Reviewerly/exHarmony huggingface dataset:
K- Means Based Probabilistic Neural Network (KPNN) For Designing Physical Machine – Classifier

Cloud Computing necessitates virtual machines that can deploy users to a machine in a sub-optimal fashion for effective and maximum utilization of resources conserving overall energy within the specified duration. PNN is an effective probabilistic classifier which has been applied for a wide variety of computer application problems. However, for big data applications, we need either pre-processing algorithms for efficient classification with lower computing time or Mathematical tracking operators to speed up a parametric approach. This paper focuses to combine the traditional K means algorithm and a PNN to process the data obtained from Google cluster to classify them into pre-specified groups so as to implement PM classifier design to monitor the Cloud usage pattern. It is found after validation of KPNN with different data sets that KPNN works better than PNN in terms of accuracy even when the number of classes increases and turns out to be a computationally attractive tool.
Novel Method of Secure Communication using Logistic Map

Significant research efforts have been invested in recent years to export new concepts for secure cryptographic methods. Many mathematicians are attracted by Chaos functions as it has sensitive nature toward its initial conditions and their colossal suitability to problems in daily life. Inspired by new researches, a new chaotic cryptography algorithm is proposed in this paper. The key feature of this approach is that instantaneous key is generated at host independently that is used to determine the type of operations on each pixel. The information available in images is 24 bit RGB these value are modified mathematically using eight reversible operations. Also during encryption, the control parameter of the chaotic system is updated timely.
Forensic Analysis of Social Media Apps

Social networks in any form, specifically online social networks (OSNs), are becoming a part of our everyday life in this new kind of millennium especially with the advanced and simple communication technologies through easily accessible devices such as smartphones and laptops and desktops. The data generated through the use of these technologies need to be analyzed for forensic purposes when criminal and terrorist activities are involved. And is useful to analyze and come to an understanding about their further intentions. In order to deal with the forensic implications of social networks, current research on both digital forensics and social networks need to be incorporated and understood. In this paper we are conducting forensic analysis on different networking sites such as Twitter, Skype, and WhatsApp on famous search engine called Firefox.
Basic Educational Content Development for Distanse Learning: Theory and Practice

With the increase in implanting the information and computing technologies into learning during 1990-2020s, it has become obvious that e-learning is going to become both a global tendency and a crucial change in the quality, form and methodology of education. The pandemic of COVID 19 with its massive global shift to distance learning brought to focus the need to revise the strategies and policies in education in general and the critical importance for appropriate digital training for university teachers in particular to boost their readiness, preparedness and ability to adapt to the changing conditions of the educational environment. The study shows that the teacher’s skills to create high-quality basic content for distance learning are crucial for successful teaching online. The plethora of conditions, criteria and factors affecting the quality of educational content for effective online teaching is examined. The article details the stages and principles of designing the basic content of an e-learning course. Using text editors and information visualization tools as examples the authors propose practical recommendations for content development. The study brings to focus some key concepts of e-learning methodology. The authors imply that to create quality content, a teacher needs knowledge and skills in pedagogical design, computing, time management and online communication. The proposed principles for the basic educational content development and criteria suggested have and educators involved in e-courses development, as well as to a wide range of users.
Accelerating Training of Deep Neural Networks on GPU using CUDA

The development of fast and efficient training algorithms for Deep Neural Networks has been a subject of interest over the past few years because the biggest drawback of Deep Neural Networks is enormous cost in computation and large time is consumed to train the parameters of Deep Neural Networks.This aspect motivated several researchers to focus on recent advancements of hardware architectures and parallel programming models and paradigms for accelerating the training of Deep Neural Networks.We revisited the concepts and mechanisms of typical Deep Neural Network training algorithms such as Backpropagation Algorithm and Boltzmann Machine Algorithm and observed that the matrix multiplication constitutes major portion of the work-load for the Deep Neural Network training process because it is carried out for a huge number of times during the training of Deep Neural Networks.With the advent of many-core GPU technologies, a matrix multiplication can be done very efficiently in parallel and this helps a lot training a Deep Neural Network not consuming time as it used to be a few years ago.CUDA is one of the high performance parallel programming models to exploit the capabilities of modern many-core GPU systems.In this paper, we propose to modify Backpropagation Algorithm and Boltzmann Machine Algorithm with CUDA parallel matrix multiplication and test on many-core GPU system.Finally we discover that the planned strategies achieve very quick training of Deep Neural Networks than classic strategies.
Measurement of soil electrical conductivity based on direct digital synthesizer (DDS) and digital oscilloscope

