Trace Data Research Articles

Faas‐sim: A trace‐driven simulation framework for serverless edge computing platforms

AbstractThis paper presentsfaas‐sim, a simulation framework tailored to serverless edge computing platforms. In serverless computing, platform operators are tasked with efficiently managing distributed computing infrastructure completely abstracted from application developers. To that end, platform operators and researchers need tools to design, build, and evaluate resource management techniques that efficiently use of infrastructure while optimizing application performance. This challenge is exacerbated in edge computing scenarios, where, compared to cloud computing, there is a lack of reference architectures, design tools, or standardized benchmarks.faas‐simbridges this gap by providing (a) a generalized model of serverless systems that builds on the function‐as‐a‐service abstraction, (b) a simulator that uses trace data from real‐world edge computing testbeds and representative workloads, and (c) a network topology generator to model and simulate distributed and heterogeneous edge‐cloud systems. We present the conceptual design, implementation, and a thorough evaluation offaas‐sim. By running experiments on both real‐world test beds and replicating them usingfaas‐sim, we show that the simulator provides accurate results and reasonable simulation performance. We have profiled a wide range of edge computing infrastructure and workloads, focusing on typical edge computing scenarios such as edge AI inference or data processing. Moreover, we present several instances where we have successfully usedfaas‐simto either design, optimize, or evaluate serverless edge computing systems.

Advancing Automated Content Analysis for a New Era of Media Effects Research: The Key Role of Transfer Learning

ABSTRACT The availability of individual-level digital trace data offers exciting new ways to study media uses and effects based on the actual content that people encountered. In this article, we argue that to really reap the benefits of this data, we need to update our methodology for automated text analysis. We review challenges for the automatic identification of theoretically relevant concepts in texts along three dimensions: format/style, language, and modality. These dimensions unveil a significantly higher level of diversity and complexity in individual-level digital trace data, as opposed to the content traditionally examined through automated text analysis in our field. Consequently, they provide a valuable perspective for exploring the limitations of traditional approaches. We argue that recent developments within the field of Natural Language Processing, in particular, transfer learning using transformer-based models, have the potential to aid the development, application, and performance of various computational tools. These tools can contribute to the meaningful categorization of the content of social (and other) media.

Language Model-Based Player Goal Recognition in Open World Digital Games

Devising models that reliably recognize player goals is a key challenge in creating player-adaptive games. Player goal recognition is the task of automatically recognizing the intent of a player from a sequence of observed player actions in a game environment. In open-world digital games, players often undertake suboptimal and varied sequences of actions to achieve goals, and the high degree of freedom afforded to players makes it challenging to identify sequential patterns that lead toward specific goals. To address these issues, we present a player goal recognition framework that utilizes a fine-tuned T5 language model, which incorporates our novel attention mechanism called Temporal Contrary Attention (TCA). The T5 language model enables the framework to exploit correlations between observations through non-sequential self-attention within input sequences, while TCA enables the framework to learn to eliminate goal hypotheses by considering counterevidence within a temporal window. We evaluate our approach using game trace data collected from 144 players' interactions with an open-world educational game. Specifically, we investigate the predictive capacity of our approach to recognize player goals as well as player plans represented as abstract actions. Results show that our approach outperforms non-linguistic machine learning approaches as well as T5 without TCA. We discuss the implications of these findings for the design and development of player goal recognition models to create player-adaptive games.

Unraveling China’s digital traces: evaluating communication scholarship through a sociotechnical lens

In the growing trend of research using digital trace data to study human activities and opinions across different contexts, networked China has emerged as a prominent area of interest. However, research that critically examines the use, strengths, and weaknesses of existing digital trace methods, and the extent to which they can reveal the true landscape of digital China remains limited. To address these gaps, this study proposes a framework for examining and evaluating the knowledge production of digital trace research within a sociotechnical system comprising state actors, platform governance, digital civil society, and international forces. We then provide the first empirical examination of the knowledge claims and epistemic approaches used in digital trace communication scholarship that has studied China across different phases in the past 30 years. Grounded in the resulting empirical evidence, we discuss two common practices in existing digital trace research on China, how these approaches and perspectives could affect the validity and reliability of offering diverse viewpoints for studying and understanding digital China, and directions for improving these practices.

