Reviewerly/exHarmony huggingface dataset:
Neural Network System for Medical Data Approximation

The paper considers the technique of medical data using neural network approximation. The method of neural networks training uses the Kachmazh's algorithm to solve linear equations systems, justifies the choice of the algorithm for solving linear equations systems using the iterative Kachmazh's method. A review of solving linear equations systems method using the Kachmazh's algorithm is carried out, the conditions of convergence are considered. The analysis of the algorithm application method in neural networks is made. An overview of the system structural scheme for medical data approximation based on the considered technique is given. The necessity of neural network systems application for medical diagnostics is substantiated.
Sorting of Low Probability of Intercept Radar Signals Based on Frequency Modulation Constancy Using Wigner-Ville Distribution

In the field of electronic warfare (EW), sorting of low probability of intercept (LPI) radar signals is very important in order to determine the capability of the intercept radar emitter. This paper presents a novel form of sorting various types of airborne radar signals using Wigner-Ville distribution (WVD). The WVD is modified using two kernel filters in order to cater for various forms of signal distortions due to noise. The sorting is carried using the instantaneous frequency (IF) obtained from this modified WVD which involves sorting the LPI radar signals into LPI-phase or LPI-frequency group. The non-LPI radar signal of constant frequency and phase parameter is also considered for the sake of test signal completion. Performance analysis is finally carried out at different signal-to-noise ratios (SNRs) in order to verify the probability of correct sorting (PCS). Results obtained showed that PCS depends on the internal modulation of the intercepted signal. The proposed method was able to easily sort non-LPI radar signal with 100% PCS achieved at minimum SNR of -12 dB while the remaining LPI radar signals required higher SNR for 100% PCS. Finally, it was shown in this paper that the proposed method outperforms previous works reviewed.
Improving Confusion-State Classifier Model Using XGBoost and Tree-Structured Parzen Estimator

Detecting confusion has been considered as a critical issue in online education platforms. Confusion emerged as an effect of the limited interaction between lecturers and learners. The confusion detection machine learning model can be used to overcome the problem. Such a model can provide the ability for online education systems to detect confusion, thus it can react accordingly. Encouraged by the need, several studies have been done to develop confusion-state classifier models. The best previous model has an average accuracy of 75%. Despite having a promising result, the model still contains several gaps that can be improved. The gaps lie in the selection of the machine learning algorithm and the absence of any hyper-parameter optimization technique. This study aims to overcome them using two approaches: replacing the machine learning algorithm with XGBoost and applying the Tree-structured Parzen Estimator (TPE) as a hyper-parameter optimization technique. The TPE was also combined with the Recursive Feature Elimination (RFE) technique. The proposed model had outperformed the previous ones by achieving an average accuracy of 87%. This study also brought out the most optimal configuration of features and hyper-parameters to build such a model. This study had presented the current confusion-state classifier model.
Predicting Available Expert Developer for newly Reported Bugs using Machine learning Algorithms

Bug triaging is a concept of assigning newly reported bug to the appropriate developer. This task of assigning bug to developer is done by manager of the project. As number of reported bugs increases this task becomes tedious because manager should have knowledge of efficiency of all developers. In order to overcome from this problem, an automated bug triaging system is introduced using machine learning algorithm. In existing papers, it has been applied on dataset without considering developers availability to solve newly assigned bugs. In this paper a new algorithm is proposed using existing machine learning algorithm to analysis the text data of a bug repository independent of developer. An extensive analysis is done on natural language processing for analysis of text data to estimate accuracy, precision, recalls and F-score using different machine learning algorithm.
Data augmentation and post selection for improved replay attack detection

Vulnerabilities of the Automatic Speaker Verification (ASV) technology have been recognized and have generated much interest to design anti-spoofing detectors. Replay attacks pose a severe threat due to the relative difficulty for detection and the ease in mounting spoofing attacks. In this paper, a high performing spoofing detection countermeasure is presented. Deep Learning (DL) based speech embedding extractors and a novel data augmentation approach are combined to improve the detection performance. To select augmented samples with high quality and diversity and avoid the bias caused by human subjective perception, we propose the use of a Support Vector Machine (SVM) based post-filter. With the generated extra informative training data, problems of over-fitting and lack of generalization can be significantly alleviated. Experimental results measured by equal error rates (EERs) indicate a relative improvement of 30% on the development and evaluation subsets. This provides the motivation for the proposed audio data augmentation and also promotes the future research on generated samples selection in the application of speaker spoofing detection.
Multi-Agent System for Threat Assessment and Action Selection under Uncertainty and Ambiguity

