Reviewerly/exHarmony huggingface dataset:
A Study of an Engine Anomaly Detection Model IForest-ADOA

Abstract In the environment of the Industrial Internet, automatic and effective anomaly detection methods are of great importance to ensure the safety and stability of engines. However, in practical engine applications, traditional machine learning anomaly detection methods often have limitations due to a large number of unmarked samples and high latitude data. In this paper, an engine anomaly detection model based on the ADOA model is proposed, combining the unsupervised learning iForest model with the semi-supervised learning ADOA model. Through the anomalous samples detected by iForest, the ADOA model is used to learn their features and patterns and perform clustering, to achieve the anomaly detection of the samples and improve its accuracy at the same time. Experiments based on a real engine dataset show that this model has good results in terms of anomaly recognition rate and anomaly misclassification rate for anomaly detection of high-dimensional data generated by real environments.
Trajectory Tracking of a Quadcopter Using Fuzzy-PD Controller

In this work, the trajectory tracking control of an unmanned aerial vehicle (UAV) has been accomplished using an adaptive proportional-derivative (PD) controller, in which fuzzy logic controllers have been utilized to determine the controller gain values resulting in better transient and steady-state characteristics. The real-time flight tests have been carried out on AR.Drone 2.0 for two different reference trajectories. The drone's discrepancies from these trajectories along with their derivatives have been employed as the input signals to the proposed control scheme. To provide means for comparison, a conventional PD controller has been also implemented. The obtained results from the real-time experiments indicate that the adaptation of the PD gains can give rise to smaller overshoot, shorter settling time, and less deviation from the target trajectory.
A novel approach of overtaking maneuvering using modified RG method

Intelligent and safe overtaking maneuvering is always a challenging task for autonomous vehicles. This paper proposes and experimentally implements a novel approach of overtaking maneuvering using modified form of Rendezvous Guidance (RG) algorithm for trajectory planning and obstacle avoidance, considering driver safety and comfort during autonomous overtaking. The simulations for all possible scenarios are conducted to ensure the effectiveness of proposed modified RG algorithm. These scenarios involved presence and absence of obstacle vehicle in overtaking lane alongside leading vehicle in driving lane. In addition, the enhanced performance of modified RG algorithm is established over conventional RG algorithm by comparative analysis. The results indicate that overtaking maneuvering period could be decreased by 10% using a modified RG algorithm and vehicle will cover less distance to complete overtaking. The efficacy of proposed method is justified by performing experiments using mobile robots. The experimental results and simulation results of modified RG algorithm are compared, and their plots are almost identical.
Adapting Procedural Content Generation to Player Personas Through Evolution

Automatically adapting game content to players opens new doors for game development. In this paper we propose an architecture using persona agents and experience metrics, which enables evolving procedurally generated levels tailored for particular player personas. Using our game, “Grave Rave”, we demonstrate that this approach successfully adapts to four rule-based persona agents over three different experience metrics. Furthermore, the adaptation is shown to be specific in nature, meaning that the levels are persona-conscious, and not just general optimizations with regard to the selected metric.
Simulator of Vehicle-Mounted Camera Video to Verify Dynamic Saliency Maps for Predicting Drivers' Gaze Points

There has been remarkable development of driver assistance systems in recent times, and it is expected that these assistance systems will significantly contribute to reducing traffic accidents caused by human attention errors. To improve the functionality of such assistance systems, several studies have been conducted on computational models to compute dynamic saliency maps for predicting the gaze points of drivers. Against this background, this study introduces a prototype simulator that can produce vehicle-mounted camera videos in various driving situations using 3D computer graphics. This simulator easily enables us to validate the effectiveness of models for computing dynamic saliency maps in various driving situations, even dangerous driving situations that are difficult to capture by actual vehicle-mounted cameras. As an initial validation of this simulator, we reproduced vehicle-mounted camera videos in situations with other cars and pedestrians. Subsequently, we applied the models to compute dynamic saliency maps, which were proposed in our previous work to reproduce vehicle-mounted camera videos. The obtained results proved the usefulness of the proposed simulator. Improvements to our models have also been discussed.
Smart Contract-Based Personal Data Protection Framework: In Cross-App Advertising

