Query: Quantitative Evaluation of Style Transfer

Relevant passages:
On the limited memory BFGS method for large scale optimization <=SEP=> We study the numerical performance of a limited memory quasi Newton method for large scale optimization which we call the L BFGS method We compare its performance with that of the method developed by Buckley and LeNir which combines cyles of BFGS steps and conjugate direction steps Our numerical tests indicate that the L BFGS method is faster than the method of Buckley and LeNir and is better able to use additional storage to accelerate convergence We show that the L BFGS method can be greatly accelerated by means of a simple scaling We then compare the L BFGSmethod with the partitioned quasi Newton method of Griewank and Toint a The results show that for some problems the partitioned quasi Newton method is clearly superior to the L BFGS method However we nd that for other problems the L BFGS method is very competitive due to its low iteration cost We also study the convergence properties of the L BFGS method and prove global convergence on uniformly convex problems

Improving the Neural Algorithm of Artistic Style <=SEP=> In this work we investigate different avenues of improving the Neural Algorithm of Artistic Style [7]. While showing great results when transferring homogeneous and repetitive patterns, the original style representation often fails to capture more complex properties, like having separate styles of foreground and background. This leads to visual artifacts and undesirable textures appearing in unexpected regions when performing style transfer. We tackle this issue with a variety of approaches, mostly by modifying the style representation in order for it to capture more information and impose a tighter constraint on the style transfer result. In our experiments, we subjectively evaluate our best method as producing from barely noticeable to significant improvements in the quality of style transfer.

Very Deep Convolutional Networks for Large-Scale Image Recognition <=SEP=> In this work we investigate the effect of the convolutional n etwork depth on its accuracy in the large-scale image recognition setting. Our main contribution is a thorough evaluation of networks of increasing depth, which shows that a significant improvement on the prior-art configurations can be achi eved by pushing the depth to 16–19 weight layers. These findings were the basis of our ImageNet Challenge 2014 submission, where our team secured the first a nd he second places in the localisation and classification tracks respec tively. We also show that our representations generalise well to other datasets, whe re t y achieve the stateof-the-art results. Importantly, we have made our two bestp rforming ConvNet models publicly available to facilitate further research o n the use of deep visual representations in computer vision.

Deep Neural Networks Reveal a Gradient in the Complexity of Neural Representations across the Ventral Stream. <=SEP=> Converging evidence suggests that the primate ventral visual pathway encodes increasingly complex stimulus features in downstream areas. We quantitatively show that there indeed exists an explicit gradient for feature complexity in the ventral pathway of the human brain. This was achieved by mapping thousands of stimulus features of increasing complexity across the cortical sheet using a deep neural network. Our approach also revealed a fine-grained functional specialization of downstream areas of the ventral stream. Furthermore, it allowed decoding of representations from human brain activity at an unsurpassed degree of accuracy, confirming the quality of the developed approach. Stimulus features that successfully explained neural responses indicate that population receptive fields were explicitly tuned for object categorization. This provides strong support for the hypothesis that object categorization is a guiding principle in the functional organization of the primate ventral stream.

High-Resolution Image Inpainting Using Multi-scale Neural Patch Synthesis <=SEP=> Recent advances in deep learning have shown exciting promise in filling large holes in natural images with semantically plausible and context aware details, impacting fundamental image manipulation tasks such as object removal. While these learning-based methods are significantly more effective in capturing high-level features than prior techniques, they can only handle very low-resolution inputs due to memory limitations and difficulty in training. Even for slightly larger images, the inpainted regions would appear blurry and unpleasant boundaries become visible. We propose a multi-scale neural patch synthesis approach based on joint optimization of image content and texture constraints, which not only preserves contextual structures but also produces high-frequency details by matching and adapting patches with the most similar mid-layer feature correlations of a deep classification network. We evaluate our method on the ImageNet and Paris Streetview datasets and achieved state-of-the-art inpainting accuracy. We show our approach produces sharper and more coherent results than prior methods, especially for high-resolution images.

