A Secure and Efficient Temporal Features Based Framework for Cloud Using MapReduce

2018 ◽  
pp. 114-123
Author(s):  
P. Srinivasa Rao ◽  
P. E. S. N. Krishna Prasad
Keyword(s):  
Temporal Features

Temporal Characteristics of Speech in Simultaneous Communication

1995 ◽  
Vol 38 (5) ◽  
pp. 1014-1024 ◽  
Author(s):  
Robert L. Whitehead ◽  
Nicholas Schiavetti ◽  
Brenda H. Whitehead ◽  
Dale Evan Metz
Keyword(s):  
Pause Duration ◽  
Vowel Duration ◽  
Vowel Perception ◽  
Temporal Characteristics ◽  
Spoken English ◽  
Temporal Features ◽  
Before And After ◽  
Word Duration ◽  
Rate Of Speech ◽  
Speech Condition

The purpose of this investigation was twofold: (a) to determine if there are changes in specific temporal characteristics of speech that occur during simultaneous communication, and (b) to determine if known temporal rules of spoken English are disrupted during simultaneous communication. Ten speakers uttered sentences consisting of a carrier phrase and experimental CVC words under conditions of: (a) speech, (b) speech combined with signed English, and (c) speech combined with signed English for every word except the CVC word that was fingerspelled. The temporal features investigated included: (a) sentence duration, (b) experimental CVC word duration, (c) vowel duration in experimental CVC words, (d) pause duration before and after experimental CVC words, and (e) consonantal effects on vowel duration. Results indicated that for all durational measures, the speech/sign/fingerspelling condition was longest, followed by the speech/sign condition, with the speech condition being shortest. It was also found that for all three speaking conditions, vowels were longer in duration when preceding voiced consonants than vowels preceding their voiceless cognates, and that a low vowel was longer in duration than a high vowel. These findings indicate that speakers consistently reduced their rate of speech when using simultaneous communication, but did not violate these specific temporal rules of English important for consonant and vowel perception.

Der Begriff ‚Anlage‘ als Bestimmung des Seins bei Martin Heidegger

2010 ◽  
Vol 2010 (1) ◽  
pp. 101-116
Author(s):  
Ales Novak
Keyword(s):  
Martin Heidegger ◽  
20Th Century ◽  
Being And Time ◽  
Temporal Features ◽  
Heaven And Earth

During the philosophical pathway of Martin Heidegger the 30s of the 20th century are a crucial period in respect of his effort to point out the temporal meaning of the notion of being. After the failure of his project of Being and Time he turned his attention towards pondering upon the (Hi)Story of being (Seinsgeschichte or Geschichte des Seins), leading him to the thought of the oblivion of being as well as of the forsakenness by the being. Within the eschatological perspectives after the end of metaphysics Heidegger arrives at the notion of Anlage, in which he means to articulate the temporal features of being corresponding to the mentioned epochal situation. The notion Anlage sums up the temporal features of setting, perpetuity, and presence, which according to Heidegger are notoriously associated with the notion of being within the metaphysics. Nonetheless, even this conceptual effort acts as a taking- off towards a far more radical phenomenology of world conceived as the fourfold of heaven and earth, the divine and the mortals.

scholarly journals A Single-Trial P300 Detector Based on Symbolized EEG and Autoencoded-(1D)CNN to Improve ITR Performance in BCIs

Sensors ◽  
2021 ◽  
Vol 21 (12) ◽  
pp. 3961
Author(s):  
Daniela De Venuto ◽  
Giovanni Mezzina
Keyword(s):  
Recognition Performance ◽  
Low Complexity ◽  
Single Trial ◽  
Eeg Signals ◽  
Speed Increase ◽  
P300 Speller ◽  
Embedded Platform ◽  
Temporal Features ◽  
High Recognition Accuracy ◽  
Car Driving