The era of big data gives rise to the development of data-driven marketing while exposing people to the threaten of data misuse and data breach. Data-driven marketing allows services to use a unique identifier to track each individual and deliver customized content. If a third party has gained unauthorized access to the behavioral data, the user’s privacy will be violated. Industry giants like Apple and Google are continuously working on user anonymity: they changed unique device identifiers to advertising ID and now manage to phase out advertising ID. At the same time, service providers and advertisers are also seeking better identity strategies. This paper mainly focuses on cross-app advertising and proposes a smart contract based personal data protection framework. Leveraging the smart contract’s advantages, we ensure transparency, tamper-resistance, and traceability during data storing and data sharing. In this framework, the user interacts with other entities anonymously, thus preventing identity leakage. In addition, we introduce a reward mechanism that allows users who authorize data sharing to be paid by the service. We then implement the proposed framework on Etherum combined with the IPFS to validate our design. Finally, we discuss the security of the current framework and possible further work.
Monitoring Activities of Daily Living with a Mobile App and Bluetooth Beacons

In this paper, we present a preliminary study on the monitoring of activities of daily living (ADL) with a mobile app. We rely on a set of Bluetooth beacons deployed around the household to perform indoor localization. The mobile app also tracks the instrumental ADL (IADL) in terms of the app usage, such as apps for social networking, financial management, and personal entertainment. Furthermore, the mobile app collects information regarding the level of physical activities and the environment such as light, temperature, air pressure using the built-in sensors of the smartphone. The latter could help infer the living conditions of the individual, even though the information is not directly about ADL. Tracking ADL in any method will inevitably intrude on the user's privacy. Our mobile app informs the user exactly what information we collect and all the data are stored locally on the smartphone. The user can view the report of the individual's ADL, and has the choice of deleting some or all data. Finally, we propose a feature extraction model for temporal ADL data and demonstrate how the features can be used to classify different behavioral patterns.
Class center attention network with spatial adaption for enhancing hepatic segments classification with low-visibility vascular

Automatic classification of hepatic segments is of great use for liver surgical resection planning. However, conventional computer-aided annotation methods have difficulty annotating cases with weakly visible hepatic vascular structures. To address this issue, we proposed a class center attention convolutional neural network with spatial adaption. In our method, an improved 2.5D input module—one with spatial adaption—was used to enhance difficult to observe information on the target slices. Furthermore, a class center attention branch was introduced, which refines the unclear boundaries caused by the low-visibility hepatic vascular system by detecting the consistency of the class centers and the voxels near boundaries. Our proposed framework is trained and evaluated on 112 CT scans which were produced by the Task08_HepaticVessel in the Medical segmentation Decathlon study. Experimental results demonstrate the final algorithm for classification of hepatic segments obtained large overlaps comparable to that of manual annotations, even if the visibility of the hepatic vascular system is low.
Graph pruning for model compression

Previous AutoML pruning works utilized individual layer features to automatically prune filters. We analyze the correlation for two layers from the different blocks which have a short-cut structure. It shows that, in one block, the deeper layer has many redundant filters which can be represented by filters in the former layer. So, it is necessary to take information from other layers into consideration in pruning. In this paper, a novel pruning method, named GraphPruning, is proposed. Any series of the network is viewed as a graph. To automatically aggregate neighboring features for each node, a graph aggregator based on graph convolution networks (GCN) is designed. In the training stage, a PruningNet that is given aggregated node features generates reasonable weights for any size of the sub-network. Subsequently, the best configuration of the Pruned Network is searched by reinforcement learning. Different from previous work, we take the node features from a well-trained graph aggregator instead of the hand-craft features, as the states in reinforcement learning. Compared with other AutoML pruning works, our method has achieved the state-of-the-art under the same conditions on ImageNet-2012.
The Eye Gaze Tracking During Online Learning with Teacher's Facial Image for Junior and Senior High School Students

In this work-in-progress paper, we indicate the progress of the experiment to clarify the effects of the teacher's facial image on the learner during online learning for junior and senior high school students. As simulated online learning, we repeated five different quizzes and explanations in the experimental procedure. The teacher's facial image was displayed during the period of the quizzes. Both quantitative and qualitative data were measured. For measuring, the eye gaze tracking system and semi-structured interview were adapted. We hypothesized that the junior and senior high school students might have secure feelings and positive impressions on the teacher's facial image because of their psychological development. Additionally, it was predicted that the gaze time would be longer than that of the graduate students. As a result of the experiment, junior and senior high school students gazed at the teacher's facial image three times longer than that of graduate students. In the interview, approximately four times larger participants than the graduate students preferred the learning video with a teacher's facial image. Although this result is according to ongoing research, we believe that sharing information at an early stage can contribute to the development of this research area.
Nexus Between Financial Innovation and Financial Intermediation in Nigeria’s Banking Sector

