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关键词： Machine learning algorithms   Data science   Prediction algorithms   Education   Machine learning   Computer science   Data models   Artificial intelligence   cognitive psychology   cognitive bias   domain neglect   machine learning  

摘要： Contribution: This article presents evidence that electrical engineering, computer science, and data science students, participating in introduction to machine learning (ML) courses, fail to interpret the performance of ML algorithms correctly, since they fail to consider the application domain. This phenomenon is referred to as the domain neglect bias and is discussed in this article from the cognitive psychology perspective. Background: ML algorithms are common components in electrical engineering, computer science, and data science curricula. Learning ML is challenging not only due to its complex mathematical and algorithmic aspects, but also due to: 1) the complexity of using these algorithms in the context of real-life situations correctly and 2) the comprehension of related social and ethical issues. Research Question: To what extent do students fail to correctly interpret the performance of ML algorithms due to the domain neglect bias and how can this bias be explained? Methodology: A questionnaire containing two questions was designed, in which the participants were asked to compare the performance of ML algorithms in the contexts of different application domains. The questions were designed to trigger the domain neglect bias which in turn, allowed quantification of its prevalence. Findings: One third of the students failed to recognize the outperforming ML algorithm due to the domain neglect bias. Educators should note this bias and develop new pedagogical methods for teaching ML performance in order to mitigate the bias.

关键词： STEM   Computer science   Career choice   Systematic mapping  

摘要： STEM disciplines are considered essential for human development, and they are associated with low unemployment rates and good economic prospects. However, many countries are faced with the problem of too few STEM graduates, which raises the question of why more students do not choose STEM majors. This study presents a systematic mapping of studies published prior to 2021 in Web of Science or Scopus in order to examine the research trends on the factors that cause students to choose a career in Computer Science, or more generically, in the STEM fields. These factors have been identified and classified in 3 categories: Environmental factors, Social influencers and Personal factors. The categories are made up of 4 levels of subcategories. We analyzed (1) the countries in which the study was conducted, (2) the characteristics of the study and (3) the frameworks used. The results show that the bulk of the studies were conducted in developed countries, mainly in North America and Europe. The frameworks most commonly used in the studies are Expectancy-Value Theory and Social Cognitive Career Theory, and consequently, the most commonly studied factors for STEM degrees are those related to personal psychological factors. For Computer Science degrees, the most frequently studied factor is career prospects. On the other hand, a small number of studies on the impact of social media on the choice of studies in the technology field were detected. Among the studies analyzed, there is great interest in determining the factors that specifically affect women and the differences between men and women, especially in studies dealing with the Computer Sciences. Furthermore, there are few studies that analyze the effect of informal educational experiences among women. Given that this kind of experiences has become very popular in recent years among women, future research should analyze their impact on the choice of STEM studies. Taking into account the current relevance of the social medi

关键词： Computer science   Decision tree   Hospital   k-Nearest neighbors (KNN)   Length of stay   Logistic regression   Machine learning   Random forest  

摘要： The construction sector in a rapidly developing country like India is a very unorganized sector. A large number of workers were affected and hospitalized during the pandemic. This situation is costing the sector heavily in several respects. This research study was conducted as part of using machine learning algorithms to improve construction company health and safety policies. LOS (length of stay) is used to predict how long a patient will stay in a hospital. Predicting LOS is very useful not only for hospitals, but also for construction companies to measure resources and reduce costs. Predicting LOS has become an important step in most hospitals before admitting patients. In this post, we used the Medical Information Mart for Intensive Care(MIMIC III) dataset and applied four different machine learning algorithms: decision tree classifier, random forest, Artificial Neural Network (ANN), and logistic regression. First, I performed data pre-processing to clean up the dataset. In the next step, we performed function selection using the Select Best algorithm with an evaluation function of chi2 to perform hot coding. We then performed a split between training and testing and applied a machine learning algorithm. The metric used for comparison was accuracy. After implementing the algorithms, the accuracy was compared. Random forest was found to perform best at 89%. Afterwards, we performed hyperparameter tuning using a grid search algorithm on a random forest to obtain higher accuracy. The final accuracy is 90%. This kind of research can help improve health security policies by introducing modern computational techniques, and can also help optimize resources. © 2023, The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of Computer Applications and Management.

关键词： Families of experiments   Linguistic patterns   Replication changes   Templates   Threats to validity  

摘要： The need of replicating empirical studies in Computer Science is widely recognized among the research community. It is essential to report the changes of each replication to promote not only the comprehensibility of the evolution of the experimental validity across a family of studies, but also replicability itself. Unfortunately, the lack of proposals for systematic reporting of changes in replications undermines these desirable objectives. The main goal of the work presented in this article is to provide researchers in Computer Science with a systematic tool-supported approach for the specification and reporting of changes in the replications of their empirical studies. Applying Design Science Research, we have developed and validated a composite artifact consisting of (i) a metamodel that formalizes all the relevant concepts related to replications and their changes; (ii) templates and linguistic patterns that facilitate their reporting; and (iii) a proof-of-concept model-based software tool that supports the proposed approach. For its validation, we have carried out a multiple case study that includes 9 families of empirical studies not only from Computer Science, but also from an area as different as Agrobiology , to check the external validity of our approach. The 9 families encompass 23 replication studies and a total of 92 replication changes, for which we have analyzed the suitability of our proposal. The multiple case study revealed some initial limitations of our approach related to threats to experimental validity and context variables. After several improvement iterations on the artifact, all of the 92 replication changes could be properly specified, including also their qualitatively estimated effects on experimental validity and their corresponding visualization. Our proposal for the specification of replication changes seems to fit the needs not only of replications in Computer Science, but also in other research areas. Nevertheless, further research

