Reviewerly/exHarmony huggingface dataset:
M-3R: A Memory Based Approach for Streaming QoE Prediction under 3R settings

The rapid growth of video traffic and demand for high quality videos has increased in the recent years. The service providers emerge out to be successful only when they provide satisfactory end user viewing experience. Due to the channel throughput fluctuations, the end users in Hypertext Transfer Protocol (HTTP) streaming endure video quality variations with time (due to bitrate adaptations) and rebuffering events (once the received video data in the buffer is played out). Hence, it is necessary to evaluate the Quality of Experience (QoE) in video streaming scenario in a continuous time (per frame) manner in order to regulate the quality deteriorations. In this paper, we have proposed M-3R predictor- A network of Long-Short Term Memory (LSTM) that evaluate the time varying streaming QoE resulting from the effects of rate adaptation and rebuffering under the 3R settings. The “3R” settings is a combined effect of several Full Reference (FR), Reduced Reference (RR), and No Reference (NR) Video Quality Assessment (VQA) metrics. Our proposed M-3R predictor was found to be effective in modelling the temporal dynamics of the streaming video QoE, that got reflected in the performance evaluation measures.
CARDS: Comprehensive AR Docent System

We present CARDS, a comprehensive Augmented Reality (AR) docent system to promote informative and engaging art experiences for exhibition visitors. While utilizing AR to add aesthetic visual effects has been actively explored, facilitating visitor engagement in educational exhibitions through AR has been relatively unexplored. Thus, the proposed system facilitates visitor engagement in informative exhibitions by providing AR-specific interaction features which include context-based sequential AR pins, position-based AR pin configuration, and orientation-based AR visual aids. With CARDS, we aim to enrich the onsite art experience of visitors by presenting information on exhibits in a systematic way, as well as to enhance visitor engagement by taking the bodily movements of visitors as interaction inputs. Furthermore, we suggest a design guideline for future educational AR guide applications using proxemic interaction methods.
Distributions-free Martingale test Distributions-shift for Swarm Behavior Prediction

The timely detection of swarm behavior is critical to ensure a reliable and appropriate response to update any behavior prediction to complex swarm phenomena. A standard assumption under the method of machine learning is that the observed data are generated from a fixed but unknown distribution. However, when solving practical swarm behavior prediction problems it is remarked that the data points are observed batch by batch. So in this paper, we are devoted to testing the assumption of distributions-shift online: the observed data arrive one by one, and after receiving each object, the machine learning algorithms give a prediction label, we would like to have a valid measure of the degree to which the evidence to against the assumption of the fixed distribution. We report the experimental performance of distributions-free martingales testing on the swarm benchmark data set, the results show a bona fide fact that the distributions-shift detection testing is an inescapable reality for the original order data set.
Gene selection using Moth Flame algorithm and classification of Gene Expression Dataset

We gather enormous volumes of data to train a model, which aids a machine's ability to learn better. Not all of this data, though, will be applicable to the model. Classes or parts of the data can be removed if they don't significantly improve our model. The model may run slowly if there is too much extra data. It's also likely that the model will become erroneous as a result of learning from this unimportant data. By using only pertinent data and removing irrelevant data, feature selection reduces the input variable of the model. Using feature selection, we may improve our model in a number of ways, including preventing over-fitting and learning from noise, increasing accuracy, and cutting training time. Three types of feature selection methodologies―the filter technique, the wrapper approach, and the embedded method―have been developed throughout the years. In this study, we present the Moth Flame Optimization Method (MFOA) algorithm for gene selection from the Gene Expression dataset. A population-based algorithm with natural inspirations is called the Moth Flame Optimization Algorithm (MFOA). It takes its cues from how moths behave in the wild. The Moth Flame Optimization Algorithm (MFOA) can converge more Quickly and requires less compute than earlier methods.
The Application of Intelligent Data Mining Model Technology in the Study of Physical Training Video System

With the rapid development of computer technology and the Internet, the world has entered the modern age of the Internet. With the rapid development of technologies such as the Internet, physical education has created a new teaching method, the video learning system. Visual education system development is an extension of the simple work of entry. You can improve the distribution of aid, increase access to education, adapt to learning needs, reduce academic stress, achieve democracy, balance education and meet the needs of the education system. Economics, Science, Technology. Its structure is very important. This article is based on a study on the use of sage data stone copying technology in reading and understanding physical education video systems, where sage data stone copy technology is used only in the text on how to read video systems. The video training program was created and the design tested. Although the PE video system has its drawbacks based on the communication technology used in this article, the overall test result is more than 32%, typically more than 30, estimating the performance of the education system.
Design and Implementation of Mobile Control APP for Teenagers Based on Big Data Technology

