Reviewerly/exHarmony huggingface dataset:
Practical Evaluation of Face Morphing Attack Detection Methods

Abstract Face morphing is a technique to combine facial images of two (or more) subjects such that the result resembles both subjects. In a morphing attack, this is exploited by, e.g., applying for a passport with the morphed image. Both subjects who contributed to the morphed image can then travel using this passport. Many state-of-the-art face recognition systems are vulnerable to morphing attacks. Morphing attack detection (MAD) methods are developed to mitigate this threat. MAD methods published in literature are often trained on a limited number of or even a single dataset where all morphed faces are created using the same procedure. The resulting MAD methods work well for these specific datasets, with reported detection rates of over 99%, but their performance collapses for face morphs created using other procedures. Often even simple image manipulations, like adding noise or smoothing cause a serious degradation in performance of the MAD methods. In addition, more advanced tools exist to manipulate the face morphs, like manual retouching or morphing artifacts can be concealed by printing and scanning a photograph (as used in the passport application process in many countries). Furthermore, datasets for training and testing MAD methods are often created by morphing images from arbitrary subjects including even male-female morphs and morphs between subjects with different skin color. Although this may result in a large number of morphed faces, the created morphs are often not convincing and certainly don’t represent a best effort attack by a criminal. A far more realistic attack would include careful selection of subjects that look alike and create high quality morphs from images of these subjects using careful (manual) post-processing. In this chapter we therefore argue that for robust evaluation of MAD methods, we require datasets with morphed images created using a large number of different morphing methods, including various ways to conceal the morphing artifacts by, e.g., adding noise, smoothing, printing and scanning, various ways of pre- and post-processing, careful selection of the subjects and multiple facial datasets. We also show the sensitivity of various MAD methods to the mentioned variations and the effect of training MAD methods on multiple datasets.
Hand Gesture Recognition as Signal for Help using Deep Neural Network

Hand gesture recognition research has increased widely and rapidly to overcome various problems in human life. When in a dangerous situation humans are required to act quickly and precisely, such as being a victim of violence, accidents, disasters, and so on. In the asking process for help, it is necessary to use an appropriate safe method, communication tools, and speed in sending messages requesting help so that something unwanted does not happen. One of the ways to overcome this problem is to use sign language such as hand gestures. So that this study designs and develops a real-time hand gesture recognition system as signal for help using Deep Neural Network. The purpose of this research is to simplify, speed up, and produce a safe way that humans can use when asking for help from others without speaking when in danger happen. Base on the experiment that real-time detection and an average accuracy of 98.79%. When the system detects a hand gesture to ask for help, a request for help message will be sent to the designated emergency email. So that the benefits derived from the design and development of this system are convenience, speed, and safety in the process of asking for help when humans are in a dangerous situation. Keywords— Hand gesture recognition, Signal for help, Deep Neural Network, Dangerous Situation, Emergency Email.
Integrasi Islam dan Budaya Lokal dalam Seni Arsitektur Masjid Kuno di Jawa: Sebuah Tinjauan Umum

Abstract This article will explain about the integration of Islam and local culture in the architecture of ancient mosques in Java. The architecture of the ancient mosque to this day is still considered to have a deep meaning. The encounter with local culture and Islam, made Islam transformed into a religion that sees the existing reality. Civilization is in the form of a physical stand firmly in its nuances of locality. Mosques are historical relics for Muslims as evidence that Islamic history has succeeded in bringing civilization to a stop in Java. The mosque is a center for da'wah, education and socio-religious activities. So the establishment of the mosque is not a matter of physical considerations, but metaphysical. The method that the author uses is the historical method. This article only looks at an overview of the architecture of ancient mosques in Java. The content of the discussion describes how Islamic architecture in Java, architectural development, integration of Islam and culture in the art of ancient mosque architecture in Java. In conclusion, there is no certain standard in building and establishing mosques in Java. The architectural style of Javanese Islamic mosques has given its own characteristics for Islam in Java.
Distance/Online Engineering Education During and After COVID-19: Graduate Teaching Assistant’s Perspective

Abstract The COVID-19 pandemic has forced universities across the globe to switch to distance/online mode of teaching since Spring 2020. The engineering college teaching community including graduate teaching assistants were severely affected by this change. Even though numerous articles cite a variety of strategies for online teaching for full time instructors, it has been identified that there are limited resources tailor made for graduate students who are teaching engineering classes. This article tries to bridge this gap by exploring some strategies for graduate teaching assistants to help them prepare for online classes and overcome the challenges posed by online/distance education from a graduate student’s experience and perspective.
Studying Security Criteria’s of Cloud and VM Platforms and Present New Innovative Solution for it

