Reviewerly/exHarmony huggingface dataset:
A Performance of Low-Cost NVIDIA Jetson Nano Embedded System in the Real-Time Siamese Single Object Tracking: A Comparison Study

The tracking of objects is a complex mission computer vision and machine learning (ML). There are several types of objects tracking like tracking one object or more than one. The tracking of one object is applied in video frames as the tracking of more than one object is applied to tracking for multiple objects in the video. Single object tracking is usually implemented using the method of correlation filter-based or of Siamese Network-based. Siamese Network, the state of art method, has an active search area, nowadays, due to its good achievements in accordance with localization real-time and accuracy application. Especially within a new surveillance system that is built on UAV to get unbounded tracking. GPU-based embedded systems give superior performance in comparison with CPU-based systems in the implementation of ML in the terms of speed. In this paper low-cost NVIDIA Jetson nano embedded system performance was evaluated for real-time Siamese single object tracking. 14 Siamese single object tracking algorithms were tested using NVIDIA Jetson nano board. The result shows that the bord gives the best performance with the Lighttrack algorithm with 8.3 frames per second speed. Such performance can be used in real-time tracking applications.
Visual dictionary of human action in vehicular environment using computer vision

Every human behavior and actions can be divided in small sub-actions and attributes. Some of these sub-actions and attributes contain visually semantic meanings to human. We call the ensemble of them as visual dictionary. The visual dictionaries help to create an action like how words from dictionary helps to create sentences. In this work, we demonstrate the effectiveness of the visual dictionary by analyzing driver behavior inside the vehicle. We take the primary driving behavior which includes two hands on the wheel and 56 secondary behaviors that include talking over handheld phone, eating sandwich, drinking from bottle, smoking, reaching for objects, and dancing. Finally, we demonstrate how each of these dictionary elements can be automatically processed from videos using computer vision.
Fast Proximal Policy Optimization

Proximal policy optimization (PPO) is one of the most promising deep reinforcement learning methods and has achieved remarkable success in a variety of challenging control tasks. However, its overall updating gradient of a batch of samples may mislead the optimization of some sub-samples. It potentially reduces the sample efficiency and degrades the final decision performance. Although the minimum operation of PPO can relieve it, its slow escape speed makes it difficult to escape the wrong optimization range within the limited epochs of the minibatch update. In this paper, we propose a novel fast version of PPO named fast-PPO that replaces the original minimum operation with two accelerating operations called linear-pulling and quadratic-pulling, respectively. Both of them can increase the updating weight of the gradient for the misled samples so that the gradient of the overall object follows their expected optimization direction. Extensive experiments on classic discrete control tasks and MuJoCo based continuous control tasks verify the effectiveness of our proposed fast PPO.
Improved Telugu Scene Text Recognition with Thin Plate Spline Transform

Srinivasa Rao, Nandam Negi, AtulScene text recognition is a difficult task because of complex backgrounds, different text orientations, varying lighting conditions and noise introduced by devices used to capture the images. The difficulty increases when the data used to train the model has very few samples like in the case of Telugu scene text recognition. This paper tries to address the issues caused by complex text shapes and the lack of huge training data for Telugu scene text recognition. We apply a thin plate spline transform (TPS) as a preprocessor to text recognizer to handle the complexity caused by the irregular text shapes. The text recognition model is based on the convolutional recurrent network (CRNN)-based model which has been used for various traditional OCR and Telugu scene detection applications. It uses a Resnet-based feature extractor which is much more successful in extracting rich features compared to VGG used in traditional convolutional recurrent network (CRNN) models. The features extracted by Resnet are passed to a bidirectional LSTM, the outputs of which are passed to a final prediction layer which uses a softmax classifier. Connectionist temporal classification (CTC) loss is used as a loss function. Instead of training from scratch, the weights for training Telugu text recognition models are loaded with weights trained on large English scene text datasets (SynthText, MJSynth) to give a good initialization for model weights. We show that above additions increase normalized edit distance of the network by large margin and produce a better scene text recognition framework for Telugu text. The recognizer is able to perform well under complex under text orientations and varying fonts, shapes and highly varying characters present in the Telugu text. We also show that the network achieves better normalized edit distance and faster convergence when loaded with weights trained on English scene text datasets when they are applied on Telugu text data. This emphasizes the use of proper weight initialization and benefits of fine tuning for producing a robust framework for Telugu scene text detection.
An AdaBoost Based - Deep Stochastic Configuration Network

