https://cis.temple.edu/~giorgio/cis587/readings/rete.html
(R1 (has-goal ?x simplify) (expression ?x 0 + ?y) ==>....) (R2 (has-goal ?x simplify) (expression ?x 0 * ?y) ==>....) and the following facts: (has-goal e1 simplicity) (expression e1 0 + 3) (has-goal e2 simplicity) (expression e2 0 + 5) (has-goal e3 simplicity) (expression e3 0 * 2) Then the Rete is +----------+ | ENTRANCE | +----------+ x ...
https://cis.temple.edu/~ingargio/cis587/readings/rete.html
(R1 (has-goal ?x simplify) (expression ?x 0 + ?y) ==>....) (R2 (has-goal ?x simplify) (expression ?x 0 * ?y) ==>....) and the following facts: (has-goal e1 simplicity) (expression e1 0 + 3) (has-goal e2 simplicity) (expression e2 0 + 5) (has-goal e3 simplicity) (expression e3 0 * 2) Then the Rete is +----------+ | ENTRANCE | +----------+ x ...
https://teaching.temple.edu/sites/teaching/files/media/document/Evaluating%20the%20Effectiveness%20of%20Turnitin%E2%80%99s%20AI%20Writing%20Indicator%20Model.pdf
Introduction: Turnitin recently developed what they call an “AI writing indicator model” that is intended to help instructors determine if a student has submitted work that is AI-generated. The model is integrated into Turnitin’s existing plagiarism detection software licensed at Temple, and is therefore convenient as it is already embedded in Canvas [1]. The process for using it is ...
https://dabi.temple.edu/external/zoran/papers/Rafaa_Complex_Net_2025.pdf
Let G denote a spatiotemporal multiplex network defined as G(V,E,L,T), where: V = v1, v2, ..., v E ..., e { n} represents the set of vertices (counties), = { e1, e2, m} represents the set of edges, L = { l1, l2, l3, l4, l5, l6} represents the set of lay-ers, and T = t1, t2, ..., t { k} represents a set of time steps.
https://cis.temple.edu/~jiewu/research/publications/Publication_files/ICDE2024_Online_Federated_Learning_on_Distributed_Unknown_Data_Using_UAVs.pdf
For the energy consumption during the learning phase, we set e1 = 0.01J and e2 = 80J [18]. To better align with real-world data collection scenarios, we design fine-grained PoI data models from three perspectives: data distribution, data generation patterns, and data quality.