Reviewerly/exHarmony huggingface dataset:
Uas Delivery Multi-Rotor Autopilot Based on Ardu-Pilot Framework Using S-Bus Protocol

Autopilot flight control is most important to assist non-professional pilot to operate small unmanned aerial vehicle (UAV). It simplifies flight operation with minor pilot intervention and maintains system control reliability and safety. A UAV flight system is designed and implemented with full autonomous operation via S-BUS and MAVLink protocols. System hardware and firmware is proposed to realize autonomous functions based on the Ardu-pilot Framework on 32-bit system-on-chip (SoC). The SoC communicates to UAV Pixhawk flight controller through S-BUS and MAVLink protocols. Path planning is constructed to direct autonomous flight control by waypoint assignments on flash memory. LiDAR device is added for detect and avoid (DAA) to obstacle and terrain to secure flight. Remote operator commands can be uplink to change flight route or abandon flight in case of emergency. With flight tests, the proposed Self-Fly exhibits excellent characteristics of constraint-free from flight controller board. One-key command for full mission automatically is designed to execute system performance.
IMAGE FEATURE EXTRACTION AND FRACTALITY EVALUATION BASED ON TWO-DIMENSIONAL CONTINUOUS WAVELET TRANSFORM: APPLICATION TO DIGITAL ELEVATION MODEL DATA

A new method to extract image features and to evaluate fractality is proposed based on two-dimensional (2D) continuous wavelet transform (CWT). Multi-directional 2D CWT coefficients are used to determine the direction and magnitude of image intensity gradients directly unlike other methods using gradient components in the horizontal and vertical directions. Image feature points are detected by comparing candidate directional 2D CWT coefficients at candidate points and their neighbors instead of gradient magnitudes or 2D CWT moduli used by traditional methods. It enables us to extract multiscale image features including line singularities such as corners which are recognized to be hardly extracted by traditional methods. This offers an advantage condition to study fractal objects consisting of lots of line singularities. It is suggested that the detected multiscale image features can reflect multiscale fractal measures to be used to evaluate fractality, that is, self-similarity across scales. From this suggestion, the method to evaluate fractality and calculate fractal dimensions using multiscale image features is proposed. The proposed method is applied to theoretical fractal models to show that the method is convenient and effective in extracting image features of them consisting of many line singularities and calculating their fractal dimensions. Finally, the method is validated using a case study dealing with fractality evaluation of geoscientific objects such as coastlines and stream networks from the digital elevation model (DEM) data.
Processing of Unstructured Information About Software Vulnerabilities

To maintain the information security of a computer system, it is necessary to regularly audit the used system components and their corresponding vulnerabilities. However, accounting for software flaws is a time-consuming task due to the constant emergence of new unstructured information about discovered vulnerabilities. To increase the speed of analyzing relevant information, the algorithm for processing vulnerability descriptions and the XML-based output data presentation format were proposed. Processing a text description means identifying a name and version of vulnerable software, as well as determining the type of a vulnerability and a level of severity. At the current stage of the research, the achieved classification accuracy is 87%.
Digital Hopf Spaces and Their Duals

In this article, we study the fundamental notions of digital Hopf and co-Hopf spaces based on pointed digital images. We show that a digital Hopf space, a digital associative Hopf space, a digital Hopf group, and a digital commutative Hopf space are unique up to digital homotopy type; that is, there is only one possible digital Hopf structure up to digital homotopy type on the underlying digital image. We also establish an equivalent condition for a digital image to be a digital Hopf space and investigate the difference between ordinary topological co-Hopf spaces and their digital counterparts by showing that any digital co-Hopf space is a digitally contractible space focusing on deep-learning methods in imaging science.
Understanding How Orthogonality of Parameters Improves Quantization of Neural Networks

We analyze why the orthogonality penalty improves quantization in deep neural networks. Using results from perturbation theory as well as through extensive experiments with Resnet50, Resnet101, and VGG19 models, we mathematically and experimentally show that improved quantization accuracy resulting from orthogonality constraint stems primarily from reduced condition numbers, which is the ratio of largest to smallest singular values of weight matrices, more so than reduced spectral norms, in contrast to the explanations in previous literature. We also show that the orthogonality penalty improves quantization even in the presence of a state-of-the-art quantized retraining method. Our results show that, when the orthogonality penalty is used with quantized retraining, ImageNet Top5 accuracy loss from 4- to 8-bit quantization is reduced by up to 7% for Resnet50, and up to 10% for Resnet101, compared to quantized retraining with no orthogonality penalty.
Optimal Design of Suspension System of Four-wheel Drive Baja Racing

