Reviewerly/exHarmony huggingface dataset:
Unmanned Boat Target Detection Based on Improved SSD

Currently, vision sensors are one of the most equipped sensors in unmanned boats, and they provide an intuitive description of the environment. Deep learning based convolutional networks can identify and classify targets well. Considering the need for real-time obstacle avoidance by unmanned boats, this section selects the lightweight model Single Shot Detector (SSD) as the recognition framework, but the SSD will appear discontinuous in the target tracking. To this end, the recognition framework of SSD&CAMShift is proposed, and CAMShift is used to obtain the pixel statistical probability of the target to achieve auxiliary tracking. The simulation results show that the designed tracker can automatically detect and track, and the data correlation between the upper and lower frames is greatly improved.
Design of Unified Controller for Obstacle Avoidance and Tracking of Wheeled Mobile Robots

The novel unified control approach is proposed to simultaneously solve tracking and obstacle avoidance problem of the wheeled mobile robot (WMR). Applying the Lyapunov's direct method, a stable unified controller is designed to simultaneously handle tracking and obstacle avoidance for this WMR system based on the potential function. By the Lyapunov stability theory, it is proved that tracking errors of the closed-loop system are asymptotical convergence, the tracking errors converge to the zero outside the obstacle detection region and the obstacle avoidance is guaranteed inside the obstacle detection region. The effectiveness and robustness of the proposed control method are validated from the simulation results.
User's activity driven short-term context inference

Customer decision making process is not invariant. Actual circumstances have a great influence on user's preference adjustments, therefore an absence of incorporating contextual information leads to sub-optimal prediction performance. A popular approach in recommender systems is to treat a context as a set of identifiable and observable attributes while assuming their full separability from an activity. In contrast, we believe that the context emerges from the activity and its change can be perceived and possibly predicted by using mined patterns of its evolution on multiple levels, starting at individual sessions. This paper presents concepts, ideas and motivation for our PhD research project.
Dependent Multilevel Interaction Network for Natural Language Inference

Neural networks have attracted great attention for natural language inference in recent years. Interactions between the premise and the hypothesis have been proved to be effective in improving the representations. Existing methods mainly focused on a single interaction, while multiple interactions have not been well studied. In this paper, we propose a dependent multilevel interaction (DMI) Network which models multiple interactions between the premise and the hypothesis to boost the performance of natural language inference. In specific, a single-interaction unit (SIU) structure with a novel combining attention mechanism is presented to capture features in an interaction. Then, we cascade a serial of SIUs in a multilevel interaction layer to obtain more comprehensive features. Experiments on two benchmark datasets, namely SciTail and SNLI, show the effectiveness of our proposed model. Our model outperforms the state-of-the-art approaches on the SciTail dataset without using any external resources. For the SNLI dataset, our model also achieves competitive results.
Exploring Local Transformation Shared Weights in Convolutional Neural Networks

We explore the methods which add domain-specific transformation knowledge to the weights of a Convolutional Neural Network (CNN), through transformation-based weight-sharing. Often, such network augmentations have demonstrated superior parameter-efficiency and better generalization abilities. We focus on two conjugate methods in literature which increase the degree of weight-sharing within CNNs, showcasing opposing philosophies. The first, transformation shared max-pooling (TSMP), explicitly injects local visual transformation invariance into network representations. The second, transformation shared kernel extension (TSKE), is simply TSMP without max-out, and therefore avoids explicit injection of local invariances. Elaborate empirical validation of these methods is presented on variations of MNIST, which evaluate various aspects of these methods. Mainly, we find that for small test-data distortions (rotation, scale and elastic), TSKE outperforms TSMP, whereas for large test-data distortions, TSMP showcases superior performance.
Multi-grained Pruning Method of Convolutional Neural Network

Although the deep learning technology has shown great power in solving the complex tasks, these neural network models are large and redundant as a matter of fact, which makes these networks difficult to be placed in embedded devices with limited memory and computing resources. In order to compress the neural network to a slimmer and smaller one, the multi-grained network pruning framework is proposed in this paper. In our framework, the pruning process was divided into the filter-level pruning and the weight-level pruning. In the process of the filter-level pruning, the importance of the filter was measured by the entropy of the activation tensor of the filter. In the other process, the dynamic recoverable pruning method was adopted to prune the weights deeply. Different from these popular pruning methods, the weight-level pruning is also taken into account based on the employment of the filter-level pruning to achieve more effectively pruning. The proposed approach is validated on two representative CNN models - AlexNet and VGG16, pre-trained on ILSVRC12. Experimental results show that AlexNet and VGG16 network models are compressed 19.75× and 22.53× respectively by this approach, which are 2.05 and 5.89 higher than the classical approaches of dynamic Network Surgery and ThiNet.
Non-positive Face Detection Based on YCgCr Color Space

