Reviewerly/exHarmony huggingface dataset:
Generation of Binary Sequences of Length 10230 Bits Having Better Odd and Even Correlation with Large Linear Complexity for Use in Global Navigation Satellites Systems (GNSS) Applications

Binary sequences used in Global Navigation Satellites Systems (GNSS) are found to be easily decoded due to less linear complexity.The correlation properties are extremely important while selecting the sequences for GNSS Applications.Linear Feedback Shift Register (LFSR) based Sequences are being used in GNSS Systems such as Global Positioning System (GPS) L2 CM (L2 frequency band Civil Moderate) Signal.Due to the short cycling of LFSR these sequences result in poor correlation properties.In this work the properties of binary sequences used in the state of the art GPS L2CM Navigation signal is explored.The odd and even correlation properties of GPS L2CM sequences are presented in detail.These sequences are analyzed for their Linear Complexity property.A method is proposed for generation of 10230 bit length binary sequences.A new set of binary sequences with set size of 47 sequences are generated using chaotic real sequences.The generated sequences are analyzed for odd correlation, even correlation and linear complexity properties.The proposed binary sequences are found to have better correlation and excellent linear complexity properties as compared to GPS L2 CM Sequences, which make them suitable for use in GNSS Applications.
Assessing the Effectiveness of Mobile-Application Technology: A Project-Based Learning

In the development of technology and its advances in this industrial revolution 4.0 era, the graduates of vocational higher education institution are required to have a ready to use skill as demanded by the job market. This study aimed to investigate the effectiveness of utilizing mobile applications regarding vocabulary acquisition which occurred within a learning process. It was a project-based learning (PBL) performed during the supervision time of the student’ final project applying dictionary applications; The Free Dictionary and Dictionary.com. A mixed method approach was implemented where the data for the quantitative study was collected from the comparison of Vocabulary Size Test (VST) Monolingual-Version A which were carried out prior to and after the PBL. Moreover, the qualitative data was obtained from semi close-ended questionnaire, and open-structured interview. The result shows the superiority of using the mobile apps through this project-based process and at the same time the apps provide contextualized problem-solving environment to their users.&#x0D; HIGHLIGHTS:&#x0D; &#x0D; Even though written reference sources still have a place for students, it cannot be denied that online reference sources have many advantages in the current process of English teaching and learning.&#x0D; Through the project-based learning, mobile application technology can facilitate the students in the language learning process effectively and efficiently, and empower themselves to get maximum results, which is ultimately improve the output of educational institutions in the future.&#x0D;
An improved adaptive genetic algorithm for mobile robot path planning analogous to TSP with constraints on city priorities

The material transportation planning with a mobile robot can be regarded as the ordered clustered traveling salesman problem. To solve such problems with different priorities at stations, an improved adaptive genetic simulated annealing algorithm is proposed. Firstly, the priority matrix is defined according to station priorities. Based on standard genetic algorithm, the generating strategy of the initial population is improved to prevent the emergence of non-feasible solutions, and an improved adaptive operator is introduced to improve the population ability for escaping local optimal solutions and avoid premature phenomena. Moreover, to speed up the convergence of the proposed algorithm, the simulated annealing strategy is utilized in mutation operations. The experimental results indicate that the proposed algorithm has the characteristics of strong ability to avoid local optima and the faster convergence speed.
English Language Education Students Perceptions of Self Efficacy in Public Speaking Class

