Reviewerly/exHarmony huggingface dataset:
Analysis of Human Intelligence in Identifying Persons Native through the Features of Facial Image

Image object classification and detection are two important basic problems in the study of computer vision. Image classification is always a challenging task for computer scientist. Classification is a well-known supervised learning technique. This is always used to extract meaningful and vital information from a large dataset. It can also be effectively used for predicting unknown classes. At present image classification accuracy is not high enough because of large number of redundant information as well as features. Primary focus should be on how human intelligence works on image classification rather than training the machine for the image classification. In this research paper a theoretical and numerical analysis of human intelligence is outlined as how human intelligence works on an image through which features and in what way other they are deciding the category of image they have perceived.
A New Effective Neural Variational Model with Mixture-of-Gaussians Prior for Text Clustering

Text clustering is one of the fundamental tasks in natural language processing and text data mining. It remains challenging because texts have complex internal structure besides the sparsity in the high-dimensional representation. In the paper, we propose a new Neural Variational model with mixture-of-Gaussians prior for Text Clustering (abbr. NVTC) to reveal the underlying textual manifold structure and cluster documents effectively. NVTC is a deep latent variable model built on the basis of the neural variational inference. In NVTC, the stochastic latent variable, which is modeled as one obeying a Gaussian mixture distribution, plays an important role in establishing the association of documents and document labels. On the other hand, by joint learning, NVTC simultaneously learns text encoded representations and cluster assignments. Experimental results demonstrate that NVTC is able to learn clustering-friendly representations of texts. It significantly outperforms several baselines including VAE+GMM, VaDE, LCK-NFC, GSDPMM and LDA on four benchmark text datasets in terms of ACC, NMI, and AMI. Furthermore, NVTC learns effective latent embeddings of texts which are interpretable by topics of texts, where each dimension of latent embeddings corresponds to a specific topic.
Self-learning Attention Global Pooling Based Image Representation for Visual Place Recognition

The visual place recognition is based on the image to determine whether the current scene match the scene in the index data set and localization. However, due to the scene appearance and the shooting position changing with time, such as distance, viewpoint, illumination, weather, season, traffic flow, etc., it is a challenge to extract more distinguishing and representative image representation by overcoming multiple variables. Recently based on convolutional neural networks, many global pooling methods have been proposed and reached the cutting edge of the state of the art, but they ignore the correlation of local regions in different channels. Therefore we propose a self-learning attention global pooling model for feature maps and performs end-to-end fine-tuning training for contrastive loss through Siamese network. The experimental results show that our method outperforms the state-of-the-art global pooling method on two public test datasets: ROxford and RParis.
Consent based Access Control Mechanism for Encrypted Medical Image

An outline which defends tolerant details during facts transfer be necessary for medical management systems. On the way to attain safety and confidentiality for facts transfer, a consent based access control system was proposed. It grants the agreement by distributing token to the data client, where the permission can only be created by official client. Thus the information stored inside the data centre can be accessed only when the data requester has the token, which is similar to the token already present inside the data centre. If the confirmation of data centre is valid, the data requester can access the original information of the user. Eventually, the user will be notified by the data centre to deserve that there is no misuse outside consent. The anticipated consent based access control method is compared with existing methods to achieve less time utilization.
An Approach to Achieve Modern Cryptographic Encryption System for Preserving Secrets using Number System Generator

In this research article, the modern cryptographic encryption system has been designed and implemented by using the method of Number System Generator. The mechanism has processed the data in a block wise manner. The block size has been fixed as 16 bit. Each bit of data is working independent to other by use of number system generator. Key has changing for every 4 bits of input. Approximately key size is assumed as in the order of N/4. Every block of data has processed by four different keys. So for every block key has changing dynamically. Two rounds of encryption process are contributed to provide final encrypted results. Encryption rounds are varied about the key selection procedure. For round1 key has generated based on the forward selection mode. In round2 key generation has made by use of reverse selection. Results of two rounds have applied together as whole. ASCII encoder is applied and contributed to enhance the security level. Also different set of data has processed and analyzed with various security parameters.
Performance Analysis of LSB Image Steganography Combined with Blowfish-RC4 Encryption in Various File Extensions

