Miracle-dz/UniFAR-FTUs huggingface dataset:
"They Can Only Ever Guide"

Unlike in commercial software development, open source software (OSS) projects do not generally have managers with direct control over how developers spend their time, yet for projects with large, diverse sets of contributors, the need exists to focus and steer development in a particular direction in a coordinated way. This is especially important for "infrastructure" projects, such as critical libraries and programming languages that many other people depend on. Some projects have taken the approach of borrowing planning tools that originated in commercial development, despite the fact that these techniques were designed for very different contexts, e.g. strong top-down control and profit motives. Little research has been done to understand how these practices are adapted to a new context. In this paper, we examine the Rust project's use of roadmaps: how has an important OSS infrastructure project adapted an inherently top-down tool to the freewheeling world of OSS? We find that because Rust's roadmaps are built in part by summarizing what motivated developers most prefer to work on, they are in some ways more a description of the motivated labor available than they are a directive that the community move in a particular direction. They allow the community to avoid wasting time on unpopular proposals by revealing that there will be little help in building them, and encouraging work on popular features by making visible the amount of consensus in those features. Roadmaps generate a collective focus without limiting the full scope of what developers work on: roadmap issues consume proportionally more effort than other issues, but constitute a minority of the work done (i.e issues and pull requests made) by both central and peripheral participants. They also create transparency among and beyond the community into what central contributors' plans are, and allow more rational decision-making by providing a way for evidence about community needs to be linked to decision-making.
Exploring the Tensions between the Owners and the Drivers of Uber Cars in Urban Bangladesh

Most scholarly discussions around ridesharing applications center on the experiences of the drivers and the riders (passengers), and thus the role of the owners of the cars, if they are different from the drivers, remain understudied. However, in many countries in the Global South, the car owners are often different from the car drivers, and the tensions between them often shape the experience with these ridesharing apps in those countries. In this paper, we address this issue based on our interview-based study in Dhaka, Bangladesh, which incorporates semi-structured interviews of 31 Uber drivers and 10 car owners. From our interviews, we identify the contract models that facilitate the partnership between prospective Uber drivers without a car and car owners seeking to rent their cars for Uber, describe the tensions between these two parties, provide a nuanced cultural portrayal of their negotiation mechanisms, and highlight the reasons for which the driver or the owner leaves Uber. Our analysis reveals how the local adoption of sharing economy amplifies existing inequalities and disrupts the prevailing social dynamics. We also connect our findings to the broader interests of CSCW around work, privacy, power and discuss their implications for design and policy formulations.
Exploring the storj network: a security analysis

The recent decade has seen a tremendous increase in volumes of data consumed and generated. Towards storing such data, companies typically make use of centralised cloud storage systems which allow for on-demand scalability and a pay-per-use model. These storage providers, while presenting many benefits, have several downsides in terms of (i) posing as a single point of failure (e.g. data breaches), (ii) featuring the potential for misuse of confidential or personal data, as well as (iii) being not nearly as competitive as they could be which has resulted in inflated prices. In recent years, along with blockchain-based technologies, novel distributed storage platforms such as Storj and Sia, have emerged that tackle these issues by enabling a dynamic storage market between hosts and renters. In comparison to traditional Peer-to-Peer (P2P) storage solutions (e.g. BitTorrrent), these platforms rely on incentivization-based P2P hosting which ensures file availability, robustness and price competitiveness, while also preserving confidentiality and ownership through end-to-end encryption. In this paper, we present a brief overview of such storage platforms, as well as provide two major contributions in the form of (i) an architectural overview, as well as (ii) a high-level security exploration on the dev. /test environment of Storj. The latter has resulted in the discovery of a DoS vulnerability, which we experimentally evaluated to be highly feasible. We have contacted Storj and they assessed that their production system1 is not vulnerable to such an attack.
Logging On, Reaching Out, and Getting By

For individuals impacted by their own or a family member's cancer, connecting with other people in similar situations can be an invaluable source of informational and emotional support. Online spaces provide opportunities for peer support that may be more accessible, given the medical and logistical restrictions on face-to-face socialisation associated with cancer and treatment. However, little is known about the impacts of online peer support. This systematic review surveys the literature on psychosocial impacts of online peer support for people impacted by cancer, integrating research from psychology, health, communications, informatics and social computing disciplines. The reviewed papers and interventions vary widely in the type of online peer support provided, who this support was intended for, and how outcomes were evaluated. Quantitative evidence suggests that online peer support may improve psychosocial wellbeing, particularly anxiety and stress, although this appears to depend on how individuals engage and interact with others. Qualitative findings suggest clear value in connecting and sharing experiences with those in similar situations, benefits which may not be well captured quantitatively. For individuals who share experiences, express emotions and feel understood and accepted by others, online peer spaces may be a valuable and viable source of support. However, this require strategic community design and management to build an active and sustainable group dynamic which can effectively and safely support people impacted by cancer.
Protect and Project

