Reviewerly/exHarmony huggingface dataset:
Complexity analysis and hardware implementation of extensible modulo addition for lightweight block cipher in Internet of Things (IOT)

This project presents complexity analysis and hardware implementation of extensible modulo addition [15] encryption algorithm on a 32-bit lightweight FPGA based block cipher called INFLEX, which is designed for the internet of things (IoT) environment, supporting 64-bits key. It is designed for constrained hardware resources yet providing a highly secure scalable configuration for the variety of applications. This characteristic is obtained by the use of generalized Feistel structure combined with an improved block inflation feature. INFLEX follows a typical ARX (Add, Rotate, XOR) round function with a distinguished feature of block expansion and collapse as per user selected control string, which makes INFLEX act as a tweakable Cipher. We have shown comparison of INFLEX algorithm robustness and immunity against linear and differential attacks and demonstrated that it outperforms one of the benchmark block Ciphers Speck32/64 proposed by national security agency (NSA).
A proposed strategy to secure web usage

With much data on the web, it can be difficult, frustrating, and seemingly impossible to find the exact information you need. There are many powerful search utilities on the web are called search engines, in addition to the visitor tracking in a web to study exactly the behavior of the web visitors, to improve the efficiency of that web. This research concentrates on a particular aspect, which is applying Data mining technique especially by association analysis algorithm on the encrypted web log files, that to ensure the privacy of the original data for these files. So since the input data introduced to mining algorithm is encrypted then the resulted association rules are encrypted that to ensure the privacy of the extracted knowledge. Then analyze the decrypted web log data file for the web usage, to study the visitor tracking. According to this study the server configurations and all the services will be improved.
DDEAS: Distributed Deduplication System with Efficient Access in Cloud Data Storage

Cloud storage service is one of the vital function of cloud computing that helps cloud users to outsource a massive volume of data without upgrading their devices. However, cloud data storage offered by Cloud Service Providers (CSPs) faces data redundancy problems. The data de-duplication technique aims to eliminate redundant data segments and keeps a single instance of the data set, even if similar data set is owned by any number of users. Since data blocks are distributed among the multiple individual servers, the user needs to download each block of the file before reconstructing the file, which reduces the system efficiency. We propose a server level data recover module in the cloud storage system to improve file access efficiency and reduce network bandwidth utilization time. In the proposed method, erasure coding is used to store blocks in distributed cloud storage and The MD5 (Message Digest 5) is used for data integrity. Executing recover algorithm helps user to directly fetch the file without downloading each block from the cloud servers. The proposed scheme improves the time efficiency of the system and quick access ability to the stored data. Thus consumes less network bandwidth and reduces user processing overhead while data file is downloading.
DATA UNCERTAINTY AND DATA QUALITY PROCESSING WITH APACHE SPARK

Spatiotemporal analysis refers to the processing of spatiotemporal data and discovering knowledge, patterns from it.A practical example is identifying different city's histories and earthquakes.This data is collecting from the source where it is being referred with countries their regions, population, speaking habits from time to time, evolutions from time to time in the cities and also the developments happened constitutes space and time, and this data are often highly noisy and sparse consequently extract useful knowledge from such noisy and sparse data is a challenging task.Another challenging problem is integrating multiplemodel data from three different sectors like countries, states, cities and their socio-economic development.There are different factors involved in ST data such as location, time, and text, these heterogeneous factors are highly coupled to reflect people's activities in a collective way, yet they have totally different modes, sizes, and allocations.How to effectively integrate those different data types for knowledge acquisition remains another challenge with the reference data structure is still unsolved.Furthermore, still, its time-consuming process to store a huge volume of spatiotemporal data which are rapidly accumulated and processing queries on the vast amount of spatiotemporal data is highly difficult in terms of space or time complexity.This paper discusses about the different sources around us to navigate and work along with the spatio data and explore more into the data set.The methods used in the analysis are EDA (Exploratory Data Analysis), Data deficiency, Data uncertainty so on.The architecture is been used in order to make a perfect their representation of the models.Lastly a model is also proposed as to work with the dataset and work more on the model with a proper consistent method.
Availability Modeling of Computing Systems with Virtual Architectures

