Reviewerly/exHarmony huggingface dataset:
A MOLLIFIER APPROACH TO THE DECONVOLUTION OF PROBABILITY DENSITIES

We use mollification to regularize the problem of deconvolution of random variables. This regularization method offers a unifying and generalizing framework in order to compare the benefits of various filter-type techniques like deconvolution kernels, Tikhonov, or spectral cutoff methods. In particular, the mollifier approach allows to relax some restrictive assumptions required for the deconvolution kernels, and has better stabilizing properties compared with spectral cutoff or Tikhonov. We show that this approach achieves optimal rates of convergence for both finitely and infinitely smoothing convolution operators under Besov and Sobolev smoothness assumptions on the unknown probability density. The qualification can be arbitrarily high depending on the choice of the mollifier function. We propose an adaptive choice of the regularization parameter using the Lepskiĭ method, and we provide simulations to compare the finite sample properties of our estimator with respect to the well-known regularization methods.
Cyber Attacks Against the PC Learning Algorithm

Data integrity is a key requirement for correct machine learning applications, such as Bayesian network structure learning algorithms. This research studies how an adversary could corrupt the PC structure learning algorithm by inserting fake data. We propose a novel measure of strength of links for Bayesian networks. We show how this measure can be used to attack the PC algorithm. We identify two subclasses of data poisoning attacks: (1) model invalidation attacks that arbitrarily break the structure of the Bayesian network model (2) targeted change attacks that achieve a specific structure. We show that model invalidation attacks require only a few “poisoned” data insertions. Targeted attacks are more difficult and require knowledge of the link strengths and a larger number of corrupt data items than the invalidation attack.
Automated Full Quantification of Left Ventricle with Deep Neural Networks

Accurate cardiac left ventricle (LV) quantification is among the most clinically important and most frequently demanded tasks for identification and diagnosis of cardiac diseases and is of great interest in the research community of medical image analysis. However, it is still a task of great challenge due to the high variability of cardiac structure across subjects and the complexity of temporal dynamics of cardiac sequences. Full quantification of cardiac LV includes simultaneously quantifying, for every frame in the whole cardiac cycle, multiple types of cardiac indices, such as cavity and myocardium areas, regional wall thicknesses, LV dimension and cardiac phase. Accurate quantification of these indices will support comprehensive global and regional cardiac function assessment. In this paper, we propose a newly designed multitask learning network which combines the segmentation task and the cardiac indices quantification task. It comprises a segmentation network based on the U-net for image representation, and then followed by a simple convolutional neural network for feature extraction of cardiac indices, two parallel recurrent neural network models are then added for temporal dynamic modeling. Then we use multitask learning to capture the existing correlations among different tasks. Experiments of 5-fold validation results show that the proposed framework achieves high accurate prediction, with average mean absolute error of 173 mm $$^{2}$$ , 2.44 mm, 1.37 mm for areas, dimensions, RWT and phase error rate 7.8%.
Efficient Data Compression Scheme for Secured Application Needs

The widespread availability of 3G/4G network could make Intelligent Traffic Systems (ITS) capable of wirelessly connecting to the data networks. The near future possibility of 5G communication would make this service more promising. The applications like traffic management and military where ITS plays an important role, demands a huge amount of secure data transmission. Massive data transfer is always a costly affair in terms of power, performance and reliability. A suitable data compression technique equipped with robust encryption methodology can leverage the burden of extensive data transportation. The shrunken data transmitted can be decrypted and decompressed at the receiver end, and there by the original information can be recovered. This paper demonstrates an efficient way of lossless data compression using a blend of Static and Instantaneous encoding named as SIN Compression. The proposed scheme encodes the data based upon a suitable threshold point that eliminates the large number of iterations used in traditional compression methods. For an experiment performed over a 16 × 16 pixel image; the SIN compression shows 60% of performance, 64% of area, and 69% of power improvement over the original lossless Huffman compression.
Fundamentals: Quantities, Definitions, and Units

