Reviewerly/exHarmony huggingface dataset:
Proposed Enhance Authentication by Development of El Gamal Subliminal Channel

The subliminal channels are covert channels that can be used to communicate secretly in a normal looking communication over an insecure channel. This channel in a cryptographic protocol such as an authentication system or a signature scheme provides an additional channel from the sender to an authorized receiver and can't be read by any unauthorized receiver. This paper proposed development for “EI-Gamal subliminal channel”, which supports authentication and security aspects, but it is inappropriate algorithm for computers network. So, the development was necessary to provide a new appropriate and powerful algorithm to be used in such networks. The proposed algorithm depends on the difficulty of factorizing the large integer number into its primitive elements p and q.
Retrieval of Encrypted Data Using Trapdoor Method in Cloud Computing

Cloud computing is a well-known innovation which licenses putting away and getting to information over Internet as opposed to putting away it on nearby machines’ hard drive. Cloud clients can empower to store their information on cloud with no nervousness about its exactness and dependability. Anyway putting away information on cloud forces certain security challenges. Redistributing information in cloud result may lose physical authority over their information. Putting away and recovering such a lot of information devours part of time as information in the cloud should be constantly put away in encoded arrangement while putting away and should be decoded while looking. There are various recommendations for executing questions over encoded information. This actualizes the customer to scramble information before re-appropriating it to the cloud in a database conspire. To stay away from this monstrous utilization of time, information looking rate can be expanded by straightforwardly seeking over scrambled information in the cloud. There are numerous techniques utilized for looking through the encoded information over cloud. In catchphrase based hunt plans overlook the semantic portrayal data of clients recovery, and can’t totally meet with clients seek goal. In this paper, propose ECSED, a novel semantic inquiry plot dependent on the idea chain of importance and the semantic connection between ideas in the scrambled datasets. ECSED utilizes two cloud servers. One cloud server is utilized to store the reCloud datasets and restore the positioned outcomes to information clients. The other cloud server is utilized to figure the comparability scores between the archives and the question and send the scores to the principal server. To additionally enhance the hunt proficiency, framework uses a tree-based file structure to sort out all the archive list vectors. At that point utilize the multi watchword positioned hunt over scrambled cloud information as our essential casing to propose two secure plans.
Real Time Sign Language Estimation System

This paper presents a system which assists people with speech and hearing disabilities to communicate freely. The model is able to extract signs from videos, by processing the video frame by frame under minimally cluttered background. This sign is then presented in readable text. The system uses a Convolutional Neural Network (CNN) and fastai - a deep learning library, along with OpenCV for webcam input and displaying the predicted sign. Experimental results show decent segmentation of signs under different backgrounds and high accuracy in gesture recognition.
A Random Projection Approach to Strengthen the Privacy Level of Medical Images

All our day’s work in this world of science is done with useful data. These useful data or information is extracted from raw facts. From the information, knowledge is gained. This knowledge is used by the customers for beneficial outcome. Here comes the concept of data mining. An important query arises as how to preserve these data. This concept is called as Privacy Preserving with Data Mining (PPDM). Many PPDM techniques are available to protect the data. The PPDM technique is useful in fields like medicine, forensics, defence, etc to preserve the confidential data. The existing techniques protect the secret data either by perturbing or by hiding them. Moreover, most of the techniques focus only on the numerical data. Very few perturbation techniques like translation, multiplicative and rotation perturb the images. But these techniques are very easily attacked by third parties since the transformation is a linear one. The Research Work aims to overcome the drawbacks of the existing perturbation techniques. The main objective of the Research Work is to improve the privacy level of the images by implementing a Random Projection (RP) technique. The remarkable features and benefits of the RP technique are pinpointed.
SCALE

SCALE provides a framework for load data from distributed load-sensitive modules for exploring force-based interaction. Force conveys not only the force vector itself but also rich information about activities, including way of touching, object location and body motion. Our system captures these interactions on a single pipeline of load data processing. Furthermore, we have expanded the interaction area from a flat 2D surface to 3D volume by building a mathematical framework, which enables us to capture the vertical height of a touch point. These technical invention opens broad applications, including general shape capturing and motion recognition. We have packaged the framework into a physical prototyping kit, and conducted a workshop with product designers to evaluate our system in practical scenarios.
Dataset

