Miracle-dz/UniFAR-FTUs huggingface dataset:
Paradoxes of Social Support in Virtual Support Communities: A Mixed-Method Inquiry of the Social Dynamics in Health and Wellness Facebook Groups

From parenting to health and wellness, the number of virtual support communities (VSCs) keeps growing. The interactive marketing discipline has primarily documented the positive social dynamics of VSCs: communities provide informational and socio-emotional support that helps members achieve their goals. Yet evidence is mounting that VSCs also exhibit judgment and pressure that ultimately hurt community relationships and engagement. We adopted a mixed-methods approach: a qualitative phase, comprised of netnography and interviews, to explore members’ experiences of a VSC and its complex social dynamics, followed by a quantitative phase to test the emerging model with cross-sectional survey data collected from members of a large number of health- and wellness-related VSCs. The two studies provide empirical evidence of many paradoxical social dynamics of VSCs and their relational and engagement consequences. We find that positive group perceptions can generate the social empathy that ensures the group's informational value is helpful to members’ goals; however, we also find that negative group perceptions create social pressure that can be helpful to relational and engagement outcomes if it increases social empathy but can also be detrimental if it turns into angst. Our findings contribute to research on VSCs, inform interactive marketing practices, and suggest further research opportunities on the social dynamics of VSCs.
I am Not What I Like: Endorsing Brands on Social Media Negatively Affects Consumers’ Self-Evaluation

To what extent do consumers incorporate the identity of brands they endorse on social media into their self-concept? We argue that, contrary to popular belief, online brand endorsements may not necessarily lead to inclusion of the brand into the self and may, consequently, lead to contrast effects that negatively affect consumers’ self-evaluations. We test our hypotheses across five studies and find that consumers who endorse a brand on social media subsequently negatively adjust their self-evaluations on traits that reflect the brand's key personality traits. This effect occurs only if they endorse the brand (i.e., by “liking” or “following” it), but not when they just visit the brand's social media page. Moreover, the effect is moderated by brand symbolism, with stronger effects if the brand is perceived to have low brand symbolism. The downwards shift in consumers’ self-evaluations negatively affects brand outcomes. We also explore the role of incentives as counter mechanism. In conclusion, our findings reveal a dark side to promoting consumer endorsement of brands on social media, one with implications not only for consumers but also for brands.
The Relationship between the Chief Executive Officer’s (CEO) Social Capital and Dynamic Capabilities: A Meta-Analysis of its Moderators

Abstract CEO social capital has shown a positive association with dynamic capabilities, although correlations have considerable heterogeneity among them. This meta-analysis estimates the correlation between CEO social capital and dynamic capabilities, and analyses moderator variables in explaining the heterogeneity in the results. Moderators are classified across four levels from macro to micro variables: country variables, firm environment, firm characteristics, and CEO variables. We apply a systematic search for studies in the Web of Science, Scopus, EBSCO, ProQuest, and Google Scholar databases. Additionally, we used a three-level random-effect meta-analysis on 89 correlations published between 2008 and 2021 from 9,272 CEOs. Findings indicate a positive correlation between CEO social capital and dynamic capabilities. Furthermore, we identified a moderator effect in the country and firm environment level. We found that the country of the study, the perception of transparency and legality in the country, and the environmental dynamism in the market moderate the size of the correlations. We also reject other moderators, including the individualism-collectivism national culture, firm size, the sources of social capital (business or political ties), and the dimension of the social capital (structural, relational or cognitive). This study contributes to the exploration of the sources of dynamic capabilities from the micro-foundation, identifies moderator variables that explain the heterogeneity in the results, and highlights the social nature of management activities which always evolve in a social context.
SRAM has no chill: exploiting power domain separation to steal on-chip secrets

The abundance of embedded systems and smart devices increases the risk of physical memory disclosure attacks. One such classic non-invasive attack exploits dynamic RAM's temperature-dependent ability to retain information across power cycles---known as a cold boot attack. When exposed to low temperatures, DRAM cells preserve their state for a short time without power, mimicking non-volatile memories in that time frame. Attackers exploit this physical phenomenon to gain access to a system's secrets, leading to data theft from encrypted storage. To prevent cold boot attacks, programmers hide secrets on-chip in Static Random-Access Memory (SRAM); by construction, on-chip SRAM is isolated from external probing and has little intrinsic capacitance, making it robust against cold boot attacks. While it is the case that SRAM protects against traditional cold boot attacks, we show that there is another way to retain information in on-chip SRAM across power cycles and software changes. This paper presents Volt Boot, an attack that demonstrates a vulnerability of on-chip volatile memories due to the physical separation common to modern system-on-chip power distribution networks. Volt Boot leverages asymmetrical power states (e.g., on vs. off) to force SRAM state retention across power cycles, eliminating the need for traditional cold boot attack enablers, such as low-temperature or intrinsic data retention time. Using several modern ARM Cortex-A devices, we demonstrate the effectiveness of the attack in caches, registers, and iRAMs. Unlike other forms of SRAM data retention attacks, Volt Boot retrieves data with 100% accuracy---without any complex post-processing.
IceBreaker: warming serverless functions better with heterogeneity

