Reviewerly/exHarmony huggingface dataset:
Profiling heliophysics data in the pythonic cloud

Analysis of long timespan heliophysics and space physics data or application of machine learning algorithms can require access to petabyte-scale and larger data sets and sufficient computational capacity to process such “big data”. We provide a summary of Python support and performance statistics for the major scientific data formats under consideration for access to heliophysics data in cloud computing environments. The Heliophysics Data Portal lists 21 different formats used in heliophysics and space physics; our study focuses on Python support for the most-used formats of CDF, FITS, and NetCDF4/HDF. In terms of package support, there is no single Python package that supports all of the common heliophysics file types, while NetCDF/HDF5 is the most supported file type. In terms of technical implementation within a cloud environment, we profile file performance in Amazon Web Services (AWS). Effective use of AWS cloud-based storage requires Python libraries designed to read their S3 storage format. In Python , S3-aware libraries exist for CDF, FITS, and NetCDF4/HDF. The existing libraries use different approaches to handling cloud-based data, each with tradeoffs. With these caveats, Python pairs well with AWS’s cloud storage within the current Python ecosystem for existing heliophysics data, and cloud performance in Python is continually improving. We recommend anyone considering cloud use or optimization of data formats for cloud use specifically profile their given data set, as instrument-specific data characteristics have a strong effect on which approach is best for cloud use.
WireSketch: Bimanual Interactions for 3D Curve Networks in VR

3D content authoring in immersive environments has the advantage of allowing users to see a design result on its actual scale in real time. We present a system to intuitively create and modify 3D curve networks using bimanual gestures in virtual reality (VR). Our system provides a rich vocabulary of interactions in which both hands are used harmoniously following simple and intuitive grammar, and supports comprehensive manipulation of 3D curve networks.
Reducing Computational Cost During Robot Navigation and Human–Robot Interaction with a Human-Inspired Reinforcement Learning Architecture

We present a new neuro-inspired reinforcement learning architecture for robot online learning and decision-making during both social and non-social scenarios. The goal is to take inspiration from the way humans dynamically and autonomously adapt their behavior according to variations in their own performance while minimizing cognitive effort. Following computational neuroscience principles, the architecture combines model-based (MB) and model-free (MF) reinforcement learning (RL). The main novelty here consists in arbitrating with a meta-controller which selects the current learning strategy according to a trade-off between efficiency and computational cost. The MB strategy, which builds a model of the long-term effects of actions and uses this model to decide through dynamic programming, enables flexible adaptation to task changes at the expense of high computation costs. The MF strategy is less flexible but also 1000 times less costly, and learns by observation of MB decisions. We test the architecture in three experiments: a navigation task in a real environment with task changes (wall configuration changes, goal location changes); a simulated object manipulation task under human teaching signals; and a simulated human–robot cooperation task to tidy up objects on a table. We show that our human-inspired strategy coordination method enables the robot to maintain an optimal performance in terms of reward and computational cost compared to an MB expert alone, which achieves the best performance but has the highest computational cost. We also show that the method makes it possible to cope with sudden changes in the environment, goal changes or changes in the behavior of the human partner during interaction tasks. The robots that performed these experiments, whether real or virtual, all used the same set of parameters, thus showing the generality of the method.
Experimental demonstration of tomography-based quantum key distribution

Tomography is a very beneficial and fundamental technique in the fields of quantum information and quantum optics, which can be applied to infer information about quantum states or quantum processes. In quantum key distribution (QKD), tomography can be proposed to improve the secure key rate by taking full advantage of data from both matched and mismatched measurement outcomes to characterize quantum channels accurately. However, to date, no experimental work has been conducted on it. In this work, we study tomography-based QKD (TB-QKD), and for the first time, to the best of our knowledge, carry out proof-of-principle experimental demonstrations by implementing Sagnac interferometers to simulate different transmission channels. Furthermore, we compare it with reference-frame-independent QKD (RFI-QKD) and demonstrate that TB-QKD can significantly outperform RFI-QKD in certain channels, e.g., amplitude damping channel or probabilistic rotation channel.
Edge-Connected Jaccard Similarity for Graph Link Prediction on FPGA

