Miracle-dz/UniFAR-FTUs huggingface dataset:
Cellular Imaging Analysis of MTT Assay Based on Tetrazolium Reduction

Background: MTT assay is a colorimetric test to evaluate cell metabolic activity of living cells via reduction of the 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide to insoluble formazan crystals by mitochondrial activities. The reduction of the tetrazolium dye is thought to occur by NADPH-dependent oxidoreductase enzymes in the cell cytosol. The MTT test is used to measure the cytotoxicity or cytostatic activity of generally plant and chemical compounds and toxic materials. In this study, it was aimed to monitor the uniformity of formazan formation at equal time intervals by visualizing the reduction of tetrazolium salts in cells. Materials and Methods: In the study, K562 cells were used to observe the reduction of tetrazolium salts to MTT formazan crystals in cells. K562 cells were seeded in culture plate under sterile conditions. After adding 10 μL of 5 mg/mL MTT solution to the culture plate, the cells were incubated for 4 h at 37 °C in a humidified environment with 5% CO2. During the culture process, the cells were imaged at 15 minuteintervals for 4 hours. Results: The behavior of viable and non-viable cells against MTT and the process of converting MTT to MTT formazan crystal by living cells were clearly monitored. Conclusions: Visual analysis of MTT reduction directly from the incubator with image recordings at equal time intervals showed the perfect homogeneity of MTT degradation of the cells over time. With our study, we can state that the MTT test is an ideal test method for cytotoxicity research.
An Upper-Division, Remote Microbiology Laboratory That Blends Virtual and Hands-on Components to Promote Student Success during the COVID-19 Pandemic

The COVID-19 shutdown forced many institutions of higher education to shift in-person teaching to emergency remote teaching. This was particularly challenging for laboratory courses, where students are expected to learn hands-on skills needed for their career goals. ABSTRACT The COVID-19 shutdown forced many institutions of higher education to shift in-person teaching to emergency remote teaching. This was particularly challenging for laboratory courses, where students are expected to learn hands-on skills needed for their career goals. Here, we describe the transformation of an upper-division microbiology laboratory to a course that seamlessly integrates online simulations with safe, hands-on experiences that can be done from home. This blended lab course helped students attain learning outcomes similar to those achieved in the in-person class. We illustrate the implementation of Unknown Portfolios to help students gain the data analysis and critical thinking skills needed to identify an unknown microorganism. Our data show that students who took these online courses mastered material as well as students who took the lab in person, demonstrating proficiency in laboratory safety skills, hands-on techniques, and theoretical class content. Last, we explore online adaptations to enhance in-person lab classes, aiming at reducing the accessibility and equity gaps inherited in many courses, as well as discussing challenges that instructors might experience in this process.
Genomic Characterization of an O101:H9-ST167 NDM-5-Producing Escherichia coli Strain from a Kitten in Italy

The high-risk clone ST167 associated with blaNDM-5 resistance determinant is currently recognized to be a source of public health concern worldwide (1–4), since it has been identified even beyond hospital borders, in companion animals, wastewater, rivers, and wildlife (5–8). In this work, we characterized an NDM-5-producing Escherichia coli ST167 collected in Italy from a liver sample of a 4-month-old cat who died from parvovirus hemorrhagic enteritis. The E. coli strain 167624 was tested for antibiotic susceptibility and sequenced using both Illumina and Nanopore technologies. Bacterial identification and antibiotic susceptibility tests were performed with the semiautomated system MicroScan autoSCAN4 (Beckman Coulter); results were interpreted according to EUCAST guidelines (v10.0-2020, http://www.eucast.org). The E. coli 167624 strain showed a multidrug-resistant (MDR) profile, being resistant to all the antibiotics tested, with the exception of colistin, amikacin, and fosfomycin (Table 1).
Structural and magnetic properties of Vanadium Doped M- Type Barium Hexaferrite (BaFe 12-x