With the development of the times, the rapid development of information, the use of mobile terminals has been integrated into our lives. Because teenagers do not have good self-control ability, teenagers addiction to mobile terminals has become a major problem in the society, the market demand of teenagers anti-addiction system is large. Teenager mobile terminal control APP based on big data technology is a control APP to prevent teenagers from indulging in mobile terminals. Its front-end is developed by Java language, with Android7.0 SDK as the main technology. The back-end mainly uses SQLite embedded database as the back-end data storage. The mobile terminal control APP records the software usage of the mobile terminal. By using the deep learning technology in big data technology to extract data and portrait, the mobile terminal is controlled and managed. When teenagers use a mobile terminal for more than a certain time, the screen will automatically lock. The guardian can also set a specific time on this APP, and the mobile terminal will be automatically closed when the arrival time is reached. Even if the distance is far away, the time to play the mobile terminal can be directly controlled.
On the Substructure Countability of Graph Neural Networks

With the empirical success of Graph Neural Networks (GNNs) on graph-related tasks, it is intriguing to investigate their theoretical power on these tasks. In this paper, we focus on GNNs’ theoretical power on substructure counting, a fundamental yet challenging task in many applications. Previous works have proven that the 2-dimensional Weisfeiler-Leman algorithm (2-WL) equivalent GNNs can only count limited substructures. However, the substructure counting ability of theoretically more powerful and computationally tractable GNNs remains unclear. In this paper, we study conditions for substructures to be theoretically countable by k-WL equivalent GNNs, and then focus on 3-WL equivalent ones, which are currently the most theoretically powerful instances with practical computational cost. Further, we propose an algorithm to determine the countability of substructures for 3-WL equivalent GNNs. Our results reveal that 3-WL equivalent GNNs can count considerably more substructures than 2-WL equivalent ones. However, the proportion of countable patterns and prediction performance decrease as the pattern size increases. Therefore, we propose a Layer Permutation Pooling (LPP) model for better substructure counting performance. LPP first decomposes the data graph into subgraphs. Then we propose a layer permutation scheme to represent each decomposed subgraph as a set of matrices. Finally, LPP utilizes a neural network to conduct predictions on matrices. We compare LPP with several state-of-the-art GNNs on various datasets. Experimental results show that LPP outperforms baselines by 84% on average with the RMSE metric.
Depth-resolved interferometric imaging utilizing a spatially quasi-incoherent light source

An interferometric technique that utilize a spatially quasi-incoherent light source to perform interferometric measurements involving diffusely scattering objects is presented. The proposed technique is demonstrated with settings that give a depth gate of 90 µm.
The Effect of The Self-Regulated Learning with The Alef Education Platform to Improve the High-Order Thinking Skills in Elementary School

Digital media has a role in learning mathematics. This study aims to determine whether the application of the self-regulated learning model with the Alef Education platform can improve the high-order thinking skills of elementary school students. The population in this study were seventh-grade students of Madrasah Tsanawiyah in the province of Bali. The sample was drawn by random clusters in stages of 324 students. Data were collected by tests. Testing the effectiveness of the HOTS test using the effect size test. Data were analyzed descriptively qualitatively and quantitatively. The implementation of the self-regulated learning model with the alef education platform can improve the high-order thinking skills of elementary school students in the Province of Bali with Es =0.83 including the criteria for high effectiveness. On the alef platform, there are interesting audiovisual learning videos, games, and tiered tests. The implication of this research is that the application of the self-regulated learning model with the alef education platform can improve the high-order thinking skills of elementary school students.
Application of edge computing in intelligent security system

The rise and development of edge computing technology have solved the problem of massive data in the era of big data, and provided a new direction for the development of intelligent security systems. Edge computing analyzes, calculates and stores the data at the edge near the data source, which reduces network transmission burden and the computing pressure of the cloud center and improves the efficiency of the whole system. In order to solve the problem of massive data in intelligent security system, a feasible architecture of intelligent security system based on edge computing is proposed, which is divided into four parts: perception layer, edge layer, transport layer and application layer. The intelligent security system based on edge computing can take full advantages of edge computing in terms of computing ability, real-time and security, and better meet the needs of real-time monitoring, risk assessment, early warning and intelligent decision-making services, so as to promote the comprehensive improvement of security level.
A Unified Framework for Adversarial Attacks on Multi-Source Domain Adaptation

