Reviewerly/exHarmony huggingface dataset:
Content-Aware Load Balancing in CDN Network

Content delivery network (CDN) is an important infrastructure to transfer video traffic which is a major component of internet traffic. One hot topic in CDN field is load balancing mechanism, i.e. redirect users to CDN servers to efficiently utilize CDN resource. Most of related works try to provide available bandwidth or reduce delay to user as much as possible according to the CDN servers' heterogeneous available bandwidth or distance to users. However, recent works find that users' quality of experience (QoE) related not only to bandwidth and delay, but also to content of videos. Specifically, different video frames require different sizes to get the same quality of experience (QoE), and the gradients of frame size-QoE curves are various. Based on this observation, we propose a novel load balancing method in CDN by taking the above factors into account. Specifically, we build an optimization model and propose a corresponding heuristic method to explore the benefit of considering the influence of video content on QoE. The numerical results show that our method can improve users' overall QoE by more than 5% compared with the traditional methods.
Identification of Key Lines of Power Grid under Typhoon Disaster Based on Situation Awareness

Typhoon disaster may cause blackout. The key lines are closely related to the occurrence of blackout. It is of great significance for disaster prevention and mitigation to accurately identify the key lines under typhoon disaster. Based on the idea of situation awareness, the key lines in the power grid under typhoon disaster are identified by considering five situations, including line fault situation, defense situation, topology situation, operation situation and fault prone situation. By using the combination of subjective and objective AHP-entropy weight, the weight of each situation is reasonably determined. And the comprehensive situation of the line is determined. The key lines are identified according to the comprehensive situation of the line. The simulation results of IEEE 39-bus system under typhoon disaster show that the proposed method can identify the key lines in the power system under typhoon disaster reasonably and comprehensively.
The Role of RNNs for Contextual Representations

Recurrent neural networks (RNNs) have been used prevalently to capture long-term dependencies of sequential inputs. In particular, for question answering systems, variants of RNNs, such as Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU), allow the positional, ordering or contextual information to be encoded into latent contextual representations. While applying RNNs for encoding this information is intuitively reasonable, no specific research has been conducted to investigate how effective is their use in such systems when the sequence of sentences is unimportant. In this paper we conduct a case study on the effectiveness of using RNNs to generate context representations using the DMN+ network. Our results based on a three-fact task in the bAbI dataset show that sequences of facts in the training dataset influence the predictive performance of the trained system. We propose two methods to resolve this problem, one is data augmentation and the other is the optimization of the DMN+ structure by replacing the GRU in the episodic memory module with a non-recurrent operation. The experimental results demonstrate that our proposed solutions can resolve the problem effectively.
Multiple Object detection using Autonomous UAVs based on YOLOv3 algorithm

Abstract Currently, there has been an enormous increase in interest towards autonomous UAVs and applications with respect to autonomous UAVs such as scientific data collection and security monitoring, infrastructure inspection, search and rescue and much more. Recognizing objects visually is a key step in such applications and is very pivotal to build a complete autonomous system. Nevertheless, the chore of detecting an object is very much challenging and is even more difficult when we have low quality images aboard low cost consumer UAVs. More frequently due to the motion of UAVs, images get blurred, noisy and even onboard cameras have low resolution hence object detection becomes more challenging since identifying objects are quite small and these tasks get even more difficult as there is a necessity of real time detection of objects. This paper focuses on all these aspects and brings an outcome for the object detection and identification.
Formal Reachability Analysis for Multi-Agent Reinforcement Learning Systems

Reachability analysis is one of the most basic and challenging problems in verification. We investigate this problem in multi-agent reinforcement learning (MARL) system by transforming the reachability analysis to decision-making problem tackled by mixed integer linear programming (MILP) solver Gurobi. We define syntax and semantics for multi-role strategy logic (mrSL) which is used to describe the reachability specification. The logic mrSL is a variant of SL to express properties, which provide a holistic perspective to describe reachability properties instead of specifying a specific agent reaches a certain state in the MARL system. And the algorithms to translate reachability property into MILP constraints are provided. A tool called MAReachAnalysis is introduced, which uses Gurobi to solve the corresponding reachability problem and evaluate it on the predator-prey task of multi-agent deep deterministic policy gradient (MADDPG) example, discussion of the experimental results obtained on a range of test cases.
On the super quantum information

Abstract A superqubit is a three dimensional super vector belonging to uOSP(1/2) super group. And considered as a super symmetric generalized qubit. To see how superqubits properties can be exploited in the field of the quantum computing, we propose some algorithms in order to perform the super teleportation. implementation and technologies. Using superqubit circuits will be an open field of research.
Prediction Method of High Frequency Electromagnetic Noise of Micro Electric Vehicle based on Wavelet Time-Frequency Analysis

