Reviewerly/exHarmony huggingface dataset:
Safety Measures and Auto Detection against SQL Injection Attacks

The SQL injection attack (SQLIA) occurred when the attacker integrating a code of a malicious SQL query into a valid query statement via a non-valid input. As a result the relational database management system will trigger these malicious query that cause to SQL injection attack. After successful execution, it may interrupts the CIA (confidentiality, integrity and availability) of web API. The vulnerability of Web Application Programming Interface (API) is the prior concern for any programming. The Web API is mainly based of Simple Object Access Protocol (SOAP) protocol which provide its own security and Representational State Transfer (REST) is provide the architectural style to security measures form transport layer. Most of the time developers or newly programmers does not follow the standards of safe programming and forget to validate their input fields in the form. This vulnerability in the web API opens the door for the threats and it’s become a cake walk for the attacker to exploit the database associated with the web API. The objective of paper is to automate the detection of SQL injection attack and secure the poorly coded web API access through large network traffic. The Snort and Moloch approaches are used to develop the hybrid model for auto detection as well as analyze the SQL injection attack for the prototype system.
Topics, Conversational Dynamics and the Root/Non-root Distinction: Adverbial Clauses at the Discourse-syntax Interface

The composition and extent of the phrasal hierarchies in the left periphery of different clause types has been a major concern in recent research, mainly concentrating on the root/non-root distinction. This chapter intends to address this issue for adverbial clauses, assuming information structure and prosodic elicitation as diagnostics; specifically, the present analysis focuses on what Topic types are admitted in the C-domains of diverse adverbial clauses, through a systematic interface investigation based on original Italian data. A novel perspective is thus proposed to shed new light on the syntactic mapping and the formal properties of (central vs. peripheral) adverbial clauses, embedding the relevant proposal in a cartographic framework of analysis.
Introduction to Scrum and Agile Concepts

Our experience is that there has been a great deal of improvement in projects during the past decade. To be more specific, we’ve seen the Agile movement make an impact on how projects deliver business value.
Development of Part of Speech Tagger Using Deep Learning

Part of speech tagging is the initial step in development of NLP (natural language processing) application. POS Tagging is sequence labelling task in which we assign Part-of-speech to every word (Wi) which is sequence in sentence and tag (Ti) to corresponding word as label such as (Wi/Ti…. Wn/Tn). In this research project part of speech tagging is perform on Hindi. Hindi is the fourth most popular language and spoken by approximately 4billion people across the globe. Hindi is free word-order language and morphologically rich language due to this applying Part of Speech tagging is very challenging task. In this paper we have shown the development of POS tagging using neural approach.
Clustering of Huge Data with Fuzzy C-Means and applying Gravitational Search Algorithm for Optimization

There is a lot of bulk data which can be efficiently structured using some Clustering mechanism, among these mechanisms Fuzzy C-Means (FCM) Clustering technique is very new and can handle this bulk data logically and in a well precise mode. FCM is a better technique when compared to K-Means as FCM is designed with Fuzzy Concerns. But clustering only cannot give precise outcome, that’s the reason we are involving an Optimization technique for tuning the results and Gravitational Search Algorithm (GSA) Optimization can makes the outcome more precise. GSA is concerned with gravity principles. GSA tailors the defects and transitions into a well structure system and finally FCM will be optimized using GSA. This System is developed with Map-Reduced method. Here in this paper, a discussion is being presented with different existing techniques that were previously used to structure the data and it is discussed how FCM with GSA is better technique when compared to those techniques and some sample Preprocessing Patterns and k-means clustering results are obtained as a first step of research.
Robust and Secured Image Steganography using Improved LSB and RC4 Cryptography with Preprocessing Operation

The security of the transmitted data over the internet has become one of the challenges sharing with data communication over computer network. In this paper, a new stegnography system is developed using Improved Least Significant Bit (ILSB) with preprocessing operation for concealing data. The system mixes several technological rules to enhance the performance of concealing scheme. The original secret image is divided into several segments then rearranged to even and odd. Rivest Cipher 4 (RC4) algorithm is applied on preprocessed image before concealing it on the cover image using ILSB. The proposed systems are simulated using MATLAB software package. The experimental results show that our suggested method successes of concealing data according to PSNR ratio, correlation and elapsed time. Preprocessing operation provides flexibility and robustness to the overall system. Improved LSB is simple and strong method for concealing images. Finally, the proposed system is very active to hide the gray scale image inside the color image
A Secure Image Watermarking Architecture based on DWT-DCT Domain and Pseudo-Random Number

