Reviewerly/exHarmony huggingface dataset:
T-Mask: An Active and Accurate Dialogue State Tracking with Token Mask Prediction

Recent dialogue state tracking (DST) usually treats utterance, system action and ontology equally to estimate the slot types and values. In this way, the expression of slot in utterance is restricted. As the main way to directly express user semantics, utterance should receive further attention and its proportion in semantic expression should be dynamic according to the content. It’s common to recognize the different importance of information in all the DST models. However, most of them pay little attention to the position of slot in utterance. In fact, position and semantics are related due to human grammatical habits and expression habits. Therefore, we propose T-Mask <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> , a model to actively and accurately learn the token mask position of slot, and we further utilize the learned position information to influence the semantic expression of utterance. We verify the effectiveness of our model on DSTC2 and WoZ2.0. On WoZ2.0, we achieve 90.84 joint goal accuracy and 97.6 turn request accuracy, which is better than most existing models.
Visual tracking based on multi-cue framework and hierarchical aggregation

Existing Siamese network based trackers define target tracking as a similarity matching problem between the initial template and search regions. However, the initial template contains extremely limited target information that ignores appearance variations over time. Aiming at this problem, a visual tracking method based on multi-cue framework and hierarchical aggregation is proposed in this paper. Firstly, we put forward a stability evaluation mechanism to select the stable template for similarity matching, so that the tracker can adapt to the target appearance changes. Secondly, we construct a multi-cue tracking framework combining the initial template branch and the stable template branch for parallel tracking, thus solving the single template limitation. Finally, we propose a hierarchical aggregation strategy that combines complementary merits to further improve tracking performance. Comprehensive experimental results on the benchmark dataset demonstrate that the proposed algorithm achieves favorable tracking performance.
A Deep Learning Approach for Robust Target Tracking in a Cluttered Environment

In a challenging environment with severe measurement errors, one of the most important factor to improve target tracking accuracy is to understand the channel characteristics of the environment and to estimate the target position in data-oriented manner. In this paper, we propose a deep learning model to estimate target position by processing time difference of arrival (TDOA) measurements in an end-to-end manner, especially for cluttered environments. The proposed TDOA image based target tracking (TITT) model converts each TDOA measurement into TDOA image, then a mask is created to handle TDOA errors. Then, by accumulating and treating TDOA images with masks as input data, we train a CNN model with fully connected layer to estimate target position through back propagation. Simulation results demonstrate that our proposed TITT model outperforms the simple deep learning model in challenging environment with many clutters.
An experimental study on visual tracking based on deep learning

Purpose In the community of visual tracking or object tracking, discriminatively learned correlation filter (DCF) has gained more importance. When it comes to speed, DCF gives the best performance. The main objective of this study is to anticipate the object visually. For tracking the object visually, the authors proposed a new model based on the convolutional regression technique. Features like HOG &amp; Harris are used for the process of feature extraction. The proposed method will give the best results when compared to other existing methods. Design/methodology/approach This paper introduces the concept and research status of tracks; later the authors focus on the representative applications of deep learning in visual tracking. Findings Better tracking algorithms are not mentioned in the existing method. Research limitations/implications Visual tracking is the ability to control eye movements using the oculomotor system (vision and eye muscles working together). Visual tracking plays an important role when it comes to identifying an object and matching it with the database images. In visual tracking, deep learning has achieved great success. Practical implications The authors implement the multiple tracking methods, for better tracking purpose. Originality/value The main theme of this paper is to review the state-of-the-art tracking methods depending on deep learning. First, we introduce the visual tracking that is carried out manually, and secondly, we studied different existing methods of visual tracking based on deep learning. For every paper, we explained the analysis and drawbacks of that tracking method. This paper introduces the concept and research status of tracks, later we focus on the representative applications of deep learning in visual tracking.
System Identification of Decision-Making Process in Gold Trading Game

The decision-making process of human are being researched in association with making psychological models and experiment technique. However, there is no mathematical models using transfer functions for representing such process as far as the authors know. In this research, a model of the decision-making process was constructed, which was consisting of proportional, integrator, and derivative elements. And, the coefficients of the each element, Kp, Ki, and Kd, were individually identified by the least-square method, using the input-output data obtained in the computer simulation game of gold trading, which was created especially for this research.In the result, Ki/Kp, the coefficient ratio between the integrator element and the proportional element, and Kd/Kp, that between the derivative element and the proportional element, were different for each individual. As these differences can be regarded as individual differences, the constructed model is considered to achieve the representation of individual properties, which is the aim of this research.However, in this created game, the recall ratio of Ki/Kp and Kd/Kp was not high respectively, the average of them was only 15%. This low recall ratio can be due to the inadequacy of the game used for the identification, or the unsuitability of the structure of the constructed model. Thus, the improvement of the gold trading game and model's structure are the next challenges. Especially, the addition of learning function to the model of the decision-making process is one of the desirable challenges, because it could work even in short and repeated process like gold trading.
Design for Terra-Reforming: Prototyping Environmentally Responsible Socio-technical Futures