A path decomposition approach for computing blocking probabilities in wavelength-routing networks <=SEP=> We study a class of circuit-switched wavelength-routing networks with fixed or alternate routing and with random wavelength allocation. We present an iterative path decomposition algorithm to evaluate accurately and efficiently the blocking performance of such networks with and without wavelength converters. Our iterative algorithm analyzes the original network by decomposing it into single-path subsystems. These subsystems are analyzed in isolation, and the individual results are appropriately combined to obtain a solution for the overall network. To analyze individual subsystems, we first construct an exact Markov process that captures the behavior of a path in terms of wavelength use. We also obtain an approximate Markov process which has a closed-form solution that can be computed efficiently for short paths. We then develop an iterative algorithm to analyze approximately arbitrarily long paths. The path decomposition approach naturally captures the correlation of both link loads and link blocking events. Our algorithm represents a simple and computationally efficient solution to the difficult problem of computing call-blocking probabilities in wavelength-routing networks. We also demonstrate how our analytical techniques can be applied to gain insight into the problem of converter placement in wavelength-routing networks.

Neural Network Implementation Using CUDA and OpenMP <=SEP=> Many algorithms for image processing and pattern recognition have recently been implemented on GPU (graphic processing unit) for faster computational times. However, the implementation using GPU encounters two problems. First, the programmer should master the fundamentals of the graphics shading languages that require the prior knowledge on computer graphics. Second, in a job which needs much cooperation between CPU and GPU, which is usual in image processings and pattern recognitions contrary to the graphics area, CPU should generate raw feature data for GPU processing as much as possible to effectively utilize GPU performance. This paper proposes more quick and efficient implementation of neural networks on both GPU and multi-core CPU. We use CUDA (compute unified device architecture) that can be easily programmed due to its simple C language-like style instead of GPU to solve the first problem. Moreover, OpenMP (Open Multi-Processing) is used to concurrently process multiple data with single instruction on multi-core CPU, which results ineffectively utilizing the memories of GPU. In the experiments, we implemented neural networks-based text detection system using the proposed architecture, and the computational times showed about 15 times faster than implementation using CPU and about 4 times faster than implementation on only GPU without OpenMP.

A neuro-stimulus chip with telemetry unit for retinal prosthetic device <=SEP=> In this retinal prosthesis project, a rehabilitative device is designed to replace the functionality of defective photoreceptors in patients suffering from retinitis pigmentosa (RP) and age-related macular degeneration (AMD). The device consists of an extraocular and an intraocular unit. The implantable component receives power and a data signal via a telemetric inductive link between the two units. The extraocular unit includes a video camera and video processing board, a telemetry protocol encoder chip, and an RF amplifier and primary coil. The intraocular unit consists of a secondary coil, a rectifier and regulator, a retinal chip with a telemetry protocol decoder, a stimulus signal generator, and an electrode array. This paper focuses on the design, fabrication, and testing of a microchip which serves as the telemetry protocol decoder and stimulus signal generator. It is fabricated by MOSIS with 1.2-mm CMOS technology and was demonstrated to provide the desired biphasic current stimulus pulses for an array of 100 retinal electrodes at video frame rates.

The Causal Foundations of Structural Equation Modeling <=SEP=> The role of causality in SEM research is widely perceived to be, on the one hand, of pivotal methodological importance and, on the other hand, confusing, enigmatic and controversial. The confusion is vividly portrayed, for example, in the influential report of Wilkinson and Task Force’s (1999) on “Statistical Methods in Psychology Journals: Guidelines and Explanations.” In discussing SEM, the report starts with the usual warning: “It is sometimes thought that correlation does not prove causation but ‘causal modeling’ does. [Wrong! There are] dangers in this practice.” But then ends with a startling conclusion: “The use of complicated causal-modeling software [read SEM] rarely yields any results that have any interpretation as causal effects.” The implication being that the entire enterprise of causal modeling, from Sewell Wright (1921) to Blalock (1964) and Duncan (1975), the entire literature in econometric research, including modern advances in graphical and nonparametric structural models have all been misguided, for they have been chasing parameters that have no causal interpretation. The motives for such overstatements notwithstanding, readers may rightly ask: “If SEM methods do not ‘prove’ causation, how can they yield results that have causal interpretation?” Put another way, if the structural coefficients that SEM researchers labor to estimate can legitimately be interpreted as causal effects then, unless these parameters are grossly misestimated, why deny SEM researchers the honor of “establishing causation” or at least of deriving some useful claims about causation. The answer is that a huge logical gap exists between “establishing causation,” which requires careful manipulative experiments, and “interpreting parameters as causal effects,” which may be based on firm scientific knowledge or on previously conducted experiments, perhaps by other researchers. One can legitimately be in a possession of a parameter that stands for a causal effect and still be unable, using statistical means alone, to determine the magnitude of that parameter given non-experimental data. As a matter of fact, we know that

