Reviewerly/exHarmony huggingface dataset:
Fraudulent Transaction Detection in FinTech using Machine Learning Algorithms

With the advancement of e-commerce, online transactions purchases using credit and debit cards have drastically increased. This has caused a burst in credit and debit card fraud and has become a profoundly significant global issue. Fraud touches every area of our lives and is a growing concern that effects both businesses and customers. As machine learning techniques provide unique and efficient solutions, they are applicable in various types of problems. Recently, machine learning algorithms have been widely applied as a data mining technique for classification problems. In this paper, a binary classification problem is considered where a transaction can be classified as either fraudulent or legitimate transaction. The goal is to classify the transactions using five different machine learning algorithms. The transaction dataset (Task1 and Task2) is preprocessed, and then SGD, DT, RF, J48 and IBk machine learning classifiers are applied. After applying the classifiers, the results are compared to analyze which classifier performs the best. Based on the experimental results, it is found that the accuracy percentage of all the five classifiers for Task1 and Task2 datasets is ranging between 97.78% to 98.1%, with no major difference. As the dataset is highly imbalanced, the kappa statistic value is also considered. For both datasets, the RF classifier had the greatest value of kappa statistics, whereas SGD and J48 had the lowest value for Task1 and Task2, respectively. Other evaluation metrics were also considered for evaluating the performance of the applied classifiers. Overall, these classifiers achieved similar results for Task2 dataset. As negative Kappa statics and MCC values were obtained, the SGD classifier for Task2 dataset had the worst results in comparison. Based on evaluation criteria such as Kappa statistic and MCC values RF outperformed the others for both the datasets.
Research of Medical Aided Diagnosis System Based on Temporal Knowledge Graph

With the advent of medical big data era, medical knowledge graph has received extensive attention. The traditional knowledge graph prediction methods are mostly aimed at static data, which is not suitable for medical diagnostic data with dynamical variation characteristics. Take the example of pulmonary embolism in the clinical medicine domain, it is a typical high-risk lethal disease, and its course of disease has the characteristics of rapid deterioration over time. Therefore, it is necessary to consider the course of disease over time to predict the complications of pulmonary embolism and propose a reasonable diagnosis and treatment recommendation, it brings huge challenge to traditional knowledge graph prediction methods. For this reason, this paper proposes a deep learning method based on embedded representation, by using GRU to introduce temporal information into the knowledge graph and using TransR to ensure the structure property of the knowledge graph, to improve the accuracy and arithmetic performance of intelligent inference of the knowledge graph. The medically aided diagnosis system we have developed has been clinically implemented in several hospitals, and the effectiveness, reliability and stability of the system have been verified through practical application.
Application of Bayesian Automated Hyperparameter Tuning on Classifiers Predicting Customer Retention in Banking Industry

The paper aims to demonstrate the comparison of accuracy metrics achieved on nine different fundamental Machine Learning (ML) classifiers. Bayesian Automated Hyperparameter Tuning, with Tree-structured Parzen Estimator, has been performed on all of nine ML classifiers predicting the customers likely to be retained by the bank. After visualizing the nature of dataset and its constraints of class imbalance and limited training examples, Feature Engineering has been performed to compensate for the constraints. The ML techniques comprise first using six classifiers (namely, K-Nearest Neighbors, Naive Bayes, Decision Tree, Random Forest, SVM, and Artificial Neural Network––ANN) individually on the dataset with their default hyperparameters with and without Feature Engineering. Second, three boosting classifiers (namely, AdaBoost, XGBoost, and GradientBoost) were used without changing their default hyperparameters. Thirdly, on each classifier, Bayesian Automated Hyperparameter tuning (AHT) with Tree-structured Parzen Estimator was performed to optimize the hyperparameters to obtain the best results on the training data. Next, AHT was performed on the three boosting classifiers as well. The cross-validation mean training accuracy achieved is comparatively quite better than those achieved on this dataset so far on Kaggle and other research papers. Besides, such an extensive comparison of nine classifiers after Bayesian AHT on Banking Industry dataset has never been made before.
Deep Learning and Late Fusion Technique in Medical X-ray Image

