Miracle-dz/UniFAR-FTUs huggingface dataset:
PENGEMBANGAN EDUCATION GAME (EDUGAME) BERBASIS ANDROID PADA MATA PELAJARAN BAHASA INGGRIS UNTUK PESERTA DIDIK SEKOLAH DASAR

ABSTRAK Mata pelajaran Bahasa Inggris dengan tema “My Body” adalah mata pelajaran yang mengajarkan peserta didik tentang anggota badan dalam Bahasa Inggris, baik cara penulisan maupun cara membacanya. Permasalahan muncul karena belajar menggunakan buku dirasa kurang maksimal karena buku hanya menyajikan tulisan dan gambar anggota badan tanpa menampilkan suara cara membacanya dalam Bahasa Inggris. Education game akan menyelesaikan permasalahan ini dengan menampilkan permainan serta visualisasi animasi bagian-bagian anggota badan diserta audio cara membacanya dalam Bahasa Inggris. Dengan adanya education game ini maka peserta didik akan lebih mudah dan cepat memahami materi pelajaran, selain itu proses pembelajaran akan lebih menghibur dan menyenangkan. Kata Kunci: education, game, Bahasa Inggris
A Data-Driven Fault Detection Approach for Dynamic Processes with Sinusoidal Disturbance

This paper presents the latest study on the data-driven process monitoring system design for the dynamic processes with sinusoidal disturbance. In the previous study, it is understood that the row space of the deterministic disturbance is essential to the subspace method aided data-driven design. Based on the previous study, this paper first determines the row space of sinusoidal disturbance. By projecting the process data into the determined subspaces, the fault detection systems can be designed based on the identified kernel subspace of the system. The performance and effectiveness of the proposed scheme are verified and demonstrated through the numerical study on randomly generated systems.
Time Scale Framework for Hybrid Systems

Many engineering systems require both continuous and discrete valued signals, as well as continuous and discrete time scales in their descriptions and are therefore known as hybrid systems. Recently, the concept of time-scale calculus was introduced as a unification of the theory of difference and differential equations, making it an ideal tool to analyze hybrid systems. The time-scale calculus can handle solutions of hybrid systems with infinitely many jumps. However, to date time-scale calculus restricts time to a non-empty closed subset of the real line which is not general enough to fully capture the dynamics of hybrid systems. A generalization is proposed to extend time-scale calculus such that the time can be defined on more suitable subsets of the real line. Stability definitions of hybrid systems using this framework are also introduced together with a Lyapunov characterization of global uniform stability. A few examples demonstrate the usefulness of this new framework.
Influence of Size and Shape on the Performance of VCMA-Based MTJs

In this paper, we investigate the relationship between size, shape, and the performance of the voltage-controlled magnetic anisotropy (VCMA)-based magnetic tunnel junctions (MTJs) suitable for gigabit scale MRAMs with 10 years of retention time. A Fokker–Planck simulation framework is developed to model the magnetization dynamics in the presence of thermal noise. Here, we numerically show that the optimization of the MTJ geometry can significantly improve the performance of the VCMA-based MTJs. Using an elliptical MTJ with aspect ratio (AR) of 3 reduces the required supply voltage and energy consumption by 80.7% and 92%, respectively, compared to a circular MTJ when the minimum feature size is 50 nm. The design requirements on the VCMA coefficient, <inline-formula> <tex-math notation="LaTeX">${\xi }$ </tex-math></inline-formula>, are also reduced by 67%. However, the influence of AR is observed to diminish with reduction in the minimum feature size of the MTJ. For instance, when the minimum feature size is 20 nm, the required supply voltage and energy consumption reduce only by 60.29% and 65.30%, respectively. We, then, perform a comprehensive scaling analysis on the VCMA-based MTJs by varying size, geometry, and <inline-formula> <tex-math notation="LaTeX">${\xi }$ </tex-math></inline-formula> of the MTJs. The predicted scaling trends are then compared with those of the STT MTJs.
An Adaptive Pre-clustering Support Vector Machine for Binary Imbalanced Classification

