Reviewerly/exHarmony huggingface dataset:
Methods for Optimizing Fuzzy Inference Systems

The world is inundated with data. For any definition of data, too, the amount generated per second is incredible. With the explosion of the Internet through the World Wide Web in the 1990s and early 2000s, as well as the more recent exponential explosion of the Internet of Things, it is without a doubt that making sense of this data is a primary research question of this century.
Assessing Quality Standards in Electronic Gates of Educational Institutions

Due to the large expansion of Electronic Gates (E-Gates) in universities and faculties, and their important role in providing various services for different types of users; it is necessary to measure the quality of the performance of these types of gates in terms of providing the required services optimally. This study aims to evaluate the availability of quality standards in E-Gates through a field study on the E-Gate of the faculty of Information Technology at the University of Benghazi. The analytical descriptive method was used to analyze the results obtained from a developed questionnaire as a study tool. Significant and important recommendations have been reached at the end of the analysis of the results.
Interpreting and Explaining Deep Neural Networks

Explainable and interpretable machine learning models and algorithms are important topics which have received growing attention from research, application and administration. Many complex Deep Neural Networks (DNNs) are often perceived as black-boxes. Researchers would like to be able to interpret what the DNN has learned in order to identify biases and failure models and improve models. In this tutorial, we will provide a comprehensive overview on methods to analyze deep neural networks and an insight how those interpretable and explainable methods help us understand time series data.
Synthetic Data Generation to Mitigate the Low/No-Shot Problem in Machine Learning

The low/no-shot problem refers to a lack of available data for training deep learning algorithms. In remote sensing, complete image data sets are rare and do not always include the targets of interest. We propose a method to rapidly generate highfidelity synthetic satellite imagery featuring targets of interest over a range of solar illuminations and platform geometries. Specifically, we used the Digital Imaging and Remote Sensing Image Generation model and a custom image simulator to produce synthetic imagery of C130 aircraft in place of real Worldview-3 imagery. Our synthetic imagery was supplemented with real Worldview-3 images to test the efficacy of training deep learning algorithms with synthetic data. We deliberately chose a challenging test case of distinguishing C130s from other aircraft, or neither. Results show a negligible improvement in automatic target classification when synthetic data is supplemented with a small amount of real imagery. However, training with synthetic data alone only achieves F1-scores in line with a random classifier, suggesting that there is still significant domain mismatch between the real and synthetic datasets.
Greg, ML – Machine Learning for Healthcare at a Scale

Abstract This paper introduces the Greg, ML platform, a machine-learning engine and toolset conceived to generate automatic diagnostic suggestions based on patient profiles. Greg, ML departs from many other experiences in machine learning for healthcare in the fact that it was designed to handle a large number of different diagnoses, in the order of the hundreds. We discuss the architecture that stands at the core of Greg, ML, designed to handle the complex challenges posed by this ambitious goal, and confirm its effectiveness with experimental results based on the working prototype we have developed. Finally, we discuss challenges and opportunities related to the use of this kind of tools in medicine, and some important lessons learned while developing the tool. In this respect, we underline that Greg, ML should be conceived primarily as a support for expert doctors in their diagnostic decisions, and can hardly replace humans in their judgment.
Automated Segmentation of Nucleus, Cytoplasm and Background of Cervical Cells from Pap-smear Images using a Trainable Pixel Level Classifier

Cervical cancer ranks as the fourth most prevalent cancer affecting women worldwide and its early detection provides the opportunity to help save life. Automated diagnosis of cervical cancer from pap-smear images enables accurate, reliable and timely analysis of the condition's progress. Cell segmentation is a fundamental aspect of successful automated pap-smear analysis. In this paper, a potent approach for segmentation of cervical cells from a pap-smear image into the nucleus, cytoplasm and background using pixel level information is proposed. A number of pixels from the nuclei, cytoplasm and background are extracted from 100 images to form a feature vector which is trained using noise reduction, edge detection and texture filters to produce a pixel level classifier. Comparison of the segmented images' nucleus and cytoplasm parameters (nucleus area, longest diameter, roundness, perimeter and cytoplasm area, longest diameter, roundness, perimeter) with the ground truth image features yielded average percentage errors of 0.14, 0.28, 0.03, 0.30, 0.15, 0.25, 0.05 and 0.39 respectively. Validation of the pixel classifier with 10fold cross-validation yielded pixel classification accuracy of 98.50%, 97.70% and 98.30% with Fast Random Forest, Naïve Bayes and J48 classification methods respectively. Comparison of the segmented nucleus and cytoplasm with the ground truth nucleus and cytoplasm segmentations resulted into a Zijdenbos similarity index greater than 0.9321 and 0.9639 for nucleus and cytoplasm segmentation respectively. The results indicated that the proposed pixel level segmentation classifier was able to extract the nucleus and cytoplasm regions accurately and worked well even though there was no significant contrast between the components in the image. The results from cross-validation and test set evaluation imply that the classifier can segment cells outside the training dataset with high precision. Choosing an appropriate feature vector for training the classifier was a great challenge and a novel task in the proposed approach. As a result, good segmentation of the nucleus and cytoplasm was attained. Given the accuracy of the classifier in segmenting the nucleus, which plays an important role in cervical cancer diagnosis, the classifier can be adopted in systems for automated diagnosis of cervical cancer from pap-smear images.
A Projected Stochastic Gradient Algorithm for Estimating Shapley Value Applied in Attribute Importance

