教学课程资源库 更多>>

关键词： Age awareness   Aging anxiety   Meaning in life   Psychological distress   Terror management theory  

摘要： Aging anxiety has been linked with negative psychological and physical consequences. However, little is known about the association between aging anxiety, meaning in life, and psychological distress among middle-aged adults. Accordingly, the current study examines the links between these constructs among this age-group. Moreover, in light of the importance of age as a cognitive category for one's self-perception, we examined the moderating role of age awareness for the aforementioned links. A convenience sample of 309 adults (age range 40-65, M = 53.92, SD = 7.38) filled out scales assessing aging anxiety, meaning in life, psychological distress, and age awareness, together with various sociodemographic scales. Results demonstrated that aging anxiety was associated with reduced meaning in life and increased psychological distress. Moreover, meaning in life mediated the aging anxiety-psychological distress link, and this mediation was moderated by age awareness. The results are discussed from the perspective of Terror Management Theory, and clinical implications are suggested.

关键词： Women in computer science   COVID-19   Work-family conflict   Women in STEM   Gender   Academia  

摘要： The COVID-19 pandemic has been challenging for college faculty, with evidence that it has the potential to exacerbate pre-pandemic gender inequities in work demands (Tugend, 2020). The impact of the pandemic may be particularly difficult for women in male-dominated STEM fields such as computer science that pose additional challenges and had high attrition rates among women faculty pre-pandemic (NSF, 2019;Weisgram & Diekman, 2017). The present study examined the mechanisms through which gender may have implications for changes in turnover intentions due to the pandemic among computer science faculty, with a focus on changes in work-family conflict and workplace attitudes. A total of 317 tenure-line and non-tenure line computer science faculty across the U.S. (54.26% women, 49.84% tenured) completed a survey that included items examining whether the pandemic changed work-family conflict, work-related attitudes (job satisfaction, sense of belonging, burnout), and turnover intentions. Results of analyses indicated that identifying as a woman indirectly predicted larger increases in turnover intentions due to the pandemic, through increased work-family conflict, burnout at work, and decreased feelings of job satisfaction. The results suggest that the pandemic has the potential to increase women's attrition from computer science faculty positions, further exacerbating their underrepresentation.

关键词： Sequences   Databases   Computer science   Feature extraction   Set theory   Learning systems   Itemsets   Constraint   negative sequential candidates (NSCs)   negative sequential patterns (NSPs)   nonoccurring behaviors (NOBs)  

摘要： The sequence analysis handles sequential discrete events and behaviors, which can be represented by temporal point processes (TPPs). However, TPP models only occurring events and behaviors. This article explores an efficient method for the negative sequential pattern (NSP) mining to leverage TPP in modeling both frequently occurring and nonoccurring events and behaviors. NSP mining is good at the challenging \ modeling of nonoccurrences of events and behaviors and their combinations with occurring events, with existing methods built on incorporating various constraints into NSP representations, e.g., simplifying NSP formulations and reducing computational costs. Such constraints restrict the flexibility of NSPs, and nonoccurring behaviors (NOBs) cannot be comprehensively exposed. This article addresses this issue by loosening some inflexible constraints in NSP mining and solves a series of consequent challenges. First, we provide a new definition of negative containment with the set theory according to the loose constraints. Second, an efficient method quickly calculates the supports of negative sequences. Our method only uses the information about the corresponding positive sequential patterns (PSPs) and avoids additional database scans. Finally, a novel and efficient algorithm, NegI-NSP, is proposed to efficiently identify highly valuable NSPs. Theoretical analyses, comparisons, and experiments on four synthetic and two real-life data sets clearly show that NegI-NSP can efficiently discover more useful NOBs.

关键词： Machine learning   Deep learning   IoT   Human activity recognition   Computer science   Data science  

摘要： The use of wearable devices, sensors, machine learning, and deep learning for human activity recognition (HAR) applications has increased in recent years. Lots of researchers introduce different methods and techniques for HAR but accuracy and efficiency are still a gap to work in the HAR domain for other researchers. In this study, we design a simple architecture of MLP with a stack of dense layers which can perform accurately and efficiently on small and large features set for HAR. This study used a dataset that contain smartphone sensors (gyroscope and accelerometer) data against six daily human activities. Each sensor has three feature values corresponding to an instance for an activity. First, we train our proposed model individually on gyroscope and accelerometer data and then we combine both sensors data to train the model. We train the model using 70% of the dataset and evaluate the performance of the model on 30% data. MLP outperforms all other stat of the art models in comparison such as random forest, decision tree, logistic regression, and K nearest neighbor. The MLP accuracy scores are 0.74, 0.77, and 0.98 using features of gyroscope, accelerometer, and combination of both respectively. The results show that the proposed approach is also good with single sensor data. In comparison with the proposed MLP, we also deployed state-of-the-art models such as LSTM, CNN, and other machine learning models. Proposed MLP outperforms all used models in terms of all evaluation parameters. We also did a statistical T-test to show the significance of the proposed approach.

