Reviewerly/exHarmony huggingface dataset:
Adaptive trajectory estimation with power limited steering model under perturbation compensation

Trajectory estimation of maneuvering objects is applied in numerous tasks like navigation, path planning and visual tracking. Many previous works get impressive results in the strictly controlled condition with accurate prior statistics and dedicated dynamic model for certain object. But in challenging conditions without dedicated dynamic model and precise prior statistics, the performance of these methods significantly declines. To solve the problem, a stochastic nonlinear model called the power-limited steering model is proposed to describe the motion of non-cooperative object. It is a natural combination of instantaneous power and instantaneous angular velocity, relying on the nonlinearity to achieve the change of states. And the renormalization group is introduced to compensate the nonlinear effect of perturbation in our model. For robust and efficient trajectory estimation, an adaptive trajectory estimation (AdaTE) algorithm is proposed. By updating the statistics and truncation time online, it corrects the estimation error caused by biased prior statistics and observation drift, while reducing the computational complexity lower than O(n). The experiment of trajectory estimation demonstrates the convergence of AdaTE, and the better robust to the biased prior statistics and the observation drift compared with several typical estimation algorithms. Other experiments demonstrate through slight modification, our method can also be applied to local navigation in random obstacle environment, and trajectory optimization in visual tracking.
UltraGroup: Towards Deploying MobileNet to Ultra-Resource-Bounded Edge Devices

Though MobileNet achieves significant memory and computing reduction by depth-wise separable convolutions, some grouping methods have showed further capabilities those resource-bounded devices on edge. Thereby we propose UltraGroup, an ultra-flexible parameter grouping method, to fit MobileNet into various kinds of resource-bounded embedded systems. Inspired by the ShuffleNet, this work largely scales the parameter quantities through Gapping and Overlap methods to search an optimized deploying balance between resource limitations and the model accuracy. Our experiment shows that 1) Gapping method makes the amount of parameters reduce by nearly half compared with original group convolution and 2) Overlap method increases the accuracy of the model, and the best accuracy has improved 1.3 times compared with the original group convolution.
Cross-Domain Recommendation System Based on Tensor Decomposition for Cybersecurity Data Analytics

In the context of personalization e-commerce cyberspace based on massive data, the traditional single-domain recommendation algorithm is difficult to adapt to cross-domain information recommendation service. Collaborative filtering is a simple and common recommendation algorithm, but when the target domain is very sparse, the performance of collaborative filtering algorithm will seriously degrade. Cross domain recommendation is an effective way to solve this problem because it is made by means of the auxiliary data domain associated with the target data domain. Most of the existing cross-domain recommendation models are based on two-dimensional rating matrix, and much other dimension information is lost, which leads to a decrease in recommended accuracy. In this paper, we propose a cross-domain recommendation method based on tensor decomposition, which can reduce the sparseness of data and improve the diversity and accuracy. It extracts the scoring patterns in different fields to fill the vacancy value in the target domain by transfer learning method. Many experiments on three public real data sets show that the proposed model’s recommendation accuracy is superior to some of the most advanced recommendation models. It can be applied to large-scale cross-domain information recommendation service and cybersecurity data analytics.
Development of Large Numbers Factorization Algorithm

The paper analyzes existing factorization algorithms and classifies them. The elaboration of the factorization algorithm which allows working with both small and large numbers is presented. Whereas the replacement of operations of addition and multiplication by the operation of addition, in comparison with the existing algorithms, a large gain of time is achieved. The initiation of operation of inequality solution allows cutting up to N <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1/4</sup> of possible candidates for a single calculation step. The evaluation of the time of the algorithm was conducted. It is comparable to the best known.
Utilizing the buckshot algorithm for efficient big data clustering in the MapReduce model

Clustering is an efficient data mining as well as machine-learning method when we need to get an insight of the objects of a dataset that could be grouped together. The K-Means algorithm and the Hierarchical Agglomerative Clustering (HAC) algorithm are two of the most known and commonly used methods of clustering; the former due to its low time cost and the latter due to its accuracy. However, even the use of K-Means in document clustering over large-scale collections can lead to unpredictable time costs. In this paper, towards the direction of the efficient handling of big text data, we present a hybrid clustering approach based on a customized version of the Buckshot algorithm, which first applies a hierarchical clustering procedure on a sample of the input dataset and then uses the results as the initial centers for a K-Means based assignment of the remaining documents, with very few iterations. We also give a highly efficient adaptation of the proposed Buckshot-based approach in the MapReduce model which is then experimentally tested using Apache Hadoop over a real cluster environment. As it comes out of the experiments, it leads to acceptable clustering quality as well as to significant execution time improvements. Preliminary results drawn from relevant experiments using the Spark framework are also presented.
An Efficient XOR Based Pixel Substitution Algorithm

