Miracle-dz/UniFAR-FTUs huggingface dataset:
Deep Radiomic Analysis Based on Modeling Information Flow in Convolutional Neural Networks

This paper proposes a novel image feature set based on a principled information theoretic analysis of the convolutional neural network (CNN). The output of convolutional filters is modeled as a random variable conditioned on the object class and network filter bank. The conditional entropy (CENT) of filter outputs is shown in theory and experiments to be a highly compact and class-informative feature that can be computed from the CNN feature maps and used to obtain higher classification accuracy than the original CNN itself. Experiments involve three binary classification tasks using the 3D brain MRI data: Alzheimer’s disease (AD) versus healthy controls (HC), young versus old age, and male versus female, where the area under the curve (AUC) values for the CENT feature classification (93.9%, 96.7%, and 71.9%) are significantly higher than the softmax output of the original CNN classifier trained for the task (81.6%, 79.4%, and 63.1%). A statistical analysis based on the Wilcoxon test identifies CENT features with significant links to brain labels, which could potentially serve as diagnostic biomarkers.
Comparing Student Reflectiveness in Online Discussion Forums across Modes of Instruction and Levels of Courses

Fostering reflective deliberation in the online classroom ensures that students reach a high level of achievement in virtual courses. Student peer exchanges were evaluated on a collaborative web site structured around interactive weekly discussions offered across an online, face-to-face, and upper- and lower-division political science courses. Findings indicate that despite differences in mode and level of instruction, the 87 students were academically reflective in their peer discussions across geographic boundaries. This study concludes that a collaboration with a peer interactive design has an important place in online classes, which is a concern for educators and university administrators when developing and delivering pedagogical content.
Customer Awareness towards Digital Certificate on E-Commerce: Does It Affect Purchase Decision?

Digital certificate on e-commerce websites has been used since several years ago as insurance of website integrity from third party, the Certification Authority (CA). In Indonesia, not all e-commerce sites already use digital certificate. Some inhibitors are the price for acquiring digital certificate form global CA and awareness of customers about its importance. Growth of e-commerce globally drive the government to arrange e-commerce roadmap to boost development of this industry in Indonesia. One of program in the roadmap is creating a local CA to publish digital certificate for local e-commerce with cheaper price. It is expected more e-commerce site will implement digital certificate and further increase customer trust and purchase. The objective of this study is to address how information security awareness, especially about digital certificate, can affect purchase decision on e-commerce site through trust. Information security awareness are measured using knowledge, attitude and behavior (KAB) model. Data were gathered form customers of top-five e-commerce sites in Indonesia. Result of this research shows behavior and attitude significantly affect trust, yet knowledge does not affect trust. Furthermore, trust also has significant effect on purchase decision.
Analyses of Time Efficiency and Speed-ups in Inference Process of Two-Stage Object Detection Algorithms

Due to the good performance of the two-stage object detection algorithms in accuracy, based on a typical two-stage method-Faster RCNN, this paper combs its framework, analyzes the time consumption and proposes four speed-ups—reducing the network layers, reducing the neurons, inputting multi-scale image and setting score threshold before sorting to reduce the computational complexity. Using the same PASCAL VOC 2007 datasets, the speed-ups proposed in this paper can improve the detection speed by 35% while keeping high detection accuracy.
A Comparison of Data Aggregation Techniques in Software-Defined Wireless Sensor Network

The Software Defined Network (SDN) concept has been proposed to be utilized in the traditional Wireless Sensor Network (WSN) applications such as Internet of Things (IoTs), traffic engineering and smart grid, etc. The SDN Concept is an ideal concept whose benefits can be observed in the smooth and much improved performances from management, reconfiguration and the wireless communication between the SDN controller and the SDN switches. However, due to the increase of data traffic, the SDN switches have found it difficult to manage the incoming data flow since their flow tables have limited space to hold flow entries, the flow tables contain rules on how to control the data overflow, which can lead to packet loss and delay. The communication between the control plane and data plane also increases as the data traffic increase, resulting in higher energy consumption. Therefore, in this paper, we will discuss the data aggregation techniques that were employed in order to tackle the aforementioned challenges in the software-defined wireless sensor networks.
Sitting Assistance considering with Posture Tolerance of its User

