In order to support the learning of novice students in Java programming, the web-based Java programming Learning Assistant System (JPLAS) has been developed. JPLAS offers several types of exercise problems to foster c...
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The potential of artificial intelligence as a tool that can be an assistant for humans is very close. This is reinforced by the breakthrough of a generative model that is considered to be able to help many tasks that ...
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This study presents a novel reverse forming (RevF) operation scheme to enhance the multi-bit programming reliability of analog resistive random-access memory (RRAM), which is essential for advancing computing-in-memor...
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This study presents a novel reverse forming (RevF) operation scheme to enhance the multi-bit programming reliability of analog resistive random-access memory (RRAM), which is essential for advancing computing-in-memory (CIM) technology. The proposed RevF method uses a series of progressively intensified RESET operations following the initial forming process. The RevF scheme significantly strengthens the unstable conductive filaments (CF) at the bottom of the HfO2 resistive switching layer (RSL) and reduces excessive oxygen ions in the TaOx thermal enhanced layer (TEL). Array-level experimental evaluations on a fully integrated 128Kb RRAM chip showcase a 3-fold reduction in the post-programming conductance instability (i.e., relaxation effect), as quantified by the relative deviation (RD), and a 10(6)-fold enhancement in the retention time. To further verify the effectiveness and advantage of the RevF scheme, a classical denoising diffusion probabilistic model (DDPM) is implemented, yielding significantly enhanced image quality compared to the conventional operation scheme.
Large Language Model (LLM) chatbots such as ChatGPT possess information not only about human languages but also computer languages. It is now possible to perform programming and software design tasks with assistance f...
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ISBN:
(纸本)9798400705793
Large Language Model (LLM) chatbots such as ChatGPT possess information not only about human languages but also computer languages. It is now possible to perform programming and software design tasks with assistance from ChatGPT. We are particularly interested in how the software development community views the use of LLM chatbots in rapid prototyping using unfamiliar programming languages. In four different tech events, several example scenarios of how a tech-savvy engineer could use ChatGPT to prototype apps in unfamiliar programming languages were demonstrated, including a health education app. The four events include an IEEE chapter workshop, an IEEE WIE (Woman In Engineering) meeting, an IEEE joint chapter talk, and a university-level computerscience class. The responses from the tech audience showed that the majority perceived value in the use of LLM chatbots in these contexts, even though there were subtle differences among different groups. This shows the need for further research on how to effectively incorporate LLM chatbots into traditional software design workflow to better serve the software development community.
In the realm of computerscience education, the demand for diverse and challenging programming exercises continues to grow. This paper presents an innovative approach to address this demand by introducing an automatic...
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This dissertation investigates computational models and methods to improve collaboration skills among students. The study targets pair programming, a popular collaborative learning practice in computerscience educati...
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This dissertation investigates computational models and methods to improve collaboration skills among students. The study targets pair programming, a popular collaborative learning practice in computerscience education. This research led to the first machine learning models capable of detecting micromanagement, exclusive language, and other types of collaborative talk during pair programming. The investigation of computational models led to a novel method for adapting pretrained language models by first training them with a multi-task learning objective. I performed computational linguistic analysis of the types of interactions commonly seen in pair programming and obtained computationally tractable features to classify collaborative talk. In addition, I evaluated a novel metric utilized in evaluating the models in this dissertation. This metric is applicable in the areas of affective systems, formative feedback systems and the broader field of computerscience. Lastly, I present a computational method, CollabAssist, for providing real-time feedback to improve collaboration. The empirical evaluation of CollabAssist demonstrated a statistically significant reduction in micromanagement during pair programming. Overall, this dissertation contributes to the development of better collaborative learning practices and facilitates greater student learning gains thereby improving students’ computerscience skills.
The design of course group is an important part of major construction. The construction of course group should be closely focused on the professional talent training objectives. The cultivation of talents for artifici...
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Generalizing work of Kunnemann, Paturi, and Schneider [ICALP 2017], we study a wide class of high-dimensional dynamic programming (DP) problems in which one must find the shortest path between two points in a high-dim...
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ISBN:
(纸本)9783959773096
Generalizing work of Kunnemann, Paturi, and Schneider [ICALP 2017], we study a wide class of high-dimensional dynamic programming (DP) problems in which one must find the shortest path between two points in a high-dimensional grid given a tensor of transition costs between nodes in the grid. This captures many classical problems which are solved using DP such as the knapsack problem, the airplane refueling problem, and the minimal-weight polygon triangulation problem. We observe that for many of these problems, the tensor naturally has low tensor rank or low slice rank. We then give new algorithms and a web of fine-grained reductions to tightly determine the complexity of these problems. For instance, we show that a polynomial speedup over the DP algorithm is possible when the tensor rank is a constant or the slice rank is 1, but that such a speedup is impossible if the tensor rank is slightly super-constant (assuming SETH) or the slice rank is at least 3 (assuming the APSP conjecture). We find that this characterizes the known complexities for many of these problems, and in some cases leads to new faster algorithms.
Dependently typed programming languages have become increasingly relevant in recent years. They have been adopted in industrial strength programming languages and have been extremely successful as the basis for theore...
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Within the current literature on multi-objective optimization, there is a scarcity of comparisons between equation-based white-box solvers to evolutionary black-box solvers. It is commonly held that when dealing with ...
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Within the current literature on multi-objective optimization, there is a scarcity of comparisons between equation-based white-box solvers to evolutionary black-box solvers. It is commonly held that when dealing with linear and quadratic models, equation-based deterministic solvers are generally the preferred choice. The present study aims at challenging this hypothesis, and we show that particularly in box-constrained mixed-integer (MI) problems it is worth employing evolutionary methods when the goal is to achieve a good approximation of a Pareto frontier. To do so, this paper compares a mathematical programming approach with an evolutionary method for set-oriented Pareto front approximation of bi-objective quadratic MI optimization problems. The focus is on convex quadratic under-constrained models wherein the decision variables are either tightly or loosely bounded by box-constraints. Through an empirical assessment of families of quadratic models across varying Hessian forms, variable ranges, and condition numbers, the study compares the performance of the CPLEX-based Diversity Maximization Approach to a state-of-the-art evolutionary multi-objective optimization meta-heuristic with MI mutation and crossover operators. We identify and explain strengths and weaknesses of both approaches when dealing with loosely bounded box-constraints, and prove a theorem regarding the potential undecidability of such multi-objective problems featuring unbounded integer decision variables. The empirical results systematically confirm that black-box and white-box solvers can be competitive, especially in the case of loose box-constraints. IEEE
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