Miracle-dz/UniFAR-FTUs huggingface dataset:
Analysis of problem solving ability in social arithmetics

Problem solving is additional curriculum important because in the learning and completion process, students use to the knowledge and skills they have mastered to solve problems in life every day. Therefore, purpose this research how the problem solving ability of students and the factors influence for the problem solving skills. The renovation used in this research is the mix quantitative and qualitative and type of research is descriptive. Sample research involved 28 students, grade VII students of state junior high school 7 Banda Aceh, data collected through tests and interviews. The data obtained were analyzed using Chi Square test. The results of this study indicate that grade VII students of state junior high school 7 Banda Aceh have good problem solving skills. When looking at the steps model problem solving Polya, the student not competent devising a plan the problem solving and checking again. Factors that influence student problem solving skills include cognitive factors (reading ability, analysis and logic, as well as skills appropriately), experience factors (background integration and familiarity with non-routine issues and strategy problem solving) and affective factors (interest in the given problem, motivation and perseverance).
Big Data Management Canvas: A Reference Model for Value Creation from Data

Many big data projects are technology-driven and thus, expensive and inefficient. It is often unclear how to exploit existing data resources and map data, systems and analytics results to actual use cases. Existing big data reference models are mostly either technological or business-oriented in nature, but do not consequently align both aspects. To address this issue, a reference model for big data management is proposed that operationalizes value creation from big data by linking business targets with technical implementation. The purpose of this model is to provide a goal- and value-oriented framework to effectively map and plan purposeful big data systems aligned with a clear value proposition. Based on an epistemic model that conceptualizes big data management as a cognitive system, the solution space of data value creation is divided into five layers: preparation, analysis, interaction, effectuation, and intelligence. To operationalize the model, each of these layers is subdivided into corresponding business and IT aspects to create a link from use cases to technological implementation. The resulting reference model, the big data management canvas, can be applied to classify and extend existing big data applications and to derive and plan new big data solutions, visions, and strategies for future projects. To validate the model in the context of existing information systems, the paper describes three cases of big data management in existing companies.
Deep Reinforcement Learning for Target Searching in Cognitive Electronic Warfare

The recent appreciation of deep reinforcement learning (DRL) arises from its successes in many domains, but the applications of DRL in practical engineering are still unsatisfactory, including optimizing control strategies in cognitive electronic warfare (CEW). CEW is a massive and challenging project, and due to the sensitivity of the data sources, there are few open studies that have investigated CEW. Moreover, the spatial sparsity, continuous action, and partially observable environment that exist in CEW have greatly limited the abilities of DRL algorithms, which strongly depend on state-value and action-value functions. In this paper, we use Python to build a 3-D space game named Explorer to simulate various CEW environments in which the electronic attacker is an unmanned combat air vehicle (UCAV) and the defender is an observation station, both of which are equipped with radar as the observation sensor. In our game, the UCAV needs to accomplish the task of detecting the target as early as possible to perform follow-up tracking and guidance tasks. To allow an ”infant” UCAV to understand what ”target searching” is, we train the UCAV’s maneuvering strategies by means of a well-designed reward shaping, a simplified constant accelerated motion control, and a deep deterministic policy gradient (DDPG) algorithm based on a generative model and variational Bayesian estimation. The experimental results show that when the operating cycle is $0.2~s$ , the search success rate of the trained UCAV in 10 000 episodes is improved by 33.36% compared with the benchmark, and the target destruction rate is similarly improved by 57.84%.
Vision-Based Approaches for Automatic Food Recognition and Dietary Assessment: A Survey

Consuming the proper amount and right type of food have been the concern of many dieticians and healthcare conventions. In addition to physical activity and exercises, maintaining a healthy diet is necessary to avoid obesity and other health-related issues, such as diabetes, stroke, and many cardiovascular diseases. Recent advancements in machine learning applications and technologies have made it possible to develop automatic or semi-automatic dietary assessment solutions, which is a more convenient approach to monitor daily food intake and control eating habits. These solutions aim to address the issues found in the traditional dietary monitoring systems that suffer from imprecision, underreporting, time consumption, and low adherence. In this paper, the recent vision-based approaches and techniques have been widely explored to outline the current approaches and methodologies used for automatic dietary assessment, their performances, feasibility, and unaddressed challenges and issues.
Enhanced Modular Multilevel Converter With Reduced Number of Cells and Harmonic Content

