural language processing (NLP) techniques, Copilot for Xcode effectively processes source code tokens and patterns within code repositories, enabling features such as code generation, autocompletion, documentation, and error detection. Software developers can also query and make "small" decisions for program composition, some of which can be made simultaneously, and this is facilitated through prompt engineering in a chat interface of Copilot for Xcode. Finally, we present simple case studies as evidence of the effectiveness of utilizing NLP in Xcode to prompt popular LLM services like OpenAI ChatGPT for program composition and design.
Analyzing the Closed-Loop Performance of Detect-And-Avoid Systems
Authors: Ítalo Romani de Oliveira, Thiago Matsumoto, Aaron Mayne, Antonio Gracia Berna
Date: 2023-07-27 00:00:00

Detect-And-Avoid (DAA) algorithms for unmanned air vehicles have industry standards called Minimum Operational Performance Standards (MOPS), establishing criteria to check whether they can ensure safe separation for all plausible operational conditions. However, these MOPS ensure performance for the avoidance maneuvers, which are open-loop, but not for the maneuvers that bring the air vehicles back to their intended courses, closing the control loop of the missions. In this paper, we analyze the closed-loop performance of existing DAA algorithms, by experimenting large numbers of traffic configurations with 4 aircraft in a delimited airspace. We measure and analyze their rates of loss of separation and timeout events, the latter happening when a chain of maneuvers exceeds the maximum supply of energy in a vehicle. We also analyze the efficiency of the closed-loop logic, expressed as the excess fuel rate, and study the relationship between safety and efficiency in these scenarios. Our results show that the inefficiency caused by DAA algorithms is very significant in dense airspaces and that it offers ample space for improvement. Performing the simulations of the closed-loop mission management logic can be highly time consuming. Despite there being Neural Network approximations of DAA logic that alleviate the computational load, none of those that we found available can properly handle cases with more than one intruder in the ownship surveillance range. Therefore, in an attempt to overcome the high computational cost of analyzing the closed-loop performance of DAA algorithms, we study the correlation between inefficiency and safety of closed-loop scenarios, and some indicators from the corresponding open-loop scenarios, such as angle of deviation and number of deviations per flight time.
Stochastic Flight Plan Optimization
Authors: Ítalo Romani de Oliveira, Steve Altus, Sergey Tiourine, Euclides C. Pinto Neto, Alexandre Leite, Felipe C. F. de Azevedo
Date: 2023-07-27 00:00:00
Tasks: Stochastic Optimization

Airline operations are subject to many uncertainties, such as weather, varying demand, maintenance events, congestion, etc. Large amounts of information are currently ignored due to difficulties in processing big data sets. We explore the use of ensemble weather forecast, which presents several distinct weather predictions for the same time horizon. So far, ensemble forecasts have been very little exploited for flight planning purposes. Currently, airlines already carry out lots of statistical analyses on past data, and devise effective policies for how much fuel and payload an aircraft should carry and how much of time buffer should be used in the schedule. But these buffers can be further reduced by doing forward-looking stochastic optimization. The use of ensemble forecast allows to select a trajectory that optimizes the expected outcome of a flight for an array of scenarios, instead of optimizing for a single one. Besides, aircraft payload is another considerable source of uncertainty. We tested stochastic optimization, first with the objective of optimizing single flights, then with the objective of optimizing whole schedules. In one of the experiments, it was observed that, in 55.8% of the cases, stochastic optimization outperforms conventional optimization in terms of fuel consumption; in only 0.4% of the cases, conventional optimization wins; and, in the remaining 43.8% of the cases, they achieve equal results. The experiments with stochastic payload demonstrated that the use of payload uncertainty can squeeze a bit more fuel savings from the flight plan outcomes. But the use of this technology is not driven only by reducing overall fuel consumption. One optimization criterion can be the minimization of diversions or fuel emergencies, that is, choosing the candidate that minimizes the maximum fuel consumption (minimax).
Commande rapprochée d'un IGBT pour l'atténuation des perturbations électromagnétiques
Authors: Daniel Sting Martinez-Padron, Nicolas Patin, Eric Monmasson
Date: 2023-07-27 00:00:00

Power transistors such as IGBTs and MOSFETs are a source of electromagnetic interference (EMI) during switching due to rapid voltage/current variations. Increasing the switching time can reduce the generation of EMI but increases losses. Several driving methods to reduce EMI have been proposed in the literature. In this work, a driving method based on the control of a grid current profile, made possible by new drivers available on the market, is proposed. This is based on the gate-source voltage of transistor as as a function of the charge injected into the gate. In order to demonstrate the performance of this method, it is evaluated by SPICE simulation using a figure of merit that enables it to be compared quantitatively with a reference method known as CATS (Commande autour de la Tension de Seuil).
Improving frequency response with synthetic damping available from fleets of distributed energy resources
Authors: Hani Mavalizadeh, Luis A. Duffaut Espinosa, Mads R. Almassalkhi
Date: 2023-07-26 00:00:00

With the increasing use of renewable generation in power systems, responsive resources will be necessary to support primary frequency control in future low-inertia/under-damped power systems. Flexible loads can provide fast-frequency response services if coordinated effectively. However, practical implementations of such synthetic damping services require both effective local sensing and control at the device level and an ability to accurately estimate online and predict the available synthetic damping from a fleet. In addition, the inherent trade-off between a fleet being available for fast frequency response while providing other ancillary services needs to be characterized. In this context, the manuscript presents a novel, fully decentralized, packet-based controller for diverse flexible loads that dynamically prioritizes and interrupts loads to engender synthetic damping suitable for primary frequency control. Moreover, the packet-based control methodology is shown to accurately characterize the available synthetic damping in real-time, which is useful to aggregators and system operators. Furthermore, spectral analysis of historical frequency regulation data is used to produce a probabilistic bound on the expected available synthetic damping for primary frequency control from a fleet and the trade-off from concurrently providing secondary frequency control services. Finally, numerical simulation on IEEE test networks demonstrates the effectiveness of the proposed methodology.