Librispeech: An ASR corpus based on public domain audio books <=SEP=> This paper introduces a new corpus of read English speech, suitable for training and evaluating speech recognition systems. The LibriSpeech corpus is derived from audiobooks that are part of the LibriVox project, and contains 1000 hours of speech sampled at 16 kHz. We have made the corpus freely available for download, along with separately prepared language-model training data and pre-built language models. We show that acoustic models trained on LibriSpeech give lower error rate on the Wall Street Journal (WSJ) test sets than models trained on WSJ itself. We are also releasing Kaldi scripts that make it easy to build these systems.

Study about IOT's application in "Digital Agriculture" construction <=SEP=> Along with acceleration of various countries' digitization and informationization, according to the 12th 5-year development plan, the internet of things (IOT) will become one of Chinese emerging strategic industries. Based on the analysis of IOT's basic concept and key technology, this paper studies IOT's application in "Digital Agriculture" construction. The main contents include: IOT's application in agricultural production, IOT's application in logistics and distribution of agricultural products, and IOT's application in agricultural products safety traceability.

A circle packing algorithm <=SEP=> A circle packing is a configuration P of circles realizing a specified pattern of tangencies. Radii of packin the euclidean and hyperbolic planes may be computed using an iterative process suggested by William T We describe an efficient implementation, discuss its performance, and illustrate recent applications. A cen is played by new and subtle monotonicity results for “flowers” of circles.  2003 Elsevier Science B.V. All rights reserved.

Help from the Sky: Leveraging UAVs for Disaster Management <=SEP=> This article presents a vision for future unmanned aerial vehicles (UAV)-assisted disaster management, considering the holistic functions of disaster prediction, assessment, and response. Here, UAVs not only survey the affected area but also assist in establishing vital wireless communication links between the survivors and nearest available cellular infrastructure. A perspective of different classes of geophysical, climate-induced, and meteorological disasters based on the extent of interaction between the UAV and terrestrially deployed wireless sensors is presented in this work, with suitable network architectures designed for each of these cases. The authors outline unique research challenges and possible solutions for maintaining connected aerial meshes for handoff between UAVs and for systems-specific, security- and energy-related issues. This article is part of a special issue on drones.

Cross-Level Sensor Network Simulation with COOJA <=SEP=> Simulators for wireless sensor networks are a valuable tool for system development. However, current simulators can only simulate a single level of a system at once. This makes system development and evolution difficult since developers cannot use the same simulator for both high-level algorithm development and low-level development such as device-driver implementations. We propose cross-level simulation, a novel type of wireless sensor network simulation that enables holistic simultaneous simulation at different levels. We present an implementation of such a simulator, COOJA, a simulator for the Contiki sensor node operating system. COOJA allows for simultaneous simulation at the network level, the operating system level, and the machine code instruction set level. With COOJA, we show the feasibility of the cross-level simulation approach

Signal Processing for FMCW SAR <=SEP=> The combination of frequency-modulated continuous-wave (FMCW) technology and synthetic aperture radar (SAR) techniques leads to lightweight cost-effective imaging sensors of high resolution. One limiting factor to the use of FMCW sensors is the well-known presence of nonlinearities in the transmitted signal. This results in contrast- and range-resolution degradation, particularly when the system is intended for high-resolution long-range applications, as it is the case for SAR. This paper presents a novel processing solution, which solves the nonlinearity problem for the whole range profile. Additionally, the conventional stop-and-go approximation used in pulse-radar algorithms is not valid in FMCW SAR applications under certain circumstances. Therefore, the motion within the sweep needs to be taken into account. Analytical development of the FMCW SAR signal model, starting from the deramped signal and without using the stop-and-go approximation, is presented in this paper. The model is then applied to stripmap, spotlight, and single-transmitter/multiple-receiver digital-beamforming SAR operational mode. The proposed algorithms are verified by processing real FMCW SAR data collected with the demonstrator system built at the Delft University of Technology.