Abstract In order to match the four-wheel drive system of Baja racing, improve the handling stability and driving comfort of the car, and optimize the parameter design of the suspension. Establish a suspension system model in ADAMS, conduct motion simulation, analyze important suspension parameters, modify the suspension hard points according to the analysis results, and optimize the suspension parameters. Finally, a set of excellent suspension parameters is obtained, and the performance of the suspension system can be improved. The suspension parameters of the simulated racing car have been determined, and other performance parameters have also been improved through hard-point modification. Using ADAMS to perform motion simulation of the suspension system saves time and can efficiently realize the optimal design of the suspension system.
Traitor tracing scheme for relational databases based on blockchain

The traceability of data leakage remains a foundational challenge faced by big data. Traditional data tracing technology is mainly based on digital fingerprint to embed lengthy code into digital works such as video, while the structured data with limited embedding space has not been given adequate consideration. In this paper, we propose a chameleon short signature by improving Khalili’s chameleon hash function and combining Boneh’s signature algorithm to achieve a one-to-many signature with a shorter message length under the same security premise. Then, we construct a traitor tracing model based on the proposed signature and design a cascade chain to complete credible data sharing and undeniable leaker detection. Security and simulation analysis show that the traitor tracing model achieves trusted data sharing and efficient traitor tracing for structured data.
The Effect of Multi-Generational Selection in Geometric Semantic Genetic Programming

Among the evolutionary methods, one that is quite prominent is genetic programming. In recent years, a variant called geometric semantic genetic programming (GSGP) was successfully applied to many real-world problems. Due to a peculiarity in its implementation, GSGP needs to store all its evolutionary history, i.e., all populations from the first one. We exploit this stored information to define a multi-generational selection scheme that is able to use individuals from older populations. We show that a limited ability to use “old” generations is actually useful for the search process, thus showing a zero-cost way of improving the performances of GSGP.
Who they become is as important as what they know. We must teach students to become agents for change so they can bring design thinking to causes they care about.

Traditionally, design education focuses on what students know and which skills they acquire. This preparation launches them intopredictable roles in well-defined fields. The catch is that such stability is no longer present in today’s economy.&#x0D; As custodians of our disciplines, we are more likely to teach software and history and aesthetics, while neglecting the struggle withidentity and values that is inevitable during the formative years of young adulthood. To prepare students for a fast-changing globaleconomy, schools must help students learn about themselves, understand their world, and feel empowered to identify and tackle social problems in innovative ways.
From <i>texts to contexts</i>: the relevance of digital ethnography in a Foucauldian discourse analysis of online gender talk in Kerala

Purpose The purpose of this paper is to foreground the importance of context in discourse analysis by drawing on a study of online gender talk on Facebook in India. Design/methodology/approach Using Foucauldian discourse analysis (FDA), this study explored participants’ use of language to construct and perform various identities in online gender talk. This study discusses the methods used and challenges in analyzing digital spaces through FDA, focusing specifically on the importance of an ethnographic perspective to contextualize online talk. Findings Engagement with the larger socio-cultural context of the subject of study through various data collection methods enhanced our understanding of the contexts behind text. It helped the authors to explore the data from multiple directions from a Foucauldian framework. This study found that people constructed a “progressive” identity when talking about gender on Facebook. Originality/value There are very few studies combining discourse analysis and digital ethnography and this paper seeks to do that. Digital ethnography helps to look beyond the text and locate text in the larger socio-cultural context. To emphasize the importance of context in discourse analysis, this study engages with both online and offline data as online talk is connected with offline contexts in many significant ways. In this paper, the authors provide a description on various methodological steps used to collect and analyze online data using FDA.
Isomorphism testing of read-once functions and polynomials

In this paper, we study the isomorphism testing problem of formulas in the Boolean and arithmetic settings. We show that isomorphism testing of read-once formulas is complete for log-space. In contrast, we observe that the problem becomes polynomial-time equivalent to the graph isomorphism problem when the input formulas can be represented as OR of two or more read-once formulas. We address the polynomial isomorphism problem, a special case of polynomial equivalence problem which in turn is important from a cryptographic perspective [Patarin EUROCRYPT '96, and Kayal SODA '11]. As our main result, we propose a deterministic polynomial time canonization scheme for polynomials computed by read-once arithmetic formulas. In contrast, we show that when the arithmetic formula is allowed to read a variable twice, this problem is at least as hard as the graph isomorphism problem.
Multi-Connection of Double Residual Block for YOLOv5 Object Detection