As a kind of two-dimensional data, digital images are different from text data with large volume of data, high redundancy and strong correlation between pixels. It is hard to achieve the expected security performances when traditional encryption method is directly used to digital images. At present, the chaos-based encryption methods are widely used in image encrypting, owing to the favorable properties of chaos mapping. Aiming at the shortages of the existed chaos methods, an efficient XOR based pixel substitution algorithm(XOR-PSA) is proposed, which reduces the time consumption by substituting the pixels on the rows and columns simultaneously. The experiments show that the proposed method performs more efficient than a mainstream method without compromising the security level.
An Efficient Copy-Move Forgery Detection Technique Using Nature-Inspired Optimization Algorithm

The effective and simplest technique to create digital forgeries is copy-move forgery where a part of an image is copied and pasted elsewhere within the same image. Many techniques have been developed to detect such forgeries in digital images. The most computationally effective and robust technique is based on speeded-up robust features (SURF) framework. However, such techniques cannot produce satisfactory results for some images. Sometimes, the matched points detected are not enough or are too less to prove that the image is not authentic. Also, false forgeries are detected in some cases. The main reason behind such a scenario is that the detection results of the SURF-based framework highly depend upon the value of parameters which are mostly determined with the user experience. The predetermined parameter values limit the application of copy-move forgery detection since they cannot be applied to all the images. Therefore, a novel approach is proposed in this paper in which SURF-based detection scheme is amalgamated with nature-inspired optimization, i.e., particle swarm optimization (SURF-PSO) algorithm. This approach utilizes particle swarm optimization for each image independently to generate the customized value of parameters for forgery detection under the SURF framework. The proposed approach is experimentally validated for copy-move forgery detection of a standard dataset. Based on the obtained results, it can be seen that as compared to the conventional SURF technique [based on predefined parameter values (SURF-PPV)], the proposed SURF-PSO outperforms in terms of higher precision and a lesser number of mismatched points.
Cluster and correlation analyses of the chemical composition of groundwater nearby MSW landfills

In areas of the European North with a low population density and poorly developed transport infrastructure, it is difficult to organize waste removal and storage at large landfills, therefore many settlements have small landfills of municipal solid waste (MSW), and the practice of placing them in swamps is very common, which leads to the migration of pollutants into adjacent rivers. Environmental monitoring nearby such landfills is hampered by high costs of periodic water sampling and chemical composition analyses. It is possible to decrease the number of substances under analysis and thus reduce costs by determining the marker substances, the concentration of which in groundwater near the landfill has stable correlations with the content of other pollutants. The article provides an analysis of the pollutant concentrations in 45 MSW landfills around the world. The results of cluster and correlation analyses made it possible to identify marker substances and establish the target correlation dependences.
k-vectors: An Alternating Minimization Algorithm for Learning Regression Functions

The k-vectors algorithm for learning regression functions proposed here is akin to the well-known k-means algorithm. Both algorithms partition the space of “features”, but in contrast to the k-means algorithm, the k-vectors algorithm aims to reconstruct the regression function of the features ("response"), rather than the features themselves. The partitioning rule of the algorithm is based on maximizing the correlation (inner product) of the feature vector x with a set of k vectors, and generates polyhedral cells, similar to the ones generated by the nearest-neighbor rule of the k-means algorithm. Similarly to k-means, the learning algorithm alternates between two types of steps. In the first type of steps, k labels are determined via a centroid-type rule (in the response space), and in the second type of steps, the k vectors which determine the partition are updated according to a multiclass classification rule, in the spirit of support vector machines. It is proved that both steps of the algorithm only require solving convex optimization problems, and that the algorithm is empirically consistent-as the length of the training sequence increases, fixed-points of the empirical algorithm tend to fixed points of the population algorithm.
A Study on Information Protection Requirement in Different Deduplication Systems

Distributed Cloud computing provides an infinite amount of virtualized resources as assets to purchasers over the whole net, whereas obscure stage and execution subtleties. With the enormous development of online computerized substance, distributed storage centers on viably be a part of warehousing assets for higher power use and value adequacy. Data deduplication is one among imperative information pressure methodology for annihilating copy duplicates of rehashing information, and has been usually used in distributed storage to minimize the overhead of the additional threshold space in order to spare the bandwidth in transmission. So as to accomplish security and benefit approval focalized encryption alongside pow convention is utilized. In this paper we are concentrating the approved information deduplication to give the information security by utilizing differential benefits of clients in the cloud design
Ambiguous Utterance Aware: Communication Learning System for Pragmatic Language Disorders Using a Humanoid Robot

