Reviewerly/exHarmony huggingface dataset:
A Watermark Approach for Image Transmission: Implementation of Channel Coding Technique with Security

In this paper an approach for secured digital image transmission with watermark is being proposed. The tremendous growth in technology for various applications demand secured communications across the wireless channels. Secured image transmission is the one of the prominent process in digital communication applications. A watermark is embedded in to the image data that is to be protected from unauthorized users. The cryptographic algorithms chosen for secured transmission led to the need for hardware implementation. In the process of secured image transmission turbo encoder is proposed for error correction. The proposed approach is realized in terms of hardware for the digital logic size, area and power consumption using Xilinx 14.2 software. Synthesizing and implementation of verilog code on the target device xc6slx150-2fgg484 for timing constraints, device utilization and performance details.&#x0D; © 2020 Elsevier Ltd. All rights reserved.&#x0D; Selection and/or Peer-review under responsibility of International Conference on Mechanical, Electronics and Computer Engineering
A Survey Paper on Cloud Security Based on Distributed Ledgers of Blockchain

Technology grows up, day to day to facilitate human beings' lifestyle, in order to protect them and their properties.Cloud computing is a platform that one hardware is shared by different clients in the virtual ways and works as standalone physical hardware, is accessed everywhere at any time through the internet.Security is an essential and a vital point to all customers is belonged in the same physical component.This survey paper discusses about cloud computing challenges, types of attacks, and currently solutions in the details.Furthermore, cloud security of models as application, network, and deployment and also services.Cloud security based on a distributed ledger helps the clients to operate within transparency manners and is decentralized to enhance cloud computing information security principals, cloud security requirements, cloud security control, and security design of cloud computing.
ADVANCES IN FUZZY TECHNIQUES AND APPLICATIONS: IN OCCASION OF LOTFI ZADEH 100 BIRTH ANNIVERSARY

Advances in FUZZY techniques and applications: in occasion of Lotfi Zadeh 100 birth anniversary. Technological and Economic Development of Economy, 27(2), pp. 280-283.
Modeling the Emergence of Value and Risk in Game Theoretical Approaches

Game theory is largely about interactions of agents whose decisions affect each other. The combination of agents’ actions corresponds to outcomes, which may impact agents’ goals, either positively or negatively. The analysis of the probable consequences and expected utilities of all possible outcomes is fundamental to support agents when deciding whether to engage in a certain strategy. In this paper, we move in this direction by initiating the investigation of the ontological foundation of the emergence of value and risk from outcomes in game theoretical approaches. In order to understand the influence of these forces over outcomes, a precise and rigorous conceptualization, based on foundational ontologies, is needed. To this aim, we present and discuss here a preliminary ontological modeling of basic concepts of game theory, as well as the relation to value, risk and outcomes.
Comprehensive Survey of Security Issues &amp; Framework in Data-Centric Cloud Applications

The emergence of the cloud computing paradigm has influenced business organizations for migration over two decades.This has enabled a more incredible communication speed paired with a better fault tolerance of web-based applications and the scope of having practically unlimited storage available for the user and business data.Such colossal data availability over cloud platforms has increased concerns about the security of the same from various threats such as unauthorized eavesdropping, deliberate malicious mutation of data, loss of data integrity, and identity theft.The presence of such security vulnerabilities has rendered cloud security to be an active research area.This article aims to deep dive into the present stateof-the-art in cloud security research and mitigation approaches.It is observed that cloud storage, cloud services are the widely used applications among the organization.However, vulnerabilities and security challenges faced by this two area is maximum.Besides, outsourcing sensitive information to cloud service provider's (CSP) end leads to trust issues among organizations.This article performs a detailed comparative analysis of the security frameworks developed towards cloud storage level, cloud service level, and cloud trust level to date and throws light on future research directives.All the existing research has been evaluated against CIA (confidentiality, Integrity, Availability) principles here.
DESIGNING A MOBILE-BASED LEARNING MEDIA EDUCATIONAL GAME APPLICATIONS

The smart phone is one part of technological developments that have been to a basic human need. Android is an operating system with an open platform. Current technological developments and needs can be made practical and attractive on Android-based phones, one of which is education. Systems development method in this study, using a design of this mobile-based educational game learning media application, i.e., a linear sequential model or often referred to as the waterfall model. This educational game is expected to facilitate student learning by way of delivery packaged as attractiveas possible to stimulate feelings of pleasure in students so that it is easy to understand the material presented.
Paramount feat to sway and purge pollution by adopting computational intelligence

