Reviewerly/exHarmony huggingface dataset:
Index Assignment Optimized for Partial Encryption

In this work, we consider the partial encryption of the bitstream of entropy-coded outputs of a scalar quantizer. More specifically, the sequence of most significant bits (MSB) is separately compressed and the resulting bitstream is encrypted, while the remaining information is compressed but not encrypted. Motivated by the observation that the mapping which assigns binary indexes to quantizer outputs controls the quality of the reconstruction at the eavesdropper and the size of the portion to be encrypted, we address the problem of optimal index assignment design. Ideally, the goal is to maximize the eavesdropper's distortion and minimize the length of the compressed MSB stream. Since these two objectives are conflicting, we formulate the problem as the maximization of a weighted sum of the distortion at the eavesdropper and the probability of the MSB being 0. We show that the problem can be cast as a maximum weighted graph matching problem, which is solvable in polynomial time. Experimental results assess the effectiveness of the proposed index assignment.
3D-MODELS AS A METHOD FOR REFLECTING VOLUME AND SPATIAL PARAMETERS OF OIL-CONTAMINED SITES

Today oil companies are trying to solve the urgent problem associated with the low efficiency of remediation work in oil-contaminated areas. Difficulties are caused by the lack of spatially ordered ideas about where and how pollution is distributed, what is its concentration and volumetric parameters in soils. The indicated problem entails an irrational waste of finance. As a rule, the return on funds spent does not exceed 30%. Obviously, the time has come to apply new approaches. We offer a technique for representing contaminants in the form of 3D models and tabular systematization of their concentrations and volumes. Scaling tabular information determines the color content of volumetric models. The described technique allows the differentiation of areas and their parts into reclamation groups. They focus on targeted use of allocated finances. Tabular systematization and spatially ordered representations of research results produce a synergistic effect. It manifests itself in obtaining a more accurate assessment of the condition of oil-contaminated sites, increasing the efficiency of restoration, the efficient use of finance.
Accelerating Bag-of-Words with SOM

We propose a fast Bag-of-Words (BoW) method for image classification, inspired by the mechanism that arrangement of neurons in visual cortex can preserve the topology of mapping from inputs, and the fact that human brain can retrieve information almost instantly. We propose algorithms for accelerating both Self-Organizing Map (SOM) training and BoW coding. First, we modify the traditional SOM based on the matrix factorization form of K-means. Utilizing the topology-preserving property of dictionary learned by SOM, the coding process of BoW can be accelerated by fast search of k-nearest neighbor codewords in the grid of SOM dictionary. We evaluate the proposed method in different coding scenarios for image classification task on MNIST and CIFAR-10 datasets. The results show that the proposed method accelerates BoW classification greatly with little loss of classification accuracy.
Structure Importance-Aware Hidden Images

Hidden images are fascinating as they present viewers a visual phenomenon of perceiving latent illusion from a single image. Recently, several solutions for automatically generating hidden images have been developed in the computer graphics community. While these solutions produce visually appealing hidden results, structure importance of the hidden object is rarely taken into account. In this paper, an automatic algorithm is presented to generate such images by integrating structure importance analysis of the hidden objects. Our algorithm is motivated by the law of closure of Gestalt psychology theory, indicating that human still can recognize an object from incomplete structural edges. We model the problem as a conditional image composition and approach it through a two-stage pipeline. The algorithm first performs a structure analysis to find the structure importance map of the object. Then the importance map is combined into a conditional mean value composition to generate the hidden image. Experimental results show that the proposed algorithm can efficiently generate visually appealing hidden images. Moreover, a user study is conducted to validate the effectiveness of the proposed algorithm.
NoSQL databases for large volumes of data

The paper is devoted to the issue of NoSQL databases and their application in gradually increasing volumes of data in databases. Concerning approaches, three are covered: key-value databases, column-oriented databases, and document databases.
Siamese Network for Classification with Optimization of AUC

It is known that RankSVM can optimize area under the ROC curve (AUC) for binary classification by maximizing the margin between the positive class and the negative class. Since the objective function of Siamese Network for rank learning is the same as RankSVM, Siamese Network can also optimize AUC for binary classification. This paper proposes a method for binary classification by combining Siamese Network for rank learning with logistic regression. The effectiveness is investigated by comparing the AUC scores of the proposed method with the standard Convolutional Neural Network. Then the proposed method is extended to multi-class classification problem by using Siamese Network and multinominal logistic regression. We extend the proposed binary classifier to multi-class classification by using one-vs-others approach.
The regularization method in the classification task according to given examples