From the Gutenberg Press to the Internet of Things, socio-technical progress has benefitted humanity in ways our ancestors likely never thought possible.Many advancements have, however, had a profoundly detrimental effect upon our planet's natural environment.Despite the growing spectre of climate change, technological systems continue to be designed and adopted with little culpability.We respond to these issues by outlining the foundations for a novel research approach -Design for Terra-Reforming (DfT) -which seeks to emphasise the environmental consequences that accompany socio-technical development as well as to help make such impacts more accountable.Reflecting on an initial case study, we discuss how DfT combines the actionable insights of physical prototyping with the future-focussed envisioning of Design Fiction prototyping.We then posit how DfT might be further developed into a critical framework for better sustainable governance of the threats future technologies may pose to Earth's climate and ecology.
Accuracy of Posture Estimation by ActiGraph and Development of Posture Prediction Model from Raw Acceleration Data

Full body motion tracking system was used to obtain accurate posture information under free-living condition and the accuracy of posture prediction by ActiGraph was verified. ActiGraph tends to detect people as standing when they are actually sitting. By combining a prediction model that detects posture change, lying posture, and walking from raw acceleration data obtained by ActiGraph, we were able to predict posture information with higher accuracy.Clinical Relevance- By combining model for predicting posture changes, lying posture, and walking from raw acceleration data obtained by ActiGraph, it is possible to obtain more accurate posture information.
Deep Learning Oriented Channel Estimation for Interference Reduction for 5G

The increasing demand for high-speed data services, such as mobile gaming, Augmented/Virtual Reality (AR/VR) applications, vehicular communications, Internet of Everything (IoE), and haptic internet, results in high user densification in 5G and beyond networks. Moreover, the ultra-dense user scenarios raise the challenge of increased interference due to the highly shared spatial resources and unknown Channel State Information (CSI). Therefore, the optimal channel estimation helps in interference cancellation; however, the conventional channel estimation techniques are imperfect. On the other hand, the Deep Learning (DL) approach confers the potential solution for the channel estimation. In this paper, we implement the Convolutional Neural Network (CNN) dL architecture for channel estimation over the range of values of SNR for Single Input Single Output OFDM network. The proposed DL-CNN approach demonstrates a 94.30% reduction in Mean Square Error (MSE) compared to the traditional interpolation method-based channel estimation at different values of SNR considering the dense scenario.
Fast and Adaptive Local Consensus Verification for Robust Feature Correspondence

In this paper, we propose a fast and robust feature correspondence verification method based on local consensus. We encode the locality invariance into a continuous probability density function and formulate the problem to be a two posteriori estimation of Bayesian models without accurate transformation estimation. The model can be used without iteration for super-efficiency, and iteratively for SOTA performance. The non-iterative version can directly output the correctness confidence of each match with linearithmic time and linear space complexities. The iterative version requires only 5-10 iterations to update parameters, which can produce SOTA precision or recall. Besides, our model provides remarkable geometry robustness, and is robust to the quality of neighbors even when the simplest spatial knn is used. Numerous experimental results demonstrate the superiority of our method.
Health management technology for estimating the remaining useful life of household appliances

In this paper, we propose a new time series-based domain adaptation technique for health management in home devices. Because it is based on domain adversarial neural network technology, it can learn labeled domain data and utilize it in unlabeled domains. In addition, by modifying the domain adaptation network to process time series data, it supports prediction of remaining useful life of time series data for home devices. We confirmed that the proposed method shows much better prediction performance compared to other network models.
Practical distributed maneuvering target tracking using delayed information of heterogeneous unregistered sensors

