Reviewerly/exHarmony huggingface dataset:
Noise sensitivity of physical reservoir computing in a ring array of atomic switches

Reservoir computing (RC) is possible using physical systems. We have previously proposed an RC for ideal atomic switches. When temporal current fluctuations (noise) from the measurement of actual atomic switches are introduced into the proposed RC, performance degrades significantly. To address this issue, we propose novel methods for increasing the operating current range and observing the atomic switch several times to determine the average noise. Consequently, the memory capacity of the RC model increased, despite the presence of noise. To improve the precision of RC, we investigated the capacity and showed that changing the time constant of atomic switches results in an improvement.
Progress Planning Method of Strength Quality Training of Volleyball Players Based on Data Mining

In order to better realize the training effect of volleyball players’ strength quality and accelerate the training progress of volleyball players’ strength quality, this paper puts forward the planning method of volleyball players’ strength quality training progress based on data mining, constructs the training management system of volleyball players’ strength quality by using data mining algorithm, and excavates the evaluation indexes of volleyball players’ strength quality training quality, Finally, through experiments, it is confirmed that the volleyball player strength quality training schedule planning method based on data mining has high practicability in the process of practical application and fully meets the research requirements.
Application of Long-Term and Short-Term Memory Neural Network in Technical Evaluation of Hurdle Track and Field

In order to study the application of long-term and short-term memory neural networks in the technical evaluation of hurdle track and field, firstly, the related contents of long-term and short-term memory neural networks and hurdle track and field sports are analyzed, the variation characteristics of kinematics and surface electromyography of swinging leg in the landing link of lower hurdle are analyzed, and the relationship between surface electromyography and kinematics parameters is analyzed. Then, the kinematics and surface EMG data of three male hurdlers in the hurdle team and two men in the College of Physical Education were collected when completing the technical link of pressing down and landing. The subject data were processed and analyzed by using the Simi motion video analysis system and DASY lab10.0 EMG analysis software. Finally, SPSS13.0 and origin are used to analyze the average, standard deviation, and correlation of the data results and draw a graph. Experiments show that neural network theory and finite element method, as the new forces of sports biomechanics, have been used to diagnose track and field technology. With the popularization of this method, it will play a greater role in the research of track and field technology.
A Hybrid Approach of Prediction Using Rating and Review Data

A collaborative filtering technique has proven to be the preferable approach for personalized recommendations. Traditionally, collaborative filtering recommends target items to those users who have similar tastes. The performance of collaborative filtering degrades significantly when a considerable number of users do not provide ratings on recommended products. In such a scenario, the dataset utilized in recommendation becomes highly sparse, and ratings become very few or none co-rated. To mitigate the problem, as mentioned earlier, and to improve the performance of collaborative filtering, we propose an approach that adopts users' textual reviews and ratings both in the rating prediction. The dataset used is Amazon fine Food Reviews containing rating and text review with 568454 reviews from October 1999 to October 2012. The proposed model is tested on the collected dataset. The experimental results provide the proper evidence that the proposed model outperforms other traditional algorithms of collaborative filtering techniques.
Go Back in Time: Generating Flashbacks in Stories with Event Temporal Prompts

Stories or narratives are comprised of a sequence of events.To compose interesting stories, professional writers often leverage a creative writing technique called flashback that inserts past events into current storylines as we commonly observe in novels and plays.However, it is challenging for machines to generate flashbacks as it requires solid understanding of event temporal order (e.g.feeling hungry before eat, not vice versa), and the creativity to arrange storylines so that earlier events do not always appear first in narrative order.Two major issues in existing systems exacerbate the challenges: 1) temporal bias in pretraining and story datasets that leads to monotonic event temporal orders; 2) lack of explicit guidance that helps machines decide where to insert flashbacks.We propose to address these issues using structured storylines to encode events and their pair-wise temporal relations ( before , after and vague ) as temporal prompts that guide how stories should unfold temporally.We leverage a Plan-and-Write framework enhanced by reinforcement learning to generate storylines and stories end-toend.Evaluation results show that the proposed method can generate more interesting stories with flashbacks while maintaining textual diversity, fluency and temporal coherence. 1
Evaluation of English Subjective Questions Based on Deep Neural Networks

