Reviewerly/exHarmony huggingface dataset:
Optimizing data security by using Integration between double stegging by pixel value difference and advanced encryption standard

Abstract Advancement of internet technology has led to massive text and multimedia data delivery over the internet, Steganography and cryptography are two prominent methods for secure communication through public network such as the Internet. Steganography predates cryptography somewhat Because of its stealthy features. In this paper, a new method has been proposed which combines cryptography and steganography to make communication even more secure. In this method first the secret data is embedded inside digital image ( cov 1 ) using adaptive pixel value difference (PVD) technique, thereafter advanced encryption standard ( AES) algorithm is applied to encrypt the previous digital image. Then encrypted digital image (cov1) will embed inside another digital image (cov2) using the same previous embedding technique. An additional layer of security has been introduced by applying double steganography to ensure the security of sensitive data, also this proposed method aims to keep the value of (PSNR) and (MSE) within accepted range and enhance the amount of secret data to be embedding. Peak signal to noise ratio (PSNR) and mean square error (MSE) are used as a benchmark to measure the performance of the proposed method.
Prototype Development of Graphical Pattern Security System on Raspberry Pi

Password is a primary security mechanism that consists of a secret phrase created using alphabetic, numeric, alphanumeric, and symbolic characters, or a combination. It is used to restrict access to a system, application, or service to only those users who have the authorization to use the system or device. Because of increased computing power, modern passwords must be very long and complicated, which will make them hard to remember. Some research studies show that it is easier for people to remember visual passwords instead of textual ones. Therefore, this research aims to enhance the authentication process's security using the graphical pattern unlock approach on Raspberry Pi. The grid size of 5 × 5 was selected in the implementation, as it provides the tradeoff between security strength and an easy pattern to remember. The proposed system could be connected to the smart home system, which can enhance their security. Prototype validation revealed that the prototype is working as intended, and the authentication process took around 1.2 s to complete.
An Efficient Approach for Query Processing of Incomplete High Dimensional Data Streams

Most recent applications such as sensor networks generate continuous data streams. Additional constraints are faced for efficient query processing of such data streams that have uncertain nature and require fast and timely processing. Traditional query processing techniques of static data process the whole data without partitioning them, which is not applicable to data streams. Applying data clustering is demanded as a preprocessing step of data streams. Thus, in this paper, we propose the Incomplete High dimensional Data streams Query processing (IHDQ) algorithm for efficiently answering data streams queries. Obtained results reveal the efficiency of clustering and query processing of the proposed IHDQ compared to the alternative state-of-the-art approaches.
A Reconcile of Density Based and Hierarchical Clustering Based on the Laws of Physics

In this paper a new approach toward data processing is proposed that is inspired by all the prominent data clustering algorithms proposed by scholars. The main motif that drove this approach was to mix hierarchical clustering methods with Gaussian Estimators as to find a hidden structure in data that was not reachable using traditional bandwidth estimators. Instead the criteria for assessing similarity among data was the principles for Newtonian Physics.
Research and Implementation of Key Technologies for Transforming Formatted Data into Natural Language Text

Office automation systems generally process data in XML and other formats when storing and exchanging information, which can make the computer more convenient for automatic processing to improve work efficiency. However, such data files are not easy for the average person to read. In everyday applications, this approach is not applicable on a large scale and requires editing and drawing natural language text reports based on formatted data. Most of this work is completed manually by the user, which is repeated in the work, resulting in no significant improvement in office efficiency. Therefore, in order to improve the real-time efficiency of work, people need to conduct in-depth research on the conversion mechanism from formatted data to natural language text. This system can carry out automatic data processing according to the characteristics of official document report of the office system, so as to reduce the burden of users.
Word Sense Disambiguation for Telugu Using Lesk

Word Sense Disambiguation (WSD) is an important field in the natural language processing domain. It is mainly used in fields such as Machine translation, Information retrieval and Text mining. These do not have a built-in word sense disambiguator and thus, it has to be performed explicitly. Up until now, this has been largely performed on sentences in the English language to identify the sense or context of a word in that sentence. This paper aims to implement it for regional languages as well, specifically, Telugu. Since Telugu vocabulary is huge, occurrences of ambiguity are common and this paper is aimed at resolving them. To achieve this, a synset will be used, which is an interface for the Telugu WordNet. The algorithm used in this paper is the Lesk algorithm which is a classical word sense disambiguation algorithm. It is based on the assumption that the correct meaning/sense of a word is dependent upon the words present in its neighborhood. More specifically, the sense or context of a word is determined by comparing the meaning of each sense of the word with the meanings of the neighboring words. This paper aims to get the best sense of words based on the neighboring words of a particular word using the Lesk algorithm
Forecasting approach for Blind Spot Collision Alert

