Reviewerly/exHarmony huggingface dataset:
Pectoral Muscles Removal in Mammogram Image by Hybrid Bounding Box and Region Growing Algorithm

Breast cancer is one of the most common causes of death among women globally. Accurate and early detection is necessary for decreasing mortality and increase treatment success rates. Mammogram image is currently one of the best ways to detect breast cancer in the early stages, but it contains many artifacts such as noise, labels, and pectoral muscles, that must be deleted or suppressed because it greatly affects the results of the diagnosis in the coming stages. Removing the pectorals muscle is the biggest problem because it possesses an intensity tissue that closely resembles the tissue of fat, glands, and tumors in the form of mammograms. In this paper, an effective algorithm has been suggested by Hybridization Bounding Box and Region growing algorithm (HBBRG) algorithm to solve the problem of pectoral muscle removal which greatly affects the results of tumor detection in the next stages by combines the Bounding Box (BB) and Region growing (RG). To perform this work, pre-processing for mammogram images was applied in two stages. In the first stage, a medium filter and binary image with a specific threshold were used to remove noise and label respectively. In the second phase, the pectoral muscles were removed by applying the (BB) and (RG) algorithm separately, and then we proposed merging the two methods to set up an HBBRG algorithm with the aim to get better results for remove pectoral muscles. The proposed algorithms were tested on all the Mammographic Image Analysis Society (MIAS) database images, and the results showed a significant advantage in the HBBRG algorithm compared to other algorithms as it achieved results in over 98% to completely remove the pectoral muscles of all types of images.
Generating Accurate Pseudo Examples for Continual Learning

Continual learning (CL) is concerned with the persistent and cumulative nature of learning. This requires a method of successfully consolidating new knowledge into long-term memory without the loss of prior knowledge. Prior research has addressed this CL retention problem through the efficient rehearsal of prior examples while learning the examples of a new task within a long-term Multiple Task Learning (MTL) network. The approach maintains or improves prior knowledge while allowing its representation to remain plastic for the integration of new task examples. Preferably, rehearsal is done using pseudo examples synthesized by the MTL network; eliminating the need to retain prior task training examples or a generate them with an additional model. Previous work has shown that to properly retain knowledge the pseudo examples must adhere to the input probability distribution of those original examples. Two approaches are investigated for creating appropriate pseudo examples from a Restricted Boltzmann Machine (RBM) autoencoder, which can reside in the lowest layers of the long-term MTL Deep Belief network. We show that appropriate pseudo examples can be reconstructed by passing uniform random examples to a generative RBM model and selecting only those with reconstruction error less than the mean training error. These pseudo examples are shown to adhere to the probability distribution of the input variables of the original training examples and retain prior task knowledge during rehearsal as well as those examples. As part of the research, we develop and test a new metric called the Autoencoder Divergence Measure for comparing the probability distributions of two datasets given to a generative RBM network based on their reconstruction mean squared error.
Simultaneous Inference for Pairwise Graphical Models with Generalized Score Matching

Probabilistic graphical models provide a flexible yet parsimonious framework for modeling dependencies among nodes in networks. There is a vast literature on parameter estimation and consistent model selection for graphical models. However, in many of the applications, scientists are also interested in quantifying the uncertainty associated with the estimated parameters and selected models, which current literature has not addressed thoroughly. In this paper, we propose a novel estimator for statistical inference on edge parameters in pairwise graphical models based on generalized Hyvarinen scoring rule. Hyvarinen scoring rule is especially useful in cases where the normalizing constant cannot be obtained efficiently in a closed form, which is a common problem for graphical models, including Ising models and truncated Gaussian graphical models. Our estimator allows us to perform statistical inference for general graphical models whereas the existing works mostly focus on statistical inference for Gaussian graphical models where finding normalizing constant is computationally tractable. Under mild conditions that are typically assumed in the literature for consistent estimation, we prove that our proposed estimator is $\sqrt{n}$-consistent and asymptotically normal, which allows us to construct confidence intervals and build hypothesis tests for edge parameters. Moreover, we show how our proposed method can be applied to test hypotheses that involve a large number of model parameters simultaneously. We illustrate validity of our estimator through extensive simulation studies on a diverse collection of data-generating processes.
Applying Halstead Software Science on Different Programming Languages for Analyzing Software Complexity

In software development, it is crucial that each and every unit of effort must be measurable. If it is not measurable, Software engineers must try to make it measurable. Software metrics are any unit of software product measurement. Software metrics have emerged as an important mechanism to find a significant estimation of software products. This estimation usually includes complexity, effort, size of the code, time consumption, etc. By applying Halstead software science, we can analyze the different complexity factors of a program. This software science can lead us to effective technical as well as managerial decisions. In this paper, Halstead metrics of the program for checking for palindrome number in five different programming languages have been taken and analysis has been presented.
Functional Differences of Neo and Ethereum as Smart Contract Platforms