&lt;em&gt;Self-efficacy is one of the most pivotal springboards determining EFL learners’ success in acquiring the target language in the communicative classroom contexts. Therefore, well-planned learning strategies and enjoyable learning environments should be provided continuously in order to breed proficient communicators in the target language. To do so, English language teachers need to shift their roles from instructors as facilitators facilitators to nurture EFL learners’ self-efficacy development continuously. This study aimed to investigate the students’ perceived self-efficacy in public speaking class. One research problem was proposed in this study namely, what are the significant factors that can heighten learners’ self-efficacy in public speaking class? This study employed a qualitative method utilizing three research instruments i.e., classroom survey, students’ speaking learning reflections, and interview to collect data from public speaking class of the English Education Study Program, Sanata Dharma University, Yogyakarta. Qualitative content analysis was employed to draw meanings out of the data gathered. Findings from this study revealed that EFL learners will be able to be proficient target language communicators when they believe in their speaking competencies and obtain meaningful feedback from the teachers.&lt;/em&gt;
PAM-4 Behavioral Modeling using Machine Learning via Laguerre-Volterra Expansion

In this paper, we demonstrate a PAM-4 IBIS-AMI model obtained from machine learning for time domain simulation in commercial simulators. More specifically, we report a Laguerre-Volterra-expanded Feed-Forward neural Network (LVFFN) with only one hidden layer and 10 neurons to model the 28Gb/s PAM-4 high speed link buffer. the LVFFN model reduces the model size and improves the computational efficiency dramatically compared to the Volterra series model and other transitional artificial neural network models. The LVFFN PAM-4 model can be implemented in IBIS-AMI, an industrial standard for high speed link modeling. Eye-diagram analysis is conducted on LVFFN PAM-4 IBIS-AMI model in commercial simulators. Our results show that our model is faster to simulate 1 million bits and is easier to implement compared to the standard PAM-4 IBIS-AMI model.
Radar Sensor Fusion via Federated Unscented Kalman Filter

In this paper, a three-dimensional maneuvering target tracking algorithm in which radar sensors their tracks are fused at a federated Unscented Kalman Filter (UKF), has been presented. The federated filtering is composed of three levels namely sensors, local processors and a global processor. In the sensor-level, target range, azimuth and elevation are all measured by radar sensors in the Sphere Coordinate System (SCS). Each local processor uses UKF to proceed state estimation in the Reference Cartesian Coordinate System (RCCS). Meanwhile, the UKF processes the recursion and update of the state vector and the error covariance matrix through the unscented transformations. Finally, the state of each local processor is transmitted to the global processor for fusing as a final track for system output and information feedback. Two tracking schemes based on SCS (non-linear model) and RCCS (pseudo-linear model) measurements have been studied and their performance evaluated using simulation data. It is concluded that federated UKF processing in the nonlinear model has computational effectiveness and supplies almost the same tracking accuracy compared with federated UKF processing in the pseudo-linear model.
Holistic Temporal Situation Interpretation for Traffic Participant Prediction

For a profound understanding of traffic situations including a prediction of traf-fic participants’ future motion, behaviors and routes it is crucial to incorporate allavailable environmental observations. The presence of sensor noise and depen-dency uncertainties, the variety of available sensor data, the complexity of largetraffic scenes and the large number of different estimation tasks with divergingrequirements require a general method that gives a robust foundation for the de-velopment of estimation applications.In this work, a general description language, called Object-Oriented Factor GraphModeling Language (OOFGML), is proposed, that unifies formulation of esti-mation tasks from the application-oriented problem description via the choiceof variable and probability distribution representation through to the inferencemethod definition in implementation. The different language properties are dis-cussed theoretically using abstract examples.The derivation of explicit application examples is shown for the automated driv-ing domain. A domain-specific ontology is defined which forms the basis forfour exemplary applications covering the broad spectrum of estimation tasks inthis domain: Basic temporal filtering, ego vehicle localization using advancedinterpretations of perceived objects, road layout perception utilizing inter-objectdependencies and finally highly integrated route, behavior and motion estima-tion to predict traffic participant’s future actions. All applications are evaluatedas proof of concept and provide an example of how their class of estimation taskscan be represented using the proposed language. The language serves as a com-mon basis and opens a new field for further research towards holistic solutionsfor automated driving.
Causal Reasoning in Multi-Object Interaction on the Traffic Scene: Occlusion-Aware Prediction of Visibility Fluent

