Reviewerly/exHarmony huggingface dataset:
Interest Points Detection Based on Sign Representations of Digital Images

In this work, we present a method for detecting interest points in digital images that is robust under a certain class of brightness transformations. Importance of such method is due to the fact that current video surveillance systems perform well under controlled environments but tend to suffer when variations in illumination are present.Novelity of the method is based on the use of so-called sign representation of images. In contrast to representation of a digital image by its brightness function, sign representation associates with an image a graph of brightness increasing relation on pixels. As a result, the sign representation determines not a single image but a class of images, whose brightness functions are differ by monotonic transforms.Other feature of the method is in interpretation of interest points. This concept in image processing theory is not rigorously defined; in general, a point of interest can be characterized by increased “complexity” of image structure in its vicinity. Since the sign representation associates with an image a directed graph, we consider interest points as “concentrators” of paths from/to vertices of the graph.The results of experiments confirm the efficiency of the method.
A proposed data acquisition system and algorithm for signal processing of moving-coil geophone’s output

Abstract The study of different types of vibrational and seismic movements is important for exploration in strata monitoring, machine health monitoring, earthquake detection, etc. In order to study these vibrational movements, it needs to be acquired first for analysis. Data acquisition using the seismic sensors is a challenging task. This paper presents a data acquisition system developed to acquire seismic signals from a moving-coil geophone. The paper also discusses a signal interpretation algorithm that is devised to perform automatic detection of a seismic event occurrence by separating through the waveform and non-waveform components in the sensor’s output using Gaussian naive Bayes classifier and Kernel density estimation technique. The proposed method is effective in the identification of a useful signal and identification of its nature of origin. Accuracy of the algorithm was 99% for the waveform classification. Sensitivity of the data acquisition system for the seismic sensors was 1.589 µm s –1 . Further, the developed data acquisition system and the algorithm can be used in mines for seismological studies aimed at separating the vibration signal generated due to explosion and the one caused due to Earth’s tectonic and seismic activities.
Presentation of a Social Responsibility Project on Social Media and MOOCs Within the Scope of Scientific Communication

The learning activities of individuals are not only limited to school, but they also continue learning in all areas of life. One of the most effective ways of achieving this in the evolving socio-cultural and technological environment is the social media environment. The authors addressed the issues separately for the deficiencies identified in the study. They offered the trainings they have done in various platforms.
Shuffled Frog Leaping Algorithm Based Neural Network and Its Application in Big Data Set

Abstract This paper studies the random optimization algorithm to reorganize the leapfrog algorithm. The big data challenge requires an effective optimization algorithm to explore potential data structures using deep neural networks. At first, we introduce the neural network classifier and compare it with the support vector machine. Neural networks are suitable for large data sets and have the complex ability to extract high-level abstract data. And then we have to introduce a large dataset covering cancer data and voice data. Both datasets have large numbers of samples with complex low-level variance. At last we have to use the reorganized leapfrog algorithm to optimize neural network parameters. The random leapfrog algorithm is efficient and robust to a local minimum. The experimental results show that the algorithm has extensive application prospects and is suitable for the classification of big dataset. The neural network parameters can effectively optimized by the improved shuffled frog leaping algorithm.
A Framework for Forecasting Outbreak of Infectious Diseases Based on Climate Variability and Social Media Content

The amount of data has risen significantly over the last few years, due to the popularity of some of the data generation sources like social media, electronic health records, sensors and online shopping sites. Analyzing, processing and storing this data is very prominent since it helps to uncover hidden patterns and unknown correlations. A big data analysis and prediction System is proposed in this context, which combines weather observations, health data and social media content in order to forecast the outbreaks of infectious diseases in a locality. Finding information about the determinants of disease outbreaks are required to reduce its effects on populations. An In-mapper combiner based MapReduce algorithm is used to calculate the mean of daily measurements of various climate parameters like temperature, atmospheric pressure, relative humidity, solar and wind. The climatic parameter that may leads to the outbreak of a disease is identified by finding the correlation between the parameters and disease incidence count. To evaluate how user’s tweeting patterns and sentiments matched with the outbreak of diseases, all tweets containing keywords related to diseases are collected using twitter streaming APIs and are analyzed and processed using Spark framework. The performance of proposed model is improved due to the presence of tweet processing. This indicates that the real-time analysis of social media data can provide more effective result rather than working on the historical data.
2VT: Visions, Technologies, and Visions of Technologies for Understanding Human Scale Spaces