Are MOOC learning designs culturally inclusive (enough)?

AbstractBackgroundExtensive research on massive open online courses (MOOCs) has focused on analysing learners' behavioural trace data to understand navigation and activity patterns, which are known to vary systematically across geo‐cultural contexts. However, the perception of learners regarding the role of different learning design elements in sustaining their engagement in the course is still unclear.ObjectivesThis study aimed to examine learners' perception of learning design elements in MOOCs and explore the ways in which these perceptions differ between geo‐cultural contexts.MethodsWe conducted interviews with 22 learners from seven geo‐cultural regions to gather insights into their learning design preferences.ResultsOur findings indicate that learners from regions such as South Asia exhibit a strong inclination towards video‐based content and a lesser preference for reading textual resources. In contrast, learners from regions such as Anglo‐Saxon demonstrate a high preference for reading texts such as articles and video transcripts.ConclusionThe observed variations in self‐reported interests in various learning design elements raise intriguing questions about the nature and extent of participation of various geo‐cultural groups. This study underscores the need to develop inclusive MOOC designs and implement learning analytics approaches that adapt to the cultural preferences of learners.

Can Google Trends predict asylum-seekers’ destination choices?

Google Trends (GT) collate the volumes of search keywords over time and by geographical location. Such data could, in theory, provide insights into people’s ex ante intentions to migrate, and hence be useful for predictive analysis of future migration. Empirically, however, the predictive power of GT is sensitive, it may vary depending on geographical context, the search keywords selected for analysis, as well as Google’s market share and its users’ characteristics and search behavior, among others. Unlike most previous studies attempting to demonstrate the benefit of using GT for forecasting migration flows, this article addresses a critical but less discussed issue: when GT cannot enhance the performances of migration models. Using EUROSTAT statistics on first-time asylum applications and a set of push-pull indicators gathered from various data sources, we train three classes of gravity models that are commonly used in the migration literature, and examine how the inclusion of GT may affect models’ abilities to predict refugees’ destination choices. The results suggest that the effects of including GT are highly contingent on the complexity of different models. Specifically, GT can only improve the performance of relatively simple models, but not of those augmented by flow Fixed-Effects or by Auto-Regressive effects. These findings call for a more comprehensive analysis of the strengths and limitations of using GT, as well as other digital trace data, in the context of modeling and forecasting migration. It is our hope that this nuanced perspective can spur further innovations in the field, and ultimately bring us closer to a comprehensive modeling framework of human migration.

Moving Between Scales

This dissertation employs the perspective of library and information science (LIS) to inform and guide methodological reflections in two interdisciplinary fields, computational social science (CSS) and digital humanities (DH). However, CSS often prioritizes recent digital trace data over archival material from earlier periods, and the large-scale methods used in DH face criticism from humanists for their incapability to capture perspectival and interpretative nuances.
 This dissertation blends humanistic and social-scientific approaches to study premodern China, emphasizing the importance of “moving between scales.” It bridges different scales of analysis by transitioning dynamically between them, examines small-scale structures to uncover how they reflect large-scale dynamics, and explores mid-range social phenomena by investigating prosopography of elite society members. Four research questions are posed: (1) What are the challenges and opportunities in investigating a social network based on biased text? (2) How can prosopographical data lead to conclusions about the shifting center of political power? (3) How can structural characteristics in multiple networks reveal changes in political culture? (4) What can bibliographic metadata reveal about the particularity of publishing history of 16th-to-19th-century Chinese books?
 By addressing these questions, this dissertation intends to leverage theories and practices of LIS to develop computational models unveiling historical trends. It seeks to contribute to CSS by enhancing social scientists’ understanding on how societies and cultural systems evolve over time, to DH by revealing the historical changes in the longue durée in premodern China, and to LIS by fulfilling its mission of providing an information perspective to traditional academic disciplines.