Registration is the most consequential topic to be dealt with in a multi-sensor tracking system. On the other hand, providing satisfactory target acceleration estimation would enhance the performance of a ground-based air defense system encountering a maneuvering target. The main novelty of the present work is addressing a new scheme to solve the registration problem in a distributed network along with estimating accurate target acceleration, simultaneously. The details of coping with three common kinds of measurement, attitude, and location biases are explored concentrating on the effects of attitude bias as the main error source from the practical viewpoint. A modified iterated extended Kalman filter is suggested as the basis to handle the multi-rate fusion problem. The probable performance degradation due to the existence of sensors measurement delay (ignored in all similar studies) is illustrated. An effective scheme that compensates for the delay with a low computation cost is introduced. The formulation is developed for a typical radar-infrared tracking system, accomplished by elucidating the required points for generalization to a desirable number of heterogeneous sensors. Calculating the Cramer-Rao bound of estimation problem, the effectiveness of proposed scheme is deliberately demonstrated through simulation analyses respecting the momentous practical aspects.
Cooperative planning of dual arm robot based on Improved Particle Swarm Optimization

The trajectory optimization of dual arm robot is a typical engineering optimization problem. Because of the collision detection, the relationship between the decision variables and the fitness function cannot be expressed directly. Particle swarm optimization (PSO) is a typical meta heuristic method, which is often used to solve engineering problems in recent years because of its easy implementation, fast computing speed and strong stability. In this paper, UR5 dual arm robot is taken as the research object. Based on quintic polynomial interpolation algorithm based path planning, a cooperative optimization method of dual arm robot is proposed, and an improved particle swarm optimization algorithm is used to plan the path of dual arm robot. Through the test of the path planning of the dual arm robot based on the improved particle swarm optimization algorithm, the experimental results show that this method can effectively solve the path planning problem of the dual arm robot.
Our Country’s Smart Agriculture Development Strategy and Path Under the Big Data Environment

With the rapid development of our country’s modern Internet, Internet of Things and other types of high-tech, smart agriculture will be an important direction for our country’s agricultural development in the future. Through the extensive application of various emerging sciences and technologies, work efficiency is improved, various natural disasters are prevented, food safety is ensured, and e-commerce sales are promoted, which will promote the development of our country’s agriculture more efficiently and safely. This is undoubtedly a strong driving force for our country to achieve the goal of building a well-off society in an all-round way. This article is to research the development strategy and approach of our country’s smart agriculture under the big data environment, and refer to related literature. Explained the research and development status of smart agriculture in our country, and then, in order to further understand the research and development of smart agriculture in our country, the questionnaire survey mainly focuses on the impact of big data on smart agriculture and the current stage. The problems in the development of smart agriculture in our country are carried out in two aspects. The survey results show that the main problem in the development of smart agriculture at this stage is that the cost of use is too high, which accounts for more than 45%, and the second is that the products used are not durable and need to be replaced from time to time.
Generating maneuverable trajectories for a reconfigurable underactuated agricultural robot

A high level of accuracy is essential for meaningful productivity measures in agriculture. However, agricultural robots are frequently used for multiple applications simultaneously or reconfigured for changing applications, both of which can reduce reading accuracy; choosing trajectories which optimize measurement accuracy by taking into account the changing dynamics of the vehicle while maximizing maneuverability is essential. The aim of this study is to produce an optimal trajectory between two points for an underactuated agricultural robot which is maneuverable in the sense that it allows readings to be carried out from all angles while respecting the dynamic constraints of the vehicle. The paths and controller are be tested on a simulation of a Husky vehicle which forms the basis for a reconfigurable agricultural robot.
Time-Varying Polar Decomposition by Continuous-Time Model and Discrete-Time Algorithm of Zeroing Neural Network Using Zhang Time Discretization (ZTD)

Time-varying polar decomposition becomes important and has many applications in potential fields. This paper first proposes a continuous-time model and a discrete-time algorithm of zeroing neural network (ZNN) for time-varying polar decomposition. The continuous-time polar decomposition (CTPD) model is obtained by using ZNN method. Besides, the discrete-time polar decomposition (DTPD) algorithm of ZNN is obtained by utilizing a 7-instant Zhang time discretization (ZTD) formula for being realized in digital computer readily. Finally, this paper investigates two numerical examples with different dimensions. The corresponding numerical results substantiate the relative effectiveness of the proposed continuous-time model and discrete-time algorithm of ZNN for time-varying polar decomposition.
Recurrent Temporal Point Process Network for First and Repeated Clinical Events