This paper categorized as such: in the first section of this paper, authors present general information about basis of cloud computing. By the means of several examples they show the importance of security in such networks. They discuss about security problems in VMware and combine it with security of cloud computing. After that they present their new novel approach for intensifying security of such network, they purpose Sequence Unscented Kalman Filtering Algorithm for acquiring better security. At the end of this paper, authors proof workability and results of applying such algorithm to the cloud computing by the means of mathematical demonstration and MATLAB simulations.
A Tertiary Review on Blockchain and Sustainability with Focus on Sustainable Development Goals

&lt;p&gt; Blockchain as a disruptive technology and a driver for social change has exhibited great potential to promote sustainable practices and help organizations and governments achieve the United Nations’ Sustainable Development Goals (SDGs). Literature reviews on blockchain and sustainability exist, but they often focus only on topics related to one or a few SDGs. There is a need to consolidate existing results in terms of SDGs and provide a comprehensive overview of the impacts that blockchain technology may have on each SDG. This paper intends to bridge this gap, presenting a tertiary review based on 34 existing literature reviews, to investigate the relationship between blockchain and sustainability in light of SDGs. The method used is a consensus-based expert elicitation with thematic analysis. The findings include a novel and comprehensive mapping of impact-based interlinkage of blockchain and SDGs and a systematic overview of drivers and barriers to adopting blockchain for sustainability. The findings reveal that blockchain can have a positive impact on all 17 SDGs through technological innovations though some negative effects can occur and impede the achievement of certain objectives. 73 positive and 10 negative linkages between blockchain adoption and the 17 SDGs as well as 45 factors that drive or hinder blockchain adoption for the achievement of SDGs have been identified. Research gaps to overcome the barriers and enhance blockchain’s positive impacts have also been identified. The findings may help managers in evaluating the applicability and tradeoffs and policymakers in making supportive measures to facilitate sustainability using blockchain. &lt;/p&gt;
Error estimates for finite differences approximations of the total variation

Abstract We present a convergence rate analysis of the Rudin–Osher–Fatemi (ROF) denoising problem for two different discretizations of the total variation. The first is the standard discretization, which induces blurring in some particular diagonal directions. We prove that in a simplified setting corresponding to such a direction, the discrete ROF energy converges to the continuous one with the rate $h^{2/3}$. The second discretization is based on dual Raviart–Thomas fields and achieves an optimal $O(h)$ convergence rate for the same quantity, for discontinuous solutions with some standard hypotheses.
The Satisfaction of Nursing Students With Medical-Surgical E-Learning in Bali

The COVID-19 pandemic has not ended yet, and so educational systems need to continue to be active and creative in establishing online learning materials. Nursing education faces challenges for many learning materials with high credits, such as Medical-Surgical Nursing. Even when altered to online methods, education still must prioritise the quality of the teaching-learning process to generate satisfactory learning. This study aimed to overview the satisfaction of nursing students towards the e-learning activities of the Medical-Surgical Nursing program during the pandemic. This was a descriptive cross-sectional study with a total of 300 participants. There were five aspects assessed, including accessibility, convenience in understanding the materials, interactivity, the accuracy of the methods used, and independence. The results showed that 110 respondents (36.7%) marked low on accessibility; 164 (54.7%) determined it was convenient to understand the materials; 126 (42%) felt that they interacted well among students and with lecturers; 185 (61.7%) thought the methods were sufficiently appropriate during the online learning process; and 141 (47%) felt independent enough to be able to do online learning. In conclusion, the students determined that the online learning process was fairly good in its accessibility, convenience in understanding the materials, interactivity, accuracy of the methods used, and independence aspects.&#x0D; Keywords: online learning, Medical-Surgical Nursing, satisfaction
Optimization of ideal temperature using Mamdani fuzzy logic alogorithm on tunnel flow type chicken cage for brooding period

Abstract Consumption of meat, particularly chicken meat, is increasing in Indonesia year after year, resulting in an increase in chicken meat output. The first step in improving the quality of chicken meat production is to boost success during the brooding stage, with one of the most critical stages being the achievement of the appropriate temperature requirement throughout the brooding period. Good broiler performance will result from uniform cage temperature during brooding. Too-cold temperatures will cause chicks to cuddle close to the heater and be too lethargic to conduct activities such as feeding and drinking. Chickens suffer from stunted growth as a result of this condition. A temperature that is excessively high, on the other hand, will force the chicks to flee the heater and seek a cooler location with better airflow. A fuzzy algorithm is an alternate way for temperature regulation that can maintain the temperature requirement of chicks during brooding. The Mamdani fuzzy approach utilized has the advantage of producing better results than other fuzzy methods. The findings of a temperature management system designed utilizing the Mamdani fuzzy approach with a tunnel flow cage design, a 100-watt bulb heater, two cooling pads, and three fans were able to maintain the appropriate temperature for 14 days in this investigation.
Structure design and simulation analysis of the water surface cleaning robot

