Multi-scale context-aware network for continuous sign language recognition

The hands and face are the most important parts for expressing sign language morphemes in sign language videos.However,we find that existing Continuous Sign Language Recognition(CSLR)methods lack the mining of hand and face information in visual backbones or use expensive and time-consuming external extractors to explore this information.In addition,the signs have different lengths,whereas previous CSLR methods typically use a fixed-length window to segment the video to capture sequential features and then perform global temporal modeling,which disturbs the perception of complete signs.In this study,we propose a Multi-Scale Context-Aware network(MSCA-Net)to solve the aforementioned problems.Our MSCA-Net contains two main modules:(1)Multi-Scale Motion Attention(MSMA),which uses the differences among frames to perceive information of the hands and face in multiple spatial scales,replacing the heavy feature extractors;and(2)Multi-Scale Temporal Modeling(MSTM),which explores crucial temporal information in the sign language video from different temporal scales.We conduct extensive experiments using three widely used sign language datasets,i.e.,RWTH-PHOENIX-Weather-2014,RWTH-PHOENIX-Weather-2014T,and CSL-Daily.The proposed MSCA-Net achieve state-of-the-art performance,demonstrating the effectiveness of our approach.

DEVELOPMENT OF A GIS DATA MODEL WITH SPATIAL,TEMPORAL AND ATTRIBUTE COMPONENTS BASED ON OBJECT-ORIENTED APPROACH

This paper presents a conceptual data model, the STA-model, for handling spatial, temporal and attribute aspects of objects in GIS. The model is developed on the basis of object-oriented modeling approach. This model includes two major parts: (a) modeling the signal objects by STA-object elements, and (b) modeling relationships between STA-objects. As an example, the STA-model is applied for modeling land cover change data with spatial, temporal and attribute components.

ISDTM:An Intrusion Signatures Description Temporal Model

ISDTM, based on an augmented Allen's interval temporal logic (ITL) and first-order predicate calculus, is a formal temporal model for representing intrusion signatures. It is augmented with some real time extensions which enhance the expressivity. Intrusion scenarios usually are the set of events and system states, where- the temporal sequence is their basic relation. Intrusion signatures description, therefore , is to represent such temporal relations in a sense. While representing these signatures, ISDTM decomposes the intrusion process into the sequence of events according to their relevant intervals, and then specifies network states in these Intervals. The uncertain intrusion signatures as well as basic temporal modes of events, which consist of the parallel mode, the sequential mode and the hybrid mode, can be succinctly and naturally represented in ISDTM. Mode chart is the visualization of intrusion signatures in ISDTM, which makes the formulas more readable. The intrusion signatures descriptions in ISDTM have advantages of compact construct, concise syntax, scalability and easy implementation.

Ensemble Based Temporal Weighting and Pareto Ranking (ETP) Model for Effective Root Cause Analysis

Root-cause identification plays a vital role in business decision making by providing effective future directions for the organizations.Aspect extraction and sentiment extraction plays a vital role in identifying the rootcauses.This paper proposes the Ensemble based temporal weighting and pareto ranking(ETP)model for Root-cause identification.Aspect extraction is performed based on rules and is followed by opinion identification using the proposed boosted ensemble model.The obtained aspects are validated and ranked using the proposed aspect weighing scheme.Pareto-rule based aspect selection is performed as the final selection mechanism and the results are presented for business decision making.Experiments were performed with the standard five product benchmark dataset.Performances on all five product reviews indicate the effective performance of the proposed model.Comparisons are performed using three standard state-of-the-art models and effectiveness is measured in terms of F-Measure and Detection rates.The results indicate improved performances exhibited by the proposed model with an increase in F-Measure levels at 1%–15%and detection rates at 4%–24%compared to the state-of-the-art models.

