Miracle-dz/UniFAR-FTUs huggingface dataset:
An Empirical Study on Customer Acceptance of FinTech 3.0 in Private Banking

The recent developments of information technology have led to more automation process that enforce a fundamental reorganization of the business models of financial services value chain and highly recognized with the term ‘Financial Technology" or "FinTech" in short. The research analyzed the important factors that affect the usage behavior as well as the effectiveness towards usage behavior of FinTech 3.0 adoptions in one of the well-known private banks in Indonesia. The findings of the test showed that four out of five variables have significant value towards the hypothesis of this research. This means the likelihood of people use FinTech is mostly determined by the perceived of usefulness, ease, and availability of required resources. This information is significant to the banks to innovate their services and products that will be in accordance with customers’ preference as well in competing with other non-financial industry. With the dynamic changes and competitive advantages offered by various parties, this research extends the existing literatures by providing real insights from the users of FinTech in one of the prominent BUKU 4 (Commercial Bank Based on Business Activities) with a core capital of more than IDR 30 trillion. In Indonesia’s banking industry, the reputation of this institution in this research is more than enough to make people choose their services and gain trust to use the technology. However, this might not be the case with other banks, considering the market competitiveness between financial and non-financial institutions.
A 0.6 V, 3.3 nW, Adjustable Gaussian Circuit for Tunable Kernel Functions

This work introduces a compact, ultra-low power (3.3nW) Gaussian circuit architecture for Kernel function emulation. It has independent and electronically adjustable mean value, amplitude and deviation, operating with 0.6V power supply. It consists of a current correlator and a bulk-controlled differential block, with all transistors operating in sub-threshold. Proper operation, accuracy and sensitivity are confirmed via post-layout simulation results and theoretical analysis. It was implemented in TSMC 90nm CMOS process and simulated using the Cadence IC Suite.
A 4-bit Bidirectional Phase Shifter for 3GHz S-Band Applications

This paper proposes a transmission-line based bidirectional reflection-type phase shifter for the 2.5 to 3.2 GHz frequency band. Open and short reflective loads are used to double the available phase shift and reduce insertion loss. The transmission line is implemented using inductors and MOS switches in a 65 nm RF CMOS process. This work achieves a total phase shift of 180° with phase resolution of 22.5° at 2.85 GHz and rms error of 1.2°. The insertion loss of the phase shifter is 5.7 dB and the loss variation is less than 0.3 dB.
Lung Segmentation-Based Pulmonary Disease Classification Using Deep Neural Networks

Interpreting chest x-ray (CXR) to find anomalies in the thoracic region is a tedious job and can consume an ample amount of radiologist’s time when there are thousands of them to process. In such scenarios, the Computer-Aided Diagnostic (CAD) systems can help radiologists by doing the trivial processing and presenting the information in a meaningful way so that, the radiologist can make more accurate decisions by spending less amount of time and energy. This research study intends to propose a better, accurate, and efficient CNN based pulmonary disease diagnosis system using CXR images. In the proposed system, the capabilities of deep neural network architecture are exploited by proposing a custom CNN architecture with additional layers and modified hyperparameters to meet the required results. The input CXR is examined for healthy or infected at the surface level and the infected images are further processed for class level label classification. The lung region is segmented from the entire input CXR image to reduce the amount of noise and increase the processing efficiency by processing less overall information. The proposed model is evaluated on the benchmark split of the NIH chest x-ray dataset and achieves better segmentation and classification results when compared to the state of the art approaches.
Applying Game Learning Analytics to a Voluntary Video Game: Intrinsic Motivation, Persistence, and Rewards in Learning to Program at an Early Age

