Reviewerly/exHarmony huggingface dataset:
Development of scale and illumination invariant feature detector with application to UAV attitude estimation

Feature detection has great importance in many applications such as vision navigation. Examining the developed detectors, it is found in many recent studies that most of the scale-invariant detectors are sensitive to illumination. In this work, we propose a novel detector that has good robustness to both scale and illumination. Motivated by the good robustness of Log-Gabor kernels toward light changes, we employ these kernels as a basis to construct the scale space. To detect potential features, we develop an effective interest points measure which is motivated by the concept of the autocorrelation and Hessian matrices. To confirm the good performance of our detector, we hold experiments on many datasets and with comparisons to common state-of-the-art methods. Furthermore, we evaluate the saliency of the detected features on a UAV attitude estimation task.
Promoting Prezi-PowerPoint presentation in mathematics learning: the development of interactive multimedia by using ADDIE model

Abstract Prezi-PowerPoint presentation results from developing learning media that combines PowerPoints with zoom-in and zoom-out effects, which are the hallmarks of Prezi. Prezi-PowerPoint presentation is a new learning medium for students designed to create an interactive and fun learning environment. The development of interactive learning media Prezi-PowerPoint presentations in mathematics learning aims to help students improve learning efficiency to achieve learning objectives. The research method used is research and development using the ADDIE model. The research implementation is limited to two media experts, one material expert, and three students. This is because the research was conducted at the time of the COVID-19 pandemic. The students response to the development of interactive learning media in the Prezi-Powerpoint presentation was good. It could happen because Prezi-PowerPoint presentation is a learning media that is new and attractive to students. Based on the feasibility test of learning media, it is obtained that the average percentage of the feasibility of developing learning media Prezi-PowerPoint presentation is suitable to use with good interpretation. Thus, Prezi-PowerPoint presentations are also relevant to use as an interactive learning media for students.
On the etymology of the Japanese plural suffix and its possible connection to Korean

Abstract This paper presents an etymological analysis of the Japanese plural suffix tachi , Old Japanese tati . I propose that tati originates from a grammaticalization of an earlier Pre-Old Japanese phonological form *totwi, the non-bound reflex of which is the Old Japanese quasi-collective marker dwoti ‘fellow (person), everyone, together’. The reconstruction of a Pre-Old Japanese stem *totwi (Pre-Proto-Japanese /*tətəj/) with quasi-collective and plural function clarifies the possible connection of the Japanese plural suffix to the Korean plural suffix tul (Middle Korean tólh ), which Whitman ( 1985 , p. 217) proposed to be cognates but which has since been criticized on phonological and distributional grounds. I show that reconstructing the earliest form of the Japanese plural suffix as /*tətəj/ resolves each of the three phonological issues with the Japano-Koreanic comparison, creates a better morphosyntactic match between the two languages, and rules out a loanword relationship of the Japanese and Korean forms.
Autonomous Vehicle Based on Deep Q-Learning and YOLOv3 with Data Augmentation

This paper presents a model which imitates human in all ways of driving without human intervention be it recognizing the car states. It incorporates the car speed, position to track, and the surrounding environments based on a combination of Deep Q-Learning and YOLOv3 on the Raspberry Pi computer. Environmental learning is done using Deep Q-Learning. Data can be collected by using Raspberry pi and a front-facing camera. A DNN model with Deep Q-Learning is trained to get high performance in recognition and control tasks. Deep Q-Learning approximates the values by using Deep Neural Networks (DNN). The neural network takes the initial state as input returns the Q-value of all possible actions as an output. Deep Q-Learning addresses correlations between samples and non-stationary by experience replay and fixed Q-targets. YOLOv3 with data augmentation is used to detect and recognize stationary and movable objects, traffic lights, and road signs. YOLOv3 has improved bounding box prediction, more accurate class predictions, and improved abilities at different scales and is much faster. It is easy to optimize since it works based on algorithms that use only one neural network to execute all the task components. YOLOv3, along with data augmentation, significantly increases the data diversity without actually collecting the new training data.
Evaluating the Performance of Novel JWT Revocation Strategy

JSON Web Tokens (JWT) provide a scalable, distributed way of user access control for modern web-based systems. The main advantage of the scheme is that the tokens are valid by themselves – through the use of digital signing – also imply its greatest weakness. Once issued, there is no trivial way to revoke a JWT token. In our work, we present a novel approach for this revocation problem, overcoming some of the problems of currently used solutions. To compare our solution to the established solutions, we also introduce the mathematical framework of comparison, which we ultimately test using real-world measurements.
A new vibration-based hybrid anomaly detection model for preventing high-power generator failures in power plants

