Miracle-dz/UniFAR-FTUs huggingface dataset:
THE UTILITARIAN EDUCATION OF THOMAS GRADGRIND

The opening page of Hard Times by the 19th century English novelist Charles Dickens is often quoted in discussions of the nature of education and teaching. In this opening, the Victorian schoolmaster Thomas Gradgrind insists that we should teach young people nothing but facts: “Facts alone are wanted in life. Plant nothing else, and root out everything else. You can only form the minds of reasoning animals upon facts: nothing else will ever be of any service to them” (Dickens, 2003, p. 1). This noted, it should also be said that Gradgrind is by no means the major villain of this novel or himself a cold or heartless man. As the story proceeds to show, Gradgrind is the very soul of not only charity, but genuine warmth and kindness, towards others in the narrative – not least towards the abandoned circus child Sissy Jupe. Still, Gradgrind is clearly in thrall to an emerging modern Western rationalempiricist mindset entirely driven by fairly basic socioeconomic concerns. His educational philosophy, as Dickens is clearly concerned to highlight, is firmly rooted in the 19th century (probably largely Benthamite) philosophy of utilitarianism that reduces human value – or what humans can be reasonably said to value – to materialist cost-benefit calculation. In consequence, Gradgrind’s education of cold fact-based reason effectively starves his own children of emotion, imagination and moral sense and succeeds only in alienating them and wrecking their lives. Towards the end of the novel, Gradgrind is brought to painful appreciation of the dire failure of his own philosophy and educational methods. Significantly, he confesses that though he had once been told that there is a wisdom of the heart as well as of the head, he had foolishly failed to believe this. Dickens’ novel, along with much other 19th century literature in the so-called “romantic” tradition (one is reminded of similar normative themes in the poetry of William Wordsworth and William Blake), mounts a devastating critique of a Western post-industrial conception of human knowledge and reason that had already gained ascendance in Dickens’ day and which continues to wield much influence in today’s world of modern and postmodern free-market capitalism and globalisation. In this regard, it is notable that the Gradgrind quote from Hard Times continues to reappear in much literature of educational theory that is critical of the test-based examination and assessment of much contemporary (global) schooling. It is an education that prizes the evidence-based knowledge of scientific research and enquiry over sources of insight that cannot be empirically validated or “proved” – as seems evident from widespread global side-lining of literature, arts and humanities in such latter-day schooling.
Desenvolvimento de Jogos Orientado a Modelo para Jogos Sérios: uma Revisão Sistemática

O desenvolvimento de jogos orientado a modelo (MDGD) se mostra uma alternativa promissora para a criação de jogos sérios por não especialistas em programação, como professores e designers, ao reduzir as barreiras técnicas de programação existentes para a introdução do conteúdo pedagógico. Este trabalho analisa e compara o uso do MDGD no processo de desenvolvimento de jogos sérios, por meio de uma revisão sistemática da literatura. Como resultados, foram analisados 11 trabalhos, em que se destaca o uso de motores de jogos para a integração do código e geração do jogo final e adoção de estruturas de dados para modelagem de elementos e suas relações. Entretanto, ainda é necessário expandir as capacidades das linguagens específicas de domínio (DSL) nos aspectos pedagógicos e para que possuam maior controle sobre conquistas e individualização da experiência do jogador. Além disso, foi evidenciada uma incongruência com a participação do especialista entre o uso e desenvolvimento, dado que o último caso é negligenciado. Como trabalhos futuros, pondera-se a necessidade de uma visão unificada de elementos comuns dentro do processo e da combinação de gêneros distintos de jogabilidade.
Simulating the Impacts of Climate Change on Maize Yields Using EPIC: A Case Study in the Eastern Cape Province of South Africa

