教学课程资源库 更多>>

关键词： Students  

摘要： Fully-online graduate degree programs are on the rise, generating new questions about how we conceptualize and measure student participation in the virtual classroom. To date, most studies examine participation patterns in single online and/or hybrid courses and do not take into account the demographic characteristics of online students. We develop and test hypotheses that address demographic differences in the nature and intensity of graduate student participation in online-only classrooms for a large degree program in computer science. This work is distinctive because it addresses participation across several classes and across multiple semesters. We select the Piazza forum because it is the required communication mechanism in the program that is the subject of our study. We extract Piazza discussion board activity logs from a sample of 1914 online computer science graduate students, specifically the numerical data indicating the type of access to Piazza students used. We distinguish between active (contributions), passive (viewership) participation and lurking behavior. Given the nature of the dependent variables of interest, we employ different forms of regression analysis. We use logistic regression to address the likelihood of non-participation in the online forum. We then use negative binomial regression to examine the intensity of passive and active engagement, and ordinary least squares regression to examine lurking behavior. We find that the intensity of participation varies by different demographic characteristics, including by age and by race/ethnicity, but not by gender. Our study also shows a notable impact of class size, where increasing class size is associated with decreasing levels of active participation and increasing lurking behavior.

关键词： Image segmentation   Semantics   Adaptation models   Logic gates   Automobiles   Training data   Computer science   Autonmous automobiles  

摘要： Self-driving cars leverage on semantic segmentation to understand an urban scene. However, it is costly to collect segmentation labels, thus, synthetic datasets are used to train segmentation models. Unfortunately, the synthetic to real domain shift causes these models to perform poorly. Prior works use adversarial training to align features of both synthetic and real-world images. We observe that background objects tend to be similar across domains, while foreground objects tend to have more variations. Using this insight, we propose an adaptation method that uses foreground and background cues and adapt them separately. We also propose a mask-aware gated discriminator that learns soft masks from the input foreground and background masks instead of naively performing binary masking that immediately removes information outside of the predicted masks. We evaluate our method on two different datasets and show that our method outperforms several state-of-the-art baselines, which verifies the effectiveness of our approach.

关键词： Task analysis   Internet of Things   Real-time systems   Energy consumption   Cryptography   Computer science   Quality of security   energy efficiency   task scheduling   real-time MPSoC   Internet of Things (IoT)  

摘要： Internet of Things (IoT) devices, such as intelligent road side units and video-based detectors, are being deployed in emerging applications like sustainable and intelligent transportation systems. The primary obstacles against the development of these IoT devices are various security threats and huge energy consumption. In this article, we study the problem of scheduling tasks onto a heterogeneous multiprocessor system on a chip (MPSoC) deployed in IoT for optimizing quality of security under energy, real-time, and task precedence constraints. We first provide a mixed-integer linear programming (MILP) formulation for allocating and scheduling dependent tasks with energy and real-time constraints on a heterogeneous MPSoC system to maximize system quality of security. In order to efficiently solve the formulated MILP, we then propose an analysis-based two-stage scheme that determines the allocation, operating frequency, and security service of tasks to maximize system quality of security while satisfying the design constraints. We finally carry out extensive simulation experiments to validate our proposed two-stage scheme and MILP approach. Simulation results demonstrate that the proposed two-stage scheme outperforms a number of representative existing approaches in saving energy and improving system quality of security. The results also show that the proposed MILP approach can achieve the best performance and the proposed two-stage scheme has a close performance to the MILP approach.

