This study employs fuzzy goal programming (FGP) and goal programming (GP) approach to plan the production of sorghum–Bengal gram–sunflower intercropping in the Northern Dry Zone of Karnataka. In this research, seven...
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This paper presents a non-intrusive load monitoring (NILM) model based on two-stage mixed-integer linear programming theory. Compared with other mixed integer optimization-based models, this paper model introduces few...
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Dynamic programming is one of the most challenging topics in data structures and algorithms courses. Students often struggle to grasp dynamic programming techniques and apply them to solve problems. The common ad...
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Continuous demands for improved performance within constrained resource budgets are driving a move from homogeneous to heterogeneous processing platforms for the implementation of today's Real-Time (RT) embedded s...
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Program synthesis constitutes a category of problems where the objective is to automatically produce computer programs that meet specified criteria. Among Genetic programming algorithms, Cartesian Genetic programming ...
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ISBN:
(纸本)9783031700545;9783031700552
Program synthesis constitutes a category of problems where the objective is to automatically produce computer programs that meet specified criteria. Among Genetic programming algorithms, Cartesian Genetic programming has been successfully used for a variety of function synthesis problems, such as circuit design, pattern analysis, and game playing. These problems are designed to work only on a single data type, for example, boolean values or entire images. Cartesian Genetic programming cannot directly be applied to problems with multiple data types, which poses a great limitation, as more realistic programs should be able to deal with different data types. Mixed-Type Cartesian Genetic programming is the only current extension of Cartesian Genetic programming which allows for processing different data types. In this work, we present and study Multimodal Adaptive Graph Evolution, a multi-chromosome generalization of Cartesian Genetic programming that groups functions by return type and constrains graph mutation based on node's type coherence. We compare Multimodal Adaptive Graph Evolution to Mixed-Type Cartesian Genetic programming on the Program Synthesis Benchmark Suite, showing that the representation and mutation constraints of Multimodal Adaptive Graph Evolution aid in the search of multimodal functions. Using Search Trajectory Networks, we find that Multimodal Adaptive Graph Evolution converges faster to a local or global minimum compared to Mixed-Type Cartesian Genetic programming and explores the solution space more effectively by creating candidate solutions with lower semantic redundancy.
Competitive programming (CP) is a mind sports activity where people solve problems using command-line computer programs to provide correct output for the given test cases. Competitors need to practice problem-solving ...
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Quantum computing has gained significant prominence within computerscience curricula. Unfortunately, the conventional mechanism for grading programming assignments (running students' code on multiple test cases a...
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ISBN:
(纸本)9798400706004
Quantum computing has gained significant prominence within computerscience curricula. Unfortunately, the conventional mechanism for grading programming assignments (running students' code on multiple test cases and verifying the output) is impractical for quantum computing. Scalable quantum computers are unlikely to be available for at least the next several years, and all known methods of simulating their behavior require exponential time. In this work, we present an efficient autograding methodology for quantum programs predicated on two observations: 1) there already exist efficient average-case methods for comparing two implementations of a quantum function for equivalency, and 2) most student submissions for an assignment are functionally identical to at least one previous submission. Our autograder maintains a database of previous submissions and iteratively compares incoming ones against this database. If no matches are found, a series of expensive simulations are performed to assign a score, and the submission along with its grade is added to the database. In the more common case of a hit, the matching score is automatically applied without the need for simulation. In a case study involving an undergraduate quantum computing course, our approach reduced total execution time needed to grade student submissions by 90% when compared to traditional autograding methods while maintaining the same granularity of feedback. We also show that our system demonstrates the largest speedups when under maximum stress, acting as a load balancer and reducing the percentage that hardware must be overprovisioned to handle the maximum load by 20%, yielding a total reduction of 92%.
In this study, artificial intelligence based tools are discussed in the context of adapting teaching methods used in computerprogramming courses. Four innovative strategies are discussed: an academic integrity and et...
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In this paper, we investigate the relationship between student experiences and transactional distance in a 12 week online undergraduate Python programming course. Transactional distance is defined as the psychological...
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ISBN:
(纸本)9798400701382
In this paper, we investigate the relationship between student experiences and transactional distance in a 12 week online undergraduate Python programming course. Transactional distance is defined as the psychological and communication space between students and instructors due to geographical separation. Although several studies have examined learning from a transactional distance perspective, there has been a lack of research which describe computing education courses from this perspective. In our online programming course context, students were introduced to Python programming concepts through pre-recorded lectures which were released every week. Towards the end of the course, we provided students with a survey. We analysed the survey responses to examine the relationship between their perceived transactional distance, sense of belonging and satisfaction in the course. We also analysed students' open-ended survey responses regarding feedback about the course, and used a thematic analysis approach to categorise these responses. We found moderate correlations between transactional distance, sense of belonging and student satisfaction based on the survey responses. This implies that reducing transactional distance can improve sense of belonging in students in an online programming course. The themes emerging from the analysis of open-ended survey responses show that feedback in an online course can be considered from a transactional distance perspective. These findings provide implications for instructors and researchers to explore the transactional distance framework as a means to improve instruction, sense of belonging and satisfaction in online programming courses.
Brain-inspired computing is a new technology that draws on the principles of brain science and is oriented to the efficient development of artificial general intelligence(AGI),and a brain-inspired computing system is ...
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Brain-inspired computing is a new technology that draws on the principles of brain science and is oriented to the efficient development of artificial general intelligence(AGI),and a brain-inspired computing system is a hierarchical system composed of neuromorphic chips,basic software and hardware,and algorithms/applications that embody this *** the system is developing rapidly,it faces various challenges and opportunities brought by interdisciplinary research,including the issue of software and hardware *** paper analyzes the status quo of brain-inspired computing *** by some design principle and methodology of general-purpose computers,it is proposed to construct"general-purpose"brain-inspired computing systems.A general-purpose brain-inspired computing system refers to a brain-inspired computing hierarchy constructed based on the design philosophy of decoupling software and hardware,which can flexibly support various brain-inspired computing applications and neuromorphic chips with different ***,this paper introduces our recent work in these aspects,including the ANN(artificial neural network)/SNN(spiking neural network)development tools,the hardware agnostic compilation infrastructure,and the chip micro-archi-tecture with high flexibility of programming and high performance;these studies show that the"general-purpose"system can remarkably improve the efficiency of application development and enhance the productivity of basic software,thereby being conductive to accelerating the advancement of various brain-inspired algorithms and *** believe that this is the key to the collaborative research and development,and the evolution of applications,basic software and chips in this field,and conducive to building a favorable software/hardware ecosystem of brain-inspired computing.
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