Climate change has been projected to impact negatively on African agricultural systems. However, there is still an insufficient understanding of the possible effects of climate change on crop yields in Africa. In this study, a previously calibrated Environmental Policy Integrated Climate (EPIC) model was used to assess the effects of future climate change on maize (Zea mays L.) yield in the Eastern Cape Province of South Africa. The study aimed to compare maize yields obtained from EPIC simulations using baseline (1980–2010) weather data with maize yields obtained from EPIC using statistically downscaled future climate data sets for two future periods (mid-century (2040–2069) and late century (2070–2099)). We used three general circulation models (GCMs): BCC-CSM1.1, GFDL-ESM2M and MIROC-ES under two Representative Concentration Pathways (RCPs), RCP 4.5 and RCP 8.5, to drive the future maize yield simulations. Simulation results showed that for all three GCMs and for both future periods, a decrease in maize production was projected. Maize yield was projected to decrease by as much as 23.8% for MIROC, RCP 8.5, (2070–2099). The temperature was projected to rise by over 50% in winter under RCP 8.5 for both future periods. For both future scenarios, rainfall was projected to decrease in the summer months while increasing in the winter months. Overall, this study provides preliminary evidence that local farmers and the Eastern Cape government can utilise to develop local climate change adaptation strategies.
Cooperation and communication in multiagent deep reinforcement learning

Acknowledgments Many thanks to my advisor Peter Stone for many years of support and encouragement throughout the PhD process. In particular, I remember a dark time when I was directionless and depressed about the prospect of finding fruitful research directions or ever graduating. You told me that all PhDs go through a period where they are lost in the woods and encouraged me to continue onward. This thesis is testament to the fact that given sufficient persistence, encouragement, and caffeine, there is a way out of the woods. Thanks to my lab-mates and colleagues who have shaped my research and added flavor to my graduate experience: for the time and feedback they so generously gave. Thanks to Kay Nettle and Amy Bush for tirelessly supporting and debug-ging the many issues that arise from setting up and maintaining a GPU cluster. The research in this thesis would have been impossible (or at least computationally intractable) without your help. Special thanks to my loving family and my fiancé Man Liang for their unconditional support throughout the ups and downs of the PhD process. Reinforcement learning is the area of machine learning concerned with learning which actions to execute in an unknown environment in order to maximize cumulative reward. As agents begin to perform tasks of genuine interest to humans, they will be faced with environments too complex for humans to predetermine the correct actions using hand-designed solutions. Instead, capable learning agents will be necessary to tackle complex real-world domains. However, traditional reinforcement learning algorithms have difficulty with domains featuring 1) high-dimensional continuous state spaces, for example pixels from a camera image, 2) high-dimensional parameterized-continuous action spaces, 3) partial observabil-ity, and 4) multiple independent learning agents. We hypothesize that deep neural networks hold the key to scaling reinforcement learning towards complex tasks. This thesis seeks to answer the following two-part question: 1) How can the power of Deep Neural Networks be leveraged to extend Reinforcement Learning to complex environments featuring partial observability, high-dimensional parameterized-continuous state and action spaces, and sparse re-wards? 2) How can multiple Deep Reinforcement Learning agents learn to cooperate in a multiagent setting? To address the first part of this question, this thesis explores the idea of using recurrent neural networks to combat partial observability experienced by agents in the domain of Atari 2600 video games. Next, we design a deep reinforcement learning agent capable of discovering effective policies for …
SNR Enhancement of Energy Detector Algorithm Using Adaptive Wiener Filter In Cognitive Radio