In this paper, we propose a different method of deepening the network model of YOLOv5s6 and design three types of Multi-Connection (MC) blocks that are suitable for specific datasets. The main purpose of Multi-Connection block is to reuse features and retain input features for passing down. Eight public datasets and one customized dataset are experimented for verification. We improve the residual block in YOLOv5. The results show that the average precision (AP) can be increased. Compared with the YOLOv5s6, YOLOv5s6 with MC type I increases the AP by 1.6% in the Global Wheat Head Dataset 2020, YOLOv5s6 with MC type II increases the AP by 2.9% in the PlanDoc dataset, and YOLOv5s6 with MC type III increases the AP by 2.9% in the PASCAL Visual Object Classes (VOC) dataset. Using the multi-connection of double residual block performs better than the original residual block of YOLOv5s6.
Methods of transmission channel insulator detection based on deep learning model

Using artificial intelligence technology to realize intelligent detection and monitoring of transmission lines is one of the key technologies in the construction of new power system. This paper introduces the target detection of transmission channel based on deep learning method. The deep learning method has the advantages of little influence of super parameters on the results, strong feature extraction ability and strong anti-interference ability. This paper mainly introduces the network framework, including YOLO, SSD, R-FCN, fast R-CNN, etc., and analyses the advantages and disadvantages of various methods. At the same time, the transmission channel insulator detection is verified for various network architectures, and the experiments show the applicability of various types to transmission channel target detection.
Investigating the usefulness of i-vectors for automatic language characterization

Work done in recent years has shown the usefulness of using automatic methods for the study of linguistic typology.However, the majority of proposed approaches come from natural language processing and require expert knowledge to predict typological information for new languages.An alternative would be to use speech-based methods that do not need extensive linguistic annotations, but considerably less work has been done in this direction.The current study aims to reduce this gap, by investigating a promising speech representation, i-vectors, which by capturing suprasegmental features of language, can be used for the automatic characterization of languages.Employing data from 24 languages, covering several linguistic families, we computed the i-vectors corresponding to each sentence and we represented the languages by their centroid i-vector.Analyzing the distance between the language centroids and phonological, inventory and syntactic distances between the same languages, we observed a significant correlation between the i-vector distance and the syntactic distance.Then, we explored in more detailed a number of syntactic features and we proposed a method for predicting the value of the most promising feature, based on the i-vector information.The obtained results, an 87% classification accuracy, are encouraging and we envision to extend this method further.
SuRe VISION

Most Home appliances nowadays are simple implementations of complex Industrial solutions. Security and Surveillance is one such domain which evolved from industry usage to common home safety usage. Home security solutions today come with hefty price points for devices and subscriptions towards data management and backups, as huge data is generated and need to be stored. We tried to find the solution of this problem by harnessing the technological capabilities and implemented a deep learning-based solution and bringing down the cost of both hardware and storage. For high safety, the HD cameras are used everywhere and the video files size is big due to the high-resolution data. Currently surveillance camera manufacturers use compression techniques to reduce the data size and bring down the file size to an optimum level where they can be viewable, but still the data size is large. With Deep Learning based Super Resolution algorithms like EDSR, WDSR, SR-GAN we can compute and generate the high quality videos from low quality videos. Our approach is to store the data with low resolution in less space and generate the high-resolution videos on demand (only when we suspect a security breach or an anonymous activity) this reduces the storage cost a lot as these kinds of situations will raise only once in a while. We also tried to implement a low- resolution camera which will serve the purpose without compromising the security.
Automatic Head Gesture Controlled Robot

In this paper, Head Gestures has been defined as the mode of conversation while interacting with the robot. The head gesture robotic is really helpful to limit human efforts and lift out fantastic results. Head gesture robot makes use of the easy module like Arduino, gyroscope and RF Module, which is discovered to be positive than different Wi-Fi module. The gyroscope relies upon the gestures of the head. Through gyroscope, a passage of data signal is obtained and it is processed with assist of aruino microcontroller. In three-axis gyroscope, one axis will govern the pace in ahead or backward course and different axis will alter the spinning mechanism. The microcontroller provided instructions to the robot to travel in the prefered direction. The primary working precept for the robotic is passage of the records indicators of gyroscope readings to the arduino board equipped on the bot. The usage of the eye blink sensor in the transmitter helps to automatic turn on/off the robot. The software compiled in that arduino runs in accordance to that value, which make the bot characteristics accordingly.
exploratory study on gathering evidences and forms of evidences for successful forensic audit