Human's can't imagine machines independent. From Vacuum Tubes technology to transistors and now with Integrated chip (IC), semiconductor technology defined another industrial revolution. The modern industrial twirl is already in transpire. Artificial intelligence (AI) is ushering in the new age of technologies that are super fast, more flexible, much potential, and initiating the world more digitally connected. No doubt the potential of IoT and AI to clean up the environment is cosmic. A computational concept like fuzzy logic, neural network, and machine learning will change the perspective of machine how they are used now. Our study reveals retrospect progression in the internet of things and artificial intelligence technologies to curb ecological pollution. New generation AI-enabled intelligent control systems are optimized with a complex algorithm to identify and eliminate pollutants for environmental clean-ups. With AI automation pollution management and mitigation, developments are examined. Adopting tech-savvy IoT and AI technology, dependency on machines can be seemly employed. For example; Transportation Management System using AI and IOT can be used to determine the best shipping routes by cutting fuel and gas consumption. To some extent ICT, wireless technology adds a contribution to reducing E-Waste. ICT together with IoT acts as a mediator while renting and sharing devices among people with different industries. AI helps to solve complex data analysis that supports Quick Decision System and policymaking. With intelligent robots, we can monitor and control acidification on Soil and water. The concept of neural network and fuzzy logic are greatly used to develop intelligent machines that predict different gas rates and chemical processes. AI machines that continuously check the pH level of water, Analysis Air Quality Index can be built to monitor water resources and atmosphere. Some AI robots can identify pollutants and helps to separate them. Now we are successful in a bifurcation of paper with plastic, Iron with aluminum. Such a method contributes to the waste management system appropriately. Our study also inspects the challenges of implementing AI, How data mining and big data management feed system.&#x0D; Authentication/Authorization work with industry and governing authority. Inspection of a biased algorithm, poor network, and security. There is no new route to achieve our goal. Our goal is to achieve paramount measures with two concepts in mind, that are Humans assist machines and Machines assist humans to control and prevent pollution in these critical situations. Both humans and machines together find an instant solution with renting and sharing that helps to minimize ecosystem effluence
Industrial Evaluation of an Architectural Assumption Documentation Tool: A Case Study

Documenting architectural assumptions effectively and systematically is of paramount importance in software development. However, the lack of tool support is a critical problem when practitioners manage assumptions in their daily work. To fill this gap, we propose Architectural Assumptions Manager (ArAM) – a tool dedicated to Architectural Assumption Documentation, which aims at alleviating the aforementioned problem. ArAM was developed as a plugin of Enterprise Architect and provides integration with Unified Modeling Language (UML) modeling. ArAM was evaluated in terms of its perceived usefulness and ease of use, through a case study with 16 architects from ten companies in China. The results indicate that ArAM is generally useful and easy to use, both in Architectural Assumption Documentation and in software development in general. Moreover, there are several points for improvement, including support for automated analysis (e.g., finding missing relationships between assumptions) and verification (e.g., verifying the correctness of existing assumptions).
Reasoning over Vision and Language: Exploring the Benefits of Supplemental Knowledge

The limits of applicability of vision-and language models are defined by the coverage of their training data. Tasks like vision question answering (VQA) often require commonsense and factual information beyond what can be learned from task-specific datasets. This paper investigates the injection of knowledge from general-purpose knowledge bases (KBs) into vision-and-language transformers. We use an auxiliary training objective that encourages the learned representations to align with graph embeddings of matching entities in a KB. We empirically study the relevance of various KBs to multiple tasks and benchmarks. The technique brings clear benefits to knowledge-demanding question answering tasks (OK-VQA, FVQA) by capturing semantic and relational knowledge absent from existing models. More surprisingly, the technique also benefits visual reasoning tasks (NLVR2, SNLI-VE). We perform probing experiments and show that the injection of additional knowledge regularizes the space of embeddings, which improves the representation of lexical and semantic similarities. The technique is model-agnostic and can expand the applicability of any vision-and-language transformer with minimal computational overhead.
Impact of Drainage Problems in the City of Jakarta

The need for land use in Jakarta can be a contributing factor to the reduced function of infiltration and water flow, resulting in changes and problems in the drainage system. It is suspected that downstream areas such as Jakarta are unable to accommodate excess water, because there may be problems with the drains or drainage systems. This study aims to determine the impact of drainage problems and to find out how to solve drainage problems in Jakarta. The method used in this research is a qualitative method with a descriptive approach carried out by studying the literature and distributing questionnaires. The research sample is residents of the Greater Jakarta area, especially Jakarta. The results showed that the problems that occurred in the operational drainage system were influenced by factors of rain intensity, catchtmen area, population growth, and environmental factors. A problematic drainage system causes flooding. To solve problems in the drainage system, namely by optimizing the drainage function, mapping the drainage area, making storage tanks and filters, implementing environmentally friendly drainage or ecodrainage, and normalizing and cleaning the drainage system.
The Impact Of Covid-19 On The Decision Making For The Customer Relation Management In The Middle East