关键词： Resumes   Interviews   Computer science   Personnel   Knowledge engineering   Industries   Training   Computer science (CS) education   resumes   student experiments  

摘要： Contribution: This study identifies which entry-level computer science (CS) resume items are most important and compares the ratings of student and recruiter participants to investigate the accuracy of student beliefs. To the authors' knowledge, this study is the first to analyze the extent to which CS students understand the resume screening process. The results of this have consequences for students in their own resume development. Background: Although prior research studies the importance of different resume items generally, little is known about resumes for CS majors, which may contain distinctive sections. Less still is known about whether students understand the resume screening process. Research Questions: Which items on entry-level CS resumes most directly influence screening decisions? What gaps exist between CS students' and recruiters' perceptions of resume items? Does the disparity in expertise between students and recruiters contribute to differences in resume screening? Methodology: 197 recruiters and 73 CS undergraduates screened randomized CS resumes. Data were analyzed using ordinary least-squares regression with interactions. Findings: Students were more likely than recruiters to move resumes to the next level and spent about 7 s less than recruiters when screening resumes. Though students correctly assessed the value of most resume items, they significantly overestimated the value of prior work experience such as internships.

关键词： Computer science   Machine learning  

摘要： Researchers are excited about the AI - but many are frustrated that its underlying engineering is cloaked in secrecy.

关键词： Turing machines  

摘要： We have defined the Conscious Turing Machine (CTM) for the purpose of investigating a Theoretical Computer Science (TCS) approach to consciousness. For this, we have hewn to the TCS demand for simplicity and understandability. The CTM is consequently and intentionally a simple machine. It is not a model of the brain, though its design has greatly benefited - and continues to benefit - from neuroscience and psychology. The CTM is a model of and for consciousness. Although it is developed to understand consciousness, the CTM offers a thoughtful and novel guide to the creation of an Artificial General Intelligence (AGI). For example, the CTM has an enormous number of powerful processors, some with specialized expertise, others unspecialized but poised to develop an expertise. For whatever problem must be dealt with, the CTM has an excellent way to utilize those processors that have the required knowledge, ability, and time to work on the problem, even if it is not aware of which ones these may be. © 2023, CC BY.

关键词： Gene expression  

摘要： Metastatic prostate cancer is one of the most common cancers in men. In the advanced stages of prostate cancer, tumours can metastasise to other tissues in the body, which is fatal. In this thesis, we performed a genetic analysis of prostate cancer tumours at different metastatic sites using data science, machine learning and topological network analysis methods. We presented a general procedure for pre-processing gene expression datasets and pre-filtering significant genes by analytical methods. We then used machine learning models for further key gene filtering and secondary site tumour classification. Finally, we performed gene co-expression network analysis and community detection on samples from different prostate cancer secondary site types. In this work, 13 of the 14,379 genes were selected as the most metastatic prostate cancer related genes, achieving approximately 92% accuracy under cross-validation. In addition, we provide preliminary insights into the co-expression patterns of genes in gene co-expression networks. Project code is available at https://***/zcablii/Master_cancer_project. © 2023, CC BY.

关键词： Philosophical aspects  

摘要： Computer science research has led to many breakthrough innovations but has also been scrutinized for enabling technology that has negative, unintended consequences for society. Given the increasing discussions of ethics in the news and among researchers, we interviewed 20 researchers in various CS sub-disciplines to identify whether and how they consider potential unintended consequences of their research innovations. We show that considering unintended consequences is generally seen as important but rarely practiced. Principal barriers are a lack of formal process and strategy as well as the academic practice that prioritizes fast progress and publications. Drawing on these findings, we discuss approaches to support researchers in routinely considering unintended consequences, from bringing diverse perspectives through community participation to increasing incentives to investigate potential consequences. We intend for our work to pave the way for routine explorations of the societal implications of technological innovations before, during, and after the research process. © 2023, CC BY.

关键词： Computer science   Data integration   Data mining   Molecular medicine   Translational research  

摘要： A long-term objective of network medicine is to replace our current, mainly phenotype-based disease definitions by subtypes of health conditions corresponding to distinct pathomechanisms. For this, molecular and health data are modeled as networks and are mined for pathomechanisms. However, many such studies rely on large-scale disease association data where diseases are annotated using the very phenotype-based disease definitions the network medicine field aims to overcome. This raises the question to which extent the biases mechanistically inadequate disease annotations introduce in disease association data distort the results of studies which use such data for pathomechanism mining. We address this question using global- and local-scale analyses of networks constructed from disease association data of various types. Our results indicate that large-scale disease association data should be used with care for pathomechanism mining and that analyses of such data should be accompanied by close-up analyses of molecular data for well-characterized patient cohorts. Large-scale disease-association data are widely used for pathomechanism mining, even if disease definitions used for annotation are mostly phenotype-based. Here, the authors show that this bias can lead to a blurred view on disease mechanisms, highlighting the need for close-up studies based on molecular data for well-characterized patient cohorts.