Reviewerly/exHarmony huggingface dataset:
Research and Analysis on Key Techniques of Digital Speckle Correlation Method

Abstract The registration of speckle images plays a key role in many fields, such as: material mechanics; accident monitoring; geodetic survey; missile guidance. Speckle image is composed of a large number of random spots, that it is difficult to obtain obvious feature values through feature extraction. Digital speckle correlation method is to directly extract the surface displacement and deformation information of objects from digital images, so all kinds of digital images can be used as processing objects. However, when the target image quality is poor or has a large deformation, its registration accuracy is difficult to reach the sub-pixel level. In this paper, we will study and analyze the key technologies of existing digital speckle correlation methods.
Texture Redefined: A Second Order Statistical Based Approach for Brodatz Dataset Samples 1–35 (A)

In this research we have used the Brodatz dataset for redefining the texture features. We have computed the second order image statistical parameters like contrast, correlation, energy and homogeneity for defining the features. These features are texture visual features which are affected by the human visual perception. We have computed these features for the first 35 textured surfaces obtained from the Brodatz dataset and on the basis of this we have concluded that which surface have obtained maximum and minimum statistical value and its effects on the human visual perception.
Detection of Conflicts of Interest in Social Networks

In this paper, we introduce a new approach to detect conflicts of interest (COIs) in social networks. We apply this approach to detect COIs in the review process of papers accepted in an international conference that is represented through a social network. This approach consists of extracting some special chains in the studied social network corresponding to conflict of interest cases where the source and target of each chain correspond to an author and a reviewer, respectively. To evaluate the proposed approach, we have conducted some experiments where a comparison with two methods in the literature has been done. The obtained results have shown some efficiency of the proposed approach.
Robotics: development factor or social isolation of the child

In this article, dealing with robotics is defined as a factor driving the development of preschool children. What was created by science fiction writers has become a natural space for the child. Children do not know a world where there are no drones, smart phones, and computers. Robotics is becoming one of the leading activities for children, which determines the development of creativity, initiative, and independence. Joint design acts as a team work environment where children learn to agree on a project topic, discuss problems in its implementation, look for information from different sources, and use digital technologies. However, immersing a child into the world of robotics can isolate him from the society; immerse him into the virtual world. “Digital flashing” of a child‟s brain can affect its cognitive methods, affecting the neural mechanisms responsible for communicating with other people. This problem is being studied by scientists from around the world. Today the world is doubling - life in two spaces - material, objective and virtual, ideal. That is why the determination of the common ground for these worlds determines the harmonization of the children development in modern space. One of this common ground could be robotics classes. In preschool education, the development of technical creativity occurs through the designers of LegoEdu. The logic of designing cognitive-research activities of children is presented.
Design Of E-Document System With Digital Signature Using User Centered Design Method

UCD (User Centered Design) is a design philosophy that places the user at the center of a system development process. The UCD approach has been supported by various techniques, methods, tools, procedures and processes that help design interactive systems that are more user-centered. This study aims to apply UCD in the design of e-document systems with digital signatures to determine user requirements for system functionality and interfaces. To find out the extent of the application of the UCD, a measurement of the level of acceptance and support of prospective users is used by making a data collection instrument in the form of a questionnaire according to the usability testing framework by using a Likert scale to calculate the variables. The results of testing of potential users showed that 85% stated that the functionality and interface design of the e-document system with digital signatures and can be developed in the next stage.
Nonlinear Dynamic System Predictions Based on Neural Networks with Fruit Fly Optimization Algorithms

Due to the nonlinear and dynamic characteristics of industrial production processes, it is rather difficult to establish accurate process prediction models using traditional techniques. The NARX neural network utilizes the past state of the system's input and output which is suitable for nonlinear dynamic system predictions. The NARX neural network uses LM algorithm to adjust the network weight, which results in excessively relying on the initial weight. In response to this problem, the combination of Fruit Fly Optimization algorithm and LM algorithm is suggested to optimize NARX network weights, leading to better performances for nonlinear dynamic system predictions. Simulation experiments on distillation columns were carried out, showing that the NARX neural network optimized by Fruit Fly Optimization algorithm is satisfied.
Application of CT-Pro Algorithm For Crime Analysis

