Reviewerly/exHarmony huggingface dataset:
Autonomous Drone for Defence Machinery Maintenance and Surveillance

This proposed research work focuses on the implementation of an autonomous unmanned aerial vehicle (UAV) which is controlled using a pix hawk flight controller. The Quad Copter is capable of navigating autonomously without any real-time input from the user and also programmed to follow a specified path autonomously. The algorithm enables a control technique by which quad copter is empowered to fly autonomously, trajectory tracking, graceful motion and accurate altitude hold performance. Surveillance and Machinery maintenance application are the primary applications designed for the Defense purposes in the Line Of Control and War-zones. This work is aimed to design a quad copter that will follow a command to fly through specified way points. The deep learning algorithm detects human motions and from data acquired camera and ultrasonic sensors to the cloud. On deviations from the standard protocol which is detected using a Sjcam 5000x elite Camera. Also, here a backup auxiliary mini drone is programmed to eject along with the data stored on the primary drone's memory on an unforeseen calamity or an attack that would damage the primary drone's ability to fly.
Temporal Conditions Suitable for Predicting Human Motion in Walking

In this paper, we clarified the temporal condition in predicting human walking through a neural network. In order to examine the relationship between human walking and prediction, we analyzed the walking motion through two different types of prediction system. One analyzed the positional error of predicted body joints to examine which moment's motion while walking influences the prediction accuracy. As a result, the error peaked around the time when the system uses the information of Toe-Off (TO) for prediction. The other analysis uses the ternary classification which predicts the sudden change of walking behaviors: turn left, turn right or stop. By dividing the temporal data into a shorter period, we examine which phase contains the essence of the proceeding direction. As a result, the correct answer rate was high in the phase around TO. From these results, it is possible to improve the accuracy of gait prediction by using the skeleton data around TO moment since the TO is the time to determine the moving direction.
Hiding data in dual color images reversibly via weighted matrix

Abstract Hiding information in dual images is a new aspect in the field of multimedia security to increase security, enhance visual quality as well as improve embedding capacity. Few data hiding schemes have been developed employing interpolated gray-scale image suffering from limited hiding capacity with poor visual quality. In this paper, a dual image-based data hiding scheme has been developed with the help of weighted matrix in such a way that more than one data bits can be embedded with only one-bit alteration in the interpolated pixel of the original cover image. The proposed scheme is innovative and realistic because no secret data bits are directly embedded within the pixel but bit alteration has been carried out alternatively in the dual image block depending on the block number. Distributing the valuable secret information within two images and updating the weighted matrix with the shared key before each iteration enhances the security of the proposed scheme. The experimental results with comparison show the effectiveness of our developed scheme which provides visual quality more than 47 dB PSNR after embedding 1,754,460 bits secret information. The intended outcome brought into the limelight some remarkable sublime characteristics in the field of image authentication, tamper detection and digital forgery detection without which the technological life is stunted. Innumerable private and public sector facet including health care, commercial security, defence, intellectual property rights gets immensely benefited from this innovative scheme.
Learning Locomotion Skills via Model-based Proximal Meta-Reinforcement Learning

Model-based reinforcement learning methods provide a promising direction for a range of automated applications, such as autonomous vehicles and legged robots, due to their sample-efficiency. However, their asymptotic performance is usually inferior compared to the state-of-the-art model-free reinforcement learning methods in locomotion control domains. One main challenge of model-based reinforcement learning is learning a dynamics model that is accurate enough for planning. This paper mitigates this issue by meta-reinforcement learning from an ensemble of dynamics models. A policy learns from dynamics models that hold different beliefs of a real environment. This procedure improves its adaptability and inaccuracy-tolerance ability. A proximal meta-reinforcement learning algorithm is introduced to improve computational efficiency and reduces variance of higher-order gradient estimation. A heteroscedastic noise is added to the training dataset, thus leading to a robust and efficient model learning. Subsequently, proximal meta-reinforcement learning maximizes the expected returns by sampling “imaginary” trajectories from the learned dynamics, which does not require real environment data and can be deployed on many servers in parallel to speed up the whole learning process. The aim of this work is to reduce the sample-complexity and computational cost of reinforcement learning in robot locomotion tasks. Simulation experiments show that the proposed algorithm achieves an asymptotic performance compared with the state-of-the-art model-free reinforcement learning methods with significantly fewer samples, which confirm our theoretical results.
IBMSDC: Intelligent Blockchain based Management System for protecting Digital Currencies Transactions

