Reviewerly/exHarmony huggingface dataset:
Analyzing the Stationarity Process in Software Effort Estimation Datasets

Software effort estimation models are typically developed based on an underlying assumption that all data points are equally relevant to the prediction of effort for future projects. The dynamic nature of several aspects of the software engineering process could mean that this assumption does not hold in at least some cases. This study employs three kernel estimator functions to test the stationarity assumption in five software engineering datasets that have been used in the construction of software effort estimation models. The kernel estimators are used in the generation of nonuniform weights which are subsequently employed in weighted linear regression modeling. In each model, older projects are assigned smaller weights while the more recently completed projects are assigned larger weights, to reflect their potentially greater relevance to present or future projects that need to be estimated. Prediction errors are compared to those obtained from uniform models. Our results indicate that, for the datasets that exhibit underlying nonstationary processes, uniform models are more accurate than the nonuniform models; that is, models based on kernel estimator functions are worse than the models where no weighting was applied. In contrast, the accuracies of uniform and nonuniform models for datasets that exhibited stationary processes were essentially equivalent. Our analysis indicates that as the heterogeneity of a dataset increases, the effect of stationarity is overridden. The results of our study also confirm prior findings that the accuracy of effort estimation models is independent of the type of kernel estimator function used in model development.
The optimal triangulation method is not really optimal

Abstract Triangulation refers to the problem of finding a 3D point from its 2D projections on multiple images taken at different camera poses. For solving this problem, it is a common practice to use the so‐called optimal triangulation method. But, the method can be optimal only if we assume no uncertainty in the camera parameters. In this paper, an extensive comparison between various existing methods for the calibrated cameras is performed, in which different poses are considered. Furthermore, uncertainty sensitivity analysis is conducted for extrinsic parameters of cameras. The results show that the optimal triangulation method is actually not the best choice when there is uncertainty in extrinsic parameters. Interestingly, it can be observed that the simple midpoint method works equally well or outperforms the optimal triangulation and many other methods. Apart from its high performance, the midpoint method has a simple closed form solution for multiple views while the optimal triangulation method is hard to be used for more than two views. Therefore, in contrast to the common practice, we argue that the simple midpoint method can be a good choice in the structure‐from‐motion process where there is uncertainty in extrinsic camera parameters.
Multi-Resolution Pre-Processing for Pattern Recognition in Images and Audio Signals

With the rapid growth of technology and the proliferation of data in this digital age, current image and audio applications require greater resolution, higher data transmission rates and better data compression techniques to meet the ever increasing demands placed on them. The research presented here investigates the impact of data compression in the automatic recognition of handwritten digit images and spoken digit audio. A Haar wavelet transform (HWT) is used to compress the original image and audio data, which is input to an artificial neural network (ANN) where the automatic digit recognition is performed. The HWT generates a signature, or fingerprint, for the data by removing redundant data using a cut-off function, a number of which are investigated. This reduced data signature enables the ANN-based recogniser to be simplified and computationally more efficient. Experimental results show that for handwritten digit images, the recognition accuracy is 94.3% with compression ratios of 80%; for spoken audio digits, the recognition accuracy is 98.8% with compression ratios of 82%.
Revisiting the Pervasiveness of Weak Keys in Network Devices

Since 2012, several academic teams reported vulnerabilities in the RSA keys of HTTPS hosts on the Internet and traced the issue to several weaknesses. In 2016, a complementary study measured the actions taken by vendors and end users over time in response to the original disclosure by analyzing a large set of RSA moduli and discovered that RSA weak keys were still widespread with almost no action taken by the vendors. In this paper, we have reappraised their investigations with a focus on larger RSA moduli (2048 bits) and attempted to provide complementary analysis on some unexpected anomalies.
WITHDRAWN: Secured medical document extraction in E-Health Care system using data mining techniques

The principle target of this examination is guaranteeing the security just as security by improving the completely homomorphism information collection. It is likewise engaged to accomplish the higher characterization brings about the illness location. The current technique for PPDM is utilized to accomplish the protection in medical care frameworks and proposed strategy for Artificial Neural Network calculation is utilized to distinguish the infection more precisely. The proposed technique builds the exhibition as far as higher exactness, review and decrease in time multifaceted nature. The proposed framework is finished by utilizing dark level co-event network highlight extraction and Artificial Neural Network method. The Artificial Neural Network scheme is utilized for ID of sick pictures and expands the grouping results altogether.
Will safety-security co-engineering pay off? A quality and cost perspective in two case studies

