Reviewerly/exHarmony huggingface dataset:
Exploration of a Solution-Centric Crime Awareness Tool

In this era of modernization, people are blessed with several computers; nevertheless, it too acts as a bane to society.Not only it impacts our youth socially and emotionally, but also physically.Computers are made to perform the computations that are not easy for a normal human brain to understand and make connections all over the world.With connections, humans also bring negative aspects that result in cybercrimes such as data theft, financial frauds, phishing attacks, cyber grooming, etc. and crimes such as acid attacks, rapes, molestation, child labor, assault, etc.Therefore, in this paper, the authors will explore the need fora solution-centric crime awareness tool that understands different types of cybercrimes, more specifically crimes against women and children, website crimes, social media crimes, etc.Moreover, their reasons, causes, and solutions at the best of understanding for a child and an older adult.It also includes the precautions for an individual to stay safe and stay aware of all the governmental agencies and NGOs for women and children.Knowing about an incident or being a victim of a crime and not reporting it is equal to promoting crimes in society.So, this approach motivates people to speak up as it is okay to ask for help.
Quantum Cryptography

Nowadays, information is exchanged among the computer network. These data and information are involved in the business, military, academic education, research and etc. which are shared around the world in both private and public networks. Since, many categories of data are required restriction on the authorization of access, modify, delete, and insert, security in communication is increasingly important to network communication. Currently, computer security applies mathematic theory to computer security for encrypting and decrypting on both sender and receiver. To use a security attack with a high-performance computer (e.g. quantum computer), an attacker can find a key and then obtains the data infeasible period. Quantum cryptography is one of the solutions that use the property of polarization to ensure that transmitted data is not trapped by an eavesdropper. Quantum cryptography is improved significantly in the last decades including the most two dominant protocols BB84 and BB92.
Unmanned Aerial Vehicle (UAV) for Parcel Delivery

Air Drones, Unmanned Aerial Vehicles (UAV), autonomous or tele-operated flying machines are the aircrafts that doesn't require a human onboard for controlling it.They can be managed manually by a remote wireless operator or automatically using the software.Our purpose in this paper is to present the use of drones for parcels and small packages delivery that can be used commercially or in emergency situations.A drone that is capable of lifting weights up to 1 -1.5 kg, an autonomous system that would control the drone completely using the Raspberry Pi a microprocessor that will connect the drone to the control system and will control the drone throughout the process.A Global Positioning System (GPS) that helps the drone to reach its destination and return to the base station.This paper presents how drones would be helpful in delivering small packages helping ecommerce expand their business, and also can used to deliver in emergency situations like deliveries of blood, medicinal goods and many other items that a person or professional can use in emergency, which takes lot of time currently for a person to deliver by hand.
Pengaruh Peran Guru Dalam Proses Pembelajaran dan Intensitas Belajar Terhadap Hasil Belajar Siswa Kelas X IPS SMA Negeri 5 Padang Pada Mata Pelajaran Ekonomi Tahun Ajaran 2018/2019

This research aim to analyze: (1) the direct effect of the teacher's role in the learning process on student intensity of learning, (2) the direct effect of the teacher's role in the learning process on student learning outcomes, (3) the direct effect of student learning intensity on student learning outcomes, (4) the indirect effect of the teacher's role in the learning process on student learning outcomes through student learning intensity. This type of research is an associative descriptive study with the population is all students of class X IPS SMA Negeri 5 Padang Academic Year 2018/2019 and a sample of 121 people. The data analysis technique used is descriptive analysis, inferential analysis with path analysis. The results showed that (1) the role of the teacher in the learning process directly affected the intensity of student learning, (2) the role of the teacher in the learning process directly affected student learning outcomes, (3) the intensity of student learning directly affected student learning outcomes, and (4) there is an indirect effect of the teacher's role in the learning process on student learning outcomes through student learning intensity.Keywords: learning outcomes, the role of teacher, intensity of learning
Research on the influence of English teaching behavior based on association rules algorithm

With the development of economic globalization, Chinese enterprises play an important role in the wave of global economy and their participation is deeper and deeper. It also brings a lot of troubles in communication. The demand for English talents is increasing, which requires our educational institutions to adjust the content of the teaching in a timely manner according to the market situation, especially in the case of a shortage of skilled personnel. Through the study of the influence of the English teaching behavior based on the association rules algorithm, this paper discussed how to carry out the teaching of English application more efficiently.
An Automatic Text Document Classification using Modified Weight and Semantic Method

