Reviewerly/exHarmony huggingface dataset:
Deep Learning Approach for Protecting Voice-Controllable Devices From Laser Attacks

The laser-based audio signal injection can be used for attacking voice controllable systems. An attacker can aim an amplitude-modulated light at the microphone's aperture, and the signal injection acts as a remote voice-command attack on voice-controllable systems. Attackers are using vulnerabilities to steal things that are in the form of physical devices or the form of virtual using making orders, withdrawal of money, etc. Therefore, detection of these signals is important because almost every device can be attacked using these amplitude-modulated laser signals. In this project, the authors use deep learning to detect the incoming signals as normal voice commands or laser-based audio signals. Mel frequency cepstral coefficients (MFCC) are derived from the audio signals to classify the input audio signals. If the audio signals are identified as laser signals, the voice command can be disabled, and an alert can be displayed to the victim. The maximum accuracy of the machine learning model was 100%, and in the real world, it's around 95%.
Military Target Recognition Technology based on WGAN-GP and XGBoost

This paper proposes a military target recognition method based on WGAN-GP and XGBoost, which expands and improves the quality of military target samples by constructing WGAN-GP, then sampling iteratively based on heuristic learning to construct an effective sample training set. On the basis of the quality training set, XGBoost is used for supervised learning, and finally a classification network model for military target recognition is obtained. Compared with methods such as CNN, KNN, and SVM, the method proposed in the article has a 1.01% to 58.84% higher overall accuracy of target sample recognition, and the overall accuracy is 27.36% 57.46% higher under the conditions of different small-scale samples.
Extraction in Detecting Tuberculosis X-Ray Results using Histogram of Oriented Gradients

Image processing is a very popular study in research. Image processing research developed from starting to improve images that have lost pixels or adding pixels to make the image clearer in providing and conveying information. However, with the support of various image processing computational techniques, it is not only conveying information but also applying machine learning techniques to carry out lessons from the available data. In this study, a training was implemented in an X-ray image of Tuberculosis. Tuberculosis is made due to the presence of bacteria that have gathered so that the results of X-rays of Tuberculosis are processed by extracting features to detect whether the results of the Tuberculosis X-ray image are positive or negative. The very simple feature extract is processed to detect the Histogram of Oriented Gradients (HOG) because it applies the gradient function to be mathematically processed in detecting Tuberculosis. In this paper, the results using HOG feature extraction based on the percentage of positive tuberculosis are 70.90%, the proportion of negative diagnosis results is 29.10%, the proportion of negative diagnosis results is 72.72%, and the positive diagnosis results are 27.28%.
A Surgical Bullet-Time Video Capturing System Depending on Surgical Situation

In recent years, capturing surgical video has become popular for education and case studies in surgical technology. However, there is always a problem of obscuration caused by the surgeon's head or hands when using a fixed surgical camera for surgery. To improve the quality of surgical videos, we propose a capturing system with multiple cameras installed between the surgeon and the patient. In this paper, we propose an effective method to automatically switch the viewpoint of multi-view surgical videos. When there is an occlusion in the current view, the system can switch to other cameras and rearrange the captured video sequences into bullet-time videos to ensure that details and surgical procedures are recorded. We also adopt a learning-based object detection method to automatically set the gaze points of certain objects from the multi-view surgical video.
A Study of the Effects of Petroleum Production on Selected Soil Fertility Attributes in Ogoni Land, Rivers State Nigeria

The physical, chemical and biological properties of soil in Khana and Gokana LGAs of Rivers State Nigeria were investigated. The main aim was to determine to what extent the soil fertility attributes in Ogoni land had been negatively affected by petroleum production in the area. Soil samples were collected from scientifically delineated spots in the study area and analyzed in the laboratory at the Institute of pollution studies, Rivers State University of Science and Technology, Port Harcourt. All the data obtained from the laboratory were analyzed statistically using one-way analysis of variance (ANOVA). Results show that soil characteristics show evidence of variation attributable to influence of petroleum production activities. It is hereby recommended that in the oil production business, government should supervise the operation of the oil companies strictly and with due regard to the welfare of the inhabitants of the communities. The oil companies should carry the host communities along in their activities. The host communities should forget the past, rely more on facts and show readiness to move forward. Oil production can still go forward to the benefit of all.
The Internal Structure of Placer Gold of the North-East of the Siberian Platform as a Reliable Search Sign of Primary Sources