While Ethereum is currently the most popular smart contract platform, there are interesting alternatives like Neo. However, Neo only rarely appears in scientific literature. Moreover, comparisons of smart contract platforms hardly employ a structured approach, but rather apply different criteria to each platform. This work performs an in-depth comparison between Ethereum and Neo in a structured manner. We derive a catalogue of criteria from related work and use it to identify differences and similarities worthy of discussion. We show how Ethereum and Neo differ in key aspects, ranging from the general goal to technical issues like the execution and fee model and practical aspects like the maturity of its features and documentation.
Alexa, How Do I Build a VUI Curriculum?

As Voice User Interfaces (VUI) become more prominent in everyday life, there is an ever growing need to ensure that designers have the appropriate knowledge and tools to be able to build usable VUIs. To do this, we need to develop a curriculum for VUI design. However, there are few resources in academia and in industry to help ground the development of a new VUI-specific HCI curriculum. We discuss the limited approaches to VUI design, and particularly to preparing designers for this emerging interaction paradigm - both academia and industry training. Grounded in this, we then describe potential avenues for understanding what a VUI curriculum may entail, what would HCI educators need from it, and how such a curriculum would be validated.
Real-Time Biometric System for Security and Surveillance Using Face Recognition

This paper-based on real-time security and surveillance because today public security problems raised widespread concern. So due to the security issue, it can use the different types of biometrics for security and surveillance and hence facial recognition has important applications in the field of biometric and numerous systems related to security and surveillance. Many methods familiarized in this field, but some problems remain not recognized because that system is only used as a biometric in small scales, but in this paper, we focus on the live security of all public and private places and multiples uses. For this real-time security used the IP CCTV cameras for surveillance purposes for this need the image as a sample of a person for model training and basic details of the person who lives inside the city. Whenever need surveillance for any suspicious person or place inside the city. Then we can find out his/her present location, and all incident is monitor by IP CCTV Cameras. This all incident update in the centralized system so that we can find the live location of the particular person anytime and anywhere within the city. This security system has many applications in real life like crime control, terrorist alert, smart society, airport security, university campus and surveillance of public places. In the future, all the data collected from "The Unique Identification Authority of India" (UIDAI).
Design and Development of a Patient-centric User Authentication System

Advancements in computer and communication technology enabled the rapid growth of e-health services, which can nowadays provide various electronic methods (e.g., obtaining online consent, exchanging health data). In this context, user authentication is an essential security task within modern healthcare systems performed daily by millions of patients across the globe. Nevertheless, most e-health service providers often employ traditional text-based password solutions which result in increased cognitive load and often lead to poor usability and security. In this paper, we present the design and development of a patient-centric user authentication system, which offers a flexible, personalized and multi-factor user authentication solution to patients. The suggested solution is currently being implemented and evaluated within the EU Serums project - Securing Medical Data in Smart Patient-Centric Healthcare Systems, which is a research project supported by the European Commission (EC) under the Horizon 2020 Framework Programme (H2020).
Football Simulation Modeling with Fuzzy Rule Interpolation-based Fuzzy Automaton

A Fuzzy Rule Interpolation-based (FRI) fuzzy automaton for controlling a football match simulation is going to be introduced in this paper. Controlling the agents (football players) of the simulation is realized by evaluating such fuzzy rule-bases, which contain only the cardinal rules able to make the system work, keeping the rule-bases as small as possible (forming so called sparse rule-bases). Classical fuzzy inference methods require complete rule-bases by design and cannot handle these kinds of sparse rule-bases. However, using sparse rule-bases to control the agents becomes possible by applying FRI. The goal of this work was to construct such a model, which employs a human-readable knowledge representation to control the agents in a football simulation. For this purpose, the application of sparse fuzzy rule-bases is well suited, as these are self-describing by their nature. An example application was also developed alongside the fuzzy automaton-based model, which is able to perform and show a lifelike football match simulation in real-time. Hence the presented model can be adapted to real robot hardware and also can be used as a reference model for fuzzy logic based machine learning methods.
A modified NEFCLASS classifier with enhanced accuracy-interpretability trade-off for datasets with skewed feature values

