Miracle-dz/UniFAR-FTUs huggingface dataset:
Classification: Naive Bayes vs Logistic Regression

Logistic regression and the naive Bayes classifier are both very popular and widely used classification techniques. They are simple, easy to implement, and provide good performance on a wide variety of problems. However, it was not too long ago that many people preferred logistic regression(a discriminative model) to naive Bayes(a generative model). In fact, many people preferred discriminative to generative models in general, since discriminative models achieve lower asymptotic errors. These preferences were tested by Ng and Jordan[1], and it was shown that a decision between which classifier is better is not always a simple answer; it was shown that naive Bayes reaches its asmyptotic error very quickly with regards to the number of training examples. Thus, if training data is scarce, one can expect naive Bayes to outperform logistic regression, but as the number of training examples grows, logistic regression will outperform naive Bayes and achieve a lower asymptotic error rate. In this paper, we will explore both logistic regression and naive Bayes classifiers by giving a background on both, describing how to implement them, and attempting to recreate several of the learning curves found in [1].
Target Mass Monitoring and Instrumentation in the Daya Bay Antineutrino Detectors

The Daya Bay experiment measures sin^2 2{\theta}_13 using functionally identical antineutrino detectors located at distances of 300 to 2000 meters from the Daya Bay nuclear power complex. Each detector consists of three nested fluid volumes surrounded by photomultiplier tubes. These volumes are coupled to overflow tanks on top of the detector to allow for thermal expansion of the liquid. Antineutrinos are detected through the inverse beta decay reaction on the proton-rich scintillator target. A precise and continuous measurement of the detector's central target mass is achieved by monitoring the the fluid level in the overflow tanks with cameras and ultrasonic and capacitive sensors. In addition, the monitoring system records detector temperature and levelness at multiple positions. This monitoring information allows the precise determination of the detectors' effective number of target protons during data taking. We present the design, calibration, installation and in-situ tests of the Daya Bay real-time antineutrino detector monitoring sensors and readout electronics.
Neural Network Programming with Java

ion 11 activation function 5, 6 ADALINE (traffic forecast) 41-45 adaptive neural networks about 179 implementation 180, 181 adaptive resonance theory (ART) 179 AND logic 37 applied unsupervised learning about 139 Kohonen neural network 140 neural network, of radial basis functions 139, 140 types of data 141 artificial intelligence 2 artificial neural networks (ANNs) about 2 need for 2 used, for diagnosing breast cancer 126-130 artificial neuron 5
The Control Strategy of Hybrid Energy Storage System charging

The Electric Car Era is coming. According to the battery charge of car, the optimized control Strategy applied to DC-DC converters of electric vehicles, it is to gain further loss reduction and improve the life of an electric car battery. The hybrid energy storage system adopts the combination of current controller and peak current controller, which improve filter performance and optimize the power quality of the hybrid energy storage system. The hybrid energy storage systems consist of battery and ultracapacitor, Finally, the simulation and experimental results verifies that the control Strategy has the ability of good performances. Introduction In order to make electric vehicles the same as fuel vehicles, they need have a fast transient acceleration energy, long-distance endurance and light-weight multiple storage devices. The introduction of power prediction feedback link in a control system can speed up the combination process between the energy storage system and the wind power system [1]. A hybrid energy storage system uses fuzzy power control theory to distribute power, with the output power of renewable energy fluctuated. When the super capacitor is fully charged, the power is provided by the storage system independently, which will reduce charge and discharge times of the battery [2]. As an optimal control method based on smoothing time and limited power, the battery maximum power and the climbing maximum power will be take into consideration In the energy storage system [3]. The integrated magnetic structure of DC-DC converter, as the connection of energy storage system and the grid and the essential part of the DC motor, which is more simpler and efficient, can significantly reduce the volume and weight of the converter [4-10]. In this paper, the proposed DCDC converter gives the specific topology and specific operating modes, as well as the battery and the super capacitor. Finally, simulation and experimental analysis shows that the performance of the hybrid energy storage system is in line with the theoretical analysis. Hybrid energy storage system Fig. 1. is a proposed hybrid energy storage system composed of DC/DC converters, super capacitors and a battery pack. DC/DC converters consist of four IGBT switches T1-T4 and their corresponding diodes (added battery) tube D1-D4, an integrated magnetic structure—selfinductance L1,L2 and mutual-inductance M, which share a core inductor. The battery pack provides power for the smooth DC motor, which is a major energy electric vehicle. The super capacitor is used to deal with the instantaneous state of peak power supply. The power management system according to the load demand determines the electrical energy flow. Proceedings of the 3rd International Conference on Material Engineering and Application (ICMEA 2016) Copyright © 2016, the Authors. Published by Atlantis Press. This is an open access article under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/). 32 Advances in Engineering Research, volume 103
Application of Holt exponential smoothing and ARIMA method for data population in West Java