The underutilization of existing radio spectrum and scarcity of available channel become a challenge for modern era communication system. Services such as high speed data communication requires more bandwidth and wide spectrum range. The rising demand in wireless services and applications bring channel constraint on the existing frequency spectrum allocation scheme. To mitigate such a problem the idea of cognitive radio technique came into picture. Cognitive radio can be defined as a radio system that can adaptively and dynamically allow user(s) to use the spectrum when the primary user is absent. The detection and decisions of the primary signal can be facilitated by spectrum sensing technique. Among those techniques, energy detector spectrum sensing is a transmitter based spectrum sensing technique which detects the presence of primary user in cognitive radio system. Detection of primary signal is made by comparing the received signal energy to that of pre-determined energy threshold value. However the performance indicator matrices show that the energy detector algorithm has poor performance when the received SNR value is too low. This thesis work attempts to analyze and compare the performance of energy detector when adaptive Weiner filter is inserted on the front end of conventional energy detector. Based on the performances matrices value, evaluation of the systems (conventional and enhanced energy detector) response will be done. The performance matrices parameters include: Receiver operating curve (ROC), Complement Receiver Operating Curve (CROC), threshold value vs false alarm probability and Detection probability Vs SNR value. The results are presented for AWGN and Rayleigh flat fading channel model. Accordingly focus is made on how the conventional energy detector behaves when the adaptive Wiener filter is inserted on the front end. Finally simulation results show that, insertion of adaptive Wiener filter in the front end of conventional energy detector has improved all the performance matrices considered in thesis work.
An Intelligent Risk Management Framework for Monitoring Vehicular Engine Health

The unwanted vehicular engine irregularities diminish vehicular competence, hinder productivity, waste time, and sluggish personal/national economic growth. Transportation sectors are essential infrastructures that require practical vulnerability assessment to avoid unexpected consequences through risk severity assessment. Artificial intelligence would be vital in the Industry 4.0 era to eliminate these issues for seamless activity and ultimate productivity. This article presents a risk management framework that includes an efficient decision model for monitoring and diagnosing vehicular engine health and condition in real-time using vulnerable components information and advanced techniques. To do this, we used the vulnerability identification frame to identify the vulnerable objects. We created a decision model that used an infrastructure vulnerability assessment model and sensor-actuator data to diagnose and categorise engine conditions as good, minor, moderate, or critical. We used machine learning and deep learning algorithms to assess the effectiveness of the risk management system’s decision model. The stacked ensemble of the deep learning algorithm outperformed other standard machine learning and deep learning algorithms in providing 80.3% decision accuracy for the 80% training data and efficiently managing large amounts of data. Anticipating the proposed framework might assist the automotive sector in advancing with cutting-edge facilities that are up to date.
Machine-Learning-Based DDoS Attack Detection Using Mutual Information and Random Forest Feature Importance Method

Cloud computing facilitates the users with on-demand services over the Internet. The services are accessible from anywhere at any time. Despite the valuable services, the paradigm is, also, prone to security issues. A Distributed Denial of Service (DDoS) attack affects the availability of cloud services and causes security threats to cloud computing. Detection of DDoS attacks is necessary for the availability of services for legitimate users. The topic has been studied by many researchers, with better accuracy for different datasets. This article presents a method for DDoS attack detection in cloud computing. The primary objective of this article is to reduce misclassification error in DDoS detection. In the proposed work, we select the most relevant features, by applying two feature selection techniques, i.e., the Mutual Information (MI) and Random Forest Feature Importance (RFFI) methods. Random Forest (RF), Gradient Boosting (GB), Weighted Voting Ensemble (WVE), K Nearest Neighbor (KNN), and Logistic Regression (LR) are applied to selected features. The experimental results show that the accuracy of RF, GB, WVE, and KNN with 19 features is 0.99. To further study these methods, misclassifications of the methods are analyzed, which lead to more accurate measurements. Extensive experiments conclude that the RF performed well in DDoS attack detection and misclassified only one attack as normal. Comparative results are presented to validate the proposed method.
Equivalence tests to support environmental biosafety decisions：theory and examples

