Reviewerly/exHarmony huggingface dataset:
Libraries in blogosphere: Modern trends

The activities of libraries in blogosphere as a segment of their presence in the media space in respect of realizing a humanistic mission of a library is considered. The statistical data on the quantity of library blogs in 2010–2017 are presented. It is indicated that the total quantity of library blogs increases, but the quantity of those keeping updated at present reduces. The updated blogs (despite of relatively small amount of subscribers) operate actively enough which is proved by the year-to-year growing quantity of posts and users’ responses. The activities university libraries in blogosphere (2016–2018) in respect of contents of content and frequency of its updating, amount of user audience, level of interaction with users and their subject preferences are analyzed. The accent is made on the statement that contents of the majority of blogs is oriented towards education and cultural growth. The importance of interaction with user groups is highlighted. The conclusions are made on the necessity of activating libraries’ activities in blogosphere on the basis of searching the cross points of libraries’ and users’ interests, studying users’ requests and accenting the attention on humanistic principles of the society development.
Parallel blind semi-fragil color image watermarking based on fast discrete cosine transform

In this paper, a completely blind semi-fragile color image watermarking method for copyright protection is proposed. The embedding algorithm is performed by swapping three pairs of mid-frequency coefficients in each 8 by 8 Discrete Cosine Transform (DCT) non-overlapped block of the fast-discrete cosine transform algorithm that utilizes the eigenvectors of a symmetric “second difference” matrix in order to achieve higher computation performance. The simplicity of the embedding, extraction and DCT computation methods, offers a significant advantage in shorter processing time. Experimental results demonstrate the watermark imperceptibility with high PSNR and SSIM values of the watermarked image (of 45 dB and 0.948, respectively); additionally, the 2D binary watermark embedded such as company trademarks or owner’s logotype can be extracted with completely blind extraction process, i.e. without the original carrier image, original watermark nor any other information derivative of them are required. Average normalized correlation of the extracted binary watermark is of 0.91 even if the watermarked image was attacked by scaling, noise addition or JPEG compression. The simulation results of the parallel implementation in multicore CPU of the proposed method have shown more efficient and effective in real-time implementation of image watermarking than commonly used DCT techniques.
Enhance image resolution with nearest neighbor pixel deconvolution

We report an image processing method which is able to break the diffraction limit for single frame images. If an image is taken by diffraction limited optical system with regularly spaced pixel detector, and if the spacing of pixels of the detector is much small than the diffraction pattern, then the spatial resolution of the image can be increased beyond the diffraction limit by using a method known as nearestneighbor- pixel-deconvolution (NNPD). Initial experiment result has proved feasibility of this method. Two images captured from outer space were processed using this method, with some new features unveiled in the processed images.
A cost-effective anomaly detection system using in-DRAM working set of active flows table

In the zettabyte era, per-flow measurement becomes more challenging owing to the growth of both traffic volumes and the number of flows. Also, swiftness of detection of anomalies becomes paramount. For fast and accurate anomaly detection, managing an accurate working set of active flows (WSAF) from massive volumes of packet influxes at line rates is a key challenge. WSAF is usually located in a very fast but expensive memory, such as TCAM or SRAM, and thus the number of entries to be stored is quite limited. To cope with the scalability issue of WSAF, we propose to use In-DRAM WSAF with scales, and put a compact data structure called FlowRegulator in front of WSAF to compensate for DRAM's slow access time by substantially reducing massive influxes to WSAF without compromising measurement accuracy. We evaluated our system in a large scale real-world experiment. As one key application, FlowRegulator detected heavy hitters with 99.8% accuracy.
Garbage collection as a joint venture

A collaborative approach to reclaiming memory in heterogeneous software systems.
Equational properties of fixed-point operations in cartesian categories: An overview

Abstract Several fixed-point models share the equational properties of iteration theories, or iteration categories, which are cartesian categories equipped with a fixed point or dagger operation subject to certain axioms. After discussing some of the basic models, we provide equational bases for iteration categories and offer an analysis of the axioms. Although iteration categories have no finite base for their identities, there exist finitely based implicational theories that capture their equational theory. We exhibit several such systems. Then we enrich iteration categories with an additive structure and exhibit interesting cases where the interaction between the iteration category structure and the additive structure can be captured by a finite number of identities. This includes the iteration category of monotonic or continuous functions over complete lattices equipped with the least fixed-point operation and the binary supremum operation as addition, the categories of simulation, bisimulation, or language equivalence classes of processes, context-free languages, and others. Finally, we exhibit a finite equational system involving residuals, which is sound and complete for monotonic or continuous functions over complete lattices in the sense that it proves all of their identities involving the operations and constants of cartesian categories, the least fixed-point operation and binary supremum, but not involving residuals.
Regularized Learning of Neural Network with Application to Sparse PCA