The accuracy-transparency trade-off is one of the most notable challenges when applying machine learning tools in the medical domain. Nefclass is a popular neuro-fuzzy classifier in medical diagnosis systems. Nefclass performs increasingly poorly as the data skewness increases. This paper presents a combined approach to improve the classification accuracy and interpretability of the Nefclass classifier, when feature values of the training and testing datasets exhibit positive skewness. The proposed model consists of two steps. Firstly, a modified Nefclass classifier embedded with a choice of two alternative discretization methods, MME and CAIM is implemented. Secondly, we devised a new rule pruning method based on the Habermans' adjusted residual to reduce the size of the resulting ruleset. This rule-pruning method improves the interpretability of Nefclass without significant accuracy deterioration. Moreover, a hybrid approach combining the two approaches provides a considerable improvement in classification accuracy and transparency of Nefclass on skewed data.
A Variational Autoencoder Mixture Model for Online Behavior Recommendation

Online behavior recommendation is an attractive research topic related to social media mining. This topic focuses on suggesting suitable behaviors for users in online platforms, including music listening, video watching, e-commerce, to name but a few to improve the user experience, an essential factor for the success of online services. A successful online behavior recommendation system should have the ability to predict behaviors that users used to performs and also suggest behaviors that users never performed before. In this paper, we develop a mixture model that contains two components to address this problem. The first component is the user-specific preference component that represents the habits of users based on their behavior history. The second component is the latent group preference component based on variational autoencoder, a deep generative neural network. This component corresponds to the hidden interests of users and allows us to discover the unseen behavior of users. We conduct experiments on various real-world datasets with different characteristics to show the performance of our model in different situations. The result indicates that our proposed model outperforms the previous mixture models for recommendation problem.
Ant Colony Optimization based Multi-Robot Planner for Combined Task Allocation and Path Finding

Nature has inspired many solutions to the problems in computer science and recently in the field of robotics as well. Ant based algorithms have been successful in solving the NP hard problems such as traveling salesman problem. In the field of multi-robots it has been used to solve path finding and task allocation problems. In industrial warehouse applications, these problems are often combined, when for example multiple robots need to pick-up objects from one location and dropoff at the other. Multiple mobile robots need to perform these task optimally and simultaneously being on the floor without collisions. In this paper, we address this problem keeping the objective of being able to obtain collision free paths for all robots in a map, assigned for all given pick-up and drop-off tasks among themselves with an optimized minimal total distance traveled by the robots. We propose a multi-robot planner inspired from ant colony optimization to solve this combined problem. This planner finds collision free paths to all tasks to be done using a spread of ants from each robot. Ignoring the ones with collisions from other ants in their determined paths, the planner rates the tasks according to the total distance traveled. Using this rating system through multiple iterations, the planner eventually selects the best task allocations with paths for all robots among given iterations. This planner or as we call it, Ant Colony Optimization based Multi-Robot Planner for Combined Task Allocation and Path Finding Ant Colony Optimization based Multi-Robot Planner for Combined Task Allocation and Path Finding (ACTF) for pick-up and drop-off tasks is presented in this paper and has been tested against other similar planners producing promising results.
Exsavi: Excavating both sample-wise and view-wise relationships to boost multi-view subspace clustering

Multi-view clustering aims to partition objects based on multiple views through unsupervised learning. To exploit cross-view information, recent advances have shifted the focus from matrix-based to tensor-based subspace learning. Though, both approaches leverage the subspace representation of the data, further higher-order statistics extraction is often overlooked. To tackle the issue, in this paper, a novel multi-view clustering method is proposed to extract both intrinsic sample-wise and view-wise statistics from multi-view data. The multi-view data is represented in tensor form and mapped into a latent tensor subspace to exploit its sample-wise relationship. Through using multi-dimensional sparse coding in its similarity tensor, the view-wise relationship is exploited and evacuated. The two relationships are jointly learned and a latent clustering structure is essentially captured in a data-driven way. We conducted extensive experiments on face, object, digital ima ge, and text datasets and compared with ten state-of-the-art methods. The experimental results demonstrated that the proposed method, named as Exsavi, outperform the baselines in terms of various evaluation metrics.
An Anonymous Protocol with User Identification and Linking Capabilities for User Privacy in a Permissioned Blockchain

A permissioned blockchain includes a user in the network after verifying the user’s identity, in contrast to Bitcoin, which is a public blockchain that allows network participation without third-party approval. The two types of permissioned blockchains are private blockchains, each consisting of one server and multiple users, and consortium blockchains, which consist of groups of private blockchains. However, a blockchain has privacy issues, such as user tracking and inference. Therefore, cryptography should be applied for user privacy in a blockchain. There is a lot of research on anonymous protocols for privacy in a blockchain. In this paper, we provide a scheme for user management, i.e., identification and authorization, in a permissioned blockchain. We also propose an anonymous protocol with user identification and transaction linking capabilities provided by the private server, strictly to solve privacy concerns.
Criticality in two-mode interferometers