Classification of images is essential in medical database because of the different image modalities such as X-ray images, Computed Tomography (CT) images, Magnetic Resonance imaging (MRI) etc. In addition to the varieties of image modalities present in different databases, they are of different body parts and need to be properly classified to enhance effective retrieval for purposes such as medical diagnosis, teaching and research. Most of the hand-crafted techniques have various limitations which reduce their potentials to accurately classify medical X-ray images. For more than a decade, various researchers have employed the use of different handcrafted techniques for medical image classification. However, the major problem associated with the techniques is their inability to extract discriminative features that are relevant enough to accurately classify medical images such as radiographic images. This study focuses on employing deep learning and fusion technique to classify medical X-ray images. The proposed technique uses a single pre-trained neural network and a late fusion technique for the classification of ImageCLEF 2007 and 2015 dataset. The employment of a single pre-trained neural network, both as a feature extractor and as a fine-tuned network, makes the technique unique especially when considering the nature of the dataset used. The combination of the posterior probabilities generated from SVM and Softmax classifiers using a single deep pre-trained neural network produces an overall classification accuracy of 95.54% in classifying the dataset into 116 categories on ImageCLEF 2007. This is the highest when compared to the use of AlexNet + SVM and fine-tuned AlexNet alone which produced 84.35% and 86.47% classification accuracies respectively. On ImageCLEF 2015, it produces an overall classification accuracy percentage of 87.72%.
Reinforcement learning for pathfinding with restricted observation space in variable complexity environments

Path finding is a common problem in computer science that has applications in robotics and autonomous systems and can be solved using reinforcement learning. Many solutions to path finding problems rely on the agent having full knowledge of its environment, however this may limit the agent from being able to act effectively in environments it has not been trained on. We modified Open AI’s Frozen Lake, a simple 2D path finding environment, to include a limited observation space. Using a variation of Q – learning, Modified Connectionist Q-Learning, an agent was trained on Frozen Lake and our new environment and tested on a series of maps it had not been trained on, as well as on a dynamic map. The agent trained on the modified,limited observation space environment was able to generalize its training to solve maps it had not seen before, in contrast to the agent trained on the Frozen Lake environment, which was not able to generalize its training.
Smart Glasses: Digital Assistance in Industry

New media developments have revolutionized the behavior of people in an unprecedented technique in the latest decades. Mobile phones have created an always online mentality. However, what is next? Recent developments underline the increase of a technology known as “Wearable devices.” Augmented reality smart glasses (ARSG) such as Microsoft HoloLens and Google Glass are very good examples of these technology. It provides huge potential for innovation for firms and manufacturing industries. ARSG is becoming very common and important technology that promotes shop floor operators to fulfill industry 4.0 requirements. Augmented reality is currently an interesting and hot research topic in manufacturing industries. The main goal of this research paper is to improve the use of smart glasses for operator training with augmented reality. It will assist to increase effectiveness and shorter learning times for the individual operator. ARSG products available in the market are very expensive. It will help to find an affordable solution for the industries. It provides new methods for reducing the efforts of the operators working online. It mainly focuses on minimizing disadvantages of the existing products.
An Adaptive Control Scheme for Human Following Behavior of Mobile Robots

This paper presents an adaptive mobile robot control scheme for autonomous human following in which the robot keeps a desired distance from the person. A human-robot interaction model in error coordinates suitable for robot following applications is proposed. An adaptive control law is presented to tackle the problem of the unknown human velocity. Simulation and experimental results are presented to illustrate the effectiveness of the proposed control scheme.
A Dynamic Resource Controller for Resolving Quality of Service Issues in Modern Streaming Processing Engines

Devising an elastic resource allocation controller of data analytical applications in virtualized data-center has received a great attention recently, mainly due to the fact that even a slight performance improvement can translate to huge monetary savings in practical large-scale execution. Apache Flink is among modern streamed data processing run-times that can provide both low latency and high throughput computation in to execute processing pipelines over high-volume and high-velocity data-items under tight latency constraints. However, a yet to be answered challenge in a large-scale platform with tens of worker nodes is how to resolve the run-time violation in the quality of service (QoS) level in a multi-tenant data streaming platforms, particularly when the amount of workload generated by different users fluctuates. Studies showed that a static resource allocation algorithm (round-robin), which is used by default in Apache Flink, suffer from lack of responsiveness to sudden traffic surges happening unpredictably during the run-time. In this paper, we address the problem of resource management in a Flink platform for ensuring different QoS enforcement levels in a platform with shared computing resources. The proposed solution applies theoretical principals borrowed from close-loop control theory to design a CPU and memory adjustment mechanism with the primary goal to fulfill the different QoS levels requested by submitted applications while the resource interference is considered as the critical performance-limiting factor. The performance evaluation is carried out by comparing the proposed resource allocation mechanism with two static heuristics (round robin and class-based weighted fair queuing) in a 80-core cluster under multiple traffic patterns resembling sudden changes in the incoming workloads of low-priory streaming applications. The experimental results confirm the stability of the proposed controller to regulate the underlying platform resources to smoothly follow the target values (QoS violation rates). Particularly, the proposed solution can achieve higher efficiency compared to the other heuristics by reducing the response-time of high priority applications by 53% while maintaining the enforced QoS levels during the burst traffic periods.
Privacy Issues in Edge Computing