Solar radiation is a generic term that refers to different magnitudes of the solar electromagnetic radiation. The quantification of solar radiation incident at the Earth’s surface is of high interest in many disciplines (radiative transfer in the atmosphere, meteorology and climatology, remote sensing of the atmosphere, solar energy studies, etc.). This multidisciplinary aspect of solar radiation sometimes produces duplication of names, definitions, or units. Moreover, different application-specific conventions for variable naming or units exist, which can be confusing. The solar irradiance that reaches a point at the Earth’s surface is basically dominated by (i) the geometric aspects of the Earth’s orbit around the Sun, and the inclination of its rotation axis in the ecliptic plane that determines the incident angle of the Sun rays; and (ii) the interaction mechanisms of solar radiation with various types of atmospheric constituents. This chapter intends to give the reader an overview of the basic definitions of the main variables that are commonly found in solar energy, and hence in this book as well. In addition, some basic aspects of solar geometry are briefly presented, followed by a concise description of the fundamentals of radiation-transfer modeling in the atmosphere. Detailed information on these topics, which is out of the scope of this book, can be found in many textbooks and the abundant literature on solar radiation, radiative transfer and atmospheric physics, to which the avid reader is referred for additional insight.
4SRS-SoaML Method for Deriving a Service-Oriented Architecture From Use Cases Within a SPEM Approach

The transformation of requirements specification into an architectural design has been a crucial endeavor for the information systems analysis and design community, with ever-new challenges to tackle. Despite the wide and diverse existing proposals, the lack of a common structure and use of different strategies makes it close to impossible to analyze or compare these approaches. Therefore, the use of model-based methods benefits from a detailed specification in order to support their analysis and evolution, also in comparison to other approaches. Following work on the derivation of a logical architecture from business-process use-cases requirements in a service-oriented approach, the authors propose a detailed specification, within a SPEM approach, of a transformation method, which they further analyze and refurbish in order to meet current and future challenges.
Mining Tree Patterns with Partially Injective Homomorphisms

One of the main differences between inductive logic programming (ILP) and graph mining lies in the pattern matching operator applied: While it is mainly defined by relational homomorphism (i.e., subsumption) in ILP, subgraph isomorphism is the most common pattern matching operator in graph mining. Using the fact that subgraph isomorphisms are injective homomorphisms, we bridge the gap between ILP and graph mining by considering a natural transition from homomorphisms to subgraph isomorphisms that is defined by partially injective homomorphisms, i.e., which require injectivity only for subsets of the vertex pairs in the pattern. Utilizing positive complexity results on deciding homomorphisms from bounded tree-width graphs, we present an algorithm mining frequent trees from arbitrary graphs w.r.t. partially injective homomorphisms. Our experimental results show that the predictive performance of the patterns obtained is comparable to that of ordinary frequent subgraphs. Thus, by preserving much from the advantageous properties of homomorphisms and subgraph isomorphisms, our approach provides a trade-off between efficiency and predictive power.
Recent Advances of Manifold Regularization

Semi-supervised learning (SSL) that can make use of a small number of labeled data with a large number of unlabeled data to produce significant improvement in learning performance has been received considerable attention. Manifold regularization is one of the most popular works that exploits the geometry of the probability distribution that generates the data and incorporates them as regularization terms. There are many representative works of manifold regularization including Laplacian regularization (LapR), Hessian regularization (HesR) and p-Laplacian regularization (pLapR). Based on the manifold regularization framework, many extensions and applications have been reported. In the chapter, we review the LapR and HesR, and we introduce an approximation algorithm of graph p-Laplacian. We study several extensions of this framework for pairwise constraint, p-Laplacian learning, hypergraph learning, etc.
Neural Network Algorithm for Solving 3d Inverse Problem of Geoelectrics

The approximating neural network algorithm for solving the inverse problems of geoelectrics in the class of grid (block) models of the medium is presented. The algorithm is based on constructing an approximate inverse operator using neural networks and makes it possible to formally obtain the solutions of the geoelectrics inverse problem with a total number of the sought parameters of the medium $$ \sim n\, \times \, 10^{ 3} $$ . The questions concerning the correctness of the problem of constructing the inverse neural network operators are considered. The a posteriori estimates of the degree of ambiguity in the inverse problem solutions are calculated. The work of the algorithm is illustrated by the examples of 2D and 3D inversions of the synthesized data and the real magnetotelluric sounding data.
Automatic Video Surveillance System for Pedestrian Crossing Using Digital Image Processing

Objective: This work proposes a novel technique to assist the physically challenged people to cross the road near the traffic signal or in the pedestrian crossing with the help of automatic video surveillance. Methods/Statistical Analysis: As per the Indian roadway corporation it is decided to minimum 1.8m (width) × 2.2m (height) is allocated for walking zone. 1800mm width is reserved for wheel chair movement in pedestrian. Detection of moving objects in a stable place needs the high security level. Findings: Image processing segmentation algorithm plays an important role to track the moving object in the fixed pedestrian crossing which can assist the physically challenged people or the elderly person. Various morphological filtering operations enhance the quality of segmenting the moving person in the video. This methodology employs histogram of Gaussian detection and object detection is done. The proposed work improves it results by 0.71% accuracy. Applications/Improvements: This work helps the physically challenged to cross the pedestrian in a safe manner and helps for autonomous vehicles. Keywords: Image Processing, Morphological Operations, Pedestrian Crossing, SVM Classification, Video Surveillance
Checking Observational Purity of Procedures

