Reviewerly/exHarmony huggingface dataset:
LSNT: A Lightweight Siamese Network Based Tracker

Trackers based on the Siamese network have achieved remarkable advancements in accuracy and robustness, yet the trackers suffer from enormous computation and memory storage cost, which hinder their rapid inference, making it difficult to deploy in calculation-constrained and memory storage-constrained scenarios. Moreover, a general depthwise correlation attempts to process the entire feature map indiscriminately, which cannot extract more robust features. In this paper, we presented a Lightweight Siamese Network based Tracker (LSNT) to tackle the above issues. In the tracking head part, an extremely efficient module is adopted to simplify the network, so as to maintain a balance with the backbone network in parameter quantity. Furthermore, a novel Spatial-Channel Attention based Depthwise (SCAD) correlation is introduced to enhance the ability of the feature fusion module. Compared with the baseline tracker, LSNT has fewer parameters and calculations, but experiments conducted on the GOT-10k benchmark demonstrate that it achieves comparable performance in terms of accuracy and robustness, thus confirming its efficiency and effectiveness.
Machine Learning based Fall Detection Algorithms for Toddlers using Public and Experimental Datasets

Falling and its adverse impacts, especially for children under the age of six, have life long consequences. Fall detection, in such cases can be paramount for administering care quickly and efficiently. This paper addresses the concern of a dearth of fall datasets, with the falls being performed by actual toddlers, and presents a novel experimental dataset for the same. It also provides a comparison between multiple publicly available datasets, which are recorded by simulating falls by adults, across deep learning and machine learning models. Using a binary classification scheme to detect falls, the machine learning models are tested for both the experimental dataset as well as the publicly available ones. In the comparison of models, the kNN model showed favorable results, having the highest accuracy to our experimental dataset, that of 96 percent, with a sensitivity of 96.5 percent and specificity of 95.8 percent. Similar tests of models with the publicly available datasets highlight the significant difference in results when utilizing fall data simulated by adults and children. The research also sheds light on the application of such models in various wearables as well as the metrics to be deliberated over when choosing a suitable algorithm for this particular implementation.
Group Shift Pointwise Convolution for Volumetric Medical Image Segmentation

Recent studies have witnessed the effectiveness of 3D convolutions on segmenting volumetric medical images. Compared with the 2D counterparts, 3D convolutions can capture the spatial context in three dimensions. Nevertheless, models employing 3D convolutions introduce more trainable parameters and are more computationally complex, which may lead easily to model overfitting especially for medical applications with limited available training data. This paper aims to improve the effectiveness and efficiency of 3D convolutions by introducing a novel Group Shift Pointwise Convolution (GSP-Conv). GSP-Conv simplifies 3D convolutions into pointwise ones with \(1\times 1\times 1\) kernels, which dramatically reduces the number of model parameters and FLOPs (e.g. \(27\times \) fewer than 3D convolutions with \(3\times 3\times 3\) kernels). Naïve pointwise convolutions with limited receptive fields cannot make full use of the spatial image context. To address this problem, we propose a parameter-free operation, Group Shift (GS), which shifts the feature maps along different spatial directions in an elegant way. With GS, pointwise convolutions can access features from different spatial locations, and the limited receptive fields of pointwise convolutions can be compensated. We evaluate the proposed method on two datasets, PROMISE12 and BraTS18. Results show that our method, with substantially decreased model complexity, achieves comparable or even better performance than models employing 3D convolutions.
Unconventional saturation effects at intermediate drive in a lossy cavity coupled to few emitters

Recent technological advancements have enabled strong light-matter interaction in highly dissipative cavity-emitter systems. However, in these systems, which are well described by the Tavis-Cummings model, the considerable loss rates render the realization of many desirable nonlinear effects, such as saturation and photon blockade, problematic. Here we present another effect occurring within the Tavis-Cummings model: A nonlinear response of the cavity for resonant external driving of intermediate strength, which makes use of large cavity dissipation rates. In this regime, $(N+1)$-photon absorption processes dominate when the cavity couples to $N$ emitters. We explore and characterize this effect in detail, and provide a picture of how the effect occurs due to destructive interference between the emitter ensemble and the external drive. We find that a central condition for the observed effect is large cooperativity; i.e., the product of the cavity and emitter decay rates is much smaller than the collective cavity-emitter interaction strength squared. Importantly, this condition does not require strong coupling. We also find an analytical expression for the critical drive strength at which the effect appears. Our results have potential for quantum state engineering, e.g., photon filtering, and could be used for the characterization of cavity-emitter systems where the number of emitters is unknown. In particular, our results open the way for investigations of unique quantum-optics applications in a variety of platforms that require neither high-quality cavities nor strong coupling.
Comment information extraction based on LSTM and Neural Networks

