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CIS 1068 Syllabus

https://cis.temple.edu/~jfiore/2024/fall/1068/handouts/syllabus/

from the course bulletin Introduction to problem solving and programming in Java, software engineering, procedural and data abstraction, and object-oriented programming, including class hierarchies, inheritance and interfaces. Data types covered include primitive data types, strings, classes, arrays, vectors, and streams. Programming techniques include at least one technique for searching and ...

CPHSSWCE0403 Prevent T2 Lifestyle Coach Training | Temple University

https://noncredit.temple.edu/search/publicCourseSearchDetails.do?method=load&courseId=30384896

Course Description Prevent T2 Lifestyle Coach Training The Center for Self-determination, Self-direction and Self-care at theTemple University School of Social Work - College of Public Health trains Lifestyle Coaches and Master Trainers in the CDC's National Diabetes Prevention Program. We offer training in-person and online, provide ongoing technical assistance and support to our trainees. We ...

Juncheng Wei, PhD | Lewis Katz School of Medicine | Lewis Katz School ...

https://medicine.temple.edu/directory/juncheng-wei-phd

Lewis Katz School of Medicine Cardiovascular Sciences Assistant Professor Center for Metabolic Disease Research Assistant Professor

Tools for Evidence Synthesis - Evidence Synthesis and Systematic ...

https://guides.temple.edu/c.php?g=78618&p=9741148

Checklists, diagrams, reporting guidelines and registration resources to help you plan your evidence synthesis review.

Learning Pixel-wise Alignment for Unsupervised Image Stitching

https://cis.temple.edu/~latecki/Papers/ACM_MM2023.pdf

ABSTRACT Image stitching aims to align a pair of images in the same view. Generating precise alignment with natural structures is challeng-ing for image stitching, as there is no wider field-of-view image as a reference, especially in non-coplanar practical scenarios. In this paper, we propose an unsupervised image stitching frame-work, breaking through the coplanar constraints in homography ...

Jason Bennett Thatcher - Jason Bennett Thatcher - Temple University

https://community.mis.temple.edu/jasonbennettthatcher/2021/03/16/strategic-directions-for-ai-the-role-of-cios-and-boards-of-directors/

We tested our model using a dataset drawn from 1,454 publicly listed firms in China. Our findings show that the presence of a CIO positively influences AI orientation and that board educational diversity, R&D experience, and AI experience positively moderate the CIO’s effect on AI orientation.

The OpenNARS implementation of the Non-Axiomatic Reasoning System

https://cis.temple.edu/~pwang/Publication/OpenNARS.pdf

Abstract. This paper describes the implementation of a Non-Axiomatic Reasoning System (NARS), a uni ed AGI system which works under the assumption of insu cient knowledge and resources (AIKR). The system's architecture, memory structure, inference engine, and control mechanism are described in detail.

Group Members and Photos – Liberles Research Group - Sites

https://sites.temple.edu/liberles/group-members/

Postdoctoral Scientist Maeva Perez is a remote post-doctoral researcher based at Hong-Kong Baptist University. She obtained her Ph.D. from Université de Montréal in September 2023. She grew up in France, Ivory Coast, and Canada, and has been living in Asia since 2020. She is interested in the ecology and evolution of deep-sea organisms. Maeva joined the group through the SMBE mentoring ...

Latest Episodes – Owl Byte - Temple University

https://podcast.cis.temple.edu/latest-episodes/

What you will discover In this episode of Owl Byte, we sit down with Professor Karl Morris, whose...

FedCPD: Personalized Federated Learning with Prototype-Enhanced ...

https://cis.temple.edu/~jiewu/research/publications/Publication_files/FedCPD.pdf

Both challenges pertain to optimizing personalized feder-ated learning, yet their solutions don’t cross-apply. Parame-ter decoupling protects local knowledge to prevent forgetting but falls short on sharing global insights, thus struggling with generalization. On the other hand, prototype learning curbs overfitting and boosts generalization by sharing class proto-types, yet it misses ...