Occlusion caused by multi-object interaction makes the traffic scene understanding intractable. In this paper, we focus on predicting the visibility status of vehicle in the framework of causality perception. The visibility fluent is employed to present the varying state of an object, involving visible and occluded. We introduce a probabilistic grammar model, named Hierarchical And-Or Graph (H-AOG), to construct the causal relations between fluents and actions. It consists of a Causal And-Or Graph (C-AOG) module and an Action And-Or Graph (A-AOG) module. An influence field is constructed by the polar coordinate transformation to model interactions in the A-AOG module. This method interprets the occurrence of occlusion due to multi-vehicle interaction. We evaluate our approach on both synthetic data and real data from the KITTI dataset. Compared to the state-of-the-art models like LSTM/GRU, it proves to achieve a promising accuracy in prediction of objects’ visibility states and have better generalization on real data.
A New Project Management Tool Based on DevSecOps

Cyber Security has become an important and crucial part of our society. Software are many times developed with little to no guidelines and security components, costing the quality and security of the software. We investigated on different agile development life-cycle models including: Behavioral Driven Development, Scaled Agile Framework and DevOps and DevSecOps. Eventually, we decided to focus on the DevSecOps development life-cycle model. Different types of DevSecOps models were found and analyzed. The characteristics of each were gathered to develop criteria for a new and effective DevSecOps model. Our results show that the DevSecOps models were more reliable than the previous models. Hence we adapted this model to develop our website to allow more security. All the selected criteria are highlighted and discussed in this study along with the website.
Continuous low-latency 3D measurements using efficient freeform GOBO pattern projection and close-to-sensor image rectification

Optical 3D measurement using active pattern projection is well known for its high precision and high 3D point density. Recently, increasing the reconstruction frame rate and the number of active sensors in a simultaneous and continuous operation regime used for sensor networks has become more important. Traditionally, light modulators such as LCoS, DMD, or GOBO (GOes Before Optics) have been used, which generate the projected pattern by blocking the light at dark areas of the pattern. In order to further increase the measurement speed and/or the number of time-sequential continuously active sensors, brighter light sources must be chosen to achieve sufficient short exposure times. Alternatively, as we show in this paper, a more efficient pattern modulator can be used. By applying an optical freeform element to generate an aperiodic sinusoidal fringe pattern, up to 100 % of the available light can be utilized. In our prototype, we show how to employ a freeform element moved in a linear bearing to create a compact low-cost, high-speed projection unit. Furthermore, to reduce the computational burden in processing numerous simultaneous image streams, we have implemented the rectification step of the 3D reconstruction pipeline into the field programmable gate array (FPGA) sensor module. Both approaches enable us to use structured light sensors for continuous high-speed 3D measurement tasks for industrial quality control. The presented prototype utilizes a single irritation-free near-infrared (NIR) LED to illuminate and reconstruct within a measurement field of approximately 300 mm × 300 mm at a measurement distance of 500 mm.
Wearable DPM System with Intelligent Imager and GPU

Dynamic Projection Mapping (DPM) can provide intelligent information visualization based on object recognition. In this paper, we propose a wearable DPM system using an intelligent imager and a GPU, and demonstrate DPM with 1,000fps high-speed target tracking and 60fps image rendering. The proposed system will show high-speed and adaptive projection as if the projection image was printed on a target.
Dual Sequential Variational Autoencoders for Fraud Detection

Fraud detection is an important research area where machine learning has a significant role to play. An important task in that context, on which the quality of the results obtained depends, is feature engineering. Unfortunately, this is very time and human consuming. Thus, in this article, we present the DuSVAE model that consists of a generative model that takes into account the sequential nature of the data. It combines two variational autoencoders that can generate a condensed representation of the input sequential data that can then be processed by a classifier to label each new sequence as fraudulent or genuine. The experiments we carried out on a large real-word dataset, from the Worldline company, demonstrate the ability of our system to better detect frauds in credit card transactions without any feature engineering effort.
Research on English Situational Teaching in Primary Schools in China-Based on the Statistics and Analysis of CNKI Journals and Theses from 2014 to 2019