This paper describes an approach to designing a multi-agent decision support system, which monitors uncertain and ambiguous dynamic environments to produce probable explanations of the current situation and identify asymmetric threat as well as selects actions to prevent catastrophic consequences or mitigate their impact while acting under resource and time constraints. The proposed system incorporates decision makers&#x0027; attitude toward risk and uncertainty in situations involving regular and high consequence rare events. An example scenario showing potentials of the described approach is also presented.
A Nuclei Segmentation Method Based on Whale Optimization Algorithm Fuzzy Clustering in Histopathological Images

A nuclei segmentation in the histopathological images is a challenging problem, specifically in the images stained with the hematoxylin and eosin dye. To alleviate this, a new whale optimization algorithm based fuzzy clustering method has been presented in this work for nuclei segmentation in the histopathological images. In the proposed method, the whale optimization algorithm is applied to find the best fuzzy clusters based on the objective function defined as compactness. The evaluation of the introduced method is conducted on the publicly available histopathological image dataset, namely H&E stained ER+ breast cancer images. The experimental results evince that the proposed approach acquires comparatively better accuracy of segmentation than the standard clustering based image segmentation methods in terms of both the qualitative and quantitative measurements. Therefore, it can be stated that the introduced method is an efficacious nuclei segmentation method for histopathological images.
Distributed Coordination of Autonomous Guided Vehicles in Multi-agent Systems with Shared Resources

This paper proposes a decoupled path planning framework to coordinate the behavior of a number of agents in dynamic environments. In this algorithm, agents can effectively conduct negotiations in a so-called incomplete information setting where only limited information about other parties is available for negotiating agents. We face the problem by breaking it into three distinct phases. The first stage is to schedule each agent individually and provide it with the lowest cost path using the Dijkstra algorithm. The next stage involves finding the best possible bids for each agent. The final stage is to reschedule each agent individually to satisfy the total demand, which is positioning all agents on their desired destinations where collisions are avoided and traveling time is optimally minimized. This cooperative pathfinding allows agents to know each other's intentions and communicate their planned paths. In the proposed dynamic system, to account for unknown and unpredictable moving obstacles, if a collision is imminent, each agent needs to decide how to proceed by researching and selecting a new path and modifying its current plan in a timely manner.
Prediction in Nature-Inspired Dynamic Optimization

In dynamic black-box optimization, time-varying objective functions are optimized. Nature-inspired optimization algorithms like evolution strategies and swarm optimization algorithms are common choices for solving dynamic optimization problems. In real-world scenarios, objective functions often change in the course of the optimization process. Frequently, their change is not random, but certain time-depending characteristics and relationships exist. Algorithms exploiting such information are often superior to naive variants based on random restarts. Predicting the moving optimum based on previous information and incorporating the predictions into the optimization process are a strategy that has attained attention in the recent past. In this chapter, we introduce the foundations of prediction-based dynamic optimization with nature-inspired methods. We present benchmark sets and quality measures and give insight into mechanisms to employ predictions into evolution strategy and particle swarm optimization-based search. Further, we show how predictive uncertainty information allows the optimizer to explore regions with higher predictive uncertainty more extensively.
Scalable Filtering of Large Graph-Coupled Hidden Markov Models

We consider the online filtering problem for a graph-coupled hidden Markov model (GHMM) with the Anonymous Influence property. Large-scale spatial processes such as forest res, social networks, disease epidemics, and robot swarms are often modeled by GHMMs with this property. We derive a scalable online recursive algorithm to produce a belief over states for each HMM node in the GHMM at each time step, given a history of noisy observations. In contrast to prior work, our algorithm is tractable for the high-dimensional discrete state spaces of GHMMs with arbitrary graph structure, and our method scales linearly with the total number of HMMs, i.e., nodes in the graph. We demonstrate the accuracy and scaling of our method using simulation experiments of a wild re model containing 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">298</sup> total states and a disease epidemic model containing 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">18</sup> states.
Towards a Reactive Game Engine

