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关键词： Game-based learning   Minecraft Education Edition   Educational technology   Lesson planning   Program evaluation  

摘要： The purpose of this research is to evaluate the potential of use for computer science course designs (CSCD) created by game-based learning method. Qualitative case study was used as the research design. CSCDs created for Minecraft Education Edition (MinecraftEdu) were examined in the context of various criteria by document analysis method, and semi-structured interviews were conducted with teachers actively using MinecraftEdu in their classes. The research revealed that the CSCDs created for MinecraftEdu primarily aimed at developing coding and design skills, underscored interdisciplinary collaboration, laid more emphasis on cognitive-psychomotor achievements with curriculum eligibility. It was acknowledged that introduction, body and conclusion parts have an elaborate design in majority of course designs according to Gagne's Events of Instruction Model, and yet, more attention should be paid to gaining the learner's attention, stimulating recall of prior learning, giving feedback, and ensuring retention and transfer. Despite increasing interest and trend towards educational games and game-based learning environments, there are limited number of studies that can help teachers evaluate the games that can be most effective in their fields and different learning contexts. Therefore, it is anticipated that this study will contribute to the related literature on game-based course designs and applicability.

关键词： Artificial intelligence   Operations research   Computer science   Optimization   Graph theory   Minimum Spanning Tree Problem   Dhouib-Matrix  

摘要： The Minimum Spanning Tree problem aims to create a subset of a graph where all the vertices are connected with the minimum edge weights and with no cycle. In this field, an innovative method entitled Dhouib-Matrix-MSTP (DM-MSTP) is designed in this research work with a time complexity independently of the number of edges O(n*log(n)) where n is the number of vertices. DM-MSTP is a constructive algorithm based on a matrix navigation with two new lists (Min-Columns and MST-Path) in order to organize the steering flow. DM-MSTP is composed of four simple steps where the first and the fourth steps are repeated only once, whereas the second and third are reiterated (n-1). For more clarification, a step-by-step application of the proposed method is presented in details. Besides, the performance of DM-MSTP is proved through different examples from the literature including a graph with negative weighted edges and complete graphs from TSP-LIB. Moreover, with a simple modification (Min by Max) the DM-MSTP is tested on the Maximum (Largest) Spanning Tree Problem. Also, DM-MSTP is applied on eight case studies and compared to six methods developed in the literature. All these experimental results in the above different environments show that DM-MSTP can rapidly plan the shortest spanning tree with a stable performance and convivial representation of the optimal tree. Hence, DM-MSTP is developed under Python programming language using Matplotlib and Numpy standard libraries.

关键词： Controllability   Color   Heuristic algorithms   Network systems   Games   Process control   Computer science   Dynamics over graphs   edge augmentation   strong structural controllability   zero forcing sets (ZFSs)  

摘要： This article studies the problem of computing a minimum zero forcing set (ZFS) in undirected graphs and presents new approaches to reducing the size of the minimum ZFS via edge augmentation. The minimum ZFS problem has numerous applications;for instance, it relates to the minimum leader selection problem for the strong structural controllability of networks defined over graphs. Computing a minimum ZFS is an NP-hard problem in general. We show that the greedy heuristic for the ZFS computation, though it typically performs well, could give arbitrarily bad solutions for some graphs. We provide a linear-time algorithm to compute a minimum ZFS in trees and a complete characterization of the minimum ZFS in the clique chain graphs. We also present a game-theoretic solution for general graphs by formalizing the minimum ZFS problem as a potential game. In addition, we consider the effect of edge augmentation on the size of the ZFS. Adding edges could improve network robustness;however, it could increase the size of the ZFS. We show that adding a set of carefully selected missing edges to a graph may actually reduce the size of the minimum ZFS. Finally, we numerically evaluate our results on random graphs.

关键词： Computer science   Neural networks   Time series   Load forecasting  

摘要： In natural language processing (NLP), transformer based models have surpassed recurrent neural networks (RNN) as state of the art, being introduced specifically to address the limitations of RNNs originating from its sequential nature. As a similar sequence modeling problem, transformer methods can be readily adapted for deep learning time series prediction. This paper proposes a sparse transformer based approach for electricity load prediction. The layers of a transformer addresses the shortcomings of RNNs and CNNs by applying the attention mechanism on the entire time series, allowing any data point in the input to influence any location in the output of the layer. This allows transformers to incorporate information from the entire sequence in a single layer. Attention computations can also be parallelized. Thus, transformers can achieve faster speeds, or trade this speed for more layers and increased complexity. In experiments on public datasets, the sparse transformer attained comparable accuracy to an RNN-based SOTA method (Liu et al., 2022) while being up to 5x faster during inference. Moreover, the proposed model is general enough to forecast the load from individual households to city levels as shown in the extensive experiments conducted.