WHU-Grace01s:A new temporal gravity field model recovered from GRACE KBRR data alone

A new temporal gravity field model called WHU-Grace01s solely recovered from Gravity Recovery and Climate Experiment （GRACE） K-Band Range Rate （KBRR） data based on dynamic integral approach is presented in this paper. After meticulously preprocessing of the GRACE KBRR data, the root mean square of its post residuals is about 0.2 micrometers per second, and seventy-two monthly temporal solutions truncated to degree and order 60 are computed for the period from January 2003 to December 2008. After applying the combi- nation filter in WHU-Grace01s, the global temporal signals show obvious periodical change rules in the large-scale fiver basins. In terms of the degree variance, our solution is smaller at high degrees, and shows a good consistency at the rest of degrees with the Release 05 models from Center for Space Research （CSR）, GeoForschungsZentrum Potsdam （GFZ） and Jet Pro- pulsion Laboratory 0PL）. Compared with other published models in terms of equivalent water height distribution, our solution is consistent with those published by CSR, GFZ, JPL, Delft institute of Earth Observation and Space system （DEOS）, Tongji University （Tongji）, Institute of Theoretical Geodesy （ITG）, Astronomical Institute in University of Bern （AIUB） and Groupe de Recherche de Geodesie Spatiale （GRGS}, which indicates that the accuracy of WHU-Grace01s has a good consistency with the previously published GRACE solutions.

Effect of Spatial and Temporal Scales on Habitat Suitability Modeling:A Case Study of Ommastrephes bartramii in the Northwest Pacific Ocean

Temporal and spatial scales play important roles in fishery ecology,and an inappropriate spatio-temporal scale may result in large errors in modeling fish distribution.The objective of this study is to evaluate the roles of spatio-temporal scales in habitat suitability modeling,with the western stock of winter-spring cohort of neon flying squid （Ornmastrephes bartramii） in the northwest Pacific Ocean as an example.In this study,the fishery-dependent data from the Chinese Mainland Squid Jigging Technical Group and sea surface temperature （SST） from remote sensing during August to October of 2003-2008 were used.We evaluated the differences in a habitat suitability index model resulting from aggregating data with 36 different spatial scales with a combination of three latitude scales （0.5°,1 ° and 2°）,four longitude scales （0.5°,1°,2° and 4°）,and three temporal scales （week,fortnight,and month）.The coefficients of variation （CV） of the weekly,biweekly and monthly suitability index （SI） were compared to determine which temporal and spatial scales of SI model are more precise.This study shows that the optimal temporal and spatial scales with the lowest CV are month,and 0.5° latitude and 0.5° longitude for O.bartramii in the northwest Pacific Ocean.This suitability index model developed with an optimal scale can be cost-effective in improving forecasting fishing ground and requires no excessive sampling efforts.We suggest that the uncertainty associated with spatial and temporal scales used in data aggregations needs to be considered in habitat suitability modeling.

Study on spatial temporal model in property management information system

Time is an important dimension for information in the geographical information system. Data, such as the historical state of target property space and related events causing the state to be changed, should be saved as important files. This should be applied to property management. This paper designs and constructs a spatial temporal model, which is suitable to the property data changing management and spatial temporal query by analyzing the basic types and characteristics of property management spatial changing time and date. This model uses current and historical situational layers to organize and set up the relationship between current situation data and historical dates according to spatial temporal topological relations in property entities. By using Map Basic, housing property management and spatial query is realized.

A novel 3-D spatial temporal channel model for high mobility HAPS-based communications scenario

A high altitude platform station （HAPS） based communications scenario for providing Intemet access and broadband multimedia services to the passengers on board of a high speed train （traveling up to 300km/h） is proposed. Regarding the addressed scenario, when the propagation link between HAPS and train is blocked by obstacles, a three-dimensional （3-D） geometrical single cylinder spatial-temporal channel model is presented, in which closed form, mathematically tractable space-time correlation functions are obtained. It shows that the correlation functions determined by the 3-D model are of significant difference with those of the conventional 2-D model. Based on the analysis model, the paper derives a realized simulation model using sum-of-sinusoids approach, and applies method of equal areas （MEA） and modified method of equal areas （MMEA） to determine the model parameters. The fitting performance of the simulation model with the analysis one is evaluated by two means-square error （MSE） performance criteria. Finally, numerical simulation results verify the mathematical analysis conclusion, when N ≥21, simulation model has an excellent fitness with the analysis one.