Design considerations and performance evaluation of 1200 V, 100 a SiC MOSFET based converter for high power density application <=SEP=> Silicon Carbide (SiC) MOSFET is capable of achieving better efficiency, power density and reliability of power converters due to its low on-state resistance, high temperature operation capability and lower switching losses compared to silicon (Si) IGBT. Operation of power converters at higher switching frequency using SiC devices allows reduction in filter size and hence improves the power to weight ratio of the converter. This paper presents switching characterization of 1200 V, 100 A SiC MOSFET module and compares efficiency of a Two Level Voltage Source Converter (2L-VSC) using SiC MOSFETs and Si IGBTs. Also, various design considerations of the 1200 V, 100 A SiC MOSFET based 2L-VSC including gate drive design, bus bar packaging and thermal management have been elaborated. The designed and developed 2L-VSC is operated to supply 35 kVA load at 20 kHz switching frequency with DC bus voltage at 800 V and the experimental results are presented.

Epigenetics and the embodiment of race: developmental origins of US racial disparities in cardiovascular health. <=SEP=> The relative contribution of genetic and environmental influences to the US black-white disparity in cardiovascular disease (CVD) is hotly debated within the public health, anthropology, and medical communities. In this article, we review evidence for developmental and epigenetic pathways linking early life environments with CVD, and critically evaluate their possible role in the origins of these racial health disparities. African Americans not only suffer from a disproportionate burden of CVD relative to whites, but also have higher rates of the perinatal health disparities now known to be the antecedents of these conditions. There is extensive evidence for a social origin to prematurity and low birth weight in African Americans, reflecting pathways such as the effects of discrimination on maternal stress physiology. In light of the inverse relationship between birth weight and adult CVD, there is now a strong rationale to consider developmental and epigenetic mechanisms as links between early life environmental factors like maternal stress during pregnancy and adult race-based health disparities in diseases like hypertension, diabetes, stroke, and coronary heart disease. The model outlined here builds upon social constructivist perspectives to highlight an important set of mechanisms by which social influences can become embodied, having durable and even transgenerational influences on the most pressing US health disparities. We conclude that environmentally responsive phenotypic plasticity, in combination with the better-studied acute and chronic effects of social-environmental exposures, provides a more parsimonious explanation than genetics for the persistence of CVD disparities between members of socially imposed racial categories.

Thermal management consumption and its effect on remaining range estimation of electric vehicles <=SEP=> This paper investigates the significance of thermal management energy consumption on the range of electric vehicles. It is necessary to combine consumption models of powertrain components such as the electric machine, power electronics and high voltage battery that take into account trip-based information containing a predicted speed profile and elevation data with a thermal management system that has the ability to incorporate predicted thermal losses of the powertrain. The thermal management system refers to the sum of all components within the refrigeration cycle of the vehicle as well as the associated control strategy. Based on the results of a real world drive study in an electric vehicle, it is demonstrated, that the range uncertainties introduced by the consumption of the thermal management are significant and should be considered when developing predictive range estimation algorithms. These uncertainties are further investigated by an energy flow based thermal system model. In order to minimize range uncertainties, a system architecture for the prediction of thermal management consumption is proposed.

A Reconfigurable Fabric for Accelerating Large-Scale Datacenter Services <=SEP=> Datacenter workloads demand high computational capabilities, flexibility, power efficiency, and low cost. It is challenging to improve all of these factors simultaneously. To advance datacenter capabilities beyond what commodity server designs can provide, we designed and built a composable, reconfigurable hardware fabric based on field programmable gate arrays (FPGA). Each server in the fabric contains one FPGA, and all FPGAs within a 48-server rack are interconnected over a low-latency, high-bandwidth network.
 We describe a medium-scale deployment of this fabric on a bed of 1632 servers, and measure its effectiveness in accelerating the ranking component of the Bing web search engine. We describe the requirements and architecture of the system, detail the critical engineering challenges and solutions needed to make the system robust in the presence of failures, and measure the performance, power, and resilience of the system. Under high load, the large-scale reconfigurable fabric improves the ranking throughput of each server by 95% at a desirable latency distribution or reduces tail latency by 29% at a fixed throughput. In other words, the reconfigurable fabric enables the same throughput using only half the number of servers.

An analysis of the GTZAN music genre dataset <=SEP=> A significant amount of work in automatic music genre recognition has used a dataset whose composition and integrity has never been formally analyzed. For the first time, we provide an analysis of its composition, and create a machine-readable index of artist and song titles. We also catalog numerous problems with its integrity, such as replications, mislabelings, and distortions.