This paper presents a new vibration-based hybrid anomaly detection model to prevent high-power generator failures in power plants. This idea was conceived as it causes large amounts of energy and economic losses once the generator remains in turn-off positions for a long time due to malfunctions. Furthermore, the failures in high-power generators, especially during peak load demanded time, can even cause systemic shortcomings. To overcome these limitations, multivariate linear regression, response surface methodology, and multi-layer perceptron-based hybrid anomaly detection model were designed to detect anomaly cases. The real-time vibration data collected by 20 distinctive sensors on powerful generators of each 360 MW from the Afşin-Elbistan B Thermal Power Plant were used. The hybrid model succeeded in determining the impact degrees of the sensors on the anomaly, and the number of effective sensors was reduced from 28 to 9. The most effective sensor for anomaly cases was determined as Pedastal 3 Vibration (x-axis), whereas the sensor with the most negligible impact was determined as Rotor 6 Vibration (y-axis). The model performance evaluation metric (R2) of the designed hybrid model was calculated over 0.92. The performance results were presented in graphs and tables.
PriPro: Towards Effective Privacy Protection on Edge-Cloud System running DNN Inference

The huge computation demand for deep learning models and limited computation resources on the edge devices calls for the cooperation between the edge device and cloud service. On a typical edge-cloud system accommodating DNN inference, a deep model is split into two partial models running on the edge device and the cloud service, respectively. The two partial models collaborate closely to satisfy the DNN inference requested by the user. However, user's privacy is vulnerable when transferring the intermediate results generated by the partial model at edge device to cloud service. Existing research works rely on metrics that are either impractical or insufficient to measure the effectiveness of privacy protection methods in the above scenario, especially from a single input aspect. In this paper, we first thoroughly analyze the state-of-the-art methods and drawbacks of existing methods from the aspects of both evaluation metrics and proposed techniques. Then, we propose a new metric system, including privacy accuracy (PA) and privacy index (PI), that can accurately measure the effectiveness of privacy protection methods. Furthermore, we propose PriPro, a privacy protection method that can dynamically inject noise to the intermediate results at various layers regarding the input features through the self-attention mechanism. The experiment results demonstrate our method outperforms existing methods for protecting user privacy on deep models such as AlexNet, VGG, and ResNet.
Generalized Net Model of Information Security Activities in the Automated Information Systems

A generalized net model of information security activities in automated information systems is constructed. The processes of information security are investigated, and the separate steps are analyzed. The constructed generalized net model describes the procedures of detecting security vulnerabilities, calculating the risk of attack, implementation of threats and security policy execution.
Detection and Blur-Removal of Single Motion Blurred Image using Deep Convolutional Neural Network

This paper proposes a simple and efficient motion blur detection and removal method based on Deep CNN. The domain of computer vision has gained significant importance in recent years due to insurgence in the fields of self-driving cars, UAVs, medical image processing, etc. Due to low light conditions and the camera’s fast motion, a large portion of image data generated is wasted. Such motion-blurred images impose a great challenge to the algorithms used for decision-making in machine vision. Although there have been significant improvements in denoising such image data, these methods are challenged by time constraints, insufficient data to train, reconstructed image quality, etc. The proposed paper employs a learning method to detect and deblur the single input image even in the absence of a ground-truth sharp image. We have used a synthetic dataset for experimental evaluation. This synthetic dataset that we have created and used for training the DCNN model has been made available for open source on Kaggle at the following link: https://www.kaggle.com/dikshaadke/motionblurdataset
Study of the Optimal Waveforms for Non-Destructive Spectral Analysis of Aqueous Solutions by Means of Audible Sound and Optimization Algorithms

Acoustic analysis of materials is a common non-destructive technique, but most efforts are focused on the ultrasonic range. In the audible range, such studies are generally devoted to audio engineering applications. Ultrasonic sound has evident advantages, but also severe limitations, like penetration depth and the use of coupling gels. We propose a biomimetic approach in the audible range to overcome some of these limitations. A total of 364 samples of water and fructose solutions with 28 concentrations between 0 g/L and 9 g/L have been analyzed inside an anechoic chamber using audible sound configurations. The spectral information from the scattered sound is used to identify and discriminate the concentration with the help of an improved grouping genetic algorithm that extracts a set of frequencies as a classifier. The fitness function of the optimization algorithm implements an extreme learning machine. The classifier obtained with this new technique is composed only by nine frequencies in the (3–15) kHz range. The results have been obtained over 20,000 independent random iterations, achieving an average classification accuracy of 98.65% for concentrations with a difference of ±0.01 g/L.
Sketch-Based Image Retrieval Using Convolutional Neural Networks Based on Feature Adaptation and Relevance Feedback

