OpenRaiser/Intern-Atlas huggingface dataset:
A Decade of Information Architecture in HCI: A Systematic Literature Review
Venue: arXiv cs.HC 2022
Year: 2022

Information Architecture (IA) is a blueprint for the information system in websites or other information-rich environments. It corresponds to how we organize, label and structure information. The importance of Information Architecture and its influence on a system's usability is vastly discussed in literature. Because of the inherent connection between Information Architecture concepts and the Human Computer Interaction (HCI) field, we decided to investigate how previous research has used Information Architecture in the context of Human Computer Interaction (IAinHCI). In order to do that, we followed a two phase process. First, we conducted a Systematic Literature Review (SLR). We queried both the ACM and IEEE databases. We filtered and assessed 311 papers that spanned a decade of research on Information Architecture. We found 25 papers that utilized Information Architecture in the context of Human Computer Interaction. Then, we followed a Background Reference Search process using the SLR resulting papers as a starting set. We assessed the eligibility of the reference list of all 25 papers and found eight additional papers that were relevant to our research question. Results of our review show that, IAinHCI papers fall under seven main categories, from IoT to the semantic web and ubiquitous technology. The website category, however, was both the most consistent over the years and the most prevalent category accounting for 67% of the papers. Our findings suggest that IA has not yet uncovered its full potential and there is still room for research to leverage and expend the IA knowledge base promising a prosperous future for Information Architecture.
Distribution Preserving Graph Representation Learning
Venue: arXiv cs.LG 2022
Year: 2022

Graph neural network (GNN) is effective to model graphs for distributed representations of nodes and an entire graph. Recently, research on the expressive power of GNN attracted growing attention. A highly-expressive GNN has the ability to generate discriminative graph representations. However, in the end-to-end training process for a certain graph learning task, a highly-expressive GNN risks generating graph representations overfitting the training data for the target task, while losing information important for the model generalization. In this paper, we propose Distribution Preserving GNN (DP-GNN) - a GNN framework that can improve the generalizability of expressive GNN models by preserving several kinds of distribution information in graph representations and node representations. Besides the generalizability, by applying an expressive GNN backbone, DP-GNN can also have high expressive power. We evaluate the proposed DP-GNN framework on multiple benchmark datasets for graph classification tasks. The experimental results demonstrate that our model achieves state-of-the-art performances.
Stability vs Implicit Bias of Gradient Methods on Separable Data and Beyond
Venue: arXiv cs.LG 2022
Year: 2022

An influential line of recent work has focused on the generalization properties of unregularized gradient-based learning procedures applied to separable linear classification with exponentially-tailed loss functions. The ability of such methods to generalize well has been attributed to the their implicit bias towards large margin predictors, both asymptotically as well as in finite time. We give an additional unified explanation for this generalization and relate it to two simple properties of the optimization objective, that we refer to as realizability and self-boundedness. We introduce a general setting of unconstrained stochastic convex optimization with these properties, and analyze generalization of gradient methods through the lens of algorithmic stability. In this broader setting, we obtain sharp stability bounds for gradient descent and stochastic gradient descent which apply even for a very large number of gradient steps, and use them to derive general generalization bounds for these algorithms. Finally, as direct applications of the general bounds, we return to the setting of linear classification with separable data and establish several novel test loss and test accuracy bounds for gradient descent and stochastic gradient descent for a variety of loss functions with different tail decay rates. In some of these cases, our bounds significantly improve upon the existing generalization error bounds in the literature.
The Unfairness of Popularity Bias in Book Recommendation
Venue: arXiv cs.IR 2022
Year: 2022

Recent studies have shown that recommendation systems commonly suffer from popularity bias. Popularity bias refers to the problem that popular items (i.e., frequently rated items) are recommended frequently while less popular items are recommended rarely or not at all. Researchers adopted two approaches to examining popularity bias: (i) from the users' perspective, by analyzing how far a recommendation system deviates from user's expectations in receiving popular items, and (ii) by analyzing the amount of exposure that long-tail items receive, measured by overall catalog coverage and novelty. In this paper, we examine the first point of view in the book domain, although the findings may be applied to other domains as well. To this end, we analyze the well-known Book-Crossing dataset and define three user groups based on their tendency towards popular items (i.e., Niche, Diverse, Bestseller-focused). Further, we evaluate the performance of nine state-of-the-art recommendation algorithms and two baselines (i.e., Random, MostPop) from both the accuracy (e.g., NDCG, Precision, Recall) and popularity bias perspectives. Our results indicate that most state-of-the-art recommendation algorithms suffer from popularity bias in the book domain, and fail to meet users' expectations with Niche and Diverse tastes despite having a larger profile size. Conversely, Bestseller-focused users are more likely to receive high-quality recommendations, both in terms of fairness and personalization. Furthermore, our study shows a tradeoff between personalization and unfairness of popularity bias in recommendation algorithms for users belonging to the Diverse and Bestseller groups, that is, algorithms with high capability of personalization suffer from the unfairness of popularity bias.
ZipZap: A Blockchain Solution for Local Energy Trading
Venue: arXiv cs.DC 2022
Year: 2022