Text mining is the process of transformation of useful information from the structured or unstructured sources. In text mining, feature extraction is one of the vital parts. This paper analyses some of the feature extraction methods and proposed the enhanced method for feature extraction. Term Frequency-Inverse Document Frequency(TF-IDF) method only assigned weight to the term based on the occurrence of the term. Now, it is enlarged to increases the weight of the most important words and decreases the weight of the less important words. This enlarged method is called as M-TF-IDF. This method does not consider the semantic similarity between the terms. Hence, Latent Semantic Analysis(LSA) method is used for feature extraction and dimensionality reduction. To analyze the performance of the proposed feature extraction methods, two benchmark datasets like Reuter-21578-R8 and 20 news group and two real time datasets like descriptive type answer dataset and crime news dataset are used. This paper used this proposed method for descriptive type answer evaluation. Manual evaluation of descriptive type paper may lead to discrepancy in the mark. It is eliminated by using this type of evaluation. The proposed method has been tested with answers written by learners of our department. It allows more accurate assessment and more effective evaluation of the learning process. This method has a lot of benefits such as reduced time and effort, efficient use of resources, reduced burden on the faculty and increased reliability of results. This proposed method also used to analyze the documents which contain the details about in and around Madurai city. Madurai is a sensitive place in the southern area of Tamilnadu in India. It has been collected from the Hindu archives. This news document has been classified like crime or not. It is also used to check in which month most crime rate occurs. This analysis used to reduce the crime rate in future. The classification algorithm Support Vector Machine(SVM) used to classify the dataset. The experimental analysis and results show that the performances of the proposed feature extraction methods are outperforming the existing feature extraction methods.
Machine Learning Algorithms for MR Brian Image Classification

Effects of Visual and Auditory Response Times in Males and Females by using Tactile and Mouse on Web Based EnvironmentClassification of MR brain images accurately is the crucial part in the medical imaging research since brain is a complex structure which needs multiple convergence approaches to classify. In this paper machine learning algorithms like SVM, HMM were employed to classify the brain images with the use of spatial features. Super pixel image segmentation approach is applied to segment the image into distinct super pixel region for which the features are calculated.
Proficient Model of Monitoring and Controlling of Low Voltage Distribution Smart Feeder Protection System Using IoT

Smart feeder is a developing power distribution network that is embedded with a blend of different kinds of energy distribution sources, as sustainable energy sources, united heat and power, and scattered energy assets.The smart feeder system, being an intermediary component for the distribution of energy to the local transformer, protects against the event of any fault, turns into a necessity in the power systems.This research work presents an advancement in monitoring and controlling the Low Voltage (LV) Smart Feeder system using the Internet of Things (IoT).The absence of data at the base feeder, the electricity administration network's status has been distinguished as the significant obstruction to its successful monitoring and control of the electrical distribution system.To tackle the issues in the recent smart feeder controlling and monitoring, authors have designed a smart feeder controlling and monitoring framework dependent on energy IoT utilizing the IoT innovations.The framework acknowledged continuous observation of feeder system devices and working conditions, information knowledge examination, alert linkage, and visual showcase.It helps backing to feeder operation and maintenance improved the activity and the intelligent smart feeder's management level.In this research work, a smart feeder protection system has been proposed with the IoT based monitoring and controlling of Voltage, Current, and Temperature (V, I, and T) of the system.
Deep Learning-Based Text Segmentation in NLP Using Fast Recurrent Neural Network with Bi-LSTM

Segmentation of Text, the undertaking of partitioning a record obsessed by adjoining sections dependent on its semantic design, is a longstanding test in language understanding. Each segment has its applicable significance. Those segments arranged as phrase, text group, point, express or any data unit relying upon the errand of the content examination. This paper proposes the profound learning-based content segmentation strategies in NLP where the content has been portioned utilizing quick tangled neural organization. We propose a bidirectional LSTM prototype where text group embedding is gotten the hang of utilizing fast RNNs and the phrases are fragmented dependent on context-oriented data. This prototype can consequently deal with variable measured setting data and present an enormous new dataset for text segmentation that is naturally divided. Besides, we build up a segmentation prototype dependent on this dataset and show that it sums up well to inconspicuous regular content. We find that albeit the segmentation precision of FRNN with Bi-LSTM segmentation is advanced than some other segmentation techniques. In the proposed framework, every content is resized obsessed by required size, which is straightforwardly exposed to preparation. That is, each resized text has foreordained and these phrases are taken as fragmented content for preparing the neural organization. The outcomes show that the proposed framework yields great segmentation rates which are practically identical to that of segmentation-based plans for manually written content.
Edge Based Segmentation in Medical Images