Research of storage technique for C-Temporal Relation Data Model

As there is datum redundancy in tradition database and temporal database in existence and the quantities of temporal database are increasing fleetly. We put forward compress storage tactics for temporal datum which combine compress technology in existence in order to settle datum redundancy in the course of temporal datum storage and temporal datum of slow acting domain and momentary acting domain are accessed by using each from independence clock method and mutual clock method .We also bring forward strategy of gridding storage to resolve the problems of temporal datum rising rapidly.

Monthly gravity field recovery from GRACE orbits and K-band measurements using variational equations approach

The Gravity Recovery and Climate Experiment（GRACE） mission can significantly improve our knowledge of the temporal variability of the Earth＇s gravity field.We obtained monthly gravity field solutions based on variational equations approach from GPS-derived positions of GRACE satellites and K-band range-rate measurements.The impact of different fixed data weighting ratios in temporal gravity field recovery while combining the two types of data was investigated for the purpose of deriving the best combined solution.The monthly gravity field solution obtained through above procedures was named as the Institute of Geodesy and Geophysics（IGG） temporal gravity field models.IGG temporal gravity field models were compared with GRACE Release05（RL05） products in following aspects：（i） the trend of the mass anomaly in China and its nearby regions within 2005-2010; （ii） the root mean squares of the global mass anomaly during 2005-2010; （iii） time-series changes in the mean water storage in the region of the Amazon Basin and the Sahara Desert between 2005 and 2010.The results showed that IGG solutions were almost consistent with GRACE RL05 products in above aspects（i）-（iii）.Changes in the annual amplitude of mean water storage in the Amazon Basin were 14.7 ± 1.2 cm for IGG,17.1 ± 1.3 cm for the Centre for Space Research（CSR）,16.4 ± 0.9 cm for the GeoForschungsZentrum（GFZ） and 16.9 ± 1.2 cm for the Jet Propulsion Laboratory（JPL） in terms of equivalent water height（EWH）,respectively.The root mean squares of the mean mass anomaly in Sahara were 1.2 cm,0.9 cm,0.9 cm and 1.2 cm for temporal gravity field models of IGG,CSR,GFZ and JPL,respectively.Comparison suggested that IGG temporal gravity field solutions were at the same accuracy level with the latest temporal gravity field solutions published by CSR,GFZ and JPL.

T=S Model to Simulate Regional Economic Development

This paper proposes a mechanism theory on regional development by using a modified Logistic model. It reveals regional evolution is an integration of fluctuation in temporal dimension and disparity in spatial dimension. T = S model is established by using Logistic model to simulate the growth of per capita GDP in China from 1990 to 1999. The result shows that T=S model accurately simulates the tracks of economic growth.

Large-eddy simulations of a forced homogeneous isotropic turbulence with polymer additives

Large-eddy simulations （LES） based on the temporal approximate deconvolution model were performed for a forced homogeneous isotropic turbulence （FHIT） with polymer additives at moderate Taylor Reynolds number. Finitely extensible nonlinear elastic in the Peterlin approximation model was adopted as the constitutive equation for the filtered conformation tensor of the polymer molecules. The LES results were verified through comparisons with the direct numerical simulation results. Using the LES database of the FHIT in the Newtonian fluid and the polymer solution flows, the polymer effects on some important parameters such as strain, vorticity, drag reduction, and so forth were studied. By extracting the vortex structures and exploring the flatness factor through a high-order correlation function of velocity derivative and wavelet analysis, it can be found that the small-scale vortex structures and small-scale intermittency in the FHIT are all inhibited due to the existence of the polymers. The extended self-similarity scaling law in the polymer solution flow shows no apparent difference from that in the Newtonian fluid flow at the currently simulated ranges of Reynolds and Weissenberg numbers.

