Miracle-dz/UniFAR-FTUs huggingface dataset:
The Effect of Internet Slack on Learning Performance

In the midst of the widespread application of the internet in learning activities, this research was conducted to assess the impact of internet abuse on classroom learning outcomes. Published research shows the impact of diverse internet uses and this study aims to show the consequences of internet abuse on learning outcomes. This research was conducted on accounting class students with a hybrid learning system, where learning is done face-to-face in class but is supported by e-learning technology. The respondents of this study were 224 accounting students. Data were taken using questionnaires and 216 questionnaires were processed using SEM-PLS. The results of this study indicate that internet abuse does not moderate the influence of interaction and social presence on classroom learning performance. These findings indicate that internet abuse is not a factor that decreases student learning performance and can be an input for the design of e-learning concepts to use the internet as an interactive learning media where activities can still be controlled.
What Is Hard about Teaching Machine Learning to Non-Majors? Insights from Classifying Instructors’ Learning Goals

Given its societal impacts and applications to numerous fields, machine learning (ML) is an important topic to understand for many students outside of computer science and statistics. However, machine-learning education research is nascent, and research on this subject for non-majors thus far has only focused on curricula and courseware. We interviewed 10 instructors of ML courses for non-majors, inquiring as to what their students find both easy and difficult about machine learning. While ML has a reputation for having algorithms that are difficult to understand, in practice our participating instructors reported that it was not the algorithms that were difficult to teach, but the higher-level design decisions. We found that the learning goals that participants described as hard to teach were consistent with higher levels of the Structure of Observed Learning Outcomes (SOLO) taxonomy, such as making design decisions and comparing/contrasting models. We also found that the learning goals that were described as easy to teach, such as following the steps of particular algorithms, were consistent with the lower levels of the SOLO taxonomy. Realizing that higher-SOLO learning goals are more difficult to teach is useful for informing course design, public outreach, and the design of educational tools for teaching ML.
A Secure Random Key Distribution Scheme Against Node Replication Attacks in Industrial Wireless Sensor Systems

With the wide deployment of wireless sensor networks in smart industrial systems, lots of unauthorized attacking from the adversary are greatly threatening the security and privacy of the entire industrial systems, of which node replication attacks can hardly be defended, since it is conducted in the physical layer. To solve this problem, we propose a secure random key distribution (SRKD) scheme, which provides a new method for the defense against the attack. Specifically, we combine a localized algorithm with a voting mechanism to support the detection and revocation of malicious nodes. We further change the meaning of the parameter ${s}$ to help prevent the replication attack. Furthermore, the experimental results show that the detection ratio of replicate nodes exceeds 90% when the number of network nodes reaches 200, which demonstrates the security and effectiveness of our scheme. Compared with existing state-of-the-art schemes, the SRKD scheme also has good storage and communication efficiency.
Group Convolutional Neural Networks for Hyperspectral Image Classification

Convolutional Neural Network (CNN) has been widely applied in hyperspectral image (HSI) classification exhibiting excellent performance. The CNN model overfitting is a common issue in this domain due to limited amount of labelled training samples. In addition, making the full use of spectral information is still considered an open problem. In this paper, we propose a novel group 2D-CNN model for spectral-spatial classification. Specifically, we propose an original multi-scale spectral feature extraction approach based on a novel concept of multi-kernel depthwise convolution. Furthermore, we exploit for the first time shuffle operation on the group convolutions in HSI spectral-spatial feature extraction to effectively limit the amount of learning parameters. As a result, we design a small and efficient network for HSI classification. Experimental results on real data demonstrate favourable performance compared to the current state-of-the-art.
Rationalizability, Observability and Common Knowledge

We study the strategic impact of players’ higher-order uncertainty over the observability of actions in general two-player games. More specifically, we consider the space of all belief hierarchies generated by the uncertainty over whether the game will be played as a static game or with perfect information. Over this space, we characterize the correspondence of a solution concept which captures the behavioral implications of Rationality and Common Belief in Rationality (RCBR), where ‘rationality’ is understood as sequential whenever the game is dynamic. We show that such a correspondence is generically single-valued, and that its structure supports a robust refinement of rationalizability, which often has very sharp implications. For instance: (i) in a class of games which includes both zero-sum games with a pure equilibrium and coordination games with a unique efficient equilibrium, RCBR generically ensures efficient equilibrium outcomes (eductive coordination); (ii) in a class of games which also includes other well-known families of coordination games, RCBR generically selects components of the Stackelberg profiles (Stackelberg selection); (iii) if it is commonly known that player 2’s action is not observable (e.g., because 1 is commonly known to move earlier, etc.), in a class of games which includes all of the above RCBR generically selects the equilibrium of the static game most favorable to player 1 (pervasiveness of first-mover advantage).
RoboWeedSupport - Detection of weed locations in leaf occluded cereal crops using a fully convolutional neural network