COVID-19 associated with universities in England, October 2020–February 2022

ObjectivesThe aim of this study was to describe the epidemiology of COVID-19 cases at universities in England (October 2020–February 2022) and investigate factors associated with rates of COVID-19 among students during autumn/winter of 2021/22. Study designThe study was an observational retrospective study using routine contact tracing data. MethodsEstimates of COVID-19 cases among students and staff at universities were described. Student cases aged 18–24 years were calculated as a percentage of all cases within that age group. Count regression was used to explore university characteristics associated with case numbers. ResultsWe identified 102,382 cases among students and 28,639 among staff. Student cases reflected trends in the wider population of the same age group, but the observed fraction aged 18–24 years who were students was consistently below the expected level (32%). Phased reopening of universities in March–May 2021 was associated with small peaks but low absolute numbers. Russell group membership, campus universities, and higher student proportions in halls of residence were all associated with increased case numbers. ConclusionsCOVID-19 case numbers among students in England varied considerably. At no time were the observed case numbers as high as expected from community prevalence. Characteristics of universities associated with higher case rates can inform future guidance for higher education settings.

The Performative Production of Trace Data in Knowledge Work

Firms increasingly harness data that are created as by-products of information systems usage to evaluate and manage employees. However, such “trace data” can be a double-edged sword. The data can provide a whole new visibility into work practices but also, make work less transparent if the employees start to change their behavior to shape the data. We study this dilemma in the context of knowledge work that has traditionally eluded behavioral measurement. We show that when knowledge workers become aware of data collection and have an interest in how their work may be represented by the data, they start to actively perform the data. We identify different patterns by which employees shape work practices to produce trace data. The changes affect not only the actions and data of the focal employee but also, the actions and data of their colleagues and subordinates. Therefore, to fully realize the potential of trace data, managers may need to get involved in designing the data and to set a trace data policy that states how the data will be used in the organization.

The Use of Mobile Assessments for Monitoring Mental Health in Youth: Umbrella Review.

Improving mental health in youth is a major concern. Future approaches to monitor and intervene in youth mental health problems should rely on mobile tools that allow for the daily monitoring of mental health both actively (eg, using ecological momentary assessments [EMAs]) and passively (eg, digital phenotyping) by capturing individuals' data. This umbrella review aims to (1) report the main characteristics of existing reviews on mental health and young people, including mobile approaches to mental health; (2) describe EMAs and trace data and the mental health conditions investigated; (3) report the main results; and (4) outline promises, limitations, and directions for future research. A systematic literature search was carried out in 9 scientific databases (Communication & Mass Media Complete, Psychology and Behavioral Sciences Collection, PsycINFO, CINAHL, ERIC, MEDLINE, the ProQuest Sociology Database, Web of Science, and PubMed) on January 30, 2022, coupled with a hand search and updated in July 2022. We included (systematic) reviews of EMAs and trace data in the context of mental health, with a specific focus on young populations, including children, adolescents, and young adults. The quality of the included reviews was evaluated using the AMSTAR (Assessment of Multiple Systematic Reviews) checklist. After the screening process, 30 reviews (published between 2016 and 2022) were included in this umbrella review, of which 21 (70%) were systematic reviews and 9 (30%) were narrative reviews. The included systematic reviews focused on symptoms of depression (5/21, 24%); bipolar disorders, schizophrenia, or psychosis (6/21, 29%); general ill-being (5/21, 24%); cognitive abilities (2/21, 9.5%); well-being (1/21, 5%); personality (1/21, 5%); and suicidal thoughts (1/21, 5%). Of the 21 systematic reviews, 15 (71%) summarized studies that used mobile apps for tracing, 2 (10%) summarized studies that used them for intervention, and 4 (19%) summarized studies that used them for both intervention and tracing. Mobile tools used in the systematic reviews were smartphones only (8/21, 38%), smartphones and wearable devices (6/21, 29%), and smartphones with other tools (7/21, 33%). In total, 29% (6/21) of the systematic reviews focused on EMAs, including ecological momentary interventions; 33% (7/21) focused on trace data; and 38% (8/21) focused on both. Narrative reviews mainly focused on the discussion of issues related to digital phenotyping, existing theoretical frameworks used, new opportunities, and practical examples. EMAs and trace data in the context of mental health assessments and interventions are promising tools. Opportunities (eg, using mobile approaches in low- and middle-income countries, integration of multimodal data, and improving self-efficacy and self-awareness on mental health) and limitations (eg, absence of theoretical frameworks, difficulty in assessing the reliability and effectiveness of such approaches, and need to appropriately assess the quality of the studies) were further discussed. PROSPERO CRD42022347717; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=347717.