Sending and exchanging important data through the internet is prone to be stolen by unauthorized parties. The number of confidential data theft cases make the need for data security important. Steganography and cryptography can be used as tools to protect the security and confidentiality of confidential data. Steganography as a tool to hide confidential data while cryptography as a tool to randomize confidential data. The combination of the Least Significant Bit (LSB)- Blowfish and Rivest Code 4 (RC4) is proposed because it can provide layered protection on confidential data. The secret data is encrypted using the Blowfish algorithm and encrypted again using the RC4 algorithm before it is pinned on the LSB media cover in the form of images. The performance analysis of the combination of LSB and Blowfish-RC4 was carried out on various image sizes with messages that had various types of file extensions. The stego image was compared to determine the level of its imperceptibility with the cover image through Mean Square Error (MSE) test, Peak Signal to Noise Ratio (PSNR) test also histogram analysis from both of that images. Based on experiments showed good PSNR results with an average above 56dB. Meanwhile, the performance of cryptographic algorithms tested through entropy tests proved to be very good because it approached perfect values.
Risk-Driven Analytics for Banking IoT Strategy

New communication technologies have a significant impact on the management of banking activities and strongly influence their ecosystem. Key innovations include big data analytics, artificial intelligence, data science, digital currency, social media, blockchain and the Internet of Things (IoT). Many of these technologies are interdependent. IoT is the interconnection of uniquely identifiable integrated computing devices within the infrastructure of the surrounding computer network. In the field of banking services, the interconnection of those integrated equipment should allow the automation of several legacy processes. As the digital transformation driven by IoT begins to take root, new business models and products emerge. This opens new frontiers for innovation that can change customer behavior in the banking industry. The objective of this chapter is to highlight and illustrate the different uses of IoT in banking. We also analyze the impact of digital risks based on the Internet of Things on the traditional banking processes. It would be of interest to bring together the different types of digital risks that have a similar impact on bank’s processes. We analyze those technologies to implement the digital transformation using new practices. We develop an approach based on a bipartite graph associating the processes and risks of the banking sector. From this, we deduce a strategy to safely lead the integration of connected objects into the banking industry.
Non-specific Facial Expression Recognition Based on Tensor and Convolution Neural Network

This paper proposes a non-specific facial expression recognition algorithm. Firstly, according to the hypothesis that "similar people have similar expressions" [1], the tensor singular value decomposition is used to find the facial expression image most similar to the non-specific face for similarity calculation, thereby updating the non-specific facial expression image. Then the updated facial expression image is input into a six-layer convolution neural network and the recognition result is obtained. The experiment was tested on the Japanese female expression database to obtain a good recognition rate. The results show that the algorithm is effective in nonspecific facial expression recognition.
Constructing and researching quasispectral orthogonal polynomials on the basis of Lagerr polynomials

An orthogonal system of polynomials is constructed in the basis of Lagerra polynomials, and the problem of approximation of a function in the constructed orthogonal basis is considered. During the computational experiment, the influence of various errors on the approximation was investigated on model problems.
Analysis of classification algorithm in pension types

Abstract The concept of data mining in its implementation is used in handling and analysing data in large capacity. Steps to extract information and to gain knowledge in supporting decision making. Data mining, in its implementation can be used in various sectors of data analysis, namely about the condition of the type of pension. The purpose of this study is to provide information, where a civil servant within the retirement limit is taken normally or earlier. The technique of using the classification method, with the decision tree C4.5 (J48), Naïve Bayes and k Nearest Neighbour algorithm. Presentation of assets of 1,316 pension data in 2012 and 2013 from Bukopin Bank customers, which are divided into training data by 65% and data testing by 35%. From the testing of three algorithms, the highest classification was produced, namely Naïve Bayes with 91%. The results of the three classifications indicate the quality of attribute determination can affect the results. And the predictive level of the three algorithms shows different results. For classification techniques with better results, improvements are needed to determine attributes and develop existing datasets.
Fast Serial Approach of Object Distance Measurement based on Deep Learning and Stereo Camera

This paper proposes an approach of real time object distance measurement using stereo camera. A convolutional neural network (CNN) detects the objects. The distance to the objects is calculated from the disparity information or depth map. Experimental results show an improvement of runtime of at least 5% compared to fastest published methods.
A Support Method for Grasping Topic Transition on the Web According to Focused Article on SNS

