pwc-archive/papers-with-abstracts huggingface dataset:
SVD-GCN: A Simplified Graph Convolution Paradigm for Recommendation
Authors: Shaowen Peng, Kazunari Sugiyama, Tsunenori Mine
Date: 2022-08-26 00:00:00
Tasks: Recommendation Systems

With the tremendous success of Graph Convolutional Networks (GCNs), they have been widely applied to recommender systems and have shown promising performance. However, most GCN-based methods rigorously stick to a common GCN learning paradigm and suffer from two limitations: (1) the limited scalability due to the high computational cost and slow training convergence; (2) the notorious over-smoothing issue which reduces performance as stacking graph convolution layers. We argue that the above limitations are due to the lack of a deep understanding of GCN-based methods. To this end, we first investigate what design makes GCN effective for recommendation. By simplifying LightGCN, we show the close connection between GCN-based and low-rank methods such as Singular Value Decomposition (SVD) and Matrix Factorization (MF), where stacking graph convolution layers is to learn a low-rank representation by emphasizing (suppressing) components with larger (smaller) singular values. Based on this observation, we replace the core design of GCN-based methods with a flexible truncated SVD and propose a simplified GCN learning paradigm dubbed SVD-GCN, which only exploits $K$-largest singular vectors for recommendation. To alleviate the over-smoothing issue, we propose a renormalization trick to adjust the singular value gap, resulting in significant improvement. Extensive experiments on three real-world datasets show that our proposed SVD-GCN not only significantly outperforms state-of-the-arts but also achieves over 100x and 10x speedups over LightGCN and MF, respectively.
Causal Inference in Recommender Systems: A Survey and Future Directions
Authors: Chen Gao, Yu Zheng, Wenjie Wang, Fuli Feng, Xiangnan He, Yong Li
Date: 2022-08-26 00:00:00
Tasks: Causal Inference, Click-Through Rate Prediction, Collaborative Filtering, Recommendation Systems, Survey

Recommender systems have become crucial in information filtering nowadays. Existing recommender systems extract user preferences based on the correlation in data, such as behavioral correlation in collaborative filtering, feature-feature, or feature-behavior correlation in click-through rate prediction. However, unfortunately, the real world is driven by causality, not just correlation, and correlation does not imply causation. For instance, recommender systems might recommend a battery charger to a user after buying a phone, where the latter can serve as the cause of the former; such a causal relation cannot be reversed. Recently, to address this, researchers in recommender systems have begun utilizing causal inference to extract causality, thereby enhancing the recommender system. In this survey, we offer a comprehensive review of the literature on causal inference-based recommendation. Initially, we introduce the fundamental concepts of both recommender system and causal inference as the foundation for subsequent content. We then highlight the typical issues faced by non-causality recommender system. Following that, we thoroughly review the existing work on causal inference-based recommender systems, based on a taxonomy of three-aspect challenges that causal inference can address. Finally, we discuss the open problems in this critical research area and suggest important potential future works.
Lib-SibGMU -- A University Library Circulation Dataset for Recommender Systems Developmen
Authors: Eduard Zubchuk, Mikhail Arhipkin, Dmitry Menshikov, Aleksandr Karaush, Nikolay Mikhaylovskiy
Date: 2022-08-25 00:00:00
Tasks: Recommendation Systems

We opensource under CC BY 4.0 license Lib-SibGMU - a university library circulation dataset - for a wide research community, and benchmark major algorithms for recommender systems on this dataset. For a recommender architecture that consists of a vectorizer that turns the history of the books borrowed into a vector, and a neighborhood-based recommender, trained separately, we show that using the fastText model as a vectorizer delivers competitive results.
Modelling the Recommender Alignment Problem
Authors: Francisco Carvalho
Date: 2022-08-25 00:00:00
Tasks: Recommendation Systems

Recommender systems (RS) mediate human experience online. Most RS act to optimize metrics that are imperfectly aligned with the best-interest of users but are easy to measure, like ad-clicks and user engagement. This has resulted in a host of hard-to-measure side-effects: political polarization, addiction, fake news. RS design faces a recommender alignment problem: that of aligning recommendations with the goals of users, system designers, and society as a whole. But how do we test and compare potential solutions to align RS? Their massive scale makes them costly and risky to test in deployment. We synthesized a simple abstract modelling framework to guide future work. To illustrate it, we construct a toy experiment where we ask: "How can we evaluate the consequences of using user retention as a reward function?" To answer the question, we learn recommender policies that optimize reward functions by controlling graph dynamics on a toy environment. Based on the effects that trained recommenders have on their environment, we conclude that engagement maximizers generally lead to worse outcomes than aligned recommenders but not always. After learning, we examine competition between RS as a potential solution to RS alignment. We find that it generally makes our toy-society better-off than it would be under the absence of recommendation or engagement maximizers. In this work, we aimed for a broad scope, touching superficially on many different points to shed light on how an end-to-end study of reward functions for recommender systems might be done. Recommender alignment is a pressing and important problem. Attempted solutions are sure to have far-reaching impacts. Here, we take a first step in developing methods to evaluating and comparing solutions with respect to their impacts on society.
The Brussels Effect and Artificial Intelligence: How EU regulation will impact the global AI market
Authors: Charlotte Siegmann, Markus Anderljung
Date: 2022-08-23 00:00:00

The European Union is likely to introduce among the first, most stringent, and most comprehensive AI regulatory regimes of the world's major jurisdictions. In this report, we ask whether the EU's upcoming regulation for AI will diffuse globally, producing a so-called "Brussels Effect". Building on and extending Anu Bradford's work, we outline the mechanisms by which such regulatory diffusion may occur. We consider both the possibility that the EU's AI regulation will incentivise changes in products offered in non-EU countries (a de facto Brussels Effect) and the possibility it will influence regulation adopted by other jurisdictions (a de jure Brussels Effect). Focusing on the proposed EU AI Act, we tentatively conclude that both de facto and de jure Brussels effects are likely for parts of the EU regulatory regime. A de facto effect is particularly likely to arise in large US tech companies with AI systems that the AI Act terms "high-risk". We argue that the upcoming regulation might be particularly important in offering the first and most influential operationalisation of what it means to develop and deploy trustworthy or human-centred AI. If the EU regime is likely to see significant diffusion, ensuring it is well-designed becomes a matter of global importance.
MonaCoBERT: Monotonic attention based ConvBERT for Knowledge Tracing
Authors: Unggi Lee, Yonghyun Park, Yujin Kim, Seongyune Choi, Hyeoncheol Kim
Date: 2022-08-19 00:00:00
Tasks: Knowledge Tracing, Management

Knowledge tracing (KT) is a field of study that predicts the future performance of students based on prior performance datasets collected from educational applications such as intelligent tutoring systems, learning management systems, and online courses. Some previous studies on KT have concentrated only on the interpretability of the model, whereas others have focused on enhancing the performance. Models that consider both interpretability and the performance improvement have been insufficient. Moreover, models that focus on performance improvements have not shown an overwhelming performance compared with existing models. In this study, we propose MonaCoBERT, which achieves the best performance on most benchmark datasets and has significant interpretability. MonaCoBERT uses a BERT-based architecture with monotonic convolutional multihead attention, which reflects forgetting behavior of the students and increases the representation power of the model. We can also increase the performance and interpretability using a classical test-theory-based (CTT-based) embedding strategy that considers the difficulty of the question. To determine why MonaCoBERT achieved the best performance and interpret the results quantitatively, we conducted ablation studies and additional analyses using Grad-CAM, UMAP, and various visualization techniques. The analysis results demonstrate that both attention components complement one another and that CTT-based embedding represents information on both global and local difficulties. We also demonstrate that our model represents the relationship between concepts.
DBE-KT22: A Knowledge Tracing Dataset Based on Online Student Evaluation
Authors: Ghodai Abdelrahman, Sherif Abdelfattah, Qing Wang, Yu Lin
Date: 2022-08-19 00:00:00
Tasks: Knowledge Tracing

Online education has gained an increasing importance over the last decade for providing affordable high-quality education to students worldwide. This has been further magnified during the global pandemic as more students switched to study online. The majority of online education tasks, e.g., course recommendation, exercise recommendation, or automated evaluation, depends on tracking students' knowledge progress. This is known as the \emph{Knowledge Tracing} problem in the literature. Addressing this problem requires collecting student evaluation data that can reflect their knowledge evolution over time. In this paper, we propose a new knowledge tracing dataset named Database Exercises for Knowledge Tracing (DBE-KT22) that is collected from an online student exercise system in a course taught at the Australian National University in Australia. We discuss the characteristics of the DBE-KT22 dataset and contrast it with the existing datasets in the knowledge tracing literature. Our dataset is available for public access through the Australian Data Archive platform.
NeurIPS Competition Instructions and Guide: Causal Insights for Learning Paths in Education
Authors: Wenbo Gong, Digory Smith, Zichao Wang, Craig Barton, Simon Woodhead, Nick Pawlowski, Joel Jennings, Cheng Zhang
Date: 2022-08-17 00:00:00
Tasks: Causal Discovery, Selection bias, Time Series, Time Series Analysis

In this competition, participants will address two fundamental causal challenges in machine learning in the context of education using time-series data. The first is to identify the causal relationships between different constructs, where a construct is defined as the smallest element of learning. The second challenge is to predict the impact of learning one construct on the ability to answer questions on other constructs. Addressing these challenges will enable optimisation of students' knowledge acquisition, which can be deployed in a real edtech solution impacting millions of students. Participants will run these tasks in an idealised environment with synthetic data and a real-world scenario with evaluation data collected from a series of A/B tests.
2060: Civilization, Energy, and Progression of Mankind on the Kardashev Scale
Authors: Antong Zhang, Jiani Yang, Yangcheng Luo, Siteng Fan
Date: 2022-08-10 00:00:00

Energy has been propelling the development of human civilization for millennia, and technologies acquiring energy beyond human and animal power have been continuously advanced and transformed. In 1964, the Kardashev Scale was proposed to quantify the relationship between energy consumption and the development of civilizations. Human civilization presently stands at Type 0.7276 on this scale. Projecting the future energy consumption, estimating the change of its constituting structure, and evaluating the influence of possible technological revolutions are critical in the context of civilization development. In this study, we use two machine learning models, random forest (RF) and autoregressive integrated moving average (ARIMA), to simulate and predict energy consumption on a global scale. We further project the position of human civilization on the Kardashev Scale in 2060. The result shows that the global energy consumption is expected to reach 928-940 EJ in 2060, with a total growth of over 50% in the coming 40 years, and our civilization is expected to achieve Type 0.7474 on the Kardashev Scale, still far away from a Type 1 civilization. Additionally, we discuss the potential energy segmentation change before 2060 and present the influence of the advent of nuclear fusion in this context.
Temporal Fuzzy Utility Maximization with Remaining Measure
Authors: Shicheng Wan, Zhenqiang Ye, Wensheng Gan, Jiahui Chen
Date: 2022-08-26 00:00:00

High utility itemset mining approaches discover hidden patterns from large amounts of temporal data. However, an inescapable problem of high utility itemset mining is that its discovered results hide the quantities of patterns, which causes poor interpretability. The results only reflect the shopping trends of customers, which cannot help decision makers quantify collected information. In linguistic terms, computers use mathematical or programming languages that are precisely formalized, but the language used by humans is always ambiguous. In this paper, we propose a novel one-phase temporal fuzzy utility itemset mining approach called TFUM. It revises temporal fuzzy-lists to maintain less but major information about potential high temporal fuzzy utility itemsets in memory, and then discovers a complete set of real interesting patterns in a short time. In particular, the remaining measure is the first adopted in the temporal fuzzy utility itemset mining domain in this paper. The remaining maximal temporal fuzzy utility is a tighter and stronger upper bound than that of previous studies adopted. Hence, it plays an important role in pruning the search space in TFUM. Finally, we also evaluate the efficiency and effectiveness of TFUM on various datasets. Extensive experimental results indicate that TFUM outperforms the state-of-the-art algorithms in terms of runtime cost, memory usage, and scalability. In addition, experiments prove that the remaining measure can significantly prune unnecessary candidates during mining.
Exploiting Deep Reinforcement Learning for Edge Caching in Cell-Free Massive MIMO Systems
Authors: Yu Zhang, Shuaifei Chen, Jiayi Zhang
Date: 2022-08-26 00:00:00
Tasks: Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning (RL)

Cell-free massive multiple-input-multiple-output is promising to meet the stringent quality-of-experience (QoE) requirements of railway wireless communications by coordinating many successional access points (APs) to serve the onboard users coherently. A key challenge is how to deliver the desired contents timely due to the radical changing propagation environment caused by the growing train speed. In this paper, we propose to proactively cache the likely-requesting contents at the upcoming APs which perform the coherent transmission to reduce end-to-end delay. A long-term QoE-maximization problem is formulated and two cache placement algorithms are proposed. One is based on heuristic convex optimization (HCO) and the other exploits deep reinforcement learning (DRL) with soft actor-critic (SAC). Compared to the conventional benchmark, numerical results show the advantage of our proposed algorithms on QoE and hit probability. With the advanced DRL model, SAC outperforms HCO on QoE by predicting the user requests accurately.
Semi-Supervised Disentanglement of Tactile Contact~Geometry from Sliding-Induced Shear
Authors: Anupam K. Gupta, Alex Church, Nathan F. Lepora
Date: 2022-08-26 00:00:00
Tasks: Disentanglement

The sense of touch is fundamental to human dexterity. When mimicked in robotic touch, particularly by use of soft optical tactile sensors, it suffers from distortion due to motion-dependent shear. This complicates tactile tasks like shape reconstruction and exploration that require information about contact geometry. In this work, we pursue a semi-supervised approach to remove shear while preserving contact-only information. We validate our approach by showing a match between the model-generated unsheared images with their counterparts from vertically tapping onto the object. The model-generated unsheared images give faithful reconstruction of contact-geometry otherwise masked by shear, along with robust estimation of object pose then used for sliding exploration and full reconstruction of several planar shapes. We show that our semi-supervised approach achieves comparable performance to its fully supervised counterpart across all validation tasks with an order of magnitude less supervision. The semi-supervised method is thus more computational and labeled sample-efficient. We expect it will have broad applicability to wide range of complex tactile exploration and manipulation tasks performed via a shear-sensitive sense of touch.
Autonomous Unmanned Aerial Vehicle Navigation using Reinforcement Learning: A Systematic Review
Authors: Fadi AlMahamid, Katarina Grolinger
Date: 2022-08-25 00:00:00
Tasks: Navigate, reinforcement-learning, Reinforcement Learning (RL)

There is an increasing demand for using Unmanned Aerial Vehicle (UAV), known as drones, in different applications such as packages delivery, traffic monitoring, search and rescue operations, and military combat engagements. In all of these applications, the UAV is used to navigate the environment autonomously - without human interaction, perform specific tasks and avoid obstacles. Autonomous UAV navigation is commonly accomplished using Reinforcement Learning (RL), where agents act as experts in a domain to navigate the environment while avoiding obstacles. Understanding the navigation environment and algorithmic limitations plays an essential role in choosing the appropriate RL algorithm to solve the navigation problem effectively. Consequently, this study first identifies the main UAV navigation tasks and discusses navigation frameworks and simulation software. Next, RL algorithms are classified and discussed based on the environment, algorithm characteristics, abilities, and applications in different UAV navigation problems, which will help the practitioners and researchers select the appropriate RL algorithms for their UAV navigation use cases. Moreover, identified gaps and opportunities will drive UAV navigation research.
Concept-Based Techniques for "Musicologist-friendly" Explanations in a Deep Music Classifier
Authors: Francesco Foscarin, Katharina Hoedt, Verena Praher, Arthur Flexer, Gerhard Widmer
Date: 2022-08-26 00:00:00

Current approaches for explaining deep learning systems applied to musical data provide results in a low-level feature space, e.g., by highlighting potentially relevant time-frequency bins in a spectrogram or time-pitch bins in a piano roll. This can be difficult to understand, particularly for musicologists without technical knowledge. To address this issue, we focus on more human-friendly explanations based on high-level musical concepts. Our research targets trained systems (post-hoc explanations) and explores two approaches: a supervised one, where the user can define a musical concept and test if it is relevant to the system; and an unsupervised one, where musical excerpts containing relevant concepts are automatically selected and given to the user for interpretation. We demonstrate both techniques on an existing symbolic composer classification system, showcase their potential, and highlight their intrinsic limitations.
Spatio-Temporal Representation Learning Enhanced Source Cell-phone Recognition from Speech Recordings
Authors: Chunyan Zeng, Shixiong Feng, Zhifeng Wang, Xiangkui Wan, Yunfan Chen, Nan Zhao
Date: 2022-08-25 00:00:00
Tasks: Representation Learning, Time Series Analysis

The existing source cell-phone recognition method lacks the long-term feature characterization of the source device, resulting in inaccurate representation of the source cell-phone related features which leads to insufficient recognition accuracy. In this paper, we propose a source cell-phone recognition method based on spatio-temporal representation learning, which includes two main parts: extraction of sequential Gaussian mean matrix features and construction of a recognition model based on spatio-temporal representation learning. In the feature extraction part, based on the analysis of time-series representation of recording source signals, we extract sequential Gaussian mean matrix with long-term and short-term representation ability by using the sensitivity of Gaussian mixture model to data distribution. In the model construction part, we design a structured spatio-temporal representation learning network C3D-BiLSTM to fully characterize the spatio-temporal information, combine 3D convolutional network and bidirectional long short-term memory network for short-term spectral information and long-time fluctuation information representation learning, and achieve accurate recognition of cell-phones by fusing spatio-temporal feature information of recording source signals. The method achieves an average accuracy of 99.03% for the closed-set recognition of 45 cell-phones under the CCNU\_Mobile dataset, and 98.18% in small sample size experiments, with recognition performance better than the existing state-of-the-art methods. The experimental results show that the method exhibits excellent recognition performance in multi-class cell-phones recognition.
From Observability to Observer Realization: A path via elementary block-diagram manipulations
Authors: Eder Baron-Prada, Renzo Caballero, Eric Feron
Date: 2022-08-26 00:00:00
Tasks: Form

Introductory state-space linear control courses focus on linear, time-invariant systems and spend intense efforts by introducing system realizations that allow the student to grasp fundamental concepts, among which controllability, observability, and controller and observer design. This note describes a graphical mechanism to transform a system expressed in observability form into its observer form based on elementary block diagram manipulations, a technique whose pedagogical usefulness tends to be overlooked. Given the well-known duality principle in linear systems with respect to controllability and observability, the proposed graphical mechanism is applicable to transform from controllability to controller realization.