Deep stochastic configuration network (DSCN) is an incremental learning method for large-scale data analysis and processing, which has the advantages of lower human intervention, higher learning efficiency and stronger generalization ability. For improving the stability of DSCN, a deep stochastic configuration network based on AdaBoost is proposed, termed as AdaBoost-DSCN. In our proposed model, the AdaBoost learning approach is adopted, the weights of the base models are adjusted adaptively according to the training results, then the base models are combined to generate a stronger model, which is beneficial to reduce the influence of random parameters on network performance of DSCN. Experimental results on complex function approximation problems and large-scale regression datasets show that AdaBoost-DSCN has higher regression accuracy for large-scale data regression analysis compared with DSCN, AdaBoost-SCN, SCN.
An Improved FairMOT Method for Crowd Tracking and Counting in Subway Passages

Although there is significant progress and wide applications in multi object tracking technology in recent years, the occlusion problem has always been an obstacle, especially in subway systems with dense crowds, high mobility, and narrow spaces. This difficulty greatly affects the accuracy of tracking and identification. Aiming at the problem of crowd occlusion in subway stations, based on the FairMOT [1], we improve the model and apply it to the subway so that it can count the number of people moving in different directions and enrich the data of pedestrian flow. However, FairMOT still cannot solve the occlusion problem in subway scenes because it cannot handle missing pedestrians effectively. Therefore, we propose three methods to solve the occlusion problem, including a new convolution branch, feature matching and handling of missing. Then, we show how to count the number of people moving in different directions. Experimental results demonstrate that our innovation can effectively improve the tracking performance not only in the case of short-distance double cross occlusion but also for long-distance dense crowd occlusion and building occlusion.
Reading Habit in the Internet Era*

The Internet and the digital revolution have deeply and sharply divided users into ‘digital immigrants’ and ‘digital natives’ with linguistic whateverism. The society is more image based than text and chat-based than reading. Reading habit is neither unambiguously definable nor accurately measurable. Proxy measures like book-buying, visiting, and borrowing from library, citations and downloads, self-reporting opinion is used to assess reading habit. Aliteracy is a predominant characteristic of the Internet era. The digital technology and the Internet have substantially affected the reading process as well as reading habit. Our ability to concentrate and read or think deeply is destroyed by digital distractions. It looks like there is a decline in reading habit due to the onslaught of electronic media. Despite increase in leisure time and income as well as considerable increase in expenditure on other forms of entertainment there is no corresponding increase in the expenditure on reading materials. The typical nature of digital reading as against print reading is characterized by convenience, devoting less time, reading shorter texts, impatience, etc, with certain values of online users as well as cognitive behaviour of students. There is a need for continuing the reading habit of print era in the digital era. Even in scholarly reading, it is imminent that the thoroughness of reading is sacrificed for speed and quantity as more and more read only shorter articles or abstracts.
Blockchain based Public Auditing Outsourcing for Cloud Storage

Cloud storage services offer flexible, convenient solutions for business and personal users to store data. Traditionally, Third Party Auditors (TPAs) are introduced to ensure data integrity for public auditing. However, TPAs may also be untrusted for forging the auditing results or colluding with cloud storage servers to deceive users. In this paper, we propose a novel Blockchain-based Public Auditing Outsourcing system without TPAs (BPAO), in which the computationally expensive operations in public auditing are outsourced through blockchain to the cloud servers without risking users' privacy. Our security analysis indicates that BPAO achieves soundness and robustness. The experimental results show that BPAO is computationally efficient for cloud storage user.
End-to-End Verifiability Degree Metric in e-Voting System

The verifiability provide protection for the votes cast and for the voters themselves in e-voting. The notion of verifiability includes Verifiability of Individual, Universal and End-to-End. End-to-End Verifiability (EV) is a type that allows voters to be able to verify after the voting process, even though the voting process has not yet been closed. Some research on End-to-End Verifiability is more for the selectors only and does not see for some e-voting phases and has not proposed any metrics. In this research, it has been proposed an End-to-End Verifiability Metric to measure the degree of End-to-End verifiability consisting of End-to-End Verifiability Metrics Before Election, End-to-End Verifiability Metrics When Election, End-to-End Verifiability Metrics After Election, End-to-End Verifiability Metrics After Vote Counting and to determine the position of degree of verifiability, followed by the degree range of verifiability.
COGNITIVE APPROACH TO MODELING POPULATIONʼS QUALITY OF LIFE

Assessing the position of economic and human capital development level, building a strategy to improve the quality of life is extremely important for the state, so the study of indicators that form the quality of life and have the greatest impact on it remains relevant.Since the very concept of quality of life is a multicomponent category, characterized by both objective and subjective indicators, we consider it appropriate to structure knowledge about the factor environment that shapes the quality of life, their reflection by forming a cognitive model, its static analysis to identify the factors that have the greatest impact on quality of life to improve its level, which reflects the purpose of the study.A cognitive model of quality of life in the form of a balanced digraph was built based on 22 indicators of socio-economic conditions of Ukrainian households, grouped into four groups:
TRI-STATE+ COMMUNICATION SYMMETRY USING THE ALGEBRAIC APPROACH