Every year many accidents involving all categories of road users. However, some accidents are related to the blind spot, due to the deficiency of visibility in this zone, also lack of information or negligence. In this work, we focused on the road safety applications. The main of our researches is to alert the risk of blind spot collision. For this we designed a new approach, namely FBSCA, Forecasting Blind Spot Collision Alert, the driver alerts his neighbors for a possible accident related to the blind spot. The most interesting scenario testing includes the case where the vehicles need to exchange information in real-time in order to avoid traffic collisions when their visibility is hindered by road obstacles. For the implementation and testing of our approach we used popular network simulation tools such MOVE, SUMO and Ns2, we provided an assessment of proposed solution in order to alert the blind spot collisions for all neighboring vehicles.
MIKiR’s approach to developing Visual Literacy Skills for PGMI Students at UIN STS Jambi through online lectures

Abstract Visual literacy is determined by one’s reading comprehension, visual literacy is always found in our environment, lecturers need to provide opportunities for students to get activities that can develop their visual literacy skills, and it is hoped that these visual skills can be taught to MI / SD students. One of the activities that can support visual literacy is the MIKiR approach. MIKiR stands for Experiencing, Interaction, Communication, and Reflection. Experiencing activities such as students observing, doing experiments, making things. Interaction such as discussing, asking, and answering questions, explaining the results of group work. Communication such as conveying work results, telling stories, expressing opinions. Reflection is like writing or mentioning what was learned today and what was not understood. Online lecture activities carried out with the MIKiR approach are able to develop student visual literacy such as students being able to present messages in text into visual forms and students being able to transform visual messages into written text. The implication of this research is that students are able to develop their visual literacy skills and students are able to choose an appropriate learning approach in improving the visual literacy of students.
Chapter 9: Integrated Geologic-Geophysical Interpretation of District-Scale Structural Frameworks: Systematic Approaches for Targeting Mineralizing Systems

The integrated interpretation of aeromagnetic data is a key exploration tool to define the concealed, potentially prospective geology that we plan to explore. It helps define the district-scale morphology of structural networks and predict which structures may be associated with the formation of mineral deposits. Aeromagnetic data is particularly useful in guiding geologic mapping, exploration targeting, and strategy because the data available is usually broad, geologic processes and features are normally well imaged in the data, and it is relatively cheap to acquire and process.
Efficient Image Segmentation of Natural Images with Noise using Energy Curved Based on Electromagnetism Optimization Algorithm

The method of digital image processing is an important step for extraction features from images, especially details from the medical image by dividing the image into various regions. In the last two decades, many significant image segmentation schemes modeled for different applications like medical to weather forecasting by images from the satellite. From available methods, multilevel thresholding of pixel values using the histogram of an image is an exceedingly important method developed for vide range of applications. Otsu’s method is the primer multilevel thresholding method based on the maximizing the inter-class variance based on the histogram. In Otsu’s method, the pixels of an image are grouped into different regions based on threshold levels calculated on the histogram of the image. All the pixels are segmented into two groups based on threshold level in the case of bi-level thresholding. The effectiveness of segmentation is depends on threshold levels, efficiency can be enhanced by computing optimized threshold level, Electromagnetism Optimization (EMO) technique used for this purpose in this paper. The techniques based on histograms of image are not holding the spatial particulars of contextual information for computing the optimized threshold levels, to nullify this shortcoming Energy Curve is used as an alternative of histogram, which is restore the special relation of pixels with its neighbor pixels. To examine the results several benchmark images considered, the final results shows that the method with Otsu’s technique with EMO on energy curve is giving better results as compared with methods based on the image histogram.
Enhancing Film Education Learning Outcomes With Virtual Experiences