This paper presents an alternative implementation of a modular multilevel converter (MMC) that generates a large number of voltage levels per phase with high-resolution voltage steps from a reduced number of cells per arm. The presented MMC employs a half-bridge (HB) chain link of medium-voltage cells and a full-bridge (FB) chain link of low-voltage cells in each of its arms. The total blocking voltage of the FB chain link is equivalent to half that of the medium-voltage HB cell. The use of HB and FB cells with two distinct rated voltages in each arm permits full exploitation of the FB cells to generate high-resolution multilevel voltage waveforms with fine stepped transitions between major voltage steps of the medium-voltage HB cells. In this manner, errors in the synthesis of the common-mode voltages of the three phase legs due to switching of the cell capacitors in and out the power path are reduced. The nested multilevel operation of the proposed MMC results in a number of voltage levels which is related to the product, rather than the sum, of the numbers of HB and FB cells. Detailed comparisons with existing MMC implementations show that the proposed MMC implementation offers the best design tradeoffs (superior ac and dc waveforms with reduced control and power circuit complexity). The validity of the proposed MMC implementation is confirmed using simulations and experiments.
Real-time PCG Anomaly Detection by Adaptive 1D Convolutional Neural Networks

The heart sound signals (Phonocardiogram - PCG) enable the earliest monitoring to detect a potential cardiovascular pathology and have recently become a crucial tool as a diagnostic test in outpatient monitoring to assess heart hemodynamic status. The need for an automated and accurate anomaly detection method for PCG has thus become imminent. To determine the state-of-the-art PCG classification algorithm, 48 international teams competed in the PhysioNet (CinC) Challenge at 2016 over the largest benchmark dataset with 3126 records with the classification outputs, normal (N), abnormal (A) and unsure - too noisy (U). In this study, our aim is to push this frontier further; however, we focus deliberately on the anomaly detection problem while assuming a reasonably high Signal-to-Noise Ratio (SNR) on the records. By using 1D Convolutional Neural Networks trained with a novel data purification approach, we aim to achieve the highest detection performance and a real-time processing ability with significantly lower delay and computational complexity. The experimental results over the high-quality subset of the same benchmark dataset shows that the proposed approach achieves both objectives. Furthermore, our findings reveal the fact that further improvements indeed require a personalized (patient-specific) approach to avoid major drawbacks of a global PCG classification approach.
Urban form, building characteristics, and residential electricity consumption: A case study in Tainan City

Which urban form factor most affects household electricity consumption? This study investigated the relationships between urban density, community layout, and land use factors and household electricity consumption simultaneously, along with building characteristics and demographic indicators. The study site involved 231 communities located in the former provincial area of Tainan City, Taiwan. Due to the area’s subtropical climate, air conditioning accounts for approximately 40% of the total yearly household electricity consumption. Of the urban form factors examined, greater population density was most strongly associated with lower household electricity consumption, followed respectively by greater urban canyon narrowness, or higher height to width ratios, and greater percentages of vacant space and building land use. Notably, both urban canyons and building land use percentages were associated with decreased consumption only after increasing past threshold levels, specifically a 1.5 height to width ratio and 40.7%, respectively. In addition, building characteristics, namely smaller household living areas and greater building age, were most strongly connected with lower household electricity consumption. In contrast, larger household living areas were linked with decreased household electricity consumption/floor area, revealing the importance of lower energy intensities of sizable scales. Of the demographic indicators studied, higher percentages of older adults were associated with lower household electricity consumption. Concerning urban form, the findings suggest that to reduce residential energy usage in a subtropical climate, buildings should be clustered to maximize the inter-building shadows resulting from narrower urban canyons, while simultaneously increasing non-built land use percentages in the adjacent areas.
Dual-mode distributed Model Predictive Control of a quadruple-tank system

In this paper, a dual-mode distributed Model Predictive Control (MPC) approach is proposed in order to reduce the on-line computational complexity of the distributed optimal control of nonlinear interconnected systems. It consists in using a nonlinear distributed MPC approach when the state variables of the overall system are far from the origin and applying a linear distributed MPC method in a neighborhood of the origin. The nonlinear distributed approach is based on first-principles (nonlinear) models of the interconnected systems dynamics. It includes a sequential linearization of these models and finding distributedly a suboptimal solution of the resulting quadratic programming problem. In order to apply the linear distributed MPC method, it is necessary first to obtain a linearized model of the overall nonlinear system in a neighborhood of the origin. The benefit of the suggested dual-mode distributed MPC approach is the reduced complexity of the on-line computations in comparison to the entirely nonlinear approach when the current overall system state is in a neighborhood of the origin. The proposed method is illustrated with simulations on the model of a quadruple-tank system.
Towards Simulated Morality Systems: Role-Playing Games as Artificial Societies