: Precursor powders of barium hexaferrite doped with vanadium, BaFe 12 - x V x O 19 with ( x = 0.1, 0.2, 0.3, 0.4, 0.5), were prepared using the ball milling technique and then sintered at different temperatures for 2 h. The structural properties of the prepared samples were investigated by X-ray diffraction (XRD) and scanning electron microscopy (SEM), while the magnetic properties were examined by the vibrating sample magnetometry (VSM). XRD and SEM studies of the samples sintered at 1100 ο C indicated the presence of Ba 3 V 2 O 8 and α-Fe 2 O 3 non-magnetic oxide phases in addition to BaM hexaferrite phase. The fractions of the non-magnetic oxide phases were found to increase with increasing x , and sintering the samples at temperatures higher than 1100 ο C was found to reduce the amounts of these non-magnetic phases only slightly. However, the addition of barium in excess of the stoichiometric ratio was found to remove the α-Fe 2 O 3 oxide, and improve the saturation magnetization of the samples significantly. In addition, washing these samples with HCl was found to improve the saturation magnetization further. The effect of sintering the samples at higher temperatures was also found to reduce the coercivity due to growth of the particle size. However, the coercivity of all samples remained high enough for potential permanent magnet and magnetic recording applications.
RepliGES and GEStory: Visual Tools for Systematizing and Consolidating Knowledge on User-Defined Gestures

The body of knowledge accumulated by gesture elicitation studies (GES), although useful, large, and extensive, is also heterogeneous, scattered in the scientific literature across different venues and fields of research, and difficult to generalize to other contexts of use represented by different gesture types, sensing devices, applications, and user categories. To address such aspects, we introduce RepliGES, a conceptual space that supports (1) replications of gesture elicitation studies to confirm, extend, and complete previous findings, (2) reuse of previously elicited gesture sets to enable new discoveries, and (3) extension and generalization of previous findings with new methods of analysis and for new user populations towards consolidated knowledge of user-defined gestures. Based on RepliGES, we introduce GEStory, an interactive design space and visual tool, to structure, visualize and identify user-defined gestures from a number of 216 published gesture elicitation studies.
DeepFakes for Privacy: Investigating the Effectiveness of State-of-the-Art Privacy-Enhancing Face Obfuscation Methods

There are many contexts in which a person’s face needs to be obfuscated for privacy, such as in social media posts. We present a user-centered analysis of the effectiveness of DeepFakes for obfuscation using synthetically generated faces, and compare it with state-of-the-art obfuscation methods: blurring, masking, pixelating, and replacement with avatars. For this, we conducted an online survey (N=110) and found that DeepFake obfuscation is a viable alternative to state-of-the-art obfuscation methods; it is as effective as masking and avatar obfuscation in concealing the identities of individuals in photos. At the same time, DeepFakes blend well with surroundings and are as aesthetically pleasing as blurring and pixelating. We discuss how DeepFake obfuscation can enhance privacy protection without negatively impacting the photo’s aesthetics.
Compensating Adaptive Mixed Criticality Scheduling

The majority of prior academic research into mixed criticality systems assumes that if high-criticality tasks continue to execute beyond the execution time limits at which they would normally finish, then further workload due to low-criticality tasks may be dropped in order to ensure that the high-criticality tasks can still meet their deadlines. Industry, however, takes a different view of the importance of low-criticality tasks, with many practical systems unable to tolerate the abandonment of such tasks. In this paper, we address the challenge of supporting genuinely graceful degradation in mixed criticality systems, thus avoiding the abandonment problem. We explore the Compensating Adaptive Mixed Criticality (C-AMC) scheduling scheme. C-AMC ensures that both high- and low-criticality tasks meet their deadlines in both normal and degraded modes. Under C-AMC, jobs of low-criticality tasks, released in degraded mode, execute imprecise versions that provide essential functionality and outputs of sufficient quality, while also reducing the overall workload. This compensates, at least in part, for the overload due to the abnormal behavior of high-criticality tasks. C-AMC is based on fixed-priority preemptive scheduling and hence provides a viable migration path along which industry can make an evolutionary transition from current practice.
AVB-aware Routing and Scheduling for Critical Traffic in Time-sensitive Networks with Preemption

The Time-Sensitive Network (TSN) amendments and protocols add capabilities on top of standard 802.1 Ethernet for guaranteeing the timeliness of both (isochronous) scheduled traffic (ST) and shaped (audio-video) communication (AVB) in distributed applications. ST streams are guaranteed via an offline computed schedule controlling the time-aware gate mechanism of IEEE 802.1Qbv, while AVB real-time streams are shaped via a credit-based shaper (CBS) and scheduler with lower-priority than ST. Although the two traffic classes use different TSN mechanisms, they are interrelated as the ST traffic class schedule influences the latency of AVB traffic. In this paper, we propose a method for the integration of the ST schedule synthesis with an analysis for the AVB class featuring IEEE 802.1Qbu frame preemption under different configurations to reduce the interference between the two classes. We first present a new worst-case response-time (WCRT) analysis for the AVB traffic class in TSN networks with preemption, considering an arbitrary number of AVB queues and different configurations for the CBS credit behavior. Then, we integrate the creation of ST schedule tables with the schedulability analysis of AVB traffic using a heuristic algorithm featuring frame preemption and a novel routing mechanism aimed at maximizing AVB schedulability. Finally, we evaluate our approach using both real-world and synthetic use cases showing the efficiency both in terms of schedule creation runtime and in terms of increasing the schedulability of lower-priority AVB traffic.
Composites: A Tangible Interaction Paradigm for Visual Data Analysis in Design Practice