A major role of ecological risk assessment(ERA)has been to provide scientific guidance on whether a future human activity will cause ecological harm,including such activities as release of a genetically modified organism(GMO),exotic species,or chemical pollutant into the environment.This requires the determination of the likelihoods that the activity:would cause a harm,and would not cause a harm.In the first case,the focus is on demonstrating the presence of a harm and developing appropriate management to mitigate such harm.This is usually evaluated using standard hypothesis analysis.In the second case,the focus is on demonstrating the absence of a harm and supporting a decision of biosafety.While most ERA researchers have focused on finding presence of harm,and some have wrongly associated the lack of detection of harm with biosafety,a novel approach in ERA would be to focus on demonstrating directly the safety of the activity.Although,some researchers have suggested that retrospective power analysis can be used to infer absence of harm,it actually provides inaccurate information about biosafety.A decision of biosafety can only be supported in a statistically sound manner by equivalence tests,described here.Using a 20%ecological equivalence standard in GMO examples,we illustrated the use of equivalence tests for two-samples with normal or binomial data and multi-sample normal data,and provided a spreadsheet calculator for each.In six of the eight examples,the effects of Cry toxins on a non-target organism were equivalent to a control,supporting a decision of biosafety.These examples also showed that demonstration of equivalence does not require large sample sizes.Although more relevant ecological equivalence standards should be developed to enable equivalence tests to become the main method to support biosafety decision making,we advocate their use for evaluating biosafety for non-target organisms because of their direct and accurate inference regarding safety.
Improved decision making in lean manufacturing using simulation-based approach

This study aims to enhance the efficacy of decision-making process in ascertaining waste by means of simulation. Waste involving any tasks that are unable to make any value addition to products from the consumer's side. Simulation can be deployed to improve the decision-making as well as knowledge of a lean practitioner (LP). To assess the production system and make decision-making more precise, a simulation-based approach was formulated and applied. The results established that production cost and waiting time can be reduced through worker arrangement.
An analysis of bus interior headroom for future design applications

This research investigated the contextual underpinning and longevity of Australian vehicle standards for route bus passenger headroom—standards developed around Internal Combustion Engine (ICE). With growing interest in Electric Vehicle (EV) technologies, Australian Design Rule (ADR) 58.6 Head Room was examined for passenger safety, comfort and relevance to potential future vehicle design. Four bus interior configurations were modelled to visualise the applications of ADR 58.6. These configurations were then analysed by comparing them to anthropometric data to determine whether accommodation was appropriate. The results found that ADR 58.6 only assures sufficient passenger accommodation when applied in a rear engine, low-entry route bus. A second, qualitative, analysis compared the ceiling height of buses to other public transport vehicle interiors. Results of this comparison contribute an understanding of what may be culturally acceptable as a new standard. Research discovered that current Australian route bus headroom standards only give a satisfactory result when applied to a specific ICE configuration. When designing for alternative ICE configurations or EV drive technologies, these standards may lead to potential problems for passenger safety and comfort. Findings highlight the need to revise ADR 58.6 ensuring relevance to advancements in technology and vehicle design. Recommendations are made to this effect.
An Evaluation of Critical Capabilities and Improvement Areas for Competitive Manufacturing in a Developed-Country Environment

This research evaluates the critical capabilities and improvement areas for competitive manufacturing in a developed-country environment. A multiple-case-study approach is applied, consisting of three manufacturing firms located in Sweden. The case study combines both quantitative and qualitative evaluations of the critical capabilities found in the extant literature. The research reveals fifteen critical capabilities and sixteen improvement areas for competitive manufacturing in a developed-country environment. The results show that the firms develop capabilities in a cumulative manner, starting with cost and quality, continuing with time and flexibility, and finally trying to improve innovation and sustainability. To develop competitive manufacturing operations in developed counties, it is therefore vital to improve capabilities related to time, flexibility, innovation, or sustainability. Irrespective of the targeted capabilities, they must contribute to cost-efficient operations and high quality must always be maintained. The efforts to develop capabilities related to innovation and sustainability may be expensive in the short term, however they can provide better cumulative results and competitiveness in the long term.
Perceptually-motivated adversarial training for deep ensemble denoising of hyperspectral images