The large amount of crime data generally becomes a pile of data that lacks of information. Data mining can be implemented in various fields such as crime. Data mining techniques can be used to find information from crime data that has been collected by the police. This study analyzed 3.198 crime data of Polresta Yogyakarta in 2016-2018. This study was conducted to determine the pattern of interrelationships between regions with potential crime in the region using association rule mining with CT-PRO algorithm. System testing was done by changing support and confidence values to find best crime patterns. The results were support and confidence values that can produce association rules are 8,59% and 70% with one rule, namely: "If the committed crime is CURAT then the crime occures in MUKIM." The rule has 70,5% confidence, 275 support count and 1,66 lift ratio which means the rule were in the strong category.
Gender Identification Using 3D Touch Sensor Extended Version

This is an extended version of our paper [1] with more results and more contributors in our study, Gender identification has immense value in science and technology. It helps optimizing a software application pertaining to users' gender. In this study, we show how the 3D touch sensor can be used for gender identification. The 3D touch sensor is newly introduced in the handheld mobile devices. This sensor measures how much force the user applies to the touch screen. Therefore, these biometric measurements can be exploited for gender identification. This study develops a special iOS application to collect 3D touch sensor's data from a user. Discrete wavelet transform based features extracted from such data can then be used to train ensemble bagged tree classifier for the purpose of gender identification. The developed iOS application and proposed classifier are tested using data collected from 185 males and 181 females. The results are evaluated in terms of sensitivity and specificity, giving averaged values of 78.2% and 79.1% respectively; however, the sensitivity/specificity could exceed 97% in some cases. Therefore, a 3D touch sensor has a potential value in providing biometric data useful for gender identification.
Exploring the effectiveness of using devices that follow the Sun

The article is devoted to the study of the effectiveness of devices that follow the Sun. For this purpose, the construction of a two-axes tracker with an automatic system of steering the solar panels on azimuth during the light period of the day and with the possibility of manually adjusting the angle of rotation of the solar panels on the zenith, depending on the season is designed. For the proposed construction, the calculation of the angles of the zenith is made and the optimal angles are selected for manual adjustment of the seasonal positions of the system. The photo modules, which are installed stationary on the developed tracker system, are selected. Structural and electrical principle diagram of the stand was developed for determining the efficiency of solar power station, equipment was selected and its manufacture made. A solar power station monitoring system has been developed, which enables to automatically visualize parameters, perform data processing in real time, create databases and perform a comparative analysis of their operational efficiency in an automatic mode. Experimental researches have been carried out on determination of the generated electric energy by static and dynamic solar power stations, on the basis of which the efficiency of tracker systems of rotation of solar panels is confirmed. The use of dynamic systems compared to static allows to increase the production of electricity by 33% under the same conditions. But with poor illumination of the panels (rain, fog, cloudy weather), the tracker's effectiveness decreases and it does not exaggerate 5% when fully covered with the sky. Therefore, the use of tracking systems in the absence of direct sunlight on the surface of photo modules is not feasible.
Application of Web-Based Plant Disease Diagnosis (Case Study: Farmers in Pagaralam City)

The purpose of this research is an expert system to diagnose diseases on web-based rice plants that can make it easy for farmers to get knowledge about rice diseases and how to handle them. Pagar Alam is a mountainous area, the majority of which are farmers. Data obtained from the Department of Agriculture of the City of Pagaralam stated that there were several types of rice planted in the Pagar Alam area, including Situbagendit, IR 64, Padi TS, Inpara 1-8, but most Cierang rice species were planted in the Pagar Alam area. It is not impossible for farmers to experience a yield setback, even to the point of crop failure due to lack of knowledge about the diseases that attack their rice plants. The system development method used is Rapid Application Development (RAD). Therefore, the researchers made an application of a Web-Based Expert System for Rice Disease Diagnosis to Improve the Welfare of Farmers in Pagar Alam City. With the application of an expert system, it can provide information on plant disease pests and can diagnose the symptoms of plant diseases, especially rice plants, as well as provide a solution to overcome them, which can later be used to reduce or reduce the risk of crop damage. The results of the study are in the form of an expert system for diagnosing diseases in web-based rice plants
Performance Analysis of Convolutional Neural Network When Augmented with New Classes in Classification