Occupancy detection, tracking, and estimation has a wide range of applications including improving building energy efficiency, safety, and security of the occupants. As depth sensors are getting cheaper, they offer a viable solution to estimate occupancy accurately in a non-privacy invasive manner. Even though there are publicly available depth datasets, they do not consider placing the sensor in the ceiling looking downwards to estimate occupancy. We deployed four Kinect for XBOX One in four CMU classrooms and conference rooms for a period of four weeks in 2017 and collected over 6 TB of depth data. We annotate this huge dataset by labelling bounding boxes around occupants and release the annotated dataset.
Requirements-Driven Visualizations for Big Data Analytics: A Model-Driven Approach

Choosing the right Visualization techniques is critical in Big Data Analytics. However, decision makers are not experts on visualization and they face up with enormous difficulties in doing so. There are currently many different (i) Big Data sources and also (ii) many different visual analytics to be chosen. Every visualization technique is not valid for every Big Data source and is not adequate for every context. In order to tackle this problem, we propose an approach, based on the Model Driven Architecture (MDA) to facilitate the selection of the right visual analytics to non-expert users. The approach is based on three different models: (i) a requirements model based on goal-oriented modeling for representing information requirements, (ii) a data representation model for representing data which will be connected to visualizations and, (iii) a visualization model for representing visualization details regardless of their implementation technology. Together with these models, a set of transformations allow us to semi-automatically obtain the corresponding implementation avoiding the intervention of the non-expert users. In this way, the great advantage of our proposal is that users no longer need to focus on the characteristics of the visualization, but rather, they focus on their information requirements and obtain the visualization that is better suited for their needs. We show the applicability of our proposal through a case study focused on a tax collection organization from a real project developed by the Spin-off company Lucentia Lab.
Discourse-Aware Neural Extractive Model for Text Summarization.

Recently BERT has been adopted in state-of-the-art text summarization modelsfor document encoding. However, such BERT-based extractive models use thesentence as the minimal selection unit, which often results in redundant oruninformative phrases in the generated summaries. As BERT is pre-trained onsentence pairs, not documents, the long-range dependencies between sentencesare not well captured. To address these issues, we present a graph-baseddiscourse-aware neural summarization model - DiscoBert. By utilizing discoursesegmentation to extract discourse units (instead of sentences) as candidates,DiscoBert provides a fine-grained granularity for extractive selection, whichhelps reduce redundancy in extracted summaries. Based on this, two discoursegraphs are further proposed: ($i$) RST Graph based on RST discourse trees; and($ii$) Coreference Graph based on coreference mentions in the document.DiscoBert first encodes the extracted discourse units with BERT, and then usesa graph convolutional network to capture the long-range dependencies amongdiscourse units through the constructed graphs. Experimental results on twopopular summarization datasets demonstrate that DiscoBert outperformsstate-of-the-art methods by a significant margin.
Study on Development of Deep Learning Fault Diagnosis Algorithm Considering Induction Motor Speed and Load Condition

The motor mechanical fault has been diagnosed under fixed driving conditions. The induction motor speed is affected not only by the input frequency but also by the load. In addition, the vibration generated by the induction motor is affected by the speed as well as the input frequency. For these reasons, a data preprocessing algorithm has been developed that shifts the measured data in the frequency domain based on motor speed. The algorithm also takes the input frequency as an input variable and removes the vibration component by the power source frequency. The data processed by the above procedure are classified through the deep learning algorithm based on CNN. As a result, a fault diagnosis system that can be applied to the industrial field has been developed by considering the motor driving conditions using the proposed algorithms.
An Optimal Strategy for Resource Utilization in Cloud Data Centers

Cloud computing has emerged in recent years as one of the most interesting developments in technology.With the gaining popularity of cloud-based solutions, more and more applications are migrating into the Cloud and thus have highly demanding critical requirements for networking resources.Virtual technology associated with a Data Center consists of a set of servers, storage and network devices, power systems, cooling systems, etc., and makes it possible for the resource management of physical machines to be more finely tuned and thus support multiple virtual machines well.The growing challenge, however, is how to efficiently provision these resources to meet the requirements of the different qualities of service levels.This paper offers and investigates a general situation wherein a datacenter can determine the cost of using resources and a Cloud service user can decide whether it will pay the price for the resource or not for an incoming task.By establishing a Continuous-Time Markov Decision Process model for both an average reward model and a discounted expected reward model, the optimal policy of each model for admitting tasks can be verified to be a State-related control limit (threshold) policy, respectively.Further, a detailed statement and verification of the upper boundaries for such an optimal policy, and a comprehensive set of experiments on the various cases to validate this proposed solution are provided.Particularly, the machine learning method is implemented to obtain the optimal threshold values by using a feed-forward neural network model.Several numerical examples are also provided on how to derive optimal threshold values.The results offered in this paper can be easily utilized to help datacenter operate in an economically optimal way when providing different needed application services to Cloud service users.
A Vehicle Target Recognition Algorithm for Wide-Angle SAR Based on Joint Feature Set Matching