Evidence is one of the powerful tool required to complete any forensic accounting assignment successfully, keeping in mind this requirement author has tried to elaborate on term evidence its meaning and the different forms in this research paper. This clarity of idea will also help forensic auditor for giving decision on many forensic audits. The researcher has tried to explain in detail the evidence, importance of evidence, forms of evidence and techniques for gathering the evidences in this paper.
A Generic Framework for Depth Reconstruction Enhancement

We propose a generic depth-refinement scheme based on GeoNet, a recent deep-learning approach for predicting depth and normals from a single color image, and extend it to be applied to any depth reconstruction task such as super resolution, denoising and deblurring, as long as the task includes a depth output. Our approach utilizes a tight coupling of the inherent geometric relationship between depth and normal maps to guide a neural network. In contrast to GeoNet, we do not utilize the original input information to the backbone reconstruction task, which leads to a generic application of our network structure. Our approach first learns a high-quality normal map from the depth image generated by the backbone method and then uses this normal map to refine the initial depth image jointly with the learned normal map. This is motivated by the fact that it is hard for neural networks to learn direct mapping between depth and normal maps without explicit geometric constraints. We show the efficiency of our method on the exemplary inverse depth-image reconstruction tasks of denoising, super resolution and removal of motion blur.
Artifact: SmartSPEC: Customizable Smart Space Datasets via Event-driven Simulations

This artifact abstract is a guideline for SmartSPEC [1], a simulator for generating customizable smart space datasets using semantic models of spaces, people, events and sensors. SmartSPEC is based on two main components: (i) Scenario Learning which produces metamodels of events and people using input seed data; and (ii) Scenario Generation which uses SmartSPEC data to generate a realistic smart space dataset. The realistic synthetic smart space dataset generated by SmartSPEC can be used for different tasks including, but not limited to, testing and validating approaches for sensor placement, location-based technologies, and sensor data management.
Policy Implications of IMF and World Bank Loans towards COVID-19 Economic Crisis on African's Development

African countries inevitably take loans due to lack of fiscal space more so, due to the devastating effects of the coronavirus pandemic in Africa, several loans and bailouts are expected from the West. These loans come with conditions which have policy implications. This study aims at examining the impact of loans received by selected African countries from the International Monetary Fund (IMF) and World Bank during the period 1994 to 2020. The study makes use of a cross sectional panel data analysis and quantile regression models. Results indicate that IMF loans had a positively but insignificant impact on GDP whilst World Bank loans and debt service to export ratio negatively affect GDP. These finding suggest that loans are not a necessary evil in Africa but what matters is targeting debt towards growth priorities. This study recommends that policies should be put to direct the use of loans towards economic growth and sustainability in Africa.
Comparative Analysis of Automatic POS Taggers Applied to German Learner Texts

The process of assigning morpho-syntactic categories of each element in the sentence including punctuation marks in a text document according to the context is called Part of Speech (POS) tagging. The article presents the analysis of testing and comparison of five part-of-speech taggers: CoreNLP, spaCy, TextBlob, RFTagger and TreeTagger, based on the texts from the annotated learner corpus of Petrozavodsk State University (PACT, Petrozavodsk Annotated Corpus of Texts). All these tools are applicable to the German language; however, learner texts have their own characteristics, primarily associated with a large number of errors. The problem of scientific research is in finding out how traditional instruments will cope with the task of automatic annotation when they come across a large number of grammatical, lexical and spelling mistakes. The conclusions were drawn about the frequency of errors in the part-of-speech identification, the tagging quality, weaknesses and strengths of each tagger.
The flipped classroom in meaningful mathematics learning: case study eighth year of basic general education

Quality academic training at the different educational levels enhances students&#x2019; meaningful learning. Due to new requirements of the Covid 19 pandemic, education started to rely more on the use of information and communication technologies. This research article aims to describe and analyze the contribution of the &#x201C;flipped classroom&#x201D; model in the meaningful learning of mathematics through the use and design of web 3.0 digital tools. The methodology implemented was experimental - exploratory research, through a structured questionnaire of 17 questions on the Likert scale, which was validated using Cronbach&#x2019;s alpha statistic with a result of 0.846. The TAM model was applied to measure the acceptance of digital resources in virtual learning and the ADDIE methodology was used in the design and application of the 3.0 technological tools for eighth-grade students of basic general education. The contrast of the hypothesis was made by the statistical test of KolmogorovSmirnov for a sample, where a value less than 0.05 was obtained. Therefore, the results proved that the flipped classroom model contributes to the meaningful learning of mathematics, allowing greater development of 30 students in their daily educational activities. In addition, there was an increase in the frequency of the use of digital documents, multimedia resources, and especially web 3.0 resources developed by the teacher to improve virtual teaching, which enhanced optimal and flexible learning within a social setting.