Imbalance classification is a common but critical problem in machine learning and artificial intelligence. Derived from structural risk minimization, a Support Vector Machine (SVM) enjoys great reputation in classification. However, the original SVM is not suitable for the imbalance classification and the existing modifications of SVM for this kind of problems fail to take the distribution of datasets into full consideration, thereby leading to some remarkable loss in their classification performance. Recently, an Adaptive Clustering by Fast Search and Find of Density Peaks (ADPclust) is proposed and performs well in finding cluster centroids in a sample space automatically and more reliably by using adaptive density peak detection and silhouette theory. Motivated by this, this work proposes an adaptive pre-clustering SVM (AP-SVM) such that the information of the original dataset distribution is well utilized to yield balanced sub-datasets for accurate and efficient classification. Specifically, AP-SVM clusters the majority into several groups given a dataset and then applies undersampling on every cluster to re-balance the dataset to be used in the SVM classification step. After experiments on 10 binary public datasets and evaluation using Area Under Curve (AUC), F-Measure, G-Mean, we well show the superiority of the proposed method over SVM, Synthetic Minority Over-sampling Technique algorithm (SMOTE), Undersampling-SVM (U-SVM), K-Means, Fuzzy C Means and EasyEnsemble.
Online Optimization in Dynamic Environments: A Regret Analysis for Sparse Problems

Time-varying systems are a challenge in many scientific and engineering areas. Usually, estimation of time-varying parameters or signals must be performed online, which calls for the development of responsive online algorithms. In this paper, we consider this problem in the context of the sparse optimization; specifically, we consider the Elastic-net model. Following the rationale in [1], we propose a novel online algorithm and we theoretically prove that it is successful in terms of dynamic regret. We then show an application to recursive identification of time-varying autoregressive models, in the case when the number of parameters to be estimated is unknown. Numerical results show the practical efficiency of the proposed method.
Artificial Swarm Intelligence employed to Amplify Diagnostic Accuracy in Radiology

Swarm Intelligence (SI) is a biological phenomenon in which groups of organisms amplify their combined brainpower by forming real-time systems. It has been studied for decades in fish schools, bird flocks, and bee swarms. Recent advances in networking and AI technologies have enabled distributed human groups to form closed-loop systems modeled after natural swarms. The process is referred to as Artificial Swarm Intelligence (ASI) and has been shown to significantly amplify group performance. The present research applies ASI technology to the field of medicine, exploring if small groups of networked radiologists can improve their diagnostic accuracy when reviewing chest X-rays for the presence of pneumonia. Performance data was collected for individual radiologists generating diagnoses alone, as well as for small groups of radiologists working together to generate diagnoses as a real-time ASI system. Diagnoses were also collected from a state-of-the-art deep learning system (CheXNet) developed at Stanford University. Results showed that small groups of networked radiologists, when working as a real-time ASI system, were significantly more accurate than the individual radiologists on their own, reducing diagnostic errors by 33%. Results also showed that small groups of networked radiologists, when working as an ASI system, were significantly more accurate (22%) than a state-of-the-art deep learning system (CheXNet).
A Feature Selection Method for Vision-Based Blood Pressure Measurement

In this paper we investigate the latest vision-based method for systolic blood pressure (SBP) and diastolic blood pressure (DBP) measurement. However, constantly blood pressure supervision needs sufficient medical equipment and may require the potential patients to tie a cuff, which is extremely inconvenient for them. What's more, continuously blood pressure measuring requires the patients to stay in the hospital and professional personnel to stand by. From the research before, we have learned that photoplethysmography (PPG) can be used to measure the blood pressure, which is known as cuffless blood pressure measurement. However, for the neonate and patients with empyrosis, photoplethysmography measuring device is still less practical and restricted in use due to the necessary contact for it to measure the systolic and diastolic blood pressure. Certain level of discomfort is still unavoidable with the use of PPG. We thus focus on remote PPG (rPPG); with green red difference (GRD) and Euler video magnification (EVM) and finite impulse response (FIR) bandpass filters, we are able to recover PPG signals from remote photoplethysmography. We propose a feature extraction measuring methods which yields a root mean square error for SBP as 11.22 mmHg and 7.83 mmHg for pulse pressure (PP) combined with the ANN model. For comparison, we've also used K nearest neighbor (KNN) and deep belief network-deep neural network (DBN-DNN).
Tunable Floating-Point for Artificial Neural Networks