Safety and Security concerns are usually interlinked while building critical software-intensive systems of systems. Several efforts try to approach both domains of expertise to increase the overall reliability of the systems and reduce costs by an earlier detection of issues and trade-offs. Despite the growing number of co-engineering practices at different life-cycle stages, there is a lack on business justifications such as economic costs of their adoption. We report on using a cost model to evaluate the convenience (or not) of adopting co-engineering practices in two industrial case studies (space and medical devices). Simulation results with the collected data suggest an improvement in quality if any of the selected co-engineering practices are integrated while cost increases in one case but reduces in the other. We discuss the results but, as they cannot be generalized, the main contribution is on proposing the cost model for answering the title's question.
Sparse Image Reconstruction by employing Adaptive Gradient Algorithm in Image Steganography

Image steganography is a well-known technique to transmit a secret message in the form of image from one place to another without giving even a small sense to the third party. Stego attack is a common problem in the field of image steganography. However secure is the key and the algorithm, the third party may attack/jam the secret message which is being communicated. This paper deals with solution to such stego attack problem by involving two levels of security and by employing sparse based image reconstruction technique to retrieve the jammed/corrupted secret message. In this paper, two models for image steganography is proposed by involving adaptive gradient algorithm for secret image reconstruction. The stego attacked/corrupted secret image can be completely reconstructed from its very few non-corrupted image pixels by adaptively estimating the gradient of the error function with respect to the pixels to be estimated subjected to l1-norm for including sparsity in the transform domain of the image. Results obtained from the simulation shows that this proposed method performs better with respect to peak signal to noise ratio and computation time as compared with other conventional image filtering techniques.
Towards a Risk Assessment Matrix for Information Security Workarounds

Workarounds are often a necessary response to obstructions or inefficiencies within organisations. Their utilisation could, however, introduce information security risk into an organisation. It is, therefore, important for organisations to firstly identify, then determine the reasons for information security workarounds, and how to assess the potential risk they pose to the organisation. Workarounds are generally triggered by human factors which can be explained with the Protection Motivation Theory, as well as environmental influences that exist within an organisation. This is shown in the paper using a flowchart to illustrate the decision-making process of employees regarding information security workarounds. Having understood why workarounds occur within a particular organisation, the value of their information security risk can be determined using a Risk Assessment Matrix for information security workarounds and an accompanying Information Security Workaround Risk Index. Using the tools proposed in this paper, information security officers can respond appropriately to information security workarounds and, where necessary, make modifications to their information security policies, depending on the potential risk associated with the identified information security workarounds.
Cross Entropy Based Thresholding Segmentation of Magnetic Resonance Prostatic Images Using Metaheuristic Algorithms

Magnetic resonance images are relevant sources of information for detecting and diagnosing a large number of illnesses and abnormalities. Segmentation of digital images helps to classify the pixels in the different regions according to their intensity level. Segmentation of digital images implemented on magnetic resonance images can help experts to improve the performance of evaluations and make a correct differential diagnosis to indicate the appropriate treatment. This chapter proposes the use of MFO, SCA and SFO algorithms to search for 2, 3, 4, 5, 8, 16 and 32 threshold values using minimum cross entropy function to segment prostatic magnetic resonance images, and implementing statistical metrics like PSNR, SSIM, and FSIM to measure the quality of a segmented image, quantified and establish the proper comparison frame, so visual comparison is not enough. The approach is tested on a set of benchmark images to demonstrate that the segmentation of digital images can improve the detection of prostatic abnormalities or illnesses.
Design and Implementation Recommender System for Iraqi Terrorist Database Based on Deep Learning

Recommendation system become important to deal with huge available information in the social media like Facebook, Twitter and other social networking to recommend and targeting the required users based on his/ her interest. Deep learning used as techniques to construct recommender system for achieving high accuracy with big available data. The proposed system used multi RBM neural network for building recommender system and measuring the interest of the users. The recommender system used to deal with security field through working on the Iraqi terrorism database obtained from global terrorist data. Many analysis process done over the data to visualize the importance of this area. The evaluation metric used Mean Squared Error (MSE) as well as Mean Absolute Error (MAE) as well as Precision and Recall and the proposed system reached good results.
Hybrid Classical-quantum Computing: Applications to Statistical Mechanics of Neocortical Interactions