While edge computing extends the computational power to the edge of networks, it helps to fix bottleneck of the bandwidth limitation and network latency problems in cloud computing. However, more edge servers and communication between edge servers and end devices bring more challenges to user’s privacy. Since edge computing causes tremendous exchange of user’s data, identity and location to edge server than cloud computing, private information is leaked easily, which makes privacy concern severe more than ever. In this chapter, we explain why privacy problems are becoming more severe than cloud computing, summarize the current privacy challenges and techniques against those privacy threats and discuss some future work about privacy issues in edge computing.
On Probabilistic Completeness of the Generalized Shape Expansion-Based Motion Planning Algorithm

A major aspect of motion planning is the use of sampling-based algorithms. Sampling-based methods are primarily used to generate a feasible collision-free path for agents in an environment known a-priori. A recently proposed motion planning algorithm, termed as 'Generalized Shape Expansion' (GSE) algorithm, is a promising option in this class of algorithm. Extensive numerical studies have suggested that the GSE outperforms several seminal algorithms in literature in terms of computational time. However, so far no guarantee of probabilistic completeness of the GSE has been presented in literature. To this end, this paper elaborates a detailed mathematical analysis of GSE, providing upper bounds on the probability of failure of the GSE algorithm. A numerical example is presented to illustrate the proof. Simulation studies are presented to compare it with prominent algorithms in the literature, particularly in terms of number of iterations to reach a feasible path.
Blockchain-Based Multi-fogcloud Authentication System

Multi-fogcloud, which comprises the cloud system, is a centralized system in which regional fogclouds are managed by a master fogcloud. In a multi-fogcloud, authentication for a regional fogcloud requires sending of an authentication request to the master fogcloud. When regional fogcloud requires excessive authentication, the master fogcloud experiences traffic failures and delays. In this paper to resolve this problem, we suggest a multi-fogcloud authentication system based on blockchain. This system distributes excessive levels of authentication requests through the region fogcloud to resolve problems. It improves authentication speeds by consolidating distributed multi-fogclouds across the blockchain network. We verify the performance of the multi-fogcloud based on blockchain through comparison with the existing multi-fogcloud system.
Classification of B-Cell Acute Lymphoblastic Leukemia Microscopic Images Using Crow Search Algorithm

The objective of this research work is to use Crow Search Algorithm for classification of blood smear microscopic images into two classes: Leukemic B-lymphoblast cells (cancer cells) and normal B-lymphoid precursors (normal cells). Crow Search Algorithm is generally used to crack numerical optimization, training neural networks and feature selection problems. This research work uses Crow Search algorithm as a transformation technique to convert non-linearly separable data points as linearly separable data points. Microscopic image dataset named C-NMC is collected from cancer imaging archives website and the microscopic images of 30 cancer subjects and 30 normal subjects are considered in this analysis. To prove the significant performance of crow search algorithm as transformation technique based classifier, other popular unsupervised classification techniques like K-Means and Fuzzy C Means are used. Remarkably 87% of accuracy is achieved when crow search algorithm is used as classifier.
Data-Driven Multi-UAV Navigation in Large-Scale Dynamic Environments Under Wind Disturbances

In the near future, large amount of unmanned aerial vehicles (UAVs) are expected to appear in the airspace. To ensure the safety of the airspace, there are many daunting technical problems to tackle, one of which is how to navigate multiple UAVs safely and efficiently in the large-scale airspace with both static and dynamic obstacles under wind disturbances. This paper solves this problem by developing a novel data-driven multi-UAV navigation framework that combines A* algorithm with a state-of-the-art deep reinforcement learning (DRL) method. The A* algorithm generates a sequence of waypoints for each UAV and the DRL ensures that the UAV can reach each waypoint in order while satisfying all dynamic constraints and safety requirements. Furthermore, our framework significantly expedites the online planning procedure by offloading most computations to offline and limiting online computing to only path fine-tuning and dynamic obstacle avoidance. The simulation studies show the good performance of the proposed framework.
RC4D: A New Development of RC4 Encryption Algorithm