Learning to program at an early age has been shown to be a vehicle for the development of Computational Thinking. Game-based environments are often used to develop these skills, but they lack sufficient voluntariness to assess aspects related to intrinsic motivation, such as interests, skills, persistence in solving a problem and behavior in response to rewards. These aspects directly affect achievement and academic performance, so it is necessary to analyze possible age and gender differences in order to adjust Computational Thinking curricula. With this aim, we deployed a voluntary video game which addresses basic computational concepts, based on intrinsic motivation, and aimed at early ages. Data were collected and analyzed using game learning analytics for 15 months, during which 4124 users played more than 28187 games. The analysis shows significant age and gender differences in relation to interests, skills, achievement, and progression through attempts. It was observed that the concepts addressed were achievable between the ages of 3 and 6 years and full mastery was possible by the age of 4 years, regardless of gender, as children persist with the challenge, intrinsically motivated, until it is overcome. In terms of persistence, significantly different behaviors were observed in the face of the challenge, which can help us to adjust the different learning methodologies to each age group and gender, adapting the way we provide reinforcement and rewards, especially for boys in the more complex challenges and for girls from the age of 5 years onwards.
The effects of macrophage-stimulating protein and gamma synuclein on the development of brainstem motor systems

Disorders of motility are among the most common and debilitating neurological ailments. In most cases, treatment of these conditions is at best palliative. This is mainly due to the apparent inability of central neurons to regenerate after a given noxious event. In the past, embryos have proven to be valuable objects to study the mechanisms of growth and death in nerve cells since both are physiological events in the developing nervous system. The discovery and study of an ever-growing list of molecules that are involved in these events has significantly furthered our understanding of the conditions that have to be met for individual nerve cell populations to develop into functional structures. It also has the potential to contribute significantly to the establishment of more targeted and efficient therapeutic strategies. Here, the effects of macrophage-stimulating protein (MSP) and 7synuclein on two systems in the developing brainstem involved in controlling movement have been studied: a) the cranial motoneurons and b) the dopaminergic neurons of the substantia nigra and the ventral tegmental area. MSP exerts a variety of biological actions on many cell types, but has no known functions in the brain. To investigate whether MSP is also capable of acting as a neurotrophic factor, hypoglossal motoneurons were purified from the embryonic chicken hindbrain because these neurons are known to express the MSP receptor tyrosine kinase RON. The study shows that MSP promotes the in vitro survival of these neurons during the period of naturally occurring neuronal cell death and enhances the growth of neurites from these neurons. Furthermore, MSP mRNA was detected in the developing tongue which is the
Coverage Aware Scheduling Strategies for 3D Wireless Video Sensor Nodes to Enhance Network Lifetime

High-density wireless video sensor nodes (VSNs) having limited battery power are deployed randomly in the disaster-hit area for capturing visual data, but its local processing and transmission consume high energy. High deployment density of those VSNs results in a larger overlap in the coverage area across VSNs that can be utilized to cover the sensing region of some VSNs and shut off such VSNs to decrease energy consumption and increase network lifetime without losing much area coverage. Two advanced approaches (APP_5 and APP_6) with realistic 3D rectangular pyramid camera coverage of VSN monitoring 2D target area is proposed in this paper. These approaches reduce the number of active VSNs in the target area and energy consumption maintaining the overall coverage area above some threshold value ensuring network connectivity. The approaches are compared with the three state-of-the-art approaches EX_1, EX_2 and EX_3 in the same simulation setup. Observed that for 150 deployed VSNs over the target area of size $75\times 75$ square meters, APP_5 and APP_6 reduce energy consumption by 6.98% and 18.6% respectively from the existing approach EX_3 (producing a better result among three existing approaches in terms of energy consumption). Reducing the number of active VSNs helps decrease energy consumption at the expense of reduced area coverage. For the same node density, both APP_5 and APP_6 lose a little amount of area coverage (i.e. 0.93% and 0.95%) than the existing approach EX_2 (producing a better result among three existing approaches in terms of percentage of area coverage). Additionally, both the proposed approaches (having the same communication overhead) establish superiority by 3.19%/7.83%/4.25% from EX_1/EX_2/(EX_3) in terms of communication overhead for 100 deployed VSNs on the same target area. Finally, APP_6 substantiates superiority in terms of reduced energy consumption (11.97%) than APP_5 losing a very little percentage (0.02%) of area coverage for 150 deployed VSNs.
Development of CNN Transfer Learning for Dyslexia Handwriting Recognition