The paper presents an implementation of the regularized two-layer neural network and its application to finding sparse components. The main part of the paper concerns the learning of the sparse regularized neural network and its use as auto-encoder. A process of learning of the neural network with non-convex optimization criterion is reduced to convex optimization with constraints in an extended domain. This approach is compared with the dictionary learning procedure. The experimental part presents the comparison of our implementation and the SparsePCA procedure from the Scikit-learn package on different data sets. As a quality of the solution during experiments we take into account: the time of learning, sparsity, and quality of reconstruction. The experiments show that our approach can be competitive when a higher sparsity is needed, and in the case of a large number of attributes relative to the number of instances.
Hybrid data stream clustering by controlling decision error

Data stream clustering is an unsupervised learning method for sequential data. Data stream clustering has some challenging issues, such as handling limited memory, dealing with evolving clusters, and detecting noise data. We propose a hybrid data stream clustering method that combines model-based c lustering and density-based clustering. The proposed method finds evolving clusters quickly and obtains cluster information easily. We use multiple hypothesis testing to handle noise data by controlling a decision error. In this testing method, we employ the positive false discovery rate as the decision error. We use a density-based algorithm to discover cluster evolution from newly arrived data. Then, we estimate a Gaussian mixture model and update the clustering results by combining past cluster information and the cluster information for newly arrived data. We applied the proposed method to several synthetic and real datasets. The experimental results demonstrate that the proposed method works effectively for a data stream that includes noise data. In addition, the proposed method yields robust results relative to input parameters compared to an existing density-based data stream clustering method.
Texture Feature Extraction

This chapter focuses on another image feature called the texture feature. Two types of texture feature methods are discussed: traditional spatial methods and contemporary spectral methods. The chapter first introduces four spatial or handcrafted methods including Tamura, GLCM, MRF, and FDFractal Dimension (FD) . Readers are shown how microstructures of an image can be modeled with handcraft and statistical methods. Pros and cons of spatial methods are also discussed. In the next, four spectral texture methods are described in details including DCTDiscrete Cosine Transform (DCT) , Gabor, wavelet, and curveletCurvelet . These spectral texture methods not only match theory with data but also strengthen readers’ understanding of the powerful spectral transforms introduced in Part I. Readers are demonstrated with rich working examples and illustrations.
A Systemic Functional Approach to Clause Combining in English

SFL consists of two parts: systemic grammar and functional grammar. In systemic grammar, language is considered as a system of meaning potential, and form is the realization of meaning. In functional grammar, form is represented as a rank constituent structure, based on which, a functional syntactic structure is associated with the three metafunctions of language, including the transitivity structure realizing ideational function, the mood structure realizing interpersonal function, and the thematic and information structures realizing textual function.
Electric Power Meter Classification Based on BOW

The automatic verification of power meters is of great significance, and the key point is the classification of the power meter types. In this paper, we propose a power meter type recognition method based on machine vision. We construct a Bag-of-Words model(BOW), and extract the image features of the instrument, and construct a visual dictionary, based on which to train a support vector machine classifier to realize the automatic identification of the instrument type. The experimental results show that the proposed method achieves a classifiaction rate of 100% for several specific power meters, and is of great significance for applications.
A Memory Layout for Dynamically Routed Capsule Layers

Capsule Networks are an emerging extension of the traditional multilayer perceptron model, providing classification and estimated pose parameters through extra computational data models such as vectors and matrices. With extra data comes extra processing and meticulous data manipulation and this paper presents a scalable GPU optimization for the training and evaluation of such networks. A novel data abstraction maps the individual values of this tensor to one-dimensional arrays to 2D grids of multi-dimensional elements, enabling the removal of redundant data movement and other structural overhead found in common machine learning libraries. Assuming a single loss function, adapted for vector-based forward and backward propagation, this optimization involves combining successive operations found in processing into kernel calls, albeit at the expense of higher memory resource utilization. Despite that, this GPU based approach reached 33 times speed up in forward propagation and 130 times speed up in back propagation against optimal single threaded CPU calculations.
Detecting Position and Direction of a Device by Swept Frequency of Microwave Using Two-Dimensional Communication System

