Reviewerly/exHarmony huggingface dataset:
Approaching a Cultural Identity of Multimedia Performance in Taiwan from a Confucian Perspective of Qi

This chapter examines the aesthetic phenomenon of conveying “tian-ren-he-yi” 天人合一 (the unity of the cosmos with humanity) in multimedia performance in Taiwan to evince dialogue between Confucian notions of Qi 氣 and phenomenological interpretations of agency. Drawing out the linkage between cosmological and aesthetic experiences, the chapter employs a hybrid methodology that merges Heidegger’s phenomenological enquiry on technology with Confucian concepts of “xing” 興 (poetic arising). The term “xing” refers to the cultivation of Qi through day-to-day life practices which concomitantly nurture creativity and ethics in relation to the cosmos. Drawing on Taiwanese new media artist Hsin-Chien Huang’s VR works as a case study, the chapter suggests that “xing”, developing Qi through embodied life practices is comparable to the Hedigerrian perspective of technology as praxical knowledge. From a phenomenological perspective, mixed reality in multimedia performance lies on a continuum shifting between actual and virtual realms where participants immerse in theater spaces augmented by multimedia technologies. In contrast, the “xing” approach, embodied in people’s original life world, is a heightened state of virtuality that can simultaneously better-connect people into reality, rather than divide them from it. Evidence of mixed reality can be found in Confucian classics which note that by achieving “xing”, people experience a form of virtuality, through a quality of respons(e)-ability of “xing”, which may give rise to cosmological and aesthetic transformation. Therefore, this chapter argues that “xing” is key to the emergence of mixed reality that enables symbiosis between the human body and heart-mind and the cosmos, a state foregrounding people’s existential authenticity in the presence of virtuality.
Multi-lingual code-mixed machine translation system

The Machine Translation System has been lately attracting considerable recognition due to its extensive use in multiple fields. This study focuses on abundant raw code-mixed textual data on microblogging sites, which can't be processed directly because it's multilingual. This raises a language barrier in communication and research. In order to use this immense data source, all unwanted symbols are removed to reduce noise in the input text. Also, the emoticon adding up the intention or emotion is permuted with the corresponding textual meaning. Recurrent Neural Network in one form or the other can be used for translating multilingual text from source to target language. A large neural network is used in the process of generating the expected noise-free output in the targeted language. The extensive experiments demonstrate that removing noise and transmuted emoticons improve the result and sustain the intention or semantics better. The proposed system can save anywhere from 35% to 98% of the cost of a human translator because of higher accuracy and desired output. It also finds a place as a subsystem to real-time multi-lingual translators, manuscript translators, other systems for providing input as required targeted language thus enlarging to input domain for that system.
A Multi-User Oriented Live Free-Viewpoint Video Streaming System Based on View Interpolation

As an important application form of immersive multimedia services, free-viewpoint video (FVV) enables users with great immersive experience by strong interaction. However, the computational complexity of virtual view synthesis algorithms poses a significant challenge to the real-time performance of an FVV system. Furthermore, the individuality of user interaction makes it difficult to serve multiple users simultaneously for a system with conventional architecture. In this paper, we novelly introduce a CNN-based view interpolation algorithm to synthesis dense virtual views in real time. Based on this, we also build an end-to-end live free-viewpoint system with a multi-user oriented streaming strategy. Our system can utilize a single edge server to serve multiple users at the same time without having to bring a large view synthesis load on the client side. We analyze the whole system and show that our approaches give the user a pleasant immersive experience, in terms of both visual quality and latency.
Design of Intelligent Recognition English Translation Model based on Association Rule Mining

Due to the rapid development of globalization, the information flow between different countries shows high speed, and English has become the main language of international communication. At present, the application value of intelligent recognition technology in different fields is increasing. The English machine translation model based on modern intelligent recognition technology can improve the efficiency and accuracy of English machine translation and realize barrier free communication. However, the traditional English machine translation method based on syntactic analysis can not solve the problem of partial structural ambiguity in the massive English language in intelligent recognition technology, which has the problem of low accuracy of machine translation. With the development of modern intelligent recognition technology, there are many intelligent machine translation tools. The current machine translation results of online machine translation still have some defects, especially after the server is used to carry out comparative learning on data in different languages in the full text range, it can obtain the grammar and text correlation laws between languages, which has the disadvantages of low efficiency and low accuracy of machine translation. Therefore, the recognizable technology of association rule mining should be used to realize accurate machine translation of English.
SITF: an algorithm for secured image transmission using Fractals