The outburst media contents in today's platforms a need for securing the ownership, authentication and data tampering raised. Protection against tapering the media contents is one of the challenges facing the individuals and industry in a digital age, in another hand rise of artificial neural network models such as deep fake and media contents editors threaten the originality and ownership of media in today digital base life. Demands to secure the ownership and authenticity of data that create by author risen. Hidden the true identity and originality of the media is one way to protect digital creation. Using a watermarked content is one way to protect the ownership of the digital media. It is possible to hide the watermark in the original content to secure it from tapering or illegally usage of the product, by hiding the watermark more securely and acceptable imperceptibility to sensors it is possible to have minimised the effect of watermark process on original contents. In this paper researchers designed an architecture that enhanced and centred on robustness and security of the watermarked image. In this method, we used four random number as seeds generated by a pseudo-random number generator, randomised numbers used as Initial value for encryption of watermark image. Watermark designed in a way that has a lesser footprint on the covered image. Outputs evaluate using PSNR, SSIM and different modification attacks tested against the watermarked image. The result shows that the architecture output is in acceptance range and enhanced the security and robustness of watermarking compere to some existing watermarking architectures and algorithms.
Removal of various Noises in Dental X-ray Images using Selective Median Filter

Image processing plays major role to provide additional information in medical diagnosis. Input images contain picture information as well as noise information. Noise information is added with the images during signal acquisition stage or in the transmission of image data. Salt &amp; Pepper noise, Gaussian noise and Speckle noise is the major noises introduced in the images. Noise information may be interpreted as data and it may lead to severe problem. Linear and Non-linear filters are used to reduce these noises in the images. In medical image analysis, non-linear filters are preferred over linear filters because it preserves edge information. Dental X-ray image is used to identify the cavities and its depth. Average filter, median filter and wiener filter are the classical techniques used in many image processing applications. In this paper, three different noises (Salt &amp;pepper, Gaussian and Speckle noise) are added and different filters (Average filters, median filter, Wiener filter) performances are analysed with the PSNR, SNR and MSE. Analysis shows that median filter is suitable for reducing salt &amp; pepper noise and wiener filter is suitable for reducing Gaussian noise and speckle noise in the dental x-ray images. Selective median filter is a modified wiener filter. Median filter is used for the pixel value 0 and 255.For other pixel values wiener filter is used. Selective median filter is giving better result than traditional techniques.
Scalability Enhancement for Cloud-based Applications using Software Oriented Methods

Scalability refers to the ability of a system to handle resource utilization in a constant and smooth fashion when high or low volume of data is applied. It is among the key attractions for migration to a cloud based infrastructure. Most of the previous studies in this area are based on the enhancement of cloud scalability in terms of hardware resources and network infrastructure. However in this case the cost of additional hardware resources and expansion of network infrastructural components to improve the cloud scalability is a major hurdle. Improving scalability of software on cloud platform by improving the software design is very less explored area. This paper focuses on two major concepts that involve measuring of software scalability using different methods and secondly exploring the software design based approaches to improve scalability. At the end, researchers have also explored the use of software design patterns to enhance scalability and flexibility in software applications on available cloud platforms especially Platform-as-a-Service (PaaS).
Drivers of convergence/divergence of corporate governance codes of MENA countries

This study aims to fill an existing gap in the regional corporate governance research by investigating the extent of and the drivers behind the convergence/divergence of the corporate governance codes among Middle Eastern and North African (MENA) countries comparing to the globally known Organization for Economic Cooperation and Development (OECD) principles of corporate governance. The results of the study revealed that the convergence level of the codes among the countries and compared to OECD principles significantly varies among countries, ranging from 31% to 73%. The results show that the adopted governance principles in the codes are 'decoupled' from legitimation concerns and focus on efficiency goals. The macroeconomic factors of MENA countries do not consistently reflect the convergence score of the codes with the OECD principles. The institutional quality factors of MENA countries are not aligned with their codes. The study provides several important implications for regulators, firms and other stakeholders.
COMPARISON OF THE ONLINE EDUCATION PLATFORMS, AND INNOVATIVE SOLUTION PROPOSALS

The traditional education methods may remain incapable to meet the today's rising education demands by reason of the fact that it depends on a time, a place, and a person.On the other hand, the training programs in the online learning environments offer important advantages for meeting the rising education demands, and have been become more popular.However, the online education programs that have become more and more popular have disadvantages such as the limitation of instructor control, the boringness due to monotony and the lack of experiential learning.These disadvantages caused by the lack of platforms where online education programs are available, are being minimized by the help technological developments.In this regard, it has been needed to research what kind of disadvantages may arise based on the lack of platforms, and to create innovative solutions in the light of developing technology against these disadvantages.In line with this requirement, under the scope of this study, a comparative content analyze is performed on the most popular platforms (synchronous and asynchronous and blended) that are preferred by universities in Turkey, and it has also been examined that how these online educations can be transformed into a more effective structure with the help of developing technologies.As a result of this study it has been determined that innovative technologies such as Augmented Reality and Virtual Reality make significant contributions to the instructor control, interaction and the experiential learning (transforming theory into practice) in online educations.
A Breast Cancer Detection using Image Processing and Machine Learning Techniques

