pwc-archive/papers-with-abstracts huggingface dataset:
From Who You Know to What You Read: Augmenting Scientific Recommendations with Implicit Social Networks
Authors: Hyeonsu B. Kang, Rafal Kocielnik, Andrew Head, Jiangjiang Yang, Matt Latzke, Aniket Kittur, Daniel S. Weld, Doug Downey, Jonathan Bragg
Date: 2022-04-21 00:00:00

The ever-increasing pace of scientific publication necessitates methods for quickly identifying relevant papers. While neural recommenders trained on user interests can help, they still result in long, monotonous lists of suggested papers. To improve the discovery experience we introduce multiple new methods for \em augmenting recommendations with textual relevance messages that highlight knowledge-graph connections between recommended papers and a user's publication and interaction history. We explore associations mediated by author entities and those using citations alone. In a large-scale, real-world study, we show how our approach significantly increases engagement -- and future engagement when mediated by authors -- without introducing bias towards highly-cited authors. To expand message coverage for users with less publication or interaction history, we develop a novel method that highlights connections with proxy authors of interest to users and evaluate it in a controlled lab study. Finally, we synthesize design implications for future graph-based messages.
Content negotiation on the Web: State of the art
Authors: Yousouf Taghzouti, Antoine Zimmermann, Maxime Lefrançois
Date: 2022-04-21 00:00:00

The openness and accessibility of the Web has contributed greatly to its worldwide adoption. Uniform Resource Identifiers (URIs) are used for resource identification on the Web. A resource on the Web can be described in many ways, which makes it difficult for a user to find an adequate representation. This situation has motivated fruitful research on content negotiation to satisfy user requirements efficiently and effectively. We focus on the important topic of content negotiation, and our goal is to present the first comprehensive state of the art. Our contributions include (1) identifying the characteristics of content negotiation scenarios (styles, dimensions, and means of conveying constraints), (2) comparing and classifying existing contributions, (3) identifying use cases that the current state of content negotiation struggles to address, (4) suggesting research directions for future work. The results of the state of the art show that the problem of content negotiation is relevant and far from being solved.
Public awareness and attitudes towards search engine optimization
Authors: Dirk Lewandowski, Sebastian Schultheiß
Date: 2022-04-21 00:00:00

This research focuses on what users know about search engine optimization (SEO) and how well they can identify results that have potentially been influenced by SEO. We conducted an online survey with a sample representative of the German online population (N = 2,012). We found that 43% of users assume a better ranking can be achieved without paying money to Google. This is in stark contrast to the possibility of influence through paid advertisements, which 79% of internet users are aware of. However, only 29.2% know how ads differ from organic results. The term "search engine optimization" is known to 8.9% of users but 14.5% can correctly name at least one SEO tactic. Success in labelling results that can be influenced through SEO varies by search engine result page (SERP) complexity and devices: participants achieved higher success rates on SERPs with simple structures than on the more complex SERPs. SEO results were identified better on the small screen than on the large screen. 59.2% assumed that SEO has a (very) strong impact on rankings. SEO is more often perceived as positive (75.2%) than negative (68.4%). The insights from this study have implications for search engine providers, regulators, and information literacy.
Heterogeneous rarity patterns drive price dynamics in NFT collections
Authors: Amin Mekacher, Alberto Bracci, Matthieu Nadini, Mauro Martino, Laura Alessandretti, Luca Maria Aiello, Andrea Baronchelli
Date: 2022-04-21 00:00:00

We quantify Non Fungible Token (NFT) rarity and investigate how it impacts market behaviour by analysing a dataset of 3.7M transactions collected between January 2018 and June 2022, involving 1.4M NFTs distributed across 410 collections. First, we consider the rarity of an NFT based on the set of human-readable attributes it possesses and show that most collections present heterogeneous rarity patterns, with few rare NFTs and a large number of more common ones. Then, we analyze market performance and show that, on average, rarer NFTs: (i) sell for higher prices, (ii) are traded less frequently, (iii) guarantee higher returns on investment (ROIs), and (iv) are less risky, i.e., less prone to yield negative returns. We anticipate that these findings will be of interest to researchers as well as NFT creators, collectors, and traders.
Evolution and use of data science vocabulary. How much have we changed in 13 years?
Authors: Igor Barahona
Date: 2022-03-28 00:00:00

