Miracle-dz/UniFAR-FTUs huggingface dataset:
Learning with Stitch Samplers: Exploring Stitch Samplers as Contextual Instructions for E-textile Tutorials

The field of textile fabrication has a strong pattern-making culture that enables individuals to reproduce items at home. Electronic textile (e-textile) researchers within HCI are increasingly exploring how computing can leverage these textile pattern-making practices, accessible fabrication tools, and do-it-yourself (DIY) maker cultures to enable individuals to make technologies for themselves with soft form factors that further blend computing into our everyday environments. In this paper we focus on the pattern-sharing artifact of stitch samplers, which are used for sharing, teaching, and learning stitching techniques, and explore how the design decisions around them should be adapted for practicing e-textile exercises. To do so, we conducted three studies: (1) preliminary interviews with five modern stitch sampler designers to understand what stitch samplers are used for, (2) a think-aloud user study of our initial e-textile sampler with ten beginners, and (3) interviews with five e-textile educators to reflect on applications and to better understand the opportunities and limitations of using samplers for distance learning. This paper contributes a better understanding of how HCI researchers can incorporate craft pattern practices for learning hybrid craft techniques.
Artist Support Networks: Implications for Future Creativity Support Tools

The artist as a solitary genius does not reflect the reality of art-making. To enable art-making, artists are supported by many other people—subcontractors, collaborators, etc.—who collectively form an Artist’s Support Network. Through an interview of 14 artists, we map the space of relationship types, provided support, interactions, failures, and successes of human support relationships. Moreover, we identified the patterns by which these aspects relate to each other in different support relationships. As technologically-driven Creativity Support Tools (CSTs) emerge to augment and automate portions of the artist’s support network, the detail of these interactions becomes critical. Existing sites of collaboration in support networks invariably shape artists’ expectations. How a CST fits within existing interaction expectations will shape the design, the artist’s understanding, and ultimately, acceptance. With this lens, we reflect on how a CST’s design–and in particular, those support collaboration and AI-driven variants–will mesh with the artist’s support network.
Designing public VR installations:

Virtual Reality (VR) installations using head-mounted displays (HMD) are becoming increasingly popular in public spaces. However, VR's immersive nature engages only the HMD wearer and excludes everyone else in the public space, and there is little design knowledge of how to engage those not wearing an HMD. To address this, we draw from our experiences of having designed seven public VR installations to present a design space around the dimensions of “agency” and “interest” with four user engagement frames to articulate twelve different user roles. To guide designers to support all roles and to transition users between those roles, we complement the design space with a set of design tactics for public VR installations. We hope that these combined contributions will help designers engage more people with VR installations so that ultimately more people benefit from what VR has to offer.
From Tool to Companion: Storywriters Want AI Writers to Respect Their Personal Values and Writing Strategies

Modern large-scale language models approach the quality of human-level writing. This promises the advent of AI writing companions performing AI-led writing under human control, surpassing traditional writing tools limited to revision and ideation supports. However, human-AI co-writing may endanger writers’ control, autonomy, and ownership by overstepping co-creative boundaries. Our design workbook study with 7 hobbyists and 13 professional writers elicited three sets of primary barriers to the adoption of human-AI co-writing. Storywriters desire retaining control over writing rather than letting AI take the lead when they (1) prioritize emotional values in turning ideas into words over the productivity of AI-generated writing; (2) have high self-confidence and distrust AI in challenging sub-tasks (e.g., creating characters and dialogue); and (3) expect the AI control mechanism to mismatch their writing strategies. We lay the groundwork for AI companions that respect storywriters’ personal values and writing methods.
Klasifikasi Bahasa Isyarat Indonesia Berbasis Sinyal EMG Menggunakan Fitur Time Domain (MAV, RMS, VAR, SSI)

Penggunaan kamera yang dipakai sebagai input media bantu untuk pengenalan bahasa isyarat masih bergantung pada kondisi lingkungan. Sinyal EMG merupakan sinyal yang berasal dari pembacaan aktivitas otot tangan, sehingga sinyal EMG tidak bergantung pada kondisi lingkungan. Oleh karena itu sinyal EMG dapat dimanfaatkan untuk mengenali gerakan bahasa isyarat. Agar dapat digunakan untuk mengenali sebuah gerakan, komputer memerlukan sebuah mekanisme standar dan logis. Permasalah utama yang terjadi dalam pengenalan gerakan adalah bagaimana cara menghasilkan data yang representatif dan konsisten terhadap sampel gerakan. Sinyal EMG hasil perekaman akan dilakukan proses ekstraksi fitur berdasarkan time domain feature dengan metode MAV, RMS, VAR dan SSI. Hasil ekstraksi fitur tersebut akan digunakan sebagai input klasifikasi menggunakan metode naive bayes. Gerakan bahasa isyarat yang dikenali pada penelitian ini ada 20 gerakan. Hasil akurasi pengenalan gerakan antara data training diujikan terhadap data baru dengan perbandingan data 50:50 yaitu sebesar 79%. Jumlah perbandingan data training yang optimal digunakan untuk pengenalan 20 gerakan Bahasa isyarat Indonesia adalah ≥50% dari total data sampel dimana berada pada rata-rata 80%.
Parallel Rule Discovery from Large Datasets by Sampling

