教学课程资源库 更多>>

关键词： Random variables   Licenses   Computer simulation   Computer science   Computational modeling   Writing   Time measurement   Computer simulation   peer reviewing  

摘要： A simple mathematical model of the scientific peer reviewing process is developed. Papers and reviewers are modeled as numerical vectors, respectively representing the paper's value among multiple quality dimensions, and the importance given to these dimensions by a given reviewer. Computer simulations show that the model can reproduce various characteristics of a real-world paper decision process, and in particular its propensity to act as an "arbitrary" decision procedure for a range of submissions. A key finding of this study is that the appearance of randomness can be explained by a mismatch between high quality dimensions of a paper, and those valued by the reviewers it is assigned to. As a consequence, a program committee may exhibit arbitrariness even with a set of completely reliable reviewers. Various factors contributing to this arbitrariness are then examined, and alternate selection models are studied that could help reduce arbitrariness and reviewer effort.

关键词： (IoMT)   Machine Learning   Privacy Assurance   Stress Sleep   Stress Detection   Stress Management   Stress Eating   Physiological Stress   Food Intake Monitoring   Computer Science   Health Sciences, Mental Health   Smart Healthcare   Smart Home   Healthcare Cyber-Physical Systems (H-CPS)   Internet-of-Medical-Things  

摘要： This research proposes Stress-Lysis, iLog and SaYoPillow to automatically detect and monitor the stress levels of a person. To self manage psychological stress in the framework of smart healthcare, a deep learning based novel system (Stress-Lysis) is proposed in this dissertation. The learning system is trained such that it monitors stress levels in a person through human body temperature, rate of motion and sweat during physical activity. The proposed deep learning system has been trained with a total of 26,000 samples per dataset and demonstrates accuracy as high as 99.7%. The collected data are transmitted and stored in the cloud, which can help in real time monitoring of a person’s stress levels, thereby reducing the risk of death and expensive treatments. The proposed system has the ability to produce results with an overall accuracy of 98.3% to 99.7%, is simple to implement and its cost is moderate. Chronic stress, uncontrolled or unmonitored food consumption, and obesity are intricately connected, even involving certain neurological adaptations. In iLog we propose a system which can not only monitor but also create awareness for the user of how much food is too much. iLog provides information on the emotional state of a person along with the classification of eating behaviors to Normal-Eating or Stress-Eating. This research proposes a deep learning model for edge computing platforms which can automatically detect, classify and quantify the objects in the plate of the user. Three different paradigms where the idea of iLog can be performed are explored in this research. Two different edge platforms have been implemented in iLog. The platforms include mobile, as it is widely used, and a single board computer which can easily be a part of network for executing experiments, with iLog Glasses being the main wearable. The iLog model has produced an overall accuracy of 98% with an average precision of 85.8%. Smart-Yoga Pillow (SaYoPillow) is envisioned as a device th

关键词： Computer science  

摘要： Sparse matrix computations, in the form of solvers for systems of linear equations, eigenvalue problem or matrix factorizations constitute the main kernel in problems from fields as diverse as computational fluid dynamics, quantum many body problems, machine learning and graph analytics. Iterative eigensolvers have been preferred over the regular method because the regular method not being feasible with industrial sized matrices. Although dense linear algebra libraries like BLAS, LAPACK, SCALAPACK are well established and some vendor optimized implementation like mkl from Intel or Cray Libsci exist, it is not the same case for sparse linear algebra which is lagging far behind. The main reason behind slow progress in the standardization of sparse linear algebra or library development is the different forms and properties depending on the application area. It is worsened for deep memory hierarchies of modern architectures due to low arithmetic intensities and memory bound computations. Minimization of data movement and fast access to the matrix are critical in this case. Since the current technology is driven by deep memory architectures where we get the increased capacity at the expense of increased latency and decreased bandwidth when we go further from the processors. The key to achieve high performance in sparse matrix computations in deep memory hierarchy is to minimize data movement across layers of the memory and overlap data movement with computations. My thesis work contributes towards addressing the algorithmic challenges and developing a computational infrastructure to achieve high performance in scientific applications for both shared memory and distributed memory architectures. For this purpose, I started working on optimizing a blocked eigensolver and optimized specific computational kernels which uses a new storage format. Using this optimization as a building block, we introduce a shared memory task parallel framework focusing on optimizing the entire