Developing new ways to improve game engines and enabling the creation of more accurate experiences is an important issue, especially for real-time applications such as Virtual Reality. As this technology has evolved, the gap between rendered frames has become smaller and environments have become more complex. As a result, mistakes can arise with regard to order-dependent events. Often these mistakes result in a temporarily unstable state in the environment, and may not be visible because of the time lapse between rendered frames. These errors are compounded by events which are not directly driven by the game engine, such as user movement and input. There is a continuous interaction between the user and the environment and the precise actions that user may take often cannot be predicted a priori. Reactive programming is a still evolving paradigm that has been employed in many different applications such as robotics and user interfaces. Our research presents a reactive, dependency-based framework for game engines using associations between components to establish the order in which they update. Automated tests were developed to evaluate the framework and compare its performance to Unity3D's default update structure as well as an existing reactive programming framework.
Multi-Emotion Classification Evaluation via Twitter

Recently, twitter has become an indispensable social communication platform. It contains many contrasting views and cultures on miscellaneous topics. This gigantic information bulk has endeared the attention of researchers for its interpretation and serve it in various life applications (e.g. product customer feedback, tourism, voting, product branding, etc.). However, natural languages ambiguity is one of the researchers' limitations, where implicit and diverse emotions are implied in the same context. Emotion analysis is a recent research field that digs to predict the implied emotions in different media types especially written text. Traditional approaches focused on detecting single attitude from social media texts, which isn't considered accurate. This research proposes two Multi-Emotion classification (MEC) approaches, that mine users' attitudes in tweets. These approaches have diverse classifiers' architectures, representative features, and a number of emotions sets. These diversities contribute in each classifier's performance in emotion classification. Eight experiments are applied using two feature representation vectors and two supervised machine learning algorithms on two emotion sets. The proposed systems outperform the contemporary traditional approaches. The first Binary relevance approach achieves hamming score ranging from 0.36 to 0.53, and the second Convolutional neural network approach achieves hamming score ranging from 0.39 to 0.54.
Small-Sample Image Classification Method of Combining Prototype and Margin Learning

Image classification is a fundamental and important task in the field of computer vision and artificial intelligence. In recent years, image classification has made breakthrough progress based on deep learning on large-scale datasets. However, it still exits big challenges on small-sample image data. The main difficulty is that the deep neural network easily overfit small-sample data and has big variance. Ensemble learning is a good way to overcome overfitting and reduce the variance of the model; however, the existing ensemble methods based on deep neural network still could overfit on small-sample image data due to the big randomness of deep neural network. In this paper, we propose a new ensemble method for small-sample image classification tasks. The proposed method based on VGG16 network, we modified the structure of the VGG16 network to two branches, one branch is a classifier based on prototype learning, and the other is a classifier based on margin learning. The experimental results on two small-sample image datasets, the LabelMe dataset and the Caltech101 dataset, show that the proposed method has better performance and higher stability than other referred methods.
Robust Assessment of Real-World Adversarial Examples

We explore rigorous, systematic, and controlled experimental evaluation of adversarial examples in the real world and propose a testing regimen for evaluation of real-world adversarial objects. We show that for small scene/ environmental perturbations, large adversarial performance differences exist. Current state of adversarial reporting exists largely as a frequency count over a dynamic collections of scenes. Our work underscores the need for either a more complete report or a score that incorporates scene changes and baseline performance for models and environments tested by adversarial developers. We put forth a score that attempts to address the above issues in a straightforward exemplar application for multiple generated adversary examples. We contribute the following: 1. a testbed for adversarial assessment, 2. a score for adversarial examples, and 3. a collection of additional evaluations on testbed data.
Measuring Code Smells and Anti-Patterns