One method of time series that is often used to predict data that contains trend is Holt. Holt method using different parameters used in the original data which aims to smooth the trend value. In addition to Holt, ARIMA method can be used on a wide variety of data including data pattern containing a pattern trend. Data actual of population from 1998-2015 contains the trends so can be solved by Holt and ARIMA method to obtain the prediction value of some periods. The best method is measured by looking at the smallest MAPE and MAE error. The result using Holt method is 47.205. 749 populations in 2016, 47.535. 324 populations in 2017, and 48.041. 672 populations in 2018, with MAPE error is 0,469744 and MAE error is 189.731. While the result using ARIMA method is 46.964. 682 populations in 2016, 47.342. 189 in 2017, and 47.899. 696 in 2018, with MAPE error is 0,4380 and MAE is 176.626.
A novel method to index the psychological condition using frontal alpha asymmetry classification

This paper aims to classify the psychological activities that exist similarly during relaxed wakefulness and non-relaxed conditions originated from the alpha waves. Electroencephalographic (EEG) signal by nature is a non linear and a random signal. However, EEG is a helpful instrument to assess on the whole cerebral activity. Through the use of advanced signal processing technique, the frontal alpha asymmetry is calculated and the buried features are revealed. Features from the relaxed wakefulness and non-relaxed conditions are believed to have few levels of similar psychological state that could be grouped together. To demonstrate that, subtractive clustering is used in conjunction with the Fuzzy C-Means (FCM) to cluster the maximum numbers of group representing the different levels. The results of the study showed that there are five clusters. K-Nearest Neighbor (k-NN) is chosen as a classifier to determine the number of clusters that gives highest discrimination. Four clusters are chosen at 84.62% classification accuracy.
ON THE PERFORMANCE OF A TECHNIQUE TO ACCELERATE TIME INTEGRATION WHEN APPLIED TO SPACE STRUCTURES ANALYSES

The true behaviors of structures, specifically when subjected to earthquakes, are dynamic. Time integration is a versatile tool in analysis of the semi-discretized equations of motion. Although the implementation of time integration is simple, the computational costs are considerable and the responses are inexact. The integration step size is the main parameter affecting the accuracies and computational costs. When the excitations are available as digitized records, conventionally, the integration step sizes are to be set not larger than the excitation steps. In 2008, a technique is proposed, providing the capability to analyze with steps larger than the excitation steps, leading to considerable less computational cost. This technique is implemented in analyses of different structural systems, including bridges, tall buildings, residential buildings, etc., and the observed losses of accuracies were negligible. In this paper, after brief theoretical discussions, the technique is applied to the analysis of the three space structures, where the structural members are sized, with attention to the existing national codes. The average acceleration method of Newmark is considered as the integration method; an El Centro as well as a Loma Prieta strong ground motion record are considered as the excitations applied in the vertical direction, and the analyses are carried out considering the linear behaviors. To address the main consequence, though the smallest oscillatory periods with considerable contribution in the response were estimated approximately, the losses of accuracy in cases with the applicability of the technique were small, still depending on the structural system and excitation.
Triplet-sensitized and thermal isomerization of all-trans, 7-cis, 9-cis, 13-cis, and 15-cis isomers of. beta. -carotene: Configurational dependence of the quantum yield of isomerization via the T sub 1 state

The products of triplet-sensitized photoisomerization (excitation at 337 mn of the sensitizer, anthracene) and thermal isomerization of {beta}-carotene in {eta}-hexane, starting from the all-trans, 7-cis, 9-cis, 13-cis, and 15-cis isomers, were analyzed by HPLC. Direct photoisomerization (excitation at 488 and 337 mn) was also examined for comparison. Three different isomerization patterns were found in both triplet-sensitized and thermal isomerization: pattern A, cis to trans isomerization around each cis bond; pattern B, trans to cis isomerization in the central part of the conjugated chain; and pattern C, cis to another cis isomerization. In the T{sub 1} state, the pattern A isomerization was predominant even for the peripheral-cis (7-cis and 9-cis) isomers and its efficiency was extremely high for the central-cis (13-cis and 15-cis) isomers. In the S{sub 0} state, the pattern B isomerization, instead, was predominant for the peripheral-cis isomers, and the pattern A isomerization was predominant only for the central-cis isomers. The quantum yields of triplet-sensitized isomerization (decrease of the starting isomer per triplet species generated) were determined to be as follows: all-trans, 0.04; 7-cis, 0.12; 9-cis, 0.15; 13-cis, 0.87; and 15-cis, 0.98. Carbon-carbon {pi} bond orders of model polyenes in the T{sub 1} and S{sub 0} states were calculatedmore » by using the Pariser-Parr-Pople CI theory; bond lengths were optimized by using a bond order-bond length relationship. Isomerization characteristics in the T{sub 1} and S{sub 0} states observed were discussed based on the results of the calculations. « less
Chinese Integrated Word Identification Based on Confidence