Situational Teaching plays a crucial role in English classroom teaching in Chinese Primary Schools; hence the relevant research prospers simultaneously. This study is devoted to reveal the research characteristic on English Situational Teaching in Primary Schools in the past 6 years. Result of contrastive analysis and survey of the essays published on CNKI Journals and Theses from year 2014 to 2019 reveals: 1) In terms of the whole field, it develops steadily in spite of lack of wide coverage and depth. 2) In regarding to the research content in whole, it is relatively disproportionate, identical and superficial with too much perceptual thinking and the micro aspect of specific application and promotion but far less scientific, empirical and experimental research. Moreover, there are too many nonstandard essays and few outstanding ones. 3) As to the research method, the distribution of the employment of the 3 methods is imbalance with too little use of the experimental and quantitative ones. Furthermore, the journal authors are unskilled in employment of multiple methods. 4) With respect to the research team, it is unstable, low productive and too centralized with primary school teachers.
PENENTUAN TINGKAT PEMAHAMAN MAHASISWA TERHADAP SOCIAL DISTANCING MENGGUNAKAN ALGORITMA C4.5

Pandemic Corona Disease 19 or called Covid19 raises a situation that requires every level of society to maintain health and perform activities work from home. One suggestion from the government is to implement social distancing for the academic community, one of which is students. However, not all students can do it due to the lack of understanding of social distancing and the demands of working status, this study seeks to measure the level of understanding of students in knowing social distancing and applying it in current conditions. Based on the questionnaire data collection distributed with the number of respondents 287 students with vulnerable ages 18-25 years later conducted a classification of datamining using the C4.5 algorithm with tree modeling, the results obtained that the accuracy of 93.73%, with class precision that is predictions of students understanding social distancing ( 96.97%), students understand but have to work (100%) and students hesitate (75.71%).
[May cross-sectional studies provide causal inferences?]

Due to the flaws inherited in synchronicity, statistical association and survivor bias on variables under measurement, a common 'consensus' has been reached on "cross-sectiional studies (CSS) can lead to failure on causal inference". In this paper, under both causal thinking and diagram, the real and measured cross-sections are clearly defined that these two concepts only exist theoretically. In real CSS research, the temporal orders of measured variables are all non-synchronic, equivalent to the assumption that measurement variables are independent to each other, or there is no differentiated classification bias. Similar to cumulative case-control or historical cohort studies, both exposure and outcome that exist or occur before their measurements in cross-sectional studies, are actions of historical reconstruction or doing 'Archaeology'. One of the common preconditions for causal inference in such studies is that: there must be a causal relation between the measured variables and their historical counterparts. The measured variables are all agents of their corresponding real counterparts, and the temporal orders are not that important in causal inference. It is necessary to better understand the analytic role of the CSS.基于变量调查（或测量）的共时性、统计学关联及幸存者偏倚等原因，横断面研究被认为不能进行因果推断，这是当前的"共识"。本文基于因果思维，借助因果图，首先明确定义真实截面和测量截面，并识别截面概念仅存在于理论的特性。实际横断面研究中，测量变量的共时性并不存在，而是无一例外地表现为非共时性时序，其实质上相当于测量变量间互为独立性假设，或不存在有差别错分偏倚。类似于累积病例对照研究和历史性队列研究，横断面研究均为暴露和结局已存在或发生后进行的测量，这种测量相当于对变量值的历史重建或"考古"。这类研究进行因果推断的共性前提条件之一是，测量变量与其历史变量间必须存在着因果律。测量变量均为真实变量的替代者，测量变量间的时序在因果推断上并不重要。应加强对横断面研究分析性角色的认识。.
Learning from Web Data with Memory Module