We propose a system of detecting the position of a device embedded with an antenna by sensing the electrical power from a two-dimensional communication (2DC) sheet. The system obtains a characteristic power pattern at each position by sweeping the frequency of the microwave supplied to the 2DC sheet. Our system uses a machine learning technique to learn the accumulated power-pattern data to detect the position of a device. The position-detection accuracy of our system was 79.1% when the antenna was moved in 12mm intervals. In addition to detecting the position of a device, we also estimated the direction.
Learning deep optimizer for blind image deconvolution

In blind image deconvolution, priors are often leveraged to constrain the solution space, so as to alleviate the under-determinacy. Priors which are trained separately from the task of deconvolution tend to be unstable. We propose the Golf Optimizer, a novel but simple form of network that learns deep priors from data with better propagation behavior. Like playing golf, our method first estimates an aggressive propagation towards optimum using one network, and recurrently applies a residual CNN to learn the gradient of prior for delicate correction on restoration. Experiments show that our network achieves competitive performance on GoPro dataset, and our model is extremely lightweight compared with the state-of-the-art works.
Lightning Talk - Think Outside the Dataset: Finding Fraudulent Reviews using Cross-Dataset Analysis

Many crowd-sourced review platforms, such as Yelp, TripAdvisor, and Foursquare, have sprung up to provide a shared space for people to write reviews and rate local businesses. With the substantial impact of businesses’ online ratings on their selling [2], many businesses add themselves to multiple websites to more easily be discovered. Some might also engage in reputation management, which could range from rewarding their customers for a favorable review, or a complex review campaign, where armies of accounts post reviews to influence a business’ average review score.
Data Confidentiality, Integrity, and Authentication

Blockchain has been created in the process of development in bitcoin. It's a singly linked list of block, with each block containing a number of transactions and each list in the blocks using with cryptographic functions. The cryptographic hash function contains the hash of the previous block, timestamp, and transaction ID. Blockchain services include the authentication, confidentiality, integrity, data and resource provenance, and privacy and access control lists technologies. The authentication provider authenticates decentralized database with transactions in private-public key pair. This key-pair is used in the transport layer security with the entire network. The network legitimizes the transaction after that and adds the transaction to the blockchain. A sequence of blocks in blockchain holds the complete record of transactions like a public ledger. The integrity data written in the blockchain cannot be altered subsequently. By limiting access to the information in confidentiality, only authorized users can access the information, so that information is also protected.
A Reversible Data Hiding Scheme in Encrypted Image with LSB Compression and MSB Replacement

A new scheme of reversible data hiding in encrypted images is proposed in this paper. Content owner first encrypts divided blocks by the specific stream cipher and permutation. During data embedding, data hider embeds secret data into the compressed least significant bits (LSB) and the most significant bits (MSB) of a part of pixels in the smooth region. After the receiver receives the marked, encrypted image, he or she can obtain a directly decrypted image with encryption key. With data-hiding key, the receiver can extract the embedded data from the compressed LSB sequences and MSB layers. If the receiver has both encryption key and data-hiding key, he or she not only can extract the embedded data from the marked, encrypted image, but also can recover original image correctly. Experimental results show our scheme achieves better rate-distortion performance than some reported schemes.
Enterprise Class Deep Neural Network Architecture for recognizing objects and faces for surveillance systems

Building security systems is improving at a mammoth rate since the past decade, to cope with the threats of unauthorized access and fraudulent intentions. In high security public places like airports, embassies, corporate offices etc. only facial recognition for verification does not suffice full proof security. To predict the intention of an individual we must capture the objects around the subject/individual and his interactions with them in real time environment with good accuracy besides recognizing them. In this paper we designed an Enterprise Class Deep Neural Network (EcDNN) architecture built on the base architecture of YOLO network. Our proposed multitask learning network architecture recognizes the faces of registered individual as well as objects in the person's vicinity at one shot which achieves significant improvement in performance in terms of speed and model size without loss of precision, if it would have done separately in a cascaded model architecture. Our proposed single network architecture employing multitask learning is achieving state of the art recognition accuracy of 79 mAP at 40 fps with 33 % reduction in model size and an approximately 4x speedup with respect to the benchmark state of the art architectures, validated on standard dataset of PASCAL VOC 2012, FDDB and custom office dataset.
Near Lossless JPEG Compression Based on Masking Effect of Non-predictable Energy of Image Regions