This work uses the algebraic approach to show how we communicate when applying the quantum mechanics (QM) concept of coherence, proposing tri-state+ in quantum computing (QC). In analogy to Einstein’s stimulated emission, when explaining the thermal radiation of quantum bodies in communication, this work shows that one can use the classical Information Theory by Shannon (with two, random logical states only, “0” and “1”, emulating a relay), and add a coherent third truth value Z, as a new process that breaks the Law of the Excluded Middle (LEM). Using a well-known result in topology and projection as a “new hypothesis” here, a higher dimensional state can embed in a lower-dimensional state. This means that any three-valued logic system, breaking the LEM, can be represented in a binary logical system, obeying the LEM. This satisfies QC in behavior, offering multiple states at the same time in GF(3m), but frees the implementation to use binary logic and LEM. This promises to allow indeterminacy, such as contingency, reference failure, vagueness, majority voting, conditionals, computability, the semantic paradoxes, and many more, to play a role in logic synthesis, with a much better resolution of indeterminate contributions to obtain coherence and help cybersecurity. We establish a link between Einstein’s and Shannon’s theories in QM, hitherto not reported, and use it to provide a model for QC without relying on external devices (i.e., quantum annealing), or incurring in decoherence. By focusing on adequate software, this could replace the emphasis in QC, from hardware to software.
The Cultivation of Innovative Talents in College Music Education

With the rapid development of modern society, people pay more and more attention to education and the cultivation of innovative talents in college music teaching is very important. We need innovative talents to achieve better results, improve relevant work and promote personnel development for the development of the music industry, we should constantly innovate and make various arrangements so as to achieve better results.
vDANE: Using virtualization for improving video quality with Server and Network Assisted DASH

Summary Network Function Virtualization (NFV) offers flexibility in traffic engineering and network resource management, by taking advantage of Software Defined Networking (SDN). By using these network technologies, it is possible to enhance the performance of video streaming applications by placing network functions in suitable locations and rerouting flows. Our study addresses the “virtual cache placement” problem in dynamic networks, where traffic patterns and attachment points of the clients are changing rapidly. The cache placement is done by determining how many virtual caches are necessary to be able to provide acceptable service to the clients, as well as where to place those caches to meet demand. To this end, we provide a heuristic solution by taking advantage of NFV‐SDN and having the assistance of Server and Network Assisted DASH (SAND). Experimental results show that the proposed algorithms can improve the video client rebuffering by 150%–270% and also can provide an 8%–12% increase in average bitrate received by the client, compared to a number of benchmark algorithms. The obtained results indicate that the co‐operation between the client and the operator of an SDN‐enabled network, by exchanging client and network information, allows network resources to be efficiently used, and as a consequence, the Quality of Experience (QoE) on the client's side is improved.
Matrix Visuals

This chapter takes you through the process of creating further text-based visualizations in Power BI Desktop. More specifically, you will learn how to create and enhance matrices. Also known as crosstabs or pivot tables, these build on the table creation techniques that you learned in the previous two chapters. They allow you to create advanced table structures containing complex hierarchies of data.
Simulation Evaluation Method for Fusion Characteristics of the Optical Camouflage Pattern

A comprehensive evaluation system for a camouflage design combining local effect evaluation and global sampling is developed. Different from previous models, this method can sample and evaluate target camouflage in a wide range of combat areas, thereby obtaining a comprehensive evaluation effect. In evaluating local effects, the Gaussian pyramid model is adopted to decompose the image on a multi-scale so that it can conform to the multi-resolution property of human eyes. The Universal Image Quality Index (UIQI) conforming to features of eye movements is then adopted to measure the similarities between multi-scale targeted and background brightness, color and textural features. In terms of the imitation camouflage pattern design algorithm, uniform sampling is used to obtain the evaluation distribution in the background; while for the deformation camouflage pattern, the sampling distribution is improved to make it conform to the movement rule of the target in the background. The evaluation results of the model for different designs were investigated. It is suggested by the experimental results that the model can compare and evaluate the indicators involved in the process of camouflage design, including integration, polychromatic adaptability and algorithm stability. This method can be applied in the evaluation and contrast of camouflage pattern design algorithms, in parameter optimisation of camouflage design and in scheme comparison in engineering practice, and can provide support of evaluation methodology for camouflage design theories.
Sequents and Trees

Considers the methodology and techniques of sequent calculus to illustrate its use in proving a wide range of metatheoretical results&#x0D; &#x0D; Includes many results and their proofs that are often not well known or easily accessible&#x0D; &#x0D; Examines important and nonstandard generalized sequent calculi, like hypersequent and structured sequent calculi.
Multi-label Classification of Remote Sensing Image Based on Deep Learning on Embedded Device