In the background of artificial intelligence (AI) era, deep neural networks (DNNs) also have a far-reaching impact on all walks of life. For the field of education, the integration of deep neural network technology and language teaching is more in-depth. Targeting at the problems of high costs and low efficiency of manual evaluation, this paper aims to study the evaluation of the English subjective questions by using deep neural networks. Firstly, the surface features of the English character information, including English antisense, negative information, semantic features, word frequency, sentences length, and words order, are combined; and the calculation method of the English sentence similarity based on multifeature fusion is established through the analytic hierarchy process (AHP). Secondly, the English text recognition is composed of long and short memory lost by aggregation cross entropy. The introduction of aggregation cross entropy can effectively improve the accuracy of text recognition. Finally, the evaluation model based on deep neural network is used to evaluate the English subjective questions. The experimental results, in terms of accuracy, recall rate, and effectiveness of the automatic recognition, show that the proposed method has high accuracy and can effectively improve the quality of the English teaching and realize a personalized teaching approach.
An Analysis of Generative Methods for Multiple Image Inpainting

Image inpainting refers to the restoration of an image with missing regions in a way that is not detectable by the observer. The inpainting regions can be of any size and shape. This is an ill-posed inverse problem that does not have a unique solution. In this work, we focus on learning-based image completion methods for multiple and diverse inpainting which goal is to provide a set of distinct solutions for a given damaged image. These methods capitalize on the probabilistic nature of certain generative models to sample various solutions that coherently restore the missing content. Along the chapter, we will analyze the underlying theory and analyze the recent proposals for multiple inpainting. To investigate the pros and cons of each method, we present quantitative and qualitative comparisons, on common datasets, regarding both the quality and the diversity of the set of inpainted solutions. Our analysis allows us to identify the most successful generative strategies in both inpainting quality and inpainting diversity. This task is closely related to the learning of an accurate probability distribution of images. Depending on the dataset in use, the challenges that entail the training of such a model will be discussed through the analysis.
Direct Measurement of Atomic Entanglement via Cavity Photon Statistics

We propose an experimental scheme for the measurement of entanglement between two two-level atoms. Our scheme requires one of the two entangled atoms to interact with a cavity field dispersively, and we show that by measuring the zero time-delay second-order coherence function of the cavity field, one can measure the concurrence of an arbitrary Bell-like atomic two-qubit state. As our scheme requires only one of the atoms to interact with the measured cavity, the entanglement quantification becomes independent of the location of the other atom. Therefore, our scheme can have important implications for entanglement quantification in distributed quantum systems.
A Multiserver Configuration and Request Distribution Framework for Profit Maximization in a Three-Tier Cloud Service Architecture

The rapid development of cloud computing has generated numerous cloud service providers. In a three-tier cloud service architecture, maximizing the cloud service profit is not only a goal pursued by cloud service providers but also a potent force that drives the continuous development of cloud computing technology. The traditional methods of maximizing cloud service profits are either limited to cloud resource management or service request distribution. Few work contributes to maximizing the profit of cloud service providers while considering both resource management and request distribution. Besides, the heterogeneity of servers is also an important concern when configuring the multiserver system. Therefore, this paper proposes a multiserver configuration and request distribution framework oriented to a three-tier cloud service architecture for maximizing the profit of cloud service providers. Compared with the benchmark method, extensive numerical simulation experiments show that our proposed framework can not only maximize the profit of cloud service providers, but also has a well-time efficiency.
Parallelism measurement method for nontransparent flat parts

Nontransparent flat parts with weak rigidity are widely used in precision physics experiments, aerospace, and other fields in which parallelism is required. However, existing methods cannot meet requirements due to the limitation of measurement size and accuracy. This paper proposes a new method for measuring parallelism of nontransparent flat parts with high accuracy and then builds a submicrometer-level parallelism measuring system. The 3D model of the whole part is reconstructed by thickness and flatness, which are measured respectively. Subsequently, parallelism is evaluated by the principle of minimum directional zone. The method is verified by an experiment with a thin copper substrate sized ⊘200mm×2.48mm on the parallelism measuring system. The experiment result shows that the part's parallelism is 7.41 µm, and the expanded uncertainty of parallelism measurement system is 0.34 µm, k=2.
NOTE-TAKING DRILLING TO IMPROVE ACADEMIC LISTENING SKILLS IN THIRD SEMESTER STUDENTS, UNIVERSITAS MUHAMMADIYAH SURAKARTA

This study aims to reveal the role of note-taking in improving listening skills and exposing learning resources besides TED.COM. The skill of note-taking in academic activities is subject to the incompetency of note-taking in standard form. This research was a classroom action research approach for third-semester students at the Universitas Muhammadiyah Surakarta that focused on TED.COM’s contribution to students’ skills. The results of the initial research found obstacles, first, students were getting to know note-taking and TED.COM for the first time. Second, the high speed of native speakers impacts lost vocabulary understanding. Third, the tone, accent, and intonation are also separate challenges. The results of the cycles showed first, there was a positive change since the students got an extra note-taking theory and variations of audio listening. Second, students have varied learning resources, so it is recommended for educators to also understand student learning resources. In conclusion, TED.COM contributed to the academic listening improvement and in the future, the Podcast program can be explored for note-taking drilling besides TED.COM.
Hybrid-attention and frame difference enhanced network for micro-video venue recognition