This paper proposes a novel sitting assistance robot, which considers the posture tolerance of its user. The standing and sitting motion are different essentially because the standing is lifting motion against gravitational force and sitting is posture coordination to sitting position according to the gravity. Therefore, the robot should lead the patient’s posture within a stable range during sitting and the required performance is different from standing assistance. However, in previous studies, conventional assistive robots used the sitting motion which is "reverse" motion of standing. Furthermore, these robots helped patients by using a fixed motion reference pathway in spite of their original intention, and as the results, these robots failed to assist by confliction between their intended motion and reference path. Therefore, we propose a novel sitting assistance robot, which allows its user to move their body within a prescribed degree of posture tolerance during the process of moving from a standing to a sitting position. Our key findings cover two fundamental research topics. One is the investigation into posture tolerance during a sitting motion. The other topic is a novel assistance control algorithm that considers the investigated posture tolerance by combining position control and force control. A prototype assistive robot, based on the proposed idea was fabricated to help patients sitting down safely according to their original intention.
Meme Opinion Categorization by Using Optical Character Recognition (OCR) and Naïve Bayes Algorithm

Generally, a meme is an image which is produced by the society which is used to comment a certain event, followed with a particular template from the decent online images. The distribution of meme becomes the phenomenon and very popular in the last few years. The problem is, some of these memes contain negative contents to harm others. One type of meme image that trends in social media is aimed at government, either to support the performance of the government or to insinuate and dislike of a government. Therefore, Political view by the citizen can be identified by viral memes on the internet. The aim of this research is classifying the types of a meme by applying image processing and OCR Tesseract which are combined with Naïve Bayes Algorithm. OCR Tesseract is required to recognize text in an image, meanwhile Naive Bayes algorithm which is used to find the highest probability to classify the testing dataset into the correct category. This research uses a meme as the dataset. The result is meme which is successfully classified. The accuracy depends on the OCR result which utilizes tesseract engine.
Thinking Big: Rehabilitating a Formerly Minor Railway Supply in a Medium-density Urban Area in Central Italy

The paper describes the feasibility study for the rehabilitation of a railway corridor at Lanciano, in central Italy. Although the area is not densely populated, the need to divert commuters’ flows from passenger cars to transit is sufficient to prompt the analysis, and the choice to focus on rail rather than rubber-tired modes is dictated by the willingness to operate more environmentally-friendly services. Area features and demand patterns, all described in the paper, steer the selection to light railway, and more specifically to tram-train services. Goals, requirements and the study outcomes are duly reported. The research question behind, and the goal of the paper, is to identify drivers and barriers to resume a commercial service on an abandoned alignment, with the goal to provide evidence for feasibility studies for similar cases.
A Slow Rate Denial-of-Service Attack Against HTTP/2

HTTP/2 is the second major version of the HTTP protocol published by the IEIF. One of its purposes is to make more efficient use of the transmission efficiency of the TCP protocol. However, while improving efficiency, the emergence of the HTTP/2 protocol has also made attackers have another option to attack Web servers. This paper proposes a slow denial-of-service attack named zAttack against the HTTP/2 protocol, which sends specific request packets through a malicious client to cause the server to wait, continuously consume server resources and finally cause a denial-of-service attack. This paper uses a number of popular web servers to test this attack. The test results show that mainstream web servers that support the HTTP/2 protocol are vulnerable to this attack.
Performance Comparison of 2.5-GHz LC Voltage-Controlled Oscillator for Three Different Technology Nodes

This paper has been written to compare the performances of LC Voltage Controlled Oscillator (VCO) at 2.5 GHz to see the effects of device performances with different technology node on the same while keeping the circuits identical. Accordingly, as a case study, three instances of LC-VCO have been designed in Cadence Spectre using GPDK 45nm, 90nm, and 180nm technology and three different power supply levels of 0.8 V, 1 V, and 1.2 V respectively. ObservedPhase Noise (PN) for 45nm, 90nm, and 180nm technology are −120.8 dBc/Hz, −124.64 dBc/Hz and −126.12 dBc/Hz @ 1MHz offset respectively. Output power, Figure of Merit (FOM) and $\mathbf{K}_{\mathbf{VCO}}$ also have been measured subsequently. The best possible FOM obtained are −181.7dB, −184.9dB and −186.2dB for 45nm, 90nm and 180nm technology node respectively.
Toward a Two-Level PSO for FJS Problem

Particle swarm optimization (PSO) is a population-based stochastic algorithm designed to solve complex optimization problems such as the Flexible Job Shop Scheduling Problem (FJSP). As a metaheuristic, the performance of the PSO is heavily affected by two elements: the size of the search-space and the way of its exploration. In this paper, we present a specific PSO algorithm for the FJSP that use Lower-bounds to bypass regions not containing optimal solutions. The proposed algorithm is a two-level PSO. The upper-level handles the mapping of operations to machines while the lower-level handles the ordering of operations. The performance gain in terms of solution optimality and CPU time, obtained by our method, has been validated by external FJSP benchmarks.
Measurements and Statistical Characterization of Time-varying WiFi Indoor Radio Channel