Cloud computing services are built on the premise of high availability. These services are sold to customers who are expecting a reduced cost particularly in the area of failures and maintenance. At the Infrastructure as a Service (IaaS) layer resources is sold to customers as virtual machines (VMs) with CPU and memory specifications. Both these resources are not necessarily guaranteed. This is because virtual machines can share the same hardware resources. If resources aren't allocated properly, one virtual machine for example, may use up too much CPU power reducing the processing power available to other virtual machines. This can result in response time failures. In this research a framework is developed that integrates hardware, software and response time failures. Response time failures occur when a request is made to a server and does not complete on time. The framework allows the cloud purchaser to test the system under stressed conditions, allocating more or less virtual machines to determine the availability of the system. The framework also allows the cloud provider to separately evaluate the availability of the hardware and other software systems.
Identifying User Interests In An Online Discussion Forum With Deep Learning

The probabilistic topic model Latent Dirichlet Allocation (LDA) was deployed to model the themes of discourse in discussion threads on the social media aggregation website Reddit. Abstracting discussion threads as vectors of topic weights, these vectors were fed into several neural network architectures, each with a different number of hidden layers, to train machine learning models that could identify which discussion would be of interest for a given user to contribute. Using accuracy as the evaluation metric to determine which model framework achieved the best performance on a given user’s validation set, these selected models achieved an average accuracy of 66.1% on the test data for a sample set of 30 users. Using the predicted probabilities of interest made by these neural networks, recommender systems were further built and analyzed for each user.
The Effectiveness of Online Learning Based on The Complete Sentence Model on Student Learning Outcomes on Theme 2 Sub-Theme 1 in Second Grade Writing Skills

Children's language skills are a problem that needs attention because many children are not yet able to write and use good and correct language. The purpose of this study was to analyze the effectiveness of online learning based on the complete sentence model on student learning outcomes in Theme 2 Playing in My Environment Sub-theme 1 Playing in the Home environment writing skills for class II. This type of research is experimental research with a research design that is pre-experimental designs. The study population was class II consisting of 28 students for the 2020-2021 school year. The sample taken was class II students consisting of 28 students using nonprobability sampling techniques. In this study, the data analysis technique used is quantitative statistical data analysis techniques. The results showed that the score of the t-test showed that the results of the calculation of the score data of students with t count = 1.717804 with a price of t table = 1.701113, then t count&gt; t table. This research concludes that there is a significant increase, so online learning based on the complete sentence model is effectively used for student learning outcomes in theme 2 Playing in My Environment Sub-theme 1 Playing in the Home environment writing skills class II.
An efficient architecture for discrete wavelet transform and best-basis algorithm for images

The hardware acceleration of the wavelet transform for real-time systems has become an essential research field. In the first part of the thesis, an efficient architecture that performs both forward and inverse lifting-based discrete wavelet transform is proposed. The proposed architecture reduces the hardware requirement by exploiting the redundancy in the arithmetic operation involved in DWT computation. The proposed architecture consists of predict module, update module, address generation module, control unit and a set of registers to establish data communication between predict and update modules. The symmetrical extension of images at the boundary to reduce distorted images has been incorporated in our proposed for both (5,3) wavelet and (9,7) wavelet. Best-basis algorithm that is designed for signal compression and de-noising uses WPT to select the best-basis node for a given additive cost function. In the second part of the thesis, we propose wavelet architecture to perform WPT decomposition. A new algorithm to implement the natural logarithm function using Maclaurin series is proposed to implement the cost function used for best-basis algorithm. These architectures have been described in VHDL at the RTL level and simulated successfully using ModelSim simulation environment. These architectures are implemented in Virex ll Pro FPGA series of Xilinx.
Sentiment Analysis System for Myanmar News using K Nearest Neighbor and Naïve Bayes

With the explosive growth of internet technology, there are very large amount of information on the web for the internet users.Users not only use that information but also provide opinions for decision making process.Sentiment analysis or opinion mining is one of text classification techniques that identify and extract opinion described in a piece of text.Our aims in this paper are to develop automatic sentiment analysis system for Myanmar news and to annotate sentiment news.Therefore, this system creates sentiment annotated corpus for Myanmar news.Feature extraction and selection are very important for sentiment analysis to get higher performance.N-grams, Countvectorizer, and TF-IDF are used for feature selection and feature extraction.In this system, Myanmar news sentiment analysis system is implemented by using K Nearest Neighbor (KNN) and Naï ve Bayes machine learning algorithms.
Intelligent Hybrid Fraud Detection Using Biometric and Face Recognition