In recent years, it has become easier for many people to post information online in the form of Web articles due to the popularization of high-performance electronic terminals and social networking services (SNSs) such as Twitter. Opportunities for browsing a wide variety of information have also increased. As a result, users often collect insufficient pieces of information from many articles and need to understand the topics contained in the information. However, it is difficult for them to find articles related to the topics that they are interested in and determine topic transitions in the articles. Therefore, this research is aimed at developing novel support for understanding articles related to a topic that a user is interested in and the topic transitions from articles on SNSs. In this paper, we propose a method for extracting topic words related to an article of interest on the basis of an analysis of timelines on Twitter. Moreover, we propose a method for extracting Web articles related to the progress of topics on the basis of an analysis of parts of speech in Web articles. Furthermore, we conducted experiments in order to evaluate the usefulness of the proposed methods and acquired findings from the experimental results.
Error Resilient Transmission of Color Mosaic Images Using Combined Approach

Video streaming is extensively used in our day-to-day exercise. Remotely assisted surgery, tele-medicine and transmission of medical images are some of the significant applications where loss on image is unbearable. Error resilience technique takes very important role in these kinds of applications. Error resilient technique is a classical area of research for which several techniques are already devised and are in use in various applications. There are still more possibilities to improve the performance of these techniques. Here a combined error resilience technique is proposed for the transmission of color mosaic images. The color mosaic images are usually captured by single sensor cameras with color filter array(CFA). Color mosaic image has only one component of RGB for each pixel captured thus the storage size, cost and camera size are considerably reduced.
Image Compression and Decompression using Fence Decimation

Image Compression is a method of reducing the amount of data that we require to represent the image. Image Compression has been the most useful and very successful technologies in the field of digital image processing. Researchers have been using oversampling of images till recently. In the implementedsystem, architecture is implementedthat compresses the images using decimation of pixels. The image is prefiltered using a low-pass prefiltering process before pixel decimation to get redefined edges. In the resulting decimated image blocking artifacts are reduced, hence we can get an image that can be compressed and transmitted without any significant change to current image coding standards and systems.For the decompression procedure, the low resolution image is first decompressed then it is upscaled to its original resolution using image upscaling method and then applying edge enhancement operation. The implemented approach of pixel decimation outperforms JPEG in PSNR measure and achieves superior visual quality.
Location Tracking of Mobile Objects and the Privacy Tradeoff

The Internet of Things (IoT) and mobile sensing applications bring great potential to the modern world, as they enhance and improve quality of life. However, this technology possess security and privacy risks to people who become targets of massive data collection. Embedded sensors have infiltrated everyday personal devices, and large amounts of data are continuously collected through trace data. Location data, search queries, preferences, etc., are all being recorded. In this paper, we surveyed IoT technology and mobile sensing applications, discussed privacy implications, and summarized current proposed solutions to the privacy issue. We then proposed a new solution that represents a balance between data integrity and privacy. We proposed a combination of three existing security measures; cryptography, data obfuscation, and access control mechanisms.
Heterogeneous Transfer Clustering for Partial Co-occurrence Data

Heterogeneous transfer clustering can translate knowledge from some related heterogeneous source domains to the target domain without any supervision. Existing works usually use a large amount of complete co-occurrence data to learn the projection functions mapping heterogeneous data to a common latent feature subspace. However, in many real applications, it is not practical to collect abundant co-occurrence data, while the available co-occurrence data are always incomplete. Another commonly encountered problem is that the complex structure of real heterogeneous data may result in substantial degeneration in clustering performance. To address these issues, we propose a heterogeneous transfer clustering method specifically designed for partial co-occurrence data (HTCPC). It is superior to the existing methods in three facets. First, HTCPC fully uses the partial co-occurrence data in both source and target domains to learn a latent space, maximally extracting useful knowledge for clustering from limited information. Second, it incorporates multi-layer hidden representations, accurately preserving the complex hierarchical structure of data. Third, it enforces approximately orthogonal constraint in representations, effectively characterizing the latent subspace with minimal redundancy. An efficient algorithm has been derived and implemented to realize the proposed HTCPC. A series of experiments on the real datasets have illustrated the advantage of the proposed approach compared with state-of-the-art methods.
Research on Publication of Technical Literature based on GJB6600