To identify the word is an elementary preprocessing for Chinese information retrieval.To capture the semantic integrity and the intension of the user query,this paper analyzes the characteristics of Chinese integrated word and presents three methods for its automatic detection by combining the mutual information,the prefix-and-suffix information of integrity word and the confidence for the integrated word.We further design and realize three prototype systems for Chinese integrated word identification based on the proposed overall-confidence,partial-confidence and weighted joint-confidence methods respectively.Finally,experiments on the 2nd SIGHAN(2005) PKU test corpus show that the performance of the system is good,capable of satisfying demands on different aspects of performance.
Design and Research of an Alert Clustering Algorithm Based on Search Tree and an Alert Classified Method Based on Bayesian Classifier

How to effectively find out valuable abnormal behaviors from the numerous alarms and logs produced by all kinds of security products everyday, all of them must be analyzed and the true and non-redundant information should be extracted, which is helpful to find the real problem and then correcting actions can be taken to protect the safety of system. This is one of the biggest challenges which IDS is facing. In this paper, taking into account search tree which can decrease searching space and overlay vector, an alert clustering algorithm based on search tree is presented. So as to classify new alert and can have correlation with other alert, an alert classified method based on Bayesian classifier is emphatically proposed . At last, KDD Cup 1999 Data is used to evaluate the performance of algorithm, and the experiment results show the high efficiency of the algorithm. The applications of them to Multi-information-source intelligential security auditing system indicate that they will have a good future for implementation.
Design of automatic business process modeling method based on process logs

A new process mining method was presented based on Markov transition matrix.In this method,a business process was regarded as a finite stationary Markov chain.A transition matrix among activities was established based on process logs,and the mining rules of basic process logical relationships were defined.At the same time,a process mining algorithm was designed based on the matrix and mining rules.Furthermore,the process formal expression algorithm was separated from mining algorithm so as to satisfy the different expression requirements to the same process,and a concrete expression algorithm was provided based on WF-net.
The Fineness Hierarchy of Semantic Roles and its Application in NLP

This paper firstly discusses three kinds of level in the fineness hierarchy of semantic roles,and introduces their application in some systems of natural language processing(NPL). Then it introduces three approaches to treat semantic roles in three semantic resources for NPL:(i) the frame elements in the semantic frame which is situation based——the semantic roles in the FramNet of California University at Berkeley;(ii) the numbered pro-typical arguments which is special verb based——the semantic roles in the PropBank of Pennsylvania University;(iii) the thematic roles of arguments which is special predicate(verb or adjective) based——the semantic roles in the NetBank of Peking University.Finally,these three semantic resources(i.e.,annotation corpora) are compared from their goals,methodologies,annotation contents and constitutions.
Design and Implementation of Communications Programs of PC Computer with GPS Receiver

This paper designs a system to achieve the global position system by using global positioning system(GPS) module link serial port of the computers which receive the positioning information from the GPS module,and then deal with this information by using Visual Basic serial communication program,moreover,design an electronic map combine with the database technique.The information used in the system is gathered by a GPS receiver,and the information is dealt with by the program,then by use of electronic maps and database to achieve the function of positioning and searching.The system basically realizes the function to display latitude and longitude,speed,altitude,time,and GPS historical data query capabilities.
ASSESSMENT IN EDUCATION: THE ROLE OF FEEDBACK

The present study wants to find out the role of feedback in language classes. For this reason, four classes were selected. The subjects were selected by random sampling. The data collection was done through test, observations and interviews. An inductive thematic analysis was used to find out the subjects’ underlying concepts and themes frequently mentioned. These general themes and underlying concepts were put in some categories. Then, these categories were organized in a coherent and meaningful manner.
Arabic Text Classification Using Maximum Entropy

In organizations, a large amount of information exists in text documents. Therefore, it is important to use text mining to discover knowledge from these unstructured data. Automatic text classification considered as one of important applications in text mining. It is the process of assigning a text document to one or more predefined categories based on their content. This paper focus on classifying Arabic text documents. Arabic language is highly inflectional and derivational language which makes text mining a complex task. In our approach, we first preprocessed data using natural language processing techniques such as tokenizing, stemming and part-of-speech. Then, we used maximum entropy method to classify Arabic documents. We experimented our approach using real data, then we compared the results with other existing systems.
SignCollect: Visualising mobile data collection for Deaf people