Financial transaction is backbone of global market that make it convenient for people to make their transaction easy.People do transaction of money from everywhere using banking system through e-banking or credit card system.That is why banking sectors play a vital role in any country and provide proper structure to any country and make their economy better.However there are many fraudulent who steal the information of any person's account, use the in information for their own purposes and do illegitimate transaction.Fraudster continuously try to get user's account information by applying different tricks to commit fraud that cost huge lose to banking system as well as consumers that's why fraud become major issue for banking sectors.In Banking sectors there are a lot of transaction that operate on each day and increase day by day and it is necessary to make secure of money and transaction as well consumers information that fraudster do not steal easily.To overcome this issue there is a need of an efficient fraud detection method or system that detect fraud quickly and stop fraudster to commit fraud because it is very easy to steal any consumers login information to commit fraud like user name and password but we need a mechanism that can be impossible to crack easily and detect fraud on earlier stage.So in this research work, we discuss the method to prevent illegitimate or fraudulent transaction.For this purpose, we propose hybrid biometric and face recognition system.This system detect fraudulent transaction and stop fraudster to commit fraud and classify transaction as legitimate and illegitimate.This system is beneficial for either online or e-banking and through credit card transaction.
Interference in Japanese Learning by First-Year Students of Hospitality Department in State Polytechnic Of Bali

The second-language learning process is often constrained by the influence of mother tongue or first language-Acquisition learners, which is called interference. Interference is principal language irrelevance in bilingual due to one or more language to be introduced or Speech Communication. Based on its phenomenon, students of State Polytechnic of Bali are considered passive interference learners, putting their mother tongue (Javanese and Balinese) elements and Indonesian Language in Japanese consciously or intuitively. This study aims to disclose the type of interference by the hospitality program students for those studying Japanese in State Polytechnic of Bali and explain its factors. The method used is Descriptive Qualitative, which is concerned with taking document field notes and literature Review to represent the real phenomenon of passive Interference types. The population in this study were all students in State Polytechnic of Bali in Japanese class. In contrast, the sample was taken from the results of purposive sampling based on their 100% attendance. There were about 65 first-year or second Semester students in Japanese Class and showed their Interferences. The study results showed that about 75 Passive Interference has commonly occurred in grammatical cases, such as phonetics, morphology, and syntax. Moreover, the occurrence factors of Passive Interference are distinctive Phonetic Features, The Language pattern differences between Mother Tongues (Local Languages) – Japanese, and lack of Japanese Language Proficiency skills and its dictions.
A Parallel Processing Technique Based on GMO and BCS for Medical Image Encryption

Image encryption is a technique that provides security to an image and their data from unauthorized access in which there is the lightweight process (LWP) that can be parallelized resulting in the reduction of computation time. In this paper, parallel lossless image encryption, as well as the decryption technique, is proposed. The method is a parallel implementation of group modulo operation (GMO) based bit circular shift (BCS) of pixel bit-plane values. The backbone of this technique is circular bit rotation based on some modulo group key value. The key value used here is the result of group modulo operation. The binary bit values of pixels of the initial Image are rotated circularly to generate a new binary bit value of pixels encrypted image. The enhancement of this GMO and BCS based encryption are also given here by using the parallel implementation of the algorithm. The given results show the parallel implementation technique has the same level of encryption standard but has a better level of the time standard. As discussed in the result section, this technique can be used for medical image encryption as well as in multimedia applications where the transfer of image data is required over a network.
A Recommendation Model for Energy-Efficient Data Mining Services Based on Data Properties

Currently, there is a big increase in the usage of data analytics applications and services because of the growth in the data produced from different sources. The QoS properties such as response time and latency of these services are important factors to decide which services to select. As a result of IT expansion, energy consumption has become a big issue. Therefore, establishing a QoS-based web service recommender system that considers energy consumption as one of the essential QoS properties represents a significant step towards selecting the energy efficient web services. This dissertation presents an experimental study on energy consumption levels and latency behavior collected from a set of data mining web services running on different datasets. Our study shows that there is a strong relation between the dataset properties and the QoS properties. Based on the findings from this study, a recommender system is built which considers three dimensions (user, service, dataset). The energy consumption values of candidate services invoked by specific users can be predicted for a given dataset. Afterwards, these services can be ranked according to their predicted energy values and presented to users. We propose three approaches to build our recommender system and we treat it as a context-aware recommendation problem. The dataset is considered as contextual information and we use a context-aware matrix factorization model to predict energy values. In the first approach, we adopt the pre-filtering model where the contextual information serves as a query for filtering relevant rating data. In the second approach, we propose a new method for the pre-filtering implementation. Finally, in the last approach, we adopt the contextual modeling method and we explore different ways of representing dataset information as contextual factors to investigate their impacts on the recommendation accuracy. We compare the proposed approaches with the baseline approaches and the results show the effectiveness of the proposed ones. Also, we compare the performance of the three approaches to discover the best-fit approach when being measured using different metrics. Both prediction and recommendation accuracy of the proposed approaches are significantly better than the baseline models.
Resizable Drone Designs: for Maneuvering Through Paths of Varying Dimensions