Verifying whether a procedure is observationally pure (that is, it always returns the same result for the same input argument) is challenging when the procedure uses mutable (private) global variables, e.g., for memoization, and when the procedure is recursive. We present a deductive verification approach for this problem. Our approach encodes the procedure’s code as a logical formula, with recursive calls being modeled using a mathematical function symbol assuming that the procedure is observationally pure. Then, a theorem prover is invoked to check whether this logical formula agrees with the function symbol referred to above in terms of input-output behavior for all arguments. We prove the soundness of this approach. We then present a conservative approximation of the first approach that reduces the verification problem to one of checking whether a quantifier-free formula is satisfiable and prove the soundness of the second approach. We evaluate our approach on a set of realistic examples, using the Boogie intermediate language and theorem prover. Our evaluation shows that the invariants are easy to construct manually, and that our approach is effective at verifying observationally pure procedures.
Getting a Handle on Duplicate Ebooks

The academic library is increasingly moving to majority online collections. As the number of ebooks increase due to the current ebook purchasing model most libraries use, libraries are more likely to be duplicating titles across packages and platforms. Many libraries do not realize how many duplicate titles there are between packages or how duplicate records affect the user experience. This paper examines four ebook packages in terms of duplication and suggests practical solutions for working with duplicate records.
ALDL: a novel method for label distribution learning

Data complexity has increased manifold in the age of data-driven societies. The data has become huge and inherently complex. The single-label classification algorithms that were discrete in their operation are losing prominence since the nature of data is not monolithic anymore. There are now cases in machine learning where data may belong to more than one class or multiple classes. This nature of data has created the need for new algorithms or methods that are multi-label in nature. Label distribution learning (LDL) is a new way to view multi-labelled algorithms. It tries to quantify the degree to which a label defines an instance. Therefore, for every instance there is a label distribution. In this paper, we introduce a new learning method, namely, angular label distribution learning (ALDL). It is based on the angular distribution function, which is derived from the computation of the length of the arc connecting two points in a circle. Comparative performance evaluation in terms of mean-square error (MSE) of the proposed ALDL has been made with algorithm adaptation of k-NN (AA-kNN), multilayer perceptron, Levenberg–Marquardt neural network and layer-recurrent neural network LDL datasets. MSE is observed to decrease for the proposed ALDL. ALDL is also highly statistically significant for the real world datasets when compared with the standard algorithms for LDL.
Promoting Geospatial Service from Information to Knowledge with Spatiotemporal Semantics

With the development of geoscience, users are eager to obtain preferred service from geospatial information intelligently and automatically. However, the information grows rapidly while the service gets more complicated, which makes it difficult to find out the targeted information for an exact service in geospatial issues. In this paper, a novel method is proposed to promote the geospatial service from information to knowledge with spatiotemporal semantics. Both prompted and professional knowledge are further refined to be published as a service. In terms of an exact task, numerous related services are recombined to a service chain under user requirement. Finally, the proposed method is applied to monitor the environment on the Air Quality Index (AQI) and soil moisture (SM) in the Sensor Web service platform, the results of which indicate geospatial knowledge service (GKS) is more efficient to support spatial decision-making.
Image Statistic Models Characterize Well Log Image Quality

Assessing the image quality of well logs is essential to ensure the accuracy of their digitization and subsequent processing. Currently, the suitability of well logs for information retrieval is solely determined on the basis of subjective judgments of their image quality by human experts. The success of natural scene statistics (NSS)-based models that are used to conduct no-reference (NR) quality assessment of photographic images motivates us to try to exploit them to characterize the quality of nonphotographic images, such as well logs. Accordingly, we develop a scheme to characterize the quality of a well log as “acceptable” or “unacceptable” for subsequent processing based on the natural image quality evaluator (NIQE), a successful NR image quality assessment model based on the NSS. Our experimental results show that the objective quality scores thus obtained can be reliably used to eliminate well logs of inferior quality from the processing pipeline, which can serve as a beneficial step to reduce the human hours spent in examining well logs and to improve the rate of information retrieval as well as the accuracy of retrieved information. Source code for the trained well log image quality predictor is available at https://github.com/Somdyuti2/Well_log_IQA.
Aerial Scene Classification and Information Retrieval via Fast Kernel Based Fuzzy C-Means Clustering