Abstract With the advent of the era of big data, the amount of data has also increased geometrically. People’s ability to obtain effective information has gradually declined. At present, most e-commerce platforms only focus on the sentiment analysis of positive and negative reviews. It is difficult for users and businesses to extract user opinions and views from the massive review data. For the product review data of a certain hard disk, use the LSTM model to train the sentiment classification model. Finally, the neural network is used to find the keywords of the comment data and the word cloud diagram is used to display the analysis results. Through the research, it can be found that LSTM emotion classifier can classify comments with high accuracy and words closely related to comment emotion tendency can be found according to the weight of neural network.
Adaptive Particle Swarm Optimization Algorithm Ensemble Model Applied to Classification of Unbalanced Data

Unbalanced data classification is a major challenge in the field of data mining. Random forest, as an ensemble learning method, is usually used to solve the problem of unbalanced data classification. For the existing random forest-based classification prediction model, its hyperparameters are dependent on empirical settings, which leads to the problem of unsatisfactory model performance. In order to make random forest find the optimum modelling corresponding to the character of unbalanced data sets and improve the accuracy of prediction, we apply the improved particle swarm optimization to set reasonable hyperparameters of the model. This paper proposes a random forest-based adaptive particle swarm optimization on data classification, and an adaptive particle swarm used to optimize the hyperparameters in the random forest to ensure that the model can better predict the unbalanced data accurately. Aiming at the premature convergence that appears in the particle swarm optimization algorithm, the population is adaptively divided according to the population fitness and the adaptive update strategy is introduced to enhance the ability of particles to jump out of the local optimum. Experimental results show that our proposed algorithms outperform the traditional ones, especially regarding the evaluation criterion of F1-measure and accuracy. The results on the six keel unbalanced data set the advantages of our proposed algorithms are presented.
A Comparative Study on the Characteristics of Interactivity between Virtual Reality (VR) and Interactive Cinema

This paper compares VR cinema with interactive cinema from an interactive perspective to examine cinemas as a new medium.Rather than revealing the difference through this, the focus is on presenting a methodology that understands the cinema in a new environment that is now standing at the starting point.The development of video technology is changing not only the external elements of content but also the internal storytelling method, but the lack of killer content has always been pointed out as a problem compared to the remarkable development of technology.Therefore, it is necessary to specify the characteristics and types of interactions represented by VR (virtual reality) and new media represented by interactive media and to present directions.Therefore, the types and characteristics of interactions were compared with VR film <Help> produced in line with the new phenomenon and interactive film <Black Mirror: Bandersnatch>.Through this, we would like to present the meaning and value of the new film format from a media perspective.
Hybrid Active Contour Model for Segmentation of Synthetic and Real Images

Level set models are extensively used for image segmentation because of their capability to handle topological changes. In this paper, the proposed model uses combined local image information and global image information to evolve the con-tour around the object boundary, making it robust, irrespective of the inhomogeneity. The proposed model is capable to deal with bias conditions, such as intensity inhomogeneity and light effects. We test this model on synthetic, and real images, confirming its superiority over previous models.
Predicting Flat-Fading Channels via Meta-Learned Closed-Form Linear Filters and Equilibrium Propagation

Predicting fading channels is a classical problem with a vast array of applications, including as an enabler of artificial intelligence (AI)-based proactive resource allocation for cellular networks. Under the assumption that the fading channel follows a stationary complex Gaussian process, as for Rayleigh and Rician fading models, the optimal predictor is linear, and it can be directly computed from the Doppler spectrum via standard linear minimum mean squared error (LMMSE) estimation. However, in practice, the Doppler spectrum is unknown, and the predictor has only access to a limited time series of estimated channels. This paper proposes to leverage meta-learning in order to mitigate the requirements in terms of training data for channel fading prediction. Specifically, it first develops an offline low-complexity solution based on linear filtering via a meta-trained quadratic regularization. Then, an online method is proposed based on gradient descent and equilibrium propagation (EP). Numerical results demonstrate the advantages of the proposed approach, showing its capacity to approach the genie-aided LMMSE solution with a small number of training data points.
Animated Transitions for Multi-user Shared Large Displays

