Miracle-dz/UniFAR-FTUs huggingface dataset:
What do We Know about Computing Education for K-12 in Non-formal Settings? A Systematic Literature Review of Recent Research

Background and context. Non-formal learning for K-12 computing education enables young people to learn about computing outside the formal curriculum. Many studies have reported on non-formal initiatives but it is not always clear what children and young people have gained from their participation. Objectives. This study set out to investigate non-formal learning initiatives by means of a systematic literature review. The two research questions addressed by the study are: (1) What has been the focus of recent computing education research about K-12 initiatives for young people and (2) What is the impact of non-formal K-12 computing initiatives? Method. A systematic literature review of computing education research was conducted, focused on non-formal initiatives for young people. Research was included from any country, but must be published in English between January 2015 and April 2021. Searches using key terms were performed across three databases. 88 studies were synthesised from over 400 initial results. Findings. The vast majority of studies reported on immersive multi-day settings such as summer camps run by universities (n= 67), with fewer (n=21) reporting on regular ongoing after-school or weekend clubs. The most popular affective outcomes measured by studies were self-efficacy (n=25) and interest (n=22). Measurement of cognitive outcomes, such as knowledge (n= 13) and skills (n=17), was less prevalent. 22 different topics were identified from the studies, with most studies being programming-heavy. The majority of papers measured the short-term impact of these interventions, and generally there was an inconsistent or incomplete reporting of learner characteristics across the studies. Implications. The lack of papers investigating regular after-school initiatives suggests that the majority of non-formal learners are not being studied or that summer school findings are being wrongly extrapolated to this setting. More rigorous research is needed for regular after-school and short-term non-formal contexts to ensure that this set of learners’ experiences is understood and potentially improved.
Self-efficacy, Interest, and Belongingness – URM Students’ Momentary Experiences in CS1

Educational stakeholders want to understand and overcome the well-documented racial and gender disparities within computer science education. There are many factors that influence students’ participation, performance, and persistence in CS courses, including motivational and affective factors. Prior research in CS education has documented the influence of these factors on students’ CS outcomes generally, and on URM students in particular. What has been less investigated, is how students’ motivational and affective experiences in CS develop and evolve from moment to moment, particularly for URM students. To better understand how these experiences develop, this paper presents the results of a study using intensive longitudinal methods which examined the differences in momentary experiences between racially underrepresented students and their represented peers in undergraduate introductory computer science courses. Using the Experience Sampling Method (ESM), we solicited responses on students’ momentary self-efficacy, interest, and affective experiences 8-18 times in each of two semesters from a total of 110 CS students, of which 19 identified as racially underrepresented. Analyzing the data using a Bayesian multivariate multilevel modeling approach, we found that students’ pre-semester self-efficacy impacted their momentary frustration, interest, and self-efficacy throughout the duration of the semester, as expected. Likewise, students’ baseline interest significantly impacted their momentary self-efficacy and interest. Baseline sense of belonging, by contrast, showed no significant impact on their momentary experiences. We also examined how students’ affective experiences relate to course outcomes and found that baseline self-efficacy significantly predicts end-of-course grades for URM students. Overall, this study highlights that students’ self-efficacy and interest are important for their momentary experiences, and course outcomes for URM students, while their sense of belonging did not make a significant impact. We expected that these influences might differ in magnitude for URM students, although a larger sample size and greater statistical power is needed to substantiate this possibility. Nevertheless, the findings from this study emphasize the importance of self-efficacy, and interest for URM students’ momentary experiences, which are important for their other outcomes in CS classes.
E-FAIR-DB: Functional Dependencies to Discover Data Bias and Enhance Data Equity

