Reviewerly/exHarmony huggingface dataset:
One-Stop-Shop for the Security of IoT with Blockchain: A Systematic Literature Review

The Internet of Things (IoT) is one of the most emerging technologies, specially with the introduction of smart cities, smart homes, etc., anticipating fifty billion connected devices. IoT devices are usually limited in their processing, storage, and network bandwidth, and are more vulnerable to attacks that can be easily hacked and compromised. As IoT deals with personal life, so its security is one of the most critical challenges that need to resolve before its adoption. Distributed Ledger Technology (DLT), especially blockchain, is a relatively new technology that deals with the distributed immutable ledger. DLT is a promising technology that may solve the security issues of IoT. So a study needs to be done to know what security challenges of IoT can be solved using DLT and what are the ways in which DLT can be used to increase security. In this paper, first, we have set the research questions, then the systematic literature review has been done in view of research questions, then answers to research questions are presented, and discussion about the answers to research questions have been presented. A solution has been proposed that can be used to increase the security of IoT. Based on the review results, the paper concludes with the suggestions for using blockchain for IoT security.
Students' Attitudes to Online Reading in the Era of Technology at the University of Transport and Communications

Thanks to the remarkable development of technology 4.0, online reading is becoming more popular for students. Higher education institutions encourage their students to participate in online learning courses as well as widening their knowledge by reading online after campus–based learning, especially in the Covid19 epidemic. The study was conducted to investigate the students’ attitudes to online reading as well as the effects of online reading for UTC2 students and ways to access information accurately by collecting 200 students from five different majors, including Civil Engineering, Construction Engineering, Economics, Mechanical Engineering, and Information Technology. The qualitative approach with a well–structured questionnaire was used to collect the primary data from respondents. The findings pointed out that the students showed their positive attitudes toward online reading because online reading is an effective way to improve their knowledge and the effectiveness of online reading significantly varies with their reading purposes, technology usage, and skills.
A Comprehensive Study on Architecture of Neural Networks and Its Prospects in Cognitive Computing

This paper proffers an overview of neural network, coupled with early neural network architecture, learning methods, and applications. Basically, neural networks are simplified models of biological nervous systems and that's why they have drawn crucial attention of research community in the domain of artificial intelligence. Basically, such networks are highly interconnected networks possessing a huge number of processing elements known as neurons. Such networks learn by examples and exhibit the mapping capabilities, generalization, fault resilience conjointly with escalated rate of information processing. In the current paper, various types of learning methods employed in case of neural networks are discussed. Subsequently, the paper details the deep neural network (DNN), its key concepts, optimization strategies, activation functions used. Afterwards, logistic regression and conventional optimization approaches are described in the paper. Finally, various applications of neural networks in various domains are included in the paper before concluding it.
Neuro Linguistic Programming: A Novel Approach to Sentiment Analysis

This Paper lights on Neuro-Linguistic Programming (NLP), which is used to impact the belief system of the human mind. NLP is a novel approach to sentiment analysis (SA) by which people knows their sentiments and platform to train the human mind. It includes various neuro-linguistic techniques with the implementation of sentiments and emotions. The research aims to provide awareness to people about the changing behavior of their minds. It tells how NLP works with various sentiments and provides a significant change in behavioral patterns. There are many social and commercial areas where NLP makes its standards as compared to various other sentiment techniques.
The expired period of Liquefied Petroleum Gas (LPG) rubber hose supports consumer safety aspects

Abstract Liquefied Petroleum Gas (LPG) rubber hose has the corrosive characteristic with hydrocarbon compounds (butane and propane). This makes LPG rubber hose has an expired period. Standards or technical specifications are needed to guarantee the quality of the product and increasing product safety. Until now, the expired period of LPG hose has not been regulated in the Indonesian National Standard (SNI). This study aims to analyse expired period of LPG rubber hose for gas stoves. In this study, the descriptive qualitative method is used based on primary data (questionnaire and interview) and secondary data (literature study). The result of this study concluded that the determination of LPG rubber hose expired period based on shelf life and service life. The service life of rubber hose will vary due to different raw materials, additives, and usage conditions (time, temperature, pressure, and environment). Seventy-five percent (75%) of LPG rubber hose industry respondents conducted research on LPG rubber hoses service life and recommended service life period is one year.
Adaptive system for visualization of the electromagnetic scattering field of aircraft