Here I present an investigation on the evolution and use of vocabulary in data science in the last 13 years. Based on a rigorous statistical analysis, a database with 12,787 documents containing the words "data science" in the title, abstract or keywords is analyzed. It is proposed to classify the evolution of this discipline in three periods: emergence, growth and boom. Characteristic words and pioneering documents are identified for each period. By proposing the distinctive vocabulary and relevant topics of data science and classified in time periods, these results add value to the scientific community of this discipline.
Fairness in Graph Mining: A Survey
Authors: Yushun Dong, Jing Ma, Song Wang, Chen Chen, Jundong Li
Date: 2022-04-21 00:00:00
Tasks: Fairness, Graph Mining, Survey

Graph mining algorithms have been playing a significant role in myriad fields over the years. However, despite their promising performance on various graph analytical tasks, most of these algorithms lack fairness considerations. As a consequence, they could lead to discrimination towards certain populations when exploited in human-centered applications. Recently, algorithmic fairness has been extensively studied in graph-based applications. In contrast to algorithmic fairness on independent and identically distributed (i.i.d.) data, fairness in graph mining has exclusive backgrounds, taxonomies, and fulfilling techniques. In this survey, we provide a comprehensive and up-to-date introduction of existing literature under the context of fair graph mining. Specifically, we propose a novel taxonomy of fairness notions on graphs, which sheds light on their connections and differences. We further present an organized summary of existing techniques that promote fairness in graph mining. Finally, we summarize the widely used datasets in this emerging research field and provide insights on current research challenges and open questions, aiming at encouraging cross-breeding ideas and further advances.
INSPIRE: Distributed Bayesian Optimization for ImproviNg SPatIal REuse in Dense WLANs
Authors: Anthony Bardou, Thomas Begin
Date: 2022-03-30 00:00:00
Tasks: Bayesian Optimization, Fairness, Gaussian Processes

WLANs, which have overtaken wired networks to become the primary means of connecting devices to the Internet, are prone to performance issues due to the scarcity of space in the radio spectrum. As a response, IEEE 802.11ax and subsequent amendments aim at increasing the spatial reuse of a radio channel by allowing the dynamic update of two key parameters in wireless transmission: the transmission power (TX_POWER) and the sensitivity threshold (OBSS_PD). In this paper, we present INSPIRE, a distributed solution performing local Bayesian optimizations based on Gaussian processes to improve the spatial reuse in WLANs. INSPIRE makes no explicit assumptions about the topology of WLANs and favors altruistic behaviors of the access points, leading them to find adequate configurations of their TX_POWER and OBSS_PD parameters for the "greater good" of the WLANs. We demonstrate the superiority of INSPIRE over other state-of-the-art strategies using the ns-3 simulator and two examples inspired by real-life deployments of dense WLANs. Our results show that, in only a few seconds, INSPIRE is able to drastically increase the quality of service of operational WLANs by improving their fairness and throughput.
Distributed Learning for Vehicular Dynamic Spectrum Access in Autonomous Driving
Authors: Pawe\{l} Sroka, Adrian Kliks
Date: 2022-03-22 00:00:00
Tasks: Autonomous Driving, channel selection, Q-Learning

Reliable wireless communication between the autonomously driving cars is one of the fundamental needs for guaranteeing passenger safety and comfort. However, when the number of communicating cars increases, the transmission quality may be significantly degraded due to too high occupancy radio of the used frequency band. In this paper, we concentrate on the autonomous vehicle-platooning use-case, where intra-platoon communication is done in the dynamically selected frequency band, other than nominally devoted for such purposes. The carrier selection is done in a flexible manner with the support of the context database located at the roadside unit (edge of wireless communication infrastructure). However, as the database delivers only context information to the platoons' leaders, the final decision is made separately by the individual platoons, following the suggestions made by the artificial intelligence algorithms. In this work, we concentrate on a lightweight Q-learning solution, that could be successfully implemented in each car for dynamic channel selection.
An Exploratory Study on Code Attention in BERT
Authors: Rishab Sharma, Fuxiang Chen, Fatemeh Fard, David Lo
Date: 2022-04-05 00:00:00
Tasks: Clone Detection, Code Summarization