Unless a powerful algorithm protects data transmitted over a wireless network, it is public. It is still vulnerable to attacks despite several solutions. The paper provides a robust M-encrypt technique for securing a wireless network. M-encrypt is the best option photos sending images over a wireless network. The image is partitioned into four sections in the first step of the method. Using L-Fractal, the proposed approach encrypts each of the four sections separately. The segments are then encrypted and transformed into linear arrays before being sent across the network. To test SITF, an IEEE 802.11g and 802.16 simulation was done. Kali Linux and hacking software running on the Redmi Note 7 were used to test the susceptibility to attacks. There was no evidence of vulnerability to the attacks in the packets. Four parameters were used to assess the quality of the encrypted images. The results show that the image quality transmitted is excellent. Finally, we calculated the encryption, decryption, and overall packet transmission times on Wi-Fi and Wi-Max networks.
Staleness Analysis in Asynchronous Optimization

Distributed optimization is widely used to solve large-scale optimization problems by parallelizing gradient-based algorithms across multiple computing nodes. In asynchronous optimization, the optimization parameter is updated using stale gradients, which are gradients computed with respect to outdated parameters. Although large degrees of staleness can slow convergence, little is known about the impact of staleness and its relation to other system parameters. In this work, we analyze asynchronous optimization when implemented using either hub-and-spoke or shared memory architectures. We show that the process of gradient arrival to the master node is similar in nature to a renewal process. We derive the bandwidth requirement of the system. For the hub-and-spoke setup, we derive bounds on the expected gradient staleness and show its connection to other system parameters such as the number of workers, expected compute time, and communication delays. Our derivations reveal that it is possible to adjust gradient staleness by tuning certain parameters such as minibatch size or the number of workers. For the shared memory architecture, we show that the expected staleness is equivalent to the number of workers. Our derivations can be used in existing convergence analyses to express convergence rates in terms of other known system parameters. Such an expression gives further details on what factors impact convergence.
Research on Application of Computer Virtual Reality Technology in Sports Simulation Training

This article will combine ergonomics with arts and crafts and organize volleyball players&#x0027; ergonomics data. Using computer virtual reality technology to accurately describe the craftsmanship data of the athlete&#x0027;s human body and the three-dimensional model of volleyball. At the same time, we imported the human body model from Pro/ENGINEER into Visual Studio, and used OpenGL animation simulation to control the three-dimensional entity model to simulate various blocking actions of volleyball players. The research results show that the system can quickly make various human models by modifying the parameters, simulating athletes with different movement characteristics, and can effectively assist the volleyball team in blocking training.
Bangladeshi Currency Notes (Bdt) Recognition Using Logistic Regression

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Technology Applications in Education, Applications of Accelerometer Wearable Sensor and Internet User Gender Prediction—and FDI Attraction for Digital Sector

According to statistics by the Department of Telecommunications (Ministry of Information and Communications) in November 2016, the total number of broadband Internet subscribers’ fixed broadband reached more than 9 million subscribers and mobile broadband subscribers reached more than 12.6 million subscribers. Besides, according to the statistics of “wearesocial.net,” in January 2015, Vietnamese people Nam is ranked fourth in the world in terms of time spent using the Internet with 5.2 h per days, just behind the Philippines at 6 h, followed by Thailand with 5.5 h, and Brazil is 5.4 h/day. So we use quantitative methods and use methods of meta-analysis, comparative statistical methods, survey methods, qualitative and quantitative analysis methods to conduct a study to explore the advances of technology applications into modern education because applications such as accelerometer wearable sensor or Internet user gender prediction can be applied on to online teaching or e-learning process well. In addition to that, FDI attraction is helping provinces in Vietnam such as Thai Nguyen to develop local economic strengths, creating more employment and income for the public in the globalization era, expand productivity and quantity for digital sector.
Measuring Document Similarity with Word Embeddings

We detail a methodology for estimating the textual similarity between two documents while accounting for the possibility that two different words can have a similar meaning. We illustrate the method's usefulness in facilitating comparisons between documents with very different formats and vocabularies by textually linking occupation task and industry output descriptions with related technologies as described in patent texts; we also examine economic applications of the resultant document similarity measures. In a final application we demonstrate that the method also works well relative to alternatives for comparing documents within the same domain by showing that pairwise textual similarity between occupations' task descriptions strongly predicts the probability that a given worker will transition from one occupation to another. Finally, we offer some suggestions on other potential uses and guidance in implementing the method.
Target Extraction from Remote Sensing Image Based on Scene Recognition and Target Segmentation