Coarse to Fine: Diffusing Categories in Wikipedia <=SEP=> Automatic taxonomy construction aims to build a categorization system without human efforts. Traditional textual pattern based methods extract hyponymy relation in raw texts. However, these methods usually yield low precision and recall. In this paper, we propose a method to automatically find diffusing attributes to a category from Wikipedia infoboxes. We use the diffusing attribute to diffuse a coarsegrained category into several fine-grained subcategories and generate a finer-grained taxonomy. Experiments show our method can find proper diffusing attributes to categories across various domains.

Work Related Stress, Burnout, Job Satisfaction and General Health of Nurses <=SEP=> Gaps in research focusing on work related stress, burnout, job satisfaction and general health of nurses is evident within developing contexts like South Africa. This study identified the relationship between work related stress, burnout, job satisfaction and general health of nurses. A total of 1200 nurses from four hospitals were invited to participate in this cross-sectional study (75% response rate). Participants completed five questionnaires and multiple linear regression analysis was used to determine significant relationships between variables. Staff issues are best associated with burnout as well as job satisfaction. Burnout explained the highest amount of variance in mental health of nurses. These are known to compromise productivity and performance, as well as affect the quality of patient care. Issues, such as security risks in the workplace, affect job satisfaction and health of nurses. Although this is more salient to developing contexts it is important in developing strategies and intervention programs towards improving nurse and patient related outcomes.

Performance of PMASynRM With Ferrite Magnets for EV/HEV Applications Considering Productivity <=SEP=> Although motors that use rare-earth permanent magnets typically exhibit high performance, their cost is high, and there are concerns about the stability of the raw material supply. This paper proposes a permanent-magnet-assisted synchronous reluctance motor (PMASynRM) with a ferrite magnet that does not use rare-earth materials considering productivity. The performance of the proposed PMASynRM is evaluated based on the finite-element method and an experiment using a prototype machine. The analysis results reveal that the proposed PMASynRM has the same power density and an equivalent efficiency as rare-earth permanent-magnet synchronous motors for hybrid electric vehicles (2003 Toyota Prius). Furthermore, some experimental results are presented in order to validate the analytical results. As a result, the proposed PMASynRM was found to achieve high-power-density and high-efficiency performance.

Adaptive Speculative Processing of Out-of-Order Event Streams <=SEP=> Distributed event-based systems are used to detect meaningful events with low latency in high data-rate event streams that occur in surveillance, sports, finances, etc. However, both known approaches to dealing with the predominant out-of-order event arrival at the distributed detectors have their shortcomings: buffering approaches introduce latencies for event ordering, and stream revision approaches may result in system overloads due to unbounded retraction cascades.
 This article presents an adaptive speculative processing technique for out-of-order event streams that enhances typical buffering approaches. In contrast to other stream revision approaches developed so far, our novel technique encapsulates the event detector, uses the buffering technique to delay events but also speculatively processes a portion of it, and adapts the degree of speculation at runtime to fit the available system resources so that detection latency becomes minimal.
 Our technique outperforms known approaches on both synthetical data and real sensor data from a realtime locating system (RTLS) with several thousands of out-of-order sensor events per second. Speculative buffering exploits system resources and reduces latency by 40% on average.

Children ’ s Eye Movements during Listening : Developmental Evidence for a Constraint-Based Theory of Sentence Processing <=SEP=> Many comprehension studies of grammatical development have focused on the ultimate interpretation that children assign to sentences and phrases, yielding somewhat static snapshots of children's emerging grammatical knowledge. Studies of the dynamic processes underlying children's language comprehension have to date been rare, owing in part to the lack of online sentence processing techniques suitable for use with children. In this chapter, we describe recent work from our research group, which examines the moment-by-moment interpretation decisions of children (age 4 to 6 years) while they listen to spoken sentences. These real-time measures were obtained by recording the children's eye movements as they visually interrogated and manipulated objects in response to spoken instructions. The first of these studies established some striking developmental differences in processing ability, with the youngest children showing an inability to use relevant properties of the referential scene to resolve temporary grammatical ambiguities (Trueswell, Sekerina, Hill, & Logrip, 1999). This finding could be interpreted as support for an early encapsulated syntactic processor that has difficulty using non-syntactic information to revise parsing commitments. However, we will review evidence from a series of follow-up experiments which suggest that this pattern arises from a developing interactive parsing system. Under this account, adult and child sentence comprehension is a " perceptual guessing game " in which multiple statistical cues are used to recover detailed linguistic structure. These cues, which include lexical-distribution evidence, verb semantic biases, and referential scene information, come " online " (become automated) at different points in the course of development. The developmental timing of these effects is related to their differential reliability and ease of detection in the input.

