Reviewerly/exHarmony huggingface dataset:
Direct-Cloud, Multi-Cloud, and Connected-Cloud – Terminologies Make a Move in Cloud Computing

Cloud computing is constantly evolving with innovations. So, the cloud service providers are investing big in finding solutions for the challenges confronted by the business organizations in the ever-changing technological world. However, still, there is a little reluctance among the organizations to ultimately adopt the public cloud because the mission-critical applications and the mission-critical data require high-level security and availability which are questionable in the equally growing hacking technology. The hacker's comfort zone is Internet, and the Internet is the primary medium for communication between enterprises and cloud service providers. So, the cloud service providers come up with a solution called Direct-Cloud which bypasses the internet and establishes a private connection between the enterprise and cloud service provider. The primary objective of this paper is to familiarize the terminology direct-cloud as it makes a massive move in Cloud Computing. So, this paper presents a study that describes direct-cloud, its architecture, benefits, the comparison between different direct-cloud solutions and the guidelines to choose a suitable direct-cloud solution. Also, the terminologies Multi-Cloud and ConnectedCloud are gaining attention among the enterprises to meet the growing needs of the business. Hence the paper further explores the direct-cloud deployment in the multi-cloud and connected-cloud environment
Speech Classification using Logical ART Deep Mechanism of Machine Learning

Apart from this there are many domains including medical, voice synthesis, hate speech classification and other custom applications where classification of speech plays an important role. The conventional techniques of speech processing and classification works on a small data set also provide lower accuracy of the classification. This paper introduces a learning model using neural network (NN) for the large dataset machine training and classification using critical feature analysis for the pattern of speech spectrogram and waveforms. The performance evaluation of the proposed training model for the speech classification is validated on a single CPU and found to achieve (12-82) % of accuracy in just 5-epochs and also continuously decreases the loss at successive iteration of the epochs. This method provides learning model framework for the speech processing and classification for a very large dataset.
Intelligence-led response: turning theory into law enforcement practice in cyber security incidents

As the internet grows drastically in scale and density, the number of cyber security incidents investigated by law enforcement agencies (LEAs) is skyrocketing. Criminals may deny committing a crime, but LEAs are hindered in proving it by the limited processing capabilities of human analysis. While initial crime scene investigation emphasises finding actionable intelligence as quickly as possible, lab forensics focuses on reconstructing the case and cross-referencing the evidence to find the truth. Both are critical parts of the investigative response to cyber security incidents. This paper presents a practical digital forensic framework based on ISO/IEC 27043: 2015 activities, intended to handle digital evidence at the crime scene and lessen the caseload burden at the lab. By advocating an intelligence-led response to crime scene investigation and lab forensics, we aim to turn theory into practice for LEAs, supporting the resolution of cyber security incidents and the understanding of what happened. By working through the different processes and activities in practical exercises, we aim to enable LEAs to implement a response strategy for combating cyber crime.
Regression Trees

This textbook provides future data analysts with the tools, methods, and skills needed to answer data-focused, real-life questions; to carry out data analysis; and to visualize and interpret results to support better decisions in business, economics, and public policy. Data wrangling and exploration, regression analysis, machine learning, and causal analysis are comprehensively covered, as well as when, why, and how the methods work, and how they relate to each other. As the most effective way to communicate data analysis, running case studies play a central role in this textbook. Each case starts with an industry-relevant question and answers it by using real-world data and applying the tools and methods covered in the textbook. Learning is then consolidated by 360 practice questions and 120 data exercises. Extensive online resources, including raw and cleaned data and codes for all analysis in Stata, R, and Python, can be found at www.gabors-data-analysis.com.
Workflow Scheduling Under Secure Cloud Environment using MPSO-SA

In the distributed data-intensive computing environment, relegating certain assignments to specific machines in a protected way is a major test for the employment planning issue. The unpredictability of this issue increments with the size of the activity and it is hard to understand viably. A few metaheuristic calculations including particle swarm optimization (PSO) strategy and variable neighborhood particle swarm optimization VNPSO) system are utilized to tackle the employment planning issue in distributed computing. While allocating assignments to the machines, to fulfill the security requirements and to limit the cost capacity, we proposed an altered PSO with a scout adjustment (MPSO-SA) calculation which utilized a cyclic term called change administrator to get the best cost capacity. The exhibition of the proposed MPSO-SA booking component is contrasted and the Genetic calculation (GA), PSO and VNPSO systems and the exploratory outcome demonstrate that the proposed technique diminishes the likelihood of hazard with security requirements and it has preferable intermingling property over the current conventions.
Monitoring and Search of Coma Patients using Variable Motion Sensor System