关键词： Entropy   Feature extraction   Reliability   Adaptation models   Training   Cyberspace   Computer science   Transfer learning   partial domain adaptation   deep neural network   image classification   contrastive learning  

摘要： Partial domain adaptation (PDA) assumes that target domain class label set is a subset of that of source domain, while this problem setting is close to the actual scenario. At present, there are mainly two methods to solve the overfitting of source domain in PDA, namely the entropy minimization and the weighted self-training. However, the entropy minimization method may make the distribution prediction sharp but inaccurate for samples with relatively average prediction distribution, and cause the model to learn more error information. While the weighted self-training method will introduce erroneous noise information in the self-training process due to the existence of noise weights. Therefore, we address these issues in our work and propose self-training contrastive partial domain adaptation method (STCPDA). We present two modules to mine domain information in STCPDA. We first design self-training module based on simple samples in target domain to address the overfitting to source domain. We divide the target domain samples into simple samples with high reliability and difficult samples with low reliability, and the pseudo-labels of simple samples are selected for self-training learning. Then we construct the contrastive learning module for source and target domains. We embed contrastive learning into feature space of the two domains. By this contrastive learning module, we can fully explore the hidden information in all domain samples and make the class boundary more salient. Many experimental results on five datasets show the effectiveness and excellent classification performance of our method.

关键词： crime   criminal behavior   violent behavior   ACEs   capital punishment   youthful offenders  

摘要： In the 2005 Roper v. Simmons decision, the Supreme Court determined that capital punishment, the death penalty, was not appropriate for offenders under the age of 18. The Court accepted the argument that juveniles are less blameworthy than adult offenders due to differences in their cognitive abilities and the influence of factors outside of their control, such as household dysfunction or the existence of adverse childhood experiences (ACEs). Recent empirical literature examining both brain development and the impact of ACEs on both brain maturity and offending behaviors demonstrates that brain maturation and psychosocial maturation continue into the mid-twenties. As a result, youthful offenders, those 18 to 24 of age, may possess many of the neuropsychological deficiencies attributed to juveniles and yet be sentenced to death. This research examines if juries of capital punishment trials recognize the impact of age and ACEs when determining their sentencing decision.

关键词： allied field   computer science   sociology   cognition   collective intelligence   culture   diversity   formal models   intragroup processes   networks   social cognition  

摘要： Humans regularly solve complex problems in cooperative teams. A wide range of mechanisms have been identified that improve the quality of solutions achieved by those teams on reaching consensus. We argue that many of these mechanisms work via increasing the transient diversity of solutions while the group attempts to reach a consensus. These mechanisms can operate at the level of individual psychology (e.g., behavioral inertia), interpersonal communication (e.g., transmission noise), or group structure (e.g., sparse social networks). Transient diversity can be increased by widening the search space of possible solutions or by slowing the diffusion of information and delaying consensus. All of these mechanisms increase the quality of the solution at the cost of increased time to reach it. We review specific mechanisms that facilitate transient diversity and synthesize evidence from both empirical studies and diverse formal models-including multiarmed bandits, NK landscapes, cumulative-innovation models, and evolutionary-transmission models. Apparent exceptions to this principle occur primarily when problems are sufficiently simple that they can be solved by mere trial and error or when the incentives of team members are insufficiently aligned. This work has implications for our understanding of collective intelligence, problem solving, innovation, and cumulative cultural evolution.

关键词： O10   O32   Digital financial inclusion   Economic growth   Financial development   Technological progress  

摘要： This study employs the panel vector autoregression model to investigate how digital financial inclusion (DFI), technological advancement, environmental conditions, and economic growth are interconnected in 16 low financial development countries (LFDCs) and 29 high financial development countries (HFDCs) from 2015 to 2020. The results of the impulse response function reveal that in LFDCs, DFI enhances environmental quality while promoting technological progress. However, this improvement in environmental quality results in a decline in economic growth in these countries. In HFDCs, the promotion of DFI results in economic growth;however, it is accompanied by a decrease in environmental quality. Furthermore, the results of variance decomposition demonstrate that the interconnection among DFI, economic growth, environmental quality, and technological progress is more closely related in LFDCs than in HFDCs. Based on the findings, we recommend relevant policy implications for the respective country groups.