Reviewerly/exHarmony huggingface dataset:
Deep Instance Search Network for Remote Sensing Image Retrieval

Remote sensing image retrieval (RSIR) has always been a hot research topic in the field of remote sensing, and instance-level remote sensing image retrieval (IL-RSIR) is one of the most important challenges. In recent years, although the powerful feature description ability of convolutional neural networks (CNNs) has improved RSIR significantly, the performance is still restricted by the complexity of remote sensing images, such as the same semantic labels but different appearance characteristics. To address these problems, we propose a novel deep instance search network (DISN). It leverages two-level retrieval branches, that is, semantic feature aggregator and keypoint matcher, to integrates the semantic information and the local details of instances and enhances the instance discriminability of feature representations. Experiments on four remote sensing benchmark datasets for IL-RSIR demonstrate that our DISN can outperform some state-of-the-art methods.
Control of Bio-Inspired Multi-robots Through Gestures Using Convolutional Neural Networks in Simulated Environment

In this paper the comparison between three convolutional neural networks, used for the control of bio-inspired multi-robots in a simulated environment, is performed through manual gestures captured in real time by a webcam. The neural networks are: VGG19, GoogLeNet and Alexnet. For the training of networks and control of robots, six gestures were used, each gesture corresponding to one action, collective and individual actions were defined, the simulation contains four bio-inspired robots. In this work the performance of the networks in the classification of gestures to control robots is compared. They proved to be efficient in the classification and control of agents, with Alexnet achieving an accuracy of 98.33%, VGG19 98.06% e Googlelenet 96.94% .
Geological and geochemical signs and criteria of potential mineralization in the Tangaralakh intrusion

The scope of the discussion embraces the problems connected with development and improvement of geological information processing methods and systems, as well as modeling of primary mineral deposits in exploration of side rock mass and deep horizons of the deposits under mining and in new areas with intent to discover potential copper-nickel sulphide mineralization. The target of the research is the geological and geochemical preconditions and signs of sulphide mineralization in the potentially ore-bearing Tangaralakh field adjacent to the deposits of the Talnakh ore-magmatic system. The integrated research is based on the conventional different-rank geological, geochemical, magmatic, structural, tectonic, lithological and stratigraphical preconditions and signs of sulphide copper-nickel platinum-bearing ore in the Norilsk Region. The geochemical search of ore objects in the Norilsk Region provided the best results in case of the geological and structural analysis of spatial interactions between geochemical abnormalities as the geochemical zonality is yet the chief factor in the mineralization prediction. This method of improvement of geological-geochemical information processing and modeling of mineral deposits was tested in geological-geochemical modeling of the Talnakh ore province bodies. From the research findings, the structural and tectonic conditions of localization of the potentially ore-bearing Tangaralakh intrusion were determined. The structural modeling of geochemical field of the mineralization revealed the types of geochemical associations marking different compositions of sulphide mineralization in disseminated ore in different sections of the Tangaralakh intrusion. It is found that the geochemical zonality coincides with the mineral zonality of the disseminated ore horizon formed at different stages of orogenesis. The similarity elements are found in the geological-geochemical models of the Tangaralakh potentially ore-bearing ore field and Talnakh ore field (ore-magmatic system).
Thai Natural Language Processing Programming

The content in this chapter divided into two parts. Part 1 is about computer languages and software used to develop NLP programs, software installation, and preparation of environments suitable for the programming. Part 2 is a workshop on creating NLP programs.
Pattern recognition based on neural network for sign language interpretation system using flexes sensor

Sign Language Recognition has emerged as one of the important areas of research in Computer Vision. The difficulty faced by the researchers is that the instances of signs vary with both motion and appearance. In this paper, we introduce a sign language interpreter based on hardware and software .We suggest using gloves builder with hardware to recognize hand gestures. In this work we suggest to use a gyroscope to solve many problems accompanied the previous works (to our knowledge, our work is the first to have used gyroscope for this purpose). In addition, we suggest including neural network in the software to enhance the recognition and make the gloves suitable for any language. The proposal recovers many problems and enhances recognitions. The performance of recognition, continuous words reaches up to 83.5.
Multi-value Private Information Retrieval using Homomorphic Encryption