This study looks at the optimal duration for animated transitions between different views selected by different users operating a shared information visualization interface on a large display. In collaborative environments with a shared large screen both users are able to interact with the interface with users tending to alternate between the role of active-user and observer. This can cause problems if view updates are observed by users that do not trigger the updates themselves. These inactive users are more easily disoriented and confused by changes in the display that they themselves did not initiate. In order to reduce this sense of disorientation, animation can be used to smooth the transition between views. This study looks at the effect of different shared screen sizes on the optimal transition time. The results showed the best time for a shared large screen and a shared normal PC are both around 1000 ms. This is significantly longer than the best time on a single-user device which is in the range of 300 ms.
Motion Sensor Monitoring and Ability Evaluation of Sprint Hurdle Swing Training under the Background of Wireless Communication Network

The struggle with obstacles has very high technical requirements, and technical movements are particularly complex, requiring the coordination of the entire body of athletes. This article aims to study the training methods and training plans of sprinters, trying to gradually improve the waist strength, abdomen, and hip muscles of sprinters. Through basic strength training, we improve the ability of the body to coordinate strength and give full play to the core strength and sprint technique of the athletes. This paper proposes a motion sensor detection technology, which provides an effective tool for testing and analysis of sports training. The research object of this article is the hurdle running special physical speed, quality, and training methods. Based on a large amount of literature and theoretical analysis, an experimental group and a control group are set up, and an inductive analysis is carried out. The four aspects of peak power and half squat 1RM are discussed and analyzed in detail. The experimental results in this paper show that the extensor muscles of the experimental group increased by 56.5 J and 51.55 J, respectively, and the increase rates were 24.72% and 19.66%, respectively. In the control group, the extensor muscles increased by 85 J and 52 J under the test conditions of 60°/s and 300°/s, respectively, with an increase rate of 38.2% and 51.2%, respectively; the flexors increased by 32.6 J and 22 J, respectively, with an increase rate of 36.25% and 37.28%, respectively.
Forensic science and fractal nature analysis

Forensic photography, also referred to as crime scene photography, is an activity that records the initial appearance of the crime scene and physical evidence in order to provide a permanent record for the court. Nowadays, we cannot imagine a crime scene investigation without photographic evidence. Crime or accident scene photographs can often be reanalyzed in cold cases or when the images need to be enlarged to show critical details. Fractals are rough or fragmented geometric shapes that can be subdivided into parts, each of which is a reduced copy of the whole. Fractal dimension (FD) is an important fractal geometry feature. There are many applications of fractals in various forensic fields, including image processing, image analysis, texture segmentation, shape classification, and identifying the image features such as roughness and smoothness of an image. Fractal analysis is applicable in forensic archeology and paleontology, as well. The damaged image can be reviewed, analyzed, and reconstructed by fractal nature analysis.
A Literature Review on Virtual Reality Authentication

As virtual reality (VR) sees an increase in use in several domains such as retail, education, military; a secure authentication scheme for VR devices is necessary to keep users’ personal information safe. A smaller section of research focuses on the authentication schemes of VR devices. To further the understanding of this topic, we conducted a detailed literature review of VR authentication by exploring papers published till October 2020. A total of N = 29 papers were found. While many papers evaluate the accuracy of authentication methods, few conduct detailed user studies. In the user studies done, we found a lack of focus on diverse populations such as the elderly, with the mean age of the participants being 25.11. Our findings from the literature review give a detailed overview of VR-based authentication schemes and highlight trends as well as current research gaps. These findings drive future research direction to create robust and usable authentication strategies.
The convergence of financial sector in Asia

This study aims to identify the convergence of financial sector development and the effect of macroeconomic variables on each financial sector development indicator in Asia. The sample used consists of 24 countries in Asia during the period 2010-2018. Identification of convergence using ?-convergence absolute and conditional. Indicators are used to represent the development of the financial sector namely private credit, liquid liabilities, stock market capitalization, and stock market turnover. Empirical evidence was based on the Generalized Method of Moment (GMM) estimation technique. The results showed that there was convergence in Asia and that macroeconomic variables had a significant effect on the development of the financial sector.
Chapter 16: Giant Carlin-Type Gold Deposits of the Cortez District, Lander and Eureka Counties, Nevada