Rule discovery from large datasets is often prohibitively costly. The problem becomes more staggering when the rules are collectively defined across multiple tables. To scale with large datasets, this paper proposes a multi-round sampling strategy for rule discovery. We consider entity enhancing rules (REEs) for collective entity resolution and conflict resolution, which may carry constant patterns and machine learning predicates. We sample large datasets with accuracy bounds a and B such that at least a% of rules discovered from samples are guaranteed to hold on the entire dataset (i.e., precision), and at least B% of rules on the entire dataset can be mined from the samples (i.e., recall). We also quantify the connection between support and confidence of the rules on samples and their counterparts on the entire dataset. To scale with the number of tuple variables in collective rules, we adopt deep Q-learning to select semantically relevant predicates. To improve the recall, we develop a tableau method to recover constant patterns from the dataset. We parallelize the algorithm such that it guarantees to reduce runtime when more processors are used. Using real-life and synthetic data, we empirically verify that the method speeds up REE discovery by 12.2 times with sample ratio 10% and recall 82%.
Hybrid Deterministic and Nondeterministic Execution of Transactions in Actor Systems

The actor model has been widely adopted in building stateful middle-tiers for large-scale interactive applications, where ACID transactions are useful to ensure application correctness. In this paper, we present Snapper, a new transaction library on top of Orleans, a popular actor system. Snapper exploits the characteristics of actor-oriented programming to improve the performance of multi-actor transactions by employing deterministic transaction execution, where pre-declared actor access information is used to generate deterministic execution schedules. The deterministic execution can potentially improve transaction throughput significantly, especially with a high contention level. Besides, Snapper can also execute actor transactions using conventional nondeterministic strategies, including S2PL, to account for scenarios where actor access information cannot be pre-declared. A salient feature of Snapper is the ability to execute concurrent hybrid workloads, where some transactions are executed deterministically while the others are executed nondeterministically. This novel hybrid execution is able to take advantage of the deterministic execution while being able to account for nondeterministic workloads. Our experimental results on two benchmarks show that deterministic execution can achieve up to 2x higher throughput than nondeterministic execution under a skewed workload. Additionally, the hybrid execution strategy can achieve a throughput that is close to deterministic execution when there is only a small percentage of nondeterministic transactions running in the system.
Efficient Personalized PageRank Computation: A Spanning Forests Sampling Based Approach

Computing the personalized PageRank vector is a fundamental problem in graph analysis. In this paper, we propose several novel algorithms to efficiently compute the personalized PageRank vector with a decay factor α based on an interesting connection between the personalized PageRank values and the weights of random spanning forests of the graph. Such a connection is derived based on a newly-developed matrix forest theorem on graphs. Based on this, we present an efficient spanning forest sampling algorithm via simulating loop-erased α-random walks to estimate the personalized PageRank vector. Compared to all existing methods, a striking feature of our approach is that its performance is insensitive w.r. t. (with respect to) the parameter α. As a consequence, our algorithm is often much faster than the state-of-the-art algorithms when α is small, which is the demanding case for many graph analysis tasks. We show that our technique can significantly improve the efficiency of the state-of-the-art algorithms for answering two well-studied personalized PageRank queries, including single source query and single target query. Extensive experiments on seven large real-world graphs demonstrate the efficiency of the proposed method.
BiDEDE'22: Second International Workshop on Big Data in Emergent Distributed Environments

The Second International Workshop on Big Data in Emergent Distributed Environments (BiDEDE) focuses on scalable data management issues in emergent computing environments like (post) cloud and fog/edge/dew computing. All these computing environments aim to smoothly integrate scalable data management and processing into distributed environments, such that communication and computational costs are reduced for higher throughput, lower latencies of applications and extending battery lifetimes of nodes in companion with robust approaches to overcome failures and crashes. While there has been research in these areas for already over one decade, still many open challenges exist because of technology triggers like lightweight virtualization, increasing capabilities of nodes and increasing massive parallelization. This workshop supports lively discussions in these and related areas.
Critically Engaging with Embedded Values through Constrained Technology Design