Sketch-based Image Retrieval (SBIR) is an approach where natural images are retrieved according to the given input sketch query. SBIR has many applications, for example, searching for a product given the sketch pattern in digital catalogs, searching for missing people given their prominent features from a digital people photo repository etc. The main challenge involved in implementing such a system is the absence of semantic information in the sketch query. In this work, we propose a combination of image prepossessing and deep learning-based methods to tackle this issue. A binary image highlighting the edges in the natural image is obtained using Canny-Edge detection algorithm. The deep features were extracted by an ImageNet based CNN model. Cosine similarity and Euclidean distance measures are adopted to generate the rank list of candidate natural images. Relevance feedback using Rocchio's method is used to adapt the query of sketch images and feature weights according to relevant images and non-relevant images. During the experimental evaluation, the proposed approach achieved a Mean average precision (MAP) of 71.84%, promising performance in retrieving relevant images for the input query sketch images.
Multi-valued cognitive maps: Calculations with linguistic variables without using numbers

A concept of multi-valued cognitive maps is introduced in this paper. The concept expands the fuzzy one. However, all variables and weights are not linearly ordered in the concept, but are only partially-ordered. Such an approach allows us to operate in cognitive maps with partially-ordered linguistic variables directly, without vague fuzzification/defuzzification methods. Hence, we may consider more subtle differences in degrees of experts' uncertainty, than in the fuzzy case. We prove the convergence of such cognitive maps and give two simple computational examples which demonstrate using such a partially-ordered uncertainty degree scale compared to the fuzzy case.
A Data Chaining on Relational Database: A Case Study with Indonesian Genomics Information System

The Indonesian government has been showing solemnity in managing the national biodiversity since the ratification of the Nagoya Protocol in 2013. This protocol contains the urge of utilizing information technology to support the management process among all countries that implement the protocol. In Indonesia, information technology has not been deeply applied to solve the biodiversity resource problems, especially in genomic data management. Related to the biopiracy and management problems, the need of a sophisticated technology with high acceptability and easy operational method has been recognized. In this research, a relation database management system (RDBMS) for genomic information system has been proposed. The protection against biopiracy is accomplished by adopting the data chaining rules from blockchain technology. An encryption system has been employed to improve security and integrity. The complete proposed system, RDBMS-Chain, could perform auditing, checking, and serving integrity.
Perph: A Workload Co-location Agent with Online Performance Prediction and Resource Inference

Striking a balance between improved cluster utilization and guaranteed application QoS is a long-standing research problem in cluster resource management.The majority of current solutions require a large number of sandboxed experimentation for different workload combinations and leverage them to predict possible interference for incoming workloads.This results in nonnegligible time complexity that severely restricts its applicability to complex workload co-locations.The nature of pure offline profiling may also lead to model aging problem that drastically degrades the model precision.In this paper, we present Perph, a runtime agent on a per node basis, which decouples ML-based performance prediction and resource inference from centralized scheduler.We exploit the sensitivity of long-running applications to multi-resources for establishing a relationship between resource allocation and consequential performance.We use Online Gradient Boost Regression Tree (OGBRT) to enable the continuous model evolution.Once performance degradation is detected, resource inference is conducted to work out a proper slice of resources that will be reallocated to recover the target performance.The integration with Node Manager (NM) of Apache YARN shows that the throughput of Kafka data-streaming application is 2.0x and 1.82x times that of isolation execution schemes in native YARN and pure cgroup cpu subsystem.In TPC-C benchmarking, the throughput can also be improved by 35% and 23% respectively against YARN native and cgroup cpu subsystem.
A place-oriented approach: from field knowledge to design framework

Media architecture has become increasingly common in public spaces, including public displays, media facades and interactive installations. These design interventions bring not only new appearances to the city but also facilitate new experiences and interactions in public spaces. Along with the continued interest in developing a better understanding of the impact of media architecture on people's sense of place, we seek to develop a place-oriented approach that incorporates the consciousness of place to create installations that resonate with people and places. In this study, we reviewed existing media architecture installations to develop a better understanding of the opportunities and challenges for media architecture to support placemaking. Based on this analysis, we reflect on what we have learned from the field study and explore how we might translate field knowledge to a design framework that supports a place-oriented approach.
Evaluation Model of Physical Education Effect: On the Application of Radial Basis Function-Particle Swarm Optimization Neural Network (RBFNN-PSO)