This paper presents a method for automating weed detection in colour images despite heavy leaf occlusion. A fully convolutional neural network is used to detect the weeds. The network is trained and validated on a total of more than 17,000 annotations of weeds in images from winter wheat fields, which have been collected using a camera mounted on an all-terrain vehicle. Hereby, the network is able to automatically detect single weed instances in cereal fields despite heavy leaf occlusion.
Students’ and Instructors’ Perceptions of Five Different Active Learning Strategies Used to Teach Software Modeling

Unified modeling language (UML) is a modeling standard in the software industry. However, students face difficulties when learning how to model complete and correct UML diagrams, in part because the traditional teacher-centered approach still dominates in computer science. In an attempt to bridge this gap, and to improve the effectiveness of teaching and learning in computing courses, educators use strategies that encourage students’ active participation in the learning process. The goal of this paper is to understand how active learning strategies influence the teaching and learning process of UML diagrams. We performed four case studies in undergraduate courses at two universities. We collected: (a) students’ perceptions of each strategy through questionnaires and focus group sessions; and (b) instructors’ perceptions of the challenges faced in using such strategies through semi-structured interviews. We found fifteen factors that influence the active learning of UML diagrams. Whereas the literature shows that students often positively respond to active learning strategies, our results show that some students have had negative responses to the new strategies. Additionally, we identified four challenges that instructors may face when employing active learning strategies. The contributions of this paper include: factors perceived by students about how active learning strategies influence the learning of UML diagrams, the challenges and difficulties faced by instructors in applying these strategies, and suggestions on how to circumvent challenges noted by instructors.
Meta-Reinforcement Learning of Machining Parameters for Energy-Efficient Process Control of Flexible Turning Operations

Energy-efficient machining has become imperative for energy conservation, emission reduction, and cost saving of manufacturing sectors. Optimal machining parameter decision is regarded as an effective way to achieve energy efficient turning. For flexible machining, it is of utmost importance to determine the optimal parameters adaptive to various machines, workpieces, and tools. However, very little research has focused on this issue. Hence, this paper undertakes this challenge by integrated meta-reinforcement learning (MRL) of machining parameters to explore the commonalities of optimization models and use the knowledge to respond quickly to new machining tasks. Specifically, the optimization problem is first formulated as a finite Markov decision process (MDP). Then, the continuous parametric optimization is approached with actor-critic (AC) framework. On the basis of the framework, meta-policy training is performed to improve the generalization capacity of the optimizer. The significance of the proposed method is exemplified and elucidated by a case study with a comparative analysis. Note to Practitioners—Here, we consider a real-world application problem of energy-aware machining parameter optimization encountered in flexible turning operations, namely, design of a parametric optimization method that can be generalized to various machining tasks where multiple objectives and constraints varying with the machining configurations. This paper presents a novel meta-reinforcement learning (MRL)-based optimization method to improve the generalization by training optimizer with multiple machining tasks. To the best of our knowledge, this is the first MRL-based method of adaptive parameter decision for energy-efficient flexible machining. It should be highly emphasized that technologists benefit from the reduced decision-making time and the improved energy saving opportunity.
Factors driving the adoption of mobility-management travel app: a bayesian structural equation modelling analysis

1 The increasing complexity and mobility demand of transport services strains the transportation system 2 especially in urban areas with limited possibilities to build new infrastructure. The solution to this 3 challenge requires changes in travel behavior. One of the proposed means to induce such change is 4 mobility-management travel apps. However, understanding the motivators underlying individuals’ 5 travel intentions is essential to design and evaluate their effectiveness. This paper aims to pinpoint and 6 understand the drivers that influence individual travel decisions when using such apps. The analytical 7 framework relies on goal frame theory in which individual’s motives to use the app are grouped into 8 three overarching goals namely, 1) gain, 2) hedonic and 3) normative goals. Furthermore, technophilia, 9 social trust and place attachment are incorporated in the framework as to better explain user-sided 10 heterogeneity. The case-study focuses on a hypothetical travel information system in Lisbon (Portugal) 11 through a technology-use preference survey to 227 travelers. Bayesian Structural equation models 12 revealed that the choice drivers are specific to individual users and depends on wide ranging factors 13 that go above traditional economic and socio-demographic methods. The study revealed that firstly, trip 14 efficiency improvement, enjoyment, social interaction and eco-friendly travel promotion are among 15 those motives explaining the adoption intention. Secondly, there are different intentions among 16 individuals depending on the users’ motives. Third, technophilia exerts a positive influence on adoption 17 intention. Fourth, the social dynamic behind the system, influence positively the use of the travel app. 18 19 20 21 22 23
Creating Player Models for Linearity in Level Design