The article considers the problem of classification based on the given examples of classes. As a feature vector, a complete characteristic of object is assumed. The peculiarity of the problem being solved is that the number of examples of the class may be less than the dimension of the feature vector, and also most of the coordinates of the feature vector can be correlated. As a consequence, the feature covariance matrix calculated for the cluster of examples may be singular or ill-conditioned. This disenable a direct use of metrics based on this covariance matrix. The article presents a regularization method involving the additional use of statistical properties of the environment.
Effective Dynamic Interactive Component of Time-Varying Data Visualization

The methods of interactive visualization in web browsers are considered. The analysis of the main problems that arise during data visualization is carried out, the basic methods of improvement of data visualization operations, which vary with time, are investigated. The software for the visualization of data with the possibility of its re-use, is developed. Proposed approach allows to improve rendering and increase productivity of the web browser.
Designing market systems with content management system (CMS) WordPress

Abstract The era of industrial revolution 4.0 was marked by the application of information technology in running industrial activities, the application of information technology needed was the development of a website-based business application model to increase competitive advantage. duta dharma is one of the medium enterprises that produce various types of processed soap. The sales system at cv duta dharma uses a manual system. This is still not fallen enough to change a significant increase in development. Therefore, need to design an e-commerce website-based online marketing information system. System modeling in the e-commerce website design system cv duta dharma is designing a context diagram to find out the stakeholders, designing decomposition to find out the inputs, processes, and outputs. Then design a data flow diagram to find out the data flow in the sales system, e-commerce website duta dharma enterprises have been designed and created with Wordpress. This e-commerce website has bank transfer payment facilities and can be run well and is expected to be able to make them more known and make it easier in product marketing.
DIC Measurement of the Full Strain Field in a Tiled Laminate to Determine Local and Global Stiffness Properties

Tiled laminates (TL) are bio-inspired oblique layered materials, composed of individual stacked plies in a tiled fashion, like the tiles on a slate-roof are positioned, and joined together with a polymer to form a rigid oblique layered structural material.This concept of an oblique layered buildup has great potential in different sectors (i.e.bridge building, offshore construction, ship building, lock gates, fire resistance panels, …) as it allows manufacturing of composite plates from tiled strips of fabric which opens up the door for robotised production of panels of any shape and size.Because this is a relatively new concept, less is known about the mechanical properties of the material such as the strength, the local and global stiffness and the fracture mechanics properties.Further, the classic laminate theory does not fully describe a TL as opposed to a plane-parallel laminate.Although available, experimental data is needed to validate, calibrate and corroborate the numerical finite element (FE) models.In previous research, the longitudinal stiffness was described by strain gauges and extensometers, leading to scatter/variation in the results due to the specific location of the measurement equipment on the laminate, which cannot be justified by the FE models.This paper studies the full strain field in a TL under uniaxial tension by digital image correlation (DIC) and compares the results to the numerical FE models.As a guidance, ASTM D3039 was used to perform the tensile test even though the specimens are not symmetric or balanced as prescribed by the standard.A fine speckle pattern is applied to the specimen and a well-lit environment is created for the DIC measurement.The results show that the strains along a path in the FE models on the top and bottom of the tiled laminate show large variations and that the peak values at the free edges in the FE models need to be averaged out over a relatively large distance.Finally, the tests allow for good local and global stiffness characterisation of a TL.
Image Analysis Based System for Assessing Malaria

Malaria, a not only widespread but also a potentially fatal disease that can be found mainly in tropical regions of the world, with the World Health Organization reporting an estimated 219 million cases worldwide as of 2017 of which 435,000 were mortal. Diagnosis currently involves taking a blood sample from a patient who is presumed to be infected, which is examined under a microscope by trained experts, although reliable the process is tedious. This disease is an ever-increasing problem thereby creating a need for an automated solution to the diagnosis of malaria. The primary objective of this project is to design a tool that can diagnose malaria from an image of a blood smear that has been stained with the commonly used Giemsa stain (which highlights the parasites in a red blood cell by turning them dark purple). In this paper, we have developed a graphical user interface to assist with the separation of red blood cells and extraction of the cells infected with the malaria parasite as well as an ANN (Artificial Neural Network) for cell classification. The graphical user interface allows the user to analyse the blood sample by running a series of image processing techniques followed by the extraction of infected cells, the results have shown that these techniques could be potentially used to detect malaria. Currently, the achieved results shown that the proposed system has 92% accuracy of a database contains a large number of ground-truth images.
Updatable Lossy Trapdoor Functions Under Consecutive Leakage