It is well known that Deaf people are not comfortable with using text for communication purposes. In South Africa, Deaf people are more comfortable with South African Sign Language (SASL). It becomes problematic for researchers to collect data from Deaf people since they have to hire Sign Language interpreters to be able to communicate with Deaf people on their behalf. This project focuses on developing a mobile-based system that will be used to collect data from Deaf people in sign language. This will be achieved by converting paper-based questionnaires into a format which Deaf people prefer. Since Deaf people understand and prefer Sign Language, the text-based questions will be presented in video format, whereby a Deaf person will be interacting with the mobile phone in providing the answers in SASL. The solution will be achieved by modifying ODK. ODK is an existing tool used to collect data from mobile devices. ODK has the ability to collect pictures, videos, audio and text-based information. Forms are built using a simple Microsoft excel spreadsheet on a PC. They are then transferred from the PC to the mobile device using a web-based tool called ODK aggregate, which allows the deployment of finished forms to the mobile device. This project modifies ODK in a way that it will ask questions and communicate with the user in video format instead of text. After collecting the answers in video format using ODK collect, the videos will be stored temporarily on the mobile device storage and later transferred to a PC using ODK briefcase where it can be transcribed accordingly.
A Robust Approach for Overlay Text Localization and Extraction in Complex Video Scene

Overlay text in video carries important semantic clues for video information retrieval and summarization. In this paper, we propose a robust method that is able to accurately locate text lines and extract text even in complex video scene. In the text localization stage, this paper adopts the method based on corner point. First, corner detection is used to extract corners as text features from video frames. Then multi-layer filtering mechanism (MLFM) is used to locate the text lines, which consists of corners clustering, corners horizontal projection, background filtering and heuristic rules. This MLFM can effectively remove the isolated corners, locate the text lines accurately and remove the background or pseudo text lines automatically. In the text extraction stage, this paper proposed a twice binarization method that combines with polarity judgment on image. The polarity judgment was used as a guide to adjust the first binarization threshold when we perform the first binarization. After the first binarization, a main proportion of the image has been processed, and the rest will be processed by the second binarization. Experimental results show that this approach can fast and robustly locate text lines and extract text in video even under complex background.
Self-Segmentation of Sequences algorithm using eligibility traces in POMDP environments

This paper presents a new hierarchical reinforcement learning (RL) algorithm to speed up learning, to make it more robust to hidden states, and to handle non-deterministic problems. This on-line RL algorithm is called SSS(/spl lambda/), which extended Sun and Sessions's original Self-Segmentation of Sequences (SSS) algorithm using eligibility traces. SSS(/spl lambda/) is compared with the original SSS for partially observable navigation tasks. The results of extensive simulations demonstrate that SSS(/spl lambda/) is clearly outperforming the original SSS.
Abstraction techniques to improve scalability of equivalence verification for NCL circuits

Asynchronous NULL convention logic (NCL) circuits are dual-rail quasi-delay-insensitive circuits that have many applications in high radiation and extreme temperature fluctuation environments such as space exploration. Two abstraction techniques are proposed that can be used to drastically improve the efficiency and scalability of formal equivalence verification targeted at NCL circuits. The effectiveness of the abstraction techniques have been demonstrated using a number of multiply and accumulate circuit benchmarks.
Robust Features for Connected Hindi Digits Recognition

Connected digits recognition is important in many applications such as voice-dialing telephone, automated banking system, automatic data entry, PIN entry, etc. In this paper robust features such as Revised perceptual linear prediction (RPLP), Bark frequency cepstral coefficients (BFCC) and Mel frequency perceptual linear prediction (MF-PLP) are used for speaker-independent connected Hindi digits recognition in clean and noisy environments. The recognition performance of these features is compared with recognition performance of Mel frequency cepstral coefficient (MFCC), ΔMFCC and Perceptual linear prediction (PLP) features. MF-PLP features have shown best recognition efficiency for clean as well as for noisy database. MFCC features are calculated by using feature extraction tool of Hidden Markov model Toolkit (HTK). All other features except MFCC are calculated using Matlab and saved in HTK format. Training and testing for speech recognition is done using HTK.
Fast URL Lookup Using URL Prefix Hash Tree

In this paper, we propose an efficient URL lookup algorithm for URL list-based web contents filtering systems. Converting a URL list into URL prefix form and building a hash tree representation of them, the proposed algorithm performs tree searches for URL lookups. It eliminates redundant searches of hash table method. Experimental results show that proposed algorithm is faster, depending on the number of segment, than conventional hash table method.