Influence of the Gas Temperature in Ozone Production of Mixture N2-O2

The ozone occurs naturally in the atmosphere and presents a filter of protection, absorbing the radiations wavelengths lower than 310 nm. The industrial generation of ozone is the classical application of the non-equilibrium air plasmas at the atmospheric pressure. A low temperature is needed because the ozone quickly decays at the high temperature. This study is based on a temporal kinetic model for the production of ozone. The chemical kinetics take into account 96 reactions with 19 species atomic and molecular created in the discharge. In this work, the model allows to calculate the temporal evolution of neutral, ionized and excited species concentrations in plasma. The results show the influence of the kinetic on the ozone production yield and on the gas heating by Joule effect.

A general spatial-temporal framework for short-term building temperature forecasting at arbitrary locations with crowdsourcing weather data

Weather forecasting has been a critical component to predict and control building energy consumption for better building energy management.Without accessibility to other data sources,the onsite observed temperatures or the airport temperatures are used in forecast models.In this paper,we present a novel approach by utilizing the crowdsourcing weather data from neighboring personal weather stations(PWS)to improve the weather forecast accuracy around buildings using a general spatial-temporal modeling framework.The final forecast is based on the ensemble of local forecasts for the target location using neighboring PWSs.Our approach is distinguished from existing literature in various aspects.First,we leverage the crowdsourcing weather data from PWS in addition to public data sources.In this way,the data is at much finer time resolution(e.g.,at 5-minute frequency)and spatial resolution(e.g.,arbitrary location vs grid).Second,our proposed model incorporates spatial-temporal correlation information of weather variables between the target building and a set of neighboring PWSs so that underlying correlations can be effectively captured to improve forecasting performance.We demonstrate the performance of the proposed framework by comparing to the benchmark models on temperature forecasting for a building located at an arbitrary location at San Antonio,Texas,USA.In general,the proposed model framework equipped with machine learning technique such as Random Forest can improve forecasting by 50%compares with persistent model and has 90%chance to outperform airport forecast in short-term forecasting.In a real-time setting,the proposed model framework can provide more accurate temperature forecasting results compared with using airport temperature forecast for most forecast horizon.Moreover,we analyze the sensitivity of model parameters to gain insights on how crowdsourcing data from the neighboring personal weather stations impacts forecasting performance.Finally,we implement our model in other cities such as Syracuse and Chicago to test the model’s performance in different landforms and climate types.

Nondeterministic Temporal Relations in Multimedia Data

Synchronization is an important issue in multimedia systems which integrate a variety of temporally related media objects. One part of synchronization is the representation of temporal information. With the emerging illteractive multimedia, deterministic temporal models are replaced by nondeterministic ones with more expressiveness. This paper classifies temporal models by their expressiveness, and evaluates relevant nondeterministic temporal relations in multimedia data. Additionally, an intervalbased nondeterndnistic model based on a complete temporal operator set is proposed providing highlevel abstractions and a high degree of expressiveness for interactive multimedia systems.

Fuzzy Association Degree with Delayed Time in Temporal Data Model

This paper presents an expression of the semantic proximity. Based on the temporal data model, a method of the temporal approximation is given. Using these concepts, this paper provides an evaluated method of fuzzy and dynamic association degree with delayed time and a superposition method of association degrees. Particularly, by means of the fuzzy and dynamic association degree, the connection between the weather data of two regions can be discovered.

Forest-height inversion using repeat-pass spaceborne polInSAR data

Forest-height inversion using airborne double-antenna synthetic aperture radar(SAR)systems has been widely researched,leading to increasing accuracy.Polarimetric SAR Interferometry(PolInSAR)data from spaceborne single-antenna SAR systems,which are influenced by temporal decorrelation,have difficulty inverting forest height.Given the temporal decorrelation effect,the classical random volume over ground(RVoG)model has been proven to invert forest height with significant errors,using repeat-pass PolInSAR data.In consideration of this problem,the temporal decorrelation RVoG(TD-RVoG;based on the RVoG)model was proposed.In this study,an improved TD-RVoG model is presented,with a new temporal decorrelation function.Compared with TD-RVoG,the new model has fewer unknown parameters and can be applied in a three-stage inversion procedure.Validity of the new model is demonstrated by Advanced Land Observing Satellite/Phased Array type L-band SAR(ALOS/PALSAR)data.Results show that the improved TD-RVoG has better accuracy,with inversion error less than 1.5m.