Video game players face a fundamental challenge in managing their competing desires for both privacy and publicity, for being both apart from, and a part of, the communities in which they play. In this paper, we argue that "gamertags" are important tools for protecting gamers' privacy as well as creative outlets for expressing meaningful aspects of identity. Based on 30 semi-structured interviews focused on players' usernames, we find through the pseudonyms under which they play, gamers both hide identifying information such as their offline names and addresses while bringing attention to information that is deeply meaningful to them, such as their family nickname or favorite music. By deemphasizing some parts of their identity and by emphasizing others, players not only shape how they are perceived by other gamers, but they also attempt to preclude accidental disclosure of more identifying information. We argue that gamertag practices thus constitute an important form of boundary work through which gamers actively seek to draw lines between their offline and multiple online worlds in the ways that they wish. We argue that gamers use these names to both protect and project aspects of their identities--at times even seeking protection through projection--as a way of addressing their competing desires to both conceal and reveal different aspects of their identities. As boundary work, players' efforts to carefully protect personally-identifying information and intentionally project personally meaningful information to their communities help them better manage their online identities, relationships with others, and overall data privacy.
MUCH: exploiting pairwise hardware event monitor correlations for improved timing analysis of complex MPSoCs

Measurement-based timing analysis techniques increasingly rely on the Performance Monitoring Units (PMU) of MPSoCs, as these units implement specialized Hardware Event Monitors (HEMs) that convey detailed information about multicore interference in hardware shared resources. Unfortunately, there is an evident mismatch between the large number of HEMs (typically several hundreds) and the comparatively small number (normally less than ten) of Performance Monitoring Counters (PMCs) that can be configured to track HEMs in the PMU. Timing analysis normally require to observe a non-negligible number of HEMs per task from the same execution. However, due to the small number of PMCs, HEMs are necessarily collected across multiple runs that, despite intended to repeat the same experiment, carry out some significant variability (above 50% for some HEMs in relevant MPSoCs) caused by platform-intrinsic execution conditions. Therefore, blindly merging HEMs from different runs is not acceptable since they may easily correspond to significantly different conditions. To tackle this issue, the HRM approach has been proposed recently to merge HEMs from different runs accurately preserving their correlation w.r. t. one anchor HEM (i.e. processor cycles) building on order statistics. However, HRM do not always preserves the correlation between other pairs of HEMs that might be lost to a large extent. This paper copes with HRM limitations by proposing the MUlti-Correlation HEM reading and merging approach (MUCH). MUCH builds on multivariate Gaussian distributions to merge HEMs from different runs while preserving pairwise correlations across each individual pair of HEMs simultaneously. Our results on an NXP T2080 MPSoC used for avionics systems show that MUCH largely outperforms HRM for an identical number of input runs.
Modelling and optimization of run-of-mine stockpile recovery

Run-of-Mine stockpiles are essential components in the mining value chain because they can be used as temporary storage to balance inflow and outflow and provide an opportunity for blending material. Stockpile schedulers plan stockpile recovery to balance throughput and material specifications to deliver for the supply chain's next stage. There are technical limits on deliveries where "failure to meet" can lead to significant penalty fees, increased operational costs due to poor operational plans or over-delivery to material specifications. Currently, human experts determine the planning of stockpile recovery in practice. However, this approach is error prone due to the complex distribution of materials within a stockpile and the inability to foresee upcoming deliveries efficiently. In this paper, we model the stockpile recovery problem as a combinatorial optimization problem considering technical restrictions in real-world issues, and we investigate multiple scenarios and experiments. We apply deterministic and randomized greedy algorithms, as well as ant colony optimization algorithms integrated with local search. We compare all algorithms with a rule of thumb heuristic to evaluate our methodology's quality. Our findings show that ant colony optimization outperforms other algorithms, and the variant integrated with swap and insert local search operators finds the best solutions.
Leveling Up Teamwork in Esports

