Reviewerly/exHarmony huggingface dataset:
Semantic Map Based Image Compression via Conditional Generative Adversarial Network

Recently, deep learning methods have been applied for image compression and achieved promising results. For lossy image compression at low bit rate, the traditional compression algorithms usually introduce undesired compression artifacts, such as blocking and blurry effects. In this paper, we propose a novel semantic map based image compression framework (SMIC), restoring visually pleasing images at significantly low bit rate. At the encoder, a semantic segmentation network (SS-Net) is designed to generate a semantic map, which is encoded as the first part of the bit stream. Furthermore, a sampled image of the input image is compressed as the second part of bit stream. Then, at the decoder, in order to reconstruct high perceptual quality images, we design an image reconstruction network (Rec-Net) conditioned on the sampled image and corresponding semantic map. Experimental results demonstrate that the proposed framework can reconstruct more perceptually pleasing images at low bit rate.
Parallel Spectrum Reconstruction in Fourier Transform Imaging Spectroscopy Based on the Embedded System

In this paper, we design a parallel-processing pipeline for spectrum reconstruction in Fourier transform imaging spectroscopy (FTIS), which works well with the embedded system, NVIDIA Jetson TX2. This embedded system has great performance in parallel computing and can be developed easily by programmers using CUDA C in a single development board. This is very important for data processing on satellite and mobile devices. On the other hand, because of the huge amount of interference data acquired by the Fourier transform spectrometer, traditional interference data processing mechanism is not efficient and time-saving. These data should be processed in a fast way for real time, especially on satellite, to save memory and bandwidth. We take advantage of parallel computing to enable higher efficiency and reduced operation time. Furthermore, traditional serial algorithms for processing interferograms on the ARMs are introduced for comparison. The experimental results show that our parallel spectrum reconstruction pipeline has much higher performance than the serial one, and for huge data, our parallel mechanism also achieves great result in high performance.
Dynamic resource management algorithms for edge computing using hardware accelerators

Many Internet of Things (IoT) applications must perform their Big Data analytics tasks at the edge to meet their real-time needs and overcome the constraints on and reliability of network resources. Since traditional CPUs cannot meet these demands, solutions are sought by using accelerator hardware such as FPGAs, GPUs and TPUs to address these challenges. My doctoral research is focusing on ascertaining the feasibility of utilizing these accelerators for real-time IoT Big Data analytics, and in turn investigating dynamic resource management algorithms to schedule edge-based accelerator resources in the presence of highly dynamic IoT workloads.
Coin Lockers as a Technology-Based Public Facility

Abstract The purpose of this research is to see what the community needs by utilizing the interests of consumers who are environmentally friendly, also attractive in business opportunities. Because of that we take samples of products that have been used in Japan namely, Coin Lockers. To support the research, we used a method of exploring reviews of several articles related to our research products. The results of this Coin Lockers show are very much needed by the people, especially the Japanese people, to store some things they don’t need to carry. From this result, we know that this product is important as support for facilities that can be obtained by the community. That’s because it’s easy to use, Coin Lockers also to save the user’s money. Of course, Coin Lockers is very beneficial for business people who want to try this new business path that is environmentally friendly and quite sophisticated in our country.
Image Dehazing Framework Using Brightness-Area Suppression Mechanism

Since more and more outdoor images are often degraded by haze and suffer from bad visibility, haze removal has become an important task of image restoration in recent decades. A systematic dehazing framework based on Koschmieder model is proposed in this paper, which adopts a novel brightness-area suppression mechanism. Firstly, global brightness-area suppression blending the large-scale atmospheric veil with the result of edge-preserving filtering, could protect the white objects not becoming darker. Then, the local brightness-area suppression based on sky detection could prevent the sky region from over saturation. In addition, post-processing procedures are designed in this dehazing system in order to generate haze-free image with better visual perception. This framework is on-limits and extensible, in that, it can accept other better dehazing technique as one of the core steps inside. Experiments show that the performance of this framework outperforms multiple state-of-the-art dehazing algorithms.
A new watermarking method for vector map data: a study on information capacity of the carrier image