This article details a system titled “Camera as Mediator” that was built to aid undergraduate Film Studies students study camera placement and shot composition techniques. Learning how camera placement impacts a film is a common educational outcome in undergraduate Film Studies classes. However, building a controlled set with proper lighting, actors, and props is a cost- and time-prohibitive task in a typical introductory course. A virtual system can give students more hands-on experience and leave ample time to focus on core learning outcomes. To this end, we constructed a photorealistic virtual environment to replicate a scene from the Charlie Chaplin film “The Gold Rush” using computer-generated models and three-dimensional scans of actors dressed as the characters from the movie. An interface was designed to allow students to navigate around the scene with a virtual camera and capture shots from perspectives not visible in the original movie. By saving and comparing these different shots, students are easily able to explore how the mood and feel of a story changes with the camera's perspective. Doing this virtually enables undergraduate students in large classes and/or online classes and with potentially limited time and equipment to study film making techniques in greater depth than they would otherwise.
Using Context for Sense Preference

A system for preferring word senses according to context is described. The system uses a machine-readable dictionary (the Longman Dictionary of Contemporary English), that contains special subject coding fields, and keeps track of these codes as a lexicon is constructed for a text. By restructuring these codes into a pragmatic hierarchy, an effective structure for word sense preference is produced. This resulting structure has value for other systems of language analysis, as a source of global text knowledge, and (possibly) also as a reduced representation of text topic.
Analyzing Public’s Reaction towards Black Lives Matter Campaign using Machine learning-based Approach through Spark

Abstract Since the end of May 2020, there is a massive wave of protestant in the United States addressed to the government regarding the case of police violence towards black people. On May 25, 2020, George Floyd, a 46-years-old black American man was killed during an arrest for allegedly using a counterfeit bill in Minneapolis. This study analyzes the public’s reactions towards the Black Lives Matter campaign using a supervised machine learning-based approach. The proposed model uses logistic regression with word vectors as its feature. The model classifies the public’s reaction represented by tweets and crawls using Spark Streaming into sentiment class, i.e. positive, negative and neutral. In addition, named entity recognition analysis was also conducted in this study. The aim is to find who else besides George Floyd whose rights have been fought for by the public. SparkNLP is used to build the logistic regressions model, sentiment analysis and named entity recognition. This study finds that most of the public tweets had a negative tone addressed to the Floyd incident specifically and to the violence towards black people in general. Another finding is that the campaign not only fought for George Floyd, but also fought for the other victims like Rayshard Brooks, Dominique Fells and Eric Garner.
Mobile Application Development Ontology

Ontologies are structural knowledge components constructed to clarify concepts in a specific domain of knowledge. They are formed to represent the concepts and the relationships between these concepts in that domain. This paper proposes a Mobile Application Development Ontology that conceptualizes the knowledge in the domain of mobile application development. The use of mobile applications is flourishing as mobile devices use is pervasive. The proposed ontology aims to provide a terminology glossary reference for stakeholders in the domain of mobile application development; students, researchers, educators, mobile application analysts, and mobile application developers. The constructed ontology was visualized using a D3 library visualization tool. Applications used for the built ontology include educational purposes and glossaries for classifying concepts in the domain of mobile application development.
Theme of Modernity in the Bashkir Drama of the 1930s

The purpose of the study is to identify playwrights’ creative search in reflection of the theme of modernity and the concept of a new hero in the Bashkir drama of the 1930s. Scientific novelty of the study lies in identifying the trend of gradual transition of the Bashkir drama from musical and ethnographic plays, differing in topics and genres, to the theme of the historical past, from stage plays to realistic depiction of modernity, to dramas of characters, to psychological development of characters’ spiritual world. The research findings have shown that there appeared a qualitatively new artistic solution to topics and problems, nature of conflict and the concept of the image of a new contemporary hero in the Bashkir drama of the 1930s, as compared to previous stages of its development.
Cryptographic Analysis of DES and RSA Algorithm Using the AVISPA Tool and WSN

With the world-changing toward IoT, wireless sensors networks (WSN) are becoming more important than ever. WSN is a complex distributed system accustomed to several technological fields especially in the area of security. Wireless sensor network deals with several research issues concerning security and energy efficiency. WSN uses cryptography as a method to ensure network data security and authentication. This paper reconsiders the algorithms of cryptography and improves them by eliminating any flaws in it, using the AVISPA tool. In this paper, symmetric and asymmetric key cryptographic algorithms like DES, RSA and the improved RSA were compared using the AVISPA tool. The objective is to enhance the existing algorithms in order to remove the flaws and to provide a more secure algorithm. To augment the security, we used two sessions as compared to one in the case of existing algorithms. To conclude, the improved algorithm is more secured as compared to existing algorithms.
Arabic Sentiment Analysis Approaches: An Overview