A soil electrical conductivity (EC) measurement system based on direct digital synthesizer (DDS) and digital oscilloscope was developed. The system took the “current-voltage four-electrode method” as the design principal and adopted a six-pin structure of the probe, two center pins to measure the soil EC in shallow layer, two outside pins to measure the soil EC in deep layer, and two middle pins for inputting the driving current. A signal generating circuit using DDS technology was adopted to generate sine signals, which was connected with the two middle pins. A digital oscilloscope was used to record and store the two soil output signals with noises in microseconds, which were from the two center pins and two outside pins, respectively. Then a digital bandpass filter was used to filter the soil output signals recorded by the digital oscilloscope. Compared with the traditional analog filter circuit, the digital filter could filter out the noises of all frequency except for the frequency of the excitation source. It could improve the effect of filtering and the accuracy of the soil EC measurement system. The DDS circuit could provide more stable sine signals with larger amplitudes. The use of digital oscilloscope enables us to analyze the soil output signals in microseconds and measure the soil EC more accurately. The new soil EC measurement system based on DDS and digital oscilloscope can provide a new effective tool for soil sensing in precision agriculture. Keywords: soil electrical conductivity, direct digital synthesizer, digital oscilloscope, precision agriculture, current-voltage four-electrode method DOI: 10.25165/j.ijabe.20191206.4840 Citation: Pei X S, Meng C, Li M Z, Yang W, Zhou P. Measurement of soil electrical conductivity based on direct digital synthesizer (DDS) and digital oscilloscope. Int J Agric & Biol Eng, 2019; 12(6): 162–168.
Novel obstacle-avoiding path planning for crop protection UAV using optimized Dubins curve

In recent years, the crop protection unmanned aerial vehicle (UAV) has been raised great attention around the world due to the advantages of more efficient operation and lower requirement of special landing airport. However, there are few researches on obstacle-avoiding path planning for crop protection UAV. In this study, an improved Dubins curve algorithm was proposed for path planning with multiple obstacle constraints. First, according to the flight parameters of UAV and the types of obstacles in the field, the obstacle circle model and the small obstacle model were established. Second, after selecting the appropriate Dubins curve to generate the obstacle-avoiding path for multiple obstacles, the genetic algorithm (GA) was used to search the optimal obstacle-avoiding path. Third, for turning in the path planning, a strategy considering the size of the spray width and the UAV’s minimum turning radius was presented, which could decrease the speed change times. The results showed that the proposed algorithm can decrease the area of overlap and skip to 205.1%, while the path length increased by only 1.6% in comparison with the traditional Dubins obstacle-avoiding algorithm under the same conditions. With the increase of obstacle radius, the area of overlap and skip reduced effectively with no significant increase in path length. Therefore, the algorithm can efficiently improve the validity of path planning with multiple obstacle constraints and ensure the safety of flight. Keywords: Dubins curve, path planning, genetic algorithm, overlap and skip spray, crop protection UAV DOI: 10.25165/j.ijabe.20201304.3205 Citation: Zhang X H, Fan C G, Cao Z Y, Fang J L, Jia Y J. Novel obstacle-avoiding path planning for crop protection UAV using optimized Dubins curve. Int J Agric & Biol Eng, 2020; 13(4): 172–177.
Introducing Citation Structures

Text Encoding Initiative documents are notoriously heterogeneous in structure, since the Guidelines are intended to permit the encoding on any type of text, from tax receipts written on papyrus to Shakespeare plays or novels. Citation Structures are a new feature in the TEI Guidelines that provide a way for documents to declare their own internal structure along with a way to resolve citations conforming to that structure. This feature will allow systems ike the Distributed Text Services (DTS) API, which process heterogeneous TEI documents to handle tasks like automated table of contents generation, the extraction of structural metadata, and the resolution of citations without prior knowledge of document structure.
Cache vs. Key-Dependency: Side Channeling an Implementation of Pilsung

Over the past two decades, cache attacks have been identified as a threat to the security of cipher implementations. These attacks recover secret information by combining observations of the victim cache accesses with the knowledge of the internal structure of the cipher. So far, cache attacks have been applied to ciphers that have fixed state transformations, leaving open the question of whether using secret, key-dependent transformations enhances the security against such attacks. In this paper we investigate this question. We look at an implementation of the North Korean cipher Pilsung, as reverse-engineered by Kryptos Logic. Like AES, Pilsung is a permutation-substitution cipher, but unlike AES, both the substitution and the permutation steps in Pilsung depend on the key, and are not known to the attacker. We analyze Pilsung and design a cache-based attack. We improve the state of the art by developing techniques for reversing secret-dependent transformations. Our attack, which requires an average of eight minutes on a typical laptop computer, demonstrates that secret transformations do not necessarily protect ciphers against side channel attacks.
Mining Social Data to Identifying User Behavior in Medhelp Forum on Health-Related Topics