Abstract The paper presents a study of the watermarking method for vector map data based on the cyclic shift of the vertex list of polygonal objects. We propose a method modification to provide an accurate authentication procedure, as well as to enhance the method robustness against the map contents alteration. The main idea of the improved method is to use a noise-like image as a secondary container for a watermark represented in the form of a bit vector. An algorithm for the construction of a carrier image on the basis of the watermark sequence, as well as an algorithm for extraction of such a sequence, are given. An experimental study on the information capacity of the carrier image and its robustness against distortions simulating the embedding into real cartographic data, such as quantization and noise addition, has been carried out.
Multiple Image Deblurring with High Dynamic-Range Poisson Data

An interesting problem arising in astronomical imaging is the reconstruction of an image with high dynamic range, for example a set of bright point sources superimposed to smooth structures. A few methods have been proposed for dealing with this problem and their performance is not always satisfactory. In this paper we propose a solution based on the representation, already proposed elsewhere, of the image as the sum of a pointwise component and a smooth one, with different regularization for the two components. Our approach is in the framework of Poisson data and to this purpose we need efficient deconvolution methods. Therefore, first we briefly describe the application of the Scaled Gradient Projection (SGP) method to the case of different regularization schemes and subsequently we propose how to apply these methods to the case of multiple image deconvolution of high-dynamic range images, with specific reference to the Fizeau interferometer LBTI of the Large Binocular Telescope (LBT). The efficacy of the proposed methods is illustrated both on simulated images and on real images, observed with LBTI, of the Jovian moon Io. The software is available at http://www.oasis.unimore.it/site/home/software.html .
Standards in practice: All things considered

Both public institutions and private enterprises are motivated to follow standards in order to comply with government regulations, establish market and brand identity, and streamline processes to create modular and scalable solutions. Standards offer several key benefits from a consistent user experience, including: reduced training, fewer errors, greater usability, increasing user satisfaction, and minimizing costs. This panel covers a wide range of topics spanning different industries reflecting the diversity of organizational approaches and challenges faced by practitioners embedded in development programs. Presenters will share their methodologies as well as lessons learned from their current and past experiences.
Placement Replicas of Interrelated Data Arrays in Unreliable Distributed Systems

The problem of optimal placement of replicas of interconnected data sets in a distributed data processing system is considered in the article. Communication channels of the distributed system are not reliable. The criterion for optimizing the task is the minimum of the average system response time to requests for data. A heuristic algorithm is proposed for the task solution.
Constrained Dual Graph Regularized NMF for Image Clustering

Non-negative matrix factorization (NMF) becomes an important dimension reduction and feature extraction tool in the fields of scientific computing and computer vision. In this paper, for using the known label information in the original data, we put forward a semi-supervised NMF algorithm called constrained dual graph regularized non-negative matrix factorization (CDNMF). The new algorithm employs hard constraints to retain the priori label information of samples, constructs two association graphs to encode the geometric structures of the data manifold and the feature manifold, and incorporates the additional bi-orthogonal constraints to improve the identification ability of data in the new representation space. We have also developed an iterative optimization strategy for CDNMF and proved its convergence. Finally the clustering experiments on five standard image data sets show the effectiveness of the proposed algorithm.
Decision support systems in asset control: an approach based on Artificial Neural Network and Association Rule Mining

Equipment control represents a critical aspect of asset management because of its impact on asset's reliability. Monitoring the behavior of the asset and being able to anticipate the occurrence of critical situation like asset's failures play a vital role in the success of a company. Hence, in this paper, we propose a decision support system based on the development of an Artificial Neural Network and Association Rule Mining. The aim of this work is supporting the Operations and Maintenance managers in defining the best control policy on asset's behavior, in order to anticipate the occurrence of failing situations and find relations among operating conditions' values helpful for failures prediction and recognition. Implementing the proposed approach may support the decision maker in defining the best maintenance policy for the asset, knowing in advance the conditions leading to its failure. A brief example-case is provided for the discussion of the practical implications of the proposed approach.
RETRACTED: Energy Analysis of Integrated Energy Based on Cloud Computing

Abstract This paper uses big data technology analysis means to abstractly identifies the common characteristics of various energy-using behaviors and analyze their behaviors; establishes user energy behavior analysis based on “multi-meter unification” information acquisition, and analyses and predicts the abnormal energy-using behavior of users according to the analysis results. So then provides a powerful means of monitoring and early warning for energy-using, increasing the level of user energy-using management, and maintaining normal order of user energy-using.
Independent Fusion of Words and Image for Multimodal Machine Translation