Private information retrieval (PIR) is a privacy protection that allows users to retrieve information from a database without revealing any information about the retrieved data to the server. Since the pioneering work of Chor et al. (FOCS `95) and of Kushilevitz and Ostrovsky (FOCS `97), PIR has been extensively studied (especially the single database setting) in the past two decades. However, most protocols only allow users to retrieve only one data at a time, which leads to high communication costs. To solve this issue, this work proposes a multi-value private information retrieval protocol using group homomorphic encryption, which allows users to retrieve multiple values at a time. We compared our work with that of Ostrovsky and Skeith (PKC `07) and show that retrieving multiple data at a time significantly can significantly reduces communication costs. Furthermore, we provide rigorous proof that if the underlying group homomorphic encryption is secure, then no attacker can know what data the user has retrieved.
Data-Imagined Decision Making in Organizations: Do Visualization Tools Run in the Family?

This paper reports of an experimental crossover between two different perspectives of organizational activities: decision making and data management. Although there are ever growing contact points between the two, it is also true that in enterprises data-driven decision making often shows many room for improvements. A converging direction of these two aspects of organizational routine could be that of comparing and coupling decision making steps, activities and characteristics with data visualization properties, capabilities and enablers of information sharing and assimilation. This study goes in this direction, by proposing an exploratory analysis of decision making models and data visualization characteristics in order to extract a set of common aspects of decision making and to configure a set of connections between them and data visualization tools features. These connections may serve to investigate the strength of synergies between decision making activities and data management visualization, their effectiveness for data-driven decision making and the margin of improvements with respect to the current decision routines in enterprises. This study contributes to set the terrain for making a clearer picture of the strengths and weaknesses of data-driven decision making, to find implications for design of data visualization tools for supporting decision making activities, and to provide indications of how proactively data visualization toolboxes should run in the family at all decision levels and for each role in organizations.
Clustering via Meta-path Embedding for Heterogeneous Information Networks

A low-dimensional embedding of multiple nodes is very convenient for clustering, which is one of the most fundamental tasks for heterogeneous information networks (HINs). On the other hand, the random walk-based network embedding is proved to be equivalent to the method of matrix factorization whose computational cost is very expensive. Moreover, mapping different types of nodes into one metric space may result in incompatibility. To cope with the two challenges above, a meta-path embedding based clustering method (called MPEClus) is proposed in this paper. Firstly, the original network is transformed into several subnetworks with independent semantics specified by meta-paths to solve the incompatibility problem. Secondly, an approximate commute embedding method, bypassing eigen-decomposition to reduce computational cost, is leveraged to the representation learning of the nodes in each subnetwork. At last, a unified probabilistic generation model is designed to aggregate the vectorized representations learned in different metric spaces for clustering. Experiment results show that MPEClus is effective in HIN clustering and outperforms the state-of-the-art baselines on two real-world datasets.
A Reversible Watermarking Scheme Based on Adaptive Block Sized Integer Transform

As the foundation of reversible watermarking technology, the integer transform technique, e.g. difference expansion (DE), has been widely studied. However, most integer transform schemes use the blocks of uniform size for embedding, resulting in that the embedding performance is not as good as the recently proposed histogram-shifting method. To solve this problem, this paper proposes a reversible watermarking scheme based on adaptive block sized integer transform. After predicting the texture complexity of different regions in the cover image, more watermarks are embedded in smooth regions by using larger sized blocks as the embedding units; in textured regions, the relatively smaller sized blocks are used as the embedding units, which decreases the amount of watermarks to reduce the distortion caused by embedding. Experimental results show that the proposed algorithm provides less image distortion compared with some existing reversible watermarking algorithms with the same embedded capacity (EC).
FEPF: A knowledge Fusion and Evaluation Method based on Pagerank and Feature Selection

In recent years, with the development of various knowledge bases, the fusion of multi-source knowledge bases is a hot and difficult problem facing the field of knowledge fusion. Due to the large differences in knowledge base structure, the efficiency and accuracy of fusion are not high. Proposed Graph Structure Fusion, a totally new knowledge fusion method based on PR(PageRank) algorithm and feature selection. This method constructs a network graph for entity content. The PR value of each node is used to determine the closeness of the relationship with the target word, and the PR value is used to select Relevant entities, excluding irrelevant entities to improve computing efficiency, and then from the perspective of graph structure, fusion of multi-source knowledge base. Experiments show that the average precision of the algorithm is 92.8%.
An Image Steganalysis Model Combined with the Directionality of Residuals