Dyslexia is categorized as learning disorder that influence the ability of reading, writing and spelling. In Malaysia, “Instrumen Senarai Semak Disleksia (ISD)” that is provided by Ministry of Education is used to detect dyslexic student at early stage. However, such evaluations are time consuming, non-standardize and can lead to a biasing result since the evaluation is based on the teacher’s experiences with the student. Hence, this research focus on the development of dyslexic handwriting recognition. The purpose of this research is to develop a transfer learning of Dyslexia handwriting recognition by using Convolutional Neural Network (CNN) based on famous architecture of handwriting recognition using of LeNet-5. Data augmentation and pre-processing was employed to a total of 138,500 handwriting image dataset before feeding it into network. The hyper-parameter of the model was tuned and analyzed to classify the 3 classes of dyslexic handwriting. The developed CNN model has successfully achieved a remarkable accuracy of 95.34% in classifying 3 classes of dyslexic handwriting. From the result, the objective in developing the CNN model for dyslexia handwriting recognition was successfully achieved.
The Cubli: Modeling and Nonlinear Attitude Control Utilizing Quaternions

This paper covers the modeling and nonlinear attitude control of the Cubli, a cube with three reaction wheels mounted on orthogonal faces that becomes a reaction wheel based 3D inverted pendulum when positioned in one of its vertices. The proposed approach utilizes quaternions instead of Euler angles as feedback control states. A nice advantage of quaternions, besides the usual arguments to avoid singularities and trigonometric functions, is that it allows working out quite complex dynamic equations completely by hand utilizing vector notation. Modeling is performed utilizing Lagrange equations and it is validated through computer simulations and Poinsot trajectories analysis. The derived nonlinear control law is based on feedback linearization technique, thus being time-invariant and equivalent to a linear one dynamically linearized at the given reference. Moreover, it is characterized by only three straightforward tuning parameters. Experimental results are presented.
Classification of Growth Conditions in Paprika Leaf Using Deep Neural Network and Hyperspectral Images

Recently, the analysis research of crop's growth condition is done with the use of hyperspectral image. However, there are many factors such as physical factors and complexity of data make the hyperspectral image analysis difficult. This study presents the classification method of crop's leaf growth condition using hyperspectral image(HSI) and Deep Neural Network(DNN). Major information of plants is acquired through hyperspectral image, and the preprocessing is followed for the information to be used for DNN learning. The preprocessing is used by cutting the data in small patch size and rotating it for the models to be operated effectively. In the experiment, paprika leaves are divided into four types of leaves and backgrounds such as normal and damaged by harmful insects, and the result of the experiment showed 90.9% of accuracy. The presented method has advantages that the data generation method does not affect DNN and can classify various growth conditions that are difficult in the existing RGB image.
LEnS: Lifetime Enhancement Coding Scheme for Non-volatile Memory Processors

Limited write endurance and high write energy consumption per bit restrict the use of emerging non-volatile memory devices. Many studies concentrate on reducing the number of bit-flips per write operation to reduce bottleneck issues. In this paper, we propose a lifetime enhancement scheme for eNVM device-based memories that distributes the number of bit-flips across the bitstream and can improve the endurance and energy performance of the NVM processors. The proposed scheme significantly reduces the number of bit-flips by ≥40% in write-intensive applications and improves the processor’s performance by ~ 55% as compared to content-aware bit-shuffling scheme.
Practical Approach to Cell Replacement for Resolving Pin Inaccessibility