Recently, Digital Currencies (DC) have taken a lot of momentum in the academic and financial community intelligently. Massive contributions have been introduced in this context. With the continual development in the emergent technologies such as; Blockchain (BC), Cloud Computing (CC), Internet of Things (IoT), distributed networks and the existence of cryptographic as a security mechanism, the potential for DC to gain rapid acceptance is increasing significantly. Although the distributed nature of DC deems as core essential for various financial disputes. Government's rules and regulations represented by National Central Bank (NCB) play an important role in plugging the loopholes in monetary policies. This paper analyzes the potential security issues and economic benefits of a NCB to issue the Digital Fiat Currency (DFC) as a replacement for paper money. Adopting IBMSDC as a methodology for designing a management system that manages, control, and protect digital currencies and transactions. As an overall conclusion, studying its functionality, implementation, implications, challenges, and risks will improve not only economic stability but also cybersecurity solutions.
Domain Adaptation Meets Disentangled Representation Learning and Style Transfer

In this paper, we face the challenges of un-supervised domain adaptation and propose a novel three-in-one framework where three tasks domain adaptation, disentangled representation, and style transfer are considered simultaneously. Firstly, the learned features are disentangled into common parts and specific parts. The common parts represent the transferrable features, which are suitable for domain adaptation with less negative transfer. Conversely, the specific parts characterize the unique style of each individual domain. Based on this, the new concept of feature exchange across domains, which can not only enhance the transferability of common features but also be useful for image style transfer, is introduced. These designs allow us to introduce five types of training objectives to realize the three challenging tasks at the same time. The experimental results show that our architecture can be adaptive well to full transfer learning and partial transfer learning upon a well-learned disentangled representation. Besides, the trained network also demonstrates high potential to generate style-transferred images.
Fast Triangle Counting on GPU

Triangle counting is one of the most basic graph applications to solve many real-world problems in a wide variety of domains. Exploring the massive parallelism of the Graphics Processing Unit (GPU) to accelerate the triangle counting is prevail. We identify that the stat-of-the-art GPU-based studies that focus on improving the load balancing still exhibit inherently a large number of random accesses in degrading the performance. In this paper, we design a prefetching scheme that buffers the neighbor list of the processed vertex in advance in the fast shared memory to avoid high latency of random global memory access. Also, we adopt the degree-based graph reordering technique and design a simple heuristic to evenly distribute the workload. Compared to the state-of-the-art HEPC Graph Challenge Champion in the last year, we advance to improve the performance of triangle counting by up to 5.9× speedup with> 109 TEPS on a single GPU for many large real graphs from graph challenge datasets.
Accelerometer Data Based Cyber-Physical System for Training Intensity Estimation

The correctness of athlete’s behavior can be controlled by health care cyber-physical system containing distributed (mobile) sensors and intelligent data processing. Such cyber-physical systems determine a concrete set of events, such as jumps or falls and identify events parameters, e.g. height and duration. The proposed accelerometer data based cyber-physical system differs from existed ones by an original method for detection of various types of athlete’s behavior. A proposed cyber-physical system contains on three modules: the data acquisition module, the data processing module and the processed data visualization module. A method for jump recognition is based on high frequency accelerometer data. The system is developed using Android Studio, R Studio development environments. The results provided by accelerometer data based cyber-physical system might be used for coaches and doctor in sports medicine for decisions regarding the optimal load in future training sessions. Use cases including different experimental setup shows the efficiency of the proposed system.
Power Efficiency Containers Scheduling Approach Based on Machine Learning Technique for Cloud Computing Environment

Recently, containers have been used extensively in the cloud computing field, and several frameworks have been proposed to schedule containers using a scheduling strategy. The main idea of the different scheduling strategies consist to select the most suitable node, from a set of nodes that forms the cloud platform, to execute each new submitted container. The Spread scheduling strategy, used as the default strategy in the Docker Swarmkit container scheduling framework, consists to select, for each new container, the node with the least number of running containers. In this paper, we propose to improve the Spread strategy by presenting a new container scheduling strategy based on the power consumption of heterogeneous cloud nodes. The novelty of our approach consists to make the best compromise that allows to reduce the global power consumption of an heterogeneous cloud infrastructure. The principle of our strategy is based on learning and scheduling steps which are applied each time a new container is submitted by a user. Our proposed strategy is implemented in Go language inside Docker Swarmkit. Experiments demonstrate the potential of our strategy under different scenarios.
A k-NN Classification based VR User Verification using Eye Movement and Ocular Biomechanics