A large body of research has underscored the importance of the cognitive process of team cognition and its relation to team performance. However, little research has focused on applying such an important teamwork process to computer-mediated collaboration within a fast-paced virtual environment. In this paper, we use esports as a research platform to address this limitation due to its fast-paced nature and its heavy reliance on teamwork. We report the experience and perceptions of 20 players with regard to their descriptions of team cognition within esports. We found that esports players relied on their game experience and understanding of role interdependencies in order to develop team cognition with strangers. We also found that experienced teams utilized a mutual understanding of teammate skills and personalities in order to predict responses and limit the verbal communication required to make quick team decisions. We contribute to CSCW by extending the cognitive understanding of computer-mediated collaboration and by advancing research on team cognition and how it can occur within a fast-paced virtual environment.
Structurally enriched entity mention embedding from semi-structured textual content

In this research, we propose a novel and effective entity mention embedding framework that learns from semi-structured text corpus with annotated entity mentions without the aid of well-constructed knowledge graph or external semantic information other than the corpus itself. Based on the co-occurrence of words and entity mentions, we enrich the co-occurrence matrix with entity-entity, entity-word, and word-entity relationships as well as the simple structures within the documents. Experimentally, we show that our proposed entity mention embedding benefits from the structural information in link prediction task measured by mean reciprocal rank (MRR) and mean precision@K (MP@K) on two datasets for Named-entity recognition (NER).
Hello? Is There Anybody in There?

As social media has become more present in people's day-to-day lives, many turn to these platforms in natural disasters to keep abreast of the ever-evolving crisis situation. Facing the increasing amount of crisis-related information available on the social media platforms, users tend to focus on and reach out to authoritative sources---individuals or organizations that provide authoritative and credible crisis-related information due to their official status or position. As they provide high-quality information, response from the authoritative sources can be especially valuable to social media users directly affected in natural disasters. In this study, we aim to extend the reach of credible information during crisis, and direct the attention of authoritative users to the affected users who need their help. Specifically, we investigate what factors differentiate the tweets by regular users that receive responses from authoritative accounts from those that do not. We find that regular users' popularity and official accounts' level of busyness do not seem to affect the likelihood of tweets receiving a response. We thus explore the linguistic features of the tweets' content. Topic modeling and sentiment analysis results suggest that these linguistic aspects of the tweets may affect the response rate from authoritative sources. Our findings suggest crisis-related policy implications, as well as design implications for social media platforms where such exchanges take place, which can potentially increase the reach of credible information in a crisis and help those affected obtain the safety-critical information they need.
Towards Accessible Remote Work

Working from home has become a mainstream work practice in many organizations during the COVID-19 pandemic. While remote work has received much scholarly and public attention over the years, we still know little about how people with disabilities engage in remote work from their homes and what access means in this context. To understand and rethink accessibility in remote work, the present paper studies work-from-home practices of neurodivergent professionals who have Autism Spectrum Disorder, Attention Deficit Hyperactivity Disorder, learning disabilities (e.g., dyslexia) and psychosocial disabilities (e.g., anxiety, depression). We report on interviews with 36 US-based neurodivergent professionals who have been working from home during the pandemic. Our findings reveal that while working from home, neurodivergent professionals create accessible physical and digital workspaces, negotiate accessible communication practices, and reconcile tensions between productivity and wellbeing. Our analysis reconsiders what access means in remote work for neurodivergent professionals and offers practical insights for inclusive work practices and accessibility improvements in remote collaboration tools.
A trace-driven methodology to evaluate and optimize memory management services of distributed operating systems for lightweight manycores

Lightweight manycores belong to a new class of emerging low-power processors for the Exascale era. These processors present several challenges for the development of applications, such as distributed memory architecture, limited amount of on-chip memory and no cache coherence. Recently, distributed Operating Systems (OSs) have been proposed to address these challenges in a transparent way. In these systems, different OS services are deployed across the processor cores, being the memory management service one of the most important ones. However, the intrinsic characteristics and memory limitations of lightweight manycores bring several challenges to the design, implementation and future optimizations of memory management services. In this work, we propose a trace-driven methodology to evaluate and optimize features of a memory management service of distributed OSs for lightweight manycores. By using a compact representation of the page access pattern of the applications, our methodology is capable of mimicking the memory access pattern of the original applications on the target distributed OS running on a lightweight manycore. We integrated our methodology in a distributed OS (Nanvix) and validated it using three applications from a specific benchmark for lightweight manycores (CAP Bench). Then, we applied our methodology to carry out a case study using a software-managed cache implementation available in Nanvix. Our methodology enabled us to evaluate different page replacement policies on Kalray MPPA-256, even without the required support from the architecture to implement them.
Computation with cells enhanced by costs