PHFinder: assisted detection of point heteroplasmy in Sanger sequencing chromatograms.

Heteroplasmy is the presence of two or more organellar genomes (mitochondrial or plastid DNA) in an organism, tissue, cell or organelle. Heteroplasmy can be detected by visual inspection of Sanger sequencing chromatograms, where it appears as multiple peaks of fluorescence at a single nucleotide position. Visual inspection of chromatograms is both consuming and highly subjective, as heteroplasmy is difficult to differentiate from background noise. Few software solutions are available to automate the detection of point heteroplasmies, and those that are available are typically proprietary, lack customization or are unsuitable for automated heteroplasmy assessment in large datasets. Here, we present PHFinder, a Python-based, open-source tool to assist in the detection of point heteroplasmies in large numbers of Sanger chromatograms. PHFinder automatically identifies point heteroplasmies directly from the chromatogram trace data. The program was tested with Sanger sequencing data from 100 humpback whales (Megaptera novaeangliae) tissue samples with known heteroplasmies. PHFinder detected most (90%) of the known heteroplasmies thereby greatly reducing the amount of visual inspection required. PHFinder is flexible and enables explicit specification of key parameters to infer double peaks (i.e., heteroplasmies).

Linkage Analysis Revised – Linking Digital Traces and Survey Data

ABSTRACT Linkage analysis, i.e. linking media exposure, content, and surveys, has been a powerful tool to assess media effects. However, the development of online communication and the advent of social media brings about many challenges for traditional linkage designs. In this paper, we explain the three steps of linkage designs for online communication effects and the usage of computational approaches to capture communication exposure and content. We then review recent designs and studies that use different forms of digital trace data to link digital communication exposure, content, and surveys: Tracking data, data donations, and screenshots/screen recordings. We describe (practical) challenges and opportunities when linking digital communication traces with self-reports and show how these data could be analyzed to establish different media effects.

Fate Specification of GFAP-Negative Primitive Neural Stem Cells and Their Progeny at Clonal Resolution.

The mature brain contains an incredible number and diversity of cells that are produced and maintained by heterogeneous pools of neural stem cells (NSCs). Two distinct types of NSCs exist in the developing and adult mouse brain: Glial Fibrillary Acidic Protein (GFAP)-negative primitive (p)NSCs and downstream GFAP-positive definitive (d)NSCs. To better understand the embryonic functions of NSCs, we performed clonal lineage tracing within neurospheres grown from either pNSCs or dNSCs to enrich for their most immediate downstream neural progenitor cells (NPCs). These clonal progenitor lineage tracing data allowed us to construct a hierarchy of progenitor subtypes downstream of pNSCs and dNSCs that were then validated using single-cell transcriptomics. Further, we identify Nexn as required for neuronal specification from neuron/astrocyte progenitor cells downstream of rare pNSCs. Combined, these data provide single-cell resolution of NPC lineages downstream of rare pNSCs that likely would be missed from population-level analyses in vivo.

Organization and morphology of calcitonin gene-related peptide-immunoreactive axons in the whole mouse stomach.