An unmanned aerial vehicle, commonly known as a drone, is an aircraft without a human pilot aboard. Essentially, a drone is a flying robot that can be remotely controlled or fly autonomously through software-controlled flight plans in their embedded systems, Flying robots are increasingly adopted in search and rescue missions because of their capability to quickly collect and stream information from remote and dangerous areas. Their maneuverability and hovering capabilities allow them to navigate through complex structures, inspect damaged buildings, and even explore underground tunnels and caves. Since their size is fixed, maneuvering over the compact areas and tunnels of variable size becomes an issue. To overcome this issue, we propose a model of quadrotor design which has the capability to change its size. The arm length of the quadrotor is changed dynamically so that it can fly in areas of variable sizes that would be hard to reach with the quadrotor of fixed arm length. On the other hand, our model is cost-effective, since the arm of the drone is designed with PVC (Polyvinyl Chloride). Using this model, drones will be able to move over compact areas and passages of variable sizes, thus aiding in better exploration during search and rescue operations.
Text Mining with Topical and Informative Measures using Semantic Ontology

The text mining has been identified as dominant throughout the era in many scientific problems. Number of techniques have been identified and proposed earlier, each uses different features towards mining text information. However, they suffer to achieve higher performance in mining relevant text information or documents. To support the development of text mining tasks, an efficient topical and informative measures based algorithm is presented, which uses semantic ontology and the taxonomy as dictionary. The text features of the documents has been extracted to generate set of terms. For any document, the text features are used to remove the noisy features like stop words, stemming and tagging. With the noise removed pure terms, the method estimates the Topical Depth Similarity (TDS) and informative Depth Similarity (IDM) measures. The measures has been estimated towards each document to perform text mining. The input query has been estimated for the TDS measure to identify the category of the query. According to the category of query, the method estimates the TDS and IDS measures to mining the text. The proposed method improve the performance of text mining with reduces false classification ratio.
Japan Association of Mineralogical Sciences

Finally, some mentorship network opportunities are tied to access to large international conferences, which are not accessible for many students and early career researchers due to the cost.This disproportionately affects scientists from low-income countries, helping to perpetuate the status quo.Opportunities for mentorship vary from country to country.While it may be easier to form a mentorship network in the US, for example, the same is not true in Africa.These suggestions are meant to provide a way forward to making geochemistry an accessible field for everyone equally, but we recognize this is just the beginning of these much need conversations.
IMPACT OF CURRENT ACCOUNT IMBALANCE ON UNEMPLOYMENT

This paper tries to explore the effects of current account deficit on the prevalent unemployment that affects the development of the Indian economy.A widening in the current account imbalance and rising unemployment in recent times makes the analysis of these factors significant .Thus using the analytical framework of a bivariate Granger Causality test in a Vector Autoregressive(VAR) model, from the year 1991 to 2018, it attempts to find out if there is a significant two way relationship between the variables Current account balance(in current $US) and Unemployment (measured as a percentage of total labour force) .It also performs the Johansen's Test for Cointegration in a Vector Error Correction Model(VECM) to test for long run relationship between the said variables.Further, this paper also suggests policy measures necessary for ameliorating the conditions of the impoverished and for holistic growth of the country.
Linguistics Characteristics of Social Network

In this paper we introduce the idea of an author about the use of slang language for the period of (16th -20th) century, while other give the use of slang language in the social network and the purpose of the said and the name giving to it. Also the factors that affect the variety of language which will reflect also on the language used in the internet. Hence a new linguistic expression was rising with the name of internet linguistic. We give also some popular internet slangs and its advantages also the emoticons and its different use between males and fameless and prefer to use the capitalize letters. This study also shows the effect of virtual communication on the individuals. We conclude that analytically the internet slang is a specific variety of language and characteristics according to the kind of output, also commonly used than speech, like feedback, emoticons, multiple conversations, hypertext links, persistence, and multiple authorship. Moreover it challenged the cooperative and politeness principles, relevance theory, humor, economy, and tolerance, it express the social identity, rather than illiteracy or the character limit of message services.