Social effects of topic propagation on Weibo

Weibo,China’s largest microblogging platform,has become one of the key information-sharing platforms in modern society.This study examines topic propagation in relation to microblogging from the perspective of the“peer effect.”Using data of hot topics from Weibo,we analyze how the social effect and propagation pathway influence the topic propagation process.We propose a spatial and temporal heterogeneity diffusion model that includes endogenous and exogenous social effects and is based on but different from the Bass diffusion model.We find that most propagation pathways end after a single level of propagation.The endogenous social effect in microblogs primarily influences the inflow of topics.Such endogenous social effect,combined with the multiplier effect,motivates most users to share a microblog topic in a short period of time.The exogenous social effect primarily influences the outflow of topics,and therefore,the microblog topics of a small number of popular users’account for most of the share volume.Our results are robust to potential serial correlation,reflection problem,and potential self-selection due to user status.The findings reveal that group characteristics affect individuals’behaviors and choices in relation to the topic propagation process on microblogging platforms.The use of a spatial and temporal heterogeneity diffusion model and the robustness of the analysis process provide new information for scholars in this field.

On essential topics of BYY harmony learning： Current status, challenging issues, and gene analysis applications

As a supplementary of [Xu L. Front. Electr. Electron. Eng. China, 2010, 5（3）： 281-328], this paper outlines current status of efforts made on Bayesian Ying- Yang （BYY） harmony learning, plus gene analysis appli- cations. At the beginning, a bird＇s-eye view is provided via Gaussian mixture in comparison with typical learn- ing algorithms and model selection criteria. Particularly, semi-supervised learning is covered simply via choosing a scalar parameter. Then, essential topics and demand- ing issues about BYY system design and BYY harmony learning are systematically outlined, with a modern per- spective on Yin-Yang viewpoint discussed, another Yang factorization addressed, and coordinations across and within Ying-Yang summarized. The BYY system acts as a unified framework to accommodate unsupervised, su- pervised, and semi-supervised learning all in one formu- lation, while the best harmony learning provides novelty and strength to automatic model selection. Also, mathe- matical formulation of harmony functional has been ad- dressed as a unified scheme for measuring the proximity to be considered in a BYY system, and used as the best choice among others. Moreover, efforts are made on a number of learning tasks, including a mode-switching factor analysis proposed as a semi-blind learning frame- work for several types of independent factor analysis, a hidden Markov model （HMM） gated temporal fac- tor analysis suggested for modeling piecewise stationary temporal dependence, and a two-level hierarchical Gaus- sian mixture extended to cover semi-supervised learning, as well as a manifold learning modified to facilitate au- tomatic model selection. Finally, studies are applied to the problems of gene analysis, such as genome-wide asso- ciation, exome sequencing analysis, and gene transcrip- tional regulation.

Joint model of user check-in activities for point-of-interest recommendation

With the rapid development of location-based networks, point-of-interest（POI） recommendation has become an important means to help people discover interesting and attractive locations, especially when users travel out of town. However, because users only check-in interaction is highly sparse, which creates a big challenge for POI recommendation. To tackle this challenge, we propose a joint probabilistic generative model called geographical temporal social content popularity（GTSCP） to imitate user check-in activities in a process of decision making, which effectively integrates the geographical influence, temporal effect, social correlation, content information and popularity impact factors to overcome the data sparsity, especially for out-of-town users. Our proposed the GTSCP supports two recommendation scenarios in a joint model, i.e., home-town recommendation and out-of-town recommendation. Experimental results show that GTSCP achieves significantly superior recommendation quality compared to other state-of-the-art POI recommendation techniques.