Conventional tools for visual analytics emphasize a linear production workflow and lack organic “work surfaces. ” A better surface would simultaneously support collaborative visualization construction, data and design exploration, and reasoning. To facilitate data-driven design within existing design tools such as card sorting, we introduce Composites, a tangible, augmented reality interface for constructing visualizations on large surfaces. In response to the placement of physical sticky-notes, Composites projects visualizations and data onto large surfaces. Our spatial grammar allows the designer to flexibly construct visualizations through the use of the notes. Similar to affinity-diagramming, the designer can “connect” the physical notes to data, operations, and visualizations which can then be re-arranged based on creative needs. We develop mechanisms (sticky interactions, visual hinting, etc.) to provide guiding feedback to the end-user. By leveraging low-cost technology, Composites extends a working surface to support a broad range of workflows without limiting creative design thinking.
Vision and Natural Language for Metadata Extraction from Scientific PDF Documents: A Multimodal Approach

The challenge of automatically extracting metadata from scientific PDF documents varies depending on the diversity of layouts within the PDF collection. In some disciplines such as German social sciences, the authors are not required to generate their papers according to a specific template and they often create their own templates which yield a high appearance diversity across publications. Overcoming this diversity using only Natural Language Processing (NLP) approaches is not always effective which is reflected in the metadata unavailability of a large portion of German social science publications. Therefore, we propose in this paper a multimodal neural network model that employs NLP together with Computer Vision (CV) for metadata extraction from scientific PDF documents. The aim is to benefit from both modalities to increase the overall accuracy of metadata extraction. The extensive experiments of the proposed model on around 8800 documents proved its effectiveness over unimodal models, with an overall F1 score of 92. %. CCS CONCEPTS • Computing methodologies $\rightarrow$ Neural networks; Supervised learning; • Applied computing $\rightarrow$ Document management and text processing.
“Secure settings are quick and easy!” – Motivating End-Users to Choose Secure Smart Home Configurations

While offering many useful features, novel smart home devices also provide an attack surface to users’ allegedly secure place: their homes. Thus, it is essential to employ effective threat mitigation strategies, such as securely configuring devices. We investigate how users can be motivated to do so. To foster secure actions, we designed two types of nudges based on the Protection Motivation Theory (PMT): one with low and one with high level of detail. As such, our nudges particularly target users’ threat appraisal (including perceived severity and likelihood of threats) and self-efficacy to take action. In a randomized online experiment (N = 210), we simulated a smart home setup procedure. Participants chose significantly more secure configurations when being provided with detailed nudges, and indicated higher perceived threat and coping appraisal (i.e., higher protection motivation) after the experiment. Based on our results, we discuss the design and deployment of nudges for (future) smart home setup procedures. Our work can help to a) increase users’ threat awareness in general, and b) motivate users to take actions such as securely configuring their devices.
The Significance and Impact of Winning an Academic Award: A Study of Early Career Academics

Academic award plays an important role in an academic’s career, particularly for early career academics. Previous studies have primarily focused on the impact of awards conferred to academics who have made outstanding contributions to a specific research field, such as the Nobel Prize. In contrast, this paper aims to investigate the effect of awards conferred to academics at an earlier career stage, who have the potential to make a great impact in the future. We devise a metric named Award Change Factor (ACF), to evaluate the change of a recipient’s academic behavior after winning an academic award. Next, we propose a model to compare award recipients with academics who have similar performance before winning an academic award. In summary, we analyze the impact of an award on the recipients’ academic impact and their teams from different perspectives. Experimental results show that most recipients do have improvements in both productivity and citations after winning an academic award, while there is no significant impact on publication quality. In addition, receipt of an academic award not only expands recipients’ collaboration network, but also has a positive effect on their team size. CCS CONCEPTS • Human-centered computing → Empirical studies in collaborative and social computing; • Information systems → Information retrieval; Data mining.
Selective scrubbing based on algorithmic randomness