Graph analysis is a critical task in many fields, such as social networking, epidemiology, bioinformatics, and fraud de-tection. In particular, understanding and inferring relationships between graph elements lies at the core of many graph-based workloads. Real-world graph workloads and their associated data structures create irregular computational patterns that compli-cate the realization of high-performance kernels. Given these complications, there does not exist a de facto “best” architecture, language, or algorithmic approach that simultaneously balances performance, energy efficiency, portability, and productivity. In this paper, we realize different algorithms of edge-connected Jaccard similarity for graph link prediction and characterize their performance across a broad spectrum of graphs on an Intel Stratix 10 FPGA. By utilizing a high-level synthesis (HLS)-driven, high-productivity approach (via the C++-based SYCL language) we rapidly prototype two implementations - a from-scratch edge-centric version and a faithfully-ported commodity GPU implementation - which would have been intractable via a hardware description language. With these implementations, we further consider the benefit and necessity of four HLS-enabled optimizations, both in isolation and in concert - totaling seven distinct synthesized hardware pipelines. Leveraging real-world graphs of up to 516 million edges, we show empirically-measured speedups of up to 9.5 x over the initial HLS implementations when all optimizations work in concert.
The effect of service robot occupational gender stereotypes on customers' willingness to use them

Customers have obvious occupational gender stereotypes for service employees. In recent years, intelligent service robots have been widely used in the hospitality industry and have also been given gender characteristics to attract customers to use them. However, whether and when the usage of gendered service robots is effective remains to be explored. This research focuses on customers' occupational gender stereotypes and the gender of service robots, examining the influences of their consistency on customers' willingness to use service robots through three scenario studies. The findings suggest that: (1) The consistency between occupational gender stereotypes and service robot gender positively affects customers' willingness to use service robots. (2) Performance expectancy and trust are two psychological mechanisms underlying the above effect. (3) In the context of service failures, the consistency backfires and brings negative effects on willingness to use. This research extends the literature on customers' acceptance of anthropomorphized robots from the perspective of gender stereotypes and identifies the mechanisms behind the stereotype consistency effect. For practical implications, hotels should design and deploy gendered robots consistent with corresponding occupational gender stereotypes.
FGAC: A Fine-Grained Access Control Framework for Supply Chain Data Sharing

With the rapid development of digital economics, a large number of data have been accumulated in the supply chain system, and data islands have appeared. Data sharing is an imperative way to unlock the data value of a supply chain system. A safe and effective access control mechanism for privacy-sensitive data is key in data sharing. At present, traditional access control mechanisms are static, single-factor control, and prone to a single point of failure. For dealing with these, a fine-grained access control (FGAC) framework for supply chain data sharing is proposed, based on the blockchain Hyperledger Fabric. It augments role-based access control (RBAC) by giving different attribute keywords to different types of users. This framework is implemented in smart contract Chaincodes and quantitatively verified by using the model-checking tool UPPAAL. The experiment results show that the FGAC framework enhances the efficiency and safety in the process of data sharing for the supply chain system, compared with the existing works.
An incentive system for decentralized DAG-based platforms

Decentralized public platforms are becoming increasingly popular due to a growing number of applications for various areas ofbusiness, finance, and social life. Authorless nodes can easily join such networks without any confirmation, making a transparent system of rewards and punishments crucial for the self-sustainability of public platforms. To achieve this, a system for incentivizing and punishing Workers’ behavior should be tightly harmonized with the corresponding consensus protocol, taking into account all of its features, and facilitating a favorable and supportive environment with equal rights for all participants. The main purpose of re-wards is to incentivize Workers to follow the protocol properly, and to penalize them for any type of misbehavior. The issues of block rewarding and punishing in decentralized networks have been well studied, but the DAG referential structure of the distributed ledger forces us to design methods that are more relevant. Since referential structures cannot be reliably validated due to the fact that they are built on the basis of the instantaneous visibility of blocks by a certain node, we propose to set rewards for blocksin the DAG network based on the degree of confidence of topological structures. In doing so, all honest nodes make common decisions based only on information recorded into the ledger, without overloading the network with additional interactions, since such data are al-ways identical and available.The main goal of this work is to design a fair distribution of rewards among honest Workers and estab-lish values for penalties for faulty ones, to ensure the general economic equilibrium of the Waterfall platform. The proposedap-proach has a flexible and transparent architecture that allows for its use for a wide range of PoS-based consensus protocols. The core principles are that Workers' rewards depend on the importance of the conducted work for block producing and achieving consensusand their penalties must not be less than the potential profit from possible attacks. The incentivizing system can facilitate protection from various kinds of attacks, namely, so-called Nothing-at-stake, Rich-get-richer, Sybil, and Splitting attacks, and from some specif-ic threats related to a DAG structure.
Sudden Event Prediction Based on Event Knowledge Graph