ABSTRACT In this letter, we present a deep-learning-based methodology for recovering hyperspectral images (HSIs) distorted by Gaussian and impulsive noise. This work makes the following contribution: To begin with, the Wasserstein Generative Adversarial Network (WGAN) is used to mitigate the effects of vanishing gradient and mode collapse that can occur when training a vanilla GAN. Secondly, data are passed via three distinct pathways in a parallel ensemble to promote multiscale feature extraction. Normal and multiscale dilated 3D convolutions are utilized to train the model in each pair of parallel paths. Thirdly, features are recovered following data permutation across three different spatial planes (viz. , and planes) and after passing through parallel convolutional blocks; to promote spatio-spectral similarity within and across the different layers of the HSI data. Fourthly, by adopting Structural Similarity (SSIM) as the content loss, the issue of loss in resolution encountered during adversarial training is mitigated. Finally, the incorporation of 3D depth-wise separable convolution and batch re-normalization (BRN) solves the major issue of computational burden encountered while processing HSI data. Extensive experimental evaluation on synthetically corrupted data and real HSI data (obtained from real hyperspectral sensors) under various degradation conditions suggests that the aforementioned denoising approach could be used in real time.
Antiproliferative and Proapoptotic Effects of Phenanthrene Derivatives Isolated from Bletilla striata on A549 Lung Cancer Cells

Lung cancer continues to be the world’s leading cause of cancer death and the treatment of non-small cell lung cancer (NSCLC) has attracted much attention. The tubers of Bletilla striata are regarded as “an excellent medicine for lung diseases” and as the first choice to treat several lung diseases. In this study, seventeen phenanthrene derivatives, including two new compounds (1 and 2), were isolated from the tubers of B. striata. Most compounds showed cytotoxicity against A549 cells. An EdU proliferation assay, a cell cycle assay, a wound healing assay, a transwell migration assay, a flow cytometry assay, and a western blot assay were performed to further investigate the effect of compound 1 on A549 cells. The results showed that compound 1 inhibited cell proliferation and migration and promoted cell apoptosis in A549 cells. The mechanisms might correlate with the regulation of the Akt, MEK/ERK, and Bcl-2/Bax signaling pathways. These results suggested that the phenanthrenes of B. striata might be important and effective substances in the treatment of NSCLC.
Subacute Oral Toxicity Evaluation of Expanded-Polystyrene-Fed Tenebrio molitor Larvae (Yellow Mealworm) Powder in Sprague-Dawley Rats

Abstract Tenebrio molitor larvae, as known as edible insects, has advantages of being rich in protein, and has been recognized as a suitable alternate protein source for broiler and pig feed. Moreover, given their ability to biodegrade polystyrene, a major pollutant, Tenebrio molitor larvae has been proposed as an innovative solution to environmental problems. In the present study, we investigated the toxicity of Tenebrio molitor larvae powder (TMlp) ingested with expanded-polystyrene (W/ eps) through in vitro and in vivo experiments. The objective of this study was to determine whether TMlp W/ eps can be applied as livestock alternative protein source. For in vitro experiments, cytotoxicity test was performed to investigate the effects of TMlp-extract on the viability of estrogen-dependent MCF-7 cells. The possibility of estrogen response was investigated in two groups: Expanded-polystyrene-fed (W/ eps) TMlp group and without expanded-polystyrene-fed (W/o eps) TMlp group. For in vivo experiments, The male Sprague-Dawley rats were divided based on the dosage of TMlp administered and oral administration was performed to every day for 5 weeks. A toxicological assessments were performed, which included clinical signs, food consumption, body and organ weights, hematology, serum chemistry, and hematoxylin and eosin staining of liver and kidney. There were no specific adverse effect of TMlp W/ eps-related findings under the experimental conditions of this study, but further studies on both sexes and animal species differences should be investigated. In conclusion, TMlp W/ eps was considered non-toxic and observed to be applicable as an alternative protein source for livestock feed.