Routine breast cancer screening allows the disease to be diagnosed and treated prior to it causing noticeable symptoms. During the diagnosis process there are chances of wrong detection hence a less human interfaced system has to be developed, hence the goal of breast cancer detection using machine learning techniques is used to find it before it spreads to the larger extent. Screening refers to tests and exams used to find a disease in people who don’t have any symptom. Early detection means finding and diagnosing a disease earlier than waiting for symptoms to start causing the effect on the neighboring cells. The breast cancer is the second most death causing cancer in humans, one in every ten women are affected by the breast cancer. Breast cancer is not only affecting the women. Men are also prone to get affected by the breast cancer but in smaller rates because of the absence of milk ducts and other lobules related to women. Early detection of the breast cancer helps in reducing the death rates if treated earlier and by proper diagnosis. In this paper the discussion of the various image processing technique done on the image and the CNN, SVM algorithm implementation on dataset images for the classification of malignant and non malignant cells are used and various tests were performed using different other machine learning algorithms and there level of accuracy and difference of various parameters are discussed for image processing MATLAB coding is used.
AABN: Anonymity assessment model based on Bayesian network with application to blockchain

Blockchain is a technology that uses community validation to keep synchronized the content of ledgers replicated across multiple users, which is the underlying technology of digital currency like bitcoin. The anonymity of blockchain has caused widespread concern. In this paper, we put forward AABN, an Anonymity Assessment model based on Bayesian Network. Firstly, we investigate and analyze the anonymity assessment techniques, and focus on typical anonymity assessment schemes. Then the related concepts involved in the assessment model are introduced and the model construction process is described in detail. Finally, the anonymity in the MIX anonymous network is quantitatively evaluated using the methods of accurate reasoning and approximate reasoning respectively, and the anonymity assessment experiments under different output strategies of the MIX anonymous network are analyzed.
Sign-singularity solution in single-frame speckle interferometry with vortex-phase modulation

We propose a sign-singularity solution in single-shot digital speckle interferometry based on vortex-phase modulation. The vortex phase is introduced through the reference beam as variation information by a reflective liquid crystal spatial light modulator. The sign singularity is eliminated with the help of a single spiral speckle fringe pattern. To effectively obtain the deformation phase, the measurement process is specially designed on the consideration of the modulation principle of the vortex phase, which is discussed in terms of theoretical analysis and simulation results. Experiments on out-of-plane deformation measurement reveal the feasibility of the proposed method and suggest the potential in actual dynamic measurement applications.
Graph Neural Network based Short-term Solar Irradiance Forcasting Model Considering Surrounding Meteorological Factors

Accurate short-term solar irradiance forecasting can achieve precise solar photovoltaic (PV) power forecasting and ensure the safe and stable operation of power grid. However, the existing solar irradiance forecasting methods only based on the historical power data and meteorological information of the local PV power station itself, which is difficult to obtain sufficiently accurate forecasting results. In this paper, we propose a short-term irradiance forecasting model based on Graph Neural Network (GNN) considering surrounding meteorological factors to further improve the accuracy. Firstly, the spatio-temporal correlation stations are constructed according to geographical location and meteorological information, and simulate the spatio-temporal correlation data around the target station by utilizing the satellite image-irradiance mapping model. Secondly, based on the complex network theory, a new index is proposed to evaluate the connectivity of the graph structure, which improves the predictive ability of the GNN model. Finally, the spatio-temporal correlation around the target site is mined through GNN model to achieve the short-term irradiance forecasting. The results show that the proposed method further improves the forecasting accuracy compared with models that don’t consider surrounding meteorological factors. The reliability of the graph connectivity is directly proportional to the forecasting accuracy, which verifies the effectiveness of the proposed index.
A novel systematic byte substitution method to design strong bijective substitution box (S-box) using piece-wise-linear chaotic map