: Computer role-playing games (RPGs) often include a simulated morality system as a core design element. Games’ morality systems can include both god’s eye view aspects, in which certain actions are inherently judged by the simulated world to be good or evil, as well as social simulations, in which non-player characters (NPCs) react to judgments of the player’s and each others’ activities. Games with a larger amount of social simulation have clear afﬁnities to multi-agent systems (MAS) research on artiﬁcial societies. They differ in a number of key respects, however, due to a mixture of pragmatic game-design considerations and their typically strong embeddedness in narrative arcs, resulting in many important aspects of moral systems being represented using explicitly scripted scenarios rather than through agent-based simulations. In this position paper, we argue that these similarities and differences make RPGs a promising challenge domain for MAS research, highlighting features such as moral dilemmas situated in more organic settings than seen in game-theoretic models of social dilemmas, and heterogeneous representations of morality that use both moral calculus systems and social simulation. We illustrate some possible approaches using a case study of the morality systems in the game The Elder Scrolls IV: Oblivion .
Near-field acoustic assessment of open rotor aero-engines

The focus of this research, part sponsored by Rolls-Royce plc. , is to both model and gain an understanding of the acoustics in the near-field of the open rotor aero-engine concept. The eventual aim of such work is to develop accurate and fast prediction models which can be used to optimise engine design and operation, so that the noise produced by the open rotor can be minimised to sit well under regulation noise levels. This work will also be used to help understand the results of important experimental testing of the open rotor. Finally, such work may be useful for understanding what cabin noise levels may be, which is a strong factor in passenger comfort and is therefore another consideration for the client. Outlined in this thesis are a number of tools and methods aimed at helping to predict the open rotor’s acoustic near-field in a fast and reliable fashion. As a primer, analytical models for the tonal noise of an open rotor in the free-field are first derived. The models are based on frequency domain methods, such as those proposed by Hanson in the 1980s. All models in this thesis may be used during engine development to make acoustic predictions at various operating conditions and gain insight into how certain rotor geometric features and operating conditions affect the generated noise level and propagation. Methods for projecting noise levels from the near-field to the far-field are also presented, which is useful when validating noise measurements from experiments. The effect of scattering from the open rotor centrebody and the effect of reverberation when contained in a closed test-section wind tunnel are also modelled. Centrebody scattering and tunnel reverberation have been shown from data analysis to have an impact on open rotor acoustic measurements. The analysis presented in this thesis has led to some novel mitigation techniques and suggestions for improved open rotor experiment design. Some limited comparisons to scaled open rotor experiments are provided, in order to validate the models and highlight further issues that would occur during real engine operation. The business case for and the environmental benefits of the open rotor are clear. Efficient operation is a key driver in the development of aircraft propulsion technologies, since it can significantly drive down operational costs and the open rotor aero-engine promises a step change improvement in fuel efficiency. Another important objective during engine development is mitigating the generation of unwanted noise, in order to meet aircraft noise regulations
A transfer learning approach for sentiment classification.

The idea of developing machine learning systems or Artificial Intelligence agents that would learn from different tasks and be able to accumulate that knowledge with time so that it functions successfully on a new task that it has not seen before is an idea and a research area that is still being explored. In this work, we will lay out an algorithm that allows a machine learning system or an AI agent to learn from k different domains then uses some or no data from the new task for the system to perform strongly on that new task. In order to test our algorithm, we chose an AI task that falls under the Natural Language Processing domain and that is sentiment analysis. The idea was to combine sentiment classifiers trained on different source domains to test them on a new domain. The algorithm was tested on two benchmark datasets. The results recorded were compared against the results reported on these two datasets in 2017 and 2018. In order to combine these classifiers’ predictions, we had to assign these classifiers weights. The algorithm made use of the similarity between domains when inferring the weights for the classifiers trained on the source domains by measuring the similarity between these source domains and the domain of the new task concluding, that domain similarity could be used in computing weights for classifiers trained on previous tasks/domains.
Predicting Response Uncertainty in Online Surveys: A Proof of Concept