Event prediction is a knowledge inference problem that predicts the consequences or effects of an event based on existing information. Early work on event prediction typically modeled the event context to predict what would happen next. Moreover, the predicted outcome was often singular. These studies had difficulty coping with both the problems of predicting sudden events in unknown contexts and predicting outcomes consisting of multiple events. To address these two problems better, we present the heterogeneous graph event prediction model (HGEP), which is based on an event knowledge graph. To cope with the situation of missing contexts, we propose a representation learning method based on the heterogeneous graph transformer. We generate event scenario representations from arguments of the initial event and other related concurrent events for the event prediction. We improve the prediction ability of the HGEP model through prior knowledge provided by scenario models in the event knowledge graph. To obtain multiple prediction outcomes, we design a scoring function to calculate the score of the occurrence probability of each event class. The event classes with scores higher than a priori values are adopted as prediction outcomes. In this paper, we create an event knowledge graph in the domain of transportation for an event prediction experiment. The experimental results show that the HGEP model can effectively make predictions with event scenario representations and has a more accurate matching rate and higher precision than the baseline model.
AI Engineering: Realizing the Potential of AI

Artificial Intelligence (AI) engineering is an engineering discipline that is concerned with all aspects of development and evolution of AI systems (that is, systems that include AI components). AI engineering is primarily an extension of software engineering, but it also includes methods and technologies from data science and AI in general.
Industry experiences with large-scale refactoring

Software refactoring plays an important role in software engineering. Developers often turn to refactoring when they want to restructure software to improve its quality without changing its external behavior. Small-scale (floss) refactoring is common in industry and is often performed by a single developer in short sessions, even though developers do much of this work manually instead of using refactoring tools. However, some refactoring efforts are much larger in scale, requiring entire teams and months or years of effort, and the role of tools in these efforts is not as well studied. In this paper, we report on a survey we conducted with developers to understand large-scale refactoring and its tool support needs. Our results from 107 industry developers demonstrate that projects commonly go through multiple large-scale refactorings, each of which requires considerable effort. Our study finds that developers use several categories of tools to support large-scale refactoring and rely more heavily on general-purpose tools like IDEs than on tools designed specifically to support refactoring. Tool support varies across the different activities, with some particularly challenging activities seeing little use of tools in practice. Furthermore, our analysis suggests significant impact is possible through advances in tool support for comprehension and testing, as well as through support for the needs of business stakeholders.
Hammurabi

This paper proposes using a logic programming language to disentangle X.509 certificate validation policy from mechanism. Expressing validation policies in a logic programming language provides multiple benefits. First, policy and mechanism can be more independently written, augmented, and analyzed compared to the current practice of interweaving them within a C or C++ implementation. Once written, these policies can be easily shared and modified for use in different TLS clients. Further, logic programming allows us to determine when clients differ in their policies and use the power of imputation to automatically generate interesting certificates, e.g., a certificate that will be accepted by one browser but not by another.
An anomaly detection method based on random convolutional kernel and isolation forest for equipment state monitoring

Anomaly detection plays an essential role in health monitoring and reliability assurance of complex system. However, previous researches suffer from distraction by outliers in training and extensively relying on empiric-based feature engineering, leading to many limitations in the practical application of detection methods. In this paper, we propose an unsupervised anomaly detection method that combines random convolution kernels with isolation forest to tackle the above problems in equipment state monitoring. The random convolution kernels are applied to generate cross-dimensional and multi-scale features for multi-dimensional time series, with combining the time series decomposing method to select abnormally sensitive features for automatic feature extraction. Then, anomaly detection is performed on the obtained features using isolation forests with low requirements for purity of training sample. The verification and comparison on different types of datasets show the performance of the proposed method surpass the traditional methods in accuracy and applicability.
Conversational Agents for Information Retrieval in the Education Domain

Text-based conversational agents (CAs) are widely deployed across a number of daily tasks, including information retrieval. However, most existing agents follow a default design that disregards user needs and preferences, ultimately leading to a lack of usage and an unsatisfying user experience. To better understand how CAs can be designed in order to lead to effective system use, we deduced relevant design requirements from both literature and 13 user interviews. We built and tested a question-answering, text-based CA for an information retrieval task in an education scenario. Results from our experimental test with 41 students indicate that following a user-centered design has a significant positive effect on enjoyment and trust in a CA as opposed to deploying a default CA. If not designed with the user in mind, CAs are not necessarily more beneficial than traditional question-answering systems. Beyond practical implications for effective CA design, this paper points towards key challenges and potential research avenues when deploying social cues for CAs.
An Auto-Encoder with Genetic Algorithm for High Dimensional Data: Towards Accurate and Interpretable Outlier Detection