The space industry has always set ambitious tasks to create unique equipment, including control equipment. Ensuring electromagnetic compatibility (EMC) of such systems is carried out at all stages of the life cycle of an aircraft (AC), starting from its development, including testing and stages of operation. Digital technology is characterized by high noise immunity. However, any modern technical system in the aggregate contains sensors, communication lines and receiving antennas sensitive to the effects of external interfering signals that propagate both through wires and over the air. Hence, the relevance of ensuring the EMC of integrated technical systems that perform the functions of measurement, data processing and the formation of control actions, both digital and analog, is obvious. The electromagnetic compatibility of a sophisticated aircraft or spaceship technical system with other systems is a prerequisite for their sustainable operation. Aerospace technology, where both highly sensitive measurements and energetic control effects are closely combined in the dense layout of an aircraft, requires a solution to the EMC problem. This paper shows the need for effective technology to control the electromagnetic radiation created in the surrounding space, both by subsystems of aircraft and by the whole object. A method for visualizing 3D electromagnetic field both in space and in time is considered.
Real-time and efficient eyes and mouth state detection: an artificial intelligence application based on embedded systems

It is reported that many traffic accidents are caused by fatigued driving. Detection of the state of the eyes and mouth is usually employed to judge whether the driver is fatigued. However, the traditional image processing methods cannot achieve satisfactory detection accuracy due to the changes of illumination, head posture, and other factors in the actual environment. To date, although the methods based on deep learning have reached adequate accuracy in the tasks of object detection, they are obliged to rely on high hardware configuration to meet the real-time requirements. To achieve satisfactory accuracy in the task of eyes and mouth detection and obtain good real-time performance on embedded platforms such as NVIDIA Jetson TX2, we improve the object detection algorithm based on deep learning. In this paper, based on the original YOLOv3-Tiny structure, we not only added the structure of deep residual learning, but also calculated six new anchor boxes according to the training set using the K-means algorithm. We also used six data augmentation methods in the training set to improve the detection accuracy. The improved algorithm proposed in this paper can achieve satisfactory detection speed and accuracy on the TX2 embedded hardware configuration platform, validating that the ameliorated scheme is effective.
Blockchain Technology: Security and Privacy Issues

Distributed Ledger and Blockchain technology decentralise the way we store data and manage information. Blockchain technology is the backbone of most cryptocurrencies, but also is being explored in other serious fields, such as new governance systems with more participatory decision-making, and decentralised autonomous organisations that can operate without human intervention. In all these cases, data immutability and security are highly valuable. Blockchain technology prevents data feed failure and corruption and creates mechanisms that grant protection to distributed systems.
Acquiring a Semantic Lexicon for Natural Language Processing

This chapter explores the possibility of acquiring semantic information for natural language processing using on-line corpora. Our viewpoint is a very practical one: What kind of information can be usefully and extensively encoded on a semantic lexicon, for the purpose of NLP applications such as automatic translation, intelligent information retrieval, etc.? Several meaning representation frameworks presented in the literature are evaluated under this perspective. Methods for knowledge extraction from available on-line resources, as for example corpora, dictionaries and query collections are then examined.
Hack for Hire

Hack-for-hire services charging $100-$400 per contract were found to produce sophisticated, persistent, and personalized attacks that were able to bypass 2FA via phishing. The demand for these services, however, appears to be limited to a niche market, as evidenced by the small number of discoverable services, an even smaller number of successful services, and the fact that these attackers target only about one in a million Google users.
On HPC and Cloud Environments Integration

Recently in many scientific disciplines, e.g. physics, chemistry, biology and multidisciplinary research have shifted to computational modelling. The main instrument for such numerical experiments has been supercomputing. However, the number of supercomputers and their performance grows significantly slower than the growth of user’s demands. As a result, users of supercomputers may wait for weeks until their job will be done. At the same time the computational power of cloud computing recently grows up considerably represented by heterogeneous DC network with plenty of available resources for numerical experiments. In these circumstances, it may turn out that the time spent by the task in the system, i.e. the time spent in the queue $$+$$ computing time, in the cloud environment may be shorter than in HPC installation. There are several problems related to cloud and supercomputer environments integration. First, is how to make a decision where to send a computational task: to a supercomputer or to cloud. Secondly, these environments may have significantly different APIs, so moving a computational task from one environment to another may require a lot of code modification. Another significant problem is an automatic provisioning of virtual environment to execute the task properly. The third one is how to organize effectively migration data, computational tasks, applications and services in DC network, between DC and HPC installation? Saying effectively, we mean that network can allocate shortly, on demand, the necessary capacity in order to transfer the necessary amount of data for the right time. It is called ‘Capacity on Demand’ service. In this chapter an environment for academic multidisciplinary research – Meta Cloud Computing Environment (MC2E) is presented. This environment demonstrates the possible solutions and approaches to the problems listed above.
Recent advances on the application of big data framework based on Hadoop platform