Learning from web data has attracted lots of research interest in recent years. However, crawled web images usually have two types of noises, label noise and background noise, which induce extra difficulties in utilizing them effectively. Most existing methods either rely on human supervision or ignore the background noise. In this paper, we propose a novel method, which is capable of handling these two types of noises together, without the supervision of clean images in the training stage. Particularly, we formulate our method under the framework of multi-instance learning by grouping ROIs (i.e., images and their region proposals) from the same category into bags. ROIs in each bag are assigned with different weights based on the representative/discriminative scores of their nearest clusters, in which the clusters and their scores are obtained via our designed memory module. Our memory module could be naturally integrated with the classification module, leading to an end-to-end trainable system. Extensive experiments on four benchmark datasets demonstrate the effectiveness of our method.
Signal Denoising Based on Duffing Oscillators System

Signal denoising is an important aspect of signal processing.It is a meaningful direction to solve this problem by using chaotic oscillator.As a kind of chaotic oscillator, Duffing oscillator is often used in the field of periodic signal or pulse signal detection, but it has not been used in signal denoising.In this paper, a new Duffing oscillators system for signal denoising is proposed.The system utilizes the coupling between linear and nonlinear restoring forces of the oscillators to achieve complete synchronization of the system.Because the external signal will cause a large deviation of the oscillator's trajectory, while the Gauss white noise will only produce a small deviation.Based on this phenomenon, signal denoising can be realized.The simulation results show that, compared with the existing technology, our method is effective for the denoising of complex irregular signal, and can recover the mutation points of signal well.This method has less computational complexity and is easy to implement parallel computation.It can be used in real-time signal processing.
CLDM

Due to the huge market related with clothing, the clothing fashion analysis is receiving more and more attentions from both industry and academia. Because of the shooting conditions and the human pose in the clothing image, the clothing items may be distorted or occluded, which makes the clothing fashion applications inaccurate. In this paper, we present CLDM, a clothing landmark detector based on Mask R-CNN. CLDM detects the functional regions of cloth items, which makes the features extracted from clothes more discriminative. We propose the architecture of CLDM, and design the deep learning network, during which RestNet-FPN is adopted as the backbone and a landmark branch is designed in the perception network. We implement CLDM and experimentally validate the accuracy and efficiency of our proposed techniques.
A Real-time Traffic Sign Detection Model Based on Improved YOLOv3

Abstract Advanced driver assistance system, which can quickly and accurately detect driver assistance personnel, ensure traffic safety, and play an important role in guiding road traffic signs. However, traditional detection models often have false detection, missed detection, and predicted low accuracy of the target box, especially for the Identification of small targets, its accuracy is difficult to meet the requirements. Therefore, based on the target detection algorithm model YOLOv3, this paper replaces the common two-dimensional convolution with dilated convolution and expands the receptive field, At the end of the model, a three-layer spatial pyramid cascade structure is added to make the multi-scale feature map deep fusion. finally, the improved model is tested for performance on the GTSDB dataset. The results show that the improved model implemented in this paper can effectively solve some problems in the actual road traffic sign detection, and meet the requirements of real-time detection.
A Distributed-Ledger, Edge-Computing Architecture for Automation and Computer Integration in Semiconductor Manufacturing

Contemporary 300mm semiconductor manufacturing systems have highly automated and digitalized cyber-physical integration. They suffer from the profound problems of integrating large, centralized legacy systems with small islands of automation. With the recent advances in disruptive technologies, semiconductor manufacturing has faced dramatic pressures to reengineer its automation and computer integrated systems. This paper proposes a Distributed-Ledger, Edge-Computing Architecture (DLECA) for automation and computer integration in semiconductor manufacturing. Based on distributed ledger and edge computing technologies, DLECA establishes a decentralized software framework where manufacturing data are stored in distributed ledgers and processed locally by executing smart contracts at the edge nodes. We adopt an important topic of automation and computer integration for semiconductor research &amp;development (R&amp;D) operations as the study vehicle to illustrate the operational structure and functionality, applications, and feasibility of the proposed DLECA software framework.