Cerebellar ataxia rehabilitation trial in degenerative cerebellar diseases. <=SEP=> OBJECTIVE
To investigate short- and long-term effects of intensive rehabilitation on ataxia, gait, and activities of daily living (ADLs) in patients with degenerative cerebellar disease.


METHODS
A total of 42 patients with pure cerebellar degeneration were randomly assigned to the immediate group or the delayed-entry control group. The immediate group received 2 hours of inpatient physical and occupational therapy, focusing on coordination, balance, and ADLs, on weekdays and 1 hour on weekends for 4 weeks. The control group received the same intervention after a 4-week delay. Short-term outcome was compared between the immediate and control groups. Long-term evaluation was done in both groups at 4, 12, and 24 weeks after the intervention. Outcome measures included the assessment and rating of ataxia, Functional Independence Measure, gait speed, cadence, functional ambulation category, and number of falls.


RESULTS
The immediate group showed significantly greater functional gains in ataxia, gait speed, and ADLs than the control group. Improvement of truncal ataxia was more prominent than limb ataxia. The gains in ataxia and gait were sustained at 12 weeks and 24 weeks, respectively. At least 1 measure was better than at baseline at 24 weeks in 22 patients.


CONCLUSIONS
Short-term benefit of intensive rehabilitation was evident in patients with degenerative cerebellar diseases. Although functional status tended to decline to the baseline level within 24 weeks, gains were maintained in more than half of the participants.

ON ATTRIBUTE BASED ACCESS CONTROL SCHEME IN CLOUD ENVIRONMENT <=SEP=> *Corresponding author: Email: divyasnkl@gmail.com; Tel.: +91 9486029378

Interoperability evaluation models: A systematic review <=SEP=> Interoperability is defined as the ability for two (or more) systems or components to exchange information and to use the information that has been exchanged. There is increasing demand for interoperability between individual software systems. Developing an interoperability evaluation model between software and information systems is difficult, and becoming an important challenge. An interoperability evaluation model allows knowing the degree of interoperability, and lead to the improvement of interoperability. This paper describes the existing interoperability evaluation models, and performs a comparative analysis among their findings to determine the similarities and differences in their philosophy and implementation. This analysis yields a set of recommendations for any party that is open to the idea of creating or improving an interoperability evaluation model. 2013 Elsevier B.V. All rights reserved.

The partograph for the prevention of obstructed labor. <=SEP=> Obstructed labor is an important cause of maternal and perinatal mortality and morbidity. The partograph graphically represents key events in labor and provides an early warning system. The World Health Organization partographs are the best known partographs in low resource settings. Experiences with World Health Organization and other types of partographs in low resource settings suggest that when used with defined management protocols, this inexpensive tool can effectively monitor labor and prevent obstructed labor. However, challenges to implementation exist and these should be addressed urgently.

Action recognition in depth videos using hierarchical gaussian descriptor <=SEP=> In this paper, we propose a new approach based on distribution descriptors for action recognition in depth videos. Our local features are computed from binary patterns which incorporate the shape and motion cues for effective action recognition. Given pixel-level features, our approach estimates video local statistics in a hierarchical manner, where the distribution of pixel-level features and that of frame-level descriptors are modeled using single Gaussians. In this way, our approach constructs video descriptors directly from low-level features without resorting to codebook learning required by Bag-of-features (BoF) based approaches. In order to capture the spatial geometry and temporal order of a video, we use a spatio-temporal pyramid representation for each video. Our approach is validated on six benchmark datasets, i.e. MSRAction3D, MSRGesture3D, DHA, SKIG, UTD-MHAD and CAD-120. The experimental results show that our approach gives good performance on all the datasets. In particular, it achieves state-of-the-art accuracies on DHA, SKIG and UTD-MHAD datasets.