Decisions based on algorithms and systems generated from data have become essential tools that pervade all aspects of our daily lives; for these advances to be reliable, the results should be accurate but should also respect all the facets of data equity [11]. In this context, the concepts of Fairness and Diversity have become relevant topics of discussion within the field of Data Science Ethics and, in general, in Data Science. Although data equity is desirable, reconciling this property with accurate decision-making is a critical tradeoff, because applying a repair procedure to restore equity might modify the original data in such a way that the final decision is inaccurate w.r. t. the ultimate objective of the analysis. In this work, we propose E-FAIR-DB, a novel solution that, exploiting the notion of Functional Dependency—a type of data constraint—aims at restoring data equity by discovering and solving discrimination in datasets. The proposed solution is implemented as a pipeline that, first, mines functional dependencies to detect and evaluate fairness and diversity in the input dataset, and then, based on these understandings and on the objective of the data analysis, mitigates data bias, minimizing the number of modifications. Our tool can identify, through the mined dependencies, the attributes of the database that encompass discrimination (e.g., gender, ethnicity, or religion); then, based on these dependencies, it determines the smallest amount of data that must be added and/or removed to mitigate such bias. We evaluate our proposal both through theoretical considerations and experiments on two real-world datasets.
Comparative analysis of macrophage activation in the synovium of healthy and 49 osteoarthritic equine joints

71 Background: Synovitis is a major component of osteoarthritis and is driven primarily by 72 macrophages. Synovial macrophages are crucial keepers of joint health by driving joint 73 homeostasis, tissue repair and inflammation resolution (M2-like phenotype), but can also induce 74 an inflammatory response (M1-like phenotype) when these regulatory functions are 75 overwhelmed. Macrophage phenotypes in synovium from osteoarthritic and healthy joints are 76 poorly characterized. Defining the patterns of synovial macrophage phenotypes in normal and 77 osteoarthritic joints is paramount for developing targeted therapeutic approaches. The objective 78 of this study was to compare patterns of macrophage activation phenotypes in healthy and 79 osteoarthritic equine joints. We hypothesized that synovium from osteoarthritic joints would 80 have increased M1-like:M2-like ratios compared to normal joints. Methods: Gross evaluation of the articular surfaces, histology (H&E) and immunohistochemistry for M1-like (CD86), M2-like (CD206, IL-10), and pan macrophage (CD14) markers was 83 performed on synovial biopsies from healthy (n=29) and osteoarthritic equine joints (n=26). Cytokines (MCP-1, IL-10, PGE 2 , IL-1β, IL-6, TNF-α, IL-1ra) and growth factors (GM-CSF, SDF- 1α+β, IGF-1, and FGF-2) in synovial fluid were quantified. Results: All macrophage markers were co-expressed in all joints with minimal differences 87 between OA and normal joints. Intensity of expression varied with degree of synovial inflammation. CD14, CD86, CD206, and IL-10 were more highly expressed in grossly inflamed osteoarthritic synovium, with CD86 most highly expressed. Synovial fluid MCP-1 was higher in osteoarthritic joints while SDF-1 expression in the synovium was associated with synovial hyperplasia, consistent with 93 macrophage recruitment and activation in response to higher demands for repair. Conclusions: Macrophages are not as clearly defined in vivo as they are in vitro . The course of an effective response to injury in joints should start with inflammation and be followed inflammation resolution, both of which centrally driven by macrophages. Combined, our 97 findings suggest that homeostatic mechanisms from synovial macrophages are impaired in OA, 98 resulting in unresolved, chronic joint inflammation. Therapeutic approaches aimed at recovering mechanisms of macrophage-driven synovial homeostasis may be more effective in treating 100 osteoarthritis than simply inhibiting inflammation. macrophage marker; CD86, Cluster of differentiation 86/T cell costimulatory receptor (B7.2)— M1 marker; CD206, Cluster of differentiation 206/Mannose receptor 1 (MRC1)—
Diminished (cid:1) -cell replication contributes to reduced (cid:1) -cell mass in fetal sheep with intrauterine growth restriction