Machine Learning is enjoying an increasing success in many applications: medical, marketing, defence, cyber security, transportation. It is becoming a key tool in critical systems. However, models are often very complex and highly non-linear. This is problematic, especially for critical systems, because end-users need to fully understand the decisions of an algorithm (e.g. why an alert has been triggered or why a person has a high probability of cancer recurrence). One solution is to offer an interpretation for each individual prediction based on attribute relevance. Shapley Values allow to distribute fairly contributions for each attribute in order to understand the difference between a predicted value for an observation and a base value (e.g. the average prediction of a reference population). They come from cooperative game theory. While these values have many advantages, including their theoretical guarantees, they are however really hard to calculate. Indeed, the complexity increases exponentially with the dimension (the number of variables). In this article, we propose two novel methods to approximate these Shapley Values. The first one is an optimization of an already existing Monte Carlo scheme. It reduces the number of prediction function calls. The second method is based on a projected gradient stochastic algorithm. We prove for the second approach some probability bounds and convergence rates for the approximation errors according to the learning rate type used. Finally, we carry out experiments on simulated datasets for a classification and a regression task. We empirically show that these approaches outperform the classical Monte Carlo estimator in terms of convergence rate and number of prediction function calls, which is the major bottleneck in Shapley Value estimation for our application.
Continuous Risk Monitoring and Assessment: New Component of Continuous Assurance

ABSTRACT We propose a methodology for Continuous Risk Monitoring and Assessment (CRMA). As a new component of Continuous Assurance (CA), CRMA converts static CA systems into dynamic and adaptable systems that respond to changes in audit risks from client business risks, thereby improving the relevance and robustness of CA systems to changes in client business risks and possible business failures. The present methodology proposes to develop relevant leading and lagging indicators to monitor and assess a client's business risks and recognize emerging business risks or harmful impacts of materialized business risks in near real time. To illustrate, we develop a lagging indicator of reputational damage measured from real time postings published in social media websites. We present two cases where the present lagging indicator is measured using public Twitter messages (“tweets”) and used to assess the potential reputational damage to two large corporations.
A Content Creation Tool for AR/VR Applications in Education: The ScoolAR Framework

Nowadays, in education environments, new societal challenges and opportunities induce teachers to use new methods to improve the quality of learning. For this purpose, technology has proved to be a helpful aid in education, since it allows to ease the teaching methods, increasing the performances by introducing affordable and reliable means to convey digital contents. Studies revealed that Augmented Reality (AR) and Virtual Reality (VR) have a great potential to help two kinds of users: on one side the students, improving their knowledge and skills; on the other teachers, widening their teaching methods. In fact, the relation between AR/VR and education makes the teaching and learning experience more efficient and stimulating. Notwithstanding, a platform specifically designed does not exist for an agile creation of AR/VR contents, even for not skilled programmers. There is thus the necessity of developing new tools to enable users to become, easily, producers of such experiences. The aim of this paper is to introduce a novel framework, named ScoolAR, specifically designed to allow teachers to create tailor-made didactic proposals involving the students in the training action, thus bringing more involvement and contents awareness, managed in the realization of AR and VR applications and declined within the disciplinary topics.
Sigmoidal Salp Swarm Algorithm

The salp swarm algorithm (SSA) imitates the swarming behavior of salps while foraging in the deep ocean. This algorithm is equally good for single as well as multiobjective optimization problems. The position update process of leader salp depends on perturbation. Currently SSA used fixed value to decide perturbation that lacks diversity in solutions. In this research paper, we propose a new variant of SSA by introducing a new parameter for perturbation. The new parameter is inspired by sigmoidal decreasing function. Since it is nonlinear function; it gives improved results for complex optimization problems. The anticipated approach is named as sigmoidal salp swarm algorithm (S3A). The performance of S3A is tested over a set of fifteen benchmark problems and results proves that it improves results up to 60% in terms of mean value.
An Efficient Secure Division Protocol Using Approximate Multi-bit Product and New Constant-Round Building Blocks

