Reviewerly/exHarmony huggingface dataset:
Scanning the Voice of Your Fingerprint With Everyday Surfaces

Due to the premise of uniqueness and acceptance, fingerprint has been the most adopted biometric technologies in high-impact applications (e.g., smartphone security, monetary transactions and international-border verification). Although there are an array of commercial fingerprint scanners across different sensing modalities including optical, capacitive, thermal and ultrasonic, existing fingerprint technologies are vulnerable to spoofing attacks via fake-finger in Kang <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">et al.</i> , 2003. In this paper, we investigate a new dimension of fingerprint sensing based on the friction-excited sonic wave (in simpler words, ”voice of fingerprint”) from a user swiping his fingertip on everyday surfaces. Specifically, we develop <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SonicPrint</i> to leverage the intrinsic fingerprint ridge information in sonic wave for user identification. First, the complex ambient noise is isolated from the sonic wave using background isolation and adaptive segmentation models. Afterward, a series of multi-level friction descriptors that highlight the target fingerprint information is extracted. These descriptors are fed to a specially designed ensemble classifier for user identification. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SonicPrint</i> is practical as it leverages in-built microphones in smart devices, requiring no hardware modifications. As the first exploratory study, our experimental results with 31 participants over three different swipe actions on 12 different types of materials show up to a 98 percent identification accuracy.
Infrared Camera Assisted UAV Autonomous Control via Deep Reinforcement Learning

In this paper, we study the problem of implementing a deep reinforcement learning method that controls a physical UAV to navigate from a random initial static state to a desired destination. Different from most existing reinforcement learning applications on UAV control, which heavily depend on software simulators, we directly conduct the training process on a physical UAV. To address the challenges in training with physical UAVs, \textit{i.e.}, resource limitation of on-board chips and the data hungry issue of reinforcement learning, we propose a deep reinforcement learning control framework, and propose a reward engineering approach in the context of the considered task. We also present some basic procedures to build an indoor real drone testing environment with an OptiTrack system. Finally, the testing results of the deep reinforcement learning are presented to demonstrate the performance of the proposed framework.
Potential Fields-Aided Motion Planning for Quadcopters in Three-Dimensional Dynamic Environments

With the increasing use of unmanned aerial vehicles (UAVs), the safe operation and navigation of a UAV need to be guaranteed, and this requires a collision avoidance (CA) mechanism for UAVs. The artificial potential field (APF), a widely used CA approach, has some issues like local minima and infeasible trajectory. This paper proposes a novel approach to overcome those drawbacks by combining motion primitives (MP) and the APF. In fact, the MP generates a locally optimal and dynamically feasible trajectory for the given time duration. When the collision checker detects the risk of collision at sample points extracted from the planned trajectory, the best route among re-planned safe path candidates is selected using the APF. It is shown that the proposed approach provides smooth and feasible trajectories without any collision in three different scenarios.
Data Quality Measures and Efficient Evaluation Algorithms for Large-Scale High-Dimensional Data

Machine learning has been proven to be effective in various application areas, such as object and speech recognition on mobile systems. Since a critical key to machine learning success is the availability of large training data, many datasets are being disclosed and published online. From a data consumer or manager point of view, measuring data quality is an important first step in the learning process. We need to determine which datasets to use, update, and maintain. However, not many practical ways to measure data quality are available today, especially when it comes to large-scale high-dimensional data, such as images and videos. This paper proposes two data quality measures that can compute class separability and in-class variability, the two important aspects of data quality, for a given dataset. Classical data quality measures tend to focus only on class separability; however, we suggest that in-class variability is another important data quality factor. We provide efficient algorithms to compute our quality measures based on random projections and bootstrapping with statistical benefits on large-scale high-dimensional data. In experiments, we show that our measures are compatible with classical measures on small-scale data and can be computed much more efficiently on large-scale high-dimensional datasets.
Hand Pose Estimation in the Task of Egocentric Actions

In this article we tackle the problem of hand pose estimation when the hand is interacting with various objects from egocentric viewpoint. This entails a frequent occlusion of parts of the hand by the object and also self-occlusions of the hand. We use a Voxel-to-Voxel approach to obtain hypotheses of the hand joint locations, ensemble the hypotheses and use several post-processing strategies to improve on the results. We utilize models of prior hand pose in the form of Truncated Singular Value Decomposition (SVD) and the temporal context to produce refined hand joint locations. We present an ablation study of the methods to show the influence of individual features of the post-processing. With our method we were able to constitute state-of-the-art results on the HANDS19 Challenge: Task 2 - Depth-Based 3D Hand Pose Estimation while Interacting with Objects, with precision on unseen test data of 33.09 mm.
Phase-unwrapping method based on local polynomial models and a maximum a posteriori model correction

Recently, a theory on local polynomial approximations for phase-unwrapping algorithms, considering a state space analysis, has been proposed in Appl. Opt. 56 , 29 ( 2017 ) APOPAI 0003-6935 10.1364/AO.56.000029 . Although this work is a suitable methodology to deal with relatively low signal to noise ratios observed in the wrapped phase, the methodology has been developed only for local-polynomial phase models of order 1. The resultant proposal is an interesting Kalman filter approach for estimating the coefficient or state vectors of these local plane models. Thus, motivated by this approach and simple Bayesian theory, and considering our previous research on local polynomial models up to the third order [ Appl. Opt. 58 , 436 ( 2019 ) APOPAI 0003-6935 10.1364/AO.58.000436 ], we propose an equivalent methodology based on a simple maximum a posteriori estimation, but considering a different state space: difference vectors of coefficients for the current high-order polynomial models. Specific estimations of the covariance matrices for difference vectors, as well as noise covariance matrices involved with the correct estimation of coefficient vectors, are proposed and reconstructions with synthetic and real data are provided.
Dimensionality Reduction for Multi-Fidelity Gaussian Processes using Bayesian Adaptation

Gaussian Process (GP) meta-modeling techniques in engineering sciences have been extensively used within the small-data regime, however, numerical challenges emerge and their predictive capabilities deteriorate when the number of training data points and the dimension of the input design space increases. Such behavior is known to persist even when multi-fidelity GP formulations are being used where data becomes available to the experimenter from computer simulators of varying levels of accuracy. In this paper, we present a Bayesian framework for building computer surrogates, that rely on exploring an intermediate low dimensional design space, constructed as a linear projection of the original (typically high-dimensional) model parameter space. TheGPis then constructed as a mapping fromthe lowdimensional latent variables to the model output. The methodology consists of discovering the projection mapping that transforms the high dimensional input space to the lower dimensional latent space and training the GP model parameters. Both procedures are carried out using Markov Chain Monte Carlo (MCMC) algorithms in order to sample from the corresponding posterior parameter distributions. In addition, our proposed methodology is generalized for multi-fidelity settings where the observed data from high fidelity experiments is augmented by low fidelity simulations and the latent-variable-to-output-mapping is described by an autoregressive model that is decomposed into a low fidelity mapping and a discrepancy term, both modeled as Gaussian Processes.
A study of SLIDE Algorithm: Revolutionary AI Algorithm that Speeds Up Deep Learning on CPUs

Deep learning is the emerging technology, having endless applications and heavy investment from industries. Deep learning constructs a model from many simple computational layers stacked up, resulting in a deep network. Deep networks empower many applications in different domains.Modern advancement in deep learning have made the use of large volume of data, and problem sizes keep increasing, data sizes grow, models use more features. However, specialized hardware's are costly and challenging to generalize majority of tasks. It is becoming more challenging for training giant Neural Networks with millions of parameters and managing high speed computational power to obtain state-of-the-art accuracy.In the era of Deep Learning, it is widely accepted that for training models, Graphics Processing Units (GPUs) are chosen over Central Processing Units (CPUs) because of powerful speed of processing. But it deals with increase in computational cost and time.In this paper cost effective alternative to train Neural network on CPU has been discussed. SLIDE (Sub-Linear Deep Learning Engine) developed by Rice University Computer Scientists provides significant performance boosts and cost savings with the help of data and model parallelism and workload optimization. SLIDE is adequate to perform its machine learning algorithms using general purpose CPUs, thus eliminating the need of any specialized graphics hardware.
Open code biometric tap pad for smartphones

Poor security practices among smartphone users, such as the use of simple, easily guessed passcodes for logins, are a result of the effort required to memorize stronger ones. In this paper, we devise a concept of “open code” biometric tap pad to authenticate smartphone users, which eliminates the need of memorizing secret codes. A biometric tap pad consists of a grid of buttons each labeled with a unique digit. The user attempting to log into the phone will tap these buttons in a given sequence. He/she will not memorize this tap sequence. Instead, the sequence will be displayed on the screen. The focus here is how the user types the sequence. This typing behavior is used for authentication. An open code biometric tap pad has several advantages, such as (1) users do not need to memorize passcodes, (2) manufacturers do not need to include extra sensors, and (3) onlookers have no chance to practice shoulder-surfing. We designed three tap pads and incorporated them into an Android app. We evaluated the performance of these tap pads by experimenting with three sequence styles and five different fingers: two thumbs, two index fingers, and the “usual” finger. We collected data from 33 participants over two weeks. We tested three machine learning algorithms: Support Vector Machine, Artificial Neural Network, and Random Forest. Experimental results show significant promise of open code biometric tap pads as a solution to the problem of weak smartphone security practices used by a large segment of the population.
Trailer Hitch Assist: Lightweight Solutions for Automatically Reversing to a Trailer

This paper proposes a driver assistance function for positioning a vehicle's hitch underneath the center of a trailer's coupler. Due to a rapid development-to-production cycle, the problem was constrained to use basic ECUs, which have limited performance for real-time implementation. Based on this, we developed two practical solutions to automate the hitching process by using efficient methods that do not require any additional high-end sensors, but rely only on the vehicle's existing rear-view camera and other existing sensors.
Decentralized Fusion of Radar and Lidar Data for Tracking Extended Objects

Object tracking is a crucial task for autonomous systems, which require precise estimation of their surroundings to support decision making, planning, and control. High resolution sensors such as radar and lidar are frequently used in autonomous systems to assist in estimation of the surroundings. This estimation includes not only the kinematics of the objects such as position and velocity, but also their physical attributes such as dimensions and orientation. In this work, we propose a framework to ease the challenge of estimating extended objects by fusing radar data and lidar data. In this framework, the radar data is processed by an extended object tracker, which can process multiple measurements per object. The lidar data is preprocessed to extract bounding boxes and the bounding boxes are tracked using a conventional “point target” tracker. Further, we propose to fuse tracks of extended objects from each system using a decentralized fusion scheme. Our approach is evaluated using simulated sensor data in the context of autonomous driving in highway environments. Simulation results show that fusing extended object tracks from radar and lidar results in better tracking performance as compared to each individual sensor.
Review on Speech Recognition System for Disabled People Using Automatic Speech Recognition (ASR)

The following topics are dealt with: Internet; learning (artificial intelligence); social networking (online); diseases; remote sensing; face recognition; feature extraction; security of data; Internet of Things; text analysis.
A Table Look-Up Based Ternary Neural Network Processor

Matrix calculations dominate deep neural network (DNN) operations. Edge devices with applications in computer vision and embedded processors tend to be slower, and therefore, they are not suitable for real-time performance. Recently, dedicated processors have been developed, most of which have product-sum operation circuits. However, the area of the arithmetic circuit is large. In this paper, we propose an inference processor with a table look-up method. By storing the output of the DNN in a table in advance, an arithmetic circuit can be eliminated. We introduce ternary weights that can be realized with a small amount of memory and aid in reduction of zero weights. We show the training algorithm for the ternary weight DNN and the memory storage method by functional decomposition. The overall architecture for a sparse weight is described. To elucidate the superiority of ternary neural networks, we have compared binarization and ternarization. Experimental results show that ternarization reduced memory usage by 92% compared to binarization, while the recognition accuracy slightly improved. Thorough comparison also revealed that our proposed look-up based processor is 41 times faster than an Arduino embedded processor.
Movie Genre Filtering for Automated Parental Control

With cloud robotics, particularly robotic vision available wi-thin a household, human are able to live a convenient and safer life in an ambient assisted living environment. Recent advances in computational intelligence including neural network improves the computational capability of the robotic vision to better understand the environment. Recently, internet hoaxes that affected the social community greatly have raised strong awareness among public in parental control and the content that the youngster can view. Therefore, this paper focuses on filtering movies or videos that is not suitable for youngster by attempting to identify movie genre. Movie genre classification has been investigated in recent years, but there exist noise in normal videos referred as generic frames, as mentioned in [1], that makes differentiation movies with similar frame difficult. A filtering approach is proposed in this paper in order to identify generic frames within the video and discard them from genre classification process, in order to improve genre classification performance. Experiment shows that the filtering approach are able to improve action genre class, but have difficulties and improving other genre classes.
Optimising Cloud Servers Utilisation Based on Locust-Inspired Algorithm

The efficient management of resource sharing plays a crucial role in the cloud execution environment. Cloud computing presents this contribution by providing solid infrastructure services on a pay-as-you-go basis with assuring service availability and high service quality to meet the user needs. Cloud services are provided through various data centres that are separated around the world whereas the users have been served throughout these data centres which are represented as resources; to efficiently utilise these resources, the virtualisation is introduced as Virtual Machines (VMs). Therefore, resource allocation plays a vital role in utilising data centres perfectly. At the same time, the VMs migration from physical machine to another could attain maximum utilisation of the physical machine (server). Hence, the previous inspired algorithms could not exploit the servers efficiently. This paper introduces Locust-Inspired optimisation Algorithm (LIOA) which is an evolutionary optimisation algorithm that is used to find an optimised solution to utilise the servers efficiently. Comparing to the previous VMs migration and allocation algorithms such as Static Threshold with Minimum Migration Time (THR-MMT) and Locust-inspired scheduling Algorithm to reduce Cloud computing consumed Energy (LACE). Our algorithm presented a high probability to find the global best solution. To do so, the proposed algorithm was evaluated with the CloudSim toolkit within a real data-set. This work was compared with an existing heuristic algorithm (THR-MMT) and metaheuristic (LACE) in which LIOA outperforms the algorithms by significant enhancements in the server utilisation.
A robust and efficient framework for tubular structure segmentation in chest CT images

BACKGROUND: Tubular structure segmentation in chest CT images can reduce false positives (FPs) dramatically and improve the performance of nodules malignancy levels classification. OBJECTIVE: In this study, we present a framework that can segment the pulmonary tubular structure regions robustly and efficiently. METHODS: Firstly, we formulate a global tubular structure identification model based on Frangi filter. The model can recognize irregular vascular structures including bifurcation, small vessel, and junction, robustly and sensitively in 2D images. In addition, to segment the vessels from JVN, we design a local tubular structure identification model with a sliding window. Finally, we propose a multi-view voxel discriminating scheme on the basis of the previous two models. This scheme reduces the computational complexity of obtaining high entropy spatial tubular structure information. RESULTS: Experimental results have shown that the proposed framework achieves TPR of 85.79%, FPR of 24.83%, and ACC of 84.47% with the average elapsed time of 162.9 seconds. CONCLUSIONS: The framework provides an automated approach for effectively segmenting tubular structure from the chest CT images.
IPsec Cryptography for Data Packets Security within VPN Tunneling Networks Communications

The internet today allows us to communicate with anyone from anywhere in the world. This kind of access is available to legitimate as well as illegitimate users. Therefore, data security is required to be updated from time to time to prevent hacking attacks. To access all data with a high level of security, along with obtaining an excellent quality of service, Virtual Private Network (VPN) is considered as one of the optimum solutions. In this paper, we introduce a faster way and more confidential encryption method to packet payloads for tunnel networks, based on public-key generation by using a linear feedback shift register. In this mechanism, parallel processing of internet protocol security (IPsec) for the packets is achieved. The proposed method is considered as an approach to improve the handling of IPsec by using one of its efficient members called Encapsulating Security Payload (ESP) protocol. The latter shows superiority over other protocols utilized for the same purposes as the Secure Sockets Layer (SSL). Furthermore, it is found that the data flow is increased by using IPsec type Authentication Header (AH) protocol. Additionally, this proposed technique can satisfy a significant reduction in the delay caused by encryption by at least 15%, along with increasing the efficiency and the throughput on the Internet. Moreover, the proposed cryptography enables the development of special processors for encryption with Field Programmable Gate Array (FPGA) chips that can be available in the form of devices attached to networks.
Electric Power Equipment Image Recognition Based on Deep Forest Learning Model with Few Samples

Abstract Power system equipment have many characteristics, such as many kinds, single color, similar appearance and complex environment of transmission and substation. These characteristics make it difficult for traditional image recognition technology to achieve good recognition results. If there are few training data samples, the performance of in-depth learning recognition declines sharply. To solve these problems, this paper designs an optimization and training recognition method based on the combination of generating confrontation network and deep forest. Firstly, the generated countermeasure network is used to expand the samples of power equipment, and then the active learning method is used to optimize the generated samples. Secondly, the traditional model-enhanced sample expansion method is used to expand the optimized samples twice. Finally, the robust network model is obtained by using the deep forest method to accurately identify the power equipment. Experiments show that this method has high recognition accuracy and is superior to many other algorithms.