Reviewerly/exHarmony huggingface dataset:
Hiding and Exposing Messages by Covert Channel Features

a covert channel is a dangerous communication channel classified as an invisible and undetected security attack. Furthermore, in the different networks, there are several concerns about the packets while transmitting the message on the internet. Especially if there is a hiding data algorithm used to protect the system. Detecting systems can be done using several sectors like machine learning or using special scripting algorithms to hide these data. Based on this research content, the provided idea is that it is worth securing the transmission of packets by using covert channel features to hide the messages in the network between two parties to have a high-security level of protection. Dealing with a covert channel is to concentrates on traffic tunneling or encryption, which is a famous network security method. Furthermore, transmission using channels according to conditions for hiding the data will make the system secure and the user feels safe to send any data which cannot be tracked. Although, the feature of hiding data by using the covert channel is the absolute sense of having protecting system while using a network. Covert channels are used to make information in any transmission hidden, and use the system in save and protected mode always. The focus of the paper is a review about what is the covert channel and an example of hiding data in image type using s-Tool implementing encryption feature while transmitting the data via the internet.
Non-homogeneous Multi-robot Collaboration for Environment Mapping and Inference

Multi-robot collaboration is the subject of a great deal of research currently. This interest within the field of robotics is prominent because multiple robots can often complete complex tasks faster and more efficiently than a single robot. The majority of the research being done today is related to homogeneous robotic communities, wherein each robot has the same equipment and limitations. The scenario becomes more challenging by introducing non-homogenous teams of robots. In this paper, we explore a non-homogeneous robotic community where one robot (acting as master) is designated to map an area while a different robot (acting as slave) infers from that map. Such a system is demonstrated to be reliable within a small and primarily static environment using two mobile robots equipped with RGB-D cameras and planar LiDAR sensors. This type of role assignment allows for more specialized equipment for both robots as well as greater optimization of the robots’ tasks.
A Qualitative Study: Physics Concepts Used by Survivors in the 2018 Tsunami in Palu City

Abstract This study aimed to identify and examine the Physics concepts used by victims of the tsunami disaster in Palu City based on the earthquake, tsunami and liquefaction events that occurred on September 28, 2018 in Palu City. This research was descriptive qualitative. Data were collected by observation, interviews and documentation. Based on the results of the study, the researchers found several Physics concepts that were applied when the victims made efforts to save themselves from the tsunami. These findings were discussed and grouped according to the theme of the Physics concept, including: the concept of speed and velocity, Newton’s Second Law Concept, the concept of floating objects, and the concept of waves. The results can be used as a contextual example in learning physics. By doing so, the students will not only learn about Physics concepts, but also improve their understanding and awareness of local disasters.
Embedded Python

The C programming language has been in operation since the early 1970s and has been central to the development of computer software since then. The Python language is newer and can perform some functions that C does not. So, it is useful to be able to write a C program and have some Python code incorporated (embedded) into it. This is what this chapter will illustrate.
Sim-to-Real Autonomous Vehicle Lane Keeping using Vision

This paper presents the analysis of the J-Net deep neural network used for autonomous vehicle lane keeping, and its verification in simulated and real conditions. The transition from the simulated world on the personal computer to the real world in laboratory conditions is tackled. In the presented solution, autonomous lane keeping is achieved by analyzing information from visual sensors using a deep neural network. J-Net was developed with an aim to be implemented on an autonomous vehicle platform with limited hardware performance in terms of computing power and memory capacity. Verification of autonomous driving using J-Net was achieved in simulated conditions, using an open-source simulator for autonomous driving, and in real-world conditions. For the verification in real-world conditions, an autonomous driving system was designed and implemented in the Laboratory of Electronics at the School of Electrical Engineering, University of Belgrade.
Comprehensive Utilization Analysis of Geophysical Exploration Data Based on Data Mining Algorithm

Geophysical exploration measures the internal structure and resource distribution of the earth through geophysical instruments. A deeper understanding of the earth's internal structure and distribution of resources makes people have the ability to predict a series of geological disasters, and at the same time can help us find oil, natural gas, ore and other resources, which is of great significance for human development. In order to solve the bottleneck problem of large-scale data acquisition network directly managed by data center with the increasing number of sensors, this paper analyzes the comprehensive utilization of geophysical exploration data based on data mining algorithm.
Detecting micro-metastases in the sentinel lymph node by characterizing micro-environments

The sentinel lymph node is a predictor of breast cancer aggressiveness.<sup>1</sup> Patients with micro-metastasis (MM) are usually considered negative, yet their the hazard ratio has been reported to be 2.4 and 1.203.81 with a 95% confidence interval.<sup>2–4</sup> This work proposes an automatic detection of micro-metastasis by quantifying local cellular changes. The proposed strategy characterizes nuclei morphometry, color and texture to establish differences between MM and normal tissue. The color model is obtained from the plane [(r − b), g] while texture corresponds to the Haralick’s features from five different orders of the co-occurrence matrix.<sup>5</sup> This description is complemented by the cellular area obtained from a conventional watershed segmentation. An AdaBoost model, trained with 300 patches of 350 &times; 350 pixels (56000 &mu;m<sup>2</sup> ) randomly selected from 18 cases, was tested in a set of five different cases with approximately ten patches containing micro-metastasis. This approach obtained a best classification accuracy of 0.86, sensitivity of 0.89, specificity of 0, 83, and F-score of 0.86, while the baseline, a ResNet 50 model, obtained 0.74 of accuracy, 0.86 of sensitivity, 0, 63 of specificity, and F-score of 0.77 for exactly the same task.
Study of thin plate bending using the method of holographic moiré

Abstract The development of the technique of applying high-frequency metallized working images to products made of any materials led to the emergence of the holographic moiré method. This method is based on recording holograms in counter beams and is traditionally used to study the displacements in the working raster plane. This paper shows that if, when holograms are recorded, the registering medium is placed at a considerable distance from the working screen surface, then the recorded optical information makes it possible to determine the inclination angles of the surface under study. We present the results of a study of bending of a thin, round, rigidly clamped plate. The plate was subjected to hydrostatic bending. A good agreement between the experimental data and the theoretical solution is obtained.
Investigation of Octane Number Loss Based on Particle Swarm Optimization

The Particle swarm optimization (PSO) is a swarm intelligence algorithm that simulates the predatory behavior of birds. It is inspired by the social behavior of bird flocking. It is widely used in many fields because of its easy implementation, high accuracy, and fast convergence. In this article, we propose a method to improve the performance of the PSO algorithm by combining it with a gradient boosting regression (GBR) model. We apply our algorithm for the optimization of octane number (express in RON) loss in the gasoline industry. RON is the most significant indicator that reflects the combustion petrol performance and it is the commercial brand name of petrol (e.g., 89#, 92#, 95#). Our simulation results demonstrate that RON average loss rate was greater than 30%, under the product's sulfur content was no greater than 5μg/g (Euro VI standard is no greater than 10μg/g).
HOG Based Pedestrian Detection System for Autonomous Vehicle Operated in Limited Area

Research on autonomous vehicle is growing rapidly in recent years. Its ability to navigate without solely depending on a driver enables various applications from daily transportation to high risk expedition. In the navigation system of autonomous vehicle, pedestrian detection plays a fundamental role to avoid accident causing human fatalities. In this paper, we propose a pedestrian detection system using the Histogram of Oriented Gradient (HOG) and Support Vector Machine (SVM). This system is designed for use in limited campus area i.e. roads connecting campus buildings. We collected 2000 image samples of roads with or without people passing by. We used 90% of those samples for training the model, while another 10% was used for testing. The model is able to distinguish the number of people on the road in the field of view from zero to four people with the accuracy of 98.00%.
Investigating a Different Approaches to Resolve Binary Classification Task with Unbalanced Dataset

This work represents methods and approaches for training artificial intelligence when dataset is unbalanced. It helps to use existing dataset instead of generating new one which can be very hard and complex. To measure such methods specific performance metrics were provided. Methods and approaches were applied and results were generated.
Conversation Recommender System Based on Knowledge Graph and Time-series Feature

The conversation recommender system aims to recommend high-quality items to users through interactive conversations, which requires to be seamlessly integrated between the recommendation module and the dialog module. The knowledge graph has improved the accuracy of the dialogue recommendation system to a certain extent. However, there are still some defects that make it easy to generate more general and popular responses. For overcoming these shortcomings, this paper proposes a novel framework based on knowledge graphs and time-series features called KGTF. By serializing and modelling the dialogue content, the KGTF is able to learn the feature relationship between users and items to make more accurate recommendation. Besides, the location encoding is introduced to increase the diversity of generated responses. Experiments conducted on widely adopted benchmarks show that the proposed KGTF framework is superior to the latest KGSF method.
Performance analysis of the TensorFlow library with different optimisation algorithms

This paper presents the results of performance analysis of the Tensorflow library used in machine learning and deep neural networks. The analysis focuses on comparing the parameters obtained when training the neural network model for optimization algorithms: Adam, Nadam, AdaMax, AdaDelta, AdaGrad. Special attention has been paid to the differences between the training efficiency on tasks using microprocessor and graphics card. For the study, neural network models were created in order to recognise Polish handwritten characters. The results obtained showed that the most efficient algorithm is AdaMax, while the computer component used during the research only affects the training time of the neural network model used.
Non-cooperative Airport Panorama Surveillance

As critical air traffic control (ATC) to prevent runway incursion, video replaces the cooperative sensor, visual tracking is emerging. In this paper, we focus on visual tracking in airport panorama video. A novel Horn-Schunck density forming (HSDF) is developed that multi-objects automatically. Specifically, based on Horn-Schunck theory, to estimate the dense optical flow field of the video, and marking multi-objects automatically by density forming. On the other hand, we extend Fast Compressive Tracking (FCT) into the multi-objects framework, namely multi-objects FCT (MFCT). The benefit of MFCT is particularly attractive in airport panorama surveillance. It is verified by simulations with trials video are collected at Chengdu Shuangliu International Airport of China, which ensures the reliability of research. The work presented here is practical and straightforward; it brings an evident engineering application prospect.
An Efficient Man-Machine Recognition Method Based On Mouse Trajectory Feature De-redundancy

Behavioral authentication codes are widely used to resist abnor- mal network traffic. Mouse sliding behavior as an authentication method has the characteristics of less private information and easy data sampling. This paper analyses the attack mode of the machine sliding track data, extracts the physical quantity characteristics of the sliding path. Features importance scores are used to select the candidate features, and further Pearson correlation co- efficient is used to filter out the features with high correlation. This paper use XGBoost model as a classifier. In addition, an efficient evasion attack detection method is proposed to deal with complex human behavior evasion attacks. The experiment was carried out on two mouse sliding datasets. The experimental results show that the proposed method achieves 99.09% accuracy and 99.88% recall rate, and can complete the man-machine identification in 2ms.
E-Studio: The use of information and communication technologies in the development of drawing competences in different educational environments

In the age of COVID, art teachers face the unprecedented situation of teaching at distance a subject that involves hands-on activities with tangible tools and materials. Therefore, we have implemented e-studio workshops where the mentor and participants are not in the same room, but they interact as if they were. By using audio-video conferencing to link them together, it was possible to maintain two-way communication. This alternative option retains all the elements of live interaction. Since, unlike drawing in a real physical environment, the image on the screen that participants are asked to draw is the same for everyone, there are no views from different angles as in real space. This changes the relationship with the model and requires technical adaptations, which are presented in this article. The goals of the project included bringing distance learning closer to workshop providers, finding the optimal way to develop drawing skills at a distance, finding software that allows quality corrections, adapting the way knowledge is delivered to people with different needs, and enabling social interaction between participants and the mentor. Such an approach opened up new perspectives on the experience of drawing from a model and on the nature of drawing instruction.
A Data Recognition Technology of Aviation Virtual Instrument Screen

The instrument of the aircraft in the aviation industry shows the real-time navigation status of the aircraft, which is an important basis for judging the flight status. This paper uses the PnP algorithm to calculate the pose relationship between the camera and the target screen and solves the problem of the effect of the pose relationship between the camera and the target screen on image processing in visual inspection. And through the RAC model to improve the traditional DLT algorithm, which can quickly and accurately obtain the camera pose parameters. The algorithm uses radial constraints to add constraints to the DLT algorithm. While maintaining the low time consumption of the traditional DLT algorithm, it improves the accuracy of the DLT algorithm for camera pose estimation.
Assisting PV Experts in On-Site Condition Evaluation of PV Modules Using Weather-Independent Dark IV String Curves, Artificial Intelligence and a Web-Database

Photovoltaic (PV) modules can make a huge contribution to achieve the Sustainable Development Goals of the United Nations. To be able to make that contribution, regular check-ups and evaluation of installed PV modules are necessary as they can develop faults and degenerate over time. In this project, we improve the dark IV string curve method used for on-site fault detection and module evaluation. We do so by training artificial intelligence (AI) models to predict the maximum power point and the bright IV curve of PV modules given the weather-independent dark IV string curve. We present some background on this topic, describe the data used for training and the developed models. The results are illustrated graphically. To make the models available for PV experts in practice and to support their decision-making process, we also developed the web-database-application iPVModule for storing historical PV Module data and integrated the AI-models.
Bilder in Vorlesungen, Hörsäle als Bilder: diagrammatische Überlegungen

Abstract Visual material plays a central role in lectures to illustrate the spoken word or to show objects of knowledge. Historically, the question arises as to when which methods were used and what their functions were and still are today. In a further diagrammatic perspective on the setting of the lecture, however, other aspects of pictoriality must be included: For example, there is a tradition of storing, commenting on, processing and editing lectures by the audience, which leads, for example, to transcripts that transform the lecture medially. Yet these techniques are embedded in an ensemble of diagrammatic practices of lecture organisation, which can be understood as „instructions for use“ for both lecturers and listeners. From a diagrammatic perspective, it becomes clear that the diagrammatic orders applied in and by lectures are not simply ornaments of the lecture, but have a knowledge-constitutive effect.
Application of neural network method in engineering prediction

Abstract Data mining is widely used in engineering. Neural network method is one of the methods of data mining. It has obvious advantages in engineering prediction and numerical estimation. Based on the analysis of neural network algorithm, this paper uses neural network algorithm to predict the moisture content of used sand in casting production, and obtains an effective method to judge the moisture content by voltage.
Crowdsourced Linked Data Question Answering with AQUACOLD

There is a need for Question Answering (QA) to return accurate answers to complex natural language questions over Linked Data, improving the accessibility of Linked Data (LD) search by abstracting the complexity of SPARQL whilst retaining its expressiveness. This work presents AQUACOLD, a LD QA system which harnesses the power of crowdsourcing to meet this need.
GBNet: Gradient Boosting Network for Monocular Depth Estimation

Recently, neural networks have shown promising results in estimating depth from a single image. A large amount of per-pixel ground truth depth data is required to train the neural network in supervised learning. However, the dense depth data of ground truth is challenging to collect in realistic dynamic environments. To solve this problem, many researchers propose self- and semi-supervised learning as a credible alternative. This paper proposes a novel self- and semi-supervised monocular depth estimation method, inspired by the gradient boosting method. The existing gradient boosting method provides training to several sequential, additive, and gradual models for minimizing the error. Similarly, we design our proposed network to refine the predicted depth map sequentially and gradually generate a high-quality depth map via multi-stack CNN structures. Our method shows the state-of-the-art results for monocular depth estimation on a DDAD (Dense Depth for Autonomous Driving) dataset.
Disciplined or Agile?

Software requirements changes become necessary due to changes in customer requirements and changes in business rules and operating environments; hence, requirements development, which includes requirements changes, is a part of a software process. Previous studies have shown that failing to manage software requirements changes well is a main contributor to project failure. Given the importance of the subject, there is a plethora of efforts in academia and industry that discuss the management of requirements change in various directions, ways, and means. This chapter provided information about the current state-of-the-art approaches (i.e., Disciplined or Agile) for RCM and the research gaps in existing work. Benefits, risks, and difficulties associated with RCM are also made available to software practitioners who will be in a position of making better decisions on activities related to RCM. Better decisions can lead to better planning, which will increase the chance of project success.
A Despeckling Filter for Ultrasound Images Based on Cellular Automata Approach

In the medical profession, ultrasound imaging for clinical recognition is far better suggested than any other imaging mode because of its adaptability, suitability, safety, and viability. The images that are taken and then preprocessed as part of the procedure are crucial in medical diagnosis. Different types of noise can be added to the images during the uploading process. The more deteriorated the image, the more difficult it is to better isolate the required components, recognize them, and examine edge clarity. In this study, a comparison is made between several filters such as Gaussian, Kuan, Lee, guided, and anisotropic filters, as well as the proposed filter, which is based on the cellular automata principle. Developing the proposed filter using the cellular automata approach yields superior results in terms of image parameters accessible for consideration.
CNN-based blind SIR classification framework for STPA-BAA spectrum superposing

Spectrum sharing in the spatial domain among the plurality of wireless communication systems should be addressed due to its exhaustion. We previously conceptualized a spectrum superposing enabled by subcarrier transmission power assignment (STPA) and blind adaptive array (BAA), where co-channel interference can be suppressed even without a priori information of interferer. However, it requires knowledge of input signal-to-interference power ratio (SIR) to realize completely blind operation. This paper proposes a blind SIR estimation framework by convolutional neural network (CNN) using power spectrum images. Simulation verifies the proposed scheme can maximize throughput performance based on SIR classification results with 97% accuracy.