Extracting dense and different sizes targets from large-scale remote sensing images is a challenging task. This paper proposes a remote sensing image target extraction method based on scene recognition and target segmentation. The method recognizes images having targets first and then extracts targets via segmentation, both implements using deep network models. Firstly, cropping large-scale remote sensing images into smaller images, and classifying scenarios by whether they contain targets or not. Next, a full-resolution neural network target segmentation model with multi-source input is constructed. In the segmentation model, feature resolution retaining, and feature fusion together with data exchange mechanism lead to better feature extraction for different sizes targets and overcome the problem of gradient vanishing. Experiments for building extraction on two remote sensing data sets show that the proposed method obtains better results than the comparable deep neural network models in accuracy, and does better in targets integrity and edges smoothness.
A MVCC Approach to Parallelizing Interoperability of Consortium Blockchain

Driven in part of the rapid growth of consortium blockchain applications, blockchain interoperability becomes extremely essential to exchange transactional data among decentralized applications. To ensure the data integrity of transactions, the state-of-the-art studies of the blockchain interoperability apply data locks, which however severely decrease system efficiency. To boost interoperability performance, this paper proposes a novel approach based on multi-version concurrency control to parallelize interoperable transactions, which aims high transaction processing throughput while ensuring data integrity. The experimental evaluation with the Smallbank benchmark shows that the proposed method achieves up to 4x performance increase (in terms of processed transactions per second, TPS) compared with the existing methods, and moreover, it decreases the average latency with 58%.
Modeling of Online Learners’ Sentiments About Multigranularity Knowledge

To provide insight into online learners&#x2019; interests in various knowledge from course discussion texts, modeling learners&#x2019; sentiments and interests at different granularities are of great importance. In this article, the proposed framework combines a deep convolutional neural network and a hierarchical topic model to discover the hidden structure of online learners&#x2019; sentiments about knowledge topics. The approach is to capture multigranularity knowledge of topics of interest to learners with the hierarchical topic model and to identify information about learners&#x2019; different sentiments with the convolutional neural network. This approach not only models knowledge of hierarchical interest from general to specific but also identifies learners and their sentiment orientations to better correspond to the different granularities of knowledge. The experimental results and analysis of real-world datasets show that the proposed approach is effective and feasible.
2prong: Adaptive Video Streaming with DNN and MPC

Adaptive bitrate (ABR) algorithms are often used to optimize the quality of user experience (QoE) during video playback. In the client-side video player, the buffer size and predicted throughput are mainly used to improve user's QoE. However, due to the randomness of mobile network traffic and the heavy-tail effect of the network, it is very difficult to predict throughput. We innovatively use Bayesian neural network to dynamically evaluate video signals. Unlike previous neural network solutions, we use probability distributions instead of point estimates to predict throughput, which can effectively evaluate QoE metrics. Our contributions are to first (i) use of Bayesian neural network to guide video adaptive bitrate adaptation, and then (ii) propose a bitrate adaptive algorithm denoted 2prong, which utilizes high-dimensional contextual information such as buffer occupancy, predicted throughput and video quality to find the most valuable information for quality adaption in real time. We demonstrate the effectiveness of the 2prong algorithm using a simulation testbed. By comparing with other methods, it is demonstrated that 2prong can improve the video quality of video streaming transmission.
Bias detection in Wikipedia articles. A study on Polish and English Datasets.

Nowadays, an almost unlimited number of information sources and polarized media hamper our ability to distinguish between biased and neutral speech. It creates a need for an automatic tool that could comprehend human language and assess whether presented information is conveyed without any editorial bias. In this work, we introduce models able to detect bias on a sentence level. As other authors before, we continue to utilize Wikipedia revision history to collect examples of biased and unbiased speech. For the first time, this work introduces a corpus of labeled subjective and neutral sentences in the Polish language. Furthermore, we compare the performance of LSTM and BERT models trained on English and Polish sentences. As part of the findings, we present cues for subjectivity that were detected during the analysis. We also present a new dataset WNC-pl a Polish corpus of biased and unbiased sentences.
A Novel Clustering Algorithm via the Support and K-Nearest Neighbors of Data