In order to reduce the high-frequency electromagnetic noise of micro-electric vehicles, a method for predicting high-frequency electromagnetic noise of micro-electric vehicles based on wavelet time-frequency analysis is proposed. Through the electromagnetic structure design and excitation source, the motor vibration and noise are analyzed to obtain the high-frequency electromagnetic noise harmonics of electric vehicles. The time-step finite element method is used to calculate the electromagnetic force wave of the induction motor used for driving the electric vehicle, and the electromagnetic noise information is collected. Randomly sort the collected power supply margins of electric vehicles connected to charging, correct the corresponding data feature values, establish accurate motor CAD models and FEA models, and complete the prediction of high-frequency electromagnetic noise of micro-electric vehicles. By optimizing the shape of the permanent magnet and the stator potting and sealing, the improved motor was tested and verified, the noise results before and after optimization were compared, and the effectiveness of the motor noise reduction design was proved.
Unscented predictive filter

In this chapter, a sigma-point unscented predictive filtering approach is developed to the state estimation problem of a class of nonlinear discrete systems. This is achieved by using the unscented transformation. The state estimation accuracy ensured by the proposed approach is higher than the conventional predictive filter. The proposed filter also addresses the drawbacks of the conventional sigma-point Kalman filters. The effectiveness of the designed filter has been demonstrated in simulation with its application to estimate states of the single microsatellite attitude control system and the microsatellite formation flying system.
Architecture and Design of Oracle Scheduler

Abstract Oracle scheduler focuses on the automatic management, maintenance and monitoring of Oracle database jobs. Through it, many routine database tasks can be automatically executed, and human intervention can be reduced, so as to improve the efficiency of system operation and implement more flexible and complex task management. This paper introduces the concept and classification of Oracle scheduler, analyzes each component of Oracle scheduler, explains its overall architecture and tasks of different components, summarizes the problems needing attention in design according to design experience, and illustrates its application through the creation of Oracle scheduler object instance, job scheduling and evaluation of execution frequency.
Mapping Antimony Concentration over Geothermal Areas Using Hyperspectral and Thermal Remote Sensing

Some geothermal areas are largely covered by vegetation, making exploration and monitoring a difficult and expensive task. Therefore, looking for pathfinders/proxies in typical vegetation cover could reveal information from the hydrogeological system remotely. Buffering is a commonly used proximity technique for spatial analyses in remote sensing. Aiding to reduce the possible inaccuracies of geolocation. Variable distance isotropic buffers were utilized to extract data from a combination of different spectral images covering Waiotapu Geothermal Field in New Zealand. Correlating them with the antimony concentration of sampled kanuka plants in the Field, through a PLSR (Partial Least Squares Regression).
Personalized Online Learning with Pseudo-Ground Truth

Personalized online machine learning allows a very accurate modelling of individual behavior and demands. In particular, a system that dynamically adapts during runtime can initiate a continuous collaboration with its user where both alternately adjust to each other to maximize the system's utility. However, in application scenarios based on supervised learning it is often unclear how to obtain the required ground truth for such dynamic systems. In this paper, we focus on applications where a real-time classification of sequential data is crucial. Concretely, we propose to adapt an online personalized model solely based on pseudo-ground-truth information which is provided by another machine learning model. This model has the advantage to classify sequences in retrospective with a small delay and thus is able to achieve a higher performance than real-time systems. In particular, it is a pre-trained offline model, which means that no ground-truth information is necessary during runtime. We apply the proposal on the task of online action classification, for which the benefits of personalization have been recently emphasized.
Application of Text Dimensionality Reduction Method in Information Filtering on Railway Transit Cloud Platform

Abstract This paper focuses on the useful information data extraction of urban rail transportation’s urban rail cloud platform. Through the correlation and redundancy between feature items in the text vector space of a large amount of information data, a method of clustering text vector space dimensionality reduction is proposed and applied to the information data extraction of urban rail cloud platform. The vector aggregation theory and feature selection are applied to reduce the dimensionality of the feature space, so that the reduced dimensional feature items are more representative of the category. Through test experiments, it is proved that this text dimensionality reduction method algorithm is applied to the information data filtering of urban rail cloud platform, which effectively reduces the dimensionality of vector space and improves the accuracy and speed of text classification in urban rail cloud platform information data.
Effective Fusion of 3DCNN and Convolutional GRU for Gesture Recognition