Teja Sreenivas, K. Venkata Raju, K. Bhavya Spandana, M. Sri Harshavardhan Reddy, D. Bhavani, V.Classification of images is one of the important goals of artificial neural networks. Due to increasing efficiency and accuracy of neural networks today, neural networks have been doing more than image classification, and they are used for image captioning, text detection, and recognition. Deep learning models such as convolutional neural network, recurrent neural network, autoencoders, restricted Boltzman machines, modular neural network, and deep belief networks are widely used across the various domain of problems. The convolutional neural network has been proved successful in computer vision tasks such as object recognition and classification. This paper analyzes how the accuracy and performance of the convolutional neural network are affected while increasing number of classification classes, by augmenting with a new dataset. Through the analysis, it is propounded that the augmentation resulted in an increase in the accuracy and performance of convolutional neural network. In our experimental study, MNIST is used as the primary dataset and Fashion-MNIST is used as the augmented dataset. In our analysis, we observed five times faster convergence time for the MNIST dataset.
Employing a Continuous Measurement Process During Digital Tool Validation

As digital forensic laboratories face regulation and seek accreditation, examiners must fully validate the software and hardware used during forensic analysis. Tool validation is critical to any laboratory science as it provides analysts the empirical data necessary to draw conclusions about how laboratory instruments perform in realistic conditions. While digital forensic tools perform many common tasks, few robust validation methods exist for evaluating how tools perform those tasks. Developing validation methods that are feasible and robust is critical to both establishing digital forensics as a reliable discipline and accreditation requirements. This paper proposes a more comprehensive model of tool validation by taking continuous measurements a tool validation process. This continuous measurement process could later be repeated over all the scenarios that lab would expect to encounter in day-to-day operations. We attempt to determine whether or not a continuous model provides the sort of data necessary for empirical analysis of a digital forensic tool. Moreover, we propose a fully automated model for tool validation, permitting researchers to concentrate on validation study design rather than performing time-consuming, error-prone tasks on the test platform.
Реализация проектного обучения в техническом вузе: энтузиазм преподавателей или сопротивление

The article describes implementation of project-based learning within the scope of different academic disciplines at a technical university. The research aims at analyzing various problem points that teachers face while organizing project work of students. The reasons complicating proliferation of project-based learning are observed. Methodology includes analysis of pedagogical studies, examination of project-based activities at universities, interviewing, and questionnaire survey. The findings lead to the conclusion that project-based learning is interesting for the majority of educators and is considered to be an effective type of activity. However, it is not widely used at a technical university. This happens because of a number of difficulties that occur in the process of project planning and implementation. These are the issues of keeping up motivation, the lack of time for doing projects and presenting results, unwillingness of students to take up self-studies, problems with control and assessment, unevenly distributed learning. The author proves the necessity of teacher professional development in this sphere. The most important units of the programme have been specified. It is recommended that the course trains educators to plan projects with due regard to curriculum, to share knowledge and develop skills significant in a specific area of studies, to nurture contemporary competencies, such as confidence, perseverance, innovative thinking, creativity, flexibility, openness, sociability, teamwork. Moreover, the article describes the importance of discussing the methods of implementing short-term and international projects. According to the research, these types of project work are organized less frequently than the other ones.
A New Approach to Train LSTMs, GRUs, and Other Similar Networks for Data Generation

Deep Learning has played a major role in the field of Artificial Intelligence in solving some of the toughest problems. In this paper, a training approach for deep learning model based on LSTMs, GRUs, and other similar variants is proposed. The effectiveness of this approach is checked on two different models, the first model was based on LSTMs and the second model was based on GRUs. To maintain fairness during comparison few parameters were made constant and then different tests were carried out by varying the dropout. It was found that both LSTMs and GRUs models which were trained using the proposed approach had quickly reduced their training loss without underfitting or overfitting, on the data and converges much faster than the traditional approach. A comparative study is done on a text generation task to show the differences between the quality of data generated by our proposed model with respect to the traditional model.
The model of students’ cognitive activity in the context of interaction with the world

A model of students’ cognitive activity in the context of interaction with the world is presented. The model is based on cognitive and humanistic personality theories. The relevance of the model is confirmed by the need to increase personal activity in society in order to manage problem situations in it. The model includes levels of learning tasks, professional activity imitations and solving real problems, which allows consistent professional and personal development of students. The advantages of this model are the continuous students’ interaction with the world, which increases their social and practical activity, as well as the value of education in a changing world. The model will be able to form the students’ ability to correlate their knowledge and practical experience with problems in reality.
Comparison of Patch-Based Approaches for Interferometric Phase Filtering