The Covid-19 pandemic has caused huge changes in the social, economic, healthcare, and business aspects of life On the economic front, the pandemic caused huge disruptions in the supply chain, leading to a disruption of commerce and closure of thousands of businesses This study seeks to determine how the Covid19 pandemic has affected the customer relation management CRM function in the Middle East The research questions guiding the study are: how has the Covid-19 pandemic affected consumers’ purchasing behavior? How have these changes affected the CRM function in organizations in the Middle East The study adopts a theoretical approach and the data that informs the findings is obtained from empirical studies and other trustworthy sources structured around the context of the Middle East © 2021, Journal of Management Information and Decision Science All Rights Reserved
Using Geochemical Data to Identify Tectonic Environments

The process of discriminant analysis has been applied to major and trace elements in igneous and sedimentary rocks to seek to identify the original tectonic setting in which the rocks formed. A ‘training set’ of data from known environments is used to construct a discrimination diagram which is then used with data from unknown sources. Normally, the discrimination diagrams are based upon immobile trace element data and they have been applied predominantly to mafic igneous rocks, although there are also applications to felsic rocks and sediments. In the past, diagrams of this type have been used indiscriminately and here a robust approach is advocated for statistical analysis. Some of the diagrams presented are based upon elemental data, while others are based upon calculated discriminant functions and require some specific pre-calculation. Diagrams of this type are paradoxically accurate and at the same time geochemically opaque.
Does intonation automatically strengthen scalar implicatures?

Two mouse-tracking experiments tested predictions from two different models of scalar implicature as to whether exhaustive interpretations are computed prior to ignorance implicatures. We use different German intonational patterns to probe the availability of these interpretations (Experiments 1 and 2) and add a speaker competence manipulation in Experiment 2. Results from Experiment 1 found that deriving exhaustive interpretations with an L+H* was delayed to ignorance implicatures with an L*+H contour. Experiment 2 replicated this finding even with a strengthened competence assumption about the speaker. We interpret our processing data as providing constraints on the computational mechanisms underlying the interpretation of scalar implicatures. EARLY ACCESS
Online Dietician using Artificial intelligence

The online artificial dietician is a web page with artificial intelligence about human diets. It acts as a diet. Based on a person's schedule, body type, height and weight, a dietician it consults a person, the system too asks all this data from the user and processes it. It requires information about how many hours the user works, his height, weight, age etc. The system stores and processes this data and then calculates the nutrient B.M.I value needed to fill up user’s needs. The system then shows an appropriate diet to the users and asks if user is ok with it, else it shows other alternate diets to fill up user’s needs.
RGBD-Based Real-Time 3D Human Pose Estimation for Fitness Assessment

In this paper we present an approach to efficiently extract 3D human keypoints using a RGBD camera for static posture assessment and deploy the system in a portable computer without GPU. The approach firstly employs a lightweight model, which is an optimized version of OpenPose, for 2D human pose estimation and fuses the aligned depth data to the 2D keypoins to get 3D localization of human joints. CPU acceleration for inference is accomplished by Intel OpenVINO toolkit during this process. Then two typical postures in fitness called standing and overhead squat are assessed according to the given evaluation standards. Experiments show that this system can fulfill the tasks to extract 3D human postures and create assessment for static actions during fitness at 16 fps. For squat action this system is more robust than traditional depth-based skeleton tracking algorithms.
A new instrument in earthquake early warning system by detection and modeling of prompt gravity signals

&amp;lt;p&amp;gt;The recent finding of prompt elastogravity signals (PEGS) before the arrival of P-waves, associated with the M9.1 2011 Tohoku earthquake (Montagner et al., Nat. Comm., 2016; Vall&amp;amp;#233;e et al., Science, 2017) and a few earthquakes of magnitude larger than 8.5&amp;amp;#160; (Vall&amp;amp;#233;e and Juhel, JGR, 2019) opens the new field of speed-of-light seismology.&amp;amp;#160; The systematic detection of PEGS on real-time might help saving a few seconds before the arrival of destructive seismic waves and to obtain an accurate determination of the magnitude of the earthquake at the end of rupture. So the potential application to earthquake early warning is obvious.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;However, the use of classical broadband seismometers for detecting PEGS has severe limitations for detecting earthquakes of magnitude smaller than 8.5: first of all, the background seismic noise and second of all, the partial cancellation of the gravitational perturbation by the inertial induced acceleration recorded by seismometers (Heaton, Nature Comm., 2017).&amp;amp;#160;Two different approaches can be explored for detecting for earthquakes of magnitude smaller than 8.5. Either, by using a dense array of broadband seismometers&amp;amp;#160; (more than 100 receivers) or by designing completely new instruments such as gravity strainmeters. These new detectors must be able to measure the difference in gravity acceleration between two masses, making this instrument isolated from the seismic noise. A sensitivity of 10&amp;lt;sup&amp;gt;-15 &amp;lt;/sup&amp;gt;Hz&amp;lt;sup&amp;gt;-1/2&amp;lt;/sup&amp;gt; at 0.1 Hz is required for detecting earthquakes of M&amp;gt;7 (Juhel et al., JGR, 2019) and the technology developed by the gravitational wave physicists can be used for reaching such a sensitivity. The simulation of the expected gravity strain signals based on analytical model of gravity perturbations associated with a network-based matched filter approach show that a network of 3 gravity strainmeters might make it possible to reach such a challenging goal. Gravity strainmeters could therefore open new ways to investigate the first seconds of the earthquake rupture, speed up the estimate of earthquake magnitude, enhance tsunami warning systems and &amp;amp;#160;complement other EEWS in the future.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;&amp;amp;#160;&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;&amp;amp;#160;&amp;lt;/p&amp;gt;
Example of polynomial controller synthesis for a non-square object with one input and two outputs