It is vital to consistently screen the oblivious/extreme lethargies patients to comprehend their wellbeing condition. The primary objectives of the proposed is to achieve two things. 1) Monitoring and cautioning the restorative individual is the basic part, when the incapacitated additions cognizance utilizing movement recognition framework. 2) Continuous observing and assessment of basic signs of the patient, for example, Pulse rate and warmth and alarm the specialist at whatever point consideration is required. Wearable Motion sensor framework can be utilized to screen different body developments such and hand development as visual perception flicker development to find the cognizant condition of an individual. This framework will all around likely be exceptionally useful in helping the specialist about the wellbeing state of the other than cognizant patient and cautioning the doctor at whatever point care is required. The proposed framework will help your specialist by providing an alert about the wellbeing state of the patient, when the spot of basic signs reported.
Exploring Interactive Technology In Education Through The Use Of 3D Lenticular Projection And Volumetric displays

This project explores some of the ways interactive technology has been used in education to increase student engagement, and suggests how novel rendering and interaction technologies can be used in learning environments to increase student engagement. With the utilization of the Unity game engine, Near Field Communication using an Arduino, and Vuforia image recognition software, a 3D platform was created for a technology called the Looking Glass, which adapts 3D models to appear as 3D holograms. The idea behind the platform was to suggest ways in which technology can be used to create more engaging content in an educational setting, in order to combat the constant struggle many students have with diverting their attention away from their distracting smartphones. In the end, the goal is to suggest that in battle against distractions in a classroom, engaging content prevails over technology confiscation.
Data Retrieval

This chapter starts by introducing an example of how we can retrieve text, where every step is done manually. The chapter will describe step-by-step how we can automatize each step of the example using shell script commands, which will be introduced and explained as long as they are required. The goal is to equip the reader with a basic set of skills to retrieve data from any online database and follow the links to retrieve more information from other sources, such as literature.
Rotoscope Automation with Deep Learning

We present a deep learning based algorithm that can automatically rotoscope people on a given scene, without any user input. Current approaches to image matting require a significant amount of human input, whether by manually rotoscoping or by doing a chroma key. We show that this algorithm can perform comparably and even surpass the rotoscoping capabilities of After Effects' RotoBrush tool, in a variety of scenes comprising different lighting conditions, movements, and subjects. This makes it suitable for an integration within a VFX pipeline.
Digital watermarking of 3D disparity map based images for transaction tracking

In this thesis, the basics of disparity map and watermarking are reviewed extensively. In order to embed binary information into images, a 3D image watermarking system was proposed. This embedded information was to survive the 3D Image rendering process of Disparity maps, to help identify malicious user who would distribute the watermarked image through an unauthorized system. The proposed system adopted the concept of hidden pixel and introduced an algorithm that identifies all known hidden pixels within the image. This information is combined with the Disparity map to generate a hidden pixel disparity map (HPDM); using the information in the HPDM a decision matrix is generated. This decision matrix is used to guide the watermark embedding process to ensure that information embedded in the Left Image can survive the 3D rendering process. Using the decision matrix, the watermark detector is capable of extracting the image from either the left or right image with no effect on the overall bit rate. This achievement is due to two original additions to the detection process: (1) Reverse rendering and (2) Cyclical Redundancy check. The proposed reverse rendering process expands the decision matrix into a reduced disparity map. This reduced disparity map is used to reverse the right image into a reduced left image. The identification of the image (left or right) is achieved through the use of a CRC check, which is also capable of detecting any errors in the extracted message, thus reducing the number of misidentification. The proposed system was implemented and tested using MATLAB. The bit efficiency of the proposed system varied between 38% and 88%. This variance is caused by the complexity of the depth scene as well as the cost function used in the depth estimation process. The watermark embedding system proposed had a PSNR of 45 dB (when no mark was embedded); this value is primarily attributed to some of the quantization that occurs during the DCT transform. However, a PSNR of 33dB is attained when the watermark was added at full strength.
Pengaruh Permainan Sorting Color Dalam Meningkatkan Kemampuan Klasifikasi Pra-Matematika Di Taman Kanak-Kanak Islam Budi Mulia

