Reviewerly/exHarmony huggingface dataset:
Enhancement of pure-pursuit path-tracking algorithm with multi-goal selection

In this paper, we present an enhancement to the popular pure-pursuit algorithm, widely used in robotics and automotive applications. The original algorithm is simple and straightforward as it depends only on the very basic attributes of the kinematic model of the target mechanical system. The algorithm is usually tuned by choosing a look-ahead distance of points from the reference trajectory. On the other hand, pure-pursuit suffers from considerable tuning weaknesses highly attributed to improper selection of look-ahead distance, resulting in poor tracking performance. Our method proposes the dynamic change of lookahead distance based on selecting multiple-goal points, thus aiming a curvature fitting more the reference trajectory. Our work was motivated by an ongoing robotics and autonomous vehicle research project at our university.
Learning Fine-Grained Similarity Matching Networks for Visual Tracking

Recently, siamese trackers have been increasingly popular in visual tracking community. Despite great success, it is still difficult to perform robust tracking in various challenging scenarios. In this paper, we propose a novel similarity matching network, that effectively extracts fine-grained semantic features by adding a Classification branch and a Category-Aware module into the classical Siamese framework (CCASiam). More specifically, the supervision module can fully utilize the class information to obtain a loss for classification and the whole network performs tracking loss, so that the network can extract more discriminative features for each specific target. During online tracking, the classification branch is removed and the category-aware module is designed to guide the selection of target-active features using a ridge regression network, which avoids unnecessary calculations and over-fitting. Furthermore, we introduce different types of attention mechanisms to selectively emphasize important semantic information. Due to the fine-grained and category-aware features, CCASiam can perform high performance tracking efficiently. Extensive experimental results on several tracking benchmarks, show that the proposed tracker obtains the state-of-the-art performance with a real-time speed.
Object Detection and Localization Based on Binocular Vision for Autonomous Vehicles

Abstract Environment perception based on vision plays an important role in autonomous driving technology. Although vision perception has achieved certain results in recent years, many methods can not solve the contradiction between speed and precision. In this paper, we propose a system for fast and accurate object detection and localization based on binocular vision. For object detection, a neural network model based on YOLOv3 is proposed. Specifically, MobileNet is employed in the backbone of YOLOv3 to improve the speed of feature extraction. Then the corresponding ORB feature points are extracted from continuous stereo images which take from the binocular cameras on the moving car. Thus, the disparity of each ORB feature point is calculated. After that, we use the result of object detection to screen the ORB feature points. Finally, the depth of the targets in the traffic scene can be estimated. Experiments on the KITTI dataset show the efficacy of our system, as well as the accuracy and robustness of our object localization relative to ground truth and prior works.
Philosophical Aspects of the Development of Technique and the Destiny of a Human

Purpose: The purpose of the article is to consider two interrelated problems: the development of technology and its impact on human destiny. In this light, it reveals the aspect of conditionality of the development of modern technology achievements in psychology and philosophy, and analyzes the role of technology as a means of increasing freedom of society from the blind forces of nature. Also, it reveals methods of the resolution of contradictions found in the evolution of technique, alongside socio-political ways of dealing with negative consequences of technological development. Design/methodology/approach: The evolution of technology is considered in the philosophical aspect. The study uses the method of genetic derivation, the principles of system analysis, and the unity of historical and logical and other methods of philosophical and epistemological knowledge of the phenomena of social development. Findings: The article indicates a huge gap between the creators of new technologies and their practical users, which causes man-made crises. The development of technology is a materialized form of evolution of the noosphere. But the evolution of the “machine system” causes new forms of labor exploitation. The authors proceed from the thesis that the core of the development of modern technology is the creation of artificial intelligence and justify the position that its evolution is due not only to discoveries in the field of natural science, but also to achievements in the field of psychology, philosophy, i.e., a complex of cognitive Sciences. Originality/value: The results of the study allowed the authors to draw the following conclusions: 1) further development of the technology through improving the abilities of such neural networks, as the “obvious answer”, “classification images and accuracy of decisions”, “formulation and solution of antinomies”, etc. requires the use of the achievements of the historical development of psychology, philosophy of cognition and thinking; 2) the development of technology requires political and moral responsibility of both inventors and consumers of new technological inventions, control by civil society institutions, which can contribute to the prevention of man-made and environmental crises.
A method for calibrating forward scatter meters indoors

In this study, we developed a method for calibrating forward scatter meters (FSMs) against transmissometers for measuring visibility under laboratory conditions, and we designed and constructed a novel fog chamber for calibrating FSMs indoors. The fog chamber was used to simulate a controlled visibility environment, and a transmissometer was installed on both ends of the fog chamber as the standard. The developed method involved a comparison of the FSMs and transmissometer in a group of fog events. FSMs were installed on both sides of the optical path between the transmitter and receiver of the transmissometer. The chamber door was closed, and, after creating fog to simulate low-visibility conditions, measurements were collected from the FSMs and transmissometer every minute while the fog dissipated very slowly. The measurement errors of the FSM could then be calculated. To evaluate this method, tests were conducted in the fog chamber, and the results show that the fog chamber could simulate visibility from 10 m to 10 000 m, while the FSMs could only be calibrated below a visibility of 1500 m due to the baseline of the transmissometer. The fog homogeneity was controlled within 1.2% or less, and the stability was limited to 5% per 30 min. These parameters are reasonably homogeneous and stable for FSM—transmissometer comparison. Three PWD22 FSMs were calibrated using this method, and the calibration uncertainties were estimated to be in the range of 7.8% to 9.2%.
A Study on the Amplitude Comparison Monopulse Algorithm

This paper presents two amplitude comparison monopulse algorithms and their covariance prediction equation. The proposed algorithms are based on the iterated least-squares estimation method and include the conventional monopulse algorithm as a special case. The proposed covariance equation is simple but predicts RMS errors very accurately. This equation quantitatively states estimation accuracy in terms of major parameters of amplitude comparison monopulse radar, and is also applicable to the conventional monopulse algorithm. The proposed algorithms and covariance prediction equations are validated by the numerical simulations with 100,000 Monte Carlo runs.
On Improving The Computing Capacity of Dynamical Systems

Abstract Reservoir Computing has emerged as a practical approach for solving temporal pattern recognition problems. The procedure of preparing the system for pattern recognition is simple, provided that the dynamical system (reservoir) used for computation is complex enough. However, to achieve a sufficient reservoir complexity, one has to use many interacting elements. We propose a novel method to reduce the number of reservoir elements without reducing the computing capacity of the device. It is shown that if an auxiliary input channel can be engineered, the drive, advantageous correlations between the signal one wishes to analyse and the state of the reservoir can emerge, increasing the intelligence of the system. The method has been illustrated on the problem of electrocardiogram (ECG) signal classification. By using a reservoir with only one element, and an optimised drive, more than 93% of the signals have been correctly labelled.
Detecting Small Scale Pedestrians and Anthropomorphic Negative Samples Based on Light-Field Imaging

Great progress has been made in the field of pedestrian detection, but the following two problems have not yet been well addressed. One problem is the missed detection of small scale pedestrain as false negative failure, and the other one is confusion with anthropomorphic negative samples like vertical structures as false positive failure. In this paper, to tackle the above two problems, we use the light-field camera to capture pedestrian images for the following reasons: (i) the light-field camera can obtain multi-depth refocused images in a single exposure using one sensor, (ii) compared with 2D images, these refocused images can provide different key representations for different parts of the image. We further establish a light-field pedestrian dataset with 1766 images for pedestrian detection. A multi-focus detection network proposed in this work consists of multiple-branch detection models and takes multiple refocused images as inputs. In order to select the appropriate candidate proposal bounding box as final detection results, we design a cumulative probability selection (CPS) layer to combine each refocused image branch and accumulate the probability of each candidate neighboring proposal. Experimental results demonstrate that the proposed method outperforms state-of-the-art methods on our light-field pedestrian dataset.
Generalization performance of deep autoencoder kernels for identification of abnormalities on electrocardiograms

Modeling of deep learning (DL) structures with many hidden layers and a large number of neurons at each layer for the data analysis process has brought about the requirements for long training time and computation capacity depending on the increase in the number of optimization parameters. This chapter addresses the problem of how to reduce the training time required for DL algorithms by combining theories in deep autoencoder kernels. Existing studies in DL tend to focus on analyzing images to obtain visual arrangement and feature learning. The DL algorithms are used to handle advanced-level image processing including image generation, pose recognition, image captioning, character recognition, and more; and these need a long training process. Most research in the literature covers the application of DL on different patterns. However, little research has addressed the issue of simplifying the mathematical background while maintaining the generalization capacity and robustness of the classifiers. As a result, the aim of this chapter is to provide an overview of how the training time and generalization capability for DL algorithms including deep belief networks (DBNs) and extreme learning machine (ELM) autoencoder kernels. The DL algorithms are used to diagnose coronary artery disease (CAD) on the electrocardiogram (ECG) and fiducial features. The proposed DBN model achieved classification performance rates of 96.93%, 96.03%, and 91.23% for accuracy, sensitivity, and specificity. The best contribution is achieved using an optimized ELM autoencoder among DL algorithms.
SDVADC: Secure Deduplication and Virtual Auditing of Data in Cloud

Over the last few years, deploying data to cloud service for repository is an appealing passion that avoids efforts on significant information sustenance and administration. In distributed repository utilities, deduplication technique is often exploited to minimize the capacity and bandwidth necesseties of amenities by erasing repetitive data and caching only a solitary duplicate of them. Proof-of-Ownership mechanisms authorize any possessor of the identical information to approve to the distributed repository server that he possess the information in a dynamic way. In repository utilities with enormous information, the repository servers may intend to minimize the capacity of cached information, and the customers may want to examine the integrity of their information with a reasonable cost. We propose Secure Deduplication and Virtual Auditing of Data in Cloud (SDVADC) mechanism that realizes integrity auditing and deduplication of information in cloud. The mechanism supports secure deduplication of information and effective virtual auditing of the documents during the download process. In addition, the proposed mechanism lowers the burden of dataowner to audit documents by himself and there is no need to delegate auditing to the Third Party Auditor (TPA). Experimental results demonstrate that the virtual auditing has low auditing time cost relative to the existing public auditing schemes.
Site suitability for photovoltaic energy expansion: A Brazilian's high demand states study case

• São Paulo and Minas Gerais are two of the greatest energy consumers in Brazil. • Photovoltaic farms are proposed to diversify the energy matrix. • Site suitability was assessed through MCE-GIS-AHP. • Criteria considered geospatial, environmental and climatic factors. • Areas with high suitability and over 50 ha cover 1,322,839 ha.
WristLens

WristLens is a system for surface interaction from wrist-worn wearable devices such as smartwatches and fitness trackers. It enables eyes-free, single-handed gestures on surfaces, using an optical motion sensor embedded in a wrist-strap. This allows the user to leverage any proximate surface, including their own body, for input and interaction. An experimental study was conducted to measure the performance of gesture interaction on three different body parts. Our results show that directional gestures are accurately recognized but less so for shape gestures. Finally, we explore the interaction design space enabled by WristLens, and demonstrate novel use cases and applications, such as on-body interaction, bimanual interaction, cursor control and 3D measurement.
Dissecting Convolutional Neural Networks for Efficient Implementation on Constrained Platforms

Deep neural networks, especially Convolutional Neural Networks (CNNs), have driven unprecedented improvements in machine learning tasks in computer vision research. Most research focuses on improving inference accuracy and designing networks that are capable of reaching a target accuracy for a specific application. Networks are typically implemented on relatively unconstrained devices, such as a workstation Graphics Processing Units (GPUs), so the design and optimization techniques do not scale well to low power and resource-constrained applications like mobile, automotive, or Internet of Things (IoT). While efforts have shifted for various acceleration frameworks on constrained platforms such as Field Programmable Gate Arrays (FPGAs) and System-on-Chip (SoC) devices, obtaining the energy-efficient implementations without a significant drop in accuracy remains an art than science. In this paper, we aim to breakdown CNNs and analyze each layer independently and quantify their impact on memory footprint, power consumption, and latency. We analyze these properties of four common Convolutional Neural Network (CNN) architectures: LeNet, AlexNet, VGG-11, and VGG-16. We optimize and implement each network on a low power computing platform suitable for constrained environments. Networks are validated using the MNIST and CIFAR-10 datasets. We show that modifying specific network design parameters such as filter size, the number of fully connected layers, and subsampling techniques have a considerable impact on the overall performance and efficiency, enabling informed trade-offs and optimization. We demonstrate up to 11x reduction in power consumption compared to another FPGA platform with 96x more memory capacity, while maintaining the state-of-art classification accuracy.
RTL3D: real‐time LIDAR‐based 3D object detection with sparse CNN

LIDAR (light detection and ranging) based real‐time 3D perception is crucial for applications such as autonomous driving. However, most of the convolutional neural network (CNN) based methods are time‐consuming and computation‐intensive. These drawbacks are mainly attributed to the highly variable density of LIDAR point cloud and the complexity of their pipelines. To find a balance between speed and accuracy for 3D object detection from LIDAR, authors propose RTL3D, a computationally efficient Real‐time LIDAR‐based 3D detector. In RTL3D, an effective voxel‐wise feature representation is utilised to organise unstructured point cloud. By employing a sparse feature learning network (SFLN) on voxelised 3D data, RTL3D exploits the sparsity of point cloud and down‐samples 3D data into 2D. Basing on the generated 2D feature map, an optimised dense detection network (DDN) is applied to regress the oriented bounding box without relying on any predefined anchor boxes. The authors also introduce an incremental data augmentation approach which greatly improves the performance of RTL3D. Empirical experiments on public KITTI benchmark demonstrate that RTL3D achieves a competitive performance with state‐of‐the‐art works on 3D detection task. Owning to the simplicity of its single‐stage and anchor‐free design, RTL3D has a real‐time inference speed of 40 FPS.
A Mathematical Approach Towards Quantization of Floating Point Weights in Low Power Neural Networks

Neural networks are both compute and memory intensive, and consume significant power while inferencing. Bit reduction of weights is one of the key techniques used to make them power and area efficient without degrading performance. In this paper, we show that inferencing accuracy changes insignificantly even when floating-point weights are represented using 10-bits (lower for certain other neural networks), instead of 32-bits. We have considered a set of 8 neural networks. Further, we propose a mathematical formula for finding the optimum number of bits required to represent the exponent of floating point weights, below which the accuracy drops drastically. We also show that mantissa is highly dependent on the number of layers of a neural network and propose a mathematical proof for the same. Our simulation results show that bit reduction gives better throughput, power efficiency, and area efficiency as compared to those of the models with full precision weights.
PVF-NET: Point &amp; Voxel Fusion 3D Object Detection Framework for Point Cloud

In this paper, we present a novel 3D object detection framework for locating 3D bounding boxes of the target in autonomous driving scenes. Our proposed framework consists of two novel modules, which are twofold proposal fusion module and the RoI deep fusion module. In the former module, we utilized the 3D voxel Sparse Convolution Neural Network (CNN) and PointNet-like network to coarse generate the voxel-based and point-based proposals, where these proposals contain voxel-dense and point-wise features under the raw point cloud. Twofold proposal fusion module integrated those proposals and extended the proposal generalization, thereby dramatically improve the proposals’ recall rate in the first stage for further utilizing in the proposals refinement stage. Given the coarse integrated 3D proposals produced by the twofold proposal module, the RoI deep fusion module is proposed to abstract and aggregate the multi-scale voxel-based feature and the point-wise feature through voxel-aware pooling layer and point-aware pooling layer, respectively. Follow by that, the specific features on the different proposals are integrated via the proposals-aware fusion layer to further enrich the feature dimensionality and utilize the high-quality proposals for the proposals refinement stage to reinforce the prediction of the target bounding boxes. We conduct the experiments on KITTI dataset and evaluate our method on 3D object detection task. Our method achieved 76.79 mAP in moderate difficulty and outperformed many influential object detection models on the KITTI benchmark leaderboard.
Mining High-utility Temporal Patterns on Time Interval–based Data

In this article, we propose a novel temporal pattern mining problem, named high-utility temporal pattern mining , to fulfill the needs of various applications. Different from classical temporal pattern mining aimed at discovering frequent temporal patterns, high-utility temporal pattern mining is to find each temporal pattern whose utility is greater than or equal to the minimum-utility threshold. To facilitate efficient high-utility temporal pattern mining, several extension and pruning strategies are proposed to reduce the search space. Algorithm HUTPMiner is then proposed to efficiently mine high-utility temporal patterns with the aid of the proposed extension and pruning strategies. Experimental results show that HUTPMiner is able to prune a large number of candidates, thereby achieving high mining efficiency.
The Framework to Support the Digital Evidence Handling

Digital evidence has a different meaning from physical evidence, but even though it is different, both are a unity of evidence that supports each other in the investigation process. Unfortunately, laws and regulations generally have not been oriented to the terminology of digital evidence that should be. It becomes a research challenge in how the handling of digital evidence also gets the same treatment as physical evidence. For this reason, technical studies are needed to support the application of law and regulations for digital evidence handling. This article provides a solution in the form of digital evidence cabinets as a framework to support the centralization of digital evidence that following the applicable regulations of procedures for the management of evidence in the territory of Indonesia. This concept can translate the centralization of digital evidence through the analogy of physical cabinet and the interpretation of cabinet, rack, bags, and evidence unit with types of criminals, list of crimes, list of crime scenes and list of digital evidence at one crime scene.