Clinical applications that involves risk stratification of patients often predict the likely occurrence of an event such as the development of a complication as well as the time to the next event. Existing approaches that provide individual survival distribution across time often use baseline measurements for the risk prediction of an event occurring, and is unable to estimate the time to next event. Further, they do not deal with the complex and heterogeneous information from past visit records, and hence do not handle repeated events which are common in chronic diseases. We address the above limitations by designing a recurrent temporal time process network that incorporates longitudinal visit information to increase the accuracy of risk predictions for clinical events and to estimate the time to next event. Our proposed solution utilizes a recurrent neural network to learn a latent representation of patient visit history. This representation enables us to approximate the conditional intensity function of the temporal point process, from which we derive the survival probability function to obtain the risk of an event occurring and to estimate the time to next event. We demonstrate our approach on two real-world healthcare datasets and show that the proposed approach is able to achieve a significant performance improvement over state-of-the-art methods.
Selecting a platform for distance learning in an electronic environment

This study is dedicated to comparative analysis of known platforms used for distance learning in an electronic environment. The aim of the survey is to make a comparative analysis of the most used platforms in Bulgaria. Based on this analysis to make a choice of the qualities that should possess the "ideal" platform for Distance Learning in Electronic Environment DLEE.
Visual Question Answering on CLEVR Dataset via Multimodal Fusion and Relational Reasoning

Visual question answering (VQA) is a new interdisciplinary research area, in which a model attempts to answer a free-form question in natural language based on a given image. In this paper we improve an existing model, which leverages relational networks and attention mechanism, by decreasing its train and inference time while preserving a comparable accuracy.
Distribution-Adaptive Contexts and Probability Model Adjusting for Lossless Image Compression

Adaptive arithmetic coding (AAC) is an advanced entropy coding method. However, its performance is highly dependent on the context assignment method and the frequency table adjusting mechanisms. In this work, two techniques to improve the performance of AAC on lossless image compression are proposed. First, a method to adaptively vary the thresholds for context assignment according to the histogram is proposed. Moreover, a hyper-Laplacian probability model is applied to construct the frequency table and its parameters are adjusted adaptively according to the local weighted variance. With these techniques, lossless image compression can be performed in a much more effective way.
Dual Batch Size Training: An efficient MGD adaptive batch size method

Mini-batch Gradient Descent (MGD) has become a standard for deep learning model training. For a long period of time, the size of the mini-batch (also known as batch size) is set empirically as a fixed value, while recent works have demonstrated it could have crucial effects on the training. Although there already exist several adaptive batch size methods, they either add significant overhead to the training, or lack of robustness on various training scenarios. In this work, an adaptive batch size method to accelerate MGD training is proposed, whose basic idea is to concurrently run training with two batch sizes, and choose batch size based on the comparison of the evaluated history performance. It can be easily implemented on various deep learning platforms, and the experiment results suggest that the proposed method achieves high performance and strong robustness with acceptable and controllable overhead, which outperforms existing adaptive batch size methods.
Research on Object Detection and Location Using Semantic Segmentation

Object detection and location generally require multiple sensors and multiple deep networks to achieve. For a specific application scenario, this paper proposes an algorithm based on semantic segmentation to display object detection and location at the same time. Due to the small size of the target in the image, especially the vertex of the target occupies very few pixels, the HRnet semantic segmentation algorithm with higher resolution is adopted. Due to the unbalanced class distribution and the small number of training samples, data augmentation and Lovasz Softmax loss function are used in a targeted manner. After experimental verification, in this data set, HRnet is superior to other algorithms. And by using data augmentation and Lovasz Softmax loss function, the accuracy and IoU have been greatly improved, especially the target vertex area with few samples.
CoFirNet: Conditional Feature Vector-based Fashion Image Retrieval Network

In this paper, we present an image retrieval architecture using specific feature vectors for the fashion domain. The triplet network is widely used in an image retrieval task with a deep-learning method. The vector from the deep neural network reflects general image characteristics such as color and pattern on the image. On the other hand, the characteristics of the fashion items, such as length, fit, detailed design part, etc., are not well combined in their vector because it is difficult to obtain the individual information of the domain by a deep convolutional neural network (CNN). To consider the specific characteristics of the fashion items, we proposed a fashion image retrieval network, which is called CoFirNet, applying domain-specific feature vectors extracted from the pretrained network based on a classification scheme to distinguish the domain property in an image. The domain-specific feature vector, conditional vector, are combined with the general characteristics feature vector to build the embedding vector for the image retrieval scheme to include the characteristics of fashion items. The proposed method is evaluated, and it shows that the objective and subjective performance were higher than the compared method on the fashion images. Therefore, we believe that the proposed scheme can be a useful method and system for a specialized image search system. etc.