In the last few years, electric utility companies have increasingly invested into transactive energy systems. This trend was primarily caused by the integration of distributed energy resources (DERs) and internet-of-things (IoT) devices into their existing distribution networks. Influenced by the general interest in blockchain technologies, many industry specialists are considering new, more efficient peer-to-peer market structures for DERs. Since blockchain-based energy exchanges can automate transactions between their members and provide increased levels of security thanks to smart contracts, these new initiatives may eventually revolutionize how customers interact with utility companies. In this paper, we explore the trade-off between cost and traceability in the form of on-chain and off-chain solutions. We also propose ZipZap, a first step towards a blockchain-based local smart grid system. ZipZap is an ERC-1155 compliant solution with four different prototypes: Heavyweight, Featherweight, Lightweight and Weightless. The first three prototypes were developed in Solidity and deployed using Ethereum. Heavyweight is fully on-chain, whereas Featherweight and Lightweight showcase various levels of hybridization. Weightless, in turn, was deployed using Quorum, a gas-free alternative to Ethereum. Our evaluation uses realistic parameters and measures the impact of different types of metadata storage scopes, with some Ethereum prototypes showcasing gas cost reductions of more than 97% in comparison to our fully on-chain baseline.
ONE-NAS: An Online NeuroEvolution based Neural Architecture Search for Time Series Forecasting
Venue: arXiv cs.LG 2022
Year: 2022

Time series forecasting (TSF) is one of the most important tasks in data science, as accurate time series (TS) predictions can drive and advance a wide variety of domains including finance, transportation, health care, and power systems. However, real-world utilization of machine learning (ML) models for TSF suffers due to pretrained models being able to learn and adapt to unpredictable patterns as previously unseen data arrives over longer time scales. To address this, models must be periodically retained or redesigned, which takes significant human and computational resources. This work presents the Online NeuroEvolution based Neural Architecture Search (ONE-NAS) algorithm, which to the authors' knowledge is the first neural architecture search algorithm capable of automatically designing and training new recurrent neural networks (RNNs) in an online setting. Without any pretraining, ONE-NAS utilizes populations of RNNs which are continuously updated with new network structures and weights in response to new multivariate input data. ONE-NAS is tested on real-world large-scale multivariate wind turbine data as well a univariate Dow Jones Industrial Average (DJIA) dataset, and is shown to outperform traditional statistical time series forecasting, including naive, moving average, and exponential smoothing methods, as well as state of the art online ARIMA strategies.
Out of Hypervisor (OoH): When Nested Virtualization Becomes Practical
Venue: arXiv cs.AR 2022
Year: 2022

This paper introduces Out of Hypervisor (OoH), a new research axis close to nested virtualization. Instead of emulating a full virtual hardware inside a VM to support a hypervisor, the OoH principle is to individually expose current hypervisor-oriented hardware virtualization features to the guest OS so that its processes could also take benefit from those features. In fact, several hardware virtualization features such as Intel PML, SPP, CAT, and EPT which currently can only be used by the hypervisor also be beneficial for processes that run inside the VM. We illustrate OoH with Intel PML (Page Modification Logging), a feature which allows efficient dirty page tracking for improving VM live migration. According to the fact that dirty page tracking is at the heart of process checkpointing (CRIU) and concurrent garbage collection (Boehm), we present two OoH PML designs namely Shadow PML (SPML) and Extended PML (EPML). The former requires no hardware changes but incurs significant overhead, justifying EPML which extends PML. We evaluated and compared SPML and EPML with /proc and userfaultfd,t wo default solutions in Linux. We do this using a key-value store database as the benchmark. The results show that EPML reduces CRIU checkpointing time by about 14% while leading to a negligible overhead (of about 0.5%) compared to SPML, /proc, and userfaultfd.
Architectural Optimization and Feature Learning for High-Dimensional Time Series Datasets
Venue: arXiv cs.LG 2022
Year: 2022