In this research, we developed a communication learning support system to learn the appropriate dialogue method for communicating with people who have pragmatic language disorders. People with such disorders tend to have problems with interpersonal relationships because they cannot understand the meaning of ambiguous utterances. As a solution to this problem, we developed a communication learning support system that learns whichever dialogue method is easiest to convey to people with pragmatic language disorders, targeting typical development. Our learning support system features a humanoid robot that mimics people with pragmatic language disorders and allows the user to experience the dialogue with such a person, and the user is able to learn how to communicate with such a person while seeking actively transmitted utterances. Experimental results showed that the learning effect was stronger than when using the text-based approach. It was more enjoyable as well. Therefore, this communication learning support system shows excellent potential as a means of learning.
Xevolver: A code transformation framework for separation of system‐awareness from application codes

Summary This paper introduces the Xevolver code transformation framework to separate system‐aware code optimizations from HPC application codes. System‐aware code optimizations often make it difficult for programmers to maintain HPC application codes. On the other side, system‐aware code optimizations are mandatory to exploit the performance of target HPC systems. To achieve both high maintainability and high performance, the Xevolver framework provides an easy way to express system‐aware code optimizations as user‐defined code transformation rules. Those rules can be defined separately from HPC application codes. As a result, an HPC application code is converted into its optimized version for a particular target system just before the compilation, and standard HPC programmers do not usually need to maintain the optimized version that could be complicated and difficult‐to‐maintain. In this paper, three important components of the Xevolver framework are described, and then their practicality and benefits are demonstrated through six case studies. Accordingly, the user‐defined code transformation approach behind the Xevolver framework is promising to express system‐awareness for extracting the performance of an HPC system, and also for sharing expert knowledge and experiences about code optimizations. As the complexity and diversity of HPC system architectures are increasing in an extreme‐scale computing era, system‐aware code optimization without overcomplicating the code as discussed in this paper will become more and more important in the future.
Multi-object Tracking with Conditional Random Field

The goal of online multi-object tracking (MOT) is to estimate the tracks of multiple objects instantly with each incoming frame using up-to-the-moment information. Conventional approaches to MOT do not account for motion of the camera or background. Also, traditional optical flow features or color features do not produce satisfactory discrimination. This paper attempts to deal with such problems by turning the online MOT problem into the problem of minimizing the global energy by constructing a conditional random field model. At the same time, structural information and discriminative deep appearance features are integrated together to make data association more accurate. In this way, individual objects are not only more precisely associated across frames, but are also dynamically constrained with each other in a global manner. We conduct experiments using the MOT challenge benchmark to verify the effectiveness of our method and achieve competitive results.
Weakly Supervised Semantic Segmentation Based on Deep Learning

Weakly supervised semantic segmentation has important significance and research value in computer vision. Although the traditional semantic segmentation based on fully supervised learning has achieved great success, the training network needs to rely on a large number of pixel-level labeled data. It is difficult and time-consuming to obtain such high-quality annotation. In order to alleviate the huge burden of pixel-level labels, many weakly supervised semantic segmentation methods have emerged in recent years. This paper reviews the research progress of weakly supervised semantic segmentation. Firstly, we introduce the existing weakly supervised methods, highlighting their respective contributions and positive impact in this field. Then, datasets and metrics are described. Moreover, the results are analyzed and discussed. Finally, this work summarizes the difficulties of weakly supervised semantic segmentation and further explores the development direction of weakly supervised semantic segmentation in the future.
Secure Image Restoration and Contrast Enhancement Using Wavelet Transform and Thresholding Technique

The paper represents a methodology which involves the use of Satellite images taken from Satellite Imaging Corporation and Google Earth. This technique proposes a satellite image enhancement system in two steps which consists of restoration and contrast enhancement. The work represents a methodology for enhancement of Satellite images using wavelet transform such as Discrete wavelet transform (DWT) and Stationary wavelet transform (SWT), thresholding and contrast enhancement using equalization based on absolute difference. The result of the evaluation indicates that the technique obtains good PSNR value of satellite images. The results also strengthen that the technique fares well in preserving the information content of the satellite images and also improve the visual quality of images.
Fusion of information from multiple robots