Abstract The Cortez district is in one of the four major Carlin-type gold deposit trends in the Great Basin province of Nevada and contains three giant (&amp;gt;10 Moz) gold orebodies: Pipeline, Cortez Hills, and Goldrush, including the recently discovered Fourmile extension of the Goldrush deposit. The district has produced &amp;gt;21 Moz (653 t) of gold and contains an additional 26 Moz (809 t) in reserves and resources. The Carlin-type deposits occur in two large structural windows (Gold Acres and Cortez) of Ordovician through Devonian shelf- and slope-facies carbonate rocks exposed through deformed, time-equivalent lower Paleozoic siliciclastic rocks of the overlying Roberts Mountains thrust plate. Juxtaposition of these contrasting Paleozoic strata occurred during the late Paleozoic Antler orogeny along the Roberts Mountains thrust. Both upper and lower plate sequences were further deformed by Mesozoic compressional events. Regional extension, commencing in the Eocene, opened high- and low-angle structural conduits for mineralizing solutions and resulted in gold deposition in reactive carbonate units in structural traps, including antiforms and fault-propagated folds. The Pipeline and Cortez Hills deposits are located adjacent to the Cretaceous Gold Acres and Jurassic Mill Canyon granodioritic stocks, respectively; although these stocks are genetically unrelated to the later Carlin-type mineralization event, their thermal metamorphic aureoles may have influenced ground preparation for later gold deposition. Widespread decarbonatization, argillization, and silicification of the carbonate host rocks accompanied gold mineralization, with gold precipitated within As-rich rims on fine-grained pyrite. Pipeline and Cortez Hills also display deep supergene oxidation of the hypogene sulfide mineralization. Carlin-type mineralization in the district is believed to have been initiated in the late Eocene (&amp;gt;35 Ma) based on the age of late- to postmineral rhyolite dikes at Cortez Hills. The Carlin-type gold deposits in the district share common structural, stratigraphic, alteration, and ore mineralogic characteristics that reflect common modes of orebody formation. Ore-forming fluids were channeled along both low-angle structures (Pipeline, Goldrush/Fourmile) and high-angle features (Cortez Hills), and gold mineralization was deposited in Late Ordovician through Devonian limestone, limy mudstone, and calcareous siltstone. The Carlin-type gold fluids are interpreted to be low-salinity (2–3 wt % NaCl equiv), low-temperature (220°–270°C), and weakly acidic, analogous to those in other Carlin-type gold deposits in the Great Basin. The observed characteristics of the Cortez Carlin-type gold deposits are consistent with the recently proposed deep magmatic genetic model. Although the deposits occur over a wide geographic area in the district, it is possible that they initially formed in greater proximity to each other and were then spatially separated during Miocene and post-Miocene regional extension.
Summary of Scene Text Detection Based on Deep Learning

Scene text detection is a general text detection technology, which has become a hot research direction in the field of computer vision and document analysis in recent years, and is widely used in geographic positioning, license plate recognition, unmanned driving, and other fields. Compared with traditional document text detection, scene text changes more dramatically in font, scale, arrangement, and background. Therefore, deep learning technology has become the mainstream method in this field because of its excellent performance, which is helpful to improve the ability of text detection. This paper introduces the main research techniques of natural scene text detection and summarizes the structural characteristics of some classical network models. This paper sorts out, analyzes, and summarizes the running mechanism and performance of various network models for natural scene text detection deep learning-based in recent years. The common public datasets and their application characteristics are listed. Finally, the problems and challenges in scene text detection based on deep learning are discussed and provide an outlook on the future research directions in this field.
The Engineering Planning Method of Cloud Energy Storage System for Ancillary Service by Blockchain

To build an actual cloud energy storage system by blockchain for the ancillary service, this paper presents a prospective engineering planning method and design process to build a platform with five functions of cloud energy storage system. To demonstrate the feasibility, the engineering planning method includes the following steps. First, the detail design drawings of planning and feasibility analysis are introduced. The feasibility analysis including the project’s necessity, project alternatives, project impact, design drawings, construction scale are needed. Then, the device hardware design of blockchain platform with five functions is presented. Thirdly, the main material and equipment list, and the total investment are given. Finally, the post project evaluation is introduced. The functions include the peak load regulation, unit primary frequency regulation and automatic generation control, black start, reactive power and voltage regulation, reserve ancillary service for the integrated energy system. The paper can be referenced for planner.