The rise of gig economy platforms has highlighted the impact platform and algorithm design can have upon workers’ experiences. This paper reports on an extended series of collaborative design engagements with a private company and an international non-government organisation during the production of an Interactive Voice Response component for a gig economy platform. We present the findings of a design ethnography undertaken during this process, and discuss how design decisions reflect how each party’s values, motivations and assumptions are embedded within the final technology. There exists a need for simple methods to assist practitioners to surface and critically engage with the disparate values, priorities and assumptions held by the system’s stakeholders. We demonstrate that this can be done during the production of real-world systems through the application of constrained design as a values lever, and discuss how constraint-based values levers can support critical reflection, even in resource-constrained commercial development contexts.
BlindFL: Vertical Federated Machine Learning without Peeking into Your Data

Due to the rising concerns on privacy protection, how to build machine learning (ML) models over different data sources with security guarantees is gaining more popularity. Vertical federated learning (VFL) describes such a case where ML models are built upon the private data of different participated parties that own disjoint features for the same set of instances, which fits many real-world collaborative tasks. Nevertheless, we find that existing solutions for VFL either support limited kinds of input features or suffer from potential data leakage during the federated execution. To this end, this paper aims to investigate both the functionality and security of ML modes in the VFL scenario. To be specific, we introduce BlindFL, a novel framework for VFL training and inference. First, to address the functionality of VFL models, we propose the federated source layers to unite the data from different parties. Various kinds of features can be supported efficiently by the federated source layers, including dense, sparse, numerical, and categorical features. Second, we carefully analyze the security during the federated execution and formalize the privacy requirements. Based on the analysis, we devise secure and accurate algorithm protocols, and further prove the security guarantees under the ideal-real simulation paradigm. Extensive experiments show that BlindFL supports diverse datasets and models efficiently whilst achieves robust privacy guarantees.
Efficient Incrementialization of Correlated Nested Aggregate Queries using Relative Partial Aggregate Indexes (RPAI)

Incrementalization of queries is imperative in cases where data arrives as streams and output is latency-critical and/or desired before the full data has been received. Incremental execution computes the output at a given time by reusing the previously computed outputs or maintained views rather than re-evaluating the query from scratch. There are various approaches to perform this incrementalization ranging from query-specific algorithms and data structures (e.g., DYN, AJU) to general systems (e.g., DBToaster, Materialize). DBToaster is a state-of-the-art system that comes with an appealing theoretical background based on the idea of applying Incremental View Maintenance (IVM) recursively, maintaining a hierarchy of materialized views via delta queries. However, one key limitation of this approach is its inability to efficiently incrementalize correlated nested-aggregate queries due to an inefficient delta rule for such queries. Moreover, none of the other specialized approaches have shown efficient ways to optimize such queries either. Nonetheless, these types of queries can be found in many real-world application domains (e.g., finance), for which efficient incrementalization remains a crucial open problem. In this work, we propose an approach to incrementalize such queries based on a novel tree-based index structure called Relative Partial Aggregate Indexes (RPAI). Our approach is asymptotically faster than other systems and shows up to 1100× speedups in workloads of practical importance.
Avoiding Read Stalls on Flash Storage

When a dirty victim page is selected for replacement upon page miss, the buffer manager has to first flush the dirty victim to the storage before reading the missing page. This conventional read-after-write (RAW) protocol, while working well on hard disks, causes the problem of read stall on flash storage with asymmetric read-write speed and parallelism; because of the resource conflict for a buffer frame between write and read operations, a page-missing process has to wait for the slow write to complete to secure a clean frame for the missing page. This strict write-then-read serialization under-utilizes CPU and storage, worsening transaction throughput and latency. To avoid the read stall problem on flash storage, this paper proposes write-after-read (WAR) protocol as a new I/O architecture between buffer manager and flash storage. In WAR, foreground processes make victim frames clean instantly by temporarily copying dirty pages at LRU tail into a separate DRAM space and read their missing pages into the cleaned frames with no stall. The dirty pages will be written to the storage asynchronously. By resolving resource conflict and thus avoiding read stalls, the database engine can issue more I/Os in parallel and better utilize CPU as well as storage, improving throughput and latency. We prototype WAR in two database storage engines, MySQL/InnoDB and Zero. Our comprehensive experimental results show that WAR improves transaction throughput by up to 2.9x compared to RAW.
Compact Walks: Taming Knowledge-Graph Embeddings with Domain- and Task-Specific Pathways