Non-positive face detection is a topic worthy of further study, however, there are few paper focus on it. This paper proposes a method to detect non-positive face based on a new color space YCgCr by optimized Gaussian skin model. Firstly, YCgCr color space is compared to YCbCr to show its advantage of better clustering effect. Secondly, OTSU method is used to calculate the threshold, and applied to simple Gaussian skin model to get an improved Gaussian skin model, which achieves excellent results. And then through morphological process, including keeping the maximum connected area and neck rejection, getting an ideal face binary image finally.
Machine Learning Accelerators and More

Presents the introductory editorial for this issue of the publication.
Application of Artificial Intelligence in Digital Design of Substation

The artificial intelligence takes the computer as the core, and has great development and innovation in the research of the interdisciplinary field. The level of artificial intelligence is high, and processing speed is fast, which can ease the burden of designers, and greatly improve the designers life quality and production efficiency. With the development process of science, more and more requirements of substation digitized designing are posed. Application Imagination on Substation Design Based on Artificial Intelligence is proposed in this paper, which can design secondary physical circuit, secondary virtual circuit and intelligent label more quality and effective.
Presenting GHItaly19

This short paper introduces the contributions accepted for the 3rd Workshop on Games-Human Interaction - GHItaly19, held in connection with CHItaly 2019. Since its first edition, this series of workshops aims at focusing on every aspect of the multidisciplinary process of design and development of game interfaces. The quality of the resulting User eXperience is a highly relevant goal to achieve, in order to engage and satisfy the target users, especially when multidimensional artifacts such as video games are concerned. An important consideration concerns the multifaceted range of applications of video games, that has long overcome the borders of amusement.
A Framework for a Decentralized and Self-Organized Mail Service Network

Based on sharing economy idea, we propose a framework for a decentralized and self-organized mail service network. Availability and reliability are the essential requirements for this service. To this end, we notice major technical challenges for this network — routing maintenance and trust management. We design solutions to address them using ad-hoc and peer-to-peer network technology and blockchain technology, and bring the solutions to attention of readers for further discussion and improvement.
A Local Taylor Fitting Level Set Image Segmentation Model With Global Region-based Information

The traditional CV model based on global information has strong robustness to the initial contour and noise, while the Taylor expansion fitting model (LATE) based on local information can segment the image with intensity inhomogeneity, but is sensitive to the initial contour and noise. To solve the above problems, an adaptive level set image segmentation combining global (CV) and local information (LATE) was proposed. Firstly, the global and local energy terms were constructed respectively by using the global and local information of the image, and a weight coefficient was constructed between the global and local energy terms for adaptive adjustment. Secondly, the energy penalty term and length term are added to avoid the reinitialization of the level set function and smooth the curve evolution, thus improving the stability of numerical calculation. In the end, some comparative experiments show that the proposed model is robust to initial contour and noise, and has higher segmentation accuracy and efficiency for non-uniform gray images and natural images.
FCN Salient Object Detection Using Region Cropping

An important issue in salient object detection is how to improve the result of saliency map for the reason that it is the basis of many subsequent operations in computer vision. In this paper, we propose a region-based salient object detection model using fully convolutional neural network (FCN) with traditional visual saliency method. We introduce the region cropping and jumping operation into FCN network for a more target-oriented feature extraction, which is a low-level cue based processing. It processes the training images into patches of various sizes and makes these patches jump to convolutional layers with corresponding depths as their input data in training. This operation can preserve the main structure of objects while decrease the background redundancy. In the meantime, it also takes into account topological property, which emphasizes the topological integrity of objects. Experimental results on four datasets show that the proposed model performs effectively on salient object detection compared with other ten approaches, including state-of-the-art ones.
Algorithmic Approaches to Computational Models of Argumentation

Computational models of argumentation [1] are approaches for non-monotonic reasoning that focus on the interplay between arguments and counterarguments in order to reach conclusions.
KEMAMPUAN MENULIS CERITA FABEL MENGGUNAKAN MODEL EXAMPLES NON-EXAMPLES SISWA KELAS VII D SMP NEGERI 6 MUARO JAMBI TAHUN AJARAN 2017/2018