Target recognition is an important area in Synthetic Aperture Radar (SAR) research. Wide-angle Synthetic Aperture Radar (WSAR) has obvious advantages in target imaging resolution. This paper presents a vehicle target recognition algorithm for wide-angle SAR, which is based on joint feature set matching (JFSM). In this algorithm, firstly, the modulus stretch step is added in the imaging process of wide-angle SAR to obtain the thinned image of vehicle contour. Secondly, the gravitational-based speckle reduction algorithm is used to obtain a clearer contour image. Thirdly, the image is rotated to obtain a standard orientation image. Subsequently, the image and projection feature sets are extracted. Finally, the JFSM algorithm, which combines the image and projection sets, is used to identify the vehicle model. Experiments show that the recognition accuracy of the proposed algorithm is up to 85%. The proposed algorithm is demonstrated on the Gotcha WSAR dataset.
Context-aware Neural Machine Translation with Coreference Information

We present neural machine translation models for translating a sentence in a text by using a graph-based encoder which can consider coreference relations provided within the text explicitly.The graph-based encoder can dynamically encode the source text without attending to all tokens in the text.In experiments, our proposed models provide statistically significant improvement to the previous approach of at most 0.9 points in the BLEU score on the OpenSubtitle2018 English-to-Japanese data set.Experimental results also show that the graph-based encoder can handle a longer text well, compared with the previous approach.
One-Document Training for Vietnamese Sentiment Analysis

The traditional studies which have based on machine learning are usually supervised learning for sentiment analysis problem, this is costly time and money to build pre-labeled dataset, not domain adaptation and hard to handle unseen data. In this paper, we have approached semi-supervised learning for Vietnamese sentiment analysis, training data is only one document. Many preprocessing techniques have been performed to clean and normalize data, complemented semantic lexicons such as negation handling, intensification handling, also augmented training data from one-document training. In experiments, we have performed various aspects and obtained competitive results which may motivate next propositions.
Introduction to Evolutionary Machine Learning Techniques

This section first provides an overview of the machine learning field in artificial intelligence (AI). The most well-regarded classes of methods in AI are discussed to show where AI optimization algorithms and machine learning techniques fit in. Different types of learning are briefly covered as well including supervised, unsupervised, and reinforcement techniques. The last part of this chapter includes discussions on evolutionary machine learning, which is the focus of this book.
An Algorithm for Generating Random Numbers with Normal Distribution

A new algorithm is suggested based on the central limit theorem for generating pseudo-random numbers with a specified normal or Gaussian probability density function.The suggested algorithm is very simple but highly accurate, with an efficiency that falls between those of the Box-Muller and von Neumann rejection methods.
A Robust Model of Gated Working Memory

Gated working memory is defined as the capacity of holding arbitrary information at any time in order to be used at a later time. Based on electrophysiological recordings, several computational models have tackled the problem using dedicated and explicit mechanisms. We propose instead to consider an implicit mechanism based on a random recurrent neural network. We introduce a robust yet simple reservoir model of gated working memory with instantaneous updates. The model is able to store an arbitrary real value at random time over an extended period of time. The dynamics of the model is a line attractor that learns to exploit reentry and a nonlinearity during the training phase using only a few representative values. A deeper study of the model shows that there is actually a large range of hyperparameters for which the results hold (e.g., number of neurons, sparsity, global weight scaling) such that any large enough population, mixing excitatory and inhibitory neurons, can quickly learn to realize such gated working memory. In a nutshell, with a minimal set of hypotheses, we show that we can have a robust model of working memory. This suggests this property could be an implicit property of any random population, that can be acquired through learning. Furthermore, considering working memory to be a physically open but functionally closed system, we give account on some counterintuitive electrophysiological recordings.
Advances in Lifelog Data Organisation and Retrieval at the NTCIR-14 Lifelog-3 Task