Many micro-video related applications, such as personalized location recommendation and micro-video verification, can be benefited greatly from the venue information. Most existing works focus on integrating the information from multi-modal for exact venue category recognition. It is important to make full use of the information from different modalities. However, the performance may be limited by the lacked acoustic modality or textual descriptions in uploaded micro-videos. Therefore, in this paper visual modality is explored as the only modality according to its rich and indispensable semantic information. To this end, a hybrid-attention and frame difference enhanced network (HAFDN) is proposed to generate the comprehensive venue representation. Such network mainly contains two parallel branches: content and motion branches. Specifically, in the content branch, a domain-adaptive CNN model combined with temporal shift module (TSM) is employed to extract discriminative visual features. Then, a novel hybrid attention module (HAM) is introduced to enhance extracted features via three attention mechanisms. In HAM, channel attention, local and global spatial attention mechanisms are used to capture salient visual information from different views. In addition, convolutional Long Short-Term Memory (convLSTM) is enforced after HAM to better encode the long spatial-temporal dependency. A difference-enhanced module parallel with HAM is devised to learn the content variations among adjacent frames, which is usually ignored in prior works. Moreover, in the motion branch, 3D-CNNs and LSTM are used to capture movement variation as a supplement of content branch in a different form. Finally, the features from two branches are fused to generate robust video-level representations for predicting venue categories. Extensive experimental results on public datasets verify the effectiveness of the proposed micro-video venue recognition scheme. The source code is available at https://github.com/hs8945/HAFDN.
ImageCLEF 2020: Multimedia Retrieval in Lifelogging, Medical, Nature, and Internet Applications

This paper presents an overview of the 2020 ImageCLEF lab that will be organized as part of the Conference and Labs of the Evaluation Forum—CLEF Labs 2020 in Thessaloniki, Greece. ImageCLEF is an ongoing evaluation initiative (run since 2003) that promotes the evaluation of technologies for annotation, indexing and retrieval of visual data with the aim of providing information access to large collections of images in various usage scenarios and domains. In 2020, the 18th edition of ImageCLEF will organize four main tasks: (i) a Lifelog task (videos, images and other sources) about daily activity understanding, retrieval and summarization, (ii) a Medical task that groups three previous tasks (caption analysis, tuberculosis prediction, and medical visual question answering) with new data and adapted tasks, (iii) a Coral task about segmenting and labeling collections of coral images for 3D modeling, and a new (iv) Web user interface task addressing the problems of detecting and recognizing hand drawn website UIs (User Interfaces) for generating automatic code. The strong participation, with over 235 research groups registering and 63 submitting over 359 runs for the tasks in 2019 shows an important interest in this benchmarking campaign. We expect the new tasks to attract at least as many researchers for 2020.
Generating Video From Images using GAN and CVAE

In a given scene, people can often easily predict a lot of quick future occasions that may occur. However generalized pixel-level expectation in Machine Learning systems is difficult in light of the fact that it struggles with the ambiguity inherent in predicting what's to come. However, the objective of the paper is to concentrate on predicting the dense direction of pixels in a scene — what will move in the scene, where it will travel, and how it will deform through the span of one second for which we propose a conditional variational autoencoder as a solution for this issue. We likewise propose another structure for assessing generative models through an adversarial procedure, wherein we simultaneously train two models, a generative model G that catches the information appropriation, and a discriminative model D that gauges the likelihood that an example originated from the training data instead of G. We focus on two uses of GANs semi-supervised learning, and the age of pictures that human's find visually realistic. We present the Moments in Time Dataset, an enormous scale human-clarified assortment of one million short recordings relating to dynamic situations unfolding within three seconds.
Research on Prediction Methods of Prevalence Perception under Information Exposure

With the rapid development of information technology, the explosive growth of data information has become a common challenge and opportunity. Social network services represented by WeChat, Weibo and Twitter, drive a large... | Find, read and cite all the research you need on Tech Science Press
Fluorite Deposits

In Mongolia more than 600 fluorite deposits are known, especially in the eastern and central parts of the country which host economically important fluorite deposits. Nearly all of the fluorite deposits in Mongolia are hydrothermal type, which can be divided into two genetic groups based on their relation to volcanic and plutonic rocks. The former group is classified as epithermal fluorite deposits and fluorite deposits in the carbonatite host. The latter group is classified as types of bertrandite-phenacite-fluorite, albitite, Mo/Sn-W, and pegmatite. The total annual production of fluorite reached 527,000 tonnes in 1995. At present, Bor-Undur, Adag, Zuun Tsagaan Del, Tsagaan Elgen, Urgun, Delgerkhaan, and Bayan-Ovoo deposits are in commercial production, and further exploration is being carried out. The largest mining and ore processing complex is built on the Bor Undur deposit.
Brain Tissue Classification to Detect Focal Cortical Dysplasia in Magnetic Resonance Imaging

