This work attempts to design core layer in campus network employing best practices of network topology and routing protocol at the layer with a use case of core layer in a university campus network using software-defined networking (SDN) paradigm.
Abstract
As one of the layers in campus network, core layer or backbone network should provide interconnectivity and routing between internal and external networks, including other campus networks or the Internet. With that requirement, designing this layer to be highly available to interconnect and route traffic is a high priority. By using software-defined networking (SDN) paradigm, we attempt to design core layer in campus network employing best practices of network topology and routing protocol at the layer with a use case of core layer in a university campus network. We use RouteFlow as the SDN platform supporting a traditional routing protocol, i.e. Open Shortest Path First (OSPF), over OpenFlow (OF) network infrastructure. The experimental testbed/environment consists of two virtual machines (VMs). The first VM is used as the SDN/OF data plane with Open vSwitch in Mininet network emulator, and the second one representing SDN control plane comprising RouteFlow with POX controller. We evaluated the design by testing the interconnectivity using ping for the OF switches in the topology and hosts that are connected to the switches. We also tracked the route of the packets by monitoring traffic passed through all network interfaces of the switches using tcpdump. This case was evaluated since we need to make sure that packets were routed with the shortest path from source to destination using OSPF that was implemented in the virtual network at the top of the SDN platform.
LLMs are increasingly used for code generation, yet they frequently hallucinate non-existent software packages, creating exploitable entry points into the software supply chain. We make four contributions to this problem. First, we show that prior evaluation methodologies systematically inflate hallucination rates by misclassifying standard-library modules as hallucinations in some languages. For Python, the overestimation reaches 9.4 percentage points. Second, we evaluate seven inference-time defenses for mitigating package hallucinations, including five guided decoding strategies (Greedy, Contrastive, DoLa, Nudging, and Active Layer-Contrastive Decoding), an iterative self-refinement approach (Self-Refine), and a Retrieval-Augmented Generation (RAG)-based defense.. Across eight models spanning five families and four programming languages (Python, JavaScript, Ruby, Rust), RAG reduces the package hallucination rate (PHR) in 18 of 32 model--language configurations. Third, we introduce Package Utility (PU) to assess whether defenses preserve valid and task-relevant recommendations. Among strategies evaluated, Greedy decoding provides the strongest average mitigation--utility trade-off. Fourth, we stress-test all strategies under adversarial prompts seeded with fabricated package names and find that PHR surges by up to 45 percentage points relative to standard prompts, with Ruby consistently the most vulnerable language (80.9--95.2\%). Under adversarial conditions, RAG and Self-Refine outperform all decoding-only strategies, indicating that robust defense requires either external grounding or iterative self-verification when prompts are actively hostile. Our results recast package hallucination as both a measurement problem and a decoding-time control problem, and they demonstrate that the choice of defense must be matched to the threat model and recommendation utility.
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MAGE explains how externalized knowledge, bounded action, independent evaluation, and retained human authority can compose into a governed engineering environment, and proposes tests of when that environment turns commodity intelligence into durable engineering progress.
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An audit-and-placebo protocol is proposed that separates verifier artifacts, interaction scaffolding, and grounded feedback credit in evaluations of self-evolving test generators in evaluations of self-evolving test generators.
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It is shown that the contradiction in experience with neural surrogates in derivative-free optimisation dissolves once three factors are stated, and that these, rather than the fit accuracy a training curve reports, are what delimit when a learned local model pays.
This prototype MRG image translocation software was helpful to 69% of patients with binocular diplopia, but limited by large angle strabismus because of the limited instrument field of view.
Edsel B Ing, Kevin Sha, Sarosh Dandoti et al.· Journal of neuro-ophthalmolo...· 0 citations
A diagnostic support system based on a unified web platform that classifies patients according to the risks of developing three diseases based on regularly collected clinical or audio data using classical supervised learning algorithms is presented.
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