Reviewerly/exHarmony huggingface dataset:
Review of Authentication Algorithms for Physical Layer Attacks

Recently, Internet of Things (IoT) plays a major role in the various fields like smart cards, education, intelligent transportation, and so on. Even though IoT networks have many benefits, security in the network is a major concern, as there is a chance for leakage of information in the network. Hence, secure authentication is a major concern in the IoT networks, so that the IoT devices can control, manage, and communicate. In this paper, 30 research papers are reviewed based on different techniques adopted for physical layer attacks. The survey of various research works are made by classifying the existing research papers based on the attacks and security mechanisms, like mutual approach, lightweight scheme, dynamic approach, and cryptographic based method, and so on, are used in the IoT device security. Then, physical layer attack analysis and comprehensive threat analysis are carried out. Moreover, the research gaps and issues identified in the existing authentication techniques are listed out so that the researchers can bring a solution and enhance their researches in the future. The works reviewed in the literature are analyzed based on various aspects, like tool set, physical layer parameters and performance evaluation metrics.
Automatic Head-Nod Generation Using Utterance Text Considering Personality Traits

We propose a model for generating head nods from an utterance text considering personality traits. We have been investigating the automatic generation of body motion, such as nodding, from utterance text in dialog agent systems. Human body motion varies greatly depending on personality. Therefore, it is important to appropriately generate body motion according to the personality of the dialog agent. To construct our model, we first compiled a Japanese corpus of 24 dialogues including utterance, nod information, and personality traits (Big Five) of participants. Our nod-generation model also estimates the presence, frequency, and depth during each phrase by using various types of language information extracted from utterance text and personality traits. We evaluated how well the model can generate and estimate nods based on individual personality traits. The results indicate that our model using language information and personality trails outperformed a model using only language information.
A Securities Settlement Model Using Blockchain Technology for Central Securities Depository

Post-trade securities settlements entered the electronic age between 1980 and 2000. The introduction of technologies such as secure electronic messaging, and improvements in database technology, enabled the inception of central securities depositories (CSDs) as trusted third parties or intermediaries within the securities settlements post-trade landscape. The study reported in this chapter has a focus on CSDs and the application of the blockchain technology to securities settlements. The objective is to develop a model for securities settlements using blockchain technology for a CSD, as currently, globally, no CSD has introduced a production-ready blockchain-based solution for securities settlements. A conceptual model was created from the reported literature that was evaluated by international post-trade securities professionals. The findings have resulted in the acceptance of the main components of the model, with a focus on the cost of the solution, and with the identification of prerequisites to such a solution (e.g., legal/regulatory enablement).
Self sanitizing voice controlled intelligent wheelchair

Abstract We have developed a “Self Sanitizing Voice Controlled Intelligent Wheelchair.” The wheelchair will be totally voice controlled. There will be sanitizing system for both the wheelchair and the surrounding area. In Covid-19situation, maintaining social distance is very important. Keep this in mind we will add the alert system in the wheelchair. When any person will come very close to the wheelchair it will announce a voice alert. Also this wheelchair will climb the stair without anyone’s help. This wheelchair will be developed for the disabled person as well as for the Corona patient. The assistant of the disabled person or the disabled person can be Corona affected. As the wheelchair is voice controlled, it will not need someone to push the wheelchair. It will also helpful to carry the Corona patient, where the nurse or assistant can move the wheelchair by giving voice command from a safe distance. The wheelchair will have voice controlled sanitizing system where it will be able to sanitize itself and its surrounding area. After carrying the Corona patient this wheelchair can be sanitized without going near to it. Also this wheelchair will help to sanitize the patient’s room. So, before entering the patient’s room, by giving voice command the room can be sanitized.
Deep noise estimation and removal for real-world noisy images

This paper proposes a joint noise estimation and removal network for real-world noisy images. We observe that the noise variance depends on the image intensity and the variances of neighboring pixels are not similar due to the demosaicing interpolation. Therefore, for noise estimation, we propose to pack each 2 × 2 noisy block into a 4D vector as the input of the noise level estimation network, which makes the noise variances of adjacent pixels to be similar. This strategy not only reduces the computational complexity but also improves the estimation accuracy. For noise removal, we propose to concatenate the estimated noise variance with the noisy image as the input of the noise removal network, which takes advantages of the correlations between noise levels and image intensities. In addition, we propose a RSD (Residual SE D-Conv) block, which explores the spatial and channel correlations of realistic noise. Considering the realistic noisy and clean image pairs are limited for training, we utilize synthetic noisy images to train the network first and then fine tune the network with real images. Experimental results demonstrate that the proposed method outperforms competing methods for both noise estimation and removal tasks.
Develop on real-time image processing system based on FPGA and RAM combined with PC or Cloud system