Data mining plays an important role in all kinds of practical applications in modern society. In recent years, with the exponential growth trend of data produced and accumulated in various industries, data mining has attracted the attention of many researchers. Hadoop distributed software framework is the most commonly used framework to build cloud platform. This paper introduces the latest progress of parallel data mining applications in Hadoop platform, such as book management, cloud computing, industry, scientific research and water treatment, traffic management and other practical applications such as daily life management.
Radar signals modulation recognition based on bispectrum feature processing

Abstract Modulation recognition of radar signals is an important part of modern electronic intelligence reconnaissance and electronic support systems. In this paper, to solve the problem of low recognition accuracy and low noise resistance of radar signals under low signal-to-noise ratio(SNR), a recognition method based on variational mode decomposition(VMD) and bispectrum feature extraction is proposed. Based on the feature that bispectrum can suppress Gaussian noise, the feasibility of signals modulation recognition under low SNR is analyzed and the noise item is introduced. Due to the interference of noise item, the noise suppression effect of bispectrum is worse under 0dB. An improved VMD algorithm based on artificial bee colony(ABC) algorithm optimization and envelope entropy evaluation is proposed to preprocess the signal to improve the SNR. Finally, we designed a convolution neural network(CNN) classifier to recognize signals of different modulation types. The simulation results show that this method has better noise resistance than traditional methods, and can effectively identify different types of signals under low SNR.
Virtual reality as therapy in psychiatry

This review describes the growing research in virtual reality (VR) for healthcare purposes. In recent years, the technological improvements have expanded the possibility of investigating VR in diagnostics as well as treatment of mental and behavioural disorders. The existing literature regarding phobia, post-traumatic stress disorder, addiction, psychotic, eating and affective disorders is summarised and discussed in terms of clinical applicability.
The Effectiveness of Question and Answer Technique in Teaching Reading Comprehension at SMP Negeri 3 Ratahan

This research was conducted with the purpose to develop students’ reading comprehension of descriptive text through question and answer technique. This study is classified as quantitative through pre-experimental design with one group pre-test and post-test design. The population of this research is the students of SMP Negeri 3 Ratahan. It was limited only to the second year studens. There were 30 students. The data were collected through test. The first test is pre-test, the teacher intends to know the students’ reading comprehension without using question and answer technique. The second test is post-test in which the teacher has used question and answer technique in teaching reading comprehension. The result shows that the scores of the students in post-test are higher then those in pre-test. This is shown by the mean score of pre-test which is 5.5 and mean score of post-test which is 7.8 Keywords: Question and Answer Technique; Reading Comprehension
A Gestão do Tempo no Trabalho Docente: Uma Análise de Gênero no Contexto da Pandemia

This article seeks to demonstrate the reality experienced by teachers in Brazil and Portugal, in particular women teachers, through the proposition of remote teaching, through online classes, during the period of the COVID-19 pandemic. Education professionals, when adapting to the new organizations in distance education, proposed by the teaching networks and school regulations, were faced with a dilemma: the management of time in their daily lives. There were many challenges faced during the pandemic period and the challenges are highlighted in the responses of teachers in an online survey, conducted on google forms, proposed for 50 teachers with the adherence of 15 teachers from primary to secondary school.  Besides the online survey, the article used as a study strategy the research of university professors (Shouten, 2011; Perista, 2017; Carmim, 2020; Gonzalez, 2020; Minello, 2020) that allowed guiding the argumentation of the text and demonstrating the impact of the pandemic in the work of teachers. The objective was to understand how the interaction between education, teaching work and online classes took place for female teachers in Brazil and Portugal. Our perceptions point out that among the professions that suffered with the pandemic period was the teaching class, which when carrying out the work in online format was necessary a restructuring in the lives of female teachers. New ways of organizing teaching work, school, family and personal life became central for these women workers.
Maximum multinomial likelihood estimation in compound mixture model with application to malaria study