This paper presents analysis of temporal variations of WiFi indoor radio channel, measured by simple and affordable equipment. It has been shown that, by the normalization of the received signal amplitude to its local mean value, the whole indoor WiFi propagation environment can be represented as a Rician channel regardless of whether the visibility between the transmitter and the receiver exists or not.
An Investigation of Academic Self-Efficacy, Intrinsic Motivation and Connected Classroom Climate on College Students' Engagement in Blended Learning

Blended learning has become an important trend in the development of higher education in recent years. However, maintaining college students' engagement has been problematic. To better understand college students' engagement and continuation of blended learning, this study employed the mixed method to examine college students' engagement and three major factors (academic self-efficacy, intrinsic motivation, and connected classroom climate) that were related to students' learning. The results revealed that college students' academic self-efficacy, intrinsic motivation and connected classroom climate were found to significantly impact college students' engagement. These findings can provide instructors with some strategies to improve students' engagement in blended learning. And administrators and researchers can apply this knowledge to implement, improve, and assess blended learning.
Theoretical Analysis of REM-Based Handover Algorithm for Heterogeneous Networks

Handover has been a widely studied topic since the beginning of the mobile communications era, but with the advent of another generation, it is worth seeing it with fresh eyes. Data traffic is expected to keep growing as new use cases will coexist under the same umbrella, e.g., vehicle-to-vehicle or massive-machine-type communications. Heterogeneous networks will give way to multi-tiered networks, and mobility management will become challenging once again. Under the current approach, based uniquely on measurements, the number of handovers will soar, so will the signaling. We propose a handover algorithm that employs multidimensional radio-cognitive databases, namely radio environment maps, to predict the best network connection according to the user’s trajectory. Radio environment maps have been extensively used in spectrum-sharing scenarios, and recently, some advances in other areas have been supported by them, such as coverage deployment or interference management. We also present a geometric model that translates the 3GPP specifications into geometry and introduce a new framework that can give useful insights into our proposed technique’s performance. We validate our framework through Monte Carlo simulations, and the results show that a drastic reduction of at least 10% in the ping-pong handovers can be achieved, thus reducing the signaling needed.
Robust UWB Indoor Position Tracking Using TDOA Measurements

This paper considers the problem of ultra-wideband (UWB) indoor position tracking using time difference of arrival (TDOA) measurements. The target broadcasts UWB pulses and their time of arrivals (TOAs) are estimated at spatially distributed base stations to extract the received TDOAs for position tracking. In indoor environments, the measured TOAs may be subject to errors due to the multi-path and/or non-line-of-sight (NLOS) propagation of the UWB pulses. This could lead to large TDOA noises and severely degraded tracking performance. To address the aforementioned challenge, a new two-stage indoor tracking algorithm is developed. The first stage preprocesses the sequentially obtained TDOA measurements using an outlier-robust Kalman filter. The impact of the large TDOA errors is mitigated by assuming that the TDOA noises follow the heavy-tailed Student's $\boldsymbol{t}$-distribution. The filtered TDOAs are then fed into the second stage that utilizes a grid-based filter to estimate the current target position. The state space covered by the grid-based filter is a rectangular region that is updated recursively via bound expansion and contraction operations. The use of the recursive bound further decreases the degrading effect of NLOS TDOA noises on the position tracking accuracy. Experiments using synthetic and real-world data illustrate the good performance of the newly proposed UWB indoor tracking algorithm.
Style Transfer Based on Normalization Loss

Gatys et al. firstly proposed artistic style transfer. Based on their research, Luan et al. proposed a method stylizing a photo using the style of a reference photo. Many algorithms have shown great progress in style transfer. The combination of content loss and style loss is commonly used as objective function in style transfer. This paper proposes a new loss term called normalization loss which can replace the classic combination. Normalization loss is easier to converge. Therefore, the generation process requires less iterations. Experiments prove that our method is effective and the result images have better visual effect.
Visual Exploration of Circulation Rolls in Convective Heat Flows