This paper aims to answer how accurately we can predict player experience based on level linearity and player characteristics. Level linearity is defined and a method for measuring it is developed. A third-person Action game is developed and the player experience, together with game session data, recorded. An Artificial Neural Network is trained using supervised learning and the results show that to accurately predict player experience with level linearity, more data is needed. However, a useful method for measuring level linearity is provided for future research. Keywords— Player Modelling, Player Experience, Level Design, Level Linearity, Game Design, Supervised Learning
Development and Validation of a Multidimensional Political Behavior Scale

Years of research conducted into organizational politics has resulted in an expanded understanding of what politics “do” through the investigation of antecedents and outcomes (Lepisto & Pratt, 2012). The literature is somewhat deficient, however, in explaining and measuring what politics “are”. While there are numerous existing measures of organizational politics, the measurement and methodology in this area remains complex due to several issues. The existing literature notes design and measurement (Ferris, Adams, Kolodinsky, Hochwarter, & Ammeter, 2002; Nye & Witt, 1993), definitional (Gunn & Chen, 2006; Lepisto & Pratt, 2012), and level of analysis (Dipboye & Foster, 2006; Fedor & Maslyn, 2002) issues. This research expands the existing literature by identifying areas for improvement within the organizational politics field. Through three studies new items were created and a combination of new and existing items were reviewed and narrowed to create a twenty-six-item, behaviorally based measure of organizational politics. Analyses were conducted to establish and validate the factor structure of the new measure and nomological network relationships were reviewed. Findings show the final measure relates to known correlates of organizational politics as expected, while also providing an opportunity to examine known relationships more broadly at the dimension level due to the expanded construct coverage. POLITICAL BEHAVIOR SCALE iii
Simulator-assisted lean production training

ABSTRACT In Lean Production training and education, simulators are often used. These can take the form of for instance desktop games, computer simulations, or full-scale simulators. Many training participants perceive models for experiential learning and for continuous improvement processes as complex and abstract. Based on experiences from training sessions in a full-scale simulator Karlstad Lean Factory®, a unified model for learning and improvement work is presented. This model stimulates training transfer and is perceived as intuitive. It also shows instructional scaffolding as a learning method. Suggestions for future work include investigating synergy with Smart Manufacturing and the use of Lean Production simulators for innovative product realisation.
A Longitudinal Actor-Partner Interdependence Mediation Model of Storybook Reading: Children’s Word Use as a Mediator

ABSTRACT The present study examined the dyadic nature of fathers’ and mothers’ reported frequency of storybook reading by testing (1) the longitudinal association in parents’ frequency of storybook reading when their children were 10.47 months old and 52.95 months old, (2) the interdependence between both parents’ reported frequency of storybook reading, (3) whether children’s word use would be a potential mediator, and (4) potential moderators (e.g., education levels, language skills). The analytical sample consisted of 8,400 two-parent households from the Early Childhood Longitudinal Study-Birth Cohort (ECLS-B). The actor-partner interdependence mediation model was conducted. Findings showed a significant effect for the continuity in mothers’ storybook reading, but not fathers. Mothers’ reported storybook reading when their children were 10.47 months old significantly predicted fathers’ reported storybook reading when their children were 52.95 months old. Children’s word use was not a significant mediator. Implications and future research are discussed.
An Effort-Based Reward Approach for Allocating Load Shedding Amount in Networked Microgrids Using Multiagent System

An effort-based reward approach is proposed in this article for the allocation of load shedding amount in interconnected microgrids. In this article, effort is defined as the relative contribution of a microgrid to the network with respect to its capacity. In contrast to the absolute contribution-based reward methods, where smaller microgrids are discriminated, the proposed effort-based reward method provides a fair chance for microgrids of all sizes. Historic data of efforts of each microgrid are recorded and an index (effort index) is formulated. The effort index is used as a measure to allocate load shedding to microgrids during emergencies, where microgrids with higher effort indices receive lesser load shedding and vice versa. Different weights are defined for efforts of microgrids depending on the operation mode of the network due to the difference in the importance of power sharing in each mode. In addition, a reward compensation algorithm is devised to mitigate gaining of benefits for longer times while making lesser contributions. The proposed method is realized by using a multiagent system in java agent development framework (JADE) via agent communication language messages. The performance of the proposed method is compared with existing load shedding allocation algorithms, i.e., proportional method, bankruptcy Talmud rule, and absolute contribution-based reward methods.
dish2vec: A Comparison of Word Embedding Methods in an Unsupervised Setting