Abstract In order to effectively carry out water management and reduce people’s labor intensity, a double-hull water surface garbage cleaning robot is designed, using a pair of adjustable speed pump propellers to provide driving and steering power to the robot. It identifies the water surface garbage position through the binocular camera and algorithm, and the linkage mechanism is designed to complete the garbage pickup function. Through the Raspberry Pi as a controller, the robot has an autonomous intelligent recognition function and an autonomous recovery system. The kinematic simulation analysis shows that the robot garbage picks quickly and accurately; through the prototype function test, the robot is suitable for various complex water environment, with high operation efficiency and reliable performance, and can be widely used in water management.
Arabic as a Language of Politics: A Case Study in Corpus-based Teaching

In Arabic studies, corpus linguistics is still a fledgling discipline, but it is bound to become an unavoidable tool for specialists who seek to ground their research in actual language use. This article presents the preliminary results of an on-going research on Arabic applied linguistics. It offers a first approach to the possibility of matching Arabic as a language of politics with corpus-based teaching. The paper briefly introduces the concept of corpus-based teaching, its advantages and potential limits. It then reviews the most important Arabic corpora currently available for the language of politics, by also describing their extension, features, and limitations. Finally, it suggests some classroom activities based on arabiCorpus.
Classical simulation of quantum circuits using a multiqubit Bloch vector representation of density matrices

In the Bloch sphere picture, one finds the coefficients for expanding a single-qubit density operator in terms of the identity and Pauli matrices. A generalization to $n$ qubits via tensor products represents a density operator by a real vector of length ${4}^{n}$, conceptually similar to a state vector. Here, we study this approach for the purpose of quantum circuit simulation, including noise processes. The tensor structure leads to computationally efficient algorithms for applying circuit gates and performing few-qubit quantum operations. In view of variational circuit optimization, we study ``backpropagation'' through a quantum circuit and gradient computation based on this representation, and generalize our analysis to the Lindblad equation for modeling the (nonunitary) time evolution of a density operator.
An USBL/DR Integrated Underwater Localization Algorithm Considering Variations of Measurement Noise Covariance

Ultrashort baseline (USBL) positioning system is an important part of the integrated navigation for underwater vehicles. The single USBL positioning system has problems such as reduced accuracy of azimuth measurement due to target motion and large impact of small angular variations, especially in the region where the target is close to the conical boundary, that is, the low-elevation region. To improve the positioning accuracy of the USBL-based integrated navigation system, a tightly coupled USBL/Dead Reckoning (DR) integrated localization algorithm that considers the time varying characteristics of the measurement noise was proposed, and the filtering model of the algorithm was designed. The algorithm exploits the mechanism of the azimuth measurement covariance variation of the USBL system, constructs an adaptive measurement noise estimator for the USBL system, and applies it to the integration filtering of USBL/DR data. A nonlinear extended Kalman filtering (EKF) model was used to fuse the USBL positioning and dead reckoning trajectories. A number of simulation tests with different elevation angle settings were performed to compare the performance of the proposed algorithm with that of a conventional EKF for underwater localization. The test results reveal that the proposed algorithm can effectively reduce the positioning error caused by the change in the relative azimuth of the acoustic signal owing the motion of the mother ship and the motion of the underwater vehicle.
Effect of Rubric-Based Feedback on the Writing Skills of High School Graders

This study aimed at exploring the impact of utilizing rubric based feedback on developing the EFL writing skills of Saudi high school students. This study recruited 65 students of the second semester of high school students during the academic year 2019. The experimental group consisted of 31 students, who utilized the rubric based feedback during their writing instruction, while the control group comprised 34 students, who were taught in a traditional way in the classroom. Paired t-test revealed that there was a significant difference between mean scores of both the experimental and control group participants favoring the experimental group participants; confirming the positive impact of utilizing rubric based feedback on developing Saudi high school students' writing skills. These findings have provided evidence that the rubric based feedback has effectiveness in developing the writing skills of Saudi high school students
Optimal path planning of mobile robot based on improved ant colony algorithm