关键词： 多样性   理论计算机科学   算法   体外化   人类世  

摘要： 斯蒂格勒认为,我们这个数字化时代正是充满技术所释放的毒性的人类世时代,而且正处于其毒性最大化的时期。他的这个论断正在为2020年初突如其来的、影响全球各个国家的新冠疫情所强化。而当今人类世之所以出现如此的局面,是因为人类自启蒙运动以来过于相信科学技术所致。在斯蒂格勒看来,人类的进化是体外进化的模式,在这个进化过程中,人类的生存会不断地更加依赖外在于其躯体的技术和技术物体,也即斯蒂格勒所说的第三滞留。工业革命推动了科学技术的发展,而在此过程中出现的以人工智能的诸种形式为伪装的数字技术,以异常强大的力量将人类躯体内器官的感知、知性、理性等心智功能不断地外化于人类躯体之外的所谓高新技术物之中。这些技术物如今已到处雷同泛滥,它们是提示记忆的第三滞留的最新形式。于是,雷同的提示记忆的方式导致了雷同的心智思维方式,而雷同的心智思维方式导致了系统性的愚蠢,导致了人类熵在全球范围内的急速增长。在此意义上,斯蒂格勒才说,人类世已经达到其毒性最大化的时期。当然,斯蒂格勒并非是完全贬斥技术的人,因为在他的理论中,技术既是毒药也是解药。当今数字化的科学技术导致了人类世毒性的最大化,但要化解这种毒性却依然需要依赖于这些数字科技。这也就是斯蒂格勒为什么在这里要重新反思理论计算机科学之基础的原因。只有主动地反思科学技术才能构建心智的多样性,才能认识到科学技术并不是唯一的技术形式,进而才能保护并发展技术的多样性。斯蒂格勒似乎将这场疫情看作是能够促使人类反思当下人类世状态的契机。可是,他本人却又对当下人类世的诸种状况非常地失望,他在这个大流行病的2020年结束了自己的生命。哲学家并非一定是相信自己哲学理论的人,也并非一定是勇于践行自身哲学理论的人。苏格拉底相信并践行了自己的哲学理论。那么,于斯蒂格勒而言,其为是耶?其为非耶?

关键词： Computer science education   Computer science enrollment   Student enrollment   Human-Computer Interaction   Virtual Reality   STEM fields  

摘要： Student enrollment in STEM fields of study is critical for the future. Improving our understanding of what motivates young people to engage with material like computer programming is an essential aspect of increasing enrollment. Interest in a topic like Computer Science (CS) begins with a sense of belonging in the field. That essential sense of belonging tends to be quite evasive because it lacks a concrete definition. In this research, the goal was to dissect the main attributes associated with a sense of belonging and highlight the attributes that are key to a student’s decision to enroll in CS. The attributes determined to be vital contributors to a sense of belonging were self-efficacy, family background, goal orientation, and demographic characteristics. In order to find which of these factors associated with belonging were most important, a Virtual Reality (VR) simulation and survey were designed. A simple simulated environment was used which had participants embody an avatar that was described as an undeclared first-year college student. While in the simulation, participants were prompted to listen to an audio message from the advising office which asked them if they would like to enroll in a CS course. In the pilot study (N=10), family background was the focus, randomizing avatar gender as well as the control condition and the family background condition between participants. The feedback received from participants informed all the improvements made to the main experiment. For the main experiment (N=24), there were four slightly different audio messages each highlighting one of the four factors associated with belonging in CS. Each participant listened to all four audio messages and answered survey questions about their response to the audio. A Likert Scaled survey was used to determine how likely the participants were to enroll in the CS course given each audio prompt. Results indicated that there was a strong positive reaction to the audio message highligh