Image segmentation is the method to fragment a given image into a number of Regions or objects. The level of detail to which the partition is carried depends on the problem being solved. Edge detection is mostly used techniques in digital image processing. Edge detection will preserve the structural properties of an image and filter out unwanted dsata. In this paper, Edge detection methods such as Sobel, Prewitt, Robert, Canny, and Laplacian of Gaussian (LOG) are used. These methods are used in image segmentation. Edge detection can be enhanced by combining with denoised image. Wiener filter, Gaussian Filter and Median Filters are used for noise reductionS. The results of various methods are analyzed by implemented in MATLAB.
Concept-Drift Based Identification of Suspicious Activity at Specific IP Addresses using Machine Learning

Network Intrusion detection systems(IDS), especially those that monitor Denial of Service(DoS) attack, aim at monitoring the network traffic continuously in order to identify suspicious activity possibly initiated at one or more nodes at specific IP addresses. Traditional anomaly detection based IDS methods rely on preset bounds on the magnitude of network traffic based on statistical measures and hence are not programmable based on the changes in the network traffic dynamics. The authors proposed a methodology for monitoring the changes in the network traffic received from individual source nodes based on concept drift in order to identify suspicious activity at specific nodes. The framework applies machine learning techniques to capture the normal traffic patterns of various source nodes and accordingly defines lower and upper bounds dynamically for each node. Based on the temporal analysis in successive time windows, it is able to discriminate an abrupt change from a gradual change in the magnitude of traffic received in a time window from a node to identify suspicious activity at the corresponding IP address. The effectiveness of the methodology is tested on real world data.
Ultrasonic Sensor Based Haptic Feedback Navigational System for Deaf - Blind People

Deaf-Blindness is a rare collective disorder that affects nearly 3.5 million people in today’s world. The improvement restricts the usage of two senses and by large impacts a person’s navigational capability. A variety of aid devices are used to tackle such a disability. But one common drawback that setback them is their inability to address the collective disorder. The proposed project aims at overcoming the aforementioned drawback with the help of ultrasonic sensors and haptic feedback in the form of vibrations. These sensors and feedback mechanisms are to be controlled by a microcontroller in an Arduino. Further, a rechargeable battery shall be used to accommodate the power requirements which emphasizes on energy efficiency. The project commits to limit space constraints by proposing a compact handle design and maximizes its cost efficiency such that it is affordable for everyone equally.
Cloud Database Security in E-Voting System using Blockchain Technology

In today’s era, the cloud database security is one of the main concerns for any of the real time data accessing web/mobile applications. The cloud database protection involves accessibility and vulnerability of data, data protection, storage space, integrity and confidentiality on sensitive data. Building an electronic voting system that tries to completely fulfill the needs of the people has always been a challenge to achieve. The existing E-Voting System (E-VS) is not that much compatible with that of the current trends and does not assure to provide more security A lot of distributed ledger technologies which has been an exciting approach during existing election voting process. If we take a look on the ways of implying E-VS in a distribute ledger then Blockchain would be the right choice. As we all know that nowadays, Blockchain is one of the emerging technologies in the field of Information Technology. It normally stores information in batches called blocks which are linked together in a chronological way or method to form chain of blocks using cryptography techniques. During online voting process, many fraudulent activities happens which corrupt the entire election process. One of the major problems faced are fake voting which is obviously done by unauthorized people, inconvenient to reach to the respective places, average security level which may lead to the chances of an electoral fraud or any other malpractices.. Our proposed E-Voting System is mainly to protect the cloud database for real time data and to reduce the time consumption in voting and vote counting processes. Instead of standing in the queue for casting the vote, people can cast their votes from anywhere they want through online. The E-VS gives complete privacy and security for the online voting and makes it an ease for every individual to access it and cast their votes from anywhere possible with full pronounced security. In our proposed E-VS, Blockchain security concept called Consensus algorithm is implemented which makes it impossible for any unwanted activities to occur during election process. The E-VS system also achieves a higher level of security. Hence, the proposed system achieves data integrity, data confidentiality, eliminates storage overhead, and reduces time consumption for overall electronic voting system.
Advance Dynamic Network System of Internet of Things