Spatial experience is an important subject in various fields, and in HCI it has been mostly investigated in the urban scale. Research on human scale spaces has focused mostly on the personal meaning or aesthetic and embodied experiences in the space. Further, spatial experience is increasingly topical in envisioning how to build and interact with technologies in our everyday lived environments, particularly in so-called smart cities. This workshop brings researchers and practitioners from diverse fields to collaboratively discover new ways to understand and capture human scale spatial experience and envision its implications to future technological and creative developments in our habitats. Using a speculative design approach, we sketch concrete solutions that could help to better capture critical features of human scale spaces and allow for unique possibilities for aspects such as urban play. As a result, we hope to contribute a road map for future HCI research on human scale spatial experience and its application.
System-objective representation of conceptual knowledge with description logic

Abstract The paper describes the possibilities of applying the system-object approach “Unit-Function-Object” in terms of descriptive logic for describing conceptual knowledge. Conceptual knowledge is represented using a hierarchy of conceptual systems. The syntax and semantics of the descriptive logic ALCOIQ and SHOIQ were described. These allow us to justify the structure of the hierarchy of class systems and the mandatory implementation of the principle of monocentrism for conceptual systems. The concepts of “volume” and “content” of systems-classes were introduced. The results allow us to improve the existing ways of presenting conceptual knowledge, as well as develop models of conceptual knowledge that reflect the systemic reality that exists when solving classification problems.
Developing an augmented reality textbook for Bachelor and Master programmes “Design, technology and management of the fashion industry”

Abstract Augmented reality is a modern technology that can be used in universities, providing conditions for learning in an interactive virtual environment and achieving a high level of visualization through the means preferred by today’s digital students. AR creates conditions for easier and more accessible studying of the taught material, acquiring more knowledge in a short time, developing creative thinking of the students. AR application can increase the quality of education. In the engineering disciplines is of great importance to view the studied objects from all sides. Instead of multiple 2D images, in the textbooks could be used markers for generating AR with an interactive 3D models, providing full visualization. Students can use their mobile devices for 3D visualization of the considered objects, which makes AR easily accessible. The aim of the research is to develop topics from a textbook on “Design and modelling of women’s dresses in different silhouettes” for students majoring in “Design, technology and management of the fashion industry” using augmented reality (AR).
Identification of Flood Management due to Overflow Based on Geographics Information System (GIS) in Bandar Lampung City

Increasing the population in a city will increase the number of developments so that it will reduce water catchment areas. Of course this will have an impact on increasing the number of flood-prone areas, such as the city of Bandar Lampung, Indonesia. A flood-prone area is an area which at certain times can be affected or inundated by flood water. For this reason, it is necessary to determine in advance the criteria for flood vulnerability so that they can be used as the basis for flood management, preparedness for flood disasters, and reconstruction of the construction of embankments or other flood control structures in handling the flood disaster.
DyNetKAT: An Algebra of Dynamic Networks

Abstract We introduce a formal language for specifying dynamic updates for Software Defined Networks. Our language builds upon Network Kleene Algebra with Tests (NetKAT) and adds constructs for synchronisations and multi-packet behaviour to capture the interaction between the control- and data-plane in dynamic updates. We provide a sound and ground-complete axiomatisation of our language. We exploit the equational theory and provide an efficient method for reasoning about safety properties. We implement our equational theory in DyNetiKAT – a tool prototype, based on the Maude Rewriting Logic and the NetKAT tool, and apply it to a case study. We show that we can analyse the case study for networks with hundreds of switches using our tool prototype.
Fusion of Hand-crafted Features and Deep Semantic Features in a Unified Neural Model for Irony Detection in Microblogs