Lifelogging refers to the process of digitally capturing a continuous and detailed trace of life activities in a passive manner. In order to assist the research community to make progress in the organisation and retrieval of data from lifelog archives, a lifelog task was organised at NTCIR since edition 12. Lifelog-3 was the third running of the lifelog task (at NTCIR-14) and the Lifelog-3 task explored three different lifelog data access related challenges, the search challenge, the annotation challenge and the insights challenge. In this paper we review the dataset created for this activity, activities of participating teams who took part in these challenges and we highlight learnings for the community from the NTCIR-Lifelog challenges.
On uniform-negative binomial distribution including Gauss hypergeometric function and its application in count regression modeling

In this article, we propose a new monotonically decreasing count model which includes Gauss hypergeometric function and is suitable for Zero vertex, over dispersed dataset. Though the proposed model includes Gauss hypergeometric function, nevertheless it possesses simple and closed expressions for various distributional characteristics. An application to count regression modeling using a well-known dataset from the National Medical Expenditure Survey is discussed by considering the length of stay in hospitalization as a dependent variable and following the proposed count model. We compare our result with the classical negative binomial regression model and uniform-Poisson regression model.
Use of Robotics in the German Healthcare Sector

Assistance robots have a large potential to support patients and staff in outpatient and inpatient settings. Despite the need and large potential, the diffusion of robotic applications in the German healthcare sector is only slowly picking up pace. The objective of this study is to shed some light on the reasons and identify measures that support involved stakeholders in closing this gap in the upcoming years. Using an online survey, we addressed more than 150 clinics and nursing service providers throughout Germany with respect to the benefit of different robot application scenarios, drivers and barriers for the introduction of service robots in healthcare settings as well as estimated time savings. Concerning possible application areas, disinfection and cleaning robots are currently perceived to have the highest benefit, whereas the value of robots to support personal hygiene is considered rather low. The greatest drivers for using robot assistants in healthcare settings are their potential to save time for the staff as well as to increase employer attractiveness and higher efficiency in processes. The most frequently cited barriers are financing, data protection, legal obstacles and the importance of human contact. For three selected scenarios: assistance robots as guides, lifting robots and activation and communication robots, we further asked for the expected time savings. The results show differences between clinics as well as inpatient and outpatient nursing services. In order to accelerate the diffusion of robot assistants in Germany, several implications have to be considered: Acceptance and experience are positively correlated i.e. from a political standpoint, research programs are needed to support joint development of robot assistants by research, industry and end users. Legal and financial barriers should be reduced. For manufacturers, creating testing possibilities and close interaction with potential users for the identification of adequate scenarios and clarifying legal questions could prove to be beneficial in terms of a higher acceptance in the market.
How Do General-Purpose Sentiment Analyzers Perform when Applied to Health-Related Online Social Media Data?

Sentiment analysis has been increasingly used to analyze online social media data such as tweets and health forum posts. However, previous studies often adopted existing, general-purpose sentiment analyzers developed in non-healthcare domains, without assessing their validity and without customizing them for the specific study context. In this work, we empirically evaluated three general-purpose sentiment analyzers popularly used in previous studies (Stanford Core NLP Sentiment Analysis, TextBlob, and VADER), based on two online health datasets and a general-purpose dataset as the baseline. We illustrate that none of these general-purpose sentiment analyzers were able to produce satisfactory classifications of sentiment polarity. Further, these sentiment analyzers generated inconsistent results when applied to the same dataset, and their performance varies to a great extent across the two health datasets. Significant future work is therefore needed to develop context-specific sentiment analysis tools for analyzing online health data.
Review and Comparison of Face Detection Techniques

Automatic object detection is a common phenomenon today. To detect an object first thing is captured, is the image of the object. Now in an image categorically different types of objects are possible. Here, we are considering human face as a most common object. Day by day, the number of application based on face detection is increasing. So the demand of highly accurate and efficient face detection algorithm is on the high. In this paper, our motive is to study different types of face detection techniques and compare them. Various face detection techniques like using Haar-like cascade classifier, Local Binary Pattern cascade classifier and Support Vector Machine-based face detection methods are compared here. All these techniques are compared based on time, accuracy, low light effect, people with black face and with false object and based on memory requirement.