Approximate computing has emerged as a promising approach to energy-efficient design of digital systems in many domains such as digital signal processing, robotics, and machine learning. Numerous studies report that employing different data formats in Deep Neural Networks (DNNs), the dominant Machine Learning approach, could allow substantial improvements in power efficiency considering an acceptable quality for results. In this work, the application of Tunable Floating-Point (TFP) precision to DNN is presented. In TFP different precisions for different operations can be set by selecting a specific number of bits for significand and exponent in the floating-point representation. Flexibility in tuning the precision of given layers of the neural network may result in a more power efficient computation.
Factors Affecting Knowledge Management System Implementation in Development Planning Agency of Southern Sumatera Province

Indonesian government institutions have become more aware of the importance of knowledge management for improving organizational performance. Development Planning Agency of Southern Sumatera Province (Bappeda Sumsel) is a government institution who implemented a Knowledge Management System called Online Disposition Website. Unfortunately, the system has not been used optimally as the rate of disposition letters produced by the system is less than one percent throughout the year 2016. In this paper, we focus on finding the affecting factors of KMS implementation in Bappeda Sumsel. A survey was spread to 123 employees in Bappeda Sumsel. The research model was adapted from six latent variables of information system success model by DeLone & McLean. We empyol structural equation model to analyze survey result. We found that there is positive influence from system quality and service quality to kms use. Also, we predicate that there is positive influence from knowledge quality, service and KMS use to user satisfaction. Additionally, we found that there is positive influence from kms use dan user satisfaction to net benefit. Success model as a critical part for knowledge management system is analyzed to make sure knowledge management system is well implemented and used by the users.
Steering Behavior with Different Levels of Automation Interventions for Avoiding Collisions During Lane Change

This paper investigated how different types of automation interventions affect tactical driving behavior during lane changes. Previous research indicates that drivers trust and accept the automation as long as they have the final authority over the system. However, the need for automation to be allowed to act autonomously in some time-critical situations has also been observed. This emerged the need for a system that is able to provide more than one type of assistance function depending on the situation. How drivers trust, accept, and interact with such multiple assistance functions is crucial for system effectiveness in terms of safety. A total of 40 participants were assigned to drive in an advanced driving simulator and encounter several critical lane change scenarios under one of four driving modes: unsupported driving; haptic steering control system; automatic steering control system; and integrated steering control system that comprises both haptic and automatic steering control assistance. Measures of trust in and acceptance of the systems were recorded with questionnaires. The results showed that the integrated steering control mode was efficient in terms of safety and able to attenuate the negative effects of both haptic and automatic systems.
Object Classification using Deep Learning on Extremely Low-Resolution Time-of-Flight Data

This paper proposes two novel deep learning models for 2D and 3D classification of objects in extremely low-resolution time-of-flight imagery. The models have been developed to suit contemporary range imaging hardware based on a recently fabricated Single Photon Avalanche Diode (SPAD) camera with 64 χ 64 pixel resolution. Being the first prototype of its kind, only a small data set has been collected so far which makes it challenging for training models. To bypass this hurdle, transfer learning is applied to the widely used VGG-16 convolutional neural network (CNN), with supplementary layers added specifically to handle SPAD data. This classifier and the renowned Faster-RCNN detector offer benchmark models for comparison to a newly created 3D CNN operating on time-of-flight data acquired by the SPAD sensor. Another contribution of this work is the proposed shot noise removal algorithm which is particularly useful to mitigate the camera sensitivity in situations of excessive lighting. Models have been tested in both low-light indoor settings and outdoor daytime conditions, on eight objects exhibiting small physical dimensions, low reflectivity, featureless structures and located at ranges from 25m to 700m. Despite antagonist factors, the proposed 2D model has achieved 95% average precision and recall, with higher accuracy for the 3D model.
A Substrate Integrated Waveguide Based Antenna-Triplexer