Code smells are the design problems of software that are categorized under the names like smells, code scattering, code flaws, code tangling and code non-complying to design principles, etc. It highlights a deeper problem and significant issues in the software design model. As soon as these design problems are discovered, it has to be rectified by applying suitable refactoring techniques and improving various aspects concerning quality such as ability to maintain easily, comprehend better and reuse. The reason to rectify smells is that the existence of smells in the code can lead to downfall showcasing negative implications on the overall software functionality and the quality of software which increases the overall maintenance and a lot of cost goes waste in this process. In this paper, the recent advances and refactoring tools are being deployed to identify non-essential code smells, demarcate the existence and evolution of smells by analyzing previous versions and new versions of code.
Accelerated CNN Training through Gradient Approximation

Training deep convolutional neural networks such as VGG and ResNet by gradient descent is an expensive exercise requiring specialized hardware such as GPUs. Recent works have examined the possibility of approximating the gradient computation while maintaining the same convergence properties. While promising, the approximations only work on relatively small datasets such as MNIST. They also fail to achieve real wall-clock speedups due to lack of efficient GPU implementations of the proposed approximation methods. In this work, we explore three alternative methods to approximate gradients, with an efficient GPU kernel implementation for one of them. We achieve wall-clock speedup with ResNet-20 and VGG-19 on the CIFAR-10 dataset upwards of 7 percent, with a minimal loss in validation accuracy.
Computational Serendipitous Recommender System Frameworks: A Literature Survey

Recommender systems help users find items that are relevant or interesting. These recommendations are based on the users profile, past behavior, and items similarity. Various recommender system algorithms presented in literature have achieved high accuracy in predicting relevant recommendations similar to the users profile. Even though with high accuracy, these results tend to be over-specified with obvious recommendations that are too similar to what the user already knows, failing to arouse users long-term interest. This survey paper presents a literature review on serendipity as one of the beyond accuracy measures to overcome the over-specialization problem. The survey starts with an introduction of the concept of serendipity, followed by several definitions of serendipity found in the literature. Then the main components of serendipity are presented novelty, surprise, and value. After that, the survey discusses several well-known serendipity recommender systems. While most surveys in serendipity categorize frameworks based on types of recommender systems, this survey categorizes serendipitous frameworks based on the conceptual view of serendipity. Finally, a discussion with various evaluation strategies on serendipity will be presented.
Multi-task Cascaded and Densely Connected Convolutional Networks Applied to Human Face Detection and Facial Expression Recognition System

Face detection and recognition is an important issue and a difficult task in computer vision and human-computer interaction. Recently, with the development of deep learning, several related technologies have been proposed for face detection and facial expression recognition (FER), and the outstanding convolutional neural networks are the most common used in this field. This thesis applies the multi-task cascade convolutional neural network to face detection, and then designs the real-time FER system based on densely connected convolution network (DenseNet). The system first scales the input image to an image pyramid, and then uses the hierarchical network to determine whether a candidate window includes a human face. If a face exists, then send the candidate window to the FER system. Since DenseNet possesses the property of feature reuse, it can effectively reduce the amount of parameters and computation efforts, beneficial to develop the real-time system. In order to capture the variation of facial muscle in different expressions, this architecture adopts convolution operations with a stride 1 and tries different numbers of dense blocks. Through experiments, the proposed system can achieve real-time recognition in 30FPS and with recognition accuracy better than human eyes.
Convergence Improvement of Q-learning Based on a Personalized Recommendation System

Reinforcement Learning (RL) - a research branch of artificial intelligence - exploits the concept of reward/penalty from human learning so that the feedback information of the environment can be used for self-learning without previous knowledge. Although RL has been applied to many research fields, there are difficulties when implementing it in some real world applications. One difficulty is the tradeoff between exploration and exploitation for the agent of RL in choosing a proper action. An improper action may lead to a learning failure or an increase in learning cost. Another difficulty is that the learning agent of RL needs to interact with the environment to attain a real-time reward/penalty; however, the learning time spent in the interaction process may be too long. In order to overcome the aforementioned difficulties, this paper proposes an approach that employs a personalized recommendation system to provide a feedforward candidate action for RL to implement self-adaptive learning through teaching. A real visual tracking experiment using a pan-tilt camera system is conducted to assess the performance of the proposed approach. Experimental results show that the proposed personalized recommendation system-based approach is able to improve the effectiveness and practicality of RL.