Reviewerly/exHarmony huggingface dataset:
Image Steganography: Critical Findings through Some Novel Techniques

Word ’Steganography’ is originated from Greekand has been used in several forms for 2500 years which is a art of hiding confidential data in any digital manner in such a way that no one can concealed it. It has found practise in various section like in military, government, diplomatic, medical, personal and intelligence agencies. This survey paper put the light on the basic of image steganography along with its different techniques and sub-techniques. Cover selection with recent trend ROI (region Of Interest) is highlighted. In addition, different types of Image file used in image steganography and performance parameter are discuss well.
Calibration of Timing Parameters of Still Image Cameras and Video Cameras by a Two-dimensional LED Array

This paper presents a two dimensional 10 x 10 LED array system developed in-house at the Standards and Calibration Laboratory (SCL) for the calibration of timing parameters of still image cameras and video cameras. In this paper, the circuit design and the calibration methods for cameras of different shutter types (rolling shutter or global shutter) are presented. This LED array can also be used to verify the shutter type of cameras. The frequency of the clock signal applied in the calibration is traceable to the SI through the cesium beam frequency standard maintained at SCL.
Reading C++ Code

I suspect you already have some knowledge of C++. Maybe you already know C, Java, Perl, or other C-like languages. Maybe you know so many languages that you can readily identify common elements. Let’s test my hypothesis. Take a few minutes to read Listing 2-1, then answer the questions that follow it.
Development of Electrochemical Nanosensor for the Detection of Malaria Parasite in Clinical Samples

In this study, Metal oxide nanoparticles (MO = CuO, Fe 2 O 3 , Al 2 O 3 ) synthesized by chemical (C) and microwave (M) methods were used for sensor development and characterized. Electrochemical nanosensors were developed from the synthesized metal oxide (MO) nanoparticles by supporting it on a gold electrode (Au). The fabricated sensors were characterized, and their sensing behavior was determined using cyclic voltammetry and square wave voltammetry techniques. The activity of the developed nanosensor towards the detection of malaria biomarker (β-hematin) was determined and the optimum conditions at which the maximum detection and quantification occurred was established. β-Hematin current response at the sensors was higher compared with the bare Au electrode and followed the order Au-CuO (C) &gt; Au-CuO (M) &gt; Au-Fe₂O₃ (M) &gt; Au-Fe₂O₃ (C) &gt; Au-Al₂O₃ (M) &gt; Au-Al₂O₃ (C) &gt; bare Au. The developed sensors were stable with a relatively low current drop (10.61 - 17.35 %) in the analyte. This study demonstrated that electrocatalysis of β-hematin was more favored on Au-electrode modified with CuO nanoparticles (chemical and microwave synthesized). The outstanding properties of the Au-CuO electrodes over others include enhanced current response at lower onset potential for catalysis, high electrochemical stability in the analytes (resistance to electrode fouling) and good signal separation between malaria and Salmonella typhi antiserum (i.e little or no interference) during detection. Au-CuO sensor has the best performance towards the biomarker and quantitatively detected P. berghei in infected mice’s serum samples at 3.60 – 4.8 mM and P. falciparum in human blood serum samples at 0.65 – 1.35 mM concentration. The developed sensor successful separated β-hematin from Salmonella typhi antiserum with well-defined signal separation of 250 mV.
The English Vocabulary using Ubiquitous Learning System

Mobile education is the product of mobile communication technology, network technology, multimedia technology and contemporary education, and it is also the frontier of modern educational technology. Its promotion and development will lead to the transformation of educational technology and educational means. In this paper, the development model of educational software, such as English intelligent local learning system, mobile and mobile education support platform, PDA and palmtop computer, was discussed in this paper, so as to provide a universal learning platform for the study of English words. Using this platform, people can learn English at anytime and anywhere. The main goal of developing English word intelligence all over the world is to enable English learners to learn at any time. Based on the support platform of mobile education systems, many learning activities can be completed. Because of its running time and space limitations, people can study wherever they can, thus achieving the truly ubiquitous learning.
Research on the Blended Teaching Mode of Advanced Mathematics Based on "MOOC"

With the rapid development of the society, education is gradually changing, and a new type of teaching model has been formed in the teaching.This kind of teaching mode is based on the information network technology platform.In this research, the author studied the blended teaching mode of advanced mathematics based on "MOOC", analyzed the characteristics of the blended teaching of advanced mathematics at present stage, discussed the importance of this teaching mode in the application process, and explored how to better apply the MOOC blended teaching mode into the teaching of advanced mathematics.It is expected that this study can provide a reference for advanced mathematics teaching in colleges and universities in China.
“Once upon a time...”

