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Addressing the problem of the difficulty in providing timely and reasonable feedback evaluation for student project reports, this paper proposes a method based on LLMs (Large Language Models) that can automatically generate instant feedback evaluations for student project reports. Three LLMs, namely BART (Bidirectional and Auto-Regressive Transformer), CPTB (chatgpt_paraphraser_on_T5_base), and CGP-BLCS (chatgpt-gpt4-prompts-bart-large-cnn-samsum), were designed to generate instant text feedback pre-training models for student project reports. The effectiveness of the feedback was evaluated using ROUGE Metrics, BERT Scores, and human expert evaluations. Experiments showed that the lightweight, fine-tuned BART model, when trained on a larger dataset of 80%, generated effective feedback evaluations for student project reports. When trained on a smaller dataset of 20%, both the BART and CPTB models had unsatisfactory overall performance, while the fine-tuned CGP-BLCS model was able to generate feedback evaluations that approached human-level evaluations. The detailed descriptions of the methods used with the LLMs for generating effective text feedback evaluations for student project reports will be useful to AI computer programmers, researchers, and computer science instructional designers for improving their courses and future research.

The hypercube embedding problem, a restricted version of the general mapping problem, is the problem of mapping a set of communicating processes to a hypercube multiprocessor. The goal is to find a mapping that minimizes the length of the paths between communicating processes. Unfortunately the hypercube embedding problem has been shown to be NP-hard. Thus many heuristics have been proposed for hypercube embedding. This paper evaluates several hypercube embedding heuristics, including simulated annealing, local search, greedy, and recursive mincut bipartitioning. In addition to known heuristics, we propose a new greedy heuristic, a new Kernighan-Lin style heuristic, and some new features to enhance local search. We then assess variations of these strategies (e.g., different neighborhood structures) and combinations of them (e.g., greedy as a front end of iterative improvement heuristics). The asymptotic running times of the heuristics are given, based on efficient implementations using a priority-queue data structure.

Randomized algorithms are algorithms that employ randomness in their solution method. We show that the performance of randomized algorithms is less affected by factors that prevent most parallel deterministic algorithms from attaining their theoretical speedup bounds. A major reason is that the mapping of randomized algorithms onto multiprocessors involves very little scheduling or communication overhead. Furthermore, reliability is enhanced because the failure of a single processor leads only to degradation, not failure, of the algorithm. We present results of an extensive simulation done on a multiprocessor simulator, running a randomized branch-and-bound algorithm. The particular case we consider is the knapsack problem, due to its ease of formulation. We observe the largest speedups in precisely those problems that take large amounts of time to solve.