In recent years, many steganalysis methods using convolutional neural networks have been proposed. In the existing steganalysis networks, in order to enhance steganalysis noise and reduce the impact of image content, the high-pass filter is applied to extract residuals. However, the residual is usually directly input into a network for feature extraction, without considering further processing to enhance the statistical feature extraction of the subsequent network. Furthermore, the processing of convolutional layer in a network can be viewed as horizontal and vertical scanning maps, and the form of directions is simple. In this paper, to enrich directional features, the directionality of residuals is incorporated into the learning of network. Before feature extraction, residuals are rearranged in the direction of minor-diagonal. In addition, local binary pattern is applied to the residual map to obtain the correlation between each element in residual map and its multi-directional adjacent elements. Three spatial steganography algorithms, WOW, HUGO and S-UNIWARD, are selected in the simulation. The simulation results show that the incorporation of residual directionality into convolutional neural network can improve the steganalysis performance of the network.
Knowledge Graphs and Natural-Language Processing

Emergency-relevant data comes in many varieties. It can be high volume and high velocity, and reaction times are critical, calling for efficient and powerful techniques for data analysis and management. Knowledge graphs represent data in a rich, flexible, and uniform way that is well matched with the needs of emergency management. They build on existing standards, resources, techniques, and tools for semantic data and computing. This chapter explains the most important semantic technologies and how they support knowledge graphs. We proceed to discuss their benefits and challenges and give examples of relevant semantic data sources and vocabularies. Natural-language texts—in particular those collected from social media such as Twitter—is a type of data source that poses particular analysis challenges. We therefore include an overview of techniques for processing natural-language texts.
Efficient Electromagnetic Analysis Based on Side-channel Measurement Focusing on Physical Structures

In this paper, we study on improving efficiency of the electromagnetic analysis (EMA) based on side-channel measurement for revealing a secret key inside the cryptographic module. In this study, we focus on the specific physical structures of the printed circuit board (PCB) near the cryptographic module and conducted the EMA where the distribution of electric or magnetic field containing the secret key information is dominant. Based on the experimental results, we validated that the whole secret key information can be efficiently extracted by conducting the EMA using a single electric or magnetic probe on the location where a specific field is dominant due to the physical structure. By analyzing the physical structure of the PCB including the cryptographic module and considering the dominant field, the secret key information from the cryptographic module can be efficiently extracted.
Region Growing and Level Set Synergetic Algorithms for Image Segmentation

The application of segmentation technology in CT images can not only retain the clear characteristics of CT images, but also have the advantages of high resolution, clear anatomical relationship and clear pathological development. Level set segmentation method combines region and boundary information to solve the problems on complex texture and blurred edge of CT image. Therefore, this paper proposes an algorithm for regional growth and level set interaction for the problem of boundary sensitivity and lack of regional information in level set segmentation. In order to improve the efficiency and reduce the error of segmentation, this algorithm uses Gaussian filter to remove noise. Meaning while it adopts Ostu method to transform the image into a binary image. In addition, this algorithm provides an ideal growth environment for the seed point of the region growing algorithm correspondingly. The results of region growth are accomplished as the initial contour of the level set to modify the initial boundary. The boundary indication function introduces the region information; the level set segmentation algorithm achieves the purpose of accelerating in the pixel similar region and stopping at the target boundary as a result. Comparing the overall improved algorithms with the previous both region growing method and the level set method, the experiment results shows convincingly that the interaction between the level set segmentation and the region growing reduces the number of iterations. Under the condition of weak boundary and high noise, the boundary indicator function can accurately and completely segment the organ region and save the details of the internal organ completely, which improves the segmentation accuracy to a certain extent.
Relay strategy in online mobile games

With the booming of online mobile games (OMGs), game operators need to provide high-quality game service for users. Using relay has become the de factor approach for game streaming today, because it is easy to use (e.g., game sessions can be redirected via CDN servers) and has good scalability. Today, it has become the norm rather than the exception for game operators to hire CDN servers for their game services in a pay-per-use manner to serve massive users. Given the limited resource, selecting game sessions which are relayed has become a critical decision that can significantly affect users' quality of experience (QoE). Conventional strategies are generally rule-based, e.g., assigning game sessions to relay paths according to their past network performance, but cannot guarantee any particular QoE level because network performance dynamically changes. In this paper, we propose using data-driven approach to study network performance of game sessions in temporal and spatial patterns. Our findings indicate that there is obvious regularity for network performance of game sessions in temporal and spatial patterns. We design a machine learning-based predictive model to capture the quality of a game session given particular network performance metrics. Based on that, we strategically assign game sessions to relay paths to maximize the overall QoE. Trace-driven experiments are used to demonstrate the effectiveness and efficiency of our design.
White Blood Cells Detection and Classification Using Convolutional Neural Network