This paper studies near lossless JPEG image compression. A method of estimation of image regions masking ability (maximal level of distortions invisible for human visual system) using non-predictable energy of image regions is described. A novel method of zeroing quantized DCT coefficients of JPEG images to increase their compression ratio without introducing visible distortions is proposed. A numerical analysis of effectiveness of the proposed near lossless compression method using 300 noise free test images of TAMPERE17 database is carried out. It is shown that the proposed method provides an increase of compression ratio of JPEG images without visible distortions at about 1.35 times in average. Additionally, the proposed method results in decreasing of variability of compression ratio values for different images. It is shown that the proposed method increases minimal compression ratio for highly textured JPEG images from 1.1…1.5 times to 2 times. Carried out experiments demonstrated once again that the traditional PSNR metric does not correspond to human perception for this task.
Modeling the performance of modern sensor systems

Sensor system performance models (i.e., ATR performance models (APMs)) are needed online for decision making, multi-source fusion, sensor planning and parameter setting and offline to understand performance in operational settings for ATR development, down selects, and transition decisions and mission level simulations generally. Modern sensor systems involve resolved targets and non-human / ATR exploitation. This brings special modeling challenges, e.g., ATRs' additional sensitivities and data set mismatches. APM development is a multi-disciplinary problem. Perspectives from these disciplines will introduce both APM challenges and challenge surmounting approaches via the COMPASE Model Framework (CMF). From the confusion matrix perspective, the challenge of operating condition (OC) space resolution, which is needed for APM sensitivity and for broad model applicability, is introduced along with CMF's OC tree approach. From the sensor modeling perspective, the sensor system modeling has the challenge of taking full advantage of the rich first principles understanding of sensor effects while including effects beyond sensor image metrics. This challenge is approached in CMF through pixels-on-target adjustments based on uniqueness, clarity and conformity. From the machine learning perspective, there is the challenge of taking advantage of empirical evidence for ATR performance, even though it is non-representative in terms of OC distribution. This is being approached in CMF through stratification of OC space and explicit accounting of OC distributions. The CMF approach is realized in an APM development and assessment framework and has resulted in a suite of APMs that are being used in online and offline applications.
Denoising of Smart-phone based Fringe Projection Image using Curvelet Transform

Fourier transform profilometry (FTP) can enable three-dimensional(3-D) surface profiles through single-shot imaging, which make it one of the popular high-throughput, non-contact topography methods. Here, we developed a portable FTP using a smart phone and a micro projector. The micro projector casts a fringe pattern onto the measured object surface and the smart phone captures the resulting deformed fringe image. However, the recorded image has distortion and noise, which seriously degrade the reconstructed quality. In addition, the traditional transform, such as Fourier and wavelet, can suppress parts of high frequencies yielded by sharp changes in shape, resulting in image blurring. We then introduce Curvelet transform to inhibit the noise and preserve the high frequencies owing to its multiscale and multi-direction analysis in image processing capacities. Simulations and the experimental results show that the Curvelet transform has an excellent performance in image denoising and Curvelet transform denoising achieves high-precision 3D reconstruction.
Intelligent Medical Distance Assistance Device

The objective of this project is to realize and implement a complex system for remote monitoring of vital functions. The IOT-based health care system for the elderly is the cheapest medical device based on the IOT platform for patients and physicians. It provides a solution for measuring body parameters such as ECG, temperature, humidity and pulse. It also detects the location of patients to be sent to a doctor and in case of emergency it is possible to automatically call the emergency number accompanied by the GPS data.
Research on Image Processing Algorithm in Intelligent Vehicle System Based on Visual Navigation

In this article, the writer researches an algorithm of image navigation of smart car and introduces a kind of image navigation algorithm based on image restoration, and resolve the problem that the smart car runs in a wrong way. Firstly, this algorithm preprocesses the video image which is collected by image sensor, including binarization processing, denoising processing and background segmentation processing, so that the clear runway boundary line is available. Then, calculate the boundary equation through the boundary extraction algorithm. Judge the trend of boundary line by extract algorithm, and select the optimum fitting algorithm by the boundary line type. In this paper, the navigation algorithm we studies is more accurate than the others, and improves the ability of analyzing the route by smart car, and makes smart car runs more stably.