Constructing automation is a crucial part of IOT based smart spaces for attaining user comfortableness, energy efficiency and cost reduction. Current technologies for constructing automation are based on syntactic mapping among various sub systems of smart spaces. However, building automation process needs to consider the current status of subsystem semantically for achieving user comfortableness. Ontology is developed for semantic interaction between various subsystems. An application scenario is illustrated.
Why Business Schools Must Incorporate Cybersecurity into The Business Curriculum: Preparing the Next Generation for Success

Businesses are dependent on highly technical information systems to conduct business operations and to develop and maintain intellectual capital. The news is filled with stories of how hackers have infiltrated and breached corporate information negatively impacting consumers. This data intrusion becomes a logistical and financial nightmare for businesses and customers. Businesses are ill prepared to protect this information, yet customers expect that their confidential information is secure. Tomorrow’s business leaders must understand the value of data, the importance of data protection and the effective management of data security breaches. It is crucial that business schools incorporate cybersecurity and privacy as a core component of their curriculum.
"Category of activity: essence and application in psychological and pedagogical researches"

children with intellectual development disorders.
‘This Web Page Should Not Exist’

Plesničar and Šarf’s chapter considers the backlash to hate speech occurring on social media in the Slovenian context. When seemingly public content on Facebook was republished on a denunciatory Tumblr page, it was re-contextualised, and its authors came under greater public scrutiny. Yet when physical posters of the denunciations appeared in the nation’s capital, a counter-denunciation arose against the anonymous authors of the Tumblr page, and a broader debate emerged about the appropriateness of these tactics. This chapter draws upon a range of data to consider the socio-cultural as well as legal contexts of republishing as weaponised visibility. Not only does this case raise the issue of who is entitled to denounce, but also how the notion of ‘the public’ itself may be context-specific and contested.
Role of Artificial Intelligence in Social Media Marketing

This presented review paper encompasses all the ongoing trends of the Artificial Intelligence. This review evaluates the possibilities of Artificial Intelligence (AI) in social media marketing. This review paper aims to study the potential of AI in the social media marketing. This review paper fulfils the objectives of simulation of AI in the business organisations to enhance marketing which will in return increase sales. The paper aims examines the possibilities and strengths of AI. This review paper will explore the intervention of AI into marketing arena. The review flows from the general to specific. It evaluates the effect of AI on both the society as a whole and also specifically on the business organisation. It assesses the effect of AI on both the Social media and the business.
Logical Approach: Consistency Rules between Activity Diagram and Class Diagram

Requirements engineering (RE) is a fundamental in software development process. Requirements engineering encompasses activities ranging from requirements elicitation and analysis to specification, verification and validation. Poor requirements have been proved to be a major cause of software problems such as cost overruns, delivery delays, failure to meet expectation and degradation. Requirements validation especially models validation has gained quite an interest from a lot of researchers. In recent times, several researchers have expressed a great deal of interest in requirements validation, specifically models validation. The field of research related to consistency checking has undergone a considerable boom from time to time. Numerous methods, approaches and techniques have been recommended to address the requirements inconsistency issues, particularly in models validation. In the software development industry, UML modelling has been extensively used. The different forms of the UML model that characterise the system from various perspectives somehow establish a relation among the models to keep them inseparable from one another. This is the reason why the inconsistency becomes unavoidable. The inconsistency in the models arises when there is an overlap of the elements of the various models representing the different parts of the system and an absence of cooperation. In this paper, the emphasis is given on the consistency rules that exist between the two models. The focus is also on the class diagrams and activity, and the conversion of the rules into logical predicates, where the logical predicates are assessed with a sample case study that constitutes of the two models.
Japanese Historical Character Recognition using Deep Convolutional Neural Network (DCNN) with DropBlock Regularization

The history of a country always helps in the development of making modern society. Japanese classical handwritten/literature dataset Kuzushiji-MNIST and Kuzushiji-49 recently introduced in public domain by Center of Open Data in the Humanities (CODH). The availability of the datasets invite the researchers in different domains. The hidden treasure of information in these datasets resolved and worked by many researchers. The research on these datasets can be helpful to explore and analyze the detail of Japanese era/literature, which ultimately helps in the development of modern society. In this study, Deep Convolutional Neural Network (DCNN) with DropBlock regularization is used to recognize the hiragana characters in Japanese historical script.