Multimodal machine translation which combines visual information of image has become one of the research hotpots in recent years. Most of the existing works project the image feature into the text semantic space and merged into the model in different ways. Actually, different source words may capture different visual information. Therefore, we propose a multimodal neural machine translation (MNMT) model that integrates the words and visual information of image independently. The word itself and different key similarity information of an image are independently fused into the text semantics of the word, thereby assisting and enhancing the textual semantic and corresponding visual information of different words. And then we use them for the calculation of the context vector of the attention of decoder of our model. In this paper, different experiments are carried out on the original English-German sentence pairs of the multimodal machine translation dataset, Multi30k, and the Indonesian-Chinese sentence pairs which is manually annotated by human. Compared with the existing MNMT model based on RNN, our model has a better performance and proves the effectiveness of it.
SpecTackle - A Specification Mining Experimentation Platform

Nowadays, API Specification Mining is an important cornerstone of automated software engineering. In this paper, we introduce SpecTackle, an IDE-based experimentation platform aiming to facilitate experimentation and validation of specification mining algorithms and tools. SpecTackle strives toward (1) providing easy access to various specification mining tools, (2) simplifying configuration and usage through a shared interface, and (3) in-code visualization of pattern occurrences. The first version supports two heterogeneous mining tools, a third-party graph-based miner as well as a custom sequence mining tool. In the long term, SpecTackle envisions to also provide ground-truth benchmark projects, a unified pattern meta-model and parameter optimization for mining tools.
Indonesian Text Translator into Database Structured Query Language with Multi Parameters using Natural Language Processing

Abstract In this paper, we present the results of our research on SQL (Structured Query Language) Translator in Indonesian which aims to measure how accurate the designed system in understanding query sentences in Indonesian into SQL query languages. The methods used include pre-processing sentences, stemming, identifying keywords, until the final evaluation of sentences. We prepared 18 sentences divided into several categories as test data. The test results show that the system successfully evaluates all sentences with a total accuracy of 72.22% to 83.87%. Accuracy values were obtained by comparing similarities between systems generated queries and key answers query. In general, the system has been able to translate most of the Indonesian sentences into SQL queries.
Proposal color-histogram aware network

Visual object tracking has been a concern topic these years, and many trackers have achieved good results in various fields. These research and breakthroughs have made many improvements to solve problems such as drift, lighting, deformation and occlusion. In this paper, we use convolutional network to characterize the feature, and color features. Specifically, it contains region proposal network which including the classification branch and regression branch.
Underwater Image Enhancement of Improved Retinex Base on Statistical Learning

Underwater image enhancement technology is one of the key parts of underwater image processing, Since under water environment has greater attenuation and scattering effects on reflected light than the onshore environment that lead to uneven imaging, low contrast, color decay, etc. The underwater enhancement process is intended to highlight some key information of the image and eliminate the irrelevant information so as to make the preparations for subsequent scene segmentation, target recognition, target tracking and predictive analysis and processing. In this paper, we will focus on the problem of underwater image with low quality, and analyze the typical methods of box-based image enhancement methods. At the same time, Retinex image enhancement method based on optical analysis was introduced, and this paper proposed an improved enhancement method based on the Retinex methods. First we convert the image into HSV space for saliency analysis, next, learn the parameters of the harmonic HSV space by statistical learning. And then, we use the weights obtained by learning to predict the enhancement parameter for the decay image. Finally, the experiment verified that the method had better adaptability and better effect over the traditional Retinex algorithm in underwater image enhancement.
Research on Image Retrieval with Multi-features

Abstract This paper focuses on the key technology of image feature extraction in the image retrieval of the base ten-content, and systematically discusses the extraction technology of the low-level visual features of the image. The covered content mainly includes the color features, shape features, texture features and color space of the image. The characteristics of distribution are important. In-depth analysis and research on some key technologies in the field of image retrieval in the base ten content, including the description methods of the main low-level features (color, shape, texture, spatial position) of the image, the similarity measure between image features and the image retrieval algorithm Performance evaluation methods, etc.
Double stochastic wave models of multidimensional random fields