We present techniques to improve the understanding of pattern forming processes in Rayleigh-Bénard-type convective heat transport, through visually guided exploration of convection features in timeaveraged turbulent flows. To enable the exploration of roll-like heat transfer pathways and pattern-forming anomalies, we combine feature extraction with interactive visualization of particle trajectories. To robustly determine boundaries between circulation rolls, we propose ridge extraction in a z-averaged temperature field, and in the extracted ridge network we automatically classify topological point defects hinting at pattern forming instabilities. An importance measure based on the circular movement of particles is employed to automatically control the density of 3D trajectories and, thus, enable insights into the heat flow in the interior of rolls. A quantitative analysis of the heat transport within and across cell boundaries, as well as investigations of pattern instabilities in the vicinity of defects, is supported by interactive particle visualization including instant computations of particle density maps. We demonstrate the use of the proposed techniques to explore direct numerical simulations of the 3D Boussinesq equations of convection, giving novel insights into Rayleigh-Bénard-type convective heat transport.
Android-Based Text Recognition on Receipt Bill for Tax Sampling System

Text is an element which provides information to the readers. However, not all text is informative, some are still in need to be processed to generate information. In text processing process, data is required to be inputted into the system. The input process will be easier if the text is already in digital form. The main issue is when the text is in the non-digital form such as in the form of image. This image should be converted into a form which recognized by the machine. Therefore, an approach is required to be able to identify the text on the images, in expectation to generate text that can be processed by the machine. The method proposed for this research to identify the text is Convolutional Neural Network. Before and after entering the identification process, the input image will go through several pre-processing and post-processing phases to select which text to be displayed as a result. The testing process used images of receipt taken at the distance of 10cm and 12 cm. The result showed the accuracy rates of the testing using images of receipt taken at the distance of 10cm and 12 cm are 95% and 85% respectively.
Smoothing Batteries for PV-Diesel Microgrids

Battery storage systems used to smooth out solar PV power fluctuations can be integrated with solar PV-diesel microgrids as an enabling technology to increase the penetration of solar PV and aid microgrid stability. This paper compares the moving average and ramp rate control techniques for selecting the optimal capacity of a smoothing battery. The studies are based on a large data set of 1-minute resolution solar irradiance data for a full calendar year and across multiple locations. Results from these tests are used to derive correlations between the optimal smoothing battery capacity and daily variability indices.
Hierarchically-Fused Generative Adversarial Network for Text to Realistic Image Synthesis

In this paper, we present a novel Hierarchically-fused Generative Adversarial Network (HfGAN) for synthesizing realistic images from text descriptions. While existing approaches on this topic have achieved impressive success, to generate 256X256 images from captions, they commonly resort to coarse-to-fine scheme and associate multiple discriminators in different stages of the networks. Such a strategy is both inefficient and prone to artifacts. Motivated by the above findings, we propose an end-to-end network that can generate 256X256 photo-realistic images with only one discriminator. We fully exploit the hierarchical information from different layers and directly generate the fine-scale images by adaptively fusing features from multi-hierarchical layers. We quantitatively evaluate the synthesized images with Inception Score, Visual-semantic Similarity and average training time on the CUB birds, Oxford-102 flowers, and COCO datasets. The results show that our model is more efficient and noticeably outperforms the previous state-of-the-art methods.
Using surface electromyography for gesture detection

In this article, we present our current application research regarding measurement and processing of Electromyography data subsequently used for gesture detection. The rehabilitation area has been experiencing a huge progress in recent years. This is due to an increase in the number of patients with various types of disability, technological advance and large number of available devices. One of the rehabilitation sub-areas is the rehabilitation of patients with motor impairment.This type of rehabilitation often involves different virtual reality or augmented reality systems. Such systems are in need of accurate, inexpensive and user-friendly devices acting as controllers. In this paper we propose a system controlled via electromyography. This system is designed to aid rehabilitation of patients with impaired finger control and movements. The electromyography data are measured by Myo bracelet, processed and classified by support vector machines classifier during the offline training.
Key Directions for Industrial Agent Based Cyber-Physical Production Systems

Rapid technology advances and increasing digitalization bring new opportunities but also put new challenges to modern production systems. Industrial agent concepts and technologies can be used to address the needs of modern Cyber-Physical Production Systems (CPPS). To do so, their interplay, especially in industrial settings, needs to be better understood and assessed. Key directions for investigations that are relevant for industrial agent based CPPS are analyzed in this work, mainly the design patterns, interfaces between agents and CPPS, metrics to evaluate the quality of agent based CPPS, and distributed intelligence implemented in or by agents. Although efforts exist both in agent and CPPS domains, their amalgamation still has several aspects that are under-investigated, especially when considering large-scale systems of CPPS, and the proposed four directions discussed in this work, seem promising to address several of the key underlying challenges.