Multi-source domain adaptation studies the knowledge transferability from multiple labeled source domains to an unlabeled target domain under a distribution shift. However, little effort has been devoted to studying the adversarial vulnerability of multi-source domain adaptation approaches. Specifically, most existing techniques focus on learning the domain-invariant representation to mitigate the distribution shift across domains. In this paper, we theoretically show that the domain-invariant representation cannot guarantee the success of multi-source domain adaptation, when no labeled samples are available in the target domain. This result motivates us to propose a unified framework ( <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">AdaptAttack</monospace> ) for data poisoning adversarial attacks on multi-source domain adaptation. The key idea is to maliciously manipulate the label-informed data distributions of source domains by injecting perceptibly unnoticeable noise into the source data. In addition, it requires that the generated adversarial attacks are invisible to multi-source domain adaptation algorithms, i.e., the source classification errors and marginal discrepancies across domains are not negatively affected. Extensive experiments on public domain adaptation benchmarks confirm the effectiveness and computational efficiency of our proposed <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">AdaptAttack</monospace> framework in both white-box and black-box attack scenarios.
CasaPuNet: Channel Affine Self-Attention- Based Progressively Updated Network for Real Image Denoising

Recently, the popularity of deep learning has brought broad applications of computer vision technology in industrial information systems. However, the process of image acquisition will inevitably introduce noise, which may heavily degrade image visual quality. Most of the proposed denoising methods are nonblind and they have limited performance in removing real noise with different noise levels. To overcome this problem, we propose a deep convolutional neural network (CNN)-based blind model, i.e <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">.</i> , channel affine self-attention (CASA) based progressively updated network (CasaPuNet) for real image denoising. First, we introduce degradation mapping module (DMM) to extract degradation information, which makes the remaining subnetwork of CasaPuNet perform nonblind denoising. Then, CasaPuNet adopts a multistage architecture, which resolves the large gap between the noisy input and clean output into several small gaps and eliminates these small gaps step by step through progressive inference. Finally, a novel CASA is designed to adaptively fuse the features from multiple stages according to input statistics. Specifically, CASA extracts channel information from different features and converts them into channel weights through an affine structure for adaptive adjustment. CASA brings a significant performance gain with a small number of parameters. Extensive experiments demonstrate that CasaPuNet outperforms state-of-the-art denoising methods both quantitatively and visually.
SMOTE Inspired Extension for Differential Evolution

Although differential evolution (DE) is a well-established optimisation method, proven on a wide variety of problems, modifications are proposed on a regular basis attempting to ever more improve its performance. Typical avenues for improvement include the introduction of new (mutation) operators or parameter control schemes. Another, less common approach, is the incorporation of additional, complementary, search mechanisms. This paper proposes one such mechanism, based on the idea of producing new solutions akin to the manner of the SMOTE algorithm producing synthetic minority instances in supervised machine learning. The conducted experimental analysis showed it to be highly competitive against comparable mechanisms on the CEC2014 benchmark suite when incorporated into standard DE, whilst being especially beneficial on simpler multimodal problems. Its incorporation into improved DE variants, although still undoubtedly bringing value on these problems, does hint at complex interactions with already integrated enhancements, suggesting that extending already enhanced algorithm variants is not simple, to say the least.
Thai Scene Text Recognition with Character Combination

In recent years, scene text recognition(STR) that recognizing character sequences in natural images is in great demand beyond various fields. However, most STR studies only focus on popular scripts like Chinese or English, too little attention has been paid to minority languages. In this paper, we address problems on Thai STR, and introduce a novel strategy called Thai Character Combination(TCC), which explore original characteristics of Thai text. Unlike most other scripts, characters in Thai text can be written both horizontally and vertically, which brings big challenges to current sequence-based text recognition methods. In order to reduce complexity of structure and alleviate the misalignment problem in attention-based methods, TCC intends to combine Thai characters that stack vertically to independent combined characters. Furthermore, we establish a Thai Scene Text(TST) dataset that collected from multiple scenarios to evaluate the performance of our proposed character modeling strategy. We conduct abundant experiments and analyses to compare the recognition performance of models with and without TCC. The results indicate the effectiveness of the proposed method from multiple perspectives, especially, TCC benefits a lot for long text recognition, and there is a substantial improvement in the recognition accuracy of entire string-level.
Optimization of Fuzzy-Control Parameters for Path Tracking of a Mobile Robot Using Distributed Genetic Algorithms