Jr. Diminished (cid:1) -cell replication contributes to reduced (cid:1) -cell mass in fetal sheep with intrauterine growth restriction. Am J Physiol Regul Integr Comp Physiol R1297–R1305, 2005. doi:10.1152/ajpregu.00494. 2004. —Hu-man fetuses with severe intrauterine growth restriction (IUGR) have less pancreatic endocrine tissue and exhibit (cid:1) -cell dysfunction, which may limit (cid:1) -cell function in later life and contribute to their increased incidence of noninsulin-dependent diabetes mellitus. Three factors, replication, apoptosis, and neoformation, contribute to fetal (cid:1) -cell mass. We studied an ovine model of IUGR to understand whether nutrient deﬁcits lead to decreased rates of fetal pancreatic (cid:1) -cell replication, increased rates of apoptosis, or lower rates of differentiation. At 90% of term gestation, IUGR fetal and pancreatic weights were 58% and 59% less than pair-fed control, respectively. We identiﬁed a selective impairment of (cid:1) -cell mass compared with other pancreatic cell types in IUGR fetuses. Insulin and insulin mRNA contents were less than other pancreatic endocrine hormones in IUGR fetuses, as were pancreatic insulin positive area (42%) and (cid:1) -cell mass (76%). Pancreatic (cid:1) -cell apoptosis was not different between treat-ments. (cid:1) -cell capacity for cell cycling, determined by proliferating cell nuclear antigen (PCNA) immunostaining, was not different between treatment groups. However, the percentage of (cid:1) -cells actually undergoing mitosis was 72% lower in IUGR fetuses. These results indicate that in utero nutrient deﬁcits decrease the population of pancreatic (cid:1) -cells by lengthening G1, S, and G2 stages of interphase and decreasing mitosis near term. Diminished (cid:1) -cell mass in IUGR infants at birth, if not adequately compensated for after birth, may contribute to insufﬁcient insulin production in later life and, thus, a predisposition to noninsulin-dependent diabetes.
Structural biology of SARS-CoV-2 endoribonuclease NendoU (nsp15)

The SARS-CoV-2’s endoribonuclease (NendoU) nsp15, is an Mn2+ dependent endoribonuclease specific to uridylate that SARS-CoV-2 uses to avoid the innate immune response by managing the stray RNA generated during replication. As of the writing of this review 20 structures of SARS-CoV-2 nsp15 have been deposited into the PDB, largely solved using X-ray crystallography and some through Cryo-EM. These structures show that an nsp15 monomer consist of three conserved domains, the N-terminal oligomerization domain, the middle domain, and the catalytic NendoU domain. Enzymatically active nsp15 forms a hexamer through a dimer of trimers (point group 32), whose assembly is facilitated by the oligomerization domain. This review summarises the structural and functional information gained from SARs-CoV-2, SARs-CoV and MERS-CoV nsp15 structures, compiles the current structure-based drug design efforts, and complementary knowledge with a view to provide a clear starting point for downstream structure users interested in studying nsp15 as a novel drug target to treat COVID-19.
Proteomic Response of Sugar Beet Monosomic Addition Line M14 Guard Cells to Salt Stress

Jialin Zhang Ministry of Education, Heilongjiang University Xin Tao Ministry of Education, Heilongjiang University Shuang Wang Ministry of Education, Heilongjiang University Jinna Li Ministry of Education, Heilongjiang University He Liu Ministry of Education, Heilongjiang University Sixue Chen University of Florida Bing Yu Ministry of Education, Heilongjiang University Chunquan Ma Ministry of Education, Heilongjiang University Haiying Li (  lvzh3000@sina.com ) Ministry of Education, Heilongjiang University
Digital super-resolution microscopy using example-based algorithm

We propose a super-resolution microscopy with a confocal optical setup and an example-based algorithm. The example-based super-resolution algorithm was performed by an example database which is constructed by learning a lot of sets of a high-resolution patch and a low-resolution patch. The high-resolution patch is a part of the high-resolution image of an object model expressed in a computer, and the low-resolution patch is calculated from the high-resolution patch in consideration with a spatial property of an optical microscope. In the reconstruction process, a low-resolution image observed by the confocal optical setup with an image sensor is converted to the super-resolved high-resolution image selected by a pattern matching method from the example database. We demonstrate the adequate selection of the patch size and the weighting superposition method performs the super resolution with a low signal-to noise ratio.
BEACONS IN THE DARK: USING NOVAE AND SUPERNOVAE TO DETECT DWARF GALAXIES IN THE LOCAL UNIVERSE