Nociceptive afferent axons innervate the stomach and send signals to the brain and spinal cord. Peripheral nociceptive afferents can be detected with a variety of markers (e.g., substance P [SP] and calcitonin gene-related peptide [CGRP]). We recently examined the topographical organization and morphology of SP-immunoreactive (SP-IR) axons in the whole mouse stomach muscular layer. However, the distribution and morphological structure of CGRP-IR axons remain unclear. We used immunohistochemistry labeling and applied a combination of imaging techniques, including confocal and Zeiss Imager M2 microscopy, Neurolucida 360 tracing, and integration of axon tracing data into a 3D stomach scaffold to characterize CGRP-IR axons and terminals in the whole mouse stomach muscular layers. We found that: (1) CGRP-IR axons formed extensive terminal networks in both ventral and dorsal stomachs. (2) CGRP-IR axons densely innervated the blood vessels. (3) CGRP-IR axons ran in parallel with the longitudinal and circular muscles. Some axons ran at angles through the muscular layers. (4) They also formed varicose terminal contacts with individual myenteric ganglion neurons. (5) CGRP-IR occurred in DiI-labeled gastric-projecting neurons in the dorsal root and vagal nodose ganglia, indicating CGRP-IR axons were visceral afferent axons. (6) CGRP-IR axons did not colocalize with tyrosine hydroxylase or vesicular acetylcholine transporter axons in the stomach, indicating CGRP-IR axons were not visceral efferent axons. (7) CGRP-IR axons were traced and integrated into a 3D stomach scaffold. For the first time, we provided a topographical distribution map of CGRP-IR axon innervation of the whole stomach muscular layers at the cellular/axonal/varicosity scale.

Socio-spatial Learning Analytics in Co-located Collaborative Learning Spaces:

Socio-spatial learning analytics (SSLA) is an emerging area within learning analytics research that seeks to uncover valuable educational insights from individuals’ social and spatial data traces. These traces are captured automatically through sensing technologies in physical learning spaces, and the research is commonly based on the theoretical foundations of social constructivism and cultural anthropology. With its growing empirical basis, a systematic literature review is timely in order to provide educational researchers and practitioners with a detailed summary of the emerging works and the opportunities enabled by SSLA. This paper presents a systematic review of 25 peer-reviewed articles on SSLA published between 2011 and 2023. Descriptive, network, and thematic analyses were conducted to identify the citation networks, essential components, opportunities, and challenges enabled by SSLA. The findings illustrated that SSLA provides the opportunity to (1) contribute unobtrusive and unsupervised research methodologies, (2) support educators’ classroom orchestration through visualizations, (3) support learner reflection with continuous and reliable evidence, (4) develop novel theories about social and collaborative learning, and (5) empower educational stakeholders with the quantitative data to evaluate different learning spaces. These findings could support learning analytics and educational technology scholars and practitioners to better understand and utilize SSLA to support future educational research and practice.

Carbon Management for Intelligent Community with Combined Heat and Power Systems

In recent years, solar power technology and energy storage technology have advanced, leading to the increased use of solar power devices and energy storage systems in residential areas. Carbon management has become an important method to help the community manager guide energy consumption in a timely manner, effectively reduce the carbon emissions of the community, and reduce the substantial harm to the environment. This paper aims to study the issue of carbon management and resource allocation in an intelligent community with combined heat and power (CHP) systems and solar power. The presence of heterogeneous load demands in the power grid was considered. The main objective was to minimize the average system cost over time, which included the costs associated with the power grid and gas. The Lyapunov optimization theory was employed to solve the non-convex optimization problem of carbon management and resource allocation without energy sharing. To solve the energy-sharing problem, we designed an energy-sharing algorithm based on the Q-learning algorithm. Lastly, we conducted extensive simulations using actual trace data to validate the effectiveness of our proposed algorithms.