Cryptography is one of the essential methods for securing the information. In cryptography, there are many encryption algorithms; some of them strong where the others are broken. RC4 stream cipher one of the most common algorithms that are characterized by its speed in implementation does not need large storage space and has less complexity, but there are weaknesses in its output. Numerous researches work on the RC4 stream cipher to boost the security of it, to be strong enough. However, the biases in the output are still in most of the enhancement. The researchers claim that its swap function is responsible for those biases. They recommended to ignore some initial bytes from the key-stream output, to dispose of this before de facto encryption begins. This paper present new development over the RC4 algorithm (RC4D) via amendment in the first and second parts of the algorithm. In the first part, it increases the use of the key operations to obtain more considerable randomness, while adding one more random variable and use the Xor function in the second part. Thus, the experimental result of the NIST statistical tests and the distant-equalities statistical analysis shows the RC4D more robust than the original RC4.
CNN for object recognition implementation on FPGA using PYNQ framework

Object recognition is one of the most researched and commercialized applications of Deep Learning (DL) where Convolutional Neural Networks (CNNs) are especially accurate. The deployment of these models on embedded systems require low latency and high performance even with limited resources and energy budgets. Embedded systems with Zynq Systems on Chips (SoCs) are attractive platforms for CNNs. In this paper, we use PYNQ framework, that supports a Python-based hardware/software codesign environment to perform CNN inference for object recognition on Xilinx FPGA. We design the CNN model and train it on a CPU platform and we implement it On ZedBoard FPGA. By using only, a single ARM processor core on FPGA, we achieve 100ms latency and up to 10 image recognitions per second on the CIFAR-10 dataset with 79.90% accuracy. This model performance can be highly improved by exploring the hardware resources of the FPGA chip.
Abstractive vs Extractive Text Summarization (Output based approach) - A Comparative Study

There has been an explosion in the amount of digital data from a variety of sources, according to the IDC prediction, the total amount of digital data circulating annually around the world will hit 180 zettabytes in 2025 which pullulated from 4.4 zettabytes in 2013. These data are structured to semi-structured to fully unstructured. In today's world where machines are taking over everything the text summarization is nowhere behind it. various techniques are developed to extract the key points from text document to represent document summary. The paper briefly compares the two majorly used approaches (output based) for text summarization called extractive based text summarization and abstraction-based summarization, used in NLP for text summarization. The paper aims at reviewing the advantages, disadvantages and major features of both techniques using technique of TF-IDF Vectorisation along with word embedding vectors.
Improvement of Mixture-of-Experts-Type Model to Construct Dynamic Saliency Maps for Predicting Drivers’ Attention

Recently, there have been many studies on driving assistance systems to reduce traffic accidents. In such a situation, we proposed a method to construct dynamic saliency maps for predicting driver attention while driving in our previous work. In this construction method, multiple feature maps are first created from the video data of a car-mounted camera. Subsequently, this method computes dynamic saliency maps as combinations of the feature maps that perform center-biasing and normalization. Additionally, this method has been successfully applied to a real-world open dataset, including car-mounted camera videos. However, there is room for further improvement in the model used in this method, since the model cannot capture drivers' characteristics, so that the degree of center bias changes depending on the driving situation. In this paper, we propose an improved model that incorporates multiple center biases into the center-biasing process. It is expected that, by using this improved model, one can appropriately consider changes in the center bias depending on driving situations, and the improved model outperforms the previous model. In addition, the performance of the improved model is compared with that of the conventional model for a real-world open dataset.
Rumor-robust Decentralized Gaussian Process Learning, Fusion, and Planning for Modeling Multiple Moving Targets

This paper presents a decentralized Gaussian Process (GP) learning, fusion, and planning (RESIN) formalism for mobile sensor networks to actively learn target motion models. RESIN is characterized by both computational and communication efficiency, and the robustness to rumor propagation in sensor networks. By using the weighted exponential product rule and the Chernoff information, a rumor-robust decentralized GP fusion approach is developed to generate a globally consistent target trajectory prediction from local GP models. A decentralized information-driven path planning approach is then proposed for mobile sensors to generate informative sensing paths. A novel, constant-sized information sharing strategy is developed for sensing path coordination, and an analytical objective function is derived that significantly reduces the computational complexity of the path planning. The effectiveness of RESIN is demonstrated in simulations.
Deep Reinforcement Learning for Visual Semantic Navigation with Memory

Navigation is an important activity to be performed by mobile robots with high complexity in the context of indoor environments. Approaches as Deep Reinforcement Learning has been adopted for this purpose, from the premise of learning through experiences and taking advantage of Deep Neural Networks as Convolutional Networks, Graph Neural Networks, and Recurrent Networks. Based on the use of vision and semantic context applied in this work, the effects of adding Recurrent Networks on a learning-based navigation model are investigated, making possible the learning of better policies with the use of memory from past experiences. Results obtained show that the proposed approach gets better values in terms of qualitative as quantitative measures when compared to models without memory.