We propose that luminous transients, including novae and supernovae (SNe), can be used to detect the faintest galaxies in the universe. Beyond a few megaparsecs, dwarf galaxies with stellar masses ≲ 10 6 M ⊙ ? > will likely be too faint and/or too low in surface brightness to be directly detected in upcoming large area ground-based photometric surveys. However, single-epoch Large Synoptic Survey Telescope photometry will be able to detect novae to distances of ∼30 Mpc and SNe to gigaparsec-scale distances. Depending on the form of the stellar mass–halo mass relation and the underlying star formation histories of low-mass dwarfs, the expected nova rates will be a few to ∼100 yr−1 and the expected SN rates (including both type Ia and core-collapse) will be ∼ 10 2 − 10 4 ? > within the observable ( 4 &pgr; ? > sr) volume. The transient rate associated with intrahalo stars will be comparably large, but these transients will be located close to bright galaxies, in contrast to the dwarfs, which should trace the underlying large-scale structure of the cosmic web. Aggressive follow-up of hostless transients has the potential to uncover the predicted enormous population of low-mass field dwarf galaxies.
Work-in-Progress—Exploring Model Based Automated Response Systems to Enhance Student Outcomes

The advancement of Artificial Intelligence (AI) has sparked great excitement and expectation of its use in education. Integral to that is the idea of an Automated Response System (ARS). Huge strides have been made in the adaption of ARS in education, although a suitable theoretical framing has yet to be explored, especially in the context of designing an ARS. A previous proposal to use the community of inquiry (CoI) model was presented, but a case could be made that the link between the perception, attitudes and outcomes could be much better explored as part of the design. Hence, a different approach involving the technology acceptance model (TAM) model is explored in this paper toward its use to design an ARS to help statistics students better meet a more holistic learning outcome.
ENERGY INTENSITY OF DEVELOPMENT AND THE PRECONDITIONS FOR ITS ABATEMENT: AN ECONOMETRIC ANALYSIS, WITH EMPHASIS ON THE CIS COUNTRIES

Importance The article focuses on the issues of development of the macroeconomic and institutional environment necessary to reduce energy intensity in the CIS countries. Objectives The paper aims to develop a methodological approach based on econometric analysis of a sample of global statistical reports to justify the energy intensity abatement. Methods I use methods of comparative analysis, linear and non-linear regressions, equations with threshold values, and the methods of index numbers. Results The article proves that existing statistics can be used to justify factors influencing the energy intensity of the economy in the context of both demand and supply. As the comparative analysis shows, the regulatory quality is a key factor in reducing energy intensity in the CIS countries. Conclusions and Relevance The CIS countries have the potential for improving the energy efficiency. This requires building the potential of State institutions and focusing on the development of competitive markets for energy products, energy auditing, adoption of power-saving standards, limitation of shadow economy, and more intensive counteraction to corruption.
Medial temporal lobe atrophy on MRI differentiates Alzheimer’s disease from dementia with Lewy bodies and vascular cognitive impairment: a prospective study with pathological veriﬁcation of diagnosis