We propose a practical approach to the cell replacement problem for resolving the pin inaccessibility in the ECO (engineering-change-order) routing stage. The prior cell replacement method performs in two steps: (i) it prepares a subsidiary (i.e., alternative) cell library that includes for each cell type a set of diverse cell layouts. Then, (ii) it iteratively tries to replace the cells of routing failures with some cells in the subsidiary library during ECO routing in order to fix the routing failures. In this work, we downsize the subsidiary library produced in step (i) to speed up the sequential and time-consuming process of step (ii). Precisely, we propose a function based on the well-known formulation of Levenshtein distance to measure the degree of the pin topology difference between the layout of a cell type in the target library and a layout of the same type in the subsidiary library. Then, we update the subsidiary library to include, for each cell type, exactly one layout that has the biggest pin topology difference. Through experiments with benchmark circuits, it is shown that using the subsidiary library produced by our topology difference formulation enables to reduce the number of trials of cell replacements significantly over the conventional method while fixing almost the same amount of routing violations.
Chitosan-Integrated Single-Mode Tapered Optical Fiber DENV II E Protein Sensor

This work reports the enhancement of a single-mode tapered optical fiber (TOF) sensor for Dengue virus (DENV) II E protein using chitosan. Chitosan, an organic nanomaterial with excellent biocompatibility and abundant binding sites, was deposited on TOF as an intermediate layer between the activated taper surface and the anti-DENV II E protein antibodies. The best DENV sensing performance was attained at chitosan thickness of 17.13 nm, which was attributed to the optimum balance between the auxiliary layer thickness and penetration depth of the evanescent field. An enhanced sensitivity value of 14.19 nm/nM was achieved compared to the sensitivity obtained by standard configuration sensor. A lower dissociation constant, <inline-formula> <tex-math notation="LaTeX">${\text{K}_{d}}$ </tex-math></inline-formula> of <inline-formula> <tex-math notation="LaTeX">$1.00\times 10^{-10}$ </tex-math></inline-formula> M was also acquired, which implies a higher affinity towards the targeted E protein. Findings from this work highlight the potential of organic nanomaterial such as chitosan for biosensor enhancement and further emphasize the capability of fiber optic sensors for detection of dengue virus as well as other antigens.
Discovering Interdisciplinarily Spread Knowledge in the Academic Literature

With the increase in scientific publications and the diversification of research areas, science has become complex and interdisciplinary. Discovering important knowledge has become difficult even for researchers in specific domains. Previously proposed keyphrase extraction methods focus mainly on detecting intensively discussed topics in specific domains but do not distinguish concepts with the interdisciplinary spread from those discussed in narrow areas. Here, we propose a diffusion meme score that evaluates the knowledge diffusion distance in a paper citation network. The distance between papers that contain specific terms is measured by the network embedding space of the citation network. Using 57 million publication records from 48 years of Scopus, we evaluated newly appearing terms in and after 1975 in biomedical science papers using the proposed indicator. Approximately half of the top 20 terms were related to Nobel Prize or Clarivate Citation Laureates, and the top terms of the indicators were more likely to appear in Wikipedia than terms extracted using existing methods. Moreover, the top terms were unlikely to include specific minor diseases, which are often extracted using existing methods. Therefore, the diffusion meme score evaluates important terms from scientific literature and citation networks that are more interdisciplinary. Our method improves the understanding of young researchers regarding domains, the development of the history of science, and the evaluation of researcher contributions.
Hardware-Aware Affordance Detection for Application in Portable Embedded Systems

Affordance detection in computer vision allows segmenting an object into parts according to functions that those parts afford. Most solutions for affordance detection are developed in robotics using deep learning architectures that require substantial computing power. Therefore, these approaches are not convenient for application in embedded systems with limited resources. For instance, computer vision is used in smart prosthetic limbs, and in this context, affordance detection could be employed to determine the graspable segments of an object, which is a critical information for selecting a grasping strategy. This work proposes an affordance detection strategy based on hardware-aware deep learning solutions. Experimental results confirmed that the proposed solution achieves comparable accuracy with respect to the state-of-the-art approaches. In addition, the model was implemented on real-time embedded devices obtaining a high FPS rate, with limited power consumption. Finally, the experimental assessment in realistic conditions demonstrated that the developed method is robust and reliable. As a major outcome, the paper proposes and characterizes the first complete embedded solution for affordance detection in embedded devices. Such a solution could be used to substantially improve computer vision based prosthesis control but it is also highly relevant for other applications (e.g., resource-constrained robotic systems).
New Design and Analysis of Error-Resilient LRCs for DSSs With Silent Disk Errors