Membrane systems reveal certain computing aspects of the living cells, representing also a formal model of the cell structure and activity. We introduce membrane systems with costs by assigning both storage costs to membranes and execution costs to their rules. A variant of Maude (namely Priced-Timed Maude) is used to implement these models of the cells enhanced by costs. We can simulate and analyze the evolution of cells enhanced with costs, and verify their properties by using proper tools of the complex software platform Maude.
Service consumption planning for efficient service migration in mobile edge computing environments

Mobile Edge Computing (MEC) is the concept of placing a cloud computing server to run at the network edge near the user. The faster the user moves, the higher the probability of leaving the coverage of an MEC server. Seamless service migration should be implemented to assure service continuity. However, the time required for service migration is not negligible; thus, the frequent service migration should be avoided. In this study, we define the service consumption plan optimization problem and prove its NP-completeness. To solve the problem, we proposed a genetic algorithm-based method and conducted vast experiments to evaluate the algorithm with respect to other baselines based on real-world data sources. The results showed that the proposed method generates improved service consumption plans than other baselines in all the scenarios we set out.
Investigating behavioral differences between IoT malware via function call sequence graphs

IoT malware that infects IoT devices is rampant. Most IoT malware variants are generated by changing various behaviors such as an attack method based on existing malware families. Nearly all antivirus software only identifies the malware family's name; thus, we cannot acquire further details about differences between malware behaviors. In this paper, we propose a graph-based method for confirming differences in malware behaviors and investigating the actual conditions of malware variants. The proposed method first extracts a sequence of function calls from a binary file of malware and represents the sequence to a directed graph, which we refer to as a function call sequence graph (FCSG). Next, the method automatically checks if the FCSG matches signature-FCSGs, which are manually generated as small-scale FCSGs representing malicious behaviors of known malware such as a function of attacks and network scans. To demonstrate the usability of our proposed method, we applied the proposed method to 24,126 in-the-wild IoT malware specimens and investigated the existence of specimens with mixed behaviors from multiple malware families or were specialized for some attacking behaviors.
Can Crowds Customize Instructional Materials with Minimal Expert Guidance?

AI-based educational technologies may be most welcome in classrooms when they align with teachers' goals, preferences, and instructional practices. Teachers, however, have scarce time to make such customizations themselves. How might the crowd be leveraged to help time-strapped teachers? Crowdsourcing pipelines have traditionally focused on content generation. It is an open question how a pipeline might be designed so the crowd can succeed in a revision/customization task. In this paper, we explore an initial version of a teacher-guided crowdsourcing pipeline designed to improve the adaptive math hints of an AI-based tutoring system so they fit teachers' preferences, while requiring minimal expert guidance. In two experiments involving 144 math teachers and 481 crowdworkers, we found that such an expert-guided revision pipeline could save experts' time and produce better crowd-revised hints (in terms of teacher satisfaction) than two comparison conditions. The revised hints however, did not improve on the existing hints in the AI tutor, which were carefully-written but still have room for improvement and customization. Further analysis revealed that the main challenge for crowdworkers may lie in understanding teachers' brief written comments and implementing them in the form of effective edits, without introducing new problems. We also found that teachers preferred their own revisions over other sources of hints, and exhibited varying preferences for hints. Overall, the results confirm that there is a clear need for customizing hints to individual teachers' preferences. They also highlight the need for more elaborate scaffolds so the crowd can have specific knowledge of the requirements that teachers have for hints. The study represents a first exploration in the literature of how to support crowds with minimal expert guidance in revising and customizing instructional materials.
Challenging Passive Social Media Use

Older adults are often portrayed as passive social media users who consume content rather than actively posting content. However, this binary divide between active and passive social media use overlooks nuanced kinds of engagement online. Via an eye-tracking study of older adults' Facebook use, this work shows how not clicking or commenting on content can involve engaged kinds of social media use even if they are not visible to other users or to the platform. Older adults' decisions to not actively click or comment on social media content---an act which is often associated with non-engagement---can be intentional and relational acts of caregiving. We draw from feminist care theories to draw parallels between the invisibility of care work that older adults do on social media and the invisibility often rendered in their offline lives. We discuss theoretical, methodological, and design implications for supporting older adults as engaged participants in relational and intentional care work.