When dealing with high-dimensional data, such as in biometric, e-commerce, or industrial applications, it is extremely hard to capture the abnormalities in full space due to the curse of dimensionality. Furthermore, it is becoming increasingly complicated but essential to provide interpretations for outlier detection results in high-dimensional space as a consequence of the large number of features. To alleviate these issues, we propose a new model based on a Variational AutoEncoder and Genetic Algorithm (VAEGA) for detecting outliers in subspaces of high-dimensional data. The proposed model employs a neural network to create a probabilistic dimensionality reduction variational autoencoder (VAE) that applies its low-dimensional hidden space to characterize the high-dimensional inputs. Then, the hidden vector is sampled randomly from the hidden space to reconstruct the data so that it closely matches the input data. The reconstruction error is then computed to determine an outlier score, and samples exceeding the threshold are tentatively identified as outliers. In the second step, a genetic algorithm (GA) is used as a basis for examining and analyzing the abnormal subspace of the outlier set obtained by the VAE layer. After encoding the outlier dataset’s subspaces, the degree of anomaly for the detected subspaces is calculated using the redefined fitness function. Finally, the abnormal subspace is calculated for the detected point by selecting the subspace with the highest degree of anomaly. The clustering of abnormal subspaces helps filter outliers that are mislabeled (false positives), and the VAE layer adjusts the network weights based on the false positives. When compared to other methods using five public datasets, the VAEGA outlier detection model results are highly interpretable and outperform or have competitive performance compared to current contemporary methods.
SnapPI: Understanding Everyday Use of Personal Informatics Data Stickers on Ephemeral Social Media

Sharing personal informatics data can support accountability, connectedness, and self-expression, but people often find their data too trivial to share on social media. Ephemeral social platforms like Snapchat and Story features have emerged as spaces for sharing more trivial life events, presenting an opportunity to incorporate self-tracked data into sharing. Past work suggests that including data-driven stickers on these platforms can help add additional context to what people share, but little is known about the benefits and challenges of people's everyday experiences with this concept. To understand people's everyday use of data-driven stickers, we designed and developed SnapPI, an app for flexibly incorporating data into stickers for Snapchat. We deployed SnapPI to 21 participants for two weeks, finding that participants value aligning data sharing with Snapchat's communication and stylistic norms. Perceiving Snapchat as a playful platform, participants connected data stickers with various visual components of their Snaps. Stickers were used to incorporate personal informatics data into their existing conversations, and were edited to align with different audience needs or to be expressive. We discuss recommendations for personal data sharing, suggesting supporting flexibility in presentation and aligning with the norms of existing platforms.
Understanding Cultural Influence on Perspectives Around Contact Tracing Strategies

Contact tracing, a major way to curb COVID-19 and other epidemics, has been employed worldwide, with human interviewing and proximity tracing technology as two major approaches. While previous research has contributed some understanding of people's perspectives on contact tracing technology, much of this is based in single countries or regions where technology has been deployed. To understand how culture influences people's perceptions toward human tracing and digital tracing, we replicated a mixed-methods survey study conducted in the U.S. in South Korea and compared participants' perspectives. South Korean participants preferred digital tracing to human tracing, contrasting with the U.S. context where no strong preference was observed. We discuss how observed differences in perspective align and contrast with the country's typical cultural dimensions, such as high power distance, informing the perspective that human tracing will have greater accuracy. We emphasize the need for culturally designing contact tracing technology to highlight personal benefits regardless of cultural dimensions, and leverage technology to support social interaction in human tracing.
Revisiting Piggyback Prototyping: Examining Benefits and Tradeoffs in Extending Existing Social Computing Systems

The CSCW community has a history of designing, implementing, and evaluating novel social interactions in technology, but the process requires significant technical effort for uncertain value. We discuss the opportunities and applications of "piggyback prototyping", building and evaluating new ideas for social computing on top of existing ones, expanding on its potential to contribute design recommendations. Drawing on about 50 papers which use the method, we critically examine the intellectual and technical benefits it provides, such as ecological validity and leveraging well-tested features, as well as research-product and ethical tensions it imposes, such as limits to customization and violation of participant privacy. We discuss considerations for future researchers deciding whether to use piggyback prototyping and point to new research agendas which can reduce the burden of implementing the method.