关键词： Preprint   ArXiv   Digital repositories  

摘要： Preprints play an increasingly critical role in academic communities. There are many reasons driving researchers to post their manuscripts to preprint servers before formal submission to journals or conferences, but the use of preprints has also sparked considerable controversy, especially surrounding the claim of priority. In this paper, a case study of computer science preprints submitted to arXiv from 2008 to 2017 is conducted to quantify how many preprints have eventually been printed in peer-reviewed venues. Among those published manuscripts, some are published under different titles and without an update to their preprints on arXiv. In the case of these manuscripts, the traditional fuzzy matching method is incapable of mapping the preprint to the final published version. In view of this issue, we introduce a semantics-based mapping method with the employment of Bidirectional Encoder Representations from Transformers (BERT). With this new mapping method and a plurality of data sources, we find that 66% of all sampled preprints are published under unchanged titles and 11% are published under different titles and with other modifications. A further analysis was then performed to investigate why these preprints but not others were accepted for publication. Our comparison reveals that in the field of computer science, published preprints feature adequate revisions, multiple authorship, detailed abstract and introduction, extensive and authoritative references and available source code.

关键词： Stress response   Endocannabinoids   Brain development   Sex differences   Mental health  

摘要： Sex differences in both the endocannabinoid system and stress responses have been established for decades. While there is ample evidence that the sexes respond differently to stress and that the endocannabinoid system is involved in this response, what is less clear is whether the endocannabinoid system mediates this response to stress differently in both sexes. Also, do the sexes respond similarly to exogenous cannabinoids (CBs) following stress? Can the administration of exogenous CBs normalize the effects of stress and if so, does this happen similarly in male and female subjects? This review will attempt to delineate the stress induced neurochemical alterations in the endocannabinoid system and the resulting behavioral changes across periods of development: prenatal, early neonatal or adolescent in males and females. Within this frame work, we will then examine the neurochemical and behavioral effects of exogenous CBs and illustrate that the response to CBs is determined by the stress history of the animal. The theoretical framework for this endeavor relates to the established effects of adverse childhood experiences (ACE) in increasing substance abuse, depression and anxiety and the possibility that individuals with high ACE scores may consume cannabinoids to "self-medicate". Overall, we see that while there are instances where exogenous cannabinoids "normalize" the adverse effects produced by early stress, this normalization does not occur in all animal models with any sort of consistency. The most compelling report where CB administration appears to normalize behaviors altered by early stress, shows minimal differences between the sexes (Alteba et al., 2016). This is in stark contrast to the majority of studies on early stress and the endocannabinoid system where both sexes are included and show quite divergent, in fact opposite, effects in males and females. Frequently there is a disconnect between neurochemical changes and behavioral changes and often, exog

关键词： Computer science   Cybersecurity   Intrusion detection   Deep belief network   Feature selection   Distance   Correlation   Consistency   Information gain   Detection engine   Response engine  

摘要： The revolutionary advances in network technologies have spearheaded the design of advanced cyberattacks to surpass traditional security defense with dreadful consequences. Recently, Intrusion Detection System (IDS) is considered as a pivotal element in network security infrastructures to achieve solid line of protection against cyberattacks. The prime challenges presented to IDS are curse of high dimensionality and class imbalance that tends to increase the detection time and degrade the efficiency of IDS. As a result, feature selection plays an important role in enabling to identify the most significant features for intrusion detection. Although, several feature evaluation measures are being proposed for feature selection in literature, there is no consensus on which measures are best for intrusion detection. Therein, this work aims at recommending the most appropriate feature evaluation measure for building an efficient IDS. In this direction, four filter-based feature evaluation measures that stem from different theories such as Consistency, Correlation, Information and Distance are investigated for their potential implications in enhancing the detection ability of IDS model for different classes of attacks. Along with this, the influence of the selected features on classification accuracy of an IDS model is analyzed using four different categories of classifiers namely, K-nearest neighbors (KNN), Random Forest (RF), Support Vector Machine (SVM) and Deep Belief Network (DBN). Finally, a two-step statistical significance test is conducted on the experimental results to determine which feature evaluation measure contributes statistically significant difference in IDS performance. All the experimental comparisons are performed on two benchmark intrusion detection datasets, NSL-KDD and UNSW-NB15. In these experiments, consistency measure has best influenced the IDS model in improving the detection ability with regard to detection rate (DR), false alarm rate (FAR), ka

关键词： Sociology   Statistics   Optimization   Image processing   Cybernetics   Computer science   Indexes   Adaptive differential evolution (DE)   DE   local binary pattern (LBP) strategy   multimodal optimization problems (MMOPs)  