Abstract Typomorphic features of placer gold are carriers of the most important information necessary for the reconstruction of the history of the formation of placer and ore occurrences of gold and can be used as mineralogical criteria in the search for primary sources of placers. The study of these features is relevant for placer areas with unidentified gold sources, which include the territory of the north-east of the Siberian Platform. The internal structure of native gold is one of the most informative typomorphic features is. We studied the internal gold structures of modern placers and conglomerates of the Early Permian age in the north-east of the Siberian Platform. The purpose of the work: to identify the features of the internal structure of placer gold, depending on the chemical composition and the degree of its transformations, as well as the possibility of their use as a prospecting indicator. Structural etching of native gold was performed using the reagent: HCl + HNO3 + FeCl3 × 6H2O + CrO3 + thiurea + water. As a result of the research, it was found that the well-rounded high-grade gold of modern placers has undergone repeated redeposition through intermediate sources. The discovery of slightly rounded gold with an internal structure without signs of exogenous transformation indicates that it entered the modern alluvium from a nearby primary source. This was a prerequisite for setting up ore gold exploration, resulting in identification of apocarbonate hydrothermal-metasomatic formations with disseminated gold-sulfide mineralization were identified. Thus, the features of the internal structures of placer gold in combination with other typomorphic features are additional indications of determining the sources of formation of gold-bearing placers.
Vision: Stewardship of Smart Devices Security for the Aging Population

IoT devices can monitor the health, safety, and security of aging adults, and automate many household tasks, enabling independence far into old age. However, such devices also have many inherent vulnerabilities, which make them a popular target for cyberattacks. The heterogeneity of IoT devices and their interactions may make them susceptible to new types of attacks, and also make usability difficult for the aging population. Furthermore, the elderly may be particularly vulnerable and uncomfortable with new technologies. Existing network management interfaces are designed for domain experts, and are impracticable for non-technical users.
Deep Learning on Healthcare Ecosystem using Blockchain Based Security System

For providing immense care on the patient's health, it is obligatory to nurture the patient's health records. This health care circumstance of the patient info is stored using blockchain networks. Distinctive healthcare specialists can access the data via this shared network infrastructure-the healthcare quarter profits immensely from a decentralized and dependable database. The details would be collected and documented securely in the Mongo dB database as encoded chunk data after document validation by the CA server with minimal computation time. The details stored in this database can be accessed using RNN when the user id and password match up with the database. The interface, smart contract, user access management, monitoring, mapping modules are processed to create a blockchain. The patient's details will be collected in the P ATREC chain, and the gathered information is applied to the blockchain, where it is stored in the list of blocks. Then it generates a hash value for that information in the block. Each patient's health details can be stored in their account, which can be shared or allow access by others using smart contact after authentication. Hence, it is mandatory to securely maintain these patient health care details as a single encrypted hash value using blockchain, which cannot access.
Detection of Abnormal Activity to Alert the Nearby Persons via M-DNN Based Surveillance System

In today's environment, video surveillance is critical. When artificial intelligence, machine learning, and deep learning were introduced into the system, the technology had progressed much too far. Different methods are in place using the above combinations to help distinguish various wary activities from the live tracking of footages. Human behavior is the most unpredictable, and determining whether it is suspicious or normal is quite tough. In a theoretical setting, a deep learning approach is utilized to detect suspicious or normal behavior and sends an alarm to the nearby people if suspicious activity is predicted. In this paper, data fusion technique is used for feature extraction which gives an accurate outcome. Moreover, the classes are classified by the well effective machine learning approach of modified deep neural network (M-DNN), that predicts the classes very well. The proposed method gains 95% accuracy, as well the advanced system is contrast with previous methods like artificial neural network (ANN), random forest (RF) and support vector machine (SVM). This approach is well fitted for dynamic and static conditions.
Consideration of the side-channel attack to SPECK implemented on Arduino Uno

In this study, we examined a type of side-channel attack, the Correlation Power Analysis (CPA) attack, which analyzes the correct key of the cipher by calculating the correlation between power consumption and intermediate data. The authors also examined side-channel attacks using various attack techniques. The authors found that light-weight cipher SPECK may not resist side-channel attacks because it requires too few resources for a successful attack. Furthermore, the authors investigated the minimum number of plain texts needed for attacking. As a result, it was found that 50 plain texts are sufficient for a successful attack.
Stepping out of the Shadow