This study evaluated the relationship between financial innovation and financial intermediation in Nigeria. It seems that banks in Nigeria may have a problem of deposit-loan mismatch and losing customers to start-ups given increasing cost of deposits attributable to disruptive practice arising from financial innovations. The specific objectives of this study were to examine the relationship between financial innovation (value of the automated teller machine, internet banking, mobile banking, point of sale transactions) and financial intermediation (commercial banks deposit mobilization) in Nigeria for the period 2009–2018. This study was anchored on the financial innovation theory of Joseph Schumpeter, which states that technology creates opportunities for new profits and super profits as a result of increased investment by banks or financial institutions on products of innovation. The ordinary least square was used to estimate the parameters. The data used were extracted from the Central Bank of Nigeria statistical bulletin. The results showed that there is a positive and significant relationship between financial innovation (value of Automated Teller Machine) and financial intermediation (commercial banks deposit mobilization) in Nigeria; there is a positive but no significant relationship between financial innovation (internet banking) and financial intermediation (commercial banks deposit mobilization) in Nigeria; there is a positive but no significant relationship between financial innovation (mobile banking) and financial intermediation (commercial banks deposit mobilization) in Nigeria; and there is no positive and significant relationship between financial innovation (point of sale transactions) and financial intermediation (commercial banks deposit mobilization) in Nigeria. The f-test result showed that financial innovations proxies jointly related significantly to commercial banks’ deposits. The work concludes that financial innovations contributed to commercial banks’ deposits in Nigeria. The researchers recommended among others that banks should improve on the security of transactions done on their platforms, continue to improve and partner with start-ups in technological infrastructure, improve on power and network stability, deploy more innovative products, and improve on the efficiency of bank staff by regular training.
Thermal Image Super-Resolution: A Novel Unsupervised Approach

This paper proposes the use of a CycleGAN architecture for thermal image super-resolution under a transfer domain strategy, where middle-resolution images from one camera are transferred to a higher resolution domain of another camera. The proposed approach is trained with a large dataset acquired using three thermal cameras at different resolutions. An unsupervised learning process is followed to train the architecture. Additional loss function is proposed trying to improve results from the state of the art approaches. Following the first thermal image super-resolution challenge (PBVS-CVPR2020) evaluations are performed. A comparison with previous works is presented showing the proposed approach reaches the best results.
DAGSec: A hybrid distributed ledger architecture for the secure management of the Internet of Things

The rise of 5G mobile broadband networks creates new possibilities for the Internet of Things. Billions of devices will provide comprehensive services, including e-health applications, smart grids, and industry 4.0. Distributed ledger technologies solve most security and privacy threats of current IoT systems connected to a cloud or multi-access edge communication (MEC). Unfortunately, the volume of transactions imposed by 5G networks prevents blockchain-based solutions due to scalability issues. Nonetheless, emerging solutions based on directed acyclic graphs (DAG), still require some form of centralization or global view. This article proposes DAGSec, a hybrid distributed ledger architecture that provides a secure Internet of Things environment with high throughput and low latency. Our proposal uses directed acyclic graphs and local transaction validation instead of global transaction validation to attain a high transaction rate. Furthermore, we propose a blockchain-based witness system to approximate chronological order of independent transactions.
D2T: A Framework For transferring detection to tracking

Object detection methods draw increasing attention in deep learning based visual tracking algorithms due to their robust discrimination and powerful regression ability. To further explore the potential of object detection methods in the visual tracking task, there are two gaps that need to be bridged. The first is the difference in object definition. Object detection is class-specific while visual tracking is class-agnostic. Moreover, visual tracking needs to differentiate the target from intra-class distractors. The second is the difference in temporal dimension. Different from object detection which processes still-image, visual tracking concentrates on objects which vary continuously with time. In this paper, we propose a Detection to Tracking (D2T) framework to address the above issues and effectively transfer existing advanced detection methods to visual tracking task. Specifically, to bridge the gap of object definition, we propose a general-to-specific network that separates learning general object features and instance-level features. To make full use of the contextual information while adapting to the appearance variation of targets, we propose a temporal strategy combining short-term constraint and long-term updating. To the best of our knowledge, our D2T framework is the first universal framework which directly transfers deep learning based object detectors to visual tracking task. It provides a novel solution to visual object tracking, and it achieves superior performance in several public datasets.
Multi-level augmented inpainting network using spatial similarity