To achieve the optimal phase sensitivity in two-mode interferometers, one key problem is how to distribute photons between the two input ports, given a fixed total average photon number. In both the $\text{SU}(2)$ and $\text{SU}(1,1)$ interferometers with a product of coherent and Fock states as inputs, we find there exists a critical value of the total photon number, below which the optimal phase sensitivity can be achieved with the second port being the vacuum state. The critical points can be analytically fixed. We further consider a more general input state as a product of coherent and squeezed Fock states. Then we analytically identify three regions in the parameter space, similar to a phase diagram, and find that critical phenomena occur in two regions. If the total average input photon number is less than the critical value, the optimal distribution tends to minimize the average photon number in the second port. Our finding indicates that, in order to get interferometers with high sensitivity, one must carefully choose the photon number distribution according to the value of the input total average photon number. This central result will be useful in theoretical studies in the field of quantum parameter estimation.
Hyperbolic-Valued Hopfield Neural Networks in Synchronous Mode

For most multistate Hopfield neural networks, the stability conditions in asynchronous mode are known, whereas those in synchronous mode are not. If they were to converge in synchronous mode, recall would be accelerated by parallel processing. Complex-valued Hopfield neural networks (CHNNs) with a projection rule do not converge in synchronous mode. In this work, we provide stability conditions for hyperbolic Hopfield neural networks (HHNNs) in synchronous mode instead of CHNNs. HHNNs provide better noise tolerance than CHNNs. In addition, the stability conditions are applied to the projection rule, and HHNNs with a projection rule converge in synchronous mode. By computer simulations, we find that the projection rule for HHNNs in synchronous mode maintains a high noise tolerance.
ARbits

Augmented reality (AR) is a unique hands-on learning tool that can help students in a pervasively misunderstood area of STEM learning, electrical circuitry. AR technology can help with the construction and debugging of circuits, leading to independent learning and reduced assistance. In this paper, we introduce ARbits, a DIY, AR-compatible electrical circuitry toolkit for children. This toolkit exposes children to the concepts of circuitry at an early age, with components that are easy for little hands to handle. We anticipate that instructors at makerspaces can use our designs to fabricate multiple electrical components for children.
On Large Batch Training and Sharp Minima: A Fokker–Planck Perspective

We study the statistical properties of the dynamic trajectory of stochastic gradient descent (SGD). We approximate the mini-batch SGD and the momentum SGD as stochastic differential equations. We exploit the continuous formulation of SDE and the theory of Fokker–Planck equations to develop new results on the escaping phenomenon and the relationship with large batch and sharp minima. In particular, we find that the stochastic process solution tends to converge to flatter minima regardless of the batch size in the asymptotic regime. However, the convergence rate is rigorously proven to depend on the batch size. These results are validated empirically with various datasets and models.
The Implementation Of An Expert System In Diagnosing Skin Diseases Using The Dempster-Shafer Method

Abstract In this study we will describe the types of diseases that often attack the skin. The skin is a very sensitive organ of the body and susceptible to disease. This study uses a dempster-shafer method that is used to diagnose symptoms of skin disease. The dempster-shafer method in this study uses rule based obtained from dermatologists. The results of this study were able to produce an accurate diagnosis of 68.8% using trial sample data. The dempster-shafer method can be applied in the diagnosis of skin diseases.
Differential Evolution with Different Crossover Operators for Solving Unconstrained Global Optimization Algorithms

The Evolutionary Algorithms (EAs) are the part of bio-inspired algorithms used for solving a wide variety of real-world optimization problems. Differential Evolution (DE) is one of the algorithms in the pool of EA. DE is designed particularly for real-valued parameter optimization problems. The mutation and crossover operators of DE are designed in such a way that they are apt for real-valued parameters. The native crossover operators of DE are Binomial Crossover and Exponential Crossover. This paper proposes to test the suitability of other Crossover operators in the EA community to DE. The empirical study presented in this paper includes four crossover operators – DE’s native crossover (binomial crossover), simple arithmetic crossover, single arithmetic crossover, and uniform arithmetic crossover. The DE with these crossover operators is implemented to solve four standard benchmarking functions with different features. The empirical results are compared based on the accuracy and speed of the algorithm. The primary observations found were the arithmetic crossover was good at achieving good optimal results than the binomial crossover. However, the binomial crossover was faster in reaching a threshold set for the optimal value in the experiment.