Nonlocal methods for interferometric phase filtering achieve competitive performance in terms of both noise eliminating and fringe details preserving, even in condition of low coherence or dense fringes. This paper compares several nonlocal filtering approaches. Experimental results validate that the nonlocal methods outperform the classic methods in terms of both denoising and texture details preservation.
Strategy Synthesis in POMDPs via Game-Based Abstractions

RSS 2019: Robust autonomy: tools for safety in real-world uncertain environments, Freiburg, Germany, Jan 23rd 2019. Workshop on Safe Autonomy
Insulator Segmentation Based on Community Detection and Hybrid Feature

Image segmentation is an important prerequisite for automatic detection of insulator’s surface defects. It is difficult to remove the interference by using traditional methods to segment insulator from aerial images with wires adhering to insulators, pole tower in a large proportion and complex background. To solve these problems, this paper proposes a segmentation method with complex network community detection and hybrid feature for aerial insulator image segmentation. We implement community segmentation for achieving a higher accuracy by using the similarity between pixels. In this method, the image is segmented into super-pixels, then the features of color and texture are calculated to get hybrid feature. Next, we set up a super-pixel network by calculating Gauss similarity of hybrid feature information. Finally, we use the complex network community detection method to extract the insulator. The experiment results demonstrate the presented method is robust, efficient and accurate.
SECOND-DX: Single-model Multi-class Extension for Sparse 3D Object Detection

3D object detection is becoming increasingly significant for emerging autonomous vehicles. Safety decision making and motion planning depend highly on the result of 3D object detection. Recent 3D detection models are optimized for cars, cyclists and pedestrians with multiple models. This is not desirable because multiple models require significant resources, which are also used for other algorithms, such as localization or object tracking. We present SECOND-DX for providing multi-class support for 3D object detection with only a single model and it enables the detection of all three classes of 3D objects scanned using LiDAR sensors in real time. We conducted experiments involving the KITTI 3D object dataset to show that SECOND-DX is more accuracy overall evaluation metrics without compromising execution speed when compared with algorithms extended to support multi-class detection with a single model. Additionally, SECOND-DX can detect pedestrian classes comparable with that of current models that are optimized to support only cyclists and pedestrians.
Face Verification Between ID Document Photos and Partial Occluded Spot Photos

ID-spot face verification is an important problem in face verification area, which aims to identify whether the spotted face is the same to the ID photo. Although some face verification systems have been deployed in many application scenarios, most of them are used in a constrained environment and many key problems need to be addressed furthermore. In this paper, we focus on a challenging ID-spot face verification task, in which the spot photo is partially occluded. Toward this end, a two-stream network is employed to learn more discriminative feature for distinguishing different ID-Spot face pairs. In addition, to suppress the negative effect of background and occlusion, a global weight pooling method is proposed, which makes the available face area more significant than the background and occlusion. The experimental results show that the proposed method obtains 10% improvements on FAR@0.01 compared with previous schemes.
Taming Service Uncertainty through Probabilistic Model Learning, Analysis and Synthesis

Cloud computing owes much of its success to the ease and cost effectiveness with which new systems can be built using remote third-party services. However, the response time, reliability and other quality-of-service (QoS) properties of these services are often uncertain. As such, ensuring that service-based systems achieve their QoS requirements is very challenging. This talk will describe how recent advances in probabilistic model learning, analysis and synthesis can help address this challenge both during service-based system design and verification, and at runtime.
Deceiving Steganalyzer Using Adversarial Noises

Nowadays, CNN based steganalyzers outperform state of the art in detecting stego images, which contain hidden messages. Adversarial noises could deceive the classifier easily and control the predicted label. In this paper, we proposed a method to decorate stego images, that is, adding some more adversarial noises to stego images, which deceives the CNN Steganalyer. Compared to the embedded messages, adversarial noises only changes less pixels. With the help of Syndrome Trellis Code (STC), messages can be extracted at high correct rate. Furthermore, we find the relationship between the amplitude of adversarial noises and STC extraction accuracy. Our results show that less and certain amplitude of adversarial noises are good protection to stego images, not only because the stego images are regarded as cover image by steganalyzer, but the extractor could totally recovery the messages.