This study constructs a new radial basis function-particle swarm optimization neural network (RBFNN-PSO) system, which is applied to the evaluation system of physical education teaching effect. In order to verify the evaluation performance of the RBFNN-PSO system, the traditional RBF neural network system is used as the control, and the training is carried out. The results show that the RBFNN-PSO system can reach the convergence value faster than the traditional RBF neural network system in the training, and the training error is smaller. The results show that the scoring error of RBFNN-PSO system is smaller than that of RBF neural network system, with higher accuracy and smaller error. The experimental results show that the RBFNN-PSO is superior to the traditional RBF neural network in error and accuracy.
Cyberbullying Predictive Model: Implementation of Machine Learning Approach

Machine learning is implemented extensively in various applications. The machine learning algorithms teach computers to do what comes naturally to humans. The objective of this study is to do comparison on the predictive models in cyberbullying detection between the basic machine learning system and the proposed system with the involvement of feature selection technique, resampling and hyperparameter optimization by using two classifiers; Support Vector Classification Linear and Decision Tree. Corpus from ASKfm used to extract word n-grams features before implemented into eight different experiments setup. Evaluation on performance metric shows that Decision Tree gives the best performance when tested using feature selection without resampling and hyperparameter optimization involvement. This shows that the proposed system is better than the basic setting in machine learning.
Video QoE Inference with Machine Learning

HTTP adaptive streaming (HAS) has become the de-facto standard for delivering video over the Internet. More content providers like YouTube and Twitch have started generating and delivering high quality streams (usually 4k resolution) with advanced end-to-end encryption mechanisms. This huge increase in HAS encrypted traffic, creates a significant challenge for network providers in understanding what is happening on their infrastructures which limits their ability to manage network infrastructures properly. Due to such invisibility, the network providers could not take appropriate decisions for better optimizations, resulting in significant revenue lost. Inferring the quality of experience (QoE) of HAS-based streaming video services is important, but recent studies highlight that most of existing solutions that rely on packet inspections, showing low performance in inference accuracy. To address this issue, we develop a machine learning powered system that infers QoE factors such as startup delay, rebuffering and selected quality, for encrypted on-demand HAS streaming video services. Our solution uses two data-driven techniques: Deep Self Organizing Map (DSOM) and Multi Layer Perceptron Backpropagation (MLPB), allowing efficient accuracy with low error in inferring QoE factors over several public video datasets, compared to some state-of-the-art approaches.
A Gaussian Process Enhancement to Linear Parameter Varying Models

Simulation and analysis for modern engineering systems now routinely requires the merging of multiple disciplines, physical-domains, time-scales, and data sets — all at ever increasing levels. These capabilities are especially needed in the domain of Advanced Air Mobility, where rapidly emerging vehicle designs are significantly more complex, while having to be both cost-effective and safe. To meet these engineering challenges, machine learning methods are an attractive option for merging models and data across multiple areas while providing uncertainty quantification and maintaining computational efficiency. This paper examines the use of Gaussian process machine learning to generalize and enhance the commonly used class of quasi-Linear Parameter Varying models for fast full-envelope simulation while also supporting control system design and analysis with model uncertainty. Gaussian process machine learning is selected because it: can fuse multiple data sets, enables an easy trade-off between data fitting and smoothing, provides model uncertainty quantification, scales well with increasing complexity, and does not generally require starting from a large training data set. To demonstrate the benefits of the approach, a robust stability analysis with Gaussian process uncertainty is shown for a NASA reference design of an electric quad-rotor air-taxi concept vehicle with motor parameter uncertainty.
Cooperative localization between robots using vision and path planning algorithm

The aim of this article is to provide a solution for obstacle avoidance in order to navigate a robot from an initial point to a final point. The considered robots are i) NAO Humanoid robot and ii) Rosbot 2.0 a wheeled robot. The main focus of this article is to understand the collaboration between robots to reduce positioning errors. By integrating Inverse Perspective Mapping techniques (IPM) and the A-Star algorithm the image processing robot (NAO) would be able to instruct the wheeled bot (Rosbot) to navigate around obstacles more effectively. This method shows the capability to advance the collaboration between robots in order to diagnose different collisions and develop different path following techniques. Even though odometry is a widely used technique for calculating the position of robots it has certain drawbacks in the long run. To combat this we addressed solutions to these drawbacks by using Inverse Perspective Mapping and the A-Star algorithm. When IPM is applied calculating the position of the obstacles relative to the wheeled robot makes the path network more accurate. Additionally by applying the A-Star algorithm the path network can be analyzed to find the most direct route. The attractiveness of this methodology simplifies the navigation process by analyzing the data at a birds-eye view and computing decisions that will instruct the wheeled robot to take the shortest path. The performances of the proposed approach are analyzed by means of a series of experiments performed in a static environment.