While the popularity of continuous word embeddings has increased over the past years, detailed derivations and extensive explanations of these methods lack in the academic literature. This paper elaborates on three popular word embedding meth- ods; GloVe and two versions of word2vec: continuous skip-gram and continuous bag-of-words. This research aims to enhance our understanding of both the founda- tion and extensions of these methods. In addition, this research addresses instability of the methods with respect to the hyperparameters. An example in a food menu context is used to illustrate the application of these word embedding techniques in quantitative marketing. For this specic case, the GloVe method performs best according to external expert evaluation. Nevertheless, the fact that GloVe performs best is not generalizable to other domains or data sets due to the instability of the models and the evaluation procedure
Development of a Maturity- and Quadrant Model to Assess and Classify E-purchasing solutions

The progressing Industry 4.0 drives the need for organisations to implement sophisticated e-purchasing solutions. Moreover, research has shown that the deployment and utilization of innovative e-purchasing solutions lead to a competitive advantage due to reduced transaction costs and better control of corporate spending. Nonetheless, it seems that even organisations that are willing to implement innovative solutions struggle to find their desired software in the jungle of competing vendors, due to no established structure to evaluate and classify vendors towards their Industry 4.0 maturity. Therefore, this thesis provides the first Industry 4.0 oriented maturity model with a linked quadrant matrix to assess, classify and visualise solution vendors. With regards to the research design of this study, the new model is developed based on extensive literature reviews and semi- structured interviews with Industry experts. Furthermore, the demonstration of the model encompasses the assessment of 16 e-purchasing solution vendors. The research ends with the newly developed and evaluated maturity model and linked quadrant matrix. In addition, based on the demonstration of the model an update of the technological progress on the e-purchasing solution market is provided.
The Effects of Individual and Social Learning on the Evolution of Cognitive and Communicative Aspects of Language Abilities

Recently, it is recognized that human linguistic abilities and human language are likely to have been shaped by their coevolutionary processes, in other words, the interplaying process of the biological evolution of the language faculty and the cultural evolution of language (Smith, 2018). In a general context of co-evolution of biological and cultural processes, including that of language (Christiansen and Kirby, 2003), effects of individual and social learning have been regarded as important factors. Higashi et al. (2018) constructed a computational model of evolution on a rugged fitness landscape which has many local optima, assuming the evolution of a complex and adaptive ability composed of mutually interacting traits. They showed that individual learning and social learning could work complementarily in the course of adaptive evolution on the rugged fitness landscape. That is, individual learning can find new adaptive phenotypes thanks to the diversity of genetic expressions created by social learning. Social learning also can keep a new adaptive phenotype found by individual learning in the population. We consider the human language faculty as one of such abilities at least in part because it is composed of various sub-faculty traits of language (e.g., brain structures, vocal organs). There are thought to be two sorts of primary abilities of human language (Harman, 1975). One is a cognitive ability or capacity of thoughts (e.g., building language hierarchy). The other is a communicative ability (e.g., intention sharing). However, they only assumed mutual interactions among traits within an individual, which could be regarded as the traits related to the cognitive ability of language. Thus, in order to deal with the evolution of communication ability, the existence of a communication partner and inter-individual interactions of their traits must be assumed. The purpose of this study is to clarify how cognitive and communicative abilities of language can evolve in terms of the co-evolution of linguistic abilities, based on social and individual learning. For this purpose, we extended the model of Higashi et al. (2018) by incorporating inter-individual interactions among traits into it.
Spatial Multiplexing With Limited RF Chains: Generalized Beamspace Modulation (GBM) for mmWave Massive MIMO

Millimeter wave (mmWave) massive multiple-input multiple-output (mMIMO) has been recognized as a promising candidate for 5G communications for its capability of supporting Gb/s transmission. However, it is a common exercise to deploy a limited number of radio-frequency (RF) chains at mmWave mMIMO transceivers due to hardware complexity and cost. As a result, the potential multiplexing gain (MG), which is restricted by the smaller number of RF chains at the transmitter and receiver, is markedly compromised. In order to boost the MG and spectral efficiency (SE), we innovatively develop a novel index modulation termed as the generalized beamspace modulation (GBM). The acquisition of (sub-)beamspace is owing to a natural exploitation of the unique features of mmWave mMIMO. Based on the (sub-)beamspace, a complete GBM transceiver is designed and optimized. Unlike existing alternatives that are largely digital based, our GBM is tailored for the hybrid structure of mmWave mMIMO and can, thereby, realize efficient spatial multiplexing despite the limited RF chains. Extensive analyses and simulations have demonstrated remarkable superiority of GBM over existing counterparts in terms of the error performance and SE.