Integer division is one of the most fundamental arithmetic operators and is ubiquitously used. However, the existing division protocols in secure multi-party computation (MPC) are inefficient and very complex, and this has been a barrier to applications of MPC such as secure machine learning. We already have some secure division protocols working in $$\mathbb {Z}_{2^n}$$ . However, these existing results have drawbacks that those protocols needed many communication rounds and needed to use bigger integers than in/output. In this paper, we improve a secure division protocol in two ways. First, we construct a new protocol using only the same size integers as in/output. Second, we build efficient constant-round building blocks used as subprotocols in the division protocol. With these two improvements, communication rounds of our division protocol are reduced to about 36% (87 rounds $$\rightarrow $$ 31 rounds) for 64-bit integers in comparison with the most efficient previous one.
A Comparison of Machine Learning Methods for Data Imputation

Handling missing values in a dataset is a long-standing issue across many disciplines. Missing values can arise from different sources such as mishandling of samples, measurement errors, lack of responses, or deleted values. The main problem emerging from this situation is that many algorithms can’t run with incomplete datasets. Several methods exist for handling missing values, including “SoftImpute”, “k-nearest neighbor”, “mice”, “MatrixFactorization”, and “miss- Forest”. However, performance comparisons for these methods are hard to find, as most research approaches usually face imputation as an intermediate problem of a regression or a classification task, and only focus on this task’s performance. In addition, comparisons with existing scientific work are difficult, due to the lack of evaluations on publicly-available, open-access datasets. In order to overcome the aforementioned obstacles, in this paper we are proposing four new open datasets, representing data from real use cases, collected from publicly-available existing datasets, so as anyone can have access to them and compare their experimental results. Then, we compared the performance of some of the state-of-art approaches and most frequently used methods for missing data imputation. In addition to that, we have proposed and evaluated two new approaches, one based on Denoising Autoencoders and one on bagging. All in all, 17 different methods were tested using four different real world, publicly available datasets.
Weak Grain-Like Structures

A Grain-like structure is a cascade connection of a primitive LFSR into an NFSR. It is well known that such structures generate sequences with periods multiples of the period of primitive LFSR sequences. In this paper, we study weak Grainlike structures, i.e., Grain-like structures generating at least one sequence with minimum period. Assume the orders of LFSR and NFSR are n and m respectively. We prove that weak Grain-like structures always exist when m > n. For m = n, we give three classes of weak Grain-like structures. Then we extend the method in the second class to prove that weak Grain-like structures exist when m ≥ n - lg n + 4. Moreover, our experimental data shows that, the ratio of weak Grain-like structures approximates a value which is a little more than 63% for small m = n, and weak Grain-like structures exist with m = 3 or 4 when n ≤ 18.
Internal interface diversification as a method against malware

Internal interface diversification is a proactive software security method that prevents malware from using the fundamental services provided by an operating system by uniquely diversifying internal interfaces and propagating the information only to trusted programs. There are three main internal interfaces in operating systems that have been diversified in previous studies: (1) system calls (2) library functions and (3) shell commands. Based on previous studies and our own work, we implemented diversification for all interfaces in order to test their suitability and feasibility for real-world use. All three solutions enhanced the multi-layer security of the testing environment with little to no cost on system performance. However, maintaining such diversification tools might be troublesome in large and complex systems where new software is frequently added and software versions are updated. Thus, the solutions would be ideal for IoT devices and other smaller systems which rarely require updating, as well as restricted and static systems and critical systems with high-security requirements.
Dimensionality reduction of multielement glass evidence to calculate likelihood ratios

Abstract Dimensionality reduction of multivariate elemental concentrations of glass is reported for computing likelihood ratios ( LR s). The LR s calculated using principal component analysis (PCA) and a post hoc calibration steps result in very low ( &lt; 1%) false inclusions when comparing glass samples known to originate from different sources and very low ( &lt; 1%) false exclusions when comparing glass samples known to originate from the same source. The LR s calculated using the novel PCA approach are compared with previously reported LR s calculated using a more computationally intensive Multivariate Kernel (MVK) model followed by a calibration step using a Pool Adjacent Violators (PAV) algorithm. In both cases, the calibrated LR s limited the magnitude of the misleading evidence, providing only weak to moderate support for the incorrect hypotheses. Most of the different pairs that were found to be falsely included were explained by chemical relatedness (same manufacturer of the glass sources in very close time interval between manufacture). The computation of LR s using dimensionality reduction of elemental concentrations using PCA may transfer to other multivariate data‐generating evidence types.
Bi-Directional Seed Attention Network for Interactive Image Segmentation