关键词： Computer science   Silicon   Probabilistic logic   Gold   Genomics   Computational modeling   Bioinformatics   Reproducible computation   Student Cluster Competition  

摘要： Ankit Srivastava et al. (Srivastava et al. 2020) proposed a parallel framework for Constraint-Based Bayesian Network (BN) Learning via Markov Blanket Discovery (referred to as ramBLe) and implemented it over three existing BN learning algorithms, namely, GS, IAMB and Inter-IAMB. As part of the Student Cluster Competition at SC21, we reproduce the computational efficiency of ramBLe on our assigned Oracle cluster. The cluster has 4x36 cores in total with 100 Gbps RoCE v2 support and is equipped with CentOS-compatible Oracle Linux. Our experiments, covering the same three algorithms of the original ramBLe article (Srivastava et al. 2020), evaluate the strong and weak scalability of the algorithms using real COVID-19 data sets. We verify part of the conclusions from the original article and propose our explanation of the differences obtained in our results.

关键词： Decoding   Indexes   Error correction codes   Writing   Hamming weight   Computer science   Codecs  

摘要： In the above article [1] , Lemma 2 contained an error, which also affects the proof of Theorem 1 therein. Specifically, Theorem 1 holds only when $\sigma = 0$ or when $\tau = 0$ ; otherwise, the “if” part in the theorem requires a stronger condition.

摘要： How artificial intelligence and computer science grew up together.

关键词： Adoption   pedagogical content   teacher professional development   computer science education   primary school   formal learning environments  

摘要： Context. With the introduction of Computer Science (CS) into curricula worldwide, teachers' adoption of CS pedagogical content is essential to ensure the long-term success of reform initiatives. Continuing Professional Development (CPD) programs play a key role in this process. Unfortunately, adoption is seldom evaluated in CS-CPDs or CPDs in general. The result is a dearth of studies (i) modelling teachers' adoption of CS pedagogical content or (ii) investigating factors influencing the uptake of this new discipline. Both aspects are crucial to design and characterize successful CPD programs. Objectives. We thus propose the Teachers' Adoption of CS (TACS) model to investigate factors influencing the adoption of CS pedagogical content by teachers who are following a mandatory CS-CPD program. More specifically, the model proposes that contextual factors (e.g., age, gender, and general teaching experience), prior factors (e.g., experience, and CS perception), and acceptance factors (e.g., interest, and self-efficacy) may impact teachers' adoption of CS pedagogical content. Methods. The study included 180 grades 5 and 6 teachers (students aged 9-11) that were following a mandatory CS-CPD program. The CS-CPD program involved participation in three-day-long sessions distributed over the 2019-2020 academic year. In between sessions, with the support of instructional coaches in the schools, teachers were encouraged, but not required, to adopt the CS pedagogical content. Therefore, during the CPD, and employing surveys based on the TACS model, we evaluated teachers' adoption of the proposed content and investigated how the different factors influenced it. Results. At the PD-level, the results indicate that self-efficacy and interest queried during the CS-CPD are indicative of CS pedagogical content adoption. To shed more light on the relationshipbetween these metrics, a more in-depth analysis was conducted with n = 92 teachers whose responses could be matched between sessio

关键词： Synchronization   Control systems   Low latency communication   Computer science   Research and development   Registers   Protocols   State synchronization   programmable switches   approximate techniques  

摘要： Programmable switches empower stateful packet processing, in which incoming packets continuously update states in the data plane, while applications in the control plane read and write states. However, since the data plane and control plane are separated, a consistent view of states in both planes is required for stateful packet processing. Existing approaches suffer from either high latency or low accuracy. In this paper, we propose, a framework that offers approximate state synchronization with low latency and high accuracy. To achieve low latency, directly transfers states between switch ASICs and the control plane by bypassing switch operating systems. To achieve high accuracy, utilizes the resources in the switch ASIC to realize rate control in state synchronization, such that it avoids potential state loss. It also bounds the divergence between the states in the data plane and that in the control plane under limited link capacity. We prototype on Barefoot Tofino switches. The experimental results indicate that compared to existing approaches, achieves order-of-magnitude latency reduction while maintaining high accuracy of state synchronization. Also, our experiments demonstrate that provides significant latency benefits to existing network management applications and well preserves high application-level accuracy.

关键词： kombucha   acetic acid bacteria   yeast   Komagataeibacter rhaeticus   Zygosaccharomyces lentus   Debaryomyces prosopidis  

摘要： Kombucha is a popular sparkling sugared tea, fermented by a symbiotic culture of acetic acid bacteria (AAB) and yeast. The demand for kombucha continues to increase worldwide, mainly due to its perceived health benefits and appealing sensory properties. This study isolated and characterised the dominant AAB and yeast from a starter culture and kombucha broth after 0, 1, 3, 5, 7, 9, 11, and 14 days of fermentation at ambient temperature (22 & DEG;C). Yeast and AAB were isolated from the Kombucha samples using glucose yeast extract mannitol ethanol acetic acid (GYMEA) and yeast extract glucose chloramphenicol (YGC) media, respectively. The phenotypic and taxonomic identification of AAB and yeast were determined by morphological and biochemical characterisation, followed by a sequence analysis of the ribosomal RNA gene (16S rRNA for AAB and ITS for yeast). The changes in the microbial composition were associated with variations in the physico-chemical characteristics of kombucha tea, such as pH, titratable acidity, and total soluble solids (TSS). During fermentation, the acidity increased and the TSS decreased. The yield, moisture content, and water activity of the cellulosic pellicles which had developed at the end of fermentation were attributed to the presence of AAB. The dominant AAB species in the cellulosic pellicles and kombucha broth were identified as Komagataeibacter rhaeticus. The yeast isolates belonged to Debaryomyces prosopidis and Zygosaccharomyces lentus.