Online life is a stage wherein customers can make content material fabric, talks, criticism, websites, commercial center, etc., in which individuals have a sentiment that it is progressively advantageous (or shaky) depending on the use and it's far a surroundings for network arranges as appropriately. It has empowered people to uncover their private wellness stresses and remarkable assessments on begging to be proven wrong issues of general wellbeing. In front line years, further to the general long range interpersonal communication web net sites like facebook, Twitter, Google+, etc., some of online human services net sites, close by MedHelp, sufferers Like Me, support, and wellness fundamental, which adapt to clients with exact clinical inconveniences, are additionally developing. for example , in web-based social networking framework like discussions and surveys the customers attempt to unite with equivalent companions or online therapeutic experts in responding to questions on the whole with, 'Am I ordinary, or do others revel in comparable signs and manifestations?' and 'Am I managing this side effect appropirately ?' and 'How do people like me control this treatment viewpoint impact?' . in this paper we have were given were given focused on the most proficient method to find the customer direct in MedHelp discourse dialog board identifying with wellness subjects.
Image Retrieval using Autocorrelation Based Chordiogram Image Descriptor and Support Vector Machine

Nowadays, evolving systems for indexing and organizing images is more important due to proliferation of images in all domains and it has made content-based image retrieval (CBIR) as significant research area. This paper uses autocorrelation based chordiogram image descriptor (ACID) for effective image representation and Support vector machine (SVM) for effective image classification. The ACID of images is computed from Haar wavelet based multiresolution domain and it exploits shape, texture and geometric details. The proposed combination of ACID and SVM is highly compatible and is comprehensively tested on benchmark datasets namely Gardens Point Walking and St. Lucia and experimental results prove that proposed combination outperforms significantly in terms of precision and recall.
TensorFlow Basics

This short practical chapter is designed to introduce some deep learning–specific features of Swift for TensorFlow. Section 4.1 introduces the concept of tensor data structure which is essentially what neural networks make prediction from. After reading this chapter, you will be able to load the datasets (Section 4.2), write your own neural networks (Section 4.3), and train your model and test its accuracy (Section 4.4). In addition to all of this, in Section 4.5, you will also learn how to implement your own new layer, activation function, loss function, and optimizer. This will help in prototyping your research code or implementing the advanced building blocks of deep learning algorithms. This chapter requires some understanding of machine learning. We recommend you to refresh your concepts by reading Chapter 1 .
Perancangan User Interface Aplikasi Pemodelan Perangkat Lunak Menggunakan Metode User Centered Design

Software development comprises stages that are interrelated with one another. The analysis and design stages are the initial stages in software development. For novice software developers, diagram making as software modeling is something that can be done as an alternative in developing computer applications or systems. In this study, we carried the interface design for software modeling applications out. The application developed later is a web-based application as a solution to licensing problems and expensive software modeling tools. This study only focuses on application design, does not contain implementation and application testing. The method used in this research is a user-centered design method. This method was chosen because it uses the user as the center of the design. So we expect it that the design can adjust to the wants, needs, and needs of relevant users. After we have completed the design as a mockup, the next research is to implement and implement the applications that have been designed in this study based on the results of user feedback
A High Performance Model for Task Allocation in Distributed Computing System Using K-Means Clustering Technique

Tasks allocation is an important step for obtaining high performance in distributed computing system (DCS). This article attempts to develop a mathematical model for allocating the tasks to the processors in order to achieve optimal cost and optimal reliability of the system. The proposed model has been divided into two stages. Stage-I, makes the ‘n' clusters of set of ‘m' tasks by using k-means clustering technique. To use the k-means clustering techniques, the inter-task communication costs have been modified in such a way that highly communicated tasks are clustered together to minimize the communication costs between tasks. Stage-II, allocates the ‘n' clusters of tasks onto ‘n' processors to minimize the system cost. To design the mathematical model, executions costs and inter tasks communication costs have been taken in the form of matrices. To test the performance of the proposed model, many examples are considered from different research papers and results of examples have compared with some existing models.
Development of the Methodology for Metadata Extraction from Documents in the Course of Information System Integration Using the Ontological Model of the Subject Field