Abstract After the discovery of the Morris Worm in November 1988, the first Computer Emergency Response Team (CERT) was established. During the following years, other CERTs or Computer Security Incident Response Teams (CSIRTs) were established in different parts of the globe. Now, three decades later, CSIRTs have become an integral part of the cyber security ecosystem. This chapter aims to provide an insight into the evolution of CSIRTs by describing their historical background, their different types and services, as well as the challenges they are encountering as the topic of cyber security becomes more pertinent and political.
Resource efficient Multi-task Post-processing Custom Hardware for CNN-based Real-time Road segmentation and Lane detection

In this paper, we implement acceleration hardware in post-processing for road segmentation and lane detection that minimizes resource consumption for custom design of convolutional neural network (CNN) based multi-task system. The proposed hardware is a low-cost and sequential architecture that good connectivity with inference accelerator, and the proposed optimization method can reduce memory consumption in the CNN-based segmentation system. The resource utilization was estimated on the UltraScale+ vu9p customized platform. The proposed post-processing hardware consumes within 2% of resources and accelerates our multi-task CNN with an inference accelerator.
Social Media Analytics

Social media is a broad spectrum of process like data collection, data storage into databases and preparation of data which is utilized for research, decision making, marketing campaign with the aid of various tools and algorithms to measure the performance of a product. Analyzing social media to collect data and planning strategy around them has become one of the major business activity. Topics covered in this papers would be applications and impact of social media, challenges faced while retrieving data and preparing data for business intelligence, it also mentions some tools and algorithm.
A Study of a Multiple Evaluation System of College English Online Teaching in China

The comprehensive and scientific evaluation of college English teaching in online mode is an important basis for further promoting and optimizing the pragmatic reform of college English teaching. Based on the operational principle, the principle of service to students, and the feasibility principle, this study aims to construct a multiple evaluation system of online college English teaching based on three aspects: online interaction, online autonomous learning, and English online practice. A questionnaire has been conducted among undergraduates, teachers, and experts. The Analytic Hierarchy Process (AHP) has been used to analyze the relative importance of the indicators at the first two levels in the multiple evaluation system. The results revealed that the weight coefficients of teacher-student interaction, learning resources, and English listening practice are higher, while those of learning freedom and oral English practice are lower. Therefore, the online college English teaching reform should take the following measures: equipment input of college English practice teaching should be reinforced, communication channels between teachers and students should be strengthened, and online teaching resources should be enriched.
Teleportation affected by fluctuating electromagnetic field with a boundary

We study the quantum teleportation under fluctuating electromagnetic field in the presence of a perfectly reflecting boundary. The noisy scheme of quantum teleportation affected by electromagnetic fluctuation is proposed. Then we calculate and investigate the behaviors of entanglement and fidelity, which are closely related to the plane boundary and atomic polarization. After a period of evolution, entanglement and fidelity evolve to zero and nonzero stable value respectively. Fidelity is closely related to the weight parameter and phase parameter of the teleported state. Besides, small two-atom separation makes entanglement and fidelity have better enhancement. Furthermore, the presence of boundary, atomic polarization and two-atom separation offers us more freedom to adjust the performance of the quantum teleportation. The results would give us new insight into quantum communication in an open quantum system since quantum teleportation plays an important role in quantum communication and quantum information.
Research of Self-organization Processes in Socio-technical Multivariable Dynamic Systems

Various structures of self-organization processes in multivariable dynamic control systems of socio-technical dynamic objects are considered on the example of scientific schools and educational systems. The study of the mechanism for the emergence of synergistic properties in a given class of systems is carried out at the level of their mathematical models, which are a set of interconnected nonlinear differential equations. The description of a multivariable control system is based on its structural and functional decomposition into separate real subsystems and multidimensional connections between them. This description allowed us to identify some regularities in the emergence of synergistic properties. Numerical examples are given showing the advantages of this research technology.
Reply to Carmichael on BOK and terminology

The history and philosophy of the growth of engineering knowledge is under-developed – the nearest one can get is the progress of science. That history shows the falsity of Carmichael’s assertion that the absence of an agreed set of meanings for systems terms will hold-back the development of Civil Engineering Systems.
Remarks on Quaternion Multi–Layer Neural Network Based on the Generalised HR Calculus

This study investigates a training method of a quaternion multi–layer neural network based on a gradient– descent method extended to quaternion numbers. The gradient of the cost function is calculated using the generalised ${\mathbb{H}}{\mathbb{R}}$ calculus to derive the training rule for the network parameters. Computational experiments for identifying and controlling a discrete–time nonlinear plant were conducted to evaluate the proposed method. The results confirmed the feasibility of using the G ${\mathbb{H}}{\mathbb{R}}$ calculus in the quaternion neural network and showed the capability of using the quaternion neural network for a control system application.
User Experience Estimation in Multi-Service Scenario of Cellular Network