Usually, we seek to establish the most optimal route for a given reference, and in this work, we focus on optimizing the parameters of a fuzzy controller that allows us to follow certain path. We know that the optimization of fuzzy controllers is a task with a high demand for computational resources since it is required to simulate the operation of the controller. Recently, distributed and asynchronous genetic algorithms have had good results in this type of optimization using multiple populations and processors to work in parallel with each population. A problem with these algorithms is the need to configure the number of populations and processors. In this work, we focus on testing different configurations for a distributed genetic algorithm applied to optimize membership functions of a fuzzy path tracking controller. We tested configurations with 3, 4, and 7 processors to minimize the evaluation time and minimize the RMSE. We also show that when increasing processors, we observe a reduction in execution time.
State of the Art of Wearable Sensors for Healthcare

Personalized remote healthcare monitoring is in continuous development due to the technology improvements of sensors and wearable electronic systems. A state of the art of research works on wearable sensors for healthcare applications is presented in this work. Furthermore, a state of the art of wearable devices, chest and wrist band and smartwatches available on the market for health and sport monitoring is presented in this paper. Many activity trackers are commercially available. The prices are continuously reducing and the performances are improving, but commercial devices do not provide raw data and are therefore not useful for research purposes.
Graph signal processing and tunicate swarm optimization based image steganography using hybrid chaotic map based image scrambling

This paper put forwards a secure image steganography technique for transmitting sensitive data using a Tunicate swarm optimization algorithm. A hybrid chaotic map-based image scrambling method is proposed with the combination of Tent and Tinkerbell chaotic maps. The scrambling of the secret image provides an added level of security in data hiding. The graph signal processing of cover image using the Meyer wavelet filter is performed to conceal the secret data within the transformed cover image using the alpha blending operation. To discover the optimal value of alpha for alpha blending, the tunicate swarm optimization algorithm is employed. To take the hidden information out at receiver end, the reverse approach is applied. The experimental results revealed that the embedding technique using the tunicate algorithm with a hybrid chaotic map provides better imperceptibility and robustness in comparison to the existing graph signal-based image steganography technique. The investigation is done on PSNR, MSE and SSIM performance parameters for evaluating the proposed technique.
Joint Optimization of Bandwidth Allocation and Gradient Quantization for Federated Edge Learning

Federated Edge Learning (FEEL) is becoming a popular distributed privacy-preserving machine learning (ML) framework where multiple edge devices collaboratively train an ML model with the help of an edge server. However, FEEL usually suffers from a communication bottleneck due to the limited sharing wireless spectrum as well as the large size of training parameters. In this paper, we consider gradient quantization to reduce the communication traffic and aim at minimizing the total training latency. Since the per-round latency is determined by both the bandwidth allocation scheme and gradient quantization scheme (i.e., the quantization levels of edge devices), while the number of training rounds is affected by the latter, we propose a joint optimization of bandwidth allocation and gradient quantization. Based on the analysis of total training latency, we first formulate the joint optimization problem as nonlinear integer programming. To solve this problem, we then consider a variation of this problem where the per-round latency is fixed. Although this variation is proved to be NP-hard, we show that it can be transformed into a multiple-choice knapsack problem which can be solved efficiently by a pseudopolynomial time algorithm based on dynamic programming. We further propose a ternary search based algorithm to find a near-optimal per-round latency, so that the two algorithms together can yield a near-optimal solution to the joint optimization problem. The effectiveness of our proposed approach is validated through simulation experiments.
Evaluation of Vessel Yaw Uncertainties on Multiple Hypothesis Tracker Performance

Implementing a RADAR tracking algorithm on a marine RADAR on a vessel can prove challenging due to the fact that the yaw rate of the vessel can change quickly which can inhibit the tracking algorithm from maintaining a valid track. This paper aims to quantity how much variance can be experienced in vessel yaw rate before a Multiple Hypothesis Tracking algorithm begins to fail.
HOMOTOPY PERTURBATION ANALYSIS ON SHAPE EFFECT OF VASODILATORS DURING ARTERIAL FLOW

In this study, the homotopy perturbation method was employed to study the influence of the thermal and applied magnetic fields on the movement and efficiency of differently shaped nanoparticles. Extensive Hartmann number, nanoparticle shape factor, and nanofluid volume fraction values were used to investigate the influence of differently shaped nanoparticles in a flow-through diseased artery. To analyze the flow and heat conduction behavior of nanoparticles in an artery, the wall impedance and local Nusselt number values were evaluated using the homotopy perturbation method and the results were compared with existing results for the same case. The results of both approaches were compared graphically for the volume fraction and magnetic interaction parameters. In addition, a graph was plotted to compare the behavior of nanoparticles in both the Hamilton-Crosser and modified Hamilton-Crosser models.