Several commerical quantum computers are now available that offer Hybrid Classical-Quantum computing. Application is made to a classical-quantum model of human neocortex, Statistical Mechanics of Neocortical Interactions (SMNI), which has had its applications published in many papers since 1981. However, this project only uses Classical (super-)computers.Since 2015, PATHINT, has been used as a numerical algorithm for folding path-integrals. Applications in several systems in several disciplines has generalized been from 1 dimension to N dimensions, and from classical to quantum systems, qPATHINT. Papers have applied qPATHINT to neocortical interactions and financial options.The classical space described by SMNI applies nonlinear nonequilibrium multivariate statistical mechanics to synaptic neuronal interactions, while the quantum space described by qPATHINT applies synaptic contributions from Ca2+ waves generated by astrocytes at tripartite neuron-astrocyte-neuron sites.Previous SMNI publications since 2013 have calculated the astrocyte Ca2+ wave synaptic interactions from a closed-form (analytic) expression derived by the author. However, more realistic random shocks to the Ca2+ waves from ions entering and leaving these wave packets should be included using qPATHINT between electroencephalographic (EEG) measurements which decohere the quantum wave packets.This current project extends calculations to multiple scales of interaction between classical events and expectations over the Ca2+ quantum processes to include these random shocks to fit EEG data to the SMNI model, with previous analytic forms for the quantum processes replaced by qPATHINT. The classical-quantum system is fit using the author's Adaptive Simulated Annealing (ASA) importance-sampling optimization code. Gaussian Quadratures are used for numerical calculation of momenta expectations of the astrocyte processes that contribute to SMNI synaptic interactions.This project demonstrates how some hybrid classical-quantum systems may be calculated using only classical (super-)computers.Recent calculations also are reported using the closed-form expression, with and without shocks.
Handling incomplete data classification using imputed feature selected bagging (IFBag) method

Almost all real-world datasets contain missing values. Classification of data with missing values can adversely affect the performance of a classifier if not handled correctly. A common approach used for classification with incomplete data is imputation. Imputation transforms incomplete data with missing values to complete data. Single imputation methods are mostly less accurate than multiple imputation methods which are often computationally much more expensive. This study proposes an imputed feature selected bagging (IFBag) method which uses multiple imputation, feature selection and bagging ensemble learning approach to construct a number of base classifiers to classify new incomplete instances without any need for imputation in testing phase. In bagging ensemble learning approach, data is resampled multiple times with substitution, which can lead to diversity in data thus resulting in more accurate classifiers. The experimental results show the proposed IFBag method is considerably fast and gives 97.26% accuracy for classification with incomplete data as compared to common methods used.
Multilevel Watermarking Scheme Based on Pseudo-barcoding for Handheld Mobile Devices

Multimedia content provided by smartphones, laptops and tablets equipped by digital cameras presents a huge amount of images, audio and video clips in our daily life. Implementation of watermarking schemes for copyright protection on mobile devices remains an open issue due to specific features of mobile hardware. In this paper, we present a robust watermarking scheme for embedding textual information such as GPS coordinates in images shared via social media either in default mode or as the owner intends. The proposed algorithm is an extension of barcoding technique for binary notation called pseudo-barcoding. In addition, multilevel watermarking scheme realizes embedding of a fragile logo with customizable visibility and robust textual watermark embedding in a still image or frames of a video sequence. The implementation of the proposed algorithm is optimized for handheld mobile devices.
Review Paper Connect Studiez Insight Android Application on College Management System

In today's age of advanced mobile technology, this has become our daily routine to prefer applications to websites while working. In comparison to a website, applications are easier to use. As a result, our project proposes a software called "Connect Studiez Insight" which will provide students and staff with exposure to all of our college's insights. Students and staff will benefit from this application since many administrative and academic tasks will be efficiently managed. This app will be a copy of our branch. Students can use this application to acquire information on University Results, Exam Time Tables, and various activities that will be held in college by simply clicking on the appropriate tab. Students will be able to maintain a copy of all key documents they submit to the college. The feedback system is the most essential aspect of this application since it allows students to provide feedback and raise concerns under several topics. While the system is designed in such a way that the student's name remains anonymous, implementation will be a time-consuming process. This application's distinguishing feature will be its focus on student-centered learning. It is the "Mera Sujhaav" a suggestion page, where students can submit recommendations for events, seminars, and other activities that they would like to participate in. As a result, we're concentrating on developing a project that provides the most efficient, dependable, and flexible solution for the Institute's day-to-day operations.
A Simple Deep Learning Network for Face Recognition Using Laplacianfaces