With the ever-growing popularity of microblogging platforms especially Twitter, people are increasingly interested in using these sites to convey their opinions, ideas, and beliefs. People often choose figurative language like irony or sarcasm to share their inherent emotions. Detecting and analyzing such ironic tweets might be beneficial for various natural language processing (NLP) tasks. Since tweet contents are noisy, identifying ironic tweets is a formidable task. In this paper, we present a neural model relying on high-level deep semantic features and hand-crafted features. To extract the higher-level deep features, we apply a combination of multi-kernel convolutional neural network (MKCNN) and long short-term memory (LSTM) network, where MKCNN is placed on top of the LSTM model. Besides, we exploit the lexical, sentimental, and Twitter-specific characteristics to extract a set of hand-crafted features and imputed them to a multilayer perceptron (MLP) module for better tweet representation. Feature representations from the MKCNN-LSTM module and MLP module are then combined and sent to the fully-connected irony detection module. Experiments on the SemEval-2018 irony detection dataset demonstrated the superiority of our presented model over most of the state-of-the-art methods.
Blood Leukocyte Object Detection According to Model Parameter-Transfer and Deformable Convolution

Currently, leukocyte detection has the problem of scarcity of labeled samples, so a focal dataset must be expanded by merging multiple datasets. At the same time, given the difference in the dyeing methods, dyeing time, and collection techniques, some datasets have the problem of different homology distributions. Moreover, the effect of direct training after dataset merging is not satisfactory. The morphology of the leukocyte types is also variable and stain contamination occurs, thereby leading to the misjudgment of using traditional convolutional networks. Therefore, in this paper, the model parameter-transfer method is used to alleviate the problem of less leukocyte labeled data in the training model and deformable convolution is introduced into the main network of target detection to improve the accuracy of the object detection model. First, numerous leukocyte datasets are used to train the blood leukocyte binary classification detection network, and the model parameters of the blood leukocyte binary classification detection network are transferred to the blood leukocyte multi classification detection network through the transfer of model parameters. This method can make better use of datasets of the same origin and different distributions so as to solve the problem of scarcity in blood leukocyte data sets. Finally, the multi classification detection network is trained quickly and the accurate blood leukocyte detection results are obtained through fine tuning. The experimental results show that compare our method with the traditional Faster RCNN object detection algorithm, \({mAP}_{0.5}\) is 0.056 higher, \({mAP}_{0.7}\) is 0.119 higher, with higher recall by 4%, and better accuracy by 5%. Thus, the method proposed in this paper can achieve highly accurate leukocyte detection.
Application of stochastic calculus for certain classes of quantum models

Abstract In this work, stochastic calculus is applied for describing open quantum systems. The suggested approach is based on a modified stochastic Schroedinger equation. We apply the approach to investigate a qubit relaxation in Markovian and non-Markovian regimes.
Registration of Chest Radiographs Using A Diffeomorphic Demons Based Method

Chest radiographs are widely used in clinic, typically in diagnosis and treatment of lung tumors. Accurate registration of chest radiographs is surprisingly important by providing correspondence between different images, which is however still a challenge at present. To solve this problem, we propose a diffeomorphic demons based algorithm for registration of chest radiographs in this paper. Specifically, an improved similarity metric is designed in terms of a log Euclidean formulation and solved by a diffeomorphic framework with composition of two stationary velocity fields, which averts heavy computation that often occurs in spatial transformation of the traditional log demons method. To evaluate the performance of the proposed algorithm, qualitative and quantitative validation methods are carried out and subtraction images are generated. Results show that the proposed diffeomorphic demons based algorithm obtain more accurate registration of chest radiographs compared with active demons, additive demons, and traditional log demons methods.
The Artistic Image of a City in Visual Arts

The article is dedicated to a relevant for modern science study of the artistic image of a city. The concept of the «image of a city» is an object of various humanities. The article presents and analyzes various points of view on the issue of studying the artistic image of a city in visual arts. Specificity of usage of the term «city's image» in art history was defined. An attempt was made to apply the classifications of the artistic image of a city in visual arts according to the research of T.G. Goranskaya and Yu.R. Gorelova with the example of works by Vladivostok artists. The difference in approaches to studying the image of a city was investigated. The need for complementing these classifications due to the specifics of Vladivostok as a coastal city and seaport was highlighted. Semiotic analysis of the city’s image divided into separate components seems promising and requires further research involving more extensive factual material.
Private and Fast Routing in Credit Networks

A credit network models the trust among users in a distributed peