Reviewerly/exHarmony huggingface dataset:
Improved Tree Gradient Coding with Non-uniform Computation Load

The scaling-up process of distributed machine learning systems faces two major bottlenecks – delays due to stragglers and limited communication bandwidth. Gradient Coding (GC) was proposed for mitigating stragglers in distributed learning. A major drawback of the master-worker architecture in GC for distributed learning is the bandwidth contention at the master, which increases as the cluster size increases. Tree Gradient Coding (TGC), in which the workers are arranged in a regular tree topology leads to a reduction in bandwidth contention at the master. In TGC, each node is allocated the same amount of data for computation and the nodes communicate only with its immediate parent. In this paper, an improvement in the completion time of the TGC is achieved by allocating different computation loads to the nodes at different levels. This approach takes into account the communication delay from the nodes in the lower layers to the upper layers. The computation load at each node has been derived taking the communication delay into account. The computation load, thus calculated, is also proven to be optimal. Furthermore, the improvement in completion time is shown by implementing the proposed scheme on Amazon EC2 servers.
On Simplified Models for Dynamics of Pointlike Objects

Motivation: We study the asymptotic-type dynamics of various real pointlike objects that one models by a variety of differential equations.Their response to an external force one defines solely by the trajectory of a single point.Its velocity eventually stops changing after cessation of the external force.The response of their acceleration to the long-term external force is slow and possibly nonlinear.Objective: Our objective is to present technique for making simplified models for the long-term dynamics of pointlike objects whose motion interacts with the surroundings.In the asymptotic-type long-term dynamics, the time variable ( ) , m t t ∈ +∞ and 0 m t > is large, say m t +∞  !Method: We apply Taylor series expansion to differential equations to model the acceleration of pointlike object whose response to the long-term external force is not instantaneous and possibly nonlinear.Results: We make simplified models for the long-term dynamics of pointlike objects by Taylor polynomials in time derivatives of the external force.Application: We interpret the relativistic Lorentz-Abraham-Dirac equation as an equation for modeling the long-term dynamics, where 0 m t t ≥  .This interpretation resolves the conceptual and usage controversy surrounding its troublesome application to determine the trajectory of a radiating charged particle, thus contributing to the development of more adequate modeling of physical phenomena.
Extreme Learning Machine Based on Double Kernel Risk-Sensitive Loss for Cancer Samples Classification

In recent years, Extreme Learning Machine (ELM) has attracted extensive attention in various research fields. To improve the performance of ELM, we propose an Extreme Learning Machine Based on Double Kernel Risk-Sensitive Loss (DKRSLELM) method in this paper. The Kernel Risk-Sensitive Loss (KRSL) is integrated into the objective function of ELM. This is because KRSL not only can effectively reduce the influence of noise and outliers, but also can eliminate the redundant neurons of ELM. In the experiment, we conduct a classification experiment on cancer integration data-sets. The experimental results indicate that our method can effectively improve the classification performance of ELM.
Conditional Temporal Variational AutoEncoder for Action Video Prediction

To synthesize a realistic action sequence based on a single human image, it is crucial to model both motion patterns and diversity in the action video. This paper proposes an Action Conditional Temporal Variational AutoEncoder (ACT-VAE) to improve motion prediction accuracy and capture movement diversity. ACT-VAE predicts pose sequences for an action clip from a single input image. It is implemented as a deep generative model that maintains temporal coherence according to the action category with a novel temporal modeling on latent space. Further, ACT-VAE is a general action sequence prediction framework. When connected with a plug-and-play Pose-to-Image network, ACT-VAE can synthesize image sequences. Extensive experiments bear out our approach can predict accurate pose and synthesize realistic image sequences, surpassing state-of-the-art approaches. Compared to existing methods, ACT-VAE improves model accuracy and preserves diversity.
Method of Reconstructive Reuse of Old Building Interior Space based on Environmental Data Mining

With the rapid development of social economy and the acceleration of urbanization, land resources are gradually scarce, while the population is increasing year by year. All kinds of reasons together directly lead to the increase of people's demand for housing. Although the real estate industry continues to develop in recent years, it is still unable to properly solve this problem. The improvement of material living standard and the change of family structure have aroused people's attention to the spiritual needs, making the upgrading of people's housing consumption demand. The improvement of the level of residential interior design is the key factor to realize this change, so residential interior space design is about to face a huge challenge.
Enabling Scalability of Photonic Frequency-Domain Quantum Processing

Via a reconfigurable photonic frequency circuit, we show spectral bosonic and fermionic Houng-Ou-Mandel interference between independently created pure single photons, demonstrating photon number scalability and versatility of the frequency processing approach.
Delivery Of Data Digital High Frequency Radio Wave Using Advanced Encryption Standard Security Mechanism

Information data exchange is a common thing in the world of information technology. The data sent contains important information data that is very confidential and must be kept safe. An information is said to be safe if it meets three main aspects namely confidentiality, integrity and availability. Of the three aspects can be done by implementing a security system known as cryptography. Based on the research that has been done, it was concluded that during the process of sending and securing data using HF Radio along with the encryption process and the giving of a number password to the file has been successfully carried out well. From observations of four different file types (.txt, docx,.xls. jpg) the following file sizes are obtained.txt gets 565 byte,.docx (text) gets 12,771 byte,.docx (text + image) gets 156,166 byte $(156.2\mathrm{~Kb})$,.xlsx gets 11507 byte $(11.5\mathrm{~Kb})$, and $.jpg$ gets 15382 byte $(15.3\mathrm{~Kb})$. The location of the Experiment sending data is from Jakarta - Merak with a distance of 135 Km. In future research, we propose the development of the perfection of the prototype by taking into account the weather conditions when communicating, and developing Modulator so that the accuracy and speed of data transmission is obtained.
Comments on the Papers

This final chapter includes some comments of mine about the papers in this volume and their connections with my work. I am very grateful to all the authors of these papers for their nice contributions!
Application Research of Network Computer in English Teaching of military Academy

With the rapid development and popularization of network computer technology, how to apply it to military English teaching has become a hot topic for military school teachers. This article briefly introduces the advantages of using network computer technology to assist military English teaching and detailed specific application strategies, hoping to provide some valuable reference suggestions for English teachers to better carry out military academy classroom teaching activities.
An Evolutionary Algorithm Taking Account of Epistasis among Parameters for Black-Box Discrete Optimization

We propose an evolutionary algorithm that takes account of epistasis among parameters for black-box discrete optimization problems. The black-box discrete optimization (BB-DO) is an important problem that appears in various real-world problems such as hyper-parameter optimization of machine learning and is a difficult class of optimization problems to which optimization methods that require derivative of an objective function cannot be applied. In addition, epistasis among parameters, or the dependencies among variables, makes BB-DO problems more difficult. The bayesian optimization algorithm (BOA) has been proposed as a promising method to address epistasis in BB-DO problems. However, BOA suffers from a serious problem. The problem is that the diversity of a population is likely to be lost. Therefore, BOA requires a large population size for optimization. In order to remedy the problem of BOA, we introduce three schemes to maintain the diversity of the population into BOA. In experiments, we use two benchmark problems, a 3-deceptive function and a NK-landscape, and a structural optimization of neural networks to show the effectiveness of the proposed method. The experimental results showed that the proposed method improved the number of evaluations by 31.5% and the population size by 96.9% in a 180-dimensional 3-deceptive function and found comparable or better solutions in all NK-landscape settings compared to BOA. In the structural optimization of neural networks, the proposed method improved the number of evaluations by 21.3% and the population size by 92.3% compared to BOA. In addition, the proposed method was superior to conventional optimization methods used in this field, ASNG-NAS, regularized evolution, reinforcement learning, TPE, and random search, in terms of the evaluation value.
Research on the Application of Immersive Early Childhood Education

Preschool education has aroused extensive attention in recent years. This article researches on immersive preschool education based on virtual reality technology. Design interesting teaching tasks by analyzing the interaction requirements of the system and combining Piaget's theory of children's cognitive development to explore the application of immersive bare-hand natural interaction technology in early childhood education. The paper also settles spatial trajectory recognition to identify spatial number writing by employing multiple hidden markov model regression algorithm. Through comparative investigation, it is finally concluded that the immersive education system of this article has the advantages of strong participation, low learning burden, and strong interest.
Rise and Fall of Tracing Apps

The article highlights the possible reasons for the failure, in Western countries, of so-called ‘tracing apps’, and in particular, the difficulty to transplant a model that has been successful in Far Eastern countries, technical and organizational hitches, but most important the pervasive data protection legislation. It also points out some lessons for the future in eHealth.
Wildlife surveillance using deep learning with YOLOv3 model

Identifying an animal is not so easy task because there are many different types of animals which look alike and classifying each animal is difficult. Which made us to create a model called YOLOv3. When the user is in need to find an animal, this YOLOv3 model helps that user to find the respective animal's name in an easy manner by just looking at the picture that the user has given. This research work has used the darknet algorithm, which has a pretrained dataset in YOLOv3 model. The model itself works on various ways by training and testing of a picture in the dataset. The goal is to use the YOLOv3 model in finding the animal in a smipler method. First, the input image of an animal is given and on the input image itself it gives the output as in, the name of the animal using this YOLOv3 model. The present study discusses about the state of species-specific activitiy by using the form of still image.
UAV embedded system - a selection process

Deep Neural Networks are considered a reference point in many applications, but they come with a higher cost related to the system used resources, i.e., memory and computational power. To build such real-time embedded systems is a challenge and a difficult choice, at least from the point of view of the best-fitted system like the one that should be specifically chosen for the computer vision tasks, to give an example. In this paper, such a task represents the central element of several different tested systems used to detect humans from a quadcopter. In this respect, a comparative study between detection systems implemented with one of the most general embedded platforms (i.e., a Raspberry Pi platform sustained or not by a neural engine Neural Compute Stick 2) and, respectively, with the Jetson Nano, as a dedicated embedded platform, is conducted and the results are discussed. Due to the specific characteristics of each embedded system, due to the particular hardware and software optimizations, different performances were obtained, even for similar detection systems. All these illustrate the impossibility of choosing the optimal system for a certain application only based on the catalog data analysis of both processor and system as a whole. The paper also highlights the advantages and limitations associated with each of the analyzed embedded system, specifically from the perspective of the functionality provided to the final detection system.
A Feature Learning Model Supporting Incremental Learning

In order to solve the problems of slow running speed, low classification accuracy and catastrophic forgetting existing in current incremental learning algorithms, a new feature learning model of big data that supports incremental updating was proposed. The model extracted feature vectors through deep convolutional neural network, combined new samples and typical samples in the incremental training process, and then used regular dual average soft confidence weighting algorithm (RDASCW) for online classification. The classification model uses these eigenvectors to generate prediction results. If the predicted results are inconsistent with the actual results, the classification model parameters will be updated; otherwise, the model parameters will be kept unchanged. Experimental results show that the model can dynamically learn data features and improve classification accuracy on the basis of improving data processing speed.
The Contribution of Form Repetition to Listeners’ Expectation of Givenness in Online Reference Resolution

Although it is clear that unaccented referring expressions are associated with given information in a discourse (Dahan et al., 2002), it is less clear what aspects of givenness are relevant. We examine whether listeners' expectation of givenness depends on repetition of a referring expression or on contextual evocation of a referent. The results from two visual world eye-tracking experiments suggest that for interpretation, listeners associated reduced prominence with a repeated referring expression. Listeners expect previously evoked referents to be candidates for reduced referring expressions only when they are referred to with the exact same referential form. The data also suggest that when referents are referred to with different referential forms across utterances, accenting facilitates linking those forms for co-reference.
High Density Encoding for ARRIRAW Image files

Codex High Density Encoding (HDE) is an encoding and decoding schema for large format ARRIRAW workflows to control the ever-increasing data footprint of RAW data. Studios are demanding 4K and RAW image data to archive and future-proof the negative. High Frame Rate (HFR) and High Dynamic Range (HDR) add to this large format data footprint, leading to significant cost increases as well as storage and transmission challenges. Uncompressed RAW data costs more to transport and store than compressed ProRes files. An uncompressed ARRIRAW image can be reduced with HDE to a file size not much larger than a corresponding ProRes 4444 XQ file. Except with Codex HDE, you can access the original pixel values that were encoded. Codex HDE can be used to compress raw Bayer pattern data (e.g. from a digital image sensor) in such a way that decoded data is identical to the raw data. The encoded files are typically 50-60% of their original size. A wide variety of third-party commercial applications can be used to decode files containing HDE bitstreams for data management, transcoding, color grading, visual effects and archiving. The presentation will describe the structure of data encoded with Codex HDE, along with algorithms for decoding the stored data. It is the intent of the presentation to describe the structure and encoding of all fields in the HDE bitstream, detailing how 3rd party vendors and their applications can decode HDE bitstreams and correctly identify its structure. Recent details on SMPTE RDD51 on HDE will be presented.
Prediction of Neurological Disorders Using Visual Saliency

Neurological disorders are diseases of the central and peripheral nervous system and most commonly affect middle- or old-age people. Accurate classification and early-stage prediction of such disorders are very crucial for prompt diagnosis and treatment. This chapter discusses a new framework that uses image processing techniques for detecting neurological disorders so that clinicians prevent irreversible changes that may occur in the brain. The newly proposed framework ensures reliable and accurate machine learning techniques using visual saliency algorithms to process brain magnetic resonance imaging (MRI). The authors also provide ample hints and dimensions for the researchers interested in using visual saliency features for disease prediction and detection.
The application of symbiotic architecture in the public library in Jakarta, Indonesia

Abstract Changes in people’s habits in seeking information and knowledge have a special impact on people’s behavior when visiting and using the library, this is the problem why recently library visitors have decreased this is because it is easier to access information via electronic devices easier so that it has a direct impact on the library itself. From these problems, this study will discuss how people’s habits and behavior affect the needs of library space in the digital era. The method used by the author is distributing questionnaires to library visitors containing questions about new habits in the digital age, the questionnaire researchers provide options related to new library concepts, namely the application of symbiotic architectural concepts, where libraries have additional functions that are not only a place to find information and knowledge. however, it has additional functions according to the habits of society in the digital age. The results of this study answered that library visitors prefer to do assignments rather than read or look for books and hope that a special room is provided to do the assignment. On the other hand, the symbiotic architectural concept is accepted by visitors with the choice of green open space as another function in the library.
PassFace: Enabling Practical Anti-Spoofing Facial Recognition with Camera Fingerprinting

Facial recognition has become the surge on mobile authentication scenarios and makes up a huge market share for various apps, such as MasterCard, Google Wallet, and AliPay. However, existing solutions suffer from various impersonation attacks, including photo-spoofing attack, video-replay attack, and 3D facial mask attack. State-of-the-art countermeasures either require additional user intervention or introduce specialized high-end sensors. Even introducing these extra efforts, these approaches still hardly defend the latest 3D facial mask attacks, which gradually become accessible due to the prevalence of low-cost 3D printing. In this paper, we propose an anti-spoofing facial recognition system, PassFace, which verifies the smartphone for authentication as the second factor merely using raw facial videos without any user intervention, to defeat impersonation attacks. In particular, when receiving a user’s selfie video, PassFace identifies the user’s face from the video, and meanwhile extracts the highly unique and physically irreproducible camera fingerprint, i.e., Photo Response Non-Uniformity (PRNU), built in the smartphone from key frames of the video. After that, the system compares the Peak to Correlation Energy (PCE) calculated by the estimated PRNU and the reference profile with a threshold for authentication. Experiment results demonstrate PassFace can achieve satisfactory performance in authentication and attack resistance.
A Lightweight Attention Fusion Module for Multi-sensor 3-D Object Detection

With the rapid development of autonomous vehicles, three-dimensional (3D) object detection has become more important, whose purpose is to perceive the size and accurate location of objects in the real world. Many kinds of LiDAR-camera-based 3D object detectors have been developed with two heavy neural networks to extract view-specific features, while a LiDAR-camera-based 3D detector runs very slow about 10 frames per second (FPS). To tackle this issue, this paper first presents an accuracy and efficiency multiple-sensor framework with an early-fusion method to exploit both LiDAR and camera data for fast 3D object detection. Moreover, we also present a lightweight attention fusion module to further improve the performance of our proposed framework. Massive experiments evaluated on the KITTI benchmark suite show that the proposed approach outperforms state-of-the-art LiDAR-camera-based methods on the three classes in 3D performance. Additionally, the proposed model runs at 23 frames per second (FPS), which is almost 2× faster than state-of-the-art fusion methods for LiDAR and camera.
Obstacle Avoidance for Aerial Vehicles in Autonomous Navigation

Obstacle avoidance is the back bone of autonomous navigation as it enables vehicles to reach desired location avoiding hurdles in the path. It is one of the ongoing challenging researches in the arena of cyber physical systems. In this article, comparison of various obstacle avoidance algorithms such as Artificial Potential Field (APF) approach, Vector Field Histogram (VFH) approach, Bubble Band approach, Mounted Sonar approach, Dist-Bug approach, bug-1 and bug-2 approach and Tangent Bug approach has been addressed. The main objective is obstacle avoidance, where obstacle can be in the form of radars or any other type of equipment. A novel obstacle avoidance procedure for low-altitude flying vehicles (Static Obstacle Avoidance) and high-altitude flying vehicle (Dynamic Obstacle Avoidance) has been proposed using A-Star and Deep Q-Network Reinforcement Learning techniques, respectively. Test bed has been created considering object model, sensor model, obstacle environment and controller. Implementation of the same has been done through visualization using Pygame for A-Star approach and Director for Deep Q-Network Reinforcement Learning.
The Development and Challenges of Robot Perception

As mentioned in Section I, the perception capacity of robots is of both theoretical and practical significance. With the rapid development of sensor technologies, robots can be equipped with various types of sensors to acquire the environment information. For robot visual processing problems, it is becoming difficult when the dimension of feature space is getting higher. Fortunately, the manifold learning method is proposed as a promising nonlinear dimensionality reduction (NDR) method for high-dimensional data manifolds.