Malaria can be diagnosed by the presence of parasites and symptoms (usually fever) due to the parasites. In endemic areas, however, an individual may have fever attributable either to malaria or to other causes. Thus the parasite level of an individual with fever follows a two-component mixture, with the two components corresponding to malaria and nonmalaria individuals. Furthermore, the parasite levels of nonmalaria individuals can be characterised as a mixture of a zero component and a positive distribution. In this article, we propose a nonparametric maximum multinomial likelihood approach for estimating the proportion of malaria using parasite-level data from two groups of individuals collected in two different seasons. We develop an EM-algorithm to numerically calculate the proposed estimates and further establish their convergence rates. Simulation results show that the proposed estimators are more efficient than existing nonparametric estimators. The proposed method is used to analyse malaria survey data.
Application, Classification and System Requirements of Blockchain Technology

Blockchain serves as shared ledger for many cryptocurrencies such as ethereum and bitcoin. Blockchains are now paving way for healthcare, real estate and other applications. In this paper, we described the state-of-the art of blockchain technology by untangling the complexity of blockchain systems. We also have defined the prerequisites for blockchain to work, the process of adding a new entry in blockchain though various consensus algorithms, security factors and the system requirements of blockchain systems. This paper focuses on explaining every working aspect of blockchain systems and how it can transform human lives by mentioning some latest and exciting real time use cases of blockchains.
Survey on Smartphone Securities

Abstract Recently Mobile data security has become a serious subject for everyone, who save their private and confidential data on mobile and perform financial transactions on the daily basis through their mobile. Mobile users are aware that their mobile data is no more secure after stealing their smartphone. 84% person wants advance mechanism for smartphone security so even after login the application, monitoring user can be possible throughout the session. Author conducted a survey on mobile security for 105 participants. This survey responses gave a clear out motive to do research on advance authentication method which is keystroke dynamics on smartphone.
Single image shadow removal via detection-free spatially adaptive denormalization

Removing shadows in a single image has been a challenging problem because shadows can appear in various forms due to complex physical situations, influenced by many factors such as light sources and the material’s transparency. In order to remove shadows precisely, most previous works utilized shadow mask information, which indicates the shadow region in a given image using binary representation. However, shadow mask utilization inevitably induces multiple problems, including shadow removal performance dependency and additional shadow detection process requirements. To solve these problems, the proposed algorithm is based on an image-to-image translation algorithm, which does not require additional shadow mask information. In this deep neural network , the convergence of fast learning is induced by utilizing various normalization layers. However, in a case that is very sensitive to various spatial features of an input image, such as shadow removal, the normalization process causes a problem of losing a large amount of information existing in the input image data. So, we utilize spatially adaptive denormalization(SPADE) to prevent loss of spatial features of input image data. Therefore, not only does it fundamentally solve the problem that various feature information constituting the input image is lost in the normalization process, but also enables precise shadow region removal by combining the feature map of multi-resolutions with the feature map of the decoder. In evaluation, the proposed algorithm shows that it exceeds the existing approach by about 20~30% in both PSNR and RMSE based on the ISTD large data set.
Face Detection Using Haar Cascades Classifier

In the last several years, face detection has been listed as one of the most engaging field in research.Face detection algorithms is used for detection of frontal human faces.Face detection find use is many applications such as face tracking, faces analysis and face recognition.In this paper, we are going to discuss face detection using a haar cascade classifier and OpenCV.In this study we would be focusing on some of the face detection technology in use.
Online Search of Unknown Terrains Using a Dynamical System-Based Path Planning Approach

Surveillance and exploration of large environments is a tedious task. In spaces with limited environmental cues, random-like search is an effective approach as it allows the robot to perform online coverage of environments using simple algorithm designs. One way to generate random-like scanning search is to use nonlinear dynamical systems to impart chaos into the searching robot’s controller. This will result in the generation of unpredictable yet deterministic trajectories, allowing designers to control the system and achieve a high scanning coverage of an area. However, the unpredictability comes at the cost of increased coverage time and a lack of scalability, both of which have been ignored by the state-of-the-art chaotic path planners. This work introduces a new, scalable technique that helps a robot to steer away from the obstacles and cover the entire search space in a short period of time. The technique involves coupling and manipulating two chaotic systems to reduce the coverage time and enable scanning of unknown environments with different online properties. Using this new technique resulted in an average 49% boost in the robot’s performance compared to the state-of-the-art planners. The overall search performance of the chaotic planner remained comparable to optimal systems while still ensuring unpredictable paths.