In the current research field of mobile robots, optimal path planning is a hot topic. Ant colony algorithm is diffusely used in mobile robot path planning because of its its positive feedback, distributed and so on. However, traditional basic ant colony algorithm has some disadvantages, such as easy local convergence, slow search convergence rate and low efficiency. To solve these problems, this paper proposes an improved ant colony algorithm. Based on the algorithm principle, a new method for global pheromone initialization is put forward by improving and optimizing the pheromone mechanism, and there is a certain probability to carry out “variation” operation in each search iteration process. Meanwhile, the calculation method of heuristic function is improved to reduce the possibility of premature convergence of the algorithm falling into local optimum. The two-dimensional space model of mobile robot is created by grid method. Then they are simulated and tested respectively, and the test results are compared and analyzed. The experimental results show that the improved algorithm can obviously optimize the defects of the original algorithm, which not only can plan the path faster, but also can plan the better path, and the search efficiency has been significantly improved.
Geochemical Evidence for the Origin of the Daranna Manganese Deposit, Kebbi State, Nigeria

Inorganic and organic geochemical and geostatistical studies of manganese ore deposits exposed at Daranna, near Kaoje have been carried out with the aim of characterizing and delineating the origin of the manganese ore deposits. Data were obtained from field observations and chemical analyses of 12 ore samples. Major and trace elements and organic geochemical analyses were conducted with Energy Dispersion X-ray Fluorescence Spectrometer and Rock-Eval pyrolysis method at Geo-data GmbH Garbsen, Germany. Results of the Major and trace elements, total organic carbon content abundances and the correlation among them imply that Daranna mineralizations are mainly of hydrothermal origin with little contribution from contemporaneous volcanic materials and this is confirmed by high Fe/Mn (8.274 to 24.066 wt%) and low Co/Zn ratios and trace element patterns. The significant geochemical characteristics such as high Mn content (22.660 to 62.330 wt%; average 45.919 wt%), low concentration of Fe (2.590 to 3.310 wt%; average 3.008 wt%) reveal a primary distal hydrothermal source for mineralization. The position of samples on Ni–Zn–Co and (Co/Zn)–(Co + Ni + Cu) diagrams and evaluation of these data reveals that hydrothermal activity was the main process with sedimentary influence responsible for mineralization in the Daranna manganese deposit.
Feature Transformation Framework for Enhancing Compactness and Separability of Data Points in Feature Space for Small Datasets

Compactness and separability of data points are two important properties that contribute to the accuracy of machine learning tasks such as classification and clustering. We propose a framework that enhances the goodness criteria of the two properties by transforming the data points to a subspace in the same feature space, where data points of the same class are most similar to each other. Most related research about feature engineering in the input data points space relies on manually specified transformation functions. In contrast, our work utilizes a fully automated pipeline, in which the transformation function is learnt via an autoencoder for extraction of latent representation and multi-layer perceptron (MLP) regressors for the feature mapping. We tested our framework on both standard small datasets and benchmark-simulated small datasets by taking small fractions of their samples for training. Our framework consistently produced the best results in all semi-supervised clustering experiments based on K-means and different seeding techniques, with regards to clustering metrics and execution time. In addition, it enhances the performance of linear support vector machine (LSVM) and artificial neural network (ANN) classifier, when embedded as a preprocessing step before applying the classifiers.
Distributed public key infrastructure scheme based on blockchain and decentralized undeniable attribute-based signature

A flexible and effective identity system scheme has always been one of the core needs of the information age.Traditional centralized public key infrastructure has a number of flaws, and the present distributed public key infrastructure based on blockchain has a number of issues with performance, resilience, non-repudiation, identity flexibility, and other factors.This paper innovatively combines blockchain with decentralized undeniable attribute-based signatures and proposes a novel distributed public key infrastructure, which uses threshold algorithms and attribute-based signatures for fine-grained management of identities; the paper also introduces non-interactive zero-knowledge proof to make the certificate undeniable and uses the blockchain consensus mechanism to synchronize the certificate library to achieve distributed identity authentication.Through experimental modeling and analysis combined with specific scenarios' actual landing demand, this article indicates that the solution is adequate in terms of security and usability.
LIGHT: Lightweight Authentication for Intra Embedded Integrated Electronic Systems

As embedded integrated electronic systems (EIESs) become more pervasive (including in mission-critical applications), the need to ensure the security of data exchange in such a system against various malicious activities becomes more pronounced. However, designing secure and efficient solutions, such as authentication protocols, for the many different embedded systems with varying internal communication modes remains challenging. Therefore, in this paper, we propose a lightweight authenticated key-exchange (AKE) protocol for EIESs based on half-duplex and “command/response” bus. Specifically, the proposed protocol is designed to operate on resource-constrained devices, as well as having minimal number of interactions. We then prove the security of the proposed protocol and present the security parameter selection strategy for protocol implementation based on the empirical evaluations. Moreover, efficiency analysis also shows that the protocol can be effectively deployed in the EIESs environment.