Polynomial methods for synthesizing controllers for automatic control systems with linear objects are becoming increasingly common. The synthesis of multichannel controllers is particularly difficult, which is caused by the need to use matrix polynomial calculus. However, this approach mainly considers objects with the number of inputs equal to the number of outputs. This is due to the convenience of solving a system of linear algebraic equations in matrix polynomial calculus. In this paper, we consider a polynomial method for synthesizing regulators for a non-square object, that is, one whose number of inputs is not equal to the number of outputs. The selected system contains not only a non-square object, but also a non-square controller.
Implement Artificial Intelligence Add-In to execute Digital Forensics Investigation Software.

Implement Artificial Intelligence Machine Learning Add- In to execute Digital Forensics investigations. Searching the evidence from a database feeder with the objective needs to be learned by reinforcement learning. The pro­grammed Add-In programmed with an algorithm aiming and expect the Machine through trial-and-error to achieve that goal attempting to climb over the most object recog­nition until it finds with different percentages.The Add-In will utilize the searching in the evidence copy any related pictures or photos which will be uploaded in the internal migrated database. Artificial Intelligence Add-In will help in protecting the suspect’s privacy from being seen by the digital forensic examiner/investigator. Moreover, it will illustrate related compared images to the investigator with the compared percentage and permit the digital examiner/investigator to search in a specific time frame, which usually will be in the time frame of the incidents
Disease and Data in Society: How the Pandemic Expanded Data Collection and Surveillance Systems

The COVID-19 pandemic is a global tragedy of historic proportions, and its impacts on our families, communities, and social structures will be felt for many years to come. From the significant to the mundane, COVID-19 has changed many aspects of daily life. One of the less obvious but more long-lasting changes is the expansion of data collection and surveillance systems adopted both in response to, and as a result of, the pandemic. As the United States brings the pandemic under control and shifts back to some form of normalcy, it is important to critically evaluate the data collection and surveillance systems that have become more widespread during the pandemic. Academics, historians, and journalists alike are contributing to a growing body of work that is examining the privacy impact of the technological and social changes caused by the pandemic. This Article contributes to the existing literature on privacy during the pandemic by highlighting important trends and recommending policy responses. This Article focuses in particular on two categories of surveillance and personal data collection systems that have been deployed and adapted because of the pandemic by non-government actors. The first category includes systems that collect and monitor health and health-related data in response to the spread of COVID-19. The second category includes systems of surveillance and data collection that have expanded as a result of the shift to remote work, school, and life. The main focus of this Article is on consumer, healthcare, workplace, and educational surveillance systems rather than the types of surveillance systems typically associated with law enforcement investigations. This Article aims to provide recommendations on how to protect privacy while responding to the pandemic and what lawmakers should do once the pandemic abates.
Mixture Models for Spherical Data with Applications to Protein Bioinformatics

Finite mixture models are fitted to spherical data. Kent distributions are used for the components of the mixture because they allow considerable flexibility. Previous work on such mixtures has used an approximate maximum likelihood estimator for the parameters of a single component. However, the approximation causes problems when using the EM algorithm to estimate the parameters in a mixture model. Hence, the exact maximum likelihood estimator is used here for the individual components. This paper is motivated by a challenging prize problem in structural bioinformatics of how proteins fold. It is known that hydrogen bonds play a key role in the folding of a protein. We explore this hydrogen bond geometry using a data set describing bonds between two amino acids in proteins. An appropriate coordinate system to represent the hydrogen bond geometry is proposed, with each bond represented as a point on a sphere. We fit mixtures of Kent distributions to different subsets of the hydrogen bond data to gain insight into how the secondary structure elements bond together, since the distribution of hydrogen bonds depends on which secondary structure elements are involved.