In the last few years, Arabic Sentiment Analysis (ASA) has become the most important trends topic of market and scientific research in Machine Learning (ML) and Natural Language Processing (NLP) fields. It tries to identify the opinion orientation (positivity, neutrality or negativity) of a piece text in different domains and it has gained significant results especially in the field of education, in other words ASA is utilized to ameliorate education by trying to understand the learning degree of students and monitoring the performance of students. Due to Arabic complexities at the level of orthography, morphology and dialects. Sentiment analysis for Arabic language is very difficult and it considered as a more challenging process compared to other languages like English. In this work, we tried to give a comparative analysis of Arabic Sentiment approaches, together with various powerful existing techniques.
A <i>m</i>Θ spectrum of Reed-Muller codes

In this paper, we construct mΘ spectrum of Reed Muller codes, a family of mΘ linear codes [5] with large mΘ minimum distance. First, we cover the necessary material on the mΘ Boolean algebra. We then define the mΘ spectrum of Reed Muller codes RMΘ (r, m) as certain subspaces of the mΘ Boolean algebra . We determinate the parameters of RMΘ (r, m).
A Blockchain Data Balance Using a Generative Adversarial Network Approach: Application to Smart House IDS

The rapid development of information and communication technologies makes the Internet of Things (IoT) devices much more complex and heterogeneous. In this context, the massive end devices (IoTs) and the large volume of data raise security and privacy challenges. To tackle these issues, the joint use of the Bockchain (BC) and Machine Learning (ML) seems attractive to achieve decentralized, secure, intelligent and efficient management of networks. On the one hand, the BC can greatly facilitate the sharing of training data and ML models, the decentralization of intelligence, security, privacy and reliable ML decision-making. On the other hand, ML may have significant impacts on the development of BC in communications and networking systems, including energy and resource efficiency, scalability, security, privacy and smart contracting. An important aspect of security intends to detect unusual and potentially inappropriate activities according to traffic patterns. This paper focuses on the problem of imbalance data where the number of abnormal samples is significantly lower than that of the normal (secure) ones. In particular, this paper presents a new equilibrium model based on an exciting recent innovation in ML namely Generator Adverse Networks (GANs) to address the problem of class imbalance and data noise to Intrusion Detection System (IDS) performance. The proposed approach use is illustrated by a case study: a smart house system-based scenario.
Cyber-Physical Individual Monitoring in Indoor Internet -of- Things Systems With Edge Intelligence

This paper proposes a novel individual monitoring mechanism to support rescuing people in indoor places, named Cyber-Physical Individual monitoring (CP-Im), When firefighters go to the rescue of people existing within an indoor place where is on fire or broken down due to an earthquake or some disaster, the existence and accurate location of victims are most important information. So, this paper tries to solve such practical demand relying on the present life nature with well-deployed mobile network infrastructure and densely-populated smartphone users. CP-Im shows 98.82% of the user recognition precision and 96.5% of the user position accuracy is achieved.
A Reliable Hybrid Blockchain-Based Authentication System for IoT Network

IoT is an assembly of equipment of different calibers and functionality working towards a single goal. A blockchain is a computerized record that contains the whole history of exchanges made on the system. A multi-WSN arrangement model is structured. The hubs of the IoT are isolated into base stations, group heads, and conventional hubs as per their capacities, which encourage the administration and participation of the hubs. A hybrid blockchain model is proposed. To fit the multi-WSN arrange model better, as indicated by the various capacities and energies of various hubs, neighborhood blockchain and open blockchain are sent between group head hubs and base stations individually, and a crossbreed blockchain model is framed. A shared validation plot for IoT hubs is proposed. For group head hubs, the creators utilize the worldwide blockchain for validation, and for customary hubs, they utilize the nearby blockchain for confirmation. The proposal aims in increasing reliability by 1.17% and minimizes sinkhole attack by 2.42% compared to previous contribution.
Evaluation of Loss Function for Stable Policy Learning in Dobutsu Shogi

AlphaZero achieved superhuman performance in Go, chess, and shogi, supported by massive computational re-sources. This paper investigates policy learning in AlphaZero, aiming at improving its learning efficiency. AlphaZero trains policy and value networks that are similar to those in a standard reinforcement learning setting, but in a different way closely coupled with game tree search in self-play. Inspired by recent studies that investigate policy optimization in tree search, we apply PPO, which is a state-of-the method for learning policy in reinforcement learning, into AlphaZero's style learning. Then, we show its effectiveness in a small but interesting game where the database made by retrograde analysis is available. The results support that our method improves the sample efficiency in the learning.