Disk scrubbing is a background process to fix read errors by reading the disks. However, scrubbing the entire storage array can significantly increase the system load and degrade system performance when there is high incoming IO. Deciding "which disk to scrub" complemented with "when to scrub" can significantly improve the data centre's overall reliability and power saving. We present a solution on an open-source SMART dataset that performs selective scrubbing and designs a scrub frequency based on the scrub cycle. The method leverages an algorithmic randomness framework to quantify the health of the concerned drives and ranks them for selective scrubbing.
An approach for increasing the throughput of a CNN-based industrial quality inspections system with constrained devices

Convolutional neural networks (CNNs) with their variants are widely used in image classification tasks and often reach or even surpass human-level accuracy. Consequently, these models are applied in industrial settings to support or replace manual quality inspection of products. For scenarios like these, constrained devices with a focus on deep learning applications, such as a Jetson Nano, were developed. However, in-line quality inspections at a high speed and therefore high object throughput, pose a challenge for these limited computational resources of such devices. To reduce the overall inference time, both hard- and software optimizations of the quality inspection system are needed. This paper proposes a two-step approach that combines the detection of multiple objects in one camera frame with hardware-specific inference compilers to increase the performance and therefore the overall throughput of the system. An experimental setup of an actual production line is used to quantify the improvements. The results show that the throughput of a CNN-based quality inspection system can be significantly increased from 4 classifications per second up to 56. Therefore, the applicability of constrained devices in industrial use cases is improved, whereas further optimization approaches were identified and outlined in the paper.
TJOSConf: Automatic and Safe System Environment Operations Platform

With exploding number of servers in large IT corporations, system environment management of servers at scale is a big challenge. System environment needs to be regularly updated to meet the system demand of services or fix emerging bugs. It is of vital importance to ensure stability of services during these system updates. However, the heterogeneity of workload and diversity of update scripts make it difficult to estimate how a system update affects the servers and services running on them. Unexpected failures caused by various reasons can also arise during or after update execution, making the situation even worse. This paper aims to solve the system management challenge with an operations platform called TJOSConf. During system updates, TJOSConf is able to interact with services to make sure that expected impacts and unexpected failures can be recognized and handled in time, making system update highly automatic and safe. Various system update scripts can be easily incorporated into TJOSConf as plugins, making the platform scalable. Use cases of TJOSConf in Alibaba prove its effectiveness.
FaaS in the age of (sub-)μs I/O: a performance analysis of snapshotting

Although serverless computing brings major benefits to developers, the widespread adoption of Function-as-a-Service (FaaS) creates severe challenges for the cloud providers. Irregularity in function invocation patterns and the high cost of cold starts has led them to allocate precious DRAM resources to keep function instances always warm, a clearly sub-optimal and inflexible approach. To cope with this issue, both state-of-the-art and state-of-practice approaches consider snapshotting as a viable mitigation, thus directly associating cold start latency with storage performance. Prior studies consider storage to be inert, rather than the evolving hierarchy that it truly is. In this work, we evaluate cold start and warm function invocations on instances restored from snapshots residing on devices across different layers of the modern storage hierarchy. We thoroughly analyze and characterize the observed behavior of multiple workloads and identify fundamental trade-offs among the devices. We conclude by motivating and providing suggestions for the inclusion of the modern storage hierarchy as a decisive factor in serverless resource provisioning.
How many sources are needed? : The effects of bibliographic databases on systematic review outcomes

Systematic reviews are an established method to synthesize the current state of research for a specific question to make evidence-based decisions in research, politics, and practice. A key activity of a review approach is a systematic and comprehensive search strategy to find all potentially relevant literature. Although guidelines and handbooks address relevant methodological aspects and recommend strategies, the right choice of databases and information sources is unclear. Specifically in educational research, an interdisciplinary field, with no core database at hand and multiple potentially relevant sources available, investigators lack guidance for choosing the most appropriate ones. The presented study investigates the coverage in terms of scope, similarity and combination efficiency of seven multidisciplinary, discipline-specific and nationally focused databases. The evaluation is based on relevant assessed literature of two extensive recently published reviews in German educational research that serve as gold standard to evaluate the databases. Results indicate distinct variations in the databases, while also detecting databases with equal coverage. The paper contributes to guidance in choosing databases for educational review studies, while stressing that this process depends on a review’s topical and geographical focus. Moreover, general implications resulting from the study refer to the relevance of database choice for review outcomes, the careful consideration of diverse search strategies beyond database search and a rigorous documentation of database inclusion and exclusion criteria. CCS CONCEPTS Information Systems - Information retrieval diversity, Information Systems - similarity measures, Information Systems - Combination, fusion and federated search