Serverless computing, an emerging computing model, relies on "warming up" functions prior to its anticipated execution for faster and cost-effective service to users. Unfortunately, warming up functions can be inaccurate and incur prohibitively expensive cost during the warmup period (i.e., keep-alive cost). In this paper, we introduce IceBreaker, a novel technique that reduces the service time and the "keep-alive" cost by composing a system with heterogeneous nodes (costly and cheaper). IceBreaker does so by dynamically determining the cost-effective node type to warm up a function based on the function's time-varying probability of the next invocation. By employing heterogeneity, IceBreaker allows for more number of nodes under the same cost budget and hence, keeps more number of functions warm and reduces the wait time during high load. Our real-system evaluation confirms that IceBreaker reduces the overall keep-alive cost by 45% and execution time by 27% using representative serverless applications and industry-grade workload trace. IceBreaker is the first technique to employ and leverage the idea of mixing expensive and cheaper nodes to improve both service time and keep-alive cost for serverless functions -- opening up a new research avenue of serverless computing on heterogeneous servers for researchers and practitioners.
Training Teaching Assistants by Offering an Introductory Course

Teaching assistants (TAs) are often used in computer science (CS) courses to conduct tutorials in smaller groups and to be able to provide students with one-to-one help sessions. TAs can also assist with the assessment, which can be time-consuming in large classes. Previous research does, however, indicate that TAs can be poorly prepared for their work tasks. In this experience report, we present a TA training course that addresses this issue and prepares new TAs for their responsibilities. This includes conducting tutorials, tutoring and providing students with useful feedback in lab settings, and conducting assessments. In addition, we also aimed at creating a safe space for our TAs to discuss challenges and fears that they foresee or have already experienced as TAs. To do this, we developed an introductory course consisting of five modules, that are based on previous research on TAs in CS and in the Technological Pedagogical Content Knowledge (TPACK) framework. The modules are: HR questions, classroom teaching, helping and supervising in lab sessions, assessment, and reflection and discussion. The course is given in a blended learning format, where the first three modules are student-paced online modules, while the last two are synchronous meetings. To evaluate the training, we present course evaluation results from 53 TAs who enrolled in this 6-hours course during the 2020/2021 academic year. We conclude that this TA training course has been well appreciated by almost all the participants, discuss lessons learned and future plans.
NASPipe: high performance and reproducible pipeline parallel supernet training via causal synchronous parallelism

Supernet training, a prevalent and important paradigm in Neural Architecture Search, embeds the whole DNN architecture search space into one monolithic supernet, iteratively activates a subset of the supernet (i.e., a subnet) for fitting each batch of data, and searches a high-quality subnet which meets specific requirements. Although training subnets in parallel on multiple GPUs is desirable for acceleration, there inherently exists a race hazard that concurrent subnets may access the same DNN layers. Existing systems support neither efficiently parallelizing subnets’ training executions, nor resolving the race hazard deterministically, leading to unreproducible training procedures and potentiallly non-trivial accuracy loss. We present NASPipe, the first high-performance and reproducible distributed supernet training system via causal synchronous parallel (CSP) pipeline scheduling abstraction: NASPipe partitions a supernet across GPUs and concurrently executes multiple generated sub-tasks (subnets) in a pipelined manner; meanwhile, it oversees the correlations between the subnets and deterministically resolves any causal dependency caused by subnets’ layer sharing. To obtain high performance, NASPipe’s CSP scheduler exploits the fact that the larger a supernet spans, the fewer dependencies manifest between chronologically close subnets; therefore, it aggressively schedules the subnets with larger chronological orders into execution, only if they are not causally dependent on unfinished precedent subnets. Moreover, to relieve the excessive GPU memory burden for holding the whole supernet’s parameters, NASPipe uses a context switch technique that stashes the whole supernet in CPU memory, precisely predicts the subnets’ schedule, and pre-fetches/evicts a subnet before/after its execution. The evaluation shows that NASPipe is the only system that retains supernet training reproducibility, while achieving a comparable and even higher performance (up to 7.8X) compared to three recent pipeline training systems (e.g., GPipe).
Experience Report on the Use of Breakout Rooms in a Large Online Course