In this article, a design of planar cavity-backed antenna-triplexer is proposed for multi-band communication systems. Substrate integrated waveguide (SIW) technology is used to realize a planar cavity structure. The proposed antenna operates at triple frequency bands (around 5,6.4 and 7.5 GHz) simultaneously utilizing a rectangular patch and two half-mode-like-cavity resonators on the top surface of the cavity. The cavity resonators and patch are excited by microstrip feedlines and coaxial feed, respectively. An isolation of better than 20 dB is achieved between any two input-ports which confirm the triplexing phenomenon. Finally, the proposed antenna is prototyped and experimentally verified which are agreed well with simulated ones. Additionally, the antenna has the advantages of compacted-size, low-profile with planar integrability.
Bayesian Nonparametrics and Feedback-Linearisation of Discretised Control-Affine Systems

We propose random field system identification and inversion control (RF-SIIC) as a method for simultaneous probabilistic identification and control of time-discretised control-affine systems. Identification is achieved by conditioning random field priors on observations of configurations and noisy estimates of configuration derivatives. In contrast to previous work that has utilised random fields for identification, we leverage the structural knowledge afforded by Lagrangian mechanics and learn both the drift and control input matrix functions of a control-affine system. We employ feedback-linearisation to reduce, in expectation, the uncertain nonlinear control problem to one that is easy to regulate. Our method combines the flexibility of nonparametric Bayesian learning with epistemological guarantees on the expected closed-loop trajectory. We illustrate the viability of our approach in the context of a discretised, fully-actuated mechanical system. Our simulations suggest that our approach can adapt rapidly to a priori uncertain dynamics sufficiently well to succeed in feedback-linearising and controlling the plant as desired.
Peer Assisted Study Sessions (PASS) Online: Investigating the impact of an online format across different first year university subjects

As the demand for flexible learning increases, it is important to explore and expand online learning opportunities, especially in student supported learning. Peer Assisted Study Sessions (PASS) is a student led academic support program designed to help students transition into university and increase student retention. PASS is offered in traditionally challenging first year core subj ects. Due to increased popularity of PASS, along with limited space and time availability, a synchronous online format (Blackboard Collaborate) was piloted in three first year University of Wollongong (UOW) subjects in the faculties of Business, Nursing, and Psychology. The aim was to test the effectiveness of the online delivery of PASS by comparing student final grade outcomes from online cohorts with face-to-face (F2F) modes, and those students who had not attended PASS. Results demonstrated that students who attended PASS obtained significantly higher marks compared to students that did not attend PASS. Final grade outcomes for F2F versus online also varied between subjects. The different result profiles for the three subjects suggests there may be different drivers for student success in the online space. This paper presents these findings providing consideration of different factors that may influence student success, with directions for future research.
Convolutional Neural Network Based SMS Spam Detection

SMS spam refers to undesired text message. Machine Learning methods for anti-spam filters have been noticeably effective in categorizing spam messages. Dataset used in this research is known as Tiago's dataset. Crucial step in the experiment was data preprocessing, which involved reducing text to lower case, tokenization, removing stopwords. Convolutional Neural Network was the proposed method for classification. Overall model's accuracy was 98.4%. Obtained model can be used as a tool in many applications.
A Localization Database Establishment Method Based on Crowdsourcing Inertial Sensor Data and Quality Assessment Criteria