The ability of children's mathematical classification is still low due to the lack of teacher's ability to create children's learning settings. So this study aims to improve the ability of pre-mathematical classification of early childhood. The method used in this research is literature study research. Which is motivated by an understanding of the importance of mathematical logic abilities related to child development. Data collection instruments use the collection of literature that is relevant to the object of discussion. The results of this study, indicate that the magnitude of the effect of color sorting games in improving the ability of pre-mathematical classification that can make children able to group objects according to color, shape, size and weight.
HDFS-based parallel and scalable pattern mining using clouds for incremental data

Increased usage of internet led to the migration of large amount of data to the cloud environment which uses Hadoop and map reduce framework for managing various mining applications in distributed environment. Earlier research activity in distributed mining comprises of solving complex problems using distributed computational techniques and new algorithmic designs. But as the nature of the data and user requirement becomes more complex and demanding, the existing distributed algorithms fails in multiple aspects. In our work, a new distributed frequent pattern algorithm, named Hadoop-based parallel frequent pattern mining (HPFP) has been proposed to optimally utilise the clusters efficiently and mine repeated patterns from large databases very effectively. The empirical evaluation shows that HPFP algorithm improves the performance of mining operation by increasing the level of parallelism and execution efficacy. HPFP achieves complete parallelism and delivers superior performance to become an efficient algorithm in HDFS, than existing distributed pattern mining algorithms.
Partial Face Recognition

Most current Face Identification methods get features and nodes points from still and clear facial images. But, individuals in real world may be obstructed from objects or other people who do not provide the entire image of the person to describe. Keyword-following pats key recognition methods such as: Multiple Key Points with Gbor Triangle (MKD-GTP) and Group Point Matching (RPSM) correspond to the local key points for part of face recognition.Also, on the same note they measure similarity of nodes without higher order mathematical and graphicalinformation that are prone to noise. To solve this, the TPGM method evaluates a static change that encodes the geometry of the other line of the graph, so that a extra precise and stable match with the topology can be calculated.In case to apply higher topological information having higher order, paper offer a topological method to preserve the TPSM algorithm ofconstructionofa higher order shape for each surface and evaluate the change. The paper also suggests that the selection of Viola Jones face recognition points. Deep training can be used to create and combine graphs. This article offers an in-depth study of the progress made in scientific articles and explains the accuracy of the results
In Retrospect of Cloud Security Issues

Popular computing technologies like Distributed ,Parallel ,Grid etc., have already reached their peaks in providing services and now a hybrid aspect is capturing the focus which is a combination of traditional computing technology and network technology and termed to be “Cloud Computing ”.A desperate demand for data sharing and handling enterprise applications have called upon for cloud computing .A blocking wind for leveraging cloud computing technology is the aspect of security .But the passion towards adopting cloud have overridden the security threats. This paper glances over various security threats, risks, challenges along with their resistance capabilities to overcome the vulnerabilities in the cloud and also some of the encryption techniques that are used in the cloud.
Guided Subtree Selection for Genetic Operators in Genetic Programming for Dynamic Flexible Job Shop Scheduling

© 2020, Springer Nature Switzerland AG. Dynamic flexible job shop scheduling (DFJSS) has been widely studied in both academia and industry. Both machine assignment and operation sequencing decisions need to be made simultaneously as an operation can be processed by a set of machines in DFJSS. Using scheduling heuristics to solve the DFJSS problems becomes an effective way due to its efficiency and simplicity. Genetic programming (GP) has been successfully applied to evolve scheduling heuristics for job shop scheduling automatically. However, the subtrees of the selected parents are randomly chosen in traditional GP for crossover and mutation, which may not be sufficiently effective, especially in a huge search space. This paper proposes new strategies to guide the subtree selection rather than picking them randomly. To be specific, the occurrences of features are used to measure the importance of each subtree of the selected parents. The probability to select a subtree is based on its importance and the type of genetic operators. This paper examines the proposed algorithm on six DFJSS scenarios. The results show that the proposed GP algorithm with the guided subtree selection for crossover can converge faster and achieve significantly better performance than its counterpart in half of the scenarios while no worse in all other scenarios without increasing the computational time.
Data Authorization in Social Network using Block Chain

In India most of the people are into social networks. Some people are taking advantage of this and spreading fake and irrelevant news in the society. Due to these major riots, culture oriented problems, fake medical messages are spreading over the people. Still there is no technology to identify these problems. Here we are introducing block chain to overcome all the above mentioned problems. Our method finds the solution for these major issues. Using the above mentioned explanation with SHA256 algorithms supports block chain a lot to implement. In case of any changes done in the mid node, using the hash function and SHA 256 algorithm, the man in the middle can be identified. The entire hash should be exactly match with the primary hash. So that data cannot be changed in the middle. In case of any changes occurred the data owner will be get notice.
Analisa Citra Medis Pada Pasien Stroke dengan Metoda Peregangan Kontras Berbasis ImageJ