The estimation of user experience in a wireless network has always been a research hotspot, especially for the realization of network automation. In order to solve the problem of user experience estimation in wireless networks, we propose a two-step optimization method for the selection of the kernel function and bandwidth in a naive Bayesian classifier based on kernel density estimation. This optimization method can effectively improve the accuracy of estimation. At present, research on user experience estimation in wireless networks does not include an in-depth analysis of the reasons for the decline of user experience. We established a scheme integrating user experience prediction and network fault diagnosis. Key performance indicator (KPI) data collected from an actual network were divided into five categories, which were used to estimate user experience. The results of these five estimates were counted through the voting mechanism, and the final estimation results could be obtained. At the same time, this voting mechanism can also feed back to us which KPIs lead to the reduction of user experience. In addition, this paper also puts forward the evaluation standard of the multi-service perception capability of cell-level wireless networks. We summarize the user experience estimation for three main services in a cell to obtain a cell-level user experience evaluation. The results showed that the proposed method can accurately estimate user experience and diagnosis abnormal values in a timely manner. This method can improve the efficiency of network management.
Generative Adversarial Training with Dual-Attention for Vascular Segmentation and Topological Analysis

The vascular topology is of vital importance in building a chemotherapy model for the liver cancer in rats. And segmentation of vessels in the liver is an indispensable part of vessels' topological analysis. In this paper, we proposed and validated a novel pipeline for segmenting liver vessels and extracting their skeletons for topological analysis. We employed a dual-attention based U-Net trained in a generative adversarial network (GAN) fashion to obtain precise segmentations of vessels. For subsequent topological analysis, the vessels' skeletons are extracted and classified according to their lengths and bifurcation orders. Based on 40 samples with carefully-annotated ground truth labels, our experiments revealed consistent superiority in terms of both segmentation accuracy and topology correctness, demonstrating the robustness of the proposed pipeline.
Involvement of a System to Keep Users Interested in the Contents of Ambient Persuasive Display

Ambient displays are considered to be effective for behavior change; however, with long-term use, users may lose interest in the contents and fail in changing to desirable behavior. We investigate a method to detect familiarity and distrust as a sign of loss of interest, and two types of information presentation are proposed to cope with the loss of interest. These methods are integrated into a prototype system that aims at avoiding sedentary lifestyle at home. The result of a preliminary user study shows that adapting to the user’s preference might be effective.
Modelling of the Factors Influence on the Quality of the Digitization Process of Old Books by Fuzzy Logic

Based on such pre-established priority factors as the old book condition, optical density range, software functionality and scanning devise features, using the foundations of fuzzy logic, a fuzzy knowledge base has been formed, logical equations have been constructed and the quality of the digitization process of old books has been quantified. To calculate the quantitative indicator of the digitization process quality, a defuzzification operation has been performed according to the “centre of gravity” principle.
Search Engine Optimization Method of Online Course Management Platform

Abstract This study proposes a method of keyword selection in search engine optimization to improve the accuracy of search engine and website rankings. To promote the development of scientific and technological innovation, this paper selects the innovation and entrepreneurship curriculum platform as the experimental object. By comparing different search engine optimization strategies, the keyword search volume data is analyzed based on “comprehensive index evaluation method”, which analyzes and calculates the change of keyword search comparison and the number of related keywords. Therefore, this paper will use the comprehensive index evaluation method for keyword selection, and establish a set of practical keyword selection method combined with the actual situation of innovation and enterprise curriculum platform. The results show that this program can improve search accuracy and website ranking.
Development Status of Medical Nursing Robots

With the aging population, fewer children accelerated the urbanization process, and its interpenetrating influence, the issue of patient companionship is gradually becoming a problem in the medical process. Meanwhile, the development of digital healthcare, including medical robots, has laid a huge market foundation for medical nursing robots. Meanwhile, there are some bottlenecks and deficiencies in the technology of medical care robots. The research in theoretically oriented universities and the commercialization and promotion in related companies fail to dovetail well. This paper firstly summarizes the current classification of nursing robots by combing the historical development of nursing robot technology. Secondly, based on the analysis of existing research literature, the technical bottlenecks of nursing robots and the urgency of developing related technologies are presented, mainly including robot movement methods, flexible robot arms, action recognition, intelligent dialogue, and non-contact sign monitoring. And robotics ethics are discussed. Finally, based on the discussion of the technologies, the design of functions, and the planning of application scenarios, an ideal model of a nursing robot is proposed, and a summary and outlook are given.