In this paper, we propose a breakthrough single-trial P300 detector that maximizes the information translate rate (ITR) of the brain–computer interface (BCI), keeping high recognition accuracy performance. The architecture, designed to improve the portability of the algorithm, demonstrated full implementability on a dedicated embedded platform. The proposed P300 detector is based on the combination of a novel pre-processing stage based on the EEG signals symbolization and an autoencoded convolutional neural network (CNN). The proposed system acquires data from only six EEG channels; thus, it treats them with a low-complexity preprocessing stage including baseline correction, windsorizing and symbolization. The symbolized EEG signals are then sent to an autoencoder model to emphasize those temporal features that can be meaningful for the following CNN stage. This latter consists of a seven-layer CNN, including a 1D convolutional layer and three dense ones. Two datasets have been analyzed to assess the algorithm performance: one from a P300 speller application in BCI competition III data and one from self-collected data during a fluid prototype car driving experiment. Experimental results on the P300 speller dataset showed that the proposed method achieves an average ITR (on two subjects) of 16.83 bits/min, outperforming by +5.75 bits/min the state-of-the-art for this parameter. Jointly with the speed increase, the recognition performance returned disruptive results in terms of the harmonic mean of precision and recall (F1-Score), which achieve 51.78 ± 6.24%. The same method used in the prototype car driving led to an ITR of ~33 bit/min with an F1-Score of 70.00% in a single-trial P300 detection context, allowing fluid usage of the BCI for driving purposes. The realized network has been validated on an STM32L4 microcontroller target, for complexity and implementation assessment. The implementation showed an overall resource occupation of 5.57% of the total available ROM, ~3% of the available RAM, requiring less than 3.5 ms to provide the classification outcome.

scholarly journals The role of action effects in motor sequence planning and execution: exploring the influence of temporal and spatial effect anticipation

2021 ◽  
Author(s):  
Rachel M. Brown ◽  
Erik Friedgen ◽  
Iring Koch
Keyword(s):  
Action Planning ◽  
Spatial Effects ◽  
Motor Sequence ◽  
Action Sequence ◽  
Sequential Action ◽  
Spatial Features ◽  
Temporal Features ◽  
Action Sequences ◽  
Temporal And Spatial ◽  
Anticipation Effect

AbstractActions we perform every day generate perceivable outcomes with both spatial and temporal features. According to the ideomotor principle, we plan our actions by anticipating the outcomes, but this principle does not directly address how sequential movements are influenced by different outcomes. We examined how sequential action planning is influenced by the anticipation of temporal and spatial features of action outcomes. We further explored the influence of action sequence switching. Participants performed cued sequences of button presses that generated visual effects which were either spatially compatible or incompatible with the sequences, and the spatial effects appeared after a short or long delay. The sequence cues switched or repeated across trials, and the predictability of action sequence switches was varied across groups. The results showed a delay-anticipation effect for sequential action, whereby a shorter anticipated delay between action sequences and their outcomes speeded initiation and execution of the cued action sequences. Delay anticipation was increased by predictable action switching, but it was not strongly modified by the spatial compatibility of the action outcomes. The results extend previous demonstrations of delay anticipation to the context of sequential action. The temporal delay between actions and their outcomes appears to be retrieved for sequential planning and influences both the initiation and the execution of actions.

scholarly journals Born to sing! Song development in a singing primate

2021 ◽  
Author(s):  
Chiara De Gregorio ◽  
Filippo Carugati ◽  
Vittoria Estienne ◽  
Daria Valente ◽  
Teresa Raimondi ◽  
...  
Keyword(s):  
Vocal Communication ◽  
Developmental Trajectories ◽  
Ontogenetic Changes ◽  
Call Structure ◽  
Historical View ◽  
Singing Behavior ◽  
Vocal Behaviour ◽  
Temporal Features ◽  
Vocal Activity ◽  
Song Development

Abstract In animal vocal communication, the development of adult-like vocalization is fundamental to interact appropriately with conspecifics. However, the factors that guide ontogenetic changes in the acoustic features remains poorly understood. In contrast with a historical view of nonhuman primate vocal production as substantially innate, recent research suggests that inheritance and physiological modification can only explain some of the developmental changes in call structure during growth. A particular case of acoustic communication is the indris' singing behavior, a peculiar case among Strepsirrhine primates. Thanks to a decade of intense data collection, this work provides the first long-term quantitative analysis on song development in a singing primate. To understand the ontogeny of such a complex vocal output, we investigated juvenile and sub-adult indris' vocal behaviour, and we found that young individuals started participating in the chorus years earlier than previously reported. Our results indicated that spectro-temporal song parameters underwent essential changes during growth. In particular, the age and sex of the emitter influenced the indris' vocal activity. We found that frequency parameters showed consistent changes across the sexes, but the temporal features showed different developmental trajectories for males and females. Given the low level of morphological sexual dimorphism and the marked differences in vocal behavior, we hypothesize that factors like social influences and auditory feedback may affect songs' features, resulting in high vocal flexibility in juvenile indris. This trait may be pivotal in a species that engages in choruses with rapid vocal turn-taking.

