https://cis.temple.edu/~tuf80213/courses/temple/cis1051/cis1051.pdf
Course Description CIS 1051 introduces students to computers, computer programming, and problem solving using programs written in the Python language. Topics covered include the general characteristics of computers; techniques of problem solving and algorithm specifications; and the implementation, debugging, and testing of computer programs. The goal is to learn to solve small programming ...
https://cis.temple.edu/~pwang/Publication/OpenNARS.pdf
NARS utilises the Non-Axiomatic Logic (NAL) [9] for inference and the Nars-ese language for representing statements. The language and the logic are outside the scope of this document. The aim of this paper is to describe the current implementation of NARS in detail. The following aspects of the implementa-tion are focused on: memory management with concept centric processing, non-deterministic ...
https://cis.temple.edu/tagit/publications/A_Model_of_Unified_Perception_and_Cognition.pdf
NARS (Non-Axiomatic Reasoning System) is an AGI system designed in the framework of a reasoning (or inference) system, where the notion of “reasoning” is used in its broad sense to include many cognitive functions, including learning and perception (Wang, 1995, 2006, 2013). Perception, usually taken to mean sensory understanding, is highly relevant to AI. Perception and cognition are ...
https://cis.temple.edu/~jiewu/research/publications/Publication_files/ICC2024.pdf
Abstract—Most intrusion detection systems (IDS) are vul-nerable to novel attacks and struggle to maintain a balance between high accuracy and a low false positive rate. Furthermore, the relevant features of Distributed Denial of Service (DDoS) attacks in conventional networks may not necessarily apply to the Software-defined network (SDN) environment. Additionally, weak feature selection ...
https://cis.temple.edu/~wu/research/publications/Publication_files/Joint%20Dynamic%20Grouping%20and%20Gradient%20Coding%20for%20Time-critical%20Distributed%20Machine%20Learning%20in%20Heterogeneous%20Edge%20Networks-FINAL-VERSION.pdf
Abstract—In edge networks, distributed computing resources have been widely utilized to collaboratively perform a machine learning task by multiple nodes. However, the model training time in heterogeneous edge networks is becoming longer because of excessive computation and delay caused by slow nodes, namely stragglers. The parameter server even abandons stragglers which fail to return ...
https://cis.temple.edu/~lakaemper/courses/cis350_2004/etc/moeller_triangle.pdf
1 Introduction Most collision detection algorithms, such as OBBTree [Gottschalk96], sphere hier-archies [Hubbard96] and BV-trees [Klosowski97], try to minimize the number of primitive-primitive intersections that have to be computed. Still, a fast and reliable method for computing the primitive-primitive intersection is desired. Since render-ing hardware is often targeted for triangles, the ...
https://sites.temple.edu/ticlj/files/2017/02/30.1.Scharre-TICLJ.pdf
Paul Scharre* Much of the debate on autonomous weapons presumes a choice between human versus autonomous decision-making over targeting and engagement decisions. In fact, in many situations, human-machine teaming in engagement decisions will not only be possible but preferable. Hybrid human-machine cognitive architectures will be able to leverage the precision and reliability of automation ...
https://cis.temple.edu/tagit/publications/PAGI-TR-11.pdf
October 19, 2020 NARS (Non-Axiomatic Reasoning System) can be analyzed in three parts [Wang, 1995, Wang, 2006, Wang, 2013]:
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