As our ability to sense increases, we are experiencing a transition from data-poor problems, in which the central issue is a lack of relevant data, to data-rich problems, in which the central issue is to identify a few relevant features in a sea of observations. Motivated by applications in gravitational-wave astrophysics, we study the problem of predicting the presence of transient noise artifacts in a gravitational wave detector from a rich collection of measurements from the detector and its environment. We argue that feature learning--in which relevant features are optimized from data--is critical to achieving high accuracy. We introduce models that reduce the error rate by over 60% compared to the previous state of the art, which used fixed, hand-crafted features. Feature learning is useful not only because it improves performance on prediction tasks; the results provide valuable information about patterns associated with phenomena of interest that would otherwise be undiscoverable. In our application, features found to be associated with transient noise provide diagnostic information about its origin and suggest mitigation strategies. Learning in high-dimensional settings is challenging. Through experiments with a variety of architectures, we identify two key factors in successful models: sparsity, for selecting relevant variables within the high-dimensional observations; and depth, which confers flexibility for handling complex interactions and robustness with respect to temporal variations. We illustrate their significance through systematic experiments on real detector data. Our results provide experimental corroboration of common assumptions in the machine-learning community and have direct applicability to improving our ability to sense gravitational waves, as well as to many other problem settings with similarly high-dimensional, noisy, or partly irrelevant data.
Point Label Aware Superpixels for Multi-species Segmentation of Underwater Imagery
Venue: arXiv cs.CV 2022
Year: 2022

Monitoring coral reefs using underwater vehicles increases the range of marine surveys and availability of historical ecological data by collecting significant quantities of images. Analysis of this imagery can be automated using a model trained to perform semantic segmentation, however it is too costly and time-consuming to densely label images for training supervised models. In this letter, we leverage photo-quadrat imagery labeled by ecologists with sparse point labels. We propose a point label aware method for propagating labels within superpixel regions to obtain augmented ground truth for training a semantic segmentation model. Our point label aware superpixel method utilizes the sparse point labels, and clusters pixels using learned features to accurately generate single-species segments in cluttered, complex coral images. Our method outperforms prior methods on the UCSD Mosaics dataset by 3.62% for pixel accuracy and 8.35% for mean IoU for the label propagation task, while reducing computation time reported by previous approaches by 76%. We train a DeepLabv3+ architecture and outperform state-of-the-art for semantic segmentation by 2.91% for pixel accuracy and 9.65% for mean IoU on the UCSD Mosaics dataset and by 4.19% for pixel accuracy and 14.32% mean IoU for the Eilat dataset.
Cyber Mobility Mirror: A Deep Learning-based Real-World Object Perception Platform Using Roadside LiDAR
Venue: arXiv cs.CV 2022
Year: 2022

Object perception plays a fundamental role in Cooperative Driving Automation (CDA) which is regarded as a revolutionary promoter for the next-generation transportation systems. However, the vehicle-based perception may suffer from the limited sensing range and occlusion as well as low penetration rates in connectivity. In this paper, we propose Cyber Mobility Mirror (CMM), a next-generation real-time traffic surveillance system for 3D object perception and reconstruction, to explore the potential of roadside sensors for enabling CDA in the real world. The CMM system consists of six main components: 1) the data pre-processor to retrieve and preprocess the raw data; 2) the roadside 3D object detector to generate 3D detection results; 3) the multi-object tracker to identify detected objects; 4) the global locator to map positioning information from the LiDAR coordinate to geographic coordinate using coordinate transformation; 5) the cloud-based communicator to transmit perception information from roadside sensors to equipped vehicles, and 6) the onboard advisor to reconstruct and display the real-time traffic conditions via Graphical User Interface (GUI). In this study, a field-operational system is deployed at a real-world intersection, University Avenue and Iowa Avenue in Riverside, California to assess the feasibility and performance of our CMM system. Results from field tests demonstrate that our CMM prototype system can provide satisfactory perception performance with 96.99% precision and 83.62% recall. High-fidelity real-time traffic conditions (at the object level) can be geo-localized with an average error of 0.14m and displayed on the GUI of the equipped vehicle with a frequency of 3-4 Hz.
Efficient Massively Parallel Join Optimization for Large Queries
Venue: arXiv cs.DB 2022
Year: 2022

Modern data analytical workloads often need to run queries over a large number of tables. An optimal query plan for such queries is crucial for being able to run these queries within acceptable time bounds. However, with queries involving many tables, finding the optimal join order becomes a bottleneck in query optimization. Due to the exponential nature of join order optimization, optimizers resort to heuristic solutions after a threshold number of tables. Our objective is two fold: (a) reduce the optimization time for generating optimal plans; and (b) improve the quality of the heuristic solution. In this paper, we propose a new massively parallel algorithm, MPDP, that can efficiently prune the large search space (via a novel plan enumeration technique) while leveraging the massive parallelism offered by modern hardware (Eg: GPUs). When evaluated on real-world benchmark queries with PostgreSQL, MPDP is at least an order of magnitude faster compared to state-of-the-art techniques for large analytical queries. As a result, we are able to increase the heuristic-fall-back limit from 12 relations to 25 relations with same time budget in PostgreSQL. Also, in order to handle queries with even larger number of tables, we augment MPDP to a well known heuristic, IDP$_2$ (iterative DP version 2) and a novel heuristic UnionDP. By systematically exploring a much larger search space, these heuristics provides query plans that are up to 7 times cheaper as compared to the state-of-the-art techniques while being faster to compute.
Gradient-free Multi-domain Optimization for Autonomous Systems
Venue: arXiv cs.RO 2022
Year: 2022