Representations of data have evolved considerably in the last 20 years: in terms of the scale of the data we have, the ways in which we can communicate and the expectations of our audiences. But as our tools evolve, so we have to evolve our understanding of what we’re doing, the limitations, strengths or weaknesses and how we can use visualization to support our insights and narratives.
Session details: Theme: System software and security: SEC - Computer security track

As chairs of the Computer Security track, we are pleased to welcome you to its eighteenth edition at the ACM Symposium on Applied Computing. The Program Committee for this track, as in past years, is composed of eminent representatives from both industry and academia. Here is the list of members of this year's committee, in alphabetical order:
Improving Security for Data Migration in Cloud Computing using Randomized Encryption Technique

International Journal of Computer Sciences and Engineering (A UGC Approved and indexed with DOI, ICI and Approved, DPI Digital Library) is one of the leading and growing open access, peer-reviewed, monthly, and scientific research journal for scientists, engineers, research scholars, and academicians, which gains a foothold in Asia and opens to the world, aims to publish original, theoretical and practical advances in Computer Science,Information Technology, Engineering (Software, Mechanical, Civil, Electronics & Electrical), and all interdisciplinary streams of Computing Sciences. It intends to disseminate original, scientific, theoretical or applied research in the field of Computer Sciences and allied fields. It provides a platform for publishing results and research with a strong empirical component. It aims to bridge the significant gap between research and practice by promoting the publication of original, novel, industry-relevant research.
Preface

The 2021 International Conference on Computer Network Security and Software Engineering (CNSSE 2021) was planned to be held on February 26-28, 2021 in Zhuhai, China. Due to the impact of COVID-19, many communities from all over the world were under strict health measures and strict travel restrictions, and participants of 2021 International Conference on Computer Network Security and Software Engineering (CNSSE 2021) also meet with the difficulty of travel restrictions. For communities, if our conference was not held in a virtual form, it would add huge work on traffic system, Exit-Entry system, medical system, etc., which is quite a serious issue, to actively respond to the call of the government, to strengthen the protection work, to effectively reduce people gathering and prevent COVID-19, considering the situation that most of the authors would like to publish their articles and make academic communications as scheduled, CNSSE 2021 was held on-line instead of postponing the conference. It was a challenge, not only to the organizers, but also to the participants who were delivering their speeches using videoconference tools, under different time zones. List of Committee of CNSSE 2021 are available in this pdf.
Data Stream Mining in Fog Computing Environment with Feature Selection Using Ensemble of Swarm Search Algorithms

Fog computing emerged as a distributed computing platform to process big streaming data. The design of this computing platform supports the data analytics for Internet of Things (IoT) applications where data analytics from cloud servers are handled at the edge of a sensor network. Hence, data collected by sensor-enabled device are processed by the edge of a network rather than on the central cloud server. When data stream is processed at central location it creates a performance bottleneck at a centre point of a network. The fog computing platform avoids performance bottleneck at the centre points and relieves raw data from streaming towards the centre of the network. However, data stream mining algorithms that learn and recognize patterns are not fast and accurate enough to avoid the bottleneck. In this chapter, we conducted an experiment through computer simulation of running data stream mining algorithms in fog environment. During the experiment, swarm search methods are applied to pre-process data stream in order to select accurate feature subsets and speed the local fog data analytics. The basis for apply swarm search methods is to find the best data streaming algorithm that guarantees optimal and accurate results for edge intelligence in fog computing environment. In order to achieve accuracy at the edge of a network and select the best algorithm, we applied three criteria namely recovery ability, fluctuate degree and successive time.
Thunderstruck: The ACDC Model of Flexible Sequences and Rhythms in Recurrent Neural Circuits

Adaptive sequential behavior is a hallmark of human cognition. In particular, humans can learn to produce precise spatiotemporal sequences given a certain context. For instance, musicians can not only reproduce learned action sequences in a context-dependent manner, they can also quickly and flexibly reapply them in any desired tempo or rhythm without overwriting previous learning. Existing neural network models fail to account for these properties. We argue that this limitation emerges from the fact that order information (i.e., the position of the action) and timing (i.e., the moment of response execution) are typically stored in the same neural network weights. Here, we augment a biologically plausible recurrent neural network of cortical dynamics to include a basal ganglia-thalamic module which uses reinforcement learning to dynamically modulate action. This “associative cluster-dependent chain” (ACDC) model modularly stores order and timing information in distinct loci of the network. This feature increases computational power and allows ACDC to display a wide range of temporal properties (e.g., multiple sequences, temporal shifting, rescaling, and compositionality), while still accounting for several behavioral and neurophysiological empirical observations. Finally, we apply this ACDC network to show how it can learn the famous “Thunderstruck” song intro and then flexibly play it in a “bossa nova” rhythm without further training.
Cloud Monitoring