In interactive segmentation, the role of seed information provided by the user is significant. A seed is a clue to ease the ambiguity of the problem by making the object segmentation task interactive. However, in most deep network-based works, seed information has been used as an additional channel for input images. In this paper, we propose a novel bi-directional attention module for more actively using seed information. The proposed bi-directional seed attention module (BSA) operates based on the feature map of the segmentation network and the input seed map. Through our attention module, the network feature map is affected by the seed map, while the feature also updates the seed information. As a result, our system concentrates on the seed information and more accurately derives the segmentation result required by the user. We have conducted validation experiments on the four standard benchmark datasets, including SBD, GrabCut, Berkeley, and DAVIS, and achieved the state-of-the-art results.
Possibilistic Clustering Enabled Neuro Fuzzy Logic

Artificial neural networks are a dominant force in our modern era of data-driven artificial intelligence. The adaptive neuro fuzzy inference system (ANFIS) is a neural network based on fuzzy logic versus a more traditional premise like convolution. Advantages of ANFIS include the ability to encode and potentially understand machine learned neural information in the pursuit of explainable, interpretable, and ultimately trustworthy artificial intelligence. However, real-world data is almost always imperfect, e.g., incomplete or noisy, and ANFIS is not naturally robust. Specifically, ANFIS is susceptible to over inflated uncertainty, poor antecedent (fuzzy set) data alignment, degenerate optimization conditions, and hard to interpret logic, to name a few factors. Herein, we explore the use of possibilistic clustering to identify outliers, specifically typicality degrees, to increase the robustness of ANFIS; or any fuzzy logic neuron/network. Experiments are presented that demonstrate the need and quality of the proposed solutions in the pursuit of robust interpretable machine learned neuro fuzzy logic solutions.
Private Join and Compute from PIR with Default

The private join and compute (PJC) functionality enables secure computation over data distributed across different databases, and is applicable to a wide range of applications, many of which address settings where the input databases are of significantly different sizes.
Multiple-precision matrix-vector multiplication on graphics processing units

We are considering a parallel implementation of matrix-vector multiplication (GEMV, Level 2 of the BLAS) for graphics processing units (GPUs) using multiple-precision arithmetic based on the residue number system. In our GEMV implementation, element-wise operations with multiple-precision vectors and matrices consist of several parts, each of which is calculated by a separate CUDA kernel. This feature eliminates branch divergence when performing sequential parts of multiple-precision operations and allows the full utilization of the GPU’s resources. An efficient data structure for storing arrays with multiple-precision entries provides a coalesced access pattern to the GPU global memory. We have performed a rounding error analysis and derived error bounds for the proposed GEMV implementation. Experimental results show the high efficiency of the proposed solution compared to existing high-precision packages deployed on GPU.
An Experimental Comparison of Different Object Tracking Algorithms

This paper reviews and evaluates several state of the-art object tracking algorithms. It compares the various techniques on different parameters by implementing each one of them as it is important to check their performance and robustness. These algorithms are used to recognize, detect and track objects under the influence of different problems like occluded object, background clutter, illumination variations, and deformed objects or in shadowed regions. The tools used were python and its libraries including but not restricting to OpenCV, matplotlib, Numpy and others.
Introduction to Clustering

In this chapter, the basic concepts of clustering are introduced. Moreover, the most relevant decisions to be made for the practical application of clustering methods are listed and briefly discussed. The chapter also provides an introduction to the three most important approaches to clustering, i.e., standard, fuzzy, and model-based.
Exploration of Hyperparameter in Extreme Learning Machine for Brain MRI Datasets

In past decades, it has been observed that learning speed of feed-forward neural network was too slower than its required speed, which was a major drawback for their applications. The reason behind this problem is that all the parameters of system are tuned iteratively. This paper proposes a modern learning algorithm called extreme learning machine (ELM) in which parameters are not tuned. ELM is one of the best performing classifiers. Its architecture consists of various interconnected layers made up of several neurons. With the help of subjective weights, the input layers and hidden layers are interconnected and provide output with the help of activation function or transfer function. Activation functions are nonlinear, and various activation functions may give distinct output on same dataset. Only one out of every activation function is appropriate for each sort order issue. Results demonstrate that the accuracy of ELM is predominantly influenced by the number of neurons for a certain dataset. This paper demonstrates the fluctuation of normal test precision with different activation functions using brain MRI datasets.