Many recent models in software engineering introduced deep neural models based on the Transformer architecture or use transformer-based Pre-trained Language Models (PLM) trained on code. Although these models achieve the state of the arts results in many downstream tasks such as code summarization and bug detection, they are based on Transformer and PLM, which are mainly studied in the Natural Language Processing (NLP) field. The current studies rely on the reasoning and practices from NLP for these models in code, despite the differences between natural languages and programming languages. There is also limited literature on explaining how code is modeled. Here, we investigate the attention behavior of PLM on code and compare it with natural language. We pre-trained BERT, a Transformer based PLM, on code and explored what kind of information it learns, both semantic and syntactic. We run several experiments to analyze the attention values of code constructs on each other and what BERT learns in each layer. Our analyses show that BERT pays more attention to syntactic entities, specifically identifiers and separators, in contrast to the most attended token [CLS] in NLP. This observation motivated us to leverage identifiers to represent the code sequence instead of the [CLS] token when used for code clone detection. Our results show that employing embeddings from identifiers increases the performance of BERT by 605% and 4% F1-score in its lower layers and the upper layers, respectively. When identifiers' embeddings are used in CodeBERT, a code-based PLM, the performance is improved by 21-24% in the F1-score of clone detection. The findings can benefit the research community by using code-specific representations instead of applying the common embeddings used in NLP, and open new directions for developing smaller models with similar performance.
Efficient Representations of Radiality Constraints in Optimization of Islanding and De-Energization in Distribution Grids
Authors: Joe Gorka, Line Roald
Date: 2022-04-21 00:00:00

Optimization of power distribution system topology is complicated by the requirement that the system be operated in a radial configuration. In this paper, we discuss existing methods for enforcing radiality constraints and introduce two new formulations that enable optimization over partially energized or islanded network topologies. The first builds on methods that use so-called parent-child constraints, but enforces those constraints on an abstracted network which enables an equivalent formulation with significantly less variables and constraints. The second formulation builds on existing approaches which directly generate constraints disallowing loops, and through an iterative approach seeks to limit the number of these constraints which must be enforced.
A Decentralized Analysis and Control Synthesis Approach for Networked Systems with Arbitrary Interconnections
Authors: Shirantha Welikala, Hai Lin, Panos Antsaklis
Date: 2022-04-20 00:00:00

This paper considers the problem of decentralized analysis and control synthesis to verify and ensure properties like stability and dissipativity of a large-scale networked system comprised of linear subsystems interconnected in an arbitrary topology. In particular, we design systematic networked system analysis and control synthesis processes that can be executed in a decentralized manner at the subsystem level with minimal information sharing among the subsystems. Compared to our most recent work on the same topic, we consider a substantially more generalized problem setup in this paper and develop decentralized processes to verify and ensure a broader range of networked system properties. We show that for such decentralized processes: optimizing the used subsystem indexing scheme can substantially reduce the required inter-subsystem information-sharing sessions, and in some network topologies, information sharing among only neighboring subsystems is sufficient (distributed!). Moreover, the proposed networked system analysis and control synthesis processes are compositional/resilient to subsystem removals, which enable them to conveniently and efficiently handle situations where new subsystems are being added/removed to/from an existing network. We also provide significant insights into our decentralized approach so that it can be quickly adopted to verify and ensure properties beyond the stability and dissipativity of networked systems. Towards developing such decentralized techniques, we have also derived new centralized solutions for dissipative observer and dynamic output feedback controller design problems. Subsequently, we also specialize all the derived results for discrete-time networked systems. We conclude this paper by providing several simulation results demonstrating the proposed novel decentralized analysis and control synthesis processes and dissipativity-based results.
Joint state and parameter estimation based on constrained zonotopes
Authors: Brenner S. Rego, Diego Locatelli, Davide M. Raimondo, Guilherme V. Raffo
Date: 2022-04-20 00:00:00
Tasks: parameter estimation, State Estimation

This note presents a new method for set-based joint state and parameter estimation of discrete-time systems using constrained zonotopes. This is done by extending previous set-based state estimation methods to include parameter identification in a unified framework. Unlike in interval-based methods, the existing dependencies between states and model parameters are maintained from one time step to the next, thus providing a more accurate estimation scheme. In addition, the enclosure of states and parameters is refined using measurements through generalized intersections, which are properly captured by constrained zonotopes. The advantages of the new approach are highlighted in two numerical examples.
Mobility unevenness in rock-paper-scissors models
Authors: J. Menezes, S. Rodrigues, S. Batista
Date: 2022-04-20 00:00:00

We investigate a tritrophic system whose cyclic dominance is modelled by the rock-paper-scissors game. We consider that organisms of one or two species are affected by movement limitations, which unbalances the cyclic spatial game. Performing stochastic simulations, we show that mobility unevenness controls the population dynamics. In the case of one slow species, the predominant species depends on the level of mobility restriction, with the slow species being preponderant if the mobility limitations are substantial. If two species face mobility limitations, our outcomes show that being higher dispersive does not constitute an advantage in terms of population growth. On the contrary, if organisms move with higher mobility, they expose themselves to enemies more frequently, being more vulnerable to being eliminated. Finally, our findings show that biodiversity benefits in regions where species are slowed. Biodiversity loss for high mobility organisms, common to cyclic systems, may be avoided with coexistence probability being higher for robust mobility limitations. Our results may help biologists understand the dynamics of unbalanced spatial systems where organisms' dispersal is fundamental to biodiversity conservation.
Do t-Statistic Hurdles Need to be Raised?
Authors: Andrew Y. Chen
Date: 2022-04-21 00:00:00

Many scholars have called for raising statistical hurdles to guard against false discoveries in academic publications. I show these calls may be difficult to justify empirically. Published data exhibit bias: results that fail to meet existing hurdles are often unobserved. These unobserved results must be extrapolated, which can lead to weak identification of revised hurdles. In contrast, statistics that can target only published findings (e.g. empirical Bayes shrinkage and the FDR) can be strongly identified, as data on published findings is plentiful. I demonstrate these results theoretically and in an empirical analysis of the cross-sectional return predictability literature.
From point forecasts to multivariate probabilistic forecasts: The Schaake shuffle for day-ahead electricity price forecasting
Authors: Oliver Grothe, Fabian Kächele, Fabian Krüger
Date: 2022-04-21 00:00:00
Tasks: Decision Making, Prediction Intervals, Time Series, Time Series Analysis

Modeling price risks is crucial for economic decision making in energy markets. Besides the risk of a single price, the dependence structure of multiple prices is often relevant. We therefore propose a generic and easy-to-implement method for creating multivariate probabilistic forecasts based on univariate point forecasts of day-ahead electricity prices. While each univariate point forecast refers to one of the day's 24 hours, the multivariate forecast distribution models dependencies across hours. The proposed method is based on simple copula techniques and an optional time series component. We illustrate the method for five benchmark data sets recently provided by Lago et al. (2020). Furthermore, we demonstrate an example for constructing realistic prediction intervals for the weighted sum of consecutive electricity prices, as, e.g., needed for pricing individual load profiles.
Measuring artificial intelligence: a systematic assessment and implications for governance
Authors: Kerstin Hötte, Taheya Tarannum, Vilhelm Verendel, Lauren Bennett
Date: 2022-04-21 00:00:00