The objective of this chapter is to highlight the basic concepts that underlie cloud monitoringCloud monitoring . By the end of the chapter the reader should fully understand the role monitoring plays in bringing about the fundamentals of cloud computing and optimizing the cloud.
Introducing Entity Framework Core

While data access is a very important part of most development projects, it is not something that is typically noticed by the users. Unless there are problems, such as not being able to retrieve data or not saving changes correctly, data access is just plumbing hidden away behind the veneer of the user interface.
Genetic Rules Induction Fuzzy Inference System for Classification and Regression Application in Energy Industry

Genetic fuzzy system encompasses genetic algorithm and fuzzy logic. It divulges the advantage of optimization with ease of understanding for classification and regression of energy performance of buildings, transformer, and harmonic current in energy industry. This paper presents development of a new rules induction algorithm namely genetic rules induction fuzzy inference system for classification and regression (GRIFISCnR) that combines genetic algorithm with fuzzy logic to facilitate efficient design of building, transformer and harmonic current filter in energy industry using Pittsburgh approach. GRIFISCnR possesses the rules induction capability over other algorithms for multi-class classification and regression problems without compromising on interpretability and accuracy. It manages to strike a balance between interpretability and accuracy, and yield better accuracy with lesser number of rules. It is easier to interpret and understand fuzzy rules as compared to numerical numbers.
Artificial Intelligence for early Detection of Breast Cancer and Classification of Mammographic Masses

Every single year thousands of women endure painful and invasive surgery to remove breast lesions. Most of the time the mammographic image analysis leads to false positive detection and the majority of this actions reveal the lesions to be benign. Refining present detection and diagnostic tool is a major priority of our work. MATLAB R2015a is been used to develop the algorithm, which aids in detection of breast cancer in its early stage. The algorithm comprises of image processing and applying artificial intelligence where in the system is trained with a set of images so that when the input or the test image is given, the algorithm performs the image processing techniques and then applies the Probabilistic Neural Network (PNN) technique for detection of cancer. The system performance is also been calculated in order to estimate its reliability.
Design of step-by-step teaching system for English writing based on cloud network technology

Aiming at the problems of low user satisfaction, low user interaction accuracy and high user help-seeking rate in the step-by-step teaching system of English writing designed by the present method, a step-by-step teaching system of English writing based on cloud network technology is proposed. In the functional module design part of the system, the foreground and background display modules of the system are designed. The front-end display module includes online question answering, video learning, data learning, etc. The back-end display module includes learning and data management, the design of step-by-step teaching website for English writing, online learning system and teaching mode. Through the design of functional modules and step-by-step teaching website of English writing, the design of step-by-step teaching system of English writing is finally completed. The experimental results show that the system designed by this method has higher user satisfaction, higher user interaction accuracy and lower user help-seeking rate.
Conclusion

The TRUST framework represents a radical challenge both to existing notions of untruthfulness and to the relevance of this topic to language research. This conclusion to the book <italic>All Bullshit and Lies?</italic> returns to the three central propositions of the TRUST framework concerning the <italic>scope, ethical import</italic>, and <italic>discursive analysis</italic> of untruthfulness, and teases out some of their implications, limitations, and possible future directions. It then takes up the challenge of the relevance of this topic to language research and considers the possibility of an interdisciplinary Ethical Discourse Analysis. Finally, the potential impact of the framework is discussed in terms of the fight-back against epistemic partisanship, the judgment of knowledge on purely partisan grounds. It focuses particularly on applications to education, fostering a greater awareness of the economy of truthfulness, the training of journalists, fact-checkers, and advocates, and the regulation of political advertising and social media.
A parallelistic approach toward ontology design to overcome system's nuance in decision governance

Artificial intelligence (AI) or machine intelligence (MI) can be faster and more accurate in domain specific decision tasks than human, however, AI's inability to achieve true general human creative cognitive capacity is still a deficiency in the AI or MI decision-making systems. The fundamental problem of decision-making governance, thus, poses a great challenge in advancing human-intelligent and machine-intelligent systems. The purpose of this paper is to present a noble parallelistic approach that has recently been applied to build a top-level ontology for HI-MI decision governance. The objective of this paper thus is threefold: 1) to address the gap in existing knowledge in decision governance for HI-MI systems and as a potential solution to the problem; 2) outline an integrative methodology to detailing the top to application level ontological development, resulting in; 3) a more specific 'parallelistic approach' for the development of a top ontology for human-machine intelligence decision governance.
I-Voting on Cloud Framework