Leukemia is one of the deadliest diseases in human life, it is a type of cancer that hits blood cells. The task of diagnosing Leukemia is time consuming and tedious for doctors; it is also challenging to determine the level and type of Leukemia. The diagnoses of Leukemia are achieved through identifying the changes on the White blood Cells (WBC). WBCs are divided into five types: Neutrophils, Eosinophils, Basophils, Monocytes, and Lymphocytes. In this paper, the authors propose a Convolutional Neural Network to detect and classify normal white blood cells. The program will learn about the shape and type of normal WBC by performing the following two tasks. The first task is identifying high level features of a normal white blood cell. The second task is classifying the normal white blood cell according to its type. Using a Convolutional Neural Network CNN, the system will be able to detect normal WBCs by comparing them with the high-level features of normal WBC. This process of identifying and classifying WBC can be vital for doctors and medical staff to make a decision. The proposed network achieves an accuracy up to 95.47% with a dataset including 10,000 blood cell images.
Stability Analysis for Interval Type-2 Fuzzy Systems based on Network Environments under Imperfect Premise Matching

In order to over come the shortcoming of PDC and obtain some less conservative stability condition, the imperfect premise matching concept, which the fuzzy plant and fuzzy controller share different membership function, is proposed to construct the state feedback controller to stabilize the NCSs based on the interval type-2 fuzzy systems. By introducing the information of upper and lower membership functions of both fuzzy model and fuzzy controller, the stabilization condition of the interval type-2 networked closed-loop system is established, which is less conservative than the membership independent stability condition. A numerical example is used to show the less conservative of the proposed method.
A Watermarking Scheme Based on New Adaptive Parameter Strategy with Preserved PSNR

Robust watermarking is an effective way to protect an image copyright. This paper proposes a watermarking scheme with peak signal noise ratio (PSNR) guarantee. With the resistance of discrete wavelet transform (DWT) to image processing and singular value decomposition (SVD) to geometric attacks, the scheme embeds watermark based on difference quantization. The embedding strength of watermarks relies on the embedding parameters. Unlike other schemes that use fixed embedding parameters or obtain embedding parameters by multiple experiments, the proposed scheme is based on a new adaptive embedding parameter algorithm. The algorithm establishes the associations among the embedding parameters, and host images to determine the optimal embedding parameters. An experimental comparison with other similar schemes is given at the end of paper. The comparison indicates that the proposed scheme is superior to the previous schemes from the perspectives of imperceptibility and robustness, which are two requirements of a high-quality robust watermarking.
Design of Multi-station Bar Code Scanning System Based on NJ Controller

The multi-station bar code scanning system realizes the function of identifying and uploading the material bar code to the computer for storage and query. The system is composed of the upper computer and the lower computer, and the scanner and the NJ communicate via Ethernet. When storing, first set the stored materials and station information on the host computer, and send the command to open the code scanner when the storage requirements are met. Secondly, the robot scans the materials under the control of the NJ motion controller, and then the sensor performs the material No judgment, when the material information set by the host computer and the barcode scanning information are consistent, the information is stored in the Access database, and finally the robot stores the material to the designated station. When the material information does not match or the scan code information is incomplete, manual code complement is required, and the judgment is made after the code complement is completed. If the information matches, it is stored in the database, and the robot sends the material to the designated station. At present, the system has been applied to solve the problem of automatically scanning the code and storing it in the station during the storage process.
Deep Listening to the Amazon Rainforest through Sonic Architectures

De Rerum Natura is an electroacoustic composition by the author, based on field recordings from the Brazilian Amazon rainforest. The piece is part of wider research from the author that explores the act of listening, associated visual mental imagery and dynamic subjective links between the composer's experience of listening to/recording experience of the original material and the audience's perception of the final composition as it is performed. This article focuses on the author's process of developing De Rerum Natura, based on Deep Listening. De Rerum Natura also examines the merging of the composition with the acoustics of the performance space.