Abstract Lossy trapdoor functions (LTFs), introduced by Peikert and Waters (STOC’08), have already been found to be a very useful tool in constructing complex cryptographic primitives in a black-box manner, such as one-way trapdoor functions, deterministic public-key encryption, CCA-secure public-key encryption, etc. Due to the existence of the side-channel attack, the leakage of trapdoor information in lossy trapdoor function systems can lead to the impossibility of provable security. Recently, Zhang et al. introduced a model of consecutive and continual leakage-resilient and updatable lossy trapdoor functions (ULTFs) and provided a concrete construction to achieve the security. Meanwhile, they proposed a consecutive and continual leakage-resilient public-key encryption scheme. However, in this paper, we demonstrate that the correctness of injective function can not be satisfied. Furthermore, the attacker can easily distinguish the evaluation key of ULTFs generated by the challenger according to the security model. Finally, we show two new constructions based on the continual leakage-resilient public-key encryption scheme of Brakerski et al. (FOCS 2010) and demonstrate the security of our scheme in the consecutive and continual leakage model.
Morphological Knowledge Guided Mongolian Constituent Parsing

Mongolian constituent parsing is a challenging task due to lack of hand-annotated corpus and rich morphological varying. This paper takes a self-attention neural network to deal with Mongolian constituent parsing, which follows an encoder-decoder architecture. Concerning the syntactic functions of morphemes in Mongolian words, we make morphological analysis on each word and learn a novel word representation on such basis. To fully utilize the morphological knowledge, we adopt the last suffix tag of each word in the input embedding instead of its POS. The input embedding is the accumulation of word representation, the last suffix tag and the word position. The test experiment demonstrates that our model significantly outperforms the previous Mongolian constituent parsers. We achieve 87.16% F1 on the development set and 86.23% F1 on the test set.
Basics of Algorithms Intelligent System

It is shown that the fundamental basis and the primary basis for the concepts in the field of algorithmization of modern intelligent systems lie in the philosophical knowledge of the world and the process of thinking as such. It is indicated that the creation of modern artificial intelligent systems is based primarily on the knowledge of the actual thinking of the person, his ability to create different ways, mechanisms, tools of intellectual activity. It is emphasized that algorithms are practically the first among them and have now become one of the main ways of building intelligent systems. It is stated that the theory of algorithms itself, as one of the basic and fundamental branches of mathematics, transforming in its time from philosophy, is based on studies of epistemology, the subject of which is the process of human knowledge as such, in its entirety. It is presented from the position of epistemology and ontology as fundamental sections for the philosophy that the creation of modern integrated modules of information technology systems is unthinkable without taking into account the complex of relationships, interactions in the system "man-machine", and algorithms are the leading way to develop any artificial systems, including intelligent systems. Science is an important area of human activity, always aimed at the development and systematization of objective knowledge about the real world.
Toward a framework for assessing the cyber-worthiness of complex mission critical systems

Complex military systems are typically cyber-physical systems which are the targets of high level threat actors, and must be able to operate within a highly contested cyber environment. There is an emerging need to provide a strong level of assurance against these threat actors, but the process by which this assurance can be tested and evaluated is not so clear. This paper outlines an initial framework developed through research for evaluating the cyber-worthiness of complex mission critical systems using threat models developed in SysML. The framework provides a visual model of the process by which a threat actor could attack the system. It builds on existing concepts from system safety engineering and expands on how to present the risks and mitigations in an understandable manner.
Propagation of short current pulses in Josephson transition line and ultrafast qubit control

Abstract The results of the study of the impact of single-quantum magnetic flux pulses moving along the Josephson transmission lines (JTL) on the dynamics of states of qubits magnetically coupled with such lines are presented. The JTL dynamics was calculated in the frame of resistively shunted junction model with different damping coefficients. This allowed us to find the form of control current pulses (fluxon) acting on a superconducting qubit. Numerical simulations of the simplest logic operations with superconducting qubit due to calculated fluxon impact are presented.
Development of an AI based teaching assisting system

Nowadays, people are facing the issues of declining birthrates and an aging society. Teaching resources are far from enough and the teachers usually have great amount of works, especially in countryside or small towns. Aim to reduce the work of teachers, we developed a teaching assisting system to automatically track and recognize the motions and behaviors of students based on artificial intelligence (AI) technology. The humans are detected accurately by combing the detection results of OpenPose and human area projection method. Each person is identified by fusing his/her personal information, including the features of color, face and moving history, and tracked by using an extended particle filter method based on Markov Chain Monte Carlo (MCMC). By observing the students' behaviors without getting tired with the sensor system instead of the teachers, the performances and growing processes of all the students can be analyzed, referring the excellent knowledge and experiences of professional teachers. This information can be provided to the teacher which can help the teachers to adjust guidance to the students. Moreover, the information can also be provided to education robot so that the robot can also held a class or have interactions with the students.
Communication Effiency and User Experience Analysis of Visual and Audio Feedback Cues in Human and Service Robot Voice Interaction Cycle