Autonomous systems are composed of several subsystems such as mechanical, propulsion, perception, planning and control. These are traditionally designed separately which makes performance optimization of the integrated system a significant challenge. In this paper, we study the problem of using gradient-free optimization methods to jointly optimize the multiple domains of an autonomous system to find the set of optimal architectures for both hardware and software. We specifically perform multi-domain, multi-parameter optimization on an autonomous vehicle to find the best decision-making process, motion planning and control algorithms, and the physical parameters for autonomous racing. We detail the multi-domain optimization scheme, benchmark with different core components, and provide insights for generalization to new autonomous systems. In addition, this paper provides a benchmark of the performances of six different gradient-free optimizers in three different operating environments. Our approach is validated with a case study where we describe the autonomous vehicle system architecture, optimization methods, and finally, provide an argument on gradient-free optimization being a powerful choice to improve the performance of autonomous systems in an integrated manner.
Maximising Wrenches for Kinematically Redundant Systems with Experiments on UVMS
Venue: arXiv cs.RO 2022
Year: 2022

This paper presents methods for finding optimal configurations and actuator forces/torques to maximise contact wrenches in a desired direction for Underwater Vehicles Manipulator Systems (UVMS). The wrench maximisation problem is formulated as a linear programming problem, and the optimal configuration is solved as a bi-level optimisation in the parameterised redundancy space. We additionally consider the cases of one or more manipulators with multiple contact forces, maximising wrench capability while tracking a trajectory, and generating large wrench impulses using dynamic motions. We look at the specific cases of maximising force to lift a heavy load, and maximising torque during a valve turning operation. Extensive experimental results are presented using an underwater robotic platform equipped with a 4DOF manipulator, and show significant increases in wrench capability compared to existing methods for UVMS.
Pattern Based Multivariable Regression using Deep Learning (PBMR-DP)
Venue: arXiv cs.CV 2022
Year: 2022

We propose a deep learning methodology for multivariate regression that is based on pattern recognition that triggers fast learning over sensor data. We used a conversion of sensors-to-image which enables us to take advantage of Computer Vision architectures and training processes. In addition to this data preparation methodology, we explore the use of state-of-the-art architectures to generate regression outputs to predict agricultural crop continuous yield information. Finally, we compare with some of the top models reported in MLCAS2021. We found that using a straightforward training process, we were able to accomplish an MAE of 4.394, RMSE of 5.945, and R^2 of 0.861.
Learning Parameters for a Generalized Vidale-Wolfe Response Model with Flexible Ad Elasticity and Word-of-Mouth
Venue: arXiv cs.AI 2022
Year: 2022

In this research, we investigate a generalized form of Vidale-Wolfe (GVW) model. One key element of our modeling work is that the GVW model contains two useful indexes representing advertiser's elasticity and the word-of-mouth (WoM) effect, respectively. Moreover, we discuss some desirable properties of the GVW model, and present a deep neural network (DNN)-based estimation method to learn its parameters. Furthermore, based on three realworld datasets, we conduct computational experiments to validate the GVW model and identified properties. In addition, we also discuss potential advantages of the GVW model over econometric models. The research outcome shows that both the ad elasticity index and the WoM index have significant influences on advertising responses, and the GVW model has potential advantages over econometric models of advertising, in terms of several interesting phenomena drawn from practical advertising situations. The GVW model and its deep learning-based estimation method provide a basis to support big data-driven advertising analytics and decision makings; in the meanwhile, identified properties and experimental findings of this research illuminate critical managerial insights for advertisers in various advertising forms.
Stream Containers for Resource-oriented RDF Stream Processing
Venue: arXiv cs.DB 2022
Year: 2022

We introduce Stream Containers inspired by the Linked Data Platform as an alternative way to process RDF streams. A Stream Container represents a single RDF data stream that can be accessed in a resource-oriented way which allows for better interoperability with the existing Semantic Web infrastructure. Stream Containers are managed by webservers that are responsible for implementing the S2R operator, i.e. calculating the window for their clients. The clients on the other hand can use a standard SPARQL processor in combination with HTTP requests to do RDF processing. Query results can be converted back to an RDF stream (R2S operator) by posting the data to a Stream Container. Our approach of resource-oriented RDF stream processing can lead to a better distribution of load and thus to better worldwide scalability. We give a general overview of the proposed architecture as well as the formal semantics of the overall system.