Enhancing the Accuracy of Peak Hourly Demand in Bike-Sharing Systems using a Graph Convolutional Network with Public Transit Usage Data

2021 ◽  
pp. 036119812110120
Author(s):  
Jung-Hoon Cho ◽  
Seung Woo Ham ◽  
Dong-Kyu Kim
Keyword(s):  
Adjacency Matrix ◽  
Public Transit ◽  
Demand Forecasting ◽  
The Other ◽  
Convolutional Network ◽  
Demand Pattern ◽  
Temporal Features ◽  
Unmet Demand ◽  
Bike Sharing ◽  
Main Component

With the growth of the bike-sharing system, the problem of demand forecasting has become important to the bike-sharing system. This study aims to develop a novel prediction model that enhances the accuracy of the peak hourly demand. A spatiotemporal graph convolutional network (STGCN) is constructed to consider both the spatial and temporal features. One of the model’s essential steps is determining the main component of the adjacency matrix and the node feature matrix. To achieve this, 131 days of data from the bike-sharing system in Seoul are used and experiments conducted on the models with various adjacency matrices and node feature matrices, including public transit usage. The results indicate that the STGCN models reflecting the previous demand pattern to the adjacency matrix show outstanding performance in predicting demand compared with the other models. The results also show that the model that includes bus boarding and alighting records is more accurate than the model that contains subway records, inferring that buses have a greater connection to bike-sharing than the subway. The proposed STGCN with public transit data contributes to the alleviation of unmet demand by enhancing the accuracy in predicting peak demand.

scholarly journals A Novel Time-Sensitive Composite Similarity Model for Multivariate Time-Series Correlation Analysis

Entropy ◽  
2021 ◽  
Vol 23 (6) ◽  
pp. 731
Author(s):  
Mengxia Liang ◽  
Xiaolong Wang ◽  
Shaocong Wu
Keyword(s):  
Time Series ◽  
Correlation Analysis ◽  
Stock Price ◽  
Multivariate Time Series ◽  
Temporal Features ◽  
Proposed Model ◽  
Time Series Segmentation ◽  
Similarity Model ◽  
Dynamic Time ◽  
Investment Portfolios

Finding the correlation between stocks is an effective method for screening and adjusting investment portfolios for investors. One single temporal feature or static nontemporal features are generally used in most studies to measure the similarity between stocks. However, these features are not sufficient to explore phenomena such as price fluctuations similar in shape but unequal in length which may be caused by multiple temporal features. To research stock price volatilities entirely, mining the correlation between stocks should be considered from the point view of multiple features described as time series, including closing price, etc. In this paper, a time-sensitive composite similarity model designed for multivariate time-series correlation analysis based on dynamic time warping is proposed. First, a stock is chosen as the benchmark, and the multivariate time series are segmented by the peaks and troughs time-series segmentation (PTS) algorithm. Second, similar stocks are screened out by similarity. Finally, the rate of rising or falling together between stock pairs is used to verify the proposed model’s effectiveness. Compared with other models, the composite similarity model brings in multiple temporal features and is generalizable for numerical multivariate time series in different fields. The results show that the proposed model is very promising.

A Novel Automatic Modulation Classification Method Using Attention Mechanism and Hybrid Parallel Neural Network

2021 ◽  
Vol 11 (3) ◽  
pp. 1327
Author(s):  
Rui Zhang ◽  
Zhendong Yin ◽  
Zhilu Wu ◽  
Siyang Zhou
Keyword(s):  
Neural Network ◽  
Communication Systems ◽  
Parallel Structure ◽  
Modulation Classification ◽  
Automatic Modulation Classification ◽  
Spatial Features ◽  
Temporal Features ◽  
Different Types ◽  
In Series ◽  
Parallel Network

Automatic Modulation Classification (AMC) is of paramount importance in wireless communication systems. Existing methods usually adopt a single category of neural network or stack different categories of networks in series, and rarely extract different types of features simultaneously in a proper way. When it comes to the output layer, softmax function is applied for classification to expand the inter-class distance. In this paper, we propose a hybrid parallel network for the AMC problem. Our proposed method designs a hybrid parallel structure which utilizes Convolution Neural Network (CNN) and Gate Rate Unit (GRU) to extract spatial features and temporal features respectively. Instead of superposing these two categories of features directly, three different attention mechanisms are applied to assign weights for different types of features. Finally, a cosine similarity metric named Additive Margin softmax function, which can expand the inter-class distance and compress the intra-class distance simultaneously, is adopted for output. Simulation results demonstrate that the proposed method can achieve remarkable performance on an open access dataset.

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