Miracle-dz/UniFAR-FTUs huggingface dataset:
Graphic Code: A New Machine Readable Approach

Machine Readable Codes have been used for severalpurposes. Approaches like UPC and QR Code can beseen everywhere. Recently, it has emerged a new MRC able to combine the communication power of classical methodsto a meaningful improvement on aesthetics and data capacity.This method is named Graphic Code1. Although it has beenused in previous researches, this name was firstly used publiclyat Security Document World Conference and Exhibition, 2018. Graphic Code has two major advantages over classical MRCs: aesthetics and larger coding capacity. It opens new possibilitiesfor several purposes such as identification, tracking (using aspecific border), and transferring of content to the application.This paper focuses on presenting how graphic code can be usedfor industry applications, emphasizing its uses on Augmented Reality (AR). In the first context, it is still being used for creatinglabels and validation stamps. In the second one, it can be used asa marker (identification and tracking), and to code parametersfor an AR application such as large texts, meshes of a 3D model, an image, a drawing, or other complex controls.
Automatic Classification of Reciters of Quranic Audio Clips

This paper describes a supervised classification system of Quranic audio clips of several reciters. The objective is to identify the reciter or the closest reciter to an input audio clip. It is common to find people who like to recite Quran mimicking one of the popular reciters. To achieve a practical classifier system, we constructed a representative dataset of audio clips for seven popular reciters from Saudi Arabia. Key features were extracted from the audio clips. We chose perceptual features such as pitch and tempo based features, short time energy etc. We have tried different combination of perceptual features in order to achieve better classification. We split the dataset into training and testing sets (80% & 20%, respectively). SVM is used to implement the classifier. Experimental results show that the proposed audio classifier produces promising results with an overall accuracy of 90%.
ECG-Enhanced Multi-sensor Solution for Wearable Sports Devices

Electrocardiogram (ECG) plays a crucial role in the prevention of cardiovascular diseases in humans and various types of ECG monitoring equipment are continuously being developed. Most monitoring methods place the electrodes near the heart or on both arms, based on standard ECG leads. Although accurate monitoring effect has been achieved by these conventional approaches, the wearable performance still need to be improved. This paper proposed a novel ECG-enhanced multi-sensor solution for wearable sports devices. A wireless and wearable ECG detection system based on signal acquisition from left upper-arm was designed to verify this solution method. The system has been evaluated with solid experiments proving that the system has outperformed existing similar system. Moreover, the inertial measurement unit (IMU) and electromyography (EMG) data were detected and fused by the system to determine the validity of the ECG signal. It indicated that the system can achieve a good performance of heart rate detection under different body states.
Disparity Guided Texture Inpainting for Light Field View Synthesis

Light fields, as a type of visual content, richer in textural and geometric information than traditional imaging, can exhibit strong redundancies between views. Disparity compensated prediction, as one of the view synthesis frameworks, can exploit these redundancies to achieve high coding efficiency. Properly handling texture occlusion in the prediction process is important. We propose a disparity guided texture inpainting scheme to resolve texture occlusion. It turns out that reliable disparity (depth) can be available within occluded regions. A key contribution of this paper is the incorporation of disparity to guide the pixel visiting order and the weighted-average interpolation processes of the inpainting scheme. Specifically, the paper describes a disparity-dependent boundary distance metric, which is evaluated using a Dijkstra's algorithm and used to drive inpainting decisions. Our proposed method is evaluated on a realistic dataset with complex geometry, presenting promising results.
A Deep Learning Based Collaborative Neural Network Framework for Recommender System

Deep learning based neural networks have been attracting significant interest lately, due to their success in complex automatic recognition tasks in many artificial intelligence areas such as language recognition ,computer vision and expert systems. However, in recommendation systems, they have not been exploited fully, and most of the systems rely on traditional collaborative filtering with matrix factorization approaches. In this paper we suggest a novel approach based on deep learning-based augmentation of the collaborative filtering approach for predicting user ratings for different types of media collections in online databases and libraries, including movies, music and book collections. Though there have been few approaches proposed based on deep learning for recommender systems, they were mainly used for modelling additional complementary information available in terms of images, text or acoustic information from different items in the collections. The main component, the interaction between the item and the user was still modelled with traditional matrix factorization approaches, which uses the dot product to extract the latent features between users and items. By augmenting the latent features extracted with deep learning based neural network models, it is possible to capture the hidden interactions and can lead to better recommender system performance even with sparse and unbalanced datasets. The experimental evaluation of the proposed approach based on four different publicly available datasets involving movies, music and book collections, show promising results, in terms of different performance metrics including accuracy and mean absolute error. Experiments on four datasets show the effectiveness of our proposed frameworks.
Enhanced Honey Encryption Algorithm for Increasing Message Space against Brute Force Attack