In recent years, the resolution of satellite remote sensing images has been continuously improved. People can obtain more useful data and information from remote sensing images. Remote sensing images are widely used in various fields such as resource surveys and weather forecasting. The classification of remote sensing images is a very critical link in remote sensing image processing. This article proposes a multi-label classification method for remote sensing images based on deep learning for embedded platforms. We use residual neural network as the basic model and use transfer learning to reduces the cost of model training, besides this, we use batch normalization, image augmentation, and self-defined loss evaluation index to improve the accuracy and generalization ability of the model. Finally, the model is deployed on the embedded platform NVIDIA Jetson TX2 through tensorflow lite, the model performs well on the embedded platform and has good real-time performance.
Trends for Mobile IoT Crowdsourcing Privacy and Security in the Big Data Era

From tracking pandemics to applications, such as Google Maps, Uber, environmental monitoring, journalism, healthcare, crisis/disaster response, air quality control, noise and traffic monitoring, urban planning, etc., mobile crowdsourcing (MCS) systems are interweaved with the society and daily lives. This survey outlines major security and privacy challenges in MCS systems along with solutions and approaches. Comprehensive countermeasures, leveraging the capabilities of blockchains, smart contracts, machine learning, games, incentives, spatiotemporal cloaking, etc., are presented to preserve privacy and security of mobile workers, task requestors, and other aspects of crowdsourcing systems. Security recommendations for use cases, such as Industrial IoT, Internet of Vehicles, wireless crowdsensed systems, social crowdsourcing, edge computing, personalized and privacy-preserving recommendation, and mobile worker recruitment are further elaborated.
The Use of a Bee-bot in Pre-primary and Primary Education

Modern teaching aids are part of the daily education process. Children, pupils use them to deepen the acquired knowledge. A teacher tries to alternate the teaching methods in an interesting way. The Bee-bot, a didactic toy, has been used in education for several years. The teacher decides at what intervals and in which subject he or she can use the Bee-bot. In this article we present one of a group of micro-outputs. Here we used a heuristic method, a method of discovery and observation. Also, we used the cooperation in the application of a robotic toy.
Estimating the Photon-Number Distribution of Photonic Channels for Realistic Devices and Applications in Photonic Quantum Information Processing

We present a framework based on linear estimation to reconstruct the input-output statistics of any unknown quantum photonic channel and we highlight two applications: Quantum Key Distribution and Time-Correlated Single Photon Counting with homo-dyne detectors.
Bias-Variance Tradeoffs in Single-Sample Binary Gradient Estimators

Discrete and especially binary random variables occur in many machine learning models, notably in variational autoencoders with binary latent states and in stochastic binary networks. When learning such models, a key tool is an estimator of the gradient of the expected loss with respect to the probabilities of binary variables. The straight-through (ST) estimator gained popularity due to its simplicity and efficiency, in particular in deep networks where unbiased estimators are impractical. Several techniques were proposed to improve over ST while keeping the same low computational complexity: Gumbel-Softmax, ST-Gumbel-Softmax, BayesBiNN, FouST. We conduct a theoretical analysis of bias and variance of these methods in order to understand tradeoffs and verify the originally claimed properties. The presented theoretical results allow for better understanding of these methods and in some cases reveal serious issues.
A snake-inspired path planning algorithm based on reinforcement learning and self-motion for hyper-redundant manipulators

Redundant manipulators are flexible enough to adapt to complex environments, but their controller is also required to be specific for their extra degrees of freedom. Inspired by the morphology of snakes, we propose a path planning algorithm named Swinging Search and Crawling Control, which allows the snake-like redundant manipulators to explore in complex pipeline environments without collision. The proposed algorithm consists of the Swinging Search and the Crawling Control. In Swinging Search, a collision-free manipulator configuration that of the end-effector in the target point is found by applying reinforcement learning to self-motion, instead of designing joint motion. The self-motion narrows the search space to the null space, and the reinforcement learning makes the algorithm use the information of the environment, instead of blindly searching. Then in Crawling Control, the manipulator is controlled to crawl to the target point like a snake along the collision-free configuration. It only needs to search for a collision-free configuration for the manipulator, instead of searching collision-free configurations throughout the process of path planning. Simulation experiments show that the algorithm can complete path planning tasks of hyper-redundant manipulators in complex environments. The 16 degrees of freedom and 24 degrees of freedom manipulators can achieve 83.3% and 96.7% success rates in the pipe, respectively. In the concentric pipe, the 24 degrees of freedom manipulator has a success rate of 96.1%.