Fast kernel based fuzzy C-Means clustering is proposed in this article to accomplish both accurate and robust segmentation, via integration with watershed transform and fast level set schemes. Aerial scenes are inherently linked to (noise and artifact) sensitivities, intensity inhomogeneity, blurry boundary, and information complexity. It is thus necessary to combine the edge or contour based level set method with region based fuzzy C-Means clustering. To achieve fast segmentation, watershed transform is used to secure the initial contour of the fast level set method, so that initial cluster centers of fuzzy C-Means clustering are selected on those closed contour to avoid misclassification and to enhance separability. It reduces time for lengthy computation iteration. Using multiple densely distributed aerial images, robust and fast clustering is observed after comparing between classical and fast kernel based fuzzy C-Means clustering. To further analyze the role of hybrid fast kernel based scheme on scene classification and information retrieval, frequency domain histogram analyses for several clustering cases are conducted on aerial digital images.
High resolution fiber optic spectrometer system

A high resolution fiber optic spectrometer system was set up, which consists of a tunable laser, a fiber optic power meter, GPIB-USB data acquisition cards, and a computer control platform. The wavelength absolute accuracy of the tunable laser (Santec TSL-550) is&plusmn;2.5pm, and the wavelength setting resolution can be 0.1pm, which makes the system features high resolution. However, the resolution of the laboratory fiber optic spectrometer (YOKOGAWA AQ6317C) can only be set to 20pm. The extinction ratio (ER), free spectral range (FSR) and the trough position of the spectrum of the microfiber knot resonator (MKR) is measured with our system and AQ6317C fiber optic spectrometer. The experimental results of the two methods show that the ER and FSR are offset by 0.675dB and 0.003nm, respectively, and the standard deviations are 0.17% and 0.43%, respectively. From the results, and the stability of MKR is considered, we conclude that the resolution of our system can be better than 1nm due to the resolution of the TSL-550 light source, and the overall standard deviation is also smaller, therefore, the repeatability and stability of our testing system are better than the AQ6317C fiber optic spectrometer, which can better meet the actual measurement requirements.
Fuzzy Greenberger–Horne–Zeilinger state analyzer and multiparty measurement-device-independent quantum key distribution network

Greenberger–Horne–Zeilinger (GHZ) state is an essential resource in quantum processing. We propose multiparty measurement-device-independent quantum key distribution (MDI-QKD) protocol based on a fuzzy GHZ state analyzer, which is composed of linear optical components. Our scheme requires only N detectors and N − 1 polarizing beam splitters to realize an N-party MDI-QKD network. From numerical simulations, the security communication distance is improved to 300 km, and key rate is determined to be higher than 10 − 14. Compared with the previous work, our scheme needs less optical components and detectors, is easier to extend more users, and narrows the gap between theory and practice in multiparty MDI-QKD network.
Photometric Stereo on Large Flattened Surfaces Using Image Stitching

In this paper, we proposed a method to enlarge the recovered surface normal from photometric stereo method using image stitching. We used the laser triangulation to provide the actual height as the reference to reduce the low-frequency noise from photometric stereo. The Grid-based Motion Statistics (GMS) algorithm was used to obtain the Homography matrix. We employed and tested two algorithms for stitching: graph cut and image quilting. Two splice schemes were used: post-stitching and pre-stitching. The proposed method and comparison of two schemes were tested by experiments using captured surfaces with our built devices.
Intellectualization of Knowledge Acquisition of Academic Texts as an Answer to Challenges of Modern Information Society

Extracting knowledge from an increasing information flow is one of the main challenges of modern information society. The paper considers the possibilities and means for intellectualization of this process concerning such an important information source as the academic texts. In this case the user is faced with the task of finding fragments relevant to the subject of interest, within the vast textual documents often written in a foreign language. We experimentally investigated the comparative effectiveness of TS algorithms for extended coherent academic texts. The procedure of instrumental effectiveness evaluation was substantiated. The influence of the most significant characteristics of the text, including original language, structural organization (levels of heading), subjects of research (technique, information technologies and medicine) was considered. We have shown that for the intellectualization of knowledge acquisition from academic texts it is necessary to present to the reader the results of the TS fulfilled by different algorithms, in a complex. A system of complex visualization of TS results is proposed, and an appropriate software solution is developed. The visualization system for extended coherent texts explicitly demonstrates the semantic structure of the text, which allows the user to detect and analyze not the whole text, but only fragments corresponding to his current information needs and thus getting a complete idea of the subject of interest.