Blockchain-Backed Searchable Proxy Signcryption for Cloud Personal Health Records

Patient-centered data management and sharing of personal health records (PHRs) are difficult to be realized as data is controlled by doctors/hospitals. In addition, security and privacy, oppressive costs, search and tracing unreliability, and complicated access authorization caused by traditional encryption severely hinder the widespread adoption of PHRs. To overcome these challenges, we propose a blockchain-backed data sharing framework for PHRs, where the blockchain achieves reliable search and tracing. Furthermore, we design a hybrid <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">b</b> lock <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">c</b> hain-backed <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">s</b> earchable <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">p</b> roxy <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">s</b> ign <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">c</b> ryption scheme, named <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">BC-SPSC</b> . Specifically, an identity-based proxy signature (IBPS) is utilized to perform the authorization from patients to doctors to achieve authentic patient-centricity, therefore the blockchain can relate data with associated patients and doctors during data tracing. Moreover, BC-SPSC supports two search modes. The first mode adopts attribute-based encryption with keyword-based search (SABE), where all legitimate users can implement searches, but only users whose attributes satisfy the access structure can successfully decrypt. By contrast, the second mode utilizes attribute-based searchable encryption (ABSE) to accomplish fine-grained authorization in both search and data access/decryption, that is, who can search is also constrained by data owners. Adequate performance comparisons and simulation experiments indicate significant advantages of the BC-SPSC scheme in storage and computation overheads.

The Effect of Ethnoscience-Based Science Environment Technology Society (SETS) Learning Model in Learning Physics and Biology

This study investigates the effect of the ethnoscience-based Science Environment Technology Society (SETS) learning model on learning Physics and Biology. This type of research involves systematic research, literature reviews, and meta-analyses. The data sources were taken from an analysis of 100 international journals published from 2018 to 2023. Tracing data sources through Google Scholar, Eric, ScienceDirect, and ProQuest. The process of selecting data sources must meet the inclusion criteria analyzed by the JSAP application. The inclusion criteria in this study are: 1) the journal must be published in 2018–2023; 2) SINTA and Scopus/WOS must index the journal; 3) The journal uses experimental or quasi-experimental research methods; and 4) it has an effect size that can be calculated. The data collection technique is direct observation through the journal database. The results showed that the average effect size (ES = 0.873) is a large criterion. These findings explain that the ethnoscience-based Science Environment Technology Society (SETS) model significantly affects science learning. Furthermore, this SETS learning model helps students learn physics and biology more actively and creatively. These results are very important in evaluating the implementation of the school learning system. Furthermore, this ethnoscience-based SETS model must be applied in learning biology and physics to stimulate students to think critically.

Energy management in residential communities with shared storage based on multi-agent systems: Application to smart grids

The evolution towards smart grids (SGs) is mainly characterized by the integration of renewable energy sources (RESs) throughout the grid. The intermittent nature of these sources necessitated the installation of energy storage systems (ESSs) to improve the efficiency and reliability in the power system. Moreover, the ongoing high price of batteries has encouraged the installation of shared ESSs in residential communities. However, managing the shared ESS and the energy flows in the community is considered a key challenge. In order to handle this issue, we introduce a novel energy management system (EMS), namely Energy Management In residential COmmunities with shared storage based on multi-agent systems (EMICO). It finds the optimal energy trading operations between households, as well as the operations of the shared ESS that minimize the total energy losses. We first propose a new cluster-based architecture for the residential community in which we integrate Internet of Energy (IoE) devices to manage energy flows and find the shortest path to transfer energy with minimal loss from a cluster to the other. Then, we model our energy management problem as a constrained optimization problem and we use Lagrange multiplier method to solve it in a centralized way. In order to preserve households’ privacy, we propose a decentralized approach based on multi-agent systems (MASs) to solve our problem. We test our approach on real data traces obtained from a set of households located in the United Kingdom. Numerical results show that EMICO outperforms a literature approach in terms of energy losses (up to 35.66% of reduction in energy losses), electricity bill (up to 21.21% cheaper), number of exchanged messages (up to 83.81% less messages exchanged), and length of required cables (up to 95.03% less cables required).

Forensic rhetoric: COVID-19, the forum and the boundaries of healthcare evidence

The COVID-19 pandemic has brought into sharp focus the shifting role of healthcare evidence in public health presentations. This article investigates the rhetoric of those presentations as a phenomenon indicating...