Gesture recognition is a challenging topic in the field of computer vision. This paper combines the advantages of 3DCNN and ConvRNN in spatial and temporal feature extraction respectively and proposes a network structure of “Res3D&Dilated+ConvGRU+2DCNN”. After that, a variant of ConvGRU is proposed, which further exerts the advantages of ConvGRU in temporal feature extraction. The experiment compares the effect of combining dilated convolution in the temporal dimension with 3D Resnet, and the effectiveness of the ConvGRU variant in spatiotemporal information fusion. In the 3D dilated convolutional network, we designed a suitable convolution kernel structure and step size so that subsequent modules can be reused directly. The modular structure design can be easily replaced. The comparison on the IsoGD dataset shows our network effectively integrates spatial and long-short terms features in gesture recognition videos. The variant of ConvGRU also enables each module to play its own advantages in the framework.
Speech Generation Process Control Using Sensory Feedback Mechanism

The research objective is as follows: to develop methodology and technology of laboratory experiments on the speech generation process control using the sensory feedback mechanism. The experiments are based on innovative technologies that allow the researcher to control the speaker’s articulatory process, and herein lies scientific originality of the study. The experiment results conclusively indicate that the sensory correction technique, or the sensory feedback technique (the cybernetic term), can be efficiently used to control the speech generation process.
Design of Fault Diagnosis System for Electric Vehicle Motor Based on Virtual Instrument

In the traditional electric vehicle motor fault diagnosis system, the offline diagnosis is not obvious to the hidden fault diagnosis with inconspicuous impact, which causes the system to have high delay. For this reason, a fault diagnosis system of electric vehicle motor based on virtual instrument is designed. In the hardware design, according to the characteristics of the fault signal, the signal conditioning circuit and the virtual instrument data acquisition card are designed to realize the collection of the fault signal data; in the software design, the collected fault signal is preprocessed, and the data is accessed through the file format. Resonance demodulation realizes online fault diagnosis. The test results show that the average time delay of fault signal acquisition and the feedback delay of the designed electric vehicle motor fault diagnosis system based on virtual instrument are relatively low, which meets the requirements of the system for time delay.
Fusion-Oriented Aircraft Detection in Large Scene Image Based on Tiny Darknet

High resolution and large field of view in large scene images ( ) provide rich detail information and wide viewing angle. However, the computational complexity is also drastically increased. Small object detection in large scene remote sensing images is always one of the nontrivial problems. Generally, the body length of aircraft in large scene images is only 30~80 pixels at 0.5m or 1m resolution. While convolutional neural networks (CNN) have made great breakthroughs in natural images, the fast and accurate identification of small objects has not been effectively tackled. In order to improve accuracy, this paper proposes a two-stage Fusion-oriented aircraft detection model (FOADM) and uses an adjusted detection network model Tiny Darknet based on Darknet53 to adapt for the detection of small aircraft. In the first stage, we combine Line Segment Detection (LSD) with Graph-Based Visual Saliency (GBVS) to acquire the region of interest (ROI) in downsampled images, which can reduce the worthless search to some extent. Then, in original high resolution image, the ROI is located and partitioned. In the second stage, each block is fed into the detection network Tiny Darknet for aircraft detection. Experiments demonstrate the proposed method can effectively improve the recall of detection result in high resolution.
Acoustic and Textual Data Augmentation for Code-Switching Speech Recognition in Under-Resourced Language

Under-resourced and code-switching speech recognition have recently received research interest, resulting in several robust acoustic and language modeling approaches. As Taiwanese and Mandarin have been popularly and widely used in Taiwan, this paper aims to address the under-resourced and codeswitching issues. First, phone sharing between Taiwanese and Mandarin is employed for acoustic data augmentation to construct the acoustic models of Taiwanese speech recognizer. Regarding the lack of Taiwanese text corpus, this paper translates Mandarin corpus into Taiwanese corpus based on word-to-word translation. Moreover, additional translation rules for codeswitching text are manually designed. The augmented text corpus is then used for training the code-switching language models. In the experimental results, the word error rate for code-switching speech recognition was 26.02%, which was better than that trained by the pure Taiwanese corpus.
Efficient Implementation of HashSieve Algorithm for Lattice-Based Cryptography

The security of lattice-based cryptosystems that are secure for the post-quantum period is based on the difficulty of the shortest vector problem (SVP) and the closest vector problem (CVP). In the literature, many sieving algorithms are proposed to solve these hard problems. In this paper, efficient implementation of HashSieve sieving algorithm is discussed. A modular software library to have an efficient implementation of HashSieve algorithm is developed. Modular software library is used as an infrastructure in order for the HashSieve efficient implementation to be better than the sample in the literature (Laarhoven's standard HashSieve implementation). According to the experimental results, it is observed that HashSieve efficient implementation has a better running time than the example in the literature. It is concluded that both implementations are close to each other in terms of the memory space used.
Machine learning approach for categorical document mining