&lt;p&gt;This study aims to develop medical image processing technology, especially medical images of CT scans of stroke patients. Doctors in determining the severity of stroke patients usually use medical images of CT scans and have difficulty interpreting the extent of bleeding. Solutions are used with contrast stretching which will distinguish cell tissue, skull bone and type of bleeding. This study uses contrast stretching from the results of CT Scan images produced by first turning the DICOM Image into a JPEG image using the help of the ImageJ program. The results showed that the histogram equalization method and statistical texture analysis could be used to distinguish normal MRI and abnormal MRI detected by stroke.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Keywords : &lt;/strong&gt;Stroke, MRI, Dicom, JPEG, ImageJ, Contrast Stretching&lt;strong&gt;&lt;/strong&gt;&lt;/p&gt;&lt;p&gt; &lt;/p&gt;
Using technology to enhance and encourage dance-based exercise

This study investigated the role of Self-Service Technologies (SSTs) in dance-based exercise in order to begin exploring the motivations behind the use (or not) of SSTs by ordinary men and women in this context. The research approach employed interviews to gain insights into participants’ use of SSTs and their exercise practices, in order to start establishing ways in which dance can be re/incorporated into people’s lives through the design of appropriate SSTs. Findings from this study highlight the significant opportunity to further explore how the properties of music and dance can be integrated into the design of new SSTs. Literature suggests dance could be a beneficial exercise format for many people and self-service technology abounds for exercise but is often not used consistently. Our interviews asked participants about dance-based exercise and SSTs for exercise and showed that there is an opportunity to design SSTs to help people access dance-based exercise. SSTs should help people learn dance, build confidence, and dance alone or with others. SSTs could facilitate movement and increase engagement with physical activity whilst addressing issues around logistics, confidence and dance knowledge and experience. Keywords: Public health, Computer science
Deep Light Direction Reconstruction from single RGB images

In augmented reality applications, consistent illumination between virtual and real objects is important for creating an immersive user experience. Consistent illumination can be achieved by appropriate parameterisation of the virtual illumination model, that is consistent with real-world lighting conditions. In this study, we developed a method to reconstruct the general light direction from red-green-blue (RGB) images of real-world scenes using a modified VGG-16 neural network. We reconstructed the general light direction as azimuth and elevation angles. To avoid inaccurate results caused by coordinate uncertainty occurring at steep elevation angles, we further introduced stereographically projected coordinates. Unlike recent deep-learning-based approaches for reconstructing the light source direction, our approach does not require depth information and thus does not rely on special red-green-blue- depth (RGB-D) images as input.
Security in Distributed File System

The investigation of little documents is required to give singular clients the most recent data and improved administrations. Every one of the machines is required to be under a typical director and have the option to impart safely. Huge information is the center subject in enterprises and research fields just as for society overall. Hadoop is the most generally utilized device for huge information examination in internet-based life like Google, Facebook, Yahoo, and Amazon and so on. Hadoop essentially uses Distributed File System for the capacity of an enormous volume of unstructured, ongoing information and streams at a high speed. It has given exact significance to information stockpiling in Hadoop, however, the security of information has overlooked and exceptionally least significance was given. We have algorithms or methodologies proposed.
Random Number Generator

This chapter explains how to use Excel to take a random sample of events or objects from a sampling frame that contains the list of events or objects from which you want to take a random sample. You will learn how to use Excel to create frame numbers for generating random numbers, why a set of duplicate frame numbers is important, and the Excel commands needed to sort the frame numbers into a random sequence. In addition, you will learn how to print out your result so that all of the information fits entirely onto a single page of paper. Three practice problems are given at the end of the chapter to test your Excel skills, and the answers to these problems appear in Appendix A of this book. An additional practice problem is presented in the Practice Test given in Appendix B along with its answer in Appendix C of this book.
Gesture Classification Using Machine Learning Approach

Gesture classification is vital role to convey information between communications. Classification shows correct path for predict correct communication. Machine learning provide automated improved algorithm in the set of artificial intelligence. In this paper, we proposed machine learning classification approach for hand gesture recognition. In classification system classify the hand gesture images with better accuracy.Proposed system also differentiates different activation functions for non linear transformation of hand gestures as input to complex tasks.Proposed system compares and classifies dataset using convolution neural network algorithm using different activation functions.