In the era of digitization, data security is a vital role in message transmission and all systems that deal with users require stronger encryption techniques that against brute force attack. Honey encryption (HE) algorithm is a user data protection algorithm that can deceive the attackers from unauthorized access to user, database and websites. The main part of conventional HE is distribution transforming encoder (DTE). However, the current DTE process using cumulative distribution function (CDF) has the weakness in message space limitation because CDF cannot solve the probability theory in more than four messages. So, we propose a new method in DTE process using discrete distribution function in order to solve message space limitation problem. In our proposed honeywords generation method, the current weakness of existing honeywords generation method such as storage overhead problem can be solved. In this paper, we also describe the case studies calculation of DTE in order to prove that new DTE process has no message space limitation and mathematical model using discrete distribution function for DTE process facilitates the distribution probability theory.
On Model-Free Reinforcement Learning of Reduced-Order Optimal Control for Singularly Perturbed Systems

We propose a model-free reduced-order optimal control design for linear time-invariant singularly perturbed (SP) systems using reinforcement learning (RL). Both the state and input matrices of the plant model are assumed to be completely unknown. The only assumption imposed is that the model admits a similarity transformation that results in a SP representation. We propose a variant of Adaptive Dynamic Programming (ADP) that employs only the slow states of this SP model to learn a reduced-order adaptive optimal controller. The method significantly reduces the learning time, and complexity required for the feedback control by taking advantage of this model reduction. We use approximation theorems from singular perturbation theory to establish sub-optimality of the learned controller, and to guarantee closed-loop stability. We validate our results using two representative examples - one with a standard singularly perturbed dynamics, and the other with clustered multi-agent consensus dynamics. Both examples highlight various implementation details and effectiveness of the proposed approach.
Hexagonal- Pixel Surfaces for the Reconfiguration of Inkjet-Printed Antennas

We present a new method for the reconfiguration of inkjet-printed antennas to make them operate at different frequencies while matching to different impedances other than originally designed. Suitable antennas are modified by replacing conventional ground planes with patterned structures involving optimal arrangements of hexagonal cells. The cells are switched on or off, while the optimal distributions are found via genetic algorithms to reach desired characteristics. A full-wave solver is used for the required analysis in the optimization trials without sacrificing the accuracy. The antennas are fabricated by using a low-cost inkjet-printing setup to demonstrate the effectiveness of the approach, particularly in the context of radio-frequency-identification applications.
Wavelet transform of the leakage current to monitoring the insulator surface under non-uniform desert pollution

This work is devoted to study the insulator behavior under non-uniform desert pollution. Our investigation is focused on monitoring of both position and width of polluted layer, and the electric discharge activity on the insulator surface, by the analysis of the leakage current waveform. The wavelet transform technique is used to decompose the leakage current signal at several frequency bands (details). The standard deviation (STD) is calculated for each detail. It is employed to find the details the most significant to get information on the surface state of the insulator. The STD of the high frequency details shows a good correlation with the electric discharges activity.
Accurate PCM Crosspoint Emulator and its Use on Eigenvalues Calculation

Phase-change memory (PCM) is the most mature candidate technology to be used in in-memory computing systems. For being able to experiment with large-scale PCM configurations, we developed an accurate and precise hardware PCM cell model that emulates the basic intrinsic impairments of the PCM technology, and we used it in a $\mathrm{N}\times \mathrm{N}$ PCM crosspoint emulator for matrix-vector multiplication. In this work, we present details of the architecture of a PCM cell emulator and its use in a PCM crosspoint. We also demonstrate how the PCM crosspoint is used for eigenvalue calculations of symmetric positive values matrices.
Geographic State Machine Replication

Many current online services need to serve clients distributed across geographic areas. These systems are subject to stringent availability and performance requirements. In order to meet these requirements, replication is used to tolerate the crash of servers and improve performance by deploying replicas near the clients. Coordinating geographically distributed replicas, however, is challenging. This paper presents GeoPaxos, a protocol that addresses this challenge by combining three insights. It decouples order from execution in state machine replication, it induces a partial order on the execution of operations, instead of a total order, and it exploits geographic locality, typical of geo-distributed online services. GeoPaxos outperforms state-of-the-art approaches by more than an order of magnitude in some cases. We describe GeoPaxos design and implementation in detail, and present an extensive performance evaluation.
Rubber Tapped Path Detection using K-means Color Segmentation and Distance to Boundary Feature