K-Means is a widely used algorithm among many clustering algorithms. However, in the process of clustering by K-Means, there are problems, such as the difficulty of selecting K value, high parameter dependence and sensitive outliers. Thus, we propose a novel clustering algorithm via the support and K-Nearest Neighbors (KNN) of data to solve these problems in this paper. Firstly, we introduce the concept of data support and calculate and make the supports of pair data sparse. Secondly, we calculate the sum of the supports for each data point with other points and select and consider the data point with the maximum sum of the support as the cluster center among the data set. At the same time, we use the KNN algorithm to determine the K value selection, and the initial data set is divided into M sub-clusters (M <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</inf> ). Then, we combine the M sub-clusters into the final clustering by using the aggregation function as the similarity measure. When processing data, the algorithm combines density and distance and tests it on the UCI data set. Comparing with other typical clustering algorithms, the hybrid algorithm based on distance and density has a better clustering effect.
Lifting up Imbalanced Data Classification to SVM Based Ensemble Level on Oversampling Feature Spaces

The design of supervised machine learning algorithms assumes that the training data are balanced. In terms of industrial machinery failures and medical patient detection reports, such data distributions are generally imbalanced. This type of algorithm has a poor adaptability to this type of data. In response to this problem, in this paper, an imbalanced data classification algorithm from the ensemble level is proposed, which maps the original space to multiple different feature spaces to increase the generalization ability of the ensemble classifier. To ensure the generalization ability of a single classifier, a boundary densification algorithm that densifies majority class boundaries and controls the bias of the decision plane, and can also deal with the classification problem of imbalanced datasets, is also proposed. Experimental results show that, compared with other imbalanced classification algorithms, the oversampling algorithm of the ensemble-level kernel space proposed in this study performs better in imbalanced data classification.
Hardware architecture optimization for object tracking

In this chapter, we still focus on the hardware architecture optimization for object tracking algorithm. Real-time object tracking is demanded in versatile embedded computer vision applications. To this end, we propose a low-cost high-speed very large-scale integration (VLSI) system for object tracking, based on robust and computationally simple unified textural and dynamic compressive sensing features and simple elliptic matching with fast online template updating capability. The system introduces a memory-centric architectural paradigm, multiple-level pipelines and parallel processing circuits to achieve high frame rate while consuming as few hardware resources as possible. An FPGA prototype of the proposed VLSI tracking system was implemented. Under a 100-MHz clock frequency, the prototype achieves over 600 frame/s processing speed under 320×240 image resolution and obtains robust tracking results.1
3D Curved Path Tracking Control for UAV Collision Avoidance in Low Altitude Environments

Aiming at the 3D curved path tracking problem for fixed-wing unmanned vehicle (UAV) collision avoidance in low altitude environments, a path tracking control method is proposed based on the strategy of separated guidance-control design. First, considering the characteristics of six degree of freedom (6-DOF) motion equation of UAV including strong nonlinearity, tight coupling and nonaffinity, the affine transformation is performed on the UAV model for control design. Second, the path tracking control system is divided into the inner control loop and the outer guidance loop. Based on back-stepping linear active disturbance rejection control (BLADRC), the control loop is further divided into the angular rate loop, the angle loop and the ground speed loop, thus, corresponding controllers are designed respectively. For the design of guidance loop, acceleration commands of the UAV in vertical plane and horizontal plane are obtained by the L1 guidance algorithm, and corresponding angle commands are given by command transformation. Finally, the simulation results show that the UAV can track the desired path determined by the collision avoidance decision method accurately, thus the feasibility and the effectiveness of the proposed path tracking control method is verified.
A General Calibration Method for Dual Ptz Cameras

With the increasing application of dual PTZ cameras in intelligent unmanned systems, the research in calibration method of dual PTZ cameras is getting more and more important. The intrinsic and extrinsic parameters of the dual PTZ cameras continuously change during rotation and zoom, resulting in difficulties in precisely calibration. Here, we propose a general calibration method for dual PTZ cameras in variable focal length and posture under the conditions that optical center of camera is not coincided with the horizontal and pitch rotation axes, and horizontal and pitch rotation axes are not perpendicular to each other. We establish the relationship between dual PTZ camera’s intrinsic, extrinsic parameters and feedback parameters (pan, tilt, zoom value) through fitting and calculating the previous calibration results acquired at specific angles and zoom value by Zhang's calibration method. Subsequently, we derive the calculation formula of intrinsic and extrinsic parameters under arbitrary focal length and posture based on camera’s feedback parameters. The experimental results show that intrinsic and extrinsic parameters computed by the method proposed by us can meet the precision requirements compared with the ground truth calibrated by Zhang’s method. The average focal length error is less than 4%, the cosine similarity of rotation matrix between left and right cameras is more than 99.8%, the translation vector error is less than 1%, and the recalculated Euler angle error are less than 1 degree. Our work can quickly and accurately obtain intrinsic and extrinsic parameters during the use of the dual PTZ camera.