The aim of this research is to describe the students ability in writing fable by using examples non examples model at class VII D SMP Negeri 6 Muaro Jambi Academic Year 2017/2018. This research used quantitative correlation approach. The population of the research was class VII D SMP Negeri 6 Muaro jambi. The sample of the research consisted of one class, 12 men and 18 women with total sample was 30 people. The data was collected by using pre-test by using conventional method and post-test by using examples non examples model. The result of the research shows that the value of tcount is 7.766. Meanwhile the value of ttable is required from table t with dk 28 and the siginicance () is 0,05. By comparing the value of tcount and ttable it is required tcount &gt; ttable, it means Ho is rejected and Ha is accepted. Thus, it can be concluded that the use of examples non examples model can be used in increasing the ability of the students in writing fable at class VII D SMP Negeri 6 Muaro Jambi with the mean score of post-test is 82.63 higher that the mean of pre-test 70.20. Keywords: writing, fable, model examples non examples
Research on Trajectory Optimization of Six-Axis Manipulator Based on Watchcase Polishing

In the watchcase polishing industry, due to the complexity of the watchcase surface to be polished and the variety of the watchcase surface, as well as the low manual efficiency and poor quality, this paper proposes an optimization strategy for the trajectory generation of industrial manipulators for polishing. By analyzing the effective optimization parameters of the manipulator, the spline curve interpolation satisfies the validity and rationality of the point-to-point trajectory in the polishing process. Finally, the path optimization is completed on the basis of these. A simulation experiment was carried out on a certain manipulator, and a trajectory curve for actual polishing process was obtained.
Unsupervised Discrete Representation Learning

Learning discrete representations of data is a central machine learning task because of the compactness of the representations and ease of interpretation. The task includes clustering and hash learning as special cases. Deep neural networks are promising to be used because they can model the non-linearity of data and scale to large datasets. However, their model complexity is huge, and therefore, we need to carefully regularize the networks in order to learn useful and interpretable representations that exhibit intended invariance for applications of interest. To this end, we propose a method called Information Maximizing Self-Augmented Training (IMSAT). In IMSAT, we use data augmentation to impose the invariance on discrete representations. More specifically, we encourage the predicted representations of augmented data points to be close to those of the original data points in an end-to-end fashion. At the same time, we maximize the information-theoretic dependency between data and their predicted discrete representations. Our IMSAT is able to discover interpretable representations that exhibit intended invariance. Extensive experiments on benchmark datasets show that IMSAT produces state-of-the-art results for both clustering and unsupervised hash learning.
Common Problems and Optimization Strategies of Machine Translation of Headlines of Online Scientific News

Machine translation is getting more and more used. By a contrast analysis of 188 pieces of English headlines of online news of science and technology from the website of New York Times and its Chinese counters from the website of Huanqiu, this paper summarizes the problems of machine translation of headlines of online news of science and technology. The problems are fuzzy expression, semantic exaggeration, semantic narrowing, misusing idioms, abusing foreign language and guiding the readers to get vulgar information. The translators should improve the versions by machine translation. This paper presents four optimization strategies of machine translation of headlines of online scientific news: changing complexity to brief, using appropriate rhetoric devices, use opportunely punctuations and using the idioms familiar to readers.
A robust fusion estimation with unknown cross-covariance in distributed systems

Abstract An efficient robust fusion estimation (RFE) for distributed fusion system without knowledge of the cross-covariances of sensor estimation errors is suggested. With the hypothesis that the object lying in the intersection of some ellipsoids related to sensor estimations, the robust fusion estimation is designed to be a minimax problem, which is solved by proposing a novel relaxation strategy. Some properties of the RFE are discussed, and numerical simulations are also present to compare the tracking performance of RFE with that of the centralized fusion and CI method. The numerical examples show that the average tracking performance of RFE is slightly better than that of the CI method, and the performance degradation of RFE is acceptable compared with the centralized fusion.
Text Generation Under Subject and Sentiment Features Constraints

There was less research on new text generation under subject and sentiment features constraints with deep learning method at the same time. Existing methods can generate text with inaccurate subject features and sentiment features. The new model, called Hybrid Multi-Sentiment Bidirectional Long Short-Term Memory (HM-BiLSTM) networks, generates comments under subject and sentiment features constraints by subject attention mechanism, syntactic structure characteristic constraints. Experiment results showed that the original model can generate text with higher sentiment relevance and topic relevance.
Deep Recurrent Policy Networks for Planning Under Partial Observability