The detection algorithm of recent tapped line of the Para rubber tree (Heavea brasiliensis) in the night-time farming of rubber latex is presented. Scene images are acquired by the light-assisted smartphone camera. Intensity thresholding with knearest neighbor (k-means) color clustering is proposed for segmentation. Sums of signed-derivative and average distance of image partial boundary is used as the feature. We classified the sample images and compared the performance of SVM classifier by changing the configuration of this feature
From multicore LDPC decoder implementations to FPGA decoder architectures: a case study

Last generation high-level synthesis (HLS) tools enables hardware generation of software descriptions speeding up hardware development times. LDPC codes are involved in many digital standards even 5G, generating high quality architecture from software description is needed for Software Defined Radio (SDR) or Cloud-RAN (C-RAN) systems. Previous works in the field focused on reusing many-core software description of LDPC decoding algorithm for hardware generation purpose. In this article we demonstrate that multicore oriented ones once transformed for HLS are more suitable for hardware decoder generation. Indeed, throughputs and hardware complexities observed are closer to handmade LDPC decoders in comparison with related works. Presented results open new perspective for fast integration or prototyping of ECC architectures.
A Perceptually Inspired Driver Model for Speed Control in Curves

Understanding speed control in driving is important for analysis of road geometry and for the development of driver support assistance devices. Current models for speed selection are primarily based on the relation between road geometry and observed speeds. This study proposes a more detailed model that relates individual speed control to accelerator and brake pedal, based on perception of the visual scene as captured by the Extended Tangent Point (ETP). We investigated the potential of the the Time to ETP (TETP) as input for accelerator and brake pedal control. Based on observations from driving studies, we propose a model, and tuning rules to adjust the model parameters to observed behavior. A simulator experiment showed that, after individualization of the thresholds using a binary classification method, the model is capable of accurately capturing individual speed adaptation of 15 drivers on single lane roads with multiple curves.
SAFE: Secure Appliance Scheduling for Flexible and Efficient Energy Consumption for Smart Home IoT

Smart homes (SHs) aim at forming an energy optimized environment that can efficiently regulate the use of various Internet of Things (IoT) devices in its network. Real-time electricity pricing models along with SHs provide users an opportunity to reduce their electricity expenditure by responding to the pricing that varies with different times of the day, resulting in reducing the expenditure at both customers’ and utility provider’s end. However, responding to such prices and effectively scheduling the appliances under such complex dynamics is a challenging optimization problem to be solved by the provider or by third party services. As communication in SH-IoT environment is extremely sensitive and private, reporting of such usage information to the provider to solve the optimization has a potential risk that the provider or third party services may track users’ energy consumption profile which compromises users’ privacy. To address these issues, we developed a homomorphic encryption-based alternating direction method of multipliers approach to solve the cost-aware appliance scheduling optimization in a distributed manner and schedule home appliances without leaking users’ privacy. Through extensive simulation study considering real-world datasets, we show that the proposed secure appliance scheduling for flexible and efficient energy consumption scheme, namely SAFE, effectively lowers electricity cost while preserving users’ privacy.
An Empirical Study on Factors that Influence the Digital Startup Sustainability : The Mixed Methods Approach in Indonesia

About 90% of startup implementation in Indonesia failed due to the products and markets incompatibility and the funds that run out. The Agency of Creative Economy targets to reduce failure rate to 70% because startup delivers numerous contributions to boost the digital economy. In order to achieve the target, a holistic understanding of factors that influence startup’s sustainability becomes a fundamental baseline. This research aims to identify and analyze those factors with the mixed method approach. Literature review and expert judgment are combined to provide the conceptual model with 11 hypotheses in 5 dimensions: Finance, Organization, Product, Market, and External Environment. The research found there are 10 factors identified, 1 factor namely Minimum Viable Product (MVP) is the significant factor influencing business sustainability in the digital startup based on the PLS-SEM result. The complete results have been affirmed by the expert judgment as part of triangulation process. By leveraging those factors, startup can perform the necessary strategies to develop their business and avoid the failure.
Performance Comparison of Chunk and Peer Scheduling Algorithms of Peer-to-Peer Streaming Systems

With increasing popularity of Peer to Peer systems for video streaming, it is important that the expectations of the users regarding the quality of such systems are being met. In a P2P system, the media stream is divided into small data units known as chunks. Each peer in a Peer to Peer (P2P) system has to take two important decisions at a given time. First, which chunks are to be shared and second with which peer. This paper compares the performance of different combinations of chunk/peer schedulers in terms of chunk diffusion delay, average chunk distribution delay and max chunk distribution delay. By doing so, the best possible combination of the two schedulers for the given experimental setup is explored. The results obtained under the specified experimental setup show that when chunk scheduling algorithm Deadline Based Chunk Scheduler (DLc) is combined with different peer scheduling algorithms, the best results are obtained by its combination with Chunk Earliest Free Pair Scheduler (CEFp). For a constant peer scheduler CEFp combined with different chunk schedulers, the best results are obtained by combining it with Latest Blind Chunk Scheduler (LBc). Finally, with varying neighborhood size, the best results are obtained by the combination of DLC and Chunk Almost Free Peer Scheduler (CAFp)
Designing Fast Convolutional Engines for Deep Learning Applications

Deep learning is rapidly becoming a strong boost to the already pervasive field of computer vision. State-of-the-art Convolutional Neural Networks reach accuracies comparable to human senses. However, the high computational load and low energy efficiency make their implementation on modern embedded systems hard. In this paper, several strategies for designing fast convolutional engines suitable to hardware accelerate Convolutional Neural Networks are evaluated. When implemented within a complete embedded system based on a Zynq Ultrascale+ SoC device, two of the proposed architectures achieve a peak performance of 131.6 GMAC/s at 234MHz running frequency, by occupying at most ∼13% of the DSP slices available on chip. All the proposed engines overcome state-of-the-art competitors, exhibiting a performance/DSP utilization ratio up to 29.6 times higher.
Exact Outage Probability Analysis of Dual-Transmit-Antenna V-BLAST With Optimum Ordering

An exact outage probability analysis of zero-forcing V-BLAST technique with two transmit antennas and <inline-formula><tex-math notation="LaTeX">$r$</tex-math></inline-formula> receive antennas <inline-formula><tex-math notation="LaTeX">$(r\geq 2)$</tex-math></inline-formula> is provided under a decoding order that is optimum in the sense of minimizing the outage probability. Different from the earlier approximated analyses, an exact closed-form expression on the outage probability is attained without neglecting the effect of error propagation. Assuming a one-bit feedback from the receiver to the transmitter, the optimal power and rate allocation values are numerically computed based on the exact outage probability expression under different objective functions. The presented results are also applicable for the corresponding dual scenarios that may involve antenna selection and/or user scheduling.
Study on a Novel Predictive Torque Control Strategy Based on the Finite Control Set for PMSM

In this paper, a novel predictive torque control (PTC) strategy based on the finite control set for the permanent magnet synchronous motor (PMSM) was proposed to overcome the large flux and torque ripples of the traditional direct torque control (TDTC). First, in the PTC, the complete decoupling of the stator flux and torque in the stator flux coordinate system enables a simpler PTC. Second, compared with the traditional predictive torque control (TPTC), using the magnitude comparison of the flux and torque errors, the selected voltage vector for each control cycle was reduced from eight to one, which greatly reduces the computation and complexity. What is more, the duty cycle modulation was used to fix the switching frequency to solve the problem that the switching frequencies of TDTC and TPTC are not fixed. Finally, the simulation results verified the superiority of PTC in reducing the torque and flux ripples.
Scalar Newton-based Extremum Seeking for a Class of Diffusion PDEs

This paper addresses the design and analysis of fast Newton-based extremum seeking feedback for scalar static maps in cascade with PDE dynamics in its actuation path. Although more general classes of PDE-based systems could be envisaged, we concentrate our efforts in handling diffusion PDEs. The proposed adaptive control scheme for real-time optimization follows two basic steps: first, it cancels out the effects of the actuation dynamics in the dither signals, and second, it applies a boundary control for the diffusion process via backstepping transformation. In particular, the diffusion compensator employs perturbation-based (averaging-based) estimates for the gradient and Hessian's inverse of the nonlinear-scalar static map to be optimized. The complete stability analysis of the closed-loop system is carried out using Lyapunov's method and applying averaging for infinite-dimensional systems in order to capture the infinite-dimensional state of the actuator model. Local exponential convergence to a small neighborhood of the unknown extremum is guaranteed and verified by means of a numerical example.
A Distributed Sensing Approach for Single Platform Image-Based Localization

We present a distributed image based robot (re)-localization system with four non-stereo monocular cameras using a deep Convolutional Network (Convnet). Our system trains the well known Posenet (Kendall et al 2015) CNN architecture, with minor changes, for regressing the position of a ground robot using a compound image, consisting of images from four non-stereo monocular cameras mounted on the robotic platform. The training of the network is done end-to-end without the need for any special feature engineering to handle the compound image input. Our results show that there is significant advantage to using a compound image obtained from multiple cameras with non-overlapping field of view (non-stereo) as compared to using images from single cameras for image based localization. The compound-image based training yields median accuracy of 12 cm in an indoor environment which is at least twice as good as the results obtained using the same network trained on monocular image inputs.