关键词： Film   Semiotics   Black architecture   Black culture   Hip-hop   Design  

摘要： This thesis is an investigation into the possibilities of describing and identifying Black Architecture. The studies and essays documented here will serve as foundational material for the continued investigation of these ideas. Pruitt Igoe is identified here as an archetype of inner-city public housing; the study builds itself on a critique of the international style based on this observation. This critique suggests that; while descendants of the international style such as public housing and other urban and architectural typologies are often associated with black life, they are not considerate of black society and culture. They, therefore, do not constitute black architecture. Over the last few decades, The social influence of film and video media has provided a vehicle through which images of black life within these urban and architectural strangers have become standard. Subsequently, these images perpetuate an association of ideas of black life with these settings as a necessity. This semiotic relationship between media and society can be exploited to present new ideas about what is currently so-called black space and what could someday be acknowledged as true Black space. With that, film, social, and urban semiotics are folded into this thesis, and through a deconstructive, semiotic analysis, new images of black space will be constructed.

摘要： Machine learning, which has its origins at the intersection of computer science and statistics, is now a rapidly growing area of research that is being integrated into almost every discipline in science and business such as economics, marketing and information retrieval. As a consequence of this integration, it is necessary to understand how machine learning interacts with these disciplines and to understand fundamental questions that arise at the resulting interfaces. The goal of my thesis research is to study these interdisciplinary questions at the interface of machine learning and other disciplines including mechanism design/information elicitation, preference/choice modeling, and theoretical computer science.

关键词： Modulation   Feature extraction   Training   Image recognition   Convolution   Sun   Computer science   Indoor scene recognition   privileged information   attention transfer  

摘要： RGB-D scene recognition has achieved promising performance because depth could provide complementary geometric information to RGB images. However, the inaccessibility of depth sensors severely limits RGB-D applications. In this paper, we focus on depth privileged setting, in which depth information is only available during training but not available during testing. Considering that the information obtained from RGB and depth images are complementary while attention is informative and transferable, our idea is using RGB input to hallucinate depth attention. We build our model upon modulated deformable convolutional layer and hallucinate dual attention: post-hoc importance weight and trainable spatial transformation. Specifically, we use modulation (resp., offset) learned from RGB to mimic Grad-CAM (resp., offset) learned from depth, to combine the strength of dual attention. We also design a weighted loss to avoid negative transfer according to the quality of depth attention. Extensive experiments on two benchmarks, i.e., SUN RGB-D and NYUDv2, demonstrate that our method outperforms the state-of-the-art methods for depth privileged scene recognition.

关键词： Feature extraction   Prediction algorithms   Support vector machines   Optimization   Correlation   Filtering algorithms   Computer science   Feature selection   filter   wrapper   hybrid   K-means   V-measure   random forest  

摘要： Feature selection has emerged as a craft, using which we boost the performance of our learning model. Feature or Attribute Selection is a data preprocessing technique, where only the most informative features are considered and given to the predictor. This reduces the computational overhead and improves the correctness of the classifier. Attribute Selection is commonly carried out by applying some filter or by using the performance of the learning model to gauge the quality of the attribute subset. Metric Ranked Feature Inclusion and Accuracy Ranked Feature Inclusion are the two novel hybrid feature selection methods we introduce in this paper. These algorithms follow a two-stage procedure, the first of which is feature ranking, followed by feature subset selection. They differ in the way they rank the features but follow the same subset selection technique. Multiple experiments have been conducted to assess our models. We compare our results with numerous works of the past and validate our models using 12 datasets. From the results, we infer that our algorithms perform better than many existent models.