Abstract The word text mining or document mining means automatic classification of text documents to different categories depending on their content. At present days this area of research is attracting many researchers due to increasing use of electronic documents in everyday life. In this paper authors have proposed a document mining method using Bisecting K-means algorithm, KNN classifier and Decision tree. There are many machine learning classification algorithms used for information retrieval. But most of them have very high computational complexity. So a method Bisecting K – Means clustering algorithm instead of normal K-means algorithm is used here and this approach trivially reduces the number of comparisons compared to others. The researchers also used decision tree at last to obtain the sub categories more accurately. After analysis it is found that the combination of Bisecting K Means and KNN classifier enhance the accuracy of the categorization. Accuracy of this proposed system is shown for each category at Result and discussion section.
An Amplitude Processing Approach Based on Gradient Measurement to Improve Resolution Bandwidth in Spectrum Analysis

This paper describes a Gradient-Based amplitude processing algorithm to achieve high-resolution bandwidth in spectrum analysis. The structure in which this method is applied is hybrid analog/digital spectrum analyzer that includes an analog down-converter section and a digital processing unit. The superheterodyne stage operates as a down converter block and translates consecutive frequency spans to the bandwidth of IF filter which contributes to having a wide input frequency range from 0.5 to 6 GHz. In digital section, the output of IF filter is sampled and processed during a gradient measurement algorithm. By performing this algorithm we improved the frequency resolution to 0.5 MHz for a 10 MHz-bandwidth IF filter as well as optimizing the time duration that is required to sweep the whole spectral span.
BML: A Data Mapping Language for Blockchain Platforms

Currently, all blockchain-based applications conduct two primary operations, i.e., writing data on blockchain networks and reading these data from the networks. These tasks require users to have considerable knowledge in blockchain technology, and they become even more challenging if users want to utilize different blockchain platforms to write and read data. So far, we have not had a uniform mechanism to perform write and read operations on various blockchain platforms. In addition, writing a huge amount of data on blockchain networks is a time-consuming task and requires considerable transaction fees. To address these issues, we present in the paper a data mapping language named BML. BML allows developers to uniformly define mappings for data transformation from traditional data storage mechanisms into blockchain networks. Conversely, this language also assists users in reading transformed data. Currently, BML accepts five input data sources, including XML, JSON, XLSX, SQL (relational database), NoSQL, and supports two output platforms, including Hyperledger Sawtooth and Ethereum.
Research on Problems and Countermeasures of Computer Assisted English Teaching

Abstract The current trend is that the parents of students attach great importance to the students’ English learning. Every parent hopes that their children can have fluent English [1] . This requirement has penetrated into the hearts of every parent in contemporary China. It has gradually become a social and educational development trend. Nowadays, the skills of computer-assisted education in our country are being widely popularized, and the application of computer-assisted English teaching has become very popular. According to the analysis of experts, the advanced and vivid teaching mode is just an advanced teaching method for students to learn English well. However, if computer-assisted technology is not used properly, it will be of no benefit to students in the school.
Visual Mining Method of College Students' Daily Emotion Index Based on Physiological Big Data

College Students' daily emotions are affected by many factors, and there will be many differences. Therefore, traditional methods rely on data mining algorithm to mine emotion index. However, due to the lack of unified parameter adjustment mechanism in traditional mining, the speed of obtaining information is slow, which reduces the work efficiency. Therefore, this paper studies the visualization mining of College Students' daily emotion index based on physiological big data Excavation method. By collecting the big data of College Students' physiology, this method obtains the change rules of College Students' daily emotions, and uses clustering algorithm to cross analyze the intrinsic nature of emotions, and constructs a comprehensive visual mining algorithm to visually mine the daily emotion index of college students. Experimental results: the mining speed of this research method is 271s and 346.5s faster than the two traditional methods. It can be seen that the method of this study improves the work efficiency under the premise of reliable mining.
Hierarchical Classification Boost Using Confidence Belief Propagation

Fine-grained classification (FGC) is a constant tough task due to high similarity between sub-classes. Most research ignores the fact that FGC problem is also a hierarchical classification problem. In many situations, it is not able to make classification decision for leaf nodes in hierarchical tree, due to the lack of information. Our research treats FGC problem as multi-label classification problem. In addition, a confidence belief propagation method is proposed to solve the inconsistence of multi-labels in hierarchical relationship. Experiments on bird classification dataset with three hierarchical levels show that, the proposed approach is able to improve hierarchical classification accuracy, and it is also able to provide accurate confidence score for identifying the indistinguishable cases.