摘要： The multimodal optimization problem (MMOP) requires the algorithm to find multiple global optima of the problem simultaneously. In order to solve MMOP efficiently, a novel differential evolution (DE) algorithm based on the local binary pattern (LBP) is proposed in this paper. The LBP makes use of the neighbors' information for extracting relevant pattern information, so as to identify the multiple regions of interests, which is similar to finding multiple peaks in MMOP. Inspired by the principle of LBP, this paper proposes an LBP-based adaptive DE (LBPADE) algorithm. It enables the LBP operator to form multiple niches, and further to locate multiple peak regions in MMOP. Moreover, based on the LBP niching information, we develop a niching and global interaction (NGI) mutation strategy and an adaptive parameter strategy (APS) to fully search the niching areas and maintain multiple peak regions. The proposed NGI mutation strategy incorporates information from both the niching and the global areas for effective exploration, while APS adjusts the parameters of each individual based on its own LBP information and guides the individual to the promising direction. The proposed LBPADE algorithm is evaluated on the extensive MMOPs test functions. The experimental results show that LBPADE outperforms or at least remains competitive with some state-of-the-art algorithms.

关键词： Pain   Medical treatment   Games   Bayes methods   Support vector machines   Computer science   Fatigue   Affective issues in user interaction   posture   hand movements   fingers pressure   rehabilitation   stroke   semi-Naive Bayesian classifier  

摘要： Virtual rehabilitation environments may afford greater patient personalization if they could harness the patient's affective state. Four states: anxiety, pain, engagement and tiredness (either physical or psychological), were hypothesized to be inferable from observable metrics of hand location and gripping strength-relevant for rehabilitation. Contributions are;(a) multiresolution classifier built from Semi-Naive Bayesian classifiers, and (b) establishing predictive relations for the considered states from the motor proxies capitalizing on the proposed classifier with recognition levels sufficient for exploitation. 3D hand locations and gripping strength streams were recorded from 5 post-stroke patients whilst undergoing motor rehabilitation therapy administered through virtual rehabilitation along 10 sessions over 4 weeks. Features from the streams characterized the motor dynamics, while spontaneous manifestations of the states were labelled from concomitant videos by experts for supervised classification. The new classifier was compared against baseline support vector machine (SVM) and random forest (RF) with all three exhibiting comparable performances. Inference of the aforementioned states departing from chosen motor surrogates appears feasible, expediting increased personalization of virtual motor neurorehabilitation therapies.

关键词： Prefetching   Metadata   Correlation   Indexes   Proposals   Electronic mail   Computer science   Cache memory   performance   data prefetching   pairwise-correlating data prefetching   overprediction  

摘要： Recent research revisits pairwise-correlating data prefetching due to its extremely low overhead. Pairwise-correlating data prefetching, however, cannot accurately detect where data streams end. As a result, pairwise-correlating data prefetchers either expose low accuracy or they lose timeliness when they are performing multi-degree prefetching. In this letter, we propose a novel technique to detect where data streams end and hence, control the multi-degree prefetching in the context of pairwise-correlated prefetchers. The key idea is to have a separate metadata table that operates one step ahead of the main metadata table. This way, the runahead metadata table harnesses the degree of prefetching by allowing/disallowing the main metadata table to issue prefetch requests. We evaluate our proposal in the context of a four-core chip multiprocessor and show that it significantly reduces erroneous prefetches, providing up to 16.1 percent performance improvement on top of a state-of-the-art pairwise-correlating prefetcher.

关键词： Security   Software Engineering   Requirements Engineering   Cloning   Big Data   Computer Science   Data Mining  

摘要： This issue's practitioners' Digest reports on papers from the 2018 International Conference on Mining Sof tware Repositories, the 2019 International Conference on Requirements Engineering, and the 2019 European Conference on Software Process Improvement. Feedback or suggestions are welcome. In addition, if you try or adopt any of the practices included in this article, please send me and the author(s) of the paper(s) a note about your experiences.