Focal cortical dysplasia (FCD) is a local malformation of the cortex, the main cause of refractory epilepsy in childhood and one of the most common causes in adults. The surgery decision and planning depend on the FCD localization. Although recent studies have successfully detected FCD through artificial intelligence, no study investigates the relevance and prevalence of cortical features on FCD identification and the performance of different machine learning techniques. In this study, the proposed method constructed a voxel-based set of features, e.g., texture measure, border definition, cortical thickness.
Hybrid Algorithm using the Advantage of Krill Herd Algorithm with Opposition- Based Learning for Dynamic Resource Allocation in Cloud Environment

The cloud computing systems have more consideration due to the growing control for elevated concert computing and data storage. Resource allocation plays a vital role in cloud systems. To overcome the obscurity present in resources allocation system. In this paper, we design and develop a technique for dynamic resource allocation. A Hybridized approach is designed with the help of multi-objective oppositional krill herd optimization algorithm (OKHA). It is a combination of the krill herd algorithm and Opposition-based learning (OBL), OBL is added to get enhanced performance of the krill herd algorithm. The objective of this hybridization is to reduce the cost. In this Hybridized process each task consists of two cost i.e monetary cost and computational cost. Here each task is divided into many subtasks and assigns the respective resources to it. Our proposed multi-objective optimization algorithm will decide allocation of resource for the each subtask in this process. Finally, the testing is passed out, we evaluate our proposed algorithm with PSO, and GA algorithm we verified the performance levels of our proposed Multi-objective optimization algorithm.
Sidecar based resource estimation method for virtualized environments

The widespread use of virtualization technologies in telecommunication system resulted in series of benefits, as flexibility, agility and increased resource usage efficiency.Nevertheless, the use of Virtualized Network Functions (VNF) in virtualized modules (e.g., containers, virtual machines) also means that some legacy mechanisms that are crucial for a telco grade operation are no longer efficient.Specifically, the monitoring of the resource sets (e.g., CPU power, memory capacity) allocated to VNFs cannot rely anymore on the methods developed for earlier deployment scenarios.Even the recent monitoring solutions designed for cloud environments is rendered useless if the VNF vendor and the telco solution supplier has to deploy its product into a virtualized environment, since it does not have access to the host level monitoring tools.In this paper we propose a sidecar-based solution to evaluate the resources available for a virtualized process.We evaluated the accuracy of our proposal in a proof of concept deployment, using KVM, Docker and Kubernetes virtualization technologies, respectively.We show that our proposal can provide real monitoring data and discuss its applicability.
Predicting Sentiment Polarity of Microblogs using an LSTM – CNN Deep Learning Model

In this paper we propose a novel supervised machine learning model to predict the polarity of sentiments expressed in microblogs. The proposed model has a stacked neural network structure consisting of Long Short Term Memory (LSTM) and Convolutional Neural Network (CNN) layers. In order to capture the long-term dependencies of sentiments in the text ordering of a microblog, the proposed model employs an LSTM layer. The encodings produced by the LSTM layer are then fed to a CNN layer, which generates localized patterns of higher accuracy. These patterns are capable of capturing both local and global long-term dependences in the text of the microblogs. It was observed that the proposed model performs better and gives improved prediction accuracy when compared to semantic, machine learning and deep neural network approaches such as SVM, CNN, LSTM, CNN-LSTM, etc. This paper utilizes the benchmark Stanford Large Movie Review dataset to show the significance of the new approach. The prediction accuracy of the proposed approach is comparable to other state-of-art approaches.
Exploration of Twitter Sentiments and Classification by using Deep CNN and Naive Bayes

Sentiment evaluation of tweets help the enterprises to evaluate public emotion towards the activities or products associated with them. Most of the research targeted to obtain sentiment capabilities with the help of analyzing syntactic and lexical features which can be expressed through sentiment phrases, emoticons, exclamation marks etc. In the proposed paper we introduce a phrase embedding received by means of unsupervised learning(deep learning) on large twitter texts which uses contextual semantic relationships and co-occurrence statistical characteristics between words in tweets and also con- sider the emojis to categorise the emotions whether it is positive or negative by the use of Naive Bayes. In the preceding paper which used usnsupervised learning approach for classification, has an accuracy of 87% and supervised has an accuracy of 89%. According to our context, Naive Bayes has given an accuracy of 100% and CNN has given an accuracy of 100%. As compared to machine learning. It has a higher performance on the accuracy, precision and recall.