Aimed at the challenge of generating indoor localization databases with daily life crowdsourcing-based inertial sensor data, this paper proposes an anchor point-based forward–backward smoothing method to obtain reliable localization solutions. More importantly, a quantitative framework is proposed to evaluate the quality of smartphone-based inertial sensor data automatically without user intervention. Through this framework, the reliability of each inertial sensor data can be evaluated and sorted. Tests with multiple people and multiple smartphones in a public office building and a shopping mall illustrate that the proposed method can provide a WiFi fingerprinting database that has similar accuracy to that generated by a supervised map-aided database-generation method. Therefore, the proposed method and framework can guide the promotion of crowdsourcing-based Internet of Things applications in the context of big data.
Elastic and Magnetoelastic Properties of Fe-Rich Tetragonal Y–Fe Compounds: Phenomenology and Electron Theory

To study the strain effects on the lattice stability and magnetic anisotropy (MA) of Fe-rich tetragonal Y–Fe compounds, elastic constants of Y<sub>2</sub>Fe<sub>14</sub>B-and YFe<sub>12</sub>-type compounds were calculated in the first principles, and MA of impurity-doped YFe<sub>12</sub> with tetragonal lattice strain was calculated in a real-space tight-binding method. It has been found that the YFe<sub>12</sub>-type compounds are elastically softer than Y<sub>2</sub>Fe<sub>14</sub>B, and that the uniaxial MA of Co-doped YFe<sub>12</sub> may be increased by decreasing <inline-formula> <tex-math notation="LaTeX">$c/a$ </tex-math></inline-formula>, where <inline-formula> <tex-math notation="LaTeX">$c$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$a$ </tex-math></inline-formula> are the lattice constants along c-axis and within <inline-formula> <tex-math notation="LaTeX">$ab$ </tex-math></inline-formula>-plane, respectively.
On Efficient Computation of Texture Descriptors from Sum and Difference Histograms Considering the Scales of Patterns

Several computational activities, such as image segmentation and classification, use texture information. This information, also called descriptors, is typically calculated through the Gray Level Occurrence Matrix (GLCM), which has a quadratic cost. In this work, we discuss an alternative to this method using Sum and Difference Histograms (SDH), which has linear cost. A set of nine equations that already exist in the literature, but presenting high values of difference with GLCM, were investigated and adapted in this study. The differences (which in some cases were very high) were eliminated in most of the descriptors. The few remaining differences present values very close to zero, with a ratio of 10−1. In addition, six other equations are proposed and discussed in this study. To validate these equations, a series of experiments are performed, using real and synthetic textures. Each texture contains different combinations that represent the same motif with different perspectives and scales. These served to validate the equations proposed in this study. Moreover, in applications always present the same pattern of behavior for the equivalent equations of the original ones described by using GLCM. All the results were favorable to the use of SDH that presented a reduction of the complexity wich speeds up to 98.6% the computational time.
iCASSTLE : Imbalanced Classification Algorithm for Semi Supervised Text Learning

Information in the form of text can be found in abundance in the web today, which can be mined to solve multifarious problems. Customer reviews, for instance, flow in across multiple sources in thousands per day which can be leveraged to obtain several insights. Our goal is to extract cases of a rare event e.g., recall of products, allegations of ethics or, legal concerns or, threats to product-safety, etc. from this enormous amount of data. Manual identification of such cases to be reported is extremely labour-intensive as well as time-sensitive, but failure to do so can have fatal impact on the industry's overall health and dependability; missing out on even a single case may lead to huge penalties in terms of customer experience, product liability and industry reputation. In this paper, we will discuss classification through Positive and Unlabeled data, PU classification, where the only class, for which instances are available, is a rare event. In iCASSTLE, we propose a two-staged approach where Stage I leverages three unique components of text mining to procure representative training data containing instances of both classes in the right proportion, and Stage II uses results from Stage I to run a semi-supervised classification. We applied this to multiple datasets differing in nature of Product Safety as well as nature of imbalance and iCASSTLE is proven to perform better than the state-of-the-art methods for the relevant use-cases.