The article deals with the development of an information system model designed for scientific and educational activities; discusses the state of ontologies as a semantic integration tool; considers the issue of the formation of metadata element sets; and proposes an approach to automatizing the collection of information on scientific activities in the given knowledge domain, which would combine the metasearch and information extraction methods based on ontologies.
Assessment Of Color Normalization Algorithms Under Various Illuminations

Contrast enrichment is an essential area in the field of digital image handling for human visual perception and computer vision. Digital images have become a part of our everyday life and imaging devices are everywhere, from higher end digital cameras to video cameras integrated in laptops or cell phones. Images of a scenery taken with a number of cameras will possess diverse values of color owing to the deviations in reproducing color through various devices. Humans are likely to neglect the illumination while adjudicating the appearance of an entity. However the same does not hold true for various image capturing devices where the same object will often look different under different illuminations. The goal of the computational color normalization is to account for the result of the illuminate. This paper aims at simulating and comparing few of the color normalization algorithms like Histogram Equalization, Gamma correction and White patch Retinex using MATLAB image processing toolbox and a few statistical parameters related to the processed image are found. The results indicated that the retinex algorithm performed better than the other algorithms and it could be a prospective area of research in the field of face recognition.
Implementation of Attribute Based Symmetric Encryption through Vertically Partitioned Data in PPDM

The aim of this research article is to implement the symmetric encryption techniques on the vertically partitioned database table. In privacy-preserving data mining mainly sensitive attributes to be protected so that privacy will maintain during data mining. In this proposed algorithm the datasets partitioned in the three different sets. Then for each table for the selected attributes only one symmetrical encryption as well as different symmetric encryption is implemented. Initial data size, encryption execution time and data size after encryption are observed for each file of different data size. In result and analysis examined the performance of execution time and memory occupied after each encryption techniques is discussed and found that hybrid algorithm of using different symmetric encryption for each partitioned table is good as compared to implementing only one encryption on each partitioned table. This algorithm is mainly useful to provide privacy in PPDM in case of distributed data base of health care organizations.
Transformer-based deep neural network language models for Alzheimer's disease risk assessment from targeted speech

Abstract Background : We developed transformer-based deep learning models based on natural language processing for early risk assessment of Alzheimer’s disease from the picture description test. Methods : The lack of large datasets poses the most important limitation for using complex models that do not require feature engineering. Transformer-based pre-trained deep language models have recently made a large leap in NLP research and application. These models are pre-trained on available large datasets to understand natural language texts appropriately, and are shown to subsequently perform well on classiﬁcation tasks with small training sets. The overall classiﬁcation model is a simple classiﬁer on top of the pre-trained deep language model. Results : The models are evaluated on picture description test transcripts of the Pitt corpus, which contains data of 170 AD patients with 257 interviews and 99 healthy controls with 243 interviews. The large bidirectional encoder representations from transformers (BERTLarge) embedding with logistic regression classiﬁer achieves classiﬁcation accuracy of 88.08%, which improves the state-of-the-art by 2.48%. Conclusions : Using pre-trained language models can improve AD prediction. This not only solves the problem of lack of suﬃciently large datasets, but also reduces the need for expert-deﬁned features.
Digital and Blockchain-based Legal Regimes: An Eea Case Study Based on Innovative Legislations – Comparison of French and Liechtenstein Domestic Regulations

The financial crisis of 2007/08 had shattered the global financial system and led – besides a flood of regulations – to a wide range of new concepts and business models. One of these new concepts was “Bitcoin”, a private digital monetary system, which is characterized by decentralization, transparency and immutability. To date the underlying Blockchain or Distributed Ledger Technology (DLT) has evolved and offers an extensive range of possibilities, particularly in the financial industry. So far, an EU-wide legal basis for Blockchain or DLT applications and services is missing. France and the Principality of Liechtenstein took a step forward and adopted national laws trying to offer legal certainty in this field. This article aims to provide a comparison of the two acts and underline the similarities and differences.
Blockchain Networks:

The Information Technology Laboratory (ITL) at the National Institute of Standards and Technology (NIST) promotes the U.S. economy and public welfare by providing technical leadership for the Nation's measurement and standards infrastructure.ITL develops tests, test methods, reference data, proof of concept implementations, and technical analyses to advance the development and productive use of information technology.ITL's responsibilities include the development of management, administrative, technical,