In this paper, we present our experience with the use of breakout rooms in a second year undergraduate Software Design course at a large North American institution. Following the switch to remote instruction during the coronavirus pandemic, we revamped our in-person Software Design course to be delivered as a flipped online course, making extensive use of in-lecture exercises completed during breakout rooms. We report on the structure and logistics of this lecture design (for a large class of 300+ students). To gain insights into the impact of the use of breakout rooms on student experience, we conducted weekly student surveys asking for feedback on the lectures and specifically on the use of breakout rooms. Although many students had positive feelings regarding the use of breakout rooms, a significant percentage of students (an average of 47% of the survey responses each week) expressed negative feelings toward them. In an end-of-term survey, we specifically asked students about what they felt worked best for breakout rooms in terms of group size and pre-assigned versus randomized groups, and if there were any other areas that they felt needed improvement. Some of the patterns we observed were that most students liked smaller groups (2-5 people), preferred staying in the same group throughout the semester, and enjoyed the use of breakout rooms as long as others in their room were active participants. We share the details of these survey results as well as the tips and lessons that we learned through this experience.
Finding missed optimizations through the lens of dead code elimination

Compilers are foundational software development tools and incorporate increasingly sophisticated optimizations. Due to their complexity, it is difficult to systematically identify opportunities for improving them. Indeed, the automatic discovery of missed optimizations has been an important and significant challenge. The few existing approaches either cannot accurately pinpoint missed optimizations or target only specific analyses. This paper tackles this challenge by introducing a novel, effective approach that --- in a simple and general manner --- automatically identifies a wide range of missed optimizations. Our core insight is to leverage dead code elimination (DCE) to both analyze how well compilers optimize code and identify missed optimizations: (1) insert "optimization markers" in the basic blocks of a given program, (2) compute the program's live/dead basic blocks using the "optimization markers", and (3) identify missed optimizations from how well compilers eliminate dead blocks. We essentially exploit that, since DCE heavily depends on the rest of the optimization pipeline, through the lens of DCE, one can systematically quantify how well compilers optimize code. We conduct an extensive analysis of GCC and LLVM using our approach, which (1) provides quantitative and qualitative insights regarding their optimization capabilities, and (2) uncovers a diverse set of missed optimizations. Our results also lead to 84 bug reports for GCC and LLVM, of which 62 have already been confirmed or fixed, demonstrating our work's strong practical utility. We expect that the simplicity and generality of our approach will make it widely applicable for understanding compiler performance and finding missed optimizations. This work opens and initiates this promising direction.
Factors Influencing Student Performance and Persistence in CS2

Performance in CS1 and introductory CS courses has been an area of active research in the CS education research community for more than four decades, but studies related to student performance in CS2 are not as widely available. Past studies have examined the impact of CS1 grade, prior math preparation, and other factors such as homework, test, and project grades, on the overall performance in CS2. In this work, we will build upon the existing research related to CS2 performance with an emphasis on a few factors that have not been previously considered for this course. In addition to typical factors studied by others (i.e. gender, race, CS1 performance), our work also takes into account the impact of various CS1 pathways to CS2 and the number of previous college CS courses (including transfer credits) on student performance in CS2. We also look into both persistence, by distinguishing students who stay in the course versus those who drop from the class before the mid-semester drop deadline, and performance. Gender and race were not significant factors in determining performance in CS2 but undeclared engineering majors stood out as high performers and students' CS pathway leading to CS2 was also significant. Notably, students with CS1 transfer credit had significantly lower pass rates. Students with only 1 previous CS course credit were less likely to drop or not pass the course.
Peer Grading Without Protest: The SPARK Approach to Summative Peer Assessment

As Computer Science Professors, we strive to construct courses that maximally support and contribute to student learning through carefully crafted in-class and out-of-class activities. There is evidence that homework enhances student learning, and that students are more likely to do their homework when it affects their grade. Thus, faculty often find themselves seeking the right balance between the increase in student understanding that additional graded homework might offer and the burden of grading that homework if automated grading software is not available. Peer grading may seem like the obvious solution, since it results in only a limited increase in student workload while still incentivizing homework completion. Unfortunately, students tend to distrust their peers' abilities to evaluate their work, and consequently previous approaches to summative peer assessment risk increasing both student frustration and faculty grading load. This experience report describes SPARK, our unique approach to summative peer assessment that we have successfully used over several semesters for weekly problem sets in a theoretical computer science course as well as on a more limited basis in both undergraduate and graduate robotics courses. Surveys indicate that a majority of students find it easy to use the SPARK approach to grade their peers, believe that the homework assignments helped their learning, acknowledge that they would not have put the same time and effort into their homework had it not been graded, and believe that SPARK's method of computing their final grade for an assignment is fair.
Shallow Neural Networks beat Deep Neural Networks trained with transfer learning: A Use Case based on training Neural Networks to identify Covid-19 in chest X-ray images