The purpose of this study was to determine the diagnostic accuracy of medial temporal lobe atrophy (MTA) on MRI for distinguishing Alzheimer’s disease from other dementias in autopsy conﬁrmed cases, and to determine pathological correlates of MTA in Alzheimer’s disease, dementia with Lewy bodies (DLB) and vascular cognitive impairment (VCI). We studied 46 individuals who had both antemortem MRI and an autopsy. Subjects were clinicopathologically classiﬁed as having Alzheimer’s disease (n= 11), DLB (n= 23) or VCI (n= 12). MTA was rated visually using a standardized (Scheltens) scale blind to clinical or autopsy diagnosis. Neuropathological analysis included Braak staging as well as quantitative analysis of plaques, tangles and a -synuclein Lewy body-associated pathology in the hippocampus. Correlations between MTA and pathological measures were carried out using Spearman’s rho, linear regression to assess the contributions of local pathologic changes to MTA. Receiver operator curve analysis was used to assess the diagnostic speciﬁcity of MTA for Alzheimer’s disease among individuals with Alzheimer’s disease, DLB and VCI. MTA was a highly accurate diagnostic marker for autopsy conﬁrmed Alzheimer’s disease (sensitivity of 91% and speciﬁcity of 94%) compared with DLB and VCI. Across the entire sample, correlations were observed between MTA and Braak stage ( r = 0.50, P 5 0.001), per cent area of plaques in the hippocampus ( r = 0.37, P=0.014) and per cent area of tangles in the hippocampus ( r = 0.49, P= 0.001). Linear regression showed Braak stage (P= 0.022) to be a signiﬁcant predictor of MTA but not percent area of plaques (P= 0.375), percent area of tangles (P= 0.330) or percent area of Lewy bodies (P= 0.086). MTA on MRI had robust discriminatory power for distinguishing Alzheimer’s disease from DLB and VCI in pathologically conﬁrmed cases. Pathologically, it is more strongly related to tangle rather than plaque or Lewy body pathology in the temporal lobe. It may have utility as a means for stratifying samples in vivo on the basis of putative differences in pathology. we found MTA to be a highly accurate diagnostic marker for autopsy conﬁrmed Alzheimer’s disease (sensitivity 91% and a speciﬁcity of 94%) using ROC analysis in this sample with a mean age of 80.7 years.
Deep Learning Aided Runtime Opcode Based Malware Detection by Enes

Deep Learning Aided Runtime Opcode Based Malware Detection Enes Sinan Parildi Masters of Applied Sciences Graduate Department of The Edward S. Rogers Sr. Department of Electrical Computer Engineering. University of Toronto 2019 Thousands of new malware codes are developed every day. Signature-based methods, which are employed by common malware detectors, are susceptible to code obfuscation and novel malware. In this paper, we present an alternative method for malware detection which makes use of assembly opcode sequences or frequencies obtained during runtime. First, for sequential opcode data, we utilize natural language processing and deep learning techniques to facilitate the extraction of deeper behavioral features. Due to these features, this method can be impervious to code obfuscation and effective against novel malware. Finally, these features are fed to various machine learning algorithms for classification. The experiments on 26027 samples that were conducted demonstrated high MCC (Matthew Correlation Coefficient) scores are achievable with this approach. Secondly, for the frequency dataset, we conduct another experiment that indicates the runtime opcode approach can achieve %70 accuracy for 16 malware labels on 59424 samples.
Institutional Knowledge at Singapore Management University Efficient Topological OLAP on Information Networks

We propose a framework for efﬁcient OLAP on information networks with a focus on the most interesting kind, the topological OLAP (called “ T-OLAP ”), which incurs topological changes in the underlying networks. T-OLAP operations generate new networks from the original ones by rolling up a subset of nodes chosen by certain constraint criteria. The key challenge is to efﬁciently compute measures for the newly generated networks and handle user queries with varied constraints. Two effective computational techniques, T-Distributiveness and T-Monotonicity are proposed to achieve efﬁcient query processing and cube materialization. We also provide a T-OLAP query processing framework into which these techniques are weaved. To the best of our knowledge, this is the ﬁrst work to give a framework study for topological OLAP on information networks. Experimental results demonstrate both the effectiveness and efﬁciency of our proposed framework.
In-Body Electromagnetic Sensor Combined with AI-Enhanced Electrocardiography for Pacemaker Working Status Telemonitoring

This paper describes the design and implementation of an in-body electromagnetic sensor for patients with implanted pacemakers. The sensor can either be mounted on myocardial tissue and monitor the electrocardiography (ECG) with contact electrodes or implanted under the skin and monitor the ECG with coaxial leads. A 16-bit high-resolution analog front-end (AFE) and an energy-e ﬃ cient 32-bit CPU are used for instantaneous ECG recording. Wireless data transmission between the sensor and clinician ’ s computer is achieved by an embedded low-power Bluetooth transmitter. In order to automatically recognize the working status of the pacemaker and alarm the episodes of arrhythmias caused by pacemaker malfunctions, pacing mode classi ﬁ cation and fault diagnosis on the recorded ECG were achieved based on an AI algorithm, i.e., a resource allocation network (RAN). A prototype of the sensor was implemented on a human torso, and the in vitro test results prove that the sensor can work properly for the 1-4-meter transmission range.