QMDP-net is a recurrent network architecture that combines the features of model-free learning and model-based planning for planning under partial observability. The architecture represents a policy by connecting a partially observable Markov decision process (POMDP) model with the QMDP algorithm that uses value iteration to handle the POMDP model. However, as the value iteration used in QMDP iterates through the entire state space, it may suffer from the “curse of dimensionality”. Besides, as the policies based on the QMDP will not take actions to gain information, this may lead to bad policies in domains where information gathering is necessary. To address these two issues, this paper introduces two deep recurrent policy networks, asynchronous QMDP-net and ReplicatedQ-net, based on the plain QMDP-net. The former takes advantage of the idea of asynchronous update into the value iteration process of QMDP to learn a smaller abstract state space representation for planning. The latter partially replaces the QMDP with the replicated Q-learning algorithm to take informative actions. Experimental results demonstrate the proposed networks perform better than the plain QMDP-net on the robotic tasks in simulation.
Chain-detection Between Clusters

Chains connecting two or more different clusters are a well known problem of clustering algorithms like DBSCAN or Single Linkage Clustering. Since already a small number of points resulting from, e. g., noise can form such a chain and build a bridge between different clusters, it can happen that the results of the clustering algorithm are distorted: several disparate clusters get merged into one. This single-link effect is rather known but to the best of our knowledge there are no satisfying solutions which extract those chains, yet. We present a new algorithm detecting not only straight chains between clusters, but also bent and noisy ones. Users are able to choose between eliminating one dimensional and higher dimensional chains connecting clusters to receive the underlying cluster structure. Also, the desired straightness can be set by the user. As this paper is an extension of [8], we apply our technique not only in combination with DBSCAN but also with single link hierarchical clustering. On a real world dataset containing traffic accidents in Great Britain we were able to detect chains emerging from streets between cities and villages, which led to clusters composed of diverse villages. Additionally, we analyzed the robustness regarding the variance of chains in synthetic experiments.
An Improved Model for Image Selective Segmentation Based on Local and Global Statistics

In this paper, an improved selective model is proposed for image segmentation, which is built based on local and global statistics of the image. Firstly, by utilizing the advantage of CV model which is less sensitive to original location of initial contour and can segment noisy images, and taking advantage of LCV which can efficiently solve intensity inhomogeneity, the new model integrate the local image information from local Chan-Vese (LCV) model and global statistics from CV model into selective segmentation. In addition, a weight coefficient is added to balance the relationship between global energy and local energy. Then extending the improved model to the color images. Finally, simulation experiment results show that our model can efficiently segment noisy and intensity inhomogeneity images, and is less sensitive to the initialization. New model has stronger robustness and is more efficient.
Identification of optical signatures for the augmented reality system

The article proposes a method that allows simultaneous identification of many optical signatures used for the optical control in augmented reality systems. Optical signatures are generated using appropriately coded laser pulse sequences. The developed method of identifying optical signatures allows to identify individual series of laser pulses and is based on the analysis of video sequence of images. This is an important enhancement in relation to the systems used to cooperate with objects in the augmented reality environment.
Experience of Practical Application of the Automated Information System «Document Management of Scientific and Technical Product Deliveries»

The production of scientific and technical products is one of the most promising areas in terms of implementation of automation systems. The information system «Document management of scientific and technical product deliveries» is a set of unique resources that provides each of the participants with the opportunity to operate a single database of electronic documents and data in accordance with their respective access rights. The aim of the study was to assess the experience of practical application of the automated information system «Document management of scientific and technical product deliveries» and its role in the optimisation of the organisation’s activities. The article discusses the main results of the information system application for data storage and data management enabling the marketing of scientific and technical products. The paper provides a general assessment of how the software product helps meet the requirements for monitoring the production timeframe, automate the drafting and approval of work-related and financial documentation. It describes the process of electronic document exchange using the capabilities of CALS/PLM technologies. The paper illustrates the need to use the automated document flow for production and marketing of samples of pathogenic microorganism strains, certified reference standards for commercial enterprises, non-periodical publications. It outlines the prospects for further optimisation of channels of communication with external customers, and electronic exchange of documents. The benefits from the use of this system help reduce the time spent by managers and employees on the compilation of packages of documents and on the preparation of applications — by automating routine manipulations in processing incoming information and documents received as annexes to applications and requests. This information system was analysed to illustrate the benefits of digital technologies.