VR user identification is of utmost importance especially with the increased applications of VR that will include e-payment among other applications that requires a high level of security. Biometric identification through eye movement, has been used previously due to the intrinsic characteristics of eye movement that characterises a person uniquely. In this paper, we propose using eye movement along with extraocular muscle activations in VR user verification. The muscle activations are calculated using an ocular biomechanical model. The k-NN classification results showed approximately 90% accuracy when using a feature set with eye movement parameters (3 joint angles), muscle activations for all 6 muscles along with the VR object position in 3D. The classifier is a biometric VR user verification tool that provides an easy and non-intrusive methods that can be easily integrated in different VR applications that require user verification.
Influence of the Coupling between Two Qubits in an Open Coherent Cavity: Nonclassical Information via Quasi-Probability Distributions

In this paper, we investigate the dynamics of two coupled two-level systems (or qubits) that are resonantly interacting with a microwave cavity. We examine the effects of the intrinsic decoherence rate and the coupling between the two qubits on the non-classicality of different system partitions via quasi-probability functions. New definitions for the partial Q-function and its Wehrl entropy are used to investigate the information and the quantum coherence of the phase space. The amount of the quantum coherence and non-classicality can be appropriately tuned by suitably adopting the rates of the intrinsic-decoherence and the coupling between the two qubits. The intrinsic decoherence has a pronounced effect on the negativity and the positivity of the Wigner function. The coupling between the two qubits can control the negativity and positivity of the quasi-probability functions.
Muslin: A QoE‐aware CDN resources provisioning and advertising system for cost‐efficient multisource live streaming

Summary Delivering video content with a high and fairly shared quality of experience is a challenging task in view of the drastic video traffic increase forecasts, as live video traffic will grow 15‐fold by 2022. Currently, content delivery networks provide numerous servers hosting replicas of the video content, and consuming clients are redirected to the closest server. Then, the video content is streamed using adaptive streaming solutions. However, servers and network links often become overloaded during major events, and users may experience a poor or unfairly distributed quality of experience, unless more servers are provisioned. In this paper, we propose Muslin , a streaming solution supporting a high, fairly shared end users' quality of experience for live streaming, while minimizing the required content delivery platform scale. Muslin leverages on MS‐Stream, a content delivery solution, which aggregates video content from multiple servers to offer a high quality of experience for its users. Muslin dynamically provisions servers and replicates content into servers and advertises servers to clients based on real‐time delivery conditions. We have used Muslin to replay a 1‐day video‐games event, with hundreds of clients and several test beds. Our results show that our approach outperforms traditional content delivery schemes by increasing the fairness and quality of experience at the user side with a smaller infrastructure scale.
A Face Recognition Method Based on CNN

Abstract The traditional face recognition technology is more complicated for the extraction of facial image features and the selection of classifiers, and the recognition rate is not high. With the continuous maturity of the convolutional neural network from handwritten digit recognition to face recognition, A face recognition algorithm that tests CNN using the Python+Keras framework. The method mainly involves two aspects. One is to observe the influence on the network by changing the number of neurons in the hidden layer; the other is to observe the influence on the network by changing the number of feature maps of the convolutional layer 1 and the convolutional layer 2. The best CNN model is 36-76-1024 through multiple sets of experimental tests. The model can automatically extract facial image features and classify them. Using adam optimizer and softmax classifier for face recognition can make training faster convergence and more. Effectively improve accuracy and use the Dropout method to avoid overfitting. The experimental results show that the recognition rate of the CNN model on the olivettifaces face database is 97.5%. When the optimal CNN model is used, the average recognition rate is close to 100%, which verifies the validity and accuracy of the algorithm and model.
Online Sequential Extreme Learning Machine Algorithm for Better Prediction of the Real-time Electricity Price under Dynamic Environmental Changes

Spot price forecasting plays a key element in the electricity market. However, such forecasting usually depends on the traditional offline batch learning technologies, which cannot respond to new unexpected changes in the local power system environment. Further, the local price can be affected by dynamic price changes from the connected regions. This paper proposes a novel online learning technology to take into account the above factors to supplement the traditional technologies. It can continuously monitor any potential unexpected events and the price fluctuation from the other connected regions. When the forecasted price exceeds a threshold value due to the unexpected changes, the online learning can be activated and can utilize the recent real-time price data to improve the trained result. The proposed method is validated using a numerical simulation on real market data, and the results show that the proposed method can help in improving the forecast accuracy, especially when unexpected changes occur both locally in the surrounding area.
Detecting a Business Anomaly Based on QoS Benchmarks of Resource-service Chains for Collaborative Tasks in the IoT

In the Internet of Things (IoT), the online performance of many online services is determined by their distribution resources, which are connected to many different devices. The expected performance of a resource service primarily depends on the optimal use of the service in satisfying end-to-end quality requirements to support its successful execution. Therefore, the performance of a resource service is dynamic and should be discovered as a benchmark to detect a performance anomaly online. A performance anomaly is referred to as a business anomaly because it depends on its usage. The performance is measured by the quality of service (QoS) that is possessed by a resource service. In this paper, an approach based on the resource service QoS is proposed to detect a business anomaly via mining business process data in collaborative tasks in the IoT. First, a resource-service chain (RSC) is considered to be an analysis object because resource services are employed as a “service flow” by a business process. The similarity between any two RSCs is measured according to the QoS indicator values of resource services. Based on the similarity, a clustering algorithm is presented to resolve clustering centers that are considered to be QoS benchmarks. Second, according to the QoS benchmarks of RSCs, the thresholds of QoS indicators of a business anomaly are determined. Third, an algorithm is presented to detect anomalies of the business process. Finally, the proposed approach is illustrated by a simulation experiment. The experimental results show that the approach can be used to effectively detect a business anomaly online.
Tackling the Trade-Off Between Information Processing Capacity and Rate in Delay-Based Reservoir Computers

We study the role of the system response time in the computational capacity of delay-based reservoir computers. Photonic hardware implementation of these systems offers high processing speed. However, delay-based reservoir computers have a trade-off between computational capacity and processing speed %an intrinsic speed limitation due to the non-zero response time of the nonlinear node. The reservoir state is obtained from the sampled output of the nonlinear node. We show that the computational capacity is degraded when the sampling output rate is higher than the inverse of the system response time. We find that the computational capacity depends not only on the sampling output rate but also on the misalignment between the delay time of the nonlinear node and the data injection time. We show that the capacity degradation due to the high sampling output rate can be reduced when the delay time is greater than the data injection time. We find that this mismatch gives an improvement of the performance of delay-based reservoir computers for several benchmarking tasks. Our results show that the processing speed of delay-based reservoir computers can be increased while keeping a good computational capacity by using a mismatch between delay and data injection times. It is also shown that computational capacity for high sampling output rates can be further increased by using an extra feedback line and delay times greater than the data injection time.
Hybrid consensus‐based distributed pseudomeasurement information filter for small UAVs tracking in wireless sensor network

In this study, a novel state estimation method is proposed for small unmanned aerial vehicles (UAVs) tracking in passive sensors (PSs) wireless sensor network (WSN), where the measurement equations are highly non-linear. Different from many non-linear function approximation methods, the non-linear measurement functions are recast into a linear form with respect to UAV states. The centralised 3D pseudomeasurement information filter (PMIF) is derived by embedding the linear form measurements into conventional information filtering schemes. For distributed state estimation in a not fully connected WSN, a hybrid consensus-based distributed PMF (DHCPMIF) is developed in which each PS independently calculates local contribution by using its own pseudomeasurement. To guarantee bounded estimation error as close as possible to that obtained by the optimal centralised estimator, the hybrid consensus method is developed by combining consensus on measurement and consensus on information method, which retains the advantages of both approaches, thus providing estimation accuracy improvement when the number of consensus iterations is moderate. The effectiveness of the proposed distributed filter is verified via two simulation examples involving tracking a UAV using four and eight PSs, respectively.
Using Deep Learning and Smartphone for Automatic Detection of Fall and Daily Activities

The rapid growth of elderly population makes the health of the elderly one of the major social concerns. The elderly is often facing with several physical and mental healthcare related problems, among those, instance of fall and injuries ranked at the top. If people fall unexpectedly and without timely assistance, it is easy to cause irreparable harm. Therefore, how to automatically detect fall and alert for care/attention using advanced assisted technologies is a hot area of research. In this paper, we examine six machine learning-based methods and propose and carefully configure two novel deep learning-based architectures for fall detection. We compare the relative performance of these methods using an open source dataset, MobiAct, which was collected with four simulated fall types and nine daily living activities using smartphones. Our experimental results show that the proposed long short-term memory (LSTM) deep learning model is quite effective for the fall detection classification; its accuracy reaches 98.83%, the specificity is 99.38%, the sensitivity is 90.57% and the F1 score is 90.33%. These results are better than existing machine learning methods in all types of fall and most of daily activities.