Cryptography deals with designing practical mathematical algorithms having the two primitive elements of confusion and diffusion. The security of encrypted data is highly dependent on these two primitive elements and a key. S-box is the nonlinear component present in a symmetric encryption algorithm that provides confusion. A cryptographically strong bijective S-box structure in cryptosystem ensures near-optimal resistance against cryptanalytic attacks. It provides uncertainty and nonlinearity that ensures high confidentiality and security against cryptanalysis attacks. The nonlinearity of an S-box is highly dependent on the dispersal of input data using an S-box. Cryptographic performance criteria of chaos-based S-boxes are worse than algebraic S-box design methods, especially differential probability. This article reports a novel approach to design an 8 × 8 S-box using chaos and randomization using dispersion property to S-box cryptographic properties, especially differential probability. The randomization using dispersion property is introduced within the design loop to achieve low differential uniformity possibly. Two steps are involved in generating the proposed S-box. In the first step, a piecewise linear chaotic map (PWLCM) is utilized to generate initial S-box positions. Generally, the dispersion property is a post-processing technique that measures maximum nonlinearity in a given random sequence. However, in the second step, the concept is carefully reverse engineered, and the dispersion property is used within the design loop for systematic dispersal of input substituting sequence. The proposed controlled randomization changes the probability distribution statistics of S-box's differentials. The proposed methodology systematically substitutes the S-box positions that cause output differences to recur for a given input difference. The proposed S-box is analyzed using well-established and well-known statistical cryptographic criteria of nonlinearity, strict avalanche criteria (SAC), bit independence criteria (BIC), differential probability, and linear probability. Further, the S-box's boomerang connectivity table (BCT) is generated to analyze its strength against boomerang attack. Boomerang is a relatively new attacking framework for cryptosystem. The proposed S-box is compared with the state-of-the-art latest related publications. Results show that the proposed S-box achieves an upper bound of cryptographic properties, especially differential probability. This work hypothesizes that highly dispersive hamming distances at output difference, generated a systematic S-box. The mixing property of chaos generated trajectories utilized for decimal mapping. To test the randomness of generated chaotic trajectories, a cryptographically secure pseudo-random sequence was generated using a chaotic map that was tested using the National Institute of Standards and Technology (NIST) NIST-800-22 test suit.
Application of Edge Computing and Blockchain in Smart Agriculture System

The development of Internet technology provides a lot of convenience for the promotion of smart agriculture. At present, smart agriculture has gradually realized unmanned and automatic management, which can realize monitoring, supervision, and real-time image monitoring. However, the data in smart agriculture system cannot be guaranteed to be complete and vulnerable to attack. Based on this, this paper studies and analyzes the application of edge computing and blockchain in smart agriculture systems. Based on the simple analysis of the development of smart agriculture, the edge computing framework and the advantages of blockchain are used to build the framework system of smart agriculture. The classical architecture of edge computing and the confidentiality of blockchain are used to realize the analysis and storage of data. In view of the shortcomings of crop image overlap detection, it is proposed to detect the overlapping area and determine the feature points to analyze the image based on the edge computing and hash algorithm. In terms of data integrity, based on the advantages of blockchain, an edge data detection method based on short signature is proposed, and experiments are designed to analyze the accuracy and effectiveness of the algorithm. The simulation results show that the image mosaic algorithm can extract the contour information of the image and realize the fast image matching. The edge data integrity calculation based on short signature can meet the requirements and shorten the response time.
Prediction of Bronchopneumonia Inpatients’ Total Hospitalization Expenses Based on BP Neural Network and Support Vector Machine Models

Objective. BP neural network (BPNN) model and support vector machine (SVM) model were used to predict the total hospitalization expenses of patients with bronchopneumonia. Methods. A total of 355 patients with bronchopneumonia from January 2018 to December 2020 were collected and sorted out. The data set was randomly divided into a training set ( <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mi>n</mi> <mo>=</mo> <mn>249</mn> </math> ) and a test set ( <math xmlns="http://www.w3.org/1998/Math/MathML" id="M2"> <mi>n</mi> <mo>=</mo> <mn>106</mn> </math> ) according to 7 : 3. The BPNN model and SVM model were constructed to analyze the predictors of total hospitalization expenses. The effectiveness was compared between these two prediction models. Results. The top three influencing factors and their importance for predicting total hospitalization cost by the BPNN model were hospitalization days (0.477), age (0.154), and discharge department (0.083). The top 3 factors predicted by the SVM model were hospitalization days (0.215), age (0.196), and marital status (0.172). The area under the curve of these two models is 0.838 (95% CI: 0.755~0.921) and 0.889 (95% CI: 0.819~0.959), respectively. Conclusion. Both the BPNN model and SVM model can predict the total hospitalization expenses of patients with bronchopneumonia, but the prediction effect of the SVM model is better than the BPNN model.