Online questionnaire-based research is growing at a fast pace. Mouse-tracking methods provide a potentially important data source for this research by enabling the capture of respondents’ online behaviour while answering questionnaire items. This behaviour can give insight into respondents’ perceptual, cognitive and affective processes. The present work focused on the potential use of mouse movements to indicate uncertainty when answering questionnaire items and used machine learning methods as a basis to model these. N=79 participants completed an online questionnaire while mouse data was tracked. Mouse movement features were extracted and selected for model training and testing. Using logistic regression and k-fold cross-validation, the model achieved an estimated performance accuracy of 89%. The findings show that uncertainty is indicated by an increase in the number of horizontal direction inversions and the distance covered by the mouse and by longer interaction times with and a higher number of revisits to questionnaire items that evoked uncertainty. Future work should validate these methods further.
Detection and Certification of Faint Streaks in Astronomical Images

Fast-moving celestial objects, like near-Earth objects (NEOs), orbiting space debris, or meteors, appear as streaks superimposed over the star background in images taken by an optical telescope at long exposures. As the apparent magnitude of the object increases (the object becomes fainter), its detection becomes progressively harder. We discuss a statistical procedure that makes a binary decision on the presence/absence of a streak in the image which is called streak certification. The certification is based purely on a single input image and a public star catalog, using a minimalistic statistical model. Certification accuracy greater than 90% for streaks of arbitrary orientation, longer than 500 pixels, and the signal-to-background log-ratio is better than −10dB is achieved on the same dataset as in an earlier similar method, whose performance is thus exceeded, especially for close-to-horizontal streaks. We also show that the certification decision indicates detection failure well.
What’s in the Box? Assessing the potential usability of four decades of thesis and dissertation supplementary files

Objectives : The objective of this study is to evaluate the quality and usability of supplementary data files deposited, between 1971 and 2015, to our university institutional repository. Understanding the extent to which content historically deposited in digital repositories is usable by today’s researchers can help inform digital preservation and documentation practices for researchers today. Methods : I identified all graduate-level theses and dissertations (GTDs) in the institutional repository with multiple files as a first pass at identifying documents that included supplementary data files. These GTDs were then individually examined, removing supplementary files that were artifacts of either the upload or digitization process. The remaining “true” supplementary files were then individually opened and evaluated following elements of the DATA rubric of Van Tuyl and Whitmire (2016). Results : Supplementary files were discovered in the repository dating back to 1971 in 116 GTD submissions totaling more than 25,000 files. Most GTD submissions included fewer than 30 files, though some submissions included thousands of individual data files. The most common file types submitted included imagery, tabular data, and databases, with a very large number of unknown file types. Overall, levels of documentation were poor while actionability of datasets was generally middling. Conclusions : The results presented in this study suggest that legacy data submitted to our institutional repository with GTDs is generally in poor shape with respect to Transparency and somewhat less so for Actionability. It is clear from this study and others that researchers have a long road ahead when it comes to sharing data in a way that makes it potentially useable by other researchers.
Using Recurrent Neural Networks for Action and Intention Recognition of Car Drivers

Traffic situations leading up to accidents have been shown to be greatly affected by human errors. To reduce these errors, warning systems such as Driver Alert Control, Collision Warning and Lane Departure Warning have been introduced. However, there is still room for improvement, both regarding the timing of when a warning should be given as well as the time needed to detect a hazardous situation in advance. Two factors that affect when a warning should be given are the environment and the actions of the driver. This study proposes an artificial neural network-based approach consisting of a convolutional neural network and a recurrent neural network with long short-term memory to detect and predict different actions of a driver inside a vehicle. The network achieved an accuracy of 84% while predicting the actions of the driver in the next frame, and an accuracy of 58% 20 frames ahead with a sampling rate of approximately 30 frames per second.
A Review on Software Architectures for Heterogeneous Platforms

The increasing demands for computing performance have been a reality regardless of the requirements for smaller and more energy efficient devices. Throughout the years, the strategy adopted by industry was to increase the robustness of a single processor by increasing its clock frequency and mounting more transistors so more calculations could be executed. However, it is known that the physical limits of such processors are being reached, and one way to fulfill such increasing computing demands has been to adopt a strategy based on heterogeneous computing, i.e., using a heterogeneous platform containing more than one type of processor. This way, different types of tasks can be executed by processors that are specialized in them. Heterogeneous computing, however, poses a number of challenges to software engineering, especially in the architecture and deployment phases. In this paper, we conduct an empirical study that aims at discovering the state-of-the-art in software architecture for heterogeneous computing, with focus on deployment. We conduct a systematic mapping study that retrieved 28 studies, which were critically assessed to obtain an overview of the research field. We identified gaps and trends that can be used by both researchers and practitioners as guides to further investigate the topic.