The paper deals with the development of mathematical models of random fields to describe and simulate images. In the wave model, a random field is the result of the influence of perturbations (waves) that occur at random times in random places and have random shapes. This model allows representing and simulate isotropic and anisotropic images (and their temporal sequences) defined on arbitrary areas of multidimensional space, as well as on any surfaces. The problems of correlation analysis and synthesis can be relatively easily solved. However, this model allows representing only homogeneous fields. In this paper, we consider «double stochastic» wave models, when the first wave random field (control field) sets the parameters of the second (controlled field). As a result, the controlled field becomes nonuniform, since its parameters vary randomly. We also consider options when two fields mutually influence each other. These models allow us to represent and simulate multidimensional inhomogeneous images (and their temporal sequences), as well as systems of such images with mutual correlations.
Using Feature Filtering Metrics as Meta-dimensions in Constructing Distributional Representations

Feature filtering aims to find useful and relevant features for improvement of machine learning performance, reduction of computation complexity, and disclosure of internal information interaction. We employ some popular filtering criteria as meta-dimensions for the construction of feature space, where a word or a document can be represented with significantly reduced dimensionality. The experiment results show that the meta-feature data representation we proposed requires no extra resources on pre-training to derive word embeddings, and outperforms other traditional frequency-based or learning-based embeddings in the task of sentiment analysis.
Cell Nuclei Counting and Segmentation for Histological Image Analysis

The paper describes our experiments of automated digital histological image analysis. In our study we investigated two tasks. The primary goal was to develop a simple and effective automated scheme of cell nuclei counting. The second goal was to demonstrate that for histological images with homogeneous background we can apply a binarization technique and approximately calculate the number of nuclei in the image. The experiments were done on two public datasets of histological images. The experiments have demonstrated acceptable level of calculation results.
Learning Cross Camera Invariant Features with CCSC Loss for Person Re-identification

Person re-identification (re-ID) is mainly deployed in the multi-camera surveillance scene, which means that learning cross camera invariant features is highly required. In this paper, we propose a novel loss named Cross Camera Similarity Constraint loss (CCSC loss), which makes full use of the camera ID information and the person ID information simultaneously to construct cross camera image pairs and performs cosine similarity constraint on them. The proposed CCSC loss effectively reduces the intra-class variance through forcing the whole network to extract cross camera invariant features, and it can be unified with identification loss in a multi-task manner. Extensive experiments implemented on the standard benchmark datasets including CUHK03, DukeMTMC-reid, Market-1501 and MSMT17 indicate that the proposed CCSC loss can bring a large performance boost on the strong baseline and it is also superior to other metric learning methods such as hard triplet loss and center loss. For instance, on the most challenging dataset CUHK03-Detect, Rank-1 accuracy and mAP are improved by 10.0% and 10.2% than the baseline respectively and simultaneously obtain a comparable performance with the state-of-the-art method.
Comparison and analysis of positioning accuracy between DGPS and GPS

Abstract By observing the same GPS receiver, getting positioning data of 1000 sets of GPS and DGPS each, the two groups of data were calculated by utilizing the probability and statistics, and the average position of the two was GPS: 38°52′. 111N, 121°33′.029E, DGPS:38°52′.107N, 121°33′.033E, 95% of mean square error circle radius of GPS respectively 6.975015372m, DGPS:3.267239581m. By comparison, the positioning accuracy of DGPS is more than twice that of GPS.
Exploiting Social Data to Enhance Web Search

With the strong growth of the internet, the search engine systems have been growing rapidly to allow web users to find relevant information with respect to their interests and needs. However, the amount of information is huge, searching information problems for users’ demands still remain many challenges. Many social platforms and networks (such as Twitter, Facebook or Delicious) which allow users to tag, share and organize their favorite web pages online using social annotations easily. Moreover, these social annotations could benefit web search because of user’s interesting tags and relevant information. Therefore, this paper proposed a search engine by combining following studies: (1) exploit social data from Twitter, (2) formulate query by personalized query expansion method using social annotations (SoQuES), (3) enhance the representation of documents and personalize them with social information (PerSaDoR), (4) use the social personalized ranking function (SoPRa) to re-rank search results. Furthermore, our experiment on Twitter data showed that our method could enhance the search engine user efficiently.