Since the start of the covid-19 health crisis, there have been many studies on the application of deep learning models in order to detect the virus on chest X-ray images. Training large neural networks on big data sets is a computationally intensive task, consuming a lot of power and needing a lot of time. Thus, usually only researchers in large institutions or companies have the necessary resources to bring the task to fruition. Other researchers employ transfer learning, a technique that is based on using pre-trained deep neural networks that have been trained on a similar dataset and retrain only their last neuron layers. However, using deep neural networks with transfer learning is not always the best option; in some cases, training a shallow neural network from scratch achieves better results. In this paper we compare training from scratch, shallow neural networks to transfer learning from deep neural models. Our experiments have been conducted on a publicly available dataset containing chest X-ray images concerning covid-19 patients, as well as non-covid-19 ones. Surprisingly enough, training from scratch shallow neural networks produced significantly better results in terms of both specificity and sensitivity. The results of the models’ evaluation showed that the three shallow neural networks achieved specificity rates higher than 98%, while having a sensitivity rate of 98%, exceeding the best performing pre-trained model, the DenseNet121, which achieved a specificity rate of 91.3%, while having a sensitivity rate of 98%.
THE EFFECT OF ALCOHOL ON DRIVING SKILL

To the Editor:— The recent article "The Effect of Alcohol on Driving Skill" (The Journal, November 9, p. 1600) brings out a number of interesting facts but may lead to erroneous conclusions if not carefully scrutinized. An ingenious test of driving skill, including both steering and braking, showed that the two skills were more closely correlated than either or both with the exact concentration of alcohol in the blood stream. However, it should be noted that the subjects showed a remarkably similar concentration of alcohol in the blood after the test dose, in no instance varying by more than 0.03 per cent from the average concentration to be expected (chart 1) and almost all lying in the zone characterized by the committees of the American Medical Association and the National Safety Council, as well as most students of the subject, as of "suggestive" or "presumptive" rather than of "conclusive" or
RSSD: defend against ransomware with hardware-isolated network-storage codesign and post-attack analysis

Encryption ransomware has become a notorious malware. It encrypts user data on storage devices like solid-state drives (SSDs) and demands a ransom to restore data for users. To bypass existing defenses, ransomware would keep evolving and performing new attack models. For instance, we identify and validate three new attacks, including (1) garbage-collection (GC) attack that exploits storage capacity and keeps writing data to trigger GC and force SSDs to release the retained data; (2) timing attack that intentionally slows down the pace of encrypting data and hides its I/O patterns to escape existing defense; (3) trimming attack that utilizes the trim command available in SSDs to physically erase data. To enhance the robustness of SSDs against these attacks, we propose RSSD, a ransomware-aware SSD. It redesigns the flash management of SSDs for enabling the hardware-assisted logging, which can conservatively retain older versions of user data and received storage operations in time order with low overhead. It also employs hardware-isolated NVMe over Ethernet to expand local storage capacity by transparently offloading the logs to remote cloud/servers in a secure manner. RSSD enables post-attack analysis by building a trusted evidence chain of storage operations to assist the investigation of ransomware attacks. We develop RSSD with a real-world SSD FPGA board. Our evaluation shows that RSSD can defend against new and future ransomware attacks, while introducing negligible performance overhead.
"It is the Future": Exploring Parent Perspectives of CS Education

As schools begin offering more opportunities for computer science education, it is important to consider the viewpoints of all stakeholders. Parents, in particular, can have an outsize impact on their children's educational experience and attitudes towards STEM topics, but few studies investigate parent perceptions of CS, beliefs around whether and why learning CS is important for their children, and preferences for tools to learn programming. We ran a survey with 133 parent respondents of children in 6th to 12th grade to investigate perspectives on computing and CS education. Most parents viewed CS as important for their children to learn; however, fewer actively encouraged their child to engage with CS, and those who did tended to have higher prior knowledge in CS. In considering three types of educational programming interfaces (block, text, and hybrid), parents of varying experience levels preferred the hybrid tool, commonly because they believed that it could provide the best educational value. This work contributes to understanding parent stances and preferences on CS education, as they play an important role in supporting and facilitating their children's access to and experience in CS learning opportunities.