Technical literature is the bearing of the core knowledge of equipment, and it is the basis and support for the development of comprehensive equipment support. It is of great significance for equipment support. How to make the technical literature better play the inheritance, promotion and use efficiency of knowledge is the key problem to be solved in the comprehensive equipment support. The interactive electronic technical manual uses digital forms to store and manage technical literature in the form of text, forms, images, engineering graphics, sound, video, animation, and more. It is also viewed and used interactively to provide fault reasoning guidance and knowledge push services. In 2010, China released the interactive electronic technical manual National Military Standard GJB6600, which provides a good guide and specification for the use of military equipment technical literature, but lacks the support of a fully independent and controllable publishing platform. Under the background of domestically controllable domestic control, this paper analyzes the interactive publishing requirements of equipment developers and users for technical literature, gives the index of independent controllable technical literature publishing platform, and proposes an independent and controllable interactive publishing scheme based on GJB6600 technical literature. And proposed an interactive publishing platform construction facility that can guide technical literature.
Network Parameter Generation for One-Shot Object Detection

Recent object detection models have achieved satisfactory performance by deep learning with large-scale annotated datasets. However, these models often perform poorly when the training examples are not sufficient enough, especially for one-shot object detection where there is only one training example of each target class. In this paper, we propose a network parameter generation framework for effective one-shot object detection. By leveraging the semantic relationship between the source-domain and the target-domain (new classes), our framework can generate network parameters for the new classes directly. Furthermore, we develop two implementation schemes for this framework: weighting-based parameter generation and regression-based parameter generation. These two schemes use different information to construct the relationship and transfer knowledge from the source-domain to the target-domain. Experiments on the PASCAL-OSCD benchmark show that our methods can significantly boost the performance of one-shot object detection.
Blind 3D Image Quality Assessment Based on Multi-scale Feature Learning

3D image quality assessment (3D-IQA) plays an important role in 3D multimedia applications. In recent years, convolutional neural networks (CNN) have been widely used in various images processing tasks and achieve excellent performance. In this paper, we propose a blind 3D-IQA metric based on multi-scale feature learning by using multi-column convolutional neural networks (3D-IQA-MCNN). To address the problem of limited 3D-IQA dataset size, we take patches from the left image and right image as input and use the full-reference (FR) IQA metric to approximate a reference ground-truth for training the 3D-IQA-MCNN. Then we put the patches from left image and right image into the pre-trained 3D-IQA-MCNN and obtain two quality feature vectors based on multi-scale. Finally, by regressing the quality feature vectors onto the subjective mean opinion score (MOS), the visual quality of 3D images is predicted. Experimental results show that the proposed method achieves high consistency with human subjective assessment and outperforms several state-of-the-art 3D-IQA methods.
Rethink Gaussian Denoising Prior for Real-World Image Denoising

Real-world image denoising is a challenging but significant problem in computer vision. Unlike Gaussian denoising on which most existing methods focus, the real-world noise is nonadditive, and the distributions are difficult to model. This leads to unsatisfactory performance when applying a Gaussian denoising approach on real-world denoising problems. In this paper, we propose a simple framework for effective real-world image denoising. Specifically, we investigate the intrinsic properties of the Gaussian denoising prior and demonstrate this prior can aid real-world image denoising. To leverage this prior, we fine-tune it for only one epoch on a recently proposed real-world image denoising dataset, and the learned model can enhance both visual and quantitative results (peak-signal-noise-ratio) for real-world image denoising tasks. Extensive experiments demonstrate the effectiveness of our approach, and indicate that the Gaussian denoising prior can be also transferred to real-world image denoising by exploiting appropriate training schemes.
Real-Time Incremental Clustering on Social Network Comment Streams using Enhanced IncreSTS Algorithm

Popularity of social networking sites increases day by day. Due to its high popularity, the number of comments can grow at a high rate when a social message published. The users of social networking sites may wish to get a brief knowledge of comment streams without reading the all comments. The system aim to group comments with similar opinion together and create a summary for this message after filtering abusive words from each generated comments. Since the users will request the summary at any moment, we use IncreSTS algorithm for realtime incremental clustering of social network comment streams. Besides, we extract key-terms from each clusters that help users to easily get an overview summary.
Different Types of File Format Image Compression using Transform Domain Up-Down Conversion

In any multimedia systems, reducing the file size and making it to support all types of format of system plays an important role. Image compression depends on the quality of system. There are many types of technique used to compress the image but some advantage and some disadvantage are increasing. This paper is based on up down conversion and block coding. Up down conversion is transmitter and receiver part of the system and block coding is depending on block size. Whole image is divided into sub block and change the binary form with the help of block coding. Whole process is simulated MATLAB software and calculated parameter such as MSE and PSNR. The block coding is good achievement compared to previous one.