Whilst scaling the size of multi-robot team has its pros and cons as described in the previous chapter, often the size of the environment to be monitored necessitates a multi-robot setup. Thus, if a disconnected and decentralized team of agents is considered, multiple intermediate GP models (one model per agent) will be obtained with no communication overhead. Given the limited number of observations available to each agent, each of these will have the capability to explain the environmental dynamics to a certain extent, but the question is: which model can be trusted? Simply put, ignoring some of the models generated leads to information waste whilst multiple models cannot be used to decipher the true underlying dynamics unless they are fused. To this end, this chapter describes a novel discrete weighted posterior fusion mechanism referred to as FuDGE which allows for taking the confidence over the prediction of each expert whilst fusing the posteriors. As opposed to existing works which directly perform model fusion, this approach is amicable to real robot deployment in the sense that learning is only required whilst the robots are observing the environment and individual models are being optimized. At the end of all missions, the posteriors are fused based on the quality of learnt models. This fusion approach has shown promising results when evaluated over a real-world Ozone dataset.
Information-Preserving Networks And The Mirrored Transform

A deep network with rectifier units (ReLUs) is used for building a multi-layer transform with the constraint that the input can be reconstructed in the backward flow. The structure is analysed in reference to the number of units necessary in each layer to preserve information. Specific networks with random and fixed weights are presented and the Mirrored Transform is proposed. The resulting network generalizes classical linear transforms and provides a progressive unfolding of the input space into embeddings that can determine the basis for classification and filtering in an unsupervised manner.
Scene Text Detection Via Cascade FPN and Channel Enhancement

The detection and recognition of scene text in wild environment have attracted mass of attention because of the rapid development of Convolutional Neural Networks. However, due to the high diversity of scale and shape of scene text in real world, most of the existing state-of-art algorithms may have their shortages. On the one hand, object detection based methods require quadrangle bounding box which cannot detect arbitrary text. On the other hand, the segmentation based methods fail to pay attention to the feature and channel inter-dependency, which may fall short when facing different scale texts. The Progressive Scale Expansion Network (PSENet), which can precisely detect text instances with arbitrary shapes, could be a useful and simple baseline. Based on PSENet, we additionally add more aggregation in spatial and channel of the feature map, making it possible to fully utilize feature of different scales and channels to handle arbitrary-shaped and multi-scale text instances. To accomplish this goal, we introduce the Path Aggregation Module(PAM) and Feature Selective Module(FSM). In the PAM, we implement cascade FPN structure and shortcut procedure with global information. In the FSM, we borrow the idea from SENet. Extensive experiments on Total-Text, ICDAR 2015 and ICDAR 2017 MLT validate the effectiveness of our method. On ICDAR2017, our best F-measure (75.3%) outperforms PSENet by 3.1%.
The SA Management Scheme Based on Blockchain for Convergence Service in S&amp;T

A convergence service can be built by referring the specification of well-designed services and putting together them in bottom-up manner. In the service system for delivering an S&T convergence service, a service system needs to define a service agreement (SA) and to establish to support continuously and dependably the SA. In the paper we propose a SA management scheme that is used for storing the SA values co-created into the blockchain. The proposed scheme can provide a guideline for designing the S&T service system on SOA framework with a blockchain for providing information about SA as part of service metadata.
Turn Taking Strategies Between Dana Han-Klein and Ryan Reynolds in "Talks At Google"

The study can show the use of turn taking in an interaction and also know the purpose of the strategy. This research aims: (1) to find the types of turn taking strategies that is used between Dana Han-Klein and Ryan Reynolds in “Talks At Google” and which one is dominant and (2) To describe the purposes of turn taking strategies that are used between Dana Han-Klein and Ryan Reynolds in “Talks At Google”. The researcher uses descriptive qualitative research. The data in this study are dialogues transcript that contains the types of turn taking strategies they are current speaker selects next speaker, self selection and current speaker continues. The data source is the dialogue between Dana Han-Klein and Ryan Reynolds in “Talks At Google”. In collecting data, the researcher uses the non-interactive method because the researcher has not relation and do not affect data sources. The result shows that (1) the researcher finds three strategies used by the speakers in the conversation, namely Current Speaker Selects Next Speaker strategy (45%), Self Selection strategy (51%), and Current Speaker Continues strategy (4%) and (2) there are 3 purposes of the interviewer divided into to express (8%), to get information (87%) and to get response (4%). And also finds 6 purposes of the interviewee divided into to express (20%), to explain (48%), to convey (8%), and to tell (24%).