Governing artificial intelligence (AI) inventions is high on the political agenda, but it is not clear how to define and empirically measure it. We compare four approaches to identifying patented AI inventions that reflect different ways of understanding and defining AI. Using US patents from 1990-2019, we assess the extent to which each approach qualifies AI as a general purpose technology (GPT) and study patterns of concentration, both of which are policy-relevant criteria. The four approaches overlap in only 1.37% of patents and vary in size, accounting for shares ranging between 3-17% of all US patents in 2019. The smallest set of AI patents in our sample, identified by recent AI keywords, is the most GPT-like, with high levels of growth and generality. All four approaches show that AI inventions are concentrated at a few firms, confirming concerns about market competition. Our results suggest that policy implementation may not be straightforward and should consider more than one classification method, as identifying AI inventions ultimately depends on how AI is defined.
Delamination prediction in composite panels using unsupervised-feature learning methods with wavelet-enhanced guided wave representations
Authors: Mahindra Rautela, J. Senthilnath, Ernesto Monaco, S. Gopalakrishnan
Date: 2022-04-20 00:00:00
Tasks: Anomaly Detection, Dimensionality Reduction, Philosophy, Structural Health Monitoring

With the introduction of damage tolerance-based design philosophies, the demand for reliable and robust structural health monitoring (SHM) procedures for aerospace composite structures is increasing rapidly. The performance of supervised learning algorithms for SHM depends on the amount of labeled and balanced datasets. Apart from this, collecting datasets accommodating all possible damage scenarios is cumbersome, costly, and inaccessible for aerospace applications. In this paper, we have proposed two different unsupervised-feature learning approaches where the algorithms are trained only on the baseline scenarios to learn the distribution of baseline signals. The trained unsupervised feature learner is used for delamination prediction with an anomaly detection philosophy. In the first approach, we have combined dimensionality reduction techniques (principal component analysis and independent component analysis) with a one-class support vector machine. In another approach, we have utilized deep learning-based deep convolutional autoencoders (CAE). These state-of-the-art algorithms are applied on three different guided wave-based experimental datasets. The raw guided wave signals present in the datasets are converted into wavelet-enhanced higher-order representations for training unsupervised feature-learning algorithms. We have also compared different techniques, and it is seen that CAE generates better reconstructions with lower mean squared error and can provide higher accuracy on all the datasets.
A Bitstream Feature Based Model for Video Decoding Energy Estimation
Authors: Christian Herglotz, Yongjun Wen, Bowen Dai, Matthias Kränzler, André Kaup
Date: 2022-04-21 00:00:00
Tasks: Decoder

In this paper we show that a small amount of bit stream features can be used to accurately estimate the energy consumption of state-of-the-art software and hardware accelerated decoder implementations for four different video codecs. By testing the estimation performance on HEVC, H.264, H.263, and VP9 we show that the proposed model can be used for any hybrid video codec. We test our approach on a high amount of different test sequences to prove the general validity. We show that less than 20 features are sufficient to obtain mean estimation errors that are smaller than 8%. Finally, an example will show the performance trade-offs in terms of rate, distortion, and decoding energy for all tested codecs.
Generative Compression for Face Video: A Hybrid Scheme
Authors: Anni Tang, Yan Huang, Jun Ling, ZhiYu Zhang, Yiwei Zhang, Rong Xie, Li Song
Date: 2022-04-21 00:00:00

As the latest video coding standard, versatile video coding (VVC) has shown its ability in retaining pixel quality. To excavate more compression potential for video conference scenarios under ultra-low bitrate, this paper proposes a bitrate adjustable hybrid compression scheme for face video. This hybrid scheme combines the pixel-level precise recovery capability of traditional coding with the generation capability of deep learning based on abridged information, where Pixel wise Bi-Prediction, Low-Bitrate-FOM and Lossless Keypoint Encoder collaborate to achieve PSNR up to 36.23 dB at a low bitrate of 1.47 KB/s. Without introducing any additional bitrate, our method has a clear advantage over VVC under a completely fair comparative experiment, which proves the effectiveness of our proposed scheme. Moreover, our scheme can adapt to any existing encoder / configuration to deal with different encoding requirements, and the bitrate can be dynamically adjusted according to the network condition.