Knowledge-graph (KG) embeddings have emerged as a promise in addressing challenges faced by modern biomedical research, including the growing gap between therapeutic needs and available treatments. The popularity of KG embeddings in graph analytics is on the rise, due at least partially to the presumed semanticity of the learned embeddings. Unfortunately, the ability of a node neighborhood picked up by an embedding to capture the node's semantics may depend on the characteristics of the data. One of the reasons for this problem is that KG nodes can be promiscuous, that is, associated with a number of different relationships that are not unique or indicative of the properties of the nodes. To address the promiscuity challenge and the documented runtime-performance challenge in real-life KG embedding tools, we propose to use domain- and task-specific information to specify regular-expression pathways that define neighborhoods of KG nodes of interest. Our proposed CompactWalks framework uses these semantic subgraphs to enable meaningful compact walks in random-walk based KG embedding methods. We report the results of case studies for the task of determining which pharmaceutical drugs could treat the same diseases. The findings suggest that our CompactWalks approach has the potential to address the promiscuity and runtime-performance challenges in applying embedding tools to large-scale KGs in real life, in the biomedical domain and possibly beyond.
HiEngine: How to Architect a Cloud-Native Memory-Optimized Database Engine

Fast database engines have become an essential building block in many systems and applications. Yet most of them are designed based on on-premise solutions and do not directly work in the cloud. Existing cloud-native database systems are mostly disk resident databases that follow a storage-centric design and exploit the potential of modern cloud infrastructure, such as manycore processors, large main memory and persistent memory. However, in-memory databases are infrequent and untapped. This paper presents HiEngine, Huawei's cloud-native memory-optimized in-memory database engine that endows hierarchical database architecture and fills this gap. HiEngine simultaneously (1) leverages the cloud infrastructure with reliable storage services on the compute-side (in addition to the storage tier) for fast persistence and reliability, (2) achieves main-memory database engines' high performance, and (3) retains backward compatibility with existing cloud-native database systems. HiEngine is integrated with Huawei GaussDB(for MySQL), it brings the benefits of main-memory database engines to the cloud and co-exists with disk-based engines. Compared to conventional systems, HiEngine outperforms prior storage-centric solutions by up to 7.5X and provides comparable performance to on-premise memory-optimized database engines.
Virtepex: Virtual Remote Tele-Physical Examination System

Remote strength assessment is critical for providing accessible rehabilitation, especially in the absence of in-person meetings due to the pandemic. In this paper, we introduce ”Virtepex”, an immersive exergame for remote strength assessment developed through participatory design principles. We bring out the design process starting with a needs assessment to highlight the challenges for physicians in telehealth, followed by the expert guidelines for iterative system refinement. Virtepex addresses the challenges for remote strength assessment through a marker-less and an easy-to-setup strength estimation pipeline. It utilizes an RGB-D camera for motion tracking and an inverse dynamics module for force estimation. The force estimates are used for VR object interaction and can be assessed by a physician synchronously or asynchronously for an objective evaluation. Validation by external experts shows that Virtepex produces reliable force estimates for upper body joints, indicating the potential of marker-less force estimation for future remote assessment designs.
FILA: Online Auditing of Machine Learning Model Accuracy under Finite Labelling Budget

Machine learning (ML) is increasingly adopted in industrial applications. Typically, a ML pipeline is instantiated to automate the process of collecting training data, training a model, auditing the model accuracy and generating predictions. In this paper we present a sampling based approach to audit the model accuracy in ML pipelines in an online manner, with general applicability, including entity resolution. We target an online setting in which a deployed model makes predictions, which can be selectively assessed for accuracy by humans in the loop. We present a consistent adaptive stratified sampling estimator for model accuracy and propose the Finite Labels (FILA) method to allocate samples under a finite label budget. We demonstrate that under mild statistical assumptions FILA is asymptotically optimal. We analyze the variance of the estimator under a finite labelling budget and compare our approach to other applicable techniques and analytically establish the conditions under which our proposed FILA is the method of choice. We also present an algorithm based on Thompson Sampling, named FILA-Thompson, utilizing explore-exploit trade-offs to estimate model accuracy. Finally we present the results of a thorough experimental evaluation using real benchmark data sets demonstrating the practical utility (in terms of estimation accuracy and variance minimization under finite samples) of our proposals compared to other applicable approaches.
Natto: Providing Distributed Transaction Prioritization for High-Contention Workloads

This paper introduces Natto, a geo-distributed database system that supports transaction prioritization. Instead of having each shard process transactions in their arrival order, Natto leverages network measurements to estimate the transaction arrival time at each shard, and assigns a timestamp to the transaction based on its arrival time to the furthest shard. These timestamps establish a global ordering of transactions, and introduces opportunities to selectively abort pending low-priority transactions that conflict with a high-priority transaction, or even preempt transactions that are already partially prepared. Our experiments on both Microsoft Azure and a local cluster show that Natto's tail latency for high-priority transactions are significantly lower than the tail latencies of Carousel and TAPIR, which are the current state-of-the-art in geo-distributed transaction processing systems.