With large scale automation gathering pace among various branches and services, the paper proposes an Online Voting System built on a Cloud Framework which combines the fairness and privacy of the existing system which uses Electronic Voting Machines, with the transparency and pliability of the new-age technology by using Cloud Computing Framework wherein a community cloud can secure the required biometrics and couple it with the web framework and mobile application to ensure a free-and-fair elections with the voting right at the voters fingertip. This paper proposes a Cloud Framework which can implement this Internet-Voting (I-Voting) by employing Aadhar UIDAI (Unique Identification Authority of India) secured by encryption techniques which can be deployed by a software or an application where the user can validate his biometrics and vote in quick-time, which can then be encrypted and stored in a database from where the results can be accessed quickly saving time and resources. The proposed application combines Cloud Framework, Data Security and Biometric Verification along with what advantages it offers against the existing systems.
A Novel Feature Extraction Method for Texture and Shape Analysis of Face Makeup Database

International Journal of Computer Sciences and Engineering (A UGC Approved and indexed with DOI, ICI and Approved, DPI Digital Library) is one of the leading and growing open access, peer-reviewed, monthly, and scientific research journal for scientists, engineers, research scholars, and academicians, which gains a foothold in Asia and opens to the world, aims to publish original, theoretical and practical advances in Computer Science,Information Technology, Engineering (Software, Mechanical, Civil, Electronics & Electrical), and all interdisciplinary streams of Computing Sciences. It intends to disseminate original, scientific, theoretical or applied research in the field of Computer Sciences and allied fields. It provides a platform for publishing results and research with a strong empirical component. It aims to bridge the significant gap between research and practice by promoting the publication of original, novel, industry-relevant research.
Algorithm for Protein Sequence Alignment using Hadoop

To develop an efficient system for matching the biological protein sequences and generating the scoring matrix using a distributed scan approach by applying Smith-Waterman(SW) algorithm. The algorithm generates fatest solution and the proposed system is comparing sequences with System, OpenMP and Hadoop. The comparison of the system leads in generating an efficient matrix of the protein sequence, beneficial for predicting the efficiency of the system.
A New Metadata Scheme for Multimedia and Intelligent Learning Objects

The purpose of this contribution is to improve the interoperability of educational and multimedia metadata in the context of a new application profile based on the LOM standard, without affecting their educational purpose. our metadata analysis led to the creation of new elements and new categories by strengthening the semantic representation of pedagogical objects and the different structures of multimedia documents, namely: spatial, temporal and hypermedia structures, this proposal also includes the characteristics of description visual. This contribution was essential given the absence of a metadata schema capturing multimedia and educational characteristics at the same time. the choice to gather descriptive elements based on the LOM standard, has proven to be wise since this standard is the most recognized and known in the field of eLearning. Throughout this article, we cite the advantages of pedagogical use of Multimedia, more specifically in eLearning. We then present intelligent learning environments on the one hand and educational objects on the other. Finally, we fix the new elements of our application profile, the latter is crowned with a semantic description in the form of an ontology.
Adaptive Multi-state Pipe Framework Based on Set Pair Analysis

Human Activity Recognition (HAR) is one of the main research fields in pattern recognition. In recent years, machine learning and deep learning have played important roles in Artificial Intelligence (AI) fields, and are proven to be very successful in classification tasks of HAR. However, there are two drawbacks of the mainstream frameworks: 1) all inputs are processed with the same parameters, which would cause the framework to incorrectly assign an unrealistic label to the object; 2) these frameworks lack generality in different application scenarios. In this paper, an adaptive multi-state pipe framework based on Set Pair Analysis (SPA) is presented, where pipes are mainly divided into three kinds of types: main pipe, sub-pipe and fusion pipe. In the main pipe, the input of classification tasks is preprocessed by SPA to obtain the Membership Belief Matrix (MBM). The sub-pipe shunt processing is performed according to the membership belief. The results are merged through the fusion pipe in the end. To test the performance of the proposed framework, we attempt to find the best configuration set that yields the optimal performance and evaluate the effectiveness of the new approach on the popular benchmark dataset WISDM. Experimental results demonstrate that the proposed framework can get the good performance by achieving a result of 1.4% test error.