Recently, multi-scale neural networks have shown promising improvements in image inpainting. However, most of them adopt the progressive way, in which the errors on lower scales may be propagated on higher scales. Addressing this issue, we propose a multi-level augmented inpainting network (MLA-Net) to rationally harmonize the inter- and intra-level contexts. Here, a pyramid reconstruction structure (PRS) with three parallel levels is designed to establish the inter-level relationship, which can boost the representation of the features by integrating the texture details into semantics. Then, we propose a novel spatial similarity based attention mechanism (SSA) to ensure the intra-level local continuity between the holes and related available patches. In SSA, in order to focus on the important textures and structures rather than calculating each pixel of the feature equally, a spatial map is utilized to highlight the corresponding spatial locations during the similarity computation. The experiments are evaluated on multiple challenging datasets, which demonstrate that MLA-Net can generate accurate results with better visual quality compared with the state-of-the-art methods. For the 256×256 Places2 dataset, PSNR increases 1.02 dB, while FID decreases 0.075. For the 256×256 CelebA-HQ dataset, there are 0.22 dB and 0.613 improvements in PSNR and FID.
Scalable Blockchain Anomaly Detection with Sketches

The growing popularity of Blockchain networks attracts also malicious and hacking users. Effectively detecting inappropriate and malicious activity should thus be a top priority for safeguarding blockchain networks and services. Blockchain behavior analysis can be used to detect unusual account activities or time periods with network-wide irregular properties. Thus, optimized anomaly detection based on historical data is an essential task for securing transactions and services. However, processing the complete blockchain history can be slow and costly due to its large size and rapid growth. In this paper we suggest addressing this challenge by analyzing summarized blocks data structures, called sketches, rather than the entire blockchain. Sketches are common data structures used in computer systems and blockchain networks, to allow compact data representation while supporting efficient executions of particular queries. We study how sketches can be used to detect suspicious accounts or time periods without the need to maintain or go through the entire blockchain data. We design solutions for the major known attacks and conduct experiments to evaluate them based on real Ethereum data. We compare the accuracy, run-time and memory usage of our algorithms with traditional detection algorithms relying on the complete blockchain data. Our results indicate that sketch-based anomaly detection methods can provide a practical scalable solution for detecting anomalies in blockchain networks.
Real-time Age-Group and Accurate Age Prediction with Bagging and Transfer Learning

Age prediction based on facial images has become a vital part in many scenarios since the human face contains most important bio-metric characteristics from race, gender to age. As a result, automatic age prediction has gained its potential importance. This paper addresses two approaches to this problem: accurate age and age-group prediction, both using convolutional neural networks (CNNs) and deep neural networks (DNNs) - two fundamental deep learning techniques, in combination with computer vision library, OpenCV to aid in classifying input images. This work is distinguished by two main key contributions. The first is feature extraction, where convolutional activation features could be obtained through using a novel hierarchical aggregation based neural networks to learn aging features from an abundant number of training data. The second contribution is the features classification part, in which we apply the Softmax Regression (SR) classifier to perform accurate age prediction whereas age-group prediction uses majority vote classifier. The results showed that the number and the quality of training images and subjects in the training dataset play a significant role in improving the accuracy of this work. The experimental results showed satisfactory and promising performance, which indicates another direction of further development and optimization of deep learning algorithms.
Neural Network Principles and its Application

Neural networks which also known as artificial neural networks is generally a computing dependent technique that formed and designed to create a simulation to the real brain of a human to be used as a problem solving method. Artificial neural networks gain their abilities by the method of training or learning, each method have a certain input and output which called results too, this method of learning works to create forming probability-weighted associations among both of input and the result which stored and saved across the net specifically among its data structure, any training process is depending on identifying the net difference between processed output which is usually a prediction and the real targeted output which occurs as an error, then a series of adjustments achieved to gain a proper learning result, this process called supervised learning. Artificial neural networks have found and proved itself in many applications in a variety of fields due to their capacity to recreate and simulate nonlinear phenomena. System identification and control (process control, vehicle control, quantum chemistry, trajectory prediction, and natural resource management. Etc.) In addition to face recognition which proved to be very effective. Neural network was proved to be a very promising technique in many fields due to its accuracy and problem solving properties.