Recently, erasure coding techniques are considered as essential schemes for the reliability of the modern distributed storage systems (DSSs) with the frequent node-level failure. Especially, locally repairable codes (LRCs) are widely adopted by the practical advantage of reducing the latency for repair process. However, recent researches show that many cases for system failure are also originated from the silent disk errors. For the conventional LRCs with low error correction capability, repair process from erasure coding can propagate the silent errors and thus, the DSSs become more vulnerable compared to the cases only with node failure. Therefore, we propose a mean time to data loss (MTTDL) from the modified Markov chain model in order to evaluate effects by silent disk errors. Also, new design of binary error-resilient locally repairable codes (ER-LRCs) with high error and erasure correction capabilities are proposed, which have larger values of bit-wise minimum Hamming distance than the existing LRCs. Here, ER-LRCs can be constructed by modifying the parity check matrix from well-known optimal binary and nonbinary LRCs. From the numerical analysis using the proposed Markov model with empirical parameters, it is shown that the proposed ER-LRCs have better MTTDL values when compared to the existing LRCs.
The influence of bubble plumes on air-seawater gas transfer velocities

Air-sea gas exchange is an important process in the geochemical cycling of carbon dioxide (CO{sub 2}). The air-sea flux of CO{sub 2} is determined in part by the physical forcing functions, which are parameterized in terms of the air-sea transfer velocity, k{sub L}. Past studies have attempted to correlate k{sub L} with wind speed, U. Because strong winds occur in ocean regions thought to be important sources or sinks of CO{sub 2}, accurate knowledge of k{sub L} at high U is important in estimating the global air-sea flux of CO{sub 2}. Better understanding of the physical processes affecting gas transfer at large U will increase the accuracy in estimating k{sub L} in ocean regions with high CO{sub 2}, fluxes. Increased accuracy in estimating k{sub L} will increase the accuracy in calculating the net global air-sea CO{sub 2} flux and provide more accurate boundary and initial conditions for global ocean carbon cycle models. High wind speeds are associated with the presence of whitecaps, which can increase the gas flux by generating turbulence, disrupting surface films, and creating bubble plumes. Bubble plumes will create additional turbulence, prolong the surface disruption, and transfer gas to or from individual bubbles while they are beneath the surface. These turbulence and bubble processes very effectively promote gas transfer. Because of this, it is postulated that breaking waves, if present, will dominate non-whitecap related gas exchange. Under this assumption, k{sub L} Will increase linearly with increasing fractional area whitecap coverage, W{sub c}. In support of this, researchers found k{sub L} measured in a whitecap simulation tank (WSI) was linearly correlated with bubble plume coverage, B{sub c} (the laboratory analog of W{sub c}). However, it is not definitively known how the presence of breaking waves and bubble plumes affect the dependence of k{sub L} on Schmidt number, Sc, and aqueous-phase solubility, {alpha}.
Contemporary Studies of Task-Technology Fit: A Review of the Literature

Information technology can influence task characteristics for the improvement of task performance. This review of the literature aims to explore the application of the TTF theory by exploring the scope and range of the available literature that applies the specific elements of the theory and summarizing and disseminating the research findings, specifically the environments where TTF has been applied. It was found that the education field is the most researched topic in TTF for the last three years, followed by social media, business, and management fields. Overall, the results of this study provide an understanding of applications of the theory and assists in providing avenues for future studies to investigate TTF studies further.