The principal component analysis network (PCANet) is a simple stacking-based convolutional neural networks, it achieves good precision in target recognition tests. However, the principal component analysis (PCA) is limited when dealing with high dimensional data. In this paper, the simplified local preserving projection network (SLPPNet) is proposed to improve recognition performance. In SLPPNet, LPP-like algorithm is applied to extract features, which can maintain the spatial structure of high-dimensional data. Moreover, we changed the structure of the PCANet to obtain the filter kernels. The PCANet needs to use all training images to calculate a multistage filter bank, while the SLPPNet only needs to use one training image. Hence, the SLPPNet can greatly reduce the amount of calculation. We tested the SLPPNet extensively on various databases, such as Yale, ORL, AR and Extended Yale B datasets, and compared it with the PCANet. Experimental results show that the accuracy of SLPPNet outperforms PCANet and the SLPPNet spends less time. Especially in the occlusion databases, the recognition accuracy of SLPPNet enhances remarkably.
Towards an Evaluation Framework for Expressive Stream Reasoning

Stream Reasoning, and more particularly RDF Stream Processing (RSP), has focused on processing data streams in a timely manner, while expressive reasoning techniques, such as OWL2 DL, allow to fully model and interpret their domain knowledge. However, expressive reasoning techniques have thus far mostly focused on static data, as it tends to become slow with growing datasets. Expressive Stream Reasoning aims to combine these fields and evaluate expressive reasoning techniques in a timely manner over volatile data streams through various reasoning optimizations. Both expressive reasoning and RSP have benchmarks and frameworks for evaluating and comparing proposed solutions. However, no benchmarks or evaluation frameworks for Expressive Stream Reasoning are currently available. In this paper, we propose OWL2Streams, a resource framework for the evaluation of Expressive Stream Reasoning solutions. We identified challenges and opportunities for optimizations when dealing with expressive reasoning over the combination of streams and static data. OWL2Streams proposes three streaming scenarios, each tackling different challenges.
Towards a Most Suitable Way for AGI from Cognitive Architecture, Modeling and Simulation

This paper investigates the dependencies among cognitive architecture, architecture based modeling and model based fulfilling of general artificial intelligence, and coalesces relevance studies to form a three goal-clear research direction as corresponding compartmentalization; from the perspective of ultimate goal of cognitive architecture, thus to classify the Artificial general intelligence (AGI) oriented research into different subcomponents which are both close relevance and easier to achieve. Furthermore, two study cases are applied to illustrate the full process and essential details of the implementation of cognitive architecture, cognitive modeling and AGI realization in practical project. The studies shows the proposed distinguishing method can enlighten classifying research goals quickly, finding resolving ways easily, and thereby making AGI achieving efficiently.
A Credible Individual Behavior Profiling Method for Online Payment Fraud Detection

Online payment fraud detection relies on a credible characterization of individual behaviors. Existing individual behavior profiling methods have three problems in credibility. Firstly, they solely regard users as individuals to model and fail to credibly characterize the whole behavior patterns of transactions. Secondly, they cannot make full use of the general information among similar individuals due to the heterogeneity of transaction attributes. Thirdly, they are not capable of utilizing the label information of transactions credibly. Faced with these challenges, we propose a credible method for individual behavior profiling that consists of two steps. The first step is the construction of the credible individual behavior profiling framework. To begin with, the concept of individuals is generalized and the credible transaction description is given. Afterwards, based on the co-occurrence information of transaction attributes, the credible behavior condition is defined to set a limit to individual behavior patterns. The second step is the implementation of the framework. For this purpose, the original fraud detection problem is transformed into a pseudo-recommender system problem with a co-occurrence mapping process. In order to guarantee the credible behavior constraint, a credible recommendation algorithm is designed to collaboratively restore both the ranking and rating information of the pseudo-rating matrices. Besides, an embedding based approach is adopted to parameterize the proposed model. Experiments on an online payment transaction dataset demonstrate the effectiveness of our proposed method.
Nearest vertex attraction for actively reducing loss

In recent years the neural networks have received considerable success on classification tasks. The randomly initialized network is generally expected to adjust the weights for reducing the loss, which is also indicated as the total distance from the samples to the corresponding vertices. However, the output space of the conventional neural networks suffers from the fixed relation between the labels and vertices during learning. This case forces the mapped points around the unexpected vertex across the decision boundary into the neighborhood of the correct vertex, and simultaneously the boundary points cannot obtain the substantial rectification. Therefore, this study proposes a novel nearest vertex attraction (NVA) to actively adjust the relation between the categories and the output vertices for improving the neural network classifiers. The best relation allows that the data points can be attracted by the nearest vertices to minimize the total moving distances. In this way, the mapped points that are near to the vertices and the decision boundaries obtain the decent management. We evaluated the NVA with several conventional classification techniques and other neural network classifiers on 12 public UCI datasets. The numerical experiments demonstrate that the proposed method improves performance of the neural network classifiers on the involved benchmarks.