As the most natural way of interaction, voice interaction plays a huge role in the development and progress of home service robots. Currently, most studies research voice interaction from a technical perspective. When the accuracy of speech recognition is as high as 98%, the effect of voice interaction is still not optimistic. One reason is that there is a lack of discussion of interaction cycle design from a user experience perspective, ignoring the importance of cues during interaction. The home service robot discussed in this paper uses the Turtlebot as a platform to explore the importance and necessity of different feedback cues for awakening, listening, speaking and thinking in the cycle of voice interaction. In the paper, we used the feedback cues as a variable, we designed five test systems. The experimental results show that the cues for listening are the most important and necessary for the interaction cycle. When a system removes the cues of listening, it will bring huge obstacles and negative effects to the communication process. The wake-up cues are important and necessary. When there are not wake-up cues in the system, the task completion is greatly reduced. On the contrary, the cues of thinking, which are thought to be more important, can be used to indicate that the robot has received the voice message. However, according to the experimental data, its lack did not bring much hindrance to the experiment. Removing the cues of speaking does not have a negative impact on the experiment, but rather reduces the experimental time to a certain extent.
Linear prediction filtering of per channel data for noise reduction - theory of action and application to cardiac ultrasound

Cardiac ultrasound imaging suffers from acoustic artefacts including diffraction limitation, aberration, reverberation, multipath, and electronic noise. In particular, multipath through the chest wall gives rise to a diffuse haze that obscures clinically relevant features. Linear prediction filtering applied to time-aligned array channel data has recently been introduced for white noise reduction in ultrasound. Our objectives in this work are 1) theoretical: to summarize the insights underpinning linear prediction filtering of array data thereby providing a mathematical framework to analyze its effects; and 2) experimental: to test the effectiveness of this technique in reducing multipath noise that produces diffuse cardiac haze. A signal model of array data originating from a superposition of far-field point sources can be expressed with a fully deterministic recursion and interpreted as a linear prediction model. The linear prediction filter is the minimum mean square error estimator of the predictable signal components. Linear prediction filtering is equivalent to applying a spatial filter to the channel data prior to beam-sum. The filter, combined to beamsumming, is equivalent to a applying a post-filter (i.e. mask) to the beam-summed data, whose amplitude is the DC response of the filter. Linear prediction filtering was applied on in-vivo cardiac datasets and compared to applying a Wiener postfilter designed to minimize the mean squared error of the beamformed output in the presence of white noise. Compared to Wiener filtering, the linear prediction filter was effective at reducing chamber haze levels, but it preserved sidelobe signals. The linear prediction framework may reduce scattering from structures of interest if the signal to interference ratio is low.
Saudi Arabia Market Basket Analysis

Market Basket Analysis is one of the famous Data Mining applications, where is the later extract hidden and previously unknown patterns, relationships and knowledge that are difficult to detect with traditional statistical methods [1]. This type of analysis is rarely been applied in the Arab Markets compared to the non-Arabic Markets. This paper will address the Saudi Arabia Market Basket Analysis using real data from one of the biggest Supermarkets in Saudi Arabia. At the end of this analysis study, we come up with a helpful information and facts for Saudi Arabia Markets. Finally, we come up with new knowledge about Saudi Arabia baskets which may help both marketers as well consumers in the same time.
Residual Connection-Based Multi-step Reasoning via Commonsense Knowledge for Multiple Choice Machine Reading Comprehension

Generally, the candidate options for multiple choice machine reading comprehension (MRC) are not explicitly present in the document and need to be inferred from text or even from the world’s knowledge. Previous work endeavored to improve performance with the aid of commonsense knowledge or using multi-step reasoning strategy. However, there is no model adopt multi-step reasoning with external commonsense knowledge information to solve multiple choice MRC, and two shortcomings still remain unsolved, i.e., external knowledge may involve undesirable noise and only the latest reasoning step makes contribution to the next reasoning. To address the above issues, we propose a multi-step reasoning neural network based on the strong Co-Matching model with the aid of commonsense knowledge. Firstly, we present a sentence-level knowledge interaction (SKI) module to integrate commonsense knowledge with corresponding sentence rather than the whole MRC instance. Secondly, we present a residual connection-based multi-step reasoning (RCMR) answer module, which makes the next reasoning depending on the integration of several early reasoning steps rather than only the latest reasoning step. The comparative experimental results on MCScript show that our single model achieves a promising result comparable to SOTA single model with extra samples and specifically achieves the best result for commonsense type questions.