To address the current Instant Image Process, we designed an embedded hardware system based on FPGA(Field-Programmable Gate Array) and ARM(Advanced RISC Machine) in this project, in order to realize the instant acquisition, processing, recognizing and displaying of images. The system acquires and initially processes the image via the cameras controlled by FPGA, after that the data is buffered into the SDRAM (synchronous dynamic random-access memory). And then the ARM sends the SDRAM cached data to the PC (cloud) side through the WIFI module for advanced processing. The processed data then is transmitted back to the SDRAM through WIFI and displayed by the LCD. By the above means, the system realizes the high-speed acquisition and parallel processing of images.
The Method and Tools Development for Web-Cameras Color Correction in Binocular Vision Systems

The possibilities of using an adaptation principle in application for organizing the close-loop life circuit of autonomous fly vehicles (FV) are discussed in chapter. The uncertainties arising at each stage of the life cycle of an autonomous fly vehicles (FV) are considered. To solve a problem, the approach using intelligent, rational objects, and using knowledge database tool is proposed. The main theses of rational adaptation control system (CS) are represented. The preliminary designing tools for constructing rational adaptation algorithms for motion control system (CS) are considered. The practical applications of the proposed approach at the stage of preliminary design of control systems for autonomous fly vehicles are presented.
Semantic Word Segmenting Technique Based on Artificial Bee Colony Algorithm

Because of the complexity and uncertainty of sentences, ambiguous information often appears in sentence segmentation. To the sentence ambiguity division problem, we proposed a method that can solve the semantic information in the sentence and can select the breakpoint of word dynamically. This study proposed and designed a single-objective discrete optimization model based on the ontology technology of lexeme semantic information. This model analyzed the semantic information of the sentences through the ontology data analysis, combined the case data to calculate the relevant semantic word frequency information; then proposed an optimized artificial bee colony algorithm to solve the model, by introduced an adaptive competition strategy in the selection and elimination of the population to accelerate the convergence speed of the algorithm. Finally, this study experimented on the word segmentation module of the library automatic question-answer consultation system. The experimental results showed that this method can meet the user’s requirements, improve the question segmentation accuracy, accelerate the convergence speed of the bee colony, which verified the effectiveness of the proposed method.
Scale-Reconstructible Structure from Motion Considering Amount of Refraction

In this paper, a method for scale-reconstructible Structure from Motion to improve the robustness of three-dimensional (3D) reconstruction with a thin plate is proposed. Scale-reconstructible Structure from Motion is a 3D measurement method with an absolute scale using a single camera and a refractive plate. However, previous studies need a large thick refractive plate to achieve scale-reconstructible Structure from Motion. To solve this problem, a weighting factor according to the amount of refraction for optimization of 3D reconstruction was designed. It was confirmed that optimization using the weighting factor is effective for the robust 3D reconstruction method by simulation experiment.
Digital Modulation Recognition Based on Wavelet Denoising and Convolution Neural Network

It is difficult for traditional modulation recognition methods to extract feature parameters manually and their anti-noise ability is weak. The existing deep learning methods have high computational complexity. This paper proposes a digital modulation recognition algorithm based on wavelet denoising and convolution neural network (WD-CNN). In this paper, the signal is firstly processed by wavelet threshold denoising to reduce the interference of noise. Then the signal is mapped to the constellation diagram, and the constellation diagram is input into convolution neural network to extract features after normalizing. At last the modulation mode of signal is identified by the Softmax classifier and the Gradient-weighted Class Activation Mapping (Grad-CAM) is used to explain the performance of the convolution neural network. We use MATLAB R2019b software to generate data sets under different signal-to-noise ratio (SNR) conditions and then teste the sets. The results show that this method has high accuracy of modulation recognition, strong anti-noise ability, low time complexity and low memory consumption.
Methodological approaches to the development of criteria-based assessment of educational results

The article presents the authors’ experience in developing and applying criteria for assessing the results of the educational activities of students in the system of contemporary education. The necessity and expediency of its implementation are justified by the actual difficulty of objective assessment of the quality of educational results; reduction of the motivating function of the five-point assessment; as well as natural and personal uniqueness of each student as a subject of educational activity. The analysis of domestic and foreign studies on the problem of criteria-based assessment of learning results in the system of general and higher education shows that despite various approaches to the criteria-based assessment of learning results, the methodology of its development is not sufficiently covered in pedagogical science and practice. The present research aims to develop a pedagogically appropriate methodology for constructing a criteria-based assessment of the results of students’ educational activities. This methodology includes several interrelated actions: establishing three levels of students’ mastering of the content of each discipline; developing quantitative indicators and qualitative characteristics of each level; ensuring methodological development of requirements for each task; bringing to the attention of students the content of each level of mastering of the main educational program. An important condition is the ability to repeatedly correct each task to bring it to the highest possible level of perfection. The leading methodological position is given to the idea of the human’s inherent desire for success and to be the first. As the results of the conducted study show, the implementation of the proposed methodology naturally leads to an enhancement of the education quality, which is reliable proof of its scientific novelty.
Heterogeneous mayfly optimization algorithm

It appeared that with more updating ways, the optimization algorithms would perform better in optimization. In this paper, the heterogeneous mayfly optimization (HMO) algorithm was proposed. The individuals in the HMO swarms would have five ways to update their positions. Simulation experiments verified that the HMO algorithm would significantly increase the capabilities of the original mayfly optimization (MO) algorithm.
A Distributed Framework for APT Attack Analysis

Information security is an important part of Internet security. As more and more industries rely on the Internet, it has become urgent to protect information security of these industries, spawned local area networks (LANs), intranets and so on. With the development of information sensor technology, the Internet of Things (IoT) that interconnects physical devices has emerged. As a unity of computing process and physical process, the Cyber-physical systems (CPS) is the next generation intelligent system which integrates computing, communication and controlling capabilities. CPS covers a wide range of applications and critical infrastructures, including intelligent transportation systems, telemedicine, smart grids, aerospace, and many other fields. The APT attacks are typically conducted directly against these critical infrastructures around the world, which would incur severe consequences. It is meaningful to protect these information by detecting the APT attacks timely and accurately, and effective defensive measures could be adopted. Although the APT attacks seem destructive, the attack process are complex and changeable. In essence, the attack process usually follows certain rules. In this chapter, we introduce a distributed framework for detecting the APT attacks. Cyber security knowledge graph stores existing knowledge and the attack rules, which plays an important role in analyzing the attacks. We first analyze potential attack events by the proposed distributed framework on Spark, then we mine the attack chains from massive data with the spatial and temporal characteristics. These steps could help identify complicated attacks. We also conduct extensive experiments, the results show that the analysis accuracy depends on the completeness of the cyber security knowledge graph and the precision of the detection results from security equipments. With the rational expectation about more exposure of attacks and faster upgrade of security equipments, it is sufficient and necessary to improve the cyber security knowledge graph constantly for better performance.
Peruse and Recognition of Old Kannada Stone Inscription Characters

Karnataka is known for its tradition, culture, literature and ritual knowledge abundant in our historical documents in the form of inscription. Identify and recognizing the ancient Kannada inscription characters allows archaeologists made to disclose the historical events done in ancient Karnataka history and literature work done by Kannada ancient poets. The wastefulness of this manual procedure will unfavourably influence on the future research in the field of archaeological office. To overcome from all these negative impact, an venture made by utilizing the computerized version technique and procedure system to perceive and peruse the stone engravings having old Kannada words. Regular methodologies of manual procedure requires enormous amount of time and energy. This research method has easy way to digitalization of stone engraving characters using normal camera of our mobile phone. System utilized in this research is straightforward and viable in expelling the noise and perceive the words present in inscription in various condition disorder as follows, blurred, destroyed, shaded with dust and unprotected old stone inscriptions.
Ensemble of Nested Dichotomies for Author Identification System Using Similarity-Based Textual Features

Author identification appears to be one of the blooming research areas in the domain of Natural Language Processing (NLP) which helps in identifying the particular author of a respective piece of text. Every author has a unique style of writing based on the linguistic and stylistic study that helps in identifying one author from another. This paper aims to present an author identification technique that uses similarity-based textual features to train the ensemble of nested dichotomies learning model for identification of different authors depending on their writing patterns. Experiments were performed over 12,000 passages (approx.) with 32,37,80,594 tokens from 50 authors obtained from Bangla literature and the highest accuracy of 96.67% was obtained.
Template Features Guided Siamese Keypoints Detection for Visual Object Tracking

Abstract Visual object tracking is one of the challenging tasks in computer vision. Among tracking tasks, accurately state estimation is the main challenge. Most current methods simply adopt multi-scale searches or anchors to estimate the state, which need many hyper-parameters and complex calculations. To face this challenge, we propose an anchor-free tracking framework based on Siamese network. The proposed framework consists of template features guided Siamese subnetwork and keypoints detection subnetwork. We take simplified hourglass network as backbone in Siamese subnetwork to improve the tracking efficiency and the prediction of corners around target instead of the prediction of target in keypoints detection subnetwork. We experiment our approach on OTB-2015 and VOT-2016, and our approach acquire the best precision of 0.841 on OTB-2015 and runs at 39 FPS.
Event detection of different English data sources based on transfer learning

The methods of multi-modal English event detection under a single data source and isomorphic event detection of different English data sources based on transfer learning still need to be improved. In order to improve the efficiency of English and data source time detection, based on the transfer learning algorithm, this paper proposes multi-modal event detection under a single data source and isomorphic event detection based on transfer learning for different data sources. Moreover, by stacking multiple classification models, this paper makes each feature merge with each other, and conducts confrontation training through the difference between the two classifiers to further make the distribution of different source data similar. In addition, in order to verify the algorithm proposed in this paper, a multi-source English event detection data set is collected through a data collection method. Finally, this paper uses the data set to verify the method proposed in this paper and compare it with the current most mainstream transfer learning methods. Through experimental analysis, convergence analysis, visual analysis and parameter evaluation, the effectiveness of the algorithm proposed in this paper is demonstrated.
ANALISIS NILAI KARAKTER YANG TERKANDUNG PADA BUKU FABEL ANAK

One effort to realize character education can be done by integrating it in Indonesian subjects, namely in language and literature learning. Language and literature are the right media in shaping a child's character. Basically one of the literary works known among the public is fable. Fables are children's stories whose characters are animals, but they carry out their roles like humans (personification). Basically, in character values ​​character values ​​are contained in intrinsic elements. Intrinsic element is a building element in a work, which includes the plot, setting and mandate contained in the story. The types of stories consist of: folklore, fairy tales, and fables. The formulation of the problem in this study is what are the character values ​​contained in children's fable books ?. The purpose of this study was to analyze the character values ​​contained in children's fable books. This research approach is in the form of qualitative research using descriptive methods. The data in this study are character values ​​that are character, character, character, or personality of a person. The instrument used was the presentation of data by the researchers themselves, to maintain the validity of the data, carried out by analyzing the character values ​​contained in the children's fable book. Techniques in collecting data used in this study were using literature review techniques, and analysis of children's fable books. The identification of character value analysis of each fairy tale in the fable illustrates positive values, namely courage, honesty, responsibility, forgiving, entertaining, ingenious, hard working friendship, please help, beative. The data analysis technique used is content analysis or conclusion drawing. Based on the analysis carried out that the values ​​of character are brave, honest, responsibility, forgiving, entertaining, clever, friendship, appearing in the element of someone's personality.
Entity Alignment: Optimization by Seed Selection

The knowledge representation framework of MDATA requires the fusion of multi-source and multi-dimensional data. The main steps of data fusion are entity alignment and disambiguation. The method of entity alignment mainly includes the similarity calculation of entity description text and entity embedding. The embedding-based entity alignment method usually uses pre-aligned entities as seed data, and aligns the entities in different knowledge graphs through seed entity constraints. This method relies heavily on the quality and quantity of seed entities. In this chapter, we introduce an algorithm to optimize the selection of seed entities, and select seed entity pairs through the centrality and differentiability of entities in the knowledge graph. In order to solve the problem of insufficient number of high-quality seed entities, an iterative entity alignment method is adopted. We have done experiments on DBP15K dataset, and the experimental results show that the proposed method can achieve good entity alignment even under weak supervision.