Miracle-dz/UniFAR-FTUs huggingface dataset:
COSO Framework for Warehouse Management Internal Control Evaluation: Enabling Smart Warehouse Systems

There are many ways for the company to improve its performance, one of them is optimizing the internal control of the company's activities. Internal control is intended to evaluate company activities and operations. This study took a case study at PT. XYZ related to the evaluation of internal controls in warehouse management using the COSO framework approach. From 5 elements and 17 Principle, study found, there are 2 principles that have not been applied in PT. XYZ; enforced accountability and control over technology. The recommendation given is system improvement as intended the inventory system to be more accurate and reliable to enable smart warehouse systems inside organizations.
Deep Difference Analysis in Similar-looking Face recognition

Deep convolutional neural networks (DCNNs) have recently demonstrated impressive performance in face recognition. However, there is no clear understanding of what difference they find between two similar-looking faces. In this paper, we propose a visualization method that gives insight into difference of similar-looking faces found by DCNNs. This method, used as an assistant role, could help human to identify people who try to invade the biometric system using a similar-looking face. We design a crowdsourcing task to evaluate our method. With assistance of our method, accuracy of participants is greatly increased by 8%, which is also better than the accuracy of network, while participants get little improvement with assistance of Deconvolutional network or Gradient Back-propagation. The experiment result suggests that our method makes a difference in human-machine cooperation.
Carrier transport in two-dimensional topological insulator nanoribbons in the presence of vacancy defects

Using the non-equilibrium Green’s function formalism, we study carrier transport through imperfect two-dimensional (2D) topological insulator (TI) ribbons. In particular, we investigate the effect of vacancy defects on the carrier transport in 2D TI ribbons with hexagonal lattice structure. To account for the random distribution of the vacancy defects, we present a statistical study of varying defect densities by stochastically sampling different defect configurations. We demonstrate that the topological edge states of TI ribbons are fairly robust against a high concentration (up to 2%) of defects. At very high defect densities, we observe an increased inter-edge interaction, mediated by the localisation of the edge states within the bulk region. This effect causes significant back-scattering of the, otherwise protected, edge-states at very high defect concentrations (>2%), resulting in a loss of conduction through the TI ribbon. We discuss how this coherent vacancy scattering can be used to our advantage for the development of TI-based transistors. We find that there is an optimal concentration of vacancies yielding an ON–OFF current ratio of up to two orders of magnitude. Finally, we investigate the importance of spin–orbit coupling on the robustness of the edge states in the TI ribbon and show that increased spin–orbit coupling could further increase the ON–OFF ratio.
Investigating the Greek political Twittersphere: Greek MPs and their Twitter network

Social media platforms have made an impact in politics by providing the politicians with new channels of communication and by creating more active, engaged, and participative citizens, and voters. Twitter is a social media platform that has been extensively used in campaigns and in daily communication between politicians and the public in many places of the world. The aim of this study is to investigate aspects of the Greek political Twittersphere. More specifically in this study an attempt will be made to obtain and compare Twitter metrics for the MPs (members of parliament) belonging to the two most dominant parties of Greek politics. The study will also examine aspects of the social network formed by the interconnections of all the members of the Greek parliament. At last the study will focus on citizens that follow MPs. MPs and parties that have a large number of unique followers and MPs and parties that share large number of common followers will be identified using quantitative analysis of sets. The aim of this task will be to reveal politicians and parties with their own distinct club of Twitter followers and to investigate whether common followers amongst MPs and parties result from interpretable facts such as similar agendas and present or past relations. Although this study focuses solely on Greek MPs and their Twitter presence we anticipate that the methods presented can be applied in other political Twitterspheres for investigating similar aspects.
$W$ -Band Low-Profile Monopulse Slot Array Antenna Based on Gap Waveguide Corporate-Feed Network

This paper presents a gap waveguide-based compact monopulse array antenna, which is formed with four unconnected layers, for millimeter-wave tracking applications at <inline-formula> <tex-math notation="LaTeX">$W$ </tex-math></inline-formula>-band (85–105 GHz). Recently developed gap waveguide technology removes the need for galvanic contact among metallic layers of waveguide structures, and thereby, makes the proposed antenna suitable for easy and low-cost manufacturing. In this context, a low-loss planar Magic-Tee is designed to be used in a monopulse comparator network consisting of two vertically stacked layers. The gap waveguide planar monopulse comparator network is integrated with a high-efficiency <inline-formula> <tex-math notation="LaTeX">$16\times 16$ </tex-math></inline-formula> corporate-fed slot array antenna. The measured results of the comparator network show the amplitude and phase imbalance values to be less than 0.5 dB and 2°, respectively, over the frequency band of interest. The fabricated monopulse array antenna shows relative impedance bandwidths of 21% with input reflection coefficients better than −10 dB for the sum and difference ports. The null in the difference radiation pattern is measured to be 38 dB below the peak of the sum radiation pattern at 94 GHz. The measured gain is about 30 dBi for the same frequency. The low-loss performance of the comparator network and the feed network of the proposed array, together with the simple and easy manufacturing and mechanical assembly, makes it an excellent candidate for <inline-formula> <tex-math notation="LaTeX">$W$ </tex-math></inline-formula>-band compact direction-finding systems.
Using Monte Carlo Tree Search as a Demonstrator within Asynchronous Deep RL

Deep reinforcement learning (DRL) has achieved great successes in recent years with the help of novel methods and higher compute power. However, there are still several challenges to be addressed such as convergence to locally optimal policies and long training times. In this paper, firstly, we augment Asynchronous Advantage Actor-Critic (A3C) method with a novel self-supervised auxiliary task, i.e. \emph{Terminal Prediction}, measuring temporal closeness to terminal states, namely A3C-TP. Secondly, we propose a new framework where planning algorithms such as Monte Carlo tree search or other sources of (simulated) demonstrators can be integrated to asynchronous distributed DRL methods. Compared to vanilla A3C, our proposed methods both learn faster and converge to better policies on a two-player mini version of the Pommerman game.
Solar Powered Based Water Pumping System Using Perturb and Observation MPPT Technique

This paper concentrates on solar photovoltaic(PV) water pumping system using perturb and observation maximum power point tracking(MPPT) technique. This whole system is divided into two stages. In the first stage, an arrangement of PV modules is made which is a combination of number PV cells in series or parallel to extract the solar energy and convert into electricity. To maximize the power output of PV module, perturb and observation (P&O) MPPT technique has been used. In its second stage, direct torque and flux control(DTFC) with space vector modulation(SVM) is used to control switching pulses of the voltage source inverter(VSI). The speed of induction motor drive is controlled by DTFC technique. The whole system is developed in MATLAB and outputs are observed.
Optic-electronic processing of images

Problem of technical objects low contrast image areas analysis is actual. Even now the black and white photo materials are used along with colour images. Particularly it can be attributed to X-ray control. The majority of industrially produced photo layers are colourless. As a rule, images are analyzed visually. In particular such method is used in medical X-ray control. Up to a present moment there are no reliable algorithms capable of separating pathological and non-affected areas. If an image is sufficiently clear and contrast then it is analyzed using the primary photo-image. If the image has low contrast then a researcher, firstly, converts it into a pseudo-colour or three-dimensional and then analyses it. Such procedure is determined by complex dependence of many factors, but the most important are the following three: psycho-physiological complexity to distinguish the boundaries of the image, difficulty to create an algorithm capable of recognizing the norm-pathology boundary without help of a human, and inability of an average human eye to discern more than 10 - 12 gradations on black and white image. Existence of the mentioned factors led to various approaches to obtaining higher quality of images - the image pseudo-colour coding method, and the method of 3-D conversion. In the present paper the combination of the developed and patented by the author method for optical images analysis and well known method of computer image analysis using Femtoscan software (designed for electronic microscope images analysis) is considered.
Implementation of Compact Wearable Fall Detector for the Elderly

Falling seems common for all of us, but this becomes a serious issue as a person tends to age. Falls are common among elderly as they tend to have weak muscles and bones and shiver a lot leading to fall and in case of elderly this will cause serious injury. In this paper, we propose a threshold algorithm based fall detection system using tri-axial accelerometer and tri-axial gyroscope, hence reducing software complexity and achieving high accuracy by imparting six degrees of freedom. The focus is to design a device that is very compact and can accurately detect an event of fall.
Guidance Vector Field Encoding based on Contraction Analysis

The guidance vector field encoding problem is revisited via exploiting the recently developed differential Lyapunov framework for contraction analysis. In control applications, a designed guidance vector field is utilized to guide a particle (such as a robot, or an end actuator) to converge to and circulate along a target curve, such as robot navigation, or motion planing. This paper proposes some new methods for guidance vector filed encoding. The methods analyze the guidance vector field encoding problem from a theoretical perspective providing insights into the problem, and it shows that the contraction analysis is a natural theoretical tool for guidance vector encoding. Furthermore, the differences and relations between the proposed result and the previous methods are discussed. Simulation examples are presented to illustrate the effectiveness of the proposed methods.
Comparisons Between Simulated and Measured X-band Signatures of Quad-, Hexa- and Octocopters

This paper presents and compares finite element method (FEM) and method of moments (MoM) monostatic radar cross-section (RCS) simulation results for an example quadcopter, hexacopter and octocopter at X-band, as well as comparing the simulations against measurements for quad- and hexacopters. In particular we explore the extent to which simplified CAD models can be used to estimate the scattering from multi-rotor unmanned aerial vehicles (UAVs). We also consider the importance of model complexity for micro-Doppler rotor simulations.
Adaptive Noise Reduction of Multichannel Signals Obtained by Capacitive ECG Sensors

Capacitive electrocardiogram (ECG) sensors have been developed as a non-intrusive measurement to obtain ECG in daily life. However, the sensors are sensitive to motion noise caused by user′s movement so that this paper suggests a novel noise reduction using active noise cancellation (ANC) with multichannel signals. The proposed algorithm can detect reliable QRS interval and conserve its power while noise is reduced outside the interval. As a result, the proposed algorithm achieved more accurate R peak detection of ECG measured in driving situation than single channel ANC. The method will be useful in the filed reducing noise of multichannel biomedical signal.
Design and Analysis of a High-Performance Outer Rotor Brushless DC Motor Using Loading Distribution Method for Range Hood Applications

This paper deals with the design and analysis of an outer rotor brushless DC (BLDC) motor using the loading distribution method (LDM) for range hood applications. The goal is to design the BLDC motor with decreased motor outer diameter but improved performance compared to the commercial one. The LDM is utilized to design the BLDC motor by sufficiently considering the loading conditions, especially the electric loading related to the inner stator windings. As a design result, the outer diameter of the new BLDC motor is decreased by 8% with highly improved power and efficiency compared to the commercial one. The finite element method (FEM) is first to be conducted to verify the design results and to predict the performance of the designed BLDC motor. Then the analysis results are validated by experimental measurements for the prototype motor.
Power Allocation Policies for QoS Satisfaction in IoT Using NOMA

The fifth generation(5G) of mobile communications has been envisioned to enable the future Internet of Things(IoT) and massive data requirements. In Internet of things some users need to be served swiftly depending upon their functionality or requirement. Using normal schemes of orthogonal multiple access (OMA) it is not possible to serve the users quickly when the resources are occupied by other users. So, in order to give the priority to emergency things and address them quickly we are going for NOMA (Non Orthogonal Multiple Access). To address this requirement of connectivity and data quench issues, power allocation policies have been designed to serve the user depending upon their quality of service (QoS) requirements while using NOMA. While the other users are served with the best effort service using NOMA concept. The challenge here lies in serving the users when both have the same channel conditions. Precoding and detection strategies are developed to realize the potential of NOMA by modifying the channel conditions. Simulation results are provided at the end to demonstrate the performance of the proposed scheme.
Shearlet-based Loop Filter

In video coding, in-loop filtering has attracted attention due to its increasing coding performances. In this paper the shearlet-based loop filter is proposed using a sparsifying transform, the shearlet transform, which can identify the important structures of natural images such as edges in the sparse transform domain. This allows for separating efficiently the important information from noise components. Our novel approach for in-loop filtering is to apply a shearlet transform to the decoded image, separating important structures from noise and perform an inverse shearlet transform combined with Wiener filtering. This effectively removes compression artefacts due to quantization noise and keeps the important features of the original image. Simulation results show that our shearlet based loop filter can improve the state-of-the-art video coding standard HEVC through up to 10.5% bit rate reduction along with improved subjective visual quality.
Using Bidirectional Long Short-Term Memory and Conditional Random Fields for Labeling Arabic Named Entities: A Comparative Study

Named entity recognition (NER) is considered as one of the important tasks of natural languages processing (NLP). This paper presents two approaches that were developed for Arabic named entity recognition (ANER). The first approach is based on a traditional machine learning method of using the conditional random fields (CRF) trained with predefined set of syntactic and morphological features. Whereas, the second approach is based on the bidirectional long short-term memory with a conditional random fields layer (Bi-LSTM-CRF). Both approaches were evaluated using a reference dataset for ANER. Evaluation results show that the Bi-LSTM-CRF deep neural network overcomes the traditional CRF model with 15% of enhancement based on the F1 measure.
Contact properties of electroless nickel plated contacts on diffused p+ silicon surfaces.

Contact resistivity measurements for electroless nickel plated (ENP) contacts formed on heavily boron diffused p+ silicon are presented. In this work, the contact resistivity values for an alkaline based ENP solution which does not require prior surface sensitisation or activation is obtained from circular transmission line method measurements and compared to identical structures formed with evaporated aluminium contacts. It is found that in comparison to evaporated aluminium contacts, ENP contacts have a higher contact resistivity for comparative samples with identical p+ diffusion profiles in the underlying silicon. Nevertheless, contact resistivity values as low as 0.24 m$\Omega . cm ^{2}$ are measured for a boron surface concentration of $(1.4 \times 10) ^{19} cm ^{-3}$.
Double Moving Horizon Estimation: Linearization by a Nonlinear Transformation

Moving horizon estimation (MHE) is a con- strained non-convex optimization problem in principle, which needs to be solved online. One approach to avoid dealing with several local minima is to linearize the nonlinear dynamics. This type of convex approximation usually utilizes the estimated state as a linearization trajectory, providing no guarantees of stability and optimality in general. In this paper, we study the cascade of a linear and linearized observer, which is called double MHE. The first stage makes use of a model transformation, that in the nominal case is globally equivalent to the nonlinear dynamics. Since this approach does not consider the input and output disturbances optimally, the second stage uses the first stage estimates as an external signal for linearizing the nonlinear dynamics to improve the quality of estimation. The overall configuration can be transformed into two quadratic programs. This approach not only avoids solving a non-convex optimiza- tion problem, but also reduces the computational complexity significantly compared to the one needed for solving a non- convex problem. This estimation method has been validated in a simulation study, where our approach converged to the global minimum without the need to explicitly solve a non- convex optimization problem.
A Deep Reinforcement Learning Framework for Rebalancing Dockless Bike Sharing Systems

Bike sharing provides an environment-friendly way for traveling and is booming all over the world. Yet, due to the high similarity of user travel patterns, the bike imbalance problem constantly occurs, especially for dockless bike sharing systems, causing significant impact on service quality and company revenue. Thus, it has become a critical task for bike sharing operators to resolve such imbalance efficiently. In this paper, we propose a novel deep reinforcement learning framework for incentivizing users to rebalance such systems. We model the problem as a Markov decision process and take both spatial and temporal features into consideration. We develop a novel deep reinforcement learning algorithm called Hierarchical Reinforcement Pricing (HRP), which builds upon the Deep Deterministic Policy Gradient algorithm. Different from existing methods that often ignore spatial information and rely heavily on accurate prediction, HRP captures both spatial and temporal dependencies using a divide-and-conquer structure with an embedded localized module. We conduct extensive experiments to evaluate HRP, based on a dataset from Mobike, a major Chinese dockless bike sharing company. Results show that HRP performs close to the 24-timeslot look-ahead optimization, and outperforms state-of-the-art methods in both service level and bike distribution. It also transfers well when applied to unseen areas.
Do Enterprise Social Networks really enhance our Performance? Exploring the Relationship between Usage Practices and Individual Task Performance

The rapid proliferation of Enterprise Social Networks (ESNs) has attracted the attention of organizational and IS researchers. Despite a growing body of literature on this emerging field, there is still a paucity of research examining how ESN usages affect employees’ task performance in the workplace. In particular, we still have a limited understanding of how different ESN usage practices can affect perceived individual task performance, and how ESNs can be used most effectively, given their manifold usage forms. Our paper explores how ESN usage frequency and different ESN usage practices impact on individual task performance. By conducting a quantitative and qualitative analysis with a survey of 9 541 participants followed by 15 interviews in a multinational and knowledge-intensive company, we found strong evidence that participants who use the ESN more frequently perceive a higher individual task performance. The study results also reveal that the relationship between ESN usage frequency and individual task performance varies depending on the usage practices. Our findings yield both theoretical implications for IS research and practical implications on how to effectively deploy ESNs in organizations so as to enhance individual employee performance.
Brief Study on Multiple Port Converters With Sliding Mode Control

Inexhaustible resources based production (through solar photovoltaics, fuel cell, wind, biomass and the other) is needed in present scenario considering several perspectives tantamount to climatic change, lacking fossil preserves (oil, coal, natural gas), fossil producers rising monetary prices and the other. DC form of power has become a significant transacting issue considering the handiness of most of the inexhaustible power production as well as the energy storing in this form. Besides, several present time loads are associated with internal manner DC power. For interfacing of all inexhaustible producers, storing appliances and loads, multiple port type converter (MPTC) seems to be convenient over traditional individual converters based interfacing in several perspectives. Controlling of these MPTCs remains a challenging goal. Several controlling procedures tantamount to hysteresis, proportional integral (PRIN), sliding mode, proportional integral derivative (PRINDE) are utilized. Considering the varying architecture of the power converting devices, the sliding mode keeps superiority in comparison to other controlling procedures. The presented brief thoroughly reviews differential MPTC perspectives as well as sliding mode controlling procedure.