Miracle-dz/UniFAR-FTUs huggingface dataset:
Lineage Resource Manager

Main memory columnar database systems such as HyPer [1], Hana [4], MemSQL [9] have rapidly grown in use for both OLTP and OLAP applications in enterprise software [5]. Lineage capture in such in-memory columnar database systems incurs memory and performance overhead[2]. Hence we want to control the lineage capture overhead in such systems. We propose a lineage resource manager that employs specific mechanisms to control the lineage capture overhead. For implementing the resource manager, we consider an in-memory columnar query execution engine that supports concurrent query execution, where operators have been instrumented to capture lineage. Controlling lineage capture overhead is more imminent in embedded analytical systems (database systems running alongside another process, not as a standalone process) like DuckDB [11] and MonetDBLite [10].
Diva: Making MVCC Systems HTAP-Friendly

Multiversion concurrency control (MVCC) and design principles thereof are ingrained in modern database management systems, thus promoting remarkable progress in managing online transaction processing (OLTP) workloads for decades. However, MVCC systems would battle two vital concerns when facing hybrid transactional/analytical processing (HTAP). The first concern is to ensure rapid version searching for analytic queries with less I/O, and the second concern is to reclaim garbage data versions promptly for easing the strain on storage footprint. These are often tightly coupled since many MVCC systems rely on unified version storage that poses a space-time tradeoff in HTAP, giving rise to disappointing performance metrics that may negatively stereotype OLTP-friendly MVCC systems. This paper refutes the stereotype resulting from coupled design concerns and addresses the core problem by proposing Diva (Decoupling Index from Version dAta) that physically separates version index from version data; for decoupled concerns, we devise independent management policies: provisional version indexing and time interval-based version garbage collection. The separation of coupled concerns would render legacy disk-based MVCC systems more HTAP-friendly. We applied Diva to two full-fledged database systems---PostgreSQL and MySQL---and demonstrated that the systems with Diva escaped the space-time tradeoff under hybrid transactional/analytical workloads.
Classifier Construction Under Budget Constraints

Search mechanisms over large assortments of items are central to the operation of many platforms. As users commonly express filtering conditions based on item properties that are not initially stored, companies must derive the missing information by training and applying binary classifiers. Choosing which classifiers to construct is however not trivial, since classifiers differ in construction costs and range of applicability. Previous work has considered the problem of selecting a classifier set of minimum construction cost, but this has been done under the (often unrealistic) assumption that the available budget is unlimited and allows to support all search queries. In practice, budget constraints require prioritizing some queries over others. To capture this consideration, we study in this work a more general model that allows assigning to each search query a score that models how important it is to compute its result set and examine the optimization problem of selecting a classifier set, whose cost is within the budget, that maximizes the overall score of the queries it can answer. We show that this generalization is likely much harder to approximate complexity-wise, even assuming limited special cases. Nevertheless, we devise a heuristic algorithm, whose effectiveness is demonstrated in our experimental study over real-world data, consisting of a public dataset and datasets provided by a large e-commerce company that include costs and scores derived by business analysts. Finally, we show that our methods are applicable also for related problems in practical settings where there is some flexibility in determining the budget.
Study on modulation amplitude stabilization method for PEM based on FPGA in atomic magnetometer

Atomic magnetometer which uses atoms as sensitive elements have ultra-high precision and has wide applications in scientific researches. The photoelastic modulation method based on photoelastic modulator (PEM) is used in the atomic magnetometer to detect the small optical rotation angle of a linearly polarized light. However, the modulation amplitude of the PEM will drift due to the environmental factors, which reduces the precision and long-term stability of the atomic magnetometer. Consequently, stabilizing the PEM’s modulation amplitude is essential to precision measurement. In this paper, a modulation amplitude stabilization method for PEM based on Field Programmable Gate Array (FPGA) is proposed. The designed control system contains an optical setup and an electrical part. The optical setup is used to measure the PEM’s modulation amplitude. The FPGA chip, with the PID control algorithm implemented in it, is used as the electrical part’s micro controller. The closed loop control method based on the photoelastic modulation detection system can directly measure the PEM’s modulation amplitude in real time, without increasing the additional optical devices. In addition, the operating speed of the modulation amplitude stabilization control system can be greatly improved because of the FPGA’s parallel computing feature, and the PID control algorithm ensures flexibility to meet different needs of the PEM’s modulation amplitude set values. The Modelsim simulation results show the correctness of the PID control algorithm, and the long-term stability of the PEM’s modulation amplitude reaches 0.35% in a 3-hour continuous measurement.
Mondrian: Spreadsheet Layout Detection

Spreadsheet datasets are valuable sources of data, but often ill-suited for machine consumption. Their unstructured nature allows users to arrange data and metadata freely in a human-readable format, often in canvas-like layouts. To extract their content, data practitioners need to resort to manual inspection and run cumbersome preparation pipelines. The Mondrian system assists users in identifying and handling multiregion layout templates: spreadsheet layouts composed of independent regions that appear repeatedly across different files. Mondrian comprises an automated approach to detect multiple regions within a single file and an algorithm that leverages mapping region layouts to graphs to compute layout similarity and identify templates. Users interact with Mondrian through a web-based visual interface, that serves as a practical toolkit to handle collections of multiregion spreadsheets and enables their automated preparation.
TimeUnion: An Efficient Architecture with Unified Data Model for Timeseries Management Systems on Hybrid Cloud Storage

Timeseries management systems have attracted considerable attention during the last decade with the rise of IoT and performance monitoring. With the rapidly increasing data scale in the production environment, deploying timeseries management systems on cloud with cloud storage is a natural trend because of its high availability, reliability, and scalability. However, the state-of-the-art designs are not tailored for cloud environments; they suffer from the limited number of timeseries a single compute node can handle because of the imbalanced resource usage. In this paper, we present TimeUnion, an efficient timeseries management system tailored for hybrid cloud storage services (e.g. fast cloud block stores and slow cloud object stores). First, we propose our unified data model to represent both independent timeseries and groups of timeseries, which is capable of handling diverse use cases of timeseries. Second, since the main bottleneck of the current timeseries systems is the overuse of memory, we introduce our exploration on memory-efficient data structures for timeseries to maximize the number of timeseries a compute instance can maintain. Third, to absorb the memory data chunks quickly, we present our time-partitioned LSM-tree with tailored architecture, compaction mechanism, out-of-order data handling, and dynamic level size adjustment for timeseries data. We prototype TimeUnion with C++ from scratch and evaluate it on AWS EC2 with AWS EBS (cloud block store) and AWS S3 (cloud object store). Compared to the storage engine of Cortex, TimeUnion can handle at least 5x more timeseries, and achieve at least 24.8% higher insertion throughput and 49.8% lower query latency. We have released the open-source code of TimeUnion for public access.
Identifying customer knowledge on social media through data analytics

Purpose Customer knowledge from social media can become an important organizational asset. The purpose of this paper is to identify useful customer knowledge including knowledge for customer, knowledge about customers and knowledge from customers from social media data and facilitate social media-based customer knowledge management. Design/methodology/approach The authors conducted a case study to analyze people’s online discussion on Twitter regarding laptop brands and manufacturers. After collecting relevant tweets using Twitter search APIs, the authors applied statistical analysis, text mining and sentiment analysis techniques to analyze the social media data set and visualize relevant insights and patterns in order to identify customer knowledge. Findings The paper identifies useful insights and knowledge from customers and knowledge about customers from social media data. Furthermore, the paper shows how the authors can use knowledge from customers and knowledge about customers to help companies develop knowledge for customers. Originality/value This is an original social media analytics study that discusses how to transform large-scale social media data into useful customer knowledge including knowledge for customer, knowledge about customers and knowledge from customers.
Approximate Range Thresholding

In this paper, we study the (approximate) Range Thresholding (RT) problem over streams. Each stream element is a d-dimensional point and with a positive integer weight. An RT query q specifies a d-dimensional axis-parallel rectangular range R(q) and a positive integer threshold τ(q). Once the query q is registered in the system, define s(q) as the total weight of the elements that satisfy: (i) they arrive after q's registration, and (ii) they fall in the range R(q). Given a real number 0 < ε < 1, the task of the system is to capture an arbitrary moment during the period between the first moment when s(q) ≥ (1 - ε)⋅ τ(q) and the first moment when s(q) ≥ τ(q). The challenge is to support a large number of RT queries simultaneously while achieving sub-quadratic overall running time and near-linear space consumption all the time. We propose a new algorithm called FastRTS, which can reduce the exponent in the poly-logarithmic factor of the state-of-the-art QGT algorithm from d+1 to d, yet slightly increasing the łog term itself. Besides, we propose two extremely effective optimization techniques which significantly improve the practical performance of FastRTS. Experimental results show that FastRTS outperforms the competitors by up to three orders of magnitude in both running time and peak memory usage.
EmotiTactor: Exploring How Designers Approach Emotional Robotic Touch

In this work, we bring designers into the exploration of emotional robotic touch, discuss their design decisions and reflect on their insights. Prior psychology findings show humans can communicate distinct emotions solely through touch. We hypothesize that similar effects might also be applicable to robotic touch. To enable designers to easily generate and modify various types of affective touch for conveying emotions (e.g., anger, happiness, etc.), we developed a platform consisting of a robotic tactor interface and a software design tool. When conducting an elicitation study with eleven interaction designers, we discovered common patterns in their generated tactile sensations for each emotion. We also illustrate the strategies, metaphors, and reactions that the designers deployed in the design process. Our findings uncover that the “otherness” of robotic touch broadens the design possibilities of emotional communication beyond mimicking interpersonal touch.
Voices of Sexual Assault Survivors: Understanding Survivors’ Experiences of Interactional Breakdowns and Design Ideas for Solutions

From initial case-reporting at the crime scene to finishing legal procedures, survivors of sexual assault navigate numerous human and technological resources provided by various organizations. During the help-seeking process, survivors unavoidably interact with multiple professional stakeholders (e.g., legal authorities) and technologies (e.g., checking their case status on a court website). In the long and complex process, survivors experience interactional breakdowns with technology, and/or humans, but few studies have explored what types of breakdowns systematically occur and how to resolve them. Thus, for this study, we conducted in-depth interviews with survivors and professionals who reside in South Korea to identify what and how breakdowns occur. Moreover, participatory design sessions were conducted with sexual assault survivors and professionals to create designs that could resolve the breakdowns. Consequently, we discovered a total of eleven breakdowns and produced solutions centered on the stakeholders (i.e., survivor and professionals). Specifically, our participants wanted an integrated system that proactively informs survivors of the holistic procedures for help-seeking and legal action, manages their case, and even interacts with legal authorities on the survivor's behalf. Based on the findings, we provide an agenda of essential designs and features that could mitigate interactional breakdowns. Additionally, we call for the HCI community to approach and solve sexual violence problems through a broad, macroscopic perspective instead of focusing on one specific technology or (social or organizational) system.
Overlap between Automated Unit and Acceptance Testing – a Systematic Literature Review

Unit and automated acceptance testing have different objectives (e.g., testing units of code versus testing complete features). Testing practices (e.g., test-first, model-based) used for one “level” of testing (for either unit or acceptance testing) may require knowledge and skills that are not applicable to the other. This makes it difficult for practitioners to gain the skills required to effectively test at all levels and form a cohesive testing strategy. The aim of this systematic literature review is to understand whether there are any automated unit testing practices that have similarities with automated acceptance testing practices (and vice versa). Understanding these similarities can enable skill transfer across testing activities at different levels. This systematic literature review focuses on empirical research with an industry focus. We found that test-driven development (TDD) and model-based test generation (MBTG) are two practices widely researched for both unit testing and acceptance testing. For TDD we found that a design- and test-first mindset is required and helpful at both the unit and acceptance levels, but practitioners struggle with that practice. For MBTG we found that, despite its ability to increase code coverage, the additional manual effort to enable automated test generation may outweigh its benefits.
Experiential Value in Group Browsing of Curios on eBay and In-Person: Implications for Future Platform Design

eBay is the world's largest auction site, with a variety of antique, vintage and one-off items listed. In-person shopping for these frequently takes place within markets and antiques fairs, underpinned by a rich set of co-browsing practices. However, these collective shopping experiences are poorly supported online, with platforms predominantly designed to accommodate and support an individual user experience. Using collective autoethnography (CAE) over a period of 15 months we engaged in a series of (16) online co-browsing sessions using a configuration of video-mediated communication to co-browse eBay together, augmenting the interface to introduce sociality. This was supplemented by 8hrs of field studies split over two in-person co-browsing sessions within physical retail locations. From these studies we draw together insights on the experiential nature and practices of co-browsing on and offline, presenting recommendations to inform the design of future online retail spaces, with an aim to support better collaboration and sociality.
HTAP Databases: What is New and What is Next

Processing the mixed workloads of transactions and analytical queries in a single database system can eliminate the ETL process and enable real-time data analysis on the transaction data. However, there is no free lunch. Such systems must balance the trade-off between workload isolation and data freshness due to interweaving workloads of OLTP and OLAP. Since Gartner coined the term, Hybrid Transactional/Analytical Processing (HTAP), we have witnessed the emergence of various database systems to support HTAP. One common feature is that they leverage the best of row store and column store to achieve high quality of HTAP. As they have disparate storage strategies and processing techniques to satisfy the requirements of various HTAP applications, it is essential to understand, compare, and evaluate their key techniques. In this tutorial, we offer a comprehensive survey of HTAP databases. We introduce a taxonomy of state-of-the-art HTAP databases according to their storage strategies and architectures. We then take a deep dive into their key techniques regarding transaction processing, analytical processing, data synchronization, query optimization, and resource scheduling. We also introduce existing HTAP benchmarks. Finally, we discuss the research challenges and open problems for HTAP.
A Low Volume Hybrid Step-Down DC-DC Converter For Battery Powered Portable Applications

A Low-Volume Hybrid Step-Down DC-DC Converter For Battery Powered Portable Applications Yingxian (Mia) Ma Master of Applied Science Graduate Department of Edward S. Rogers Sr. Electrical & Computer Engineering University of Toronto March 2018 This thesis describes a hybrid step-down dc-dc converter, which, compared to conventional buck, provides a lower volume solution in battery-powered portable applications. The new architecture combines a switched-capacitor and conventional buck converter such that the size of the inductor is reduced. Moreover, the introduced topology improves efficiency by up to a 5% compared to a conventional buck. The results are verified with an experimental prototype. The thesis also compares the introduced topology with state of the art step-down topologies using an optimization tool that generates the highest efficiency solutions for a given volume constraint.
Decisions-making in Agile Information Systems Development: The Role of Empowerment and Authority

Agile information systems development (ISD) teams are claimed to work via self-organized and autonomous methods. Yet little research exists on how these teams make decisions. Therefore, this research-in-progress paper conceptualizes a multilevel model that examines decision-making–team empowerment and project managers’ decision-making authority–in agile ISD teams and its effect on team members’ work engagement and performance. Preliminary results show satisfactory psychometric criteria for the measurement instrument. Within the main study, multilevel analyses using hierarchical linear modeling and hierarchical linear regression will be used to examine cross-level effects. We expect our research to contribute to deeper understanding of agile ISD teams’ decision-making and to the limited evidence on project managers’ role in agile ISD.
A Study on Efficient Data De-Identification Method for Blockchain DID

Blockchain is a technology that enables trust-based consensus and verification based on a decentralized network. Distributed ID (DID) is based on a decentralized structure, and users have the right to manage their own ID. Recently, interest in self-sovereign identity authentication is increasing. In this paper, as a method for transparent and safe sovereignty management of data, among data pseudonymization techniques for blockchain use, various methods for data encryption processing are examined. The public key technique (homomorphic encryption) has high flexibility and security because different algorithms are applied to the entire sentence for encryption and decryption. As a result, the computational efficiency decreases. The hash function method (MD5) can maintain flexibility and is higher than the security-related two-way encryption method, but there is a threat of collision. Zero-knowledge proof is based on public key encryption based on a mutual proof method, and complex formulas are applied to processes such as personal identification, key distribution, and digital signature. It requires consensus and verification process, so the operation efficiency is lowered to the level of O (log e N) ~ O(N 2 ). In this paper, data encryption processing for blockchain DID, based on zero-knowledge proof, was proposed and a one-way encryption method considering data use range and frequency of use was proposed. Based on the content presented in the thesis, it is possible to process corrected zero-knowledge proof and to process data efficiently.
DaSEA - A Dataset for Software Ecosystem Analysis

Software package managers facilitate reuse and rapid construction of software systems. Since evermore software is distributed via package managers, researchers and practitioners require explicit data of software dependency networks that are opaquely formed by dependency relations between software packages. To reason about increasingly complex software products and ecosystems, researchers and practitioners rely either on publicly available datasets like the seemingly unattended libraries.io [14] or they mine problem-specific data from software ecosystems repeatedly and non-transparently. Therefore, we present the DaSEA dataset, which contains metadata of software packages, their versions, and dependencies from multiple ecosystems (currently six programming languages and five operating system package managers). Alongside the dataset, we provide an extensible open-source tool under the same name that is used to create updated versions of the DaSEA dataset allowing studies of evolution of software ecosystems.
A report of 34 unrecorded bacterial species in Korea , belonging to the Actinobacteria

The recently proposed phylum Actinobacteria (Ludwig et al., 2012) is composed of aerobic, Gram-staining-positive bacteria that have high-G + C contents in their genomic DNAs. They exhibit a very wide range of morphology and form cocci, rods or fragmented or branched hyphae depending on species. Some of them produce resting structure such as spores and sporangium on permanent, well-developed mycelium (Goodfellow and Cross, 1984; Ensign, 1992). The phylum Actinobacteria is well supported by analyses of the 16S and 23S rRNA, presence of conserved insertions and deletions in certain proteins, and characteristic gene rearrangements (Goodfellow and Fiedler, 2010) and encompasses six classes Actinobacteria, Acidimicrobiia , Coriobacteriia , Nitriliruptoria , Rubrobacteria and Thermoleophilia (Ludwig et al., 2012). The class Actinobacteria is composed of 15 orders Actinomycetales, Actinopolysporales, Bifidobacteriales, Catenulisporales, Corynebacteriales, Frankiales, Glycomycetales , Jiangellales , Kineosporiales , Micromonosporales, Micrococcales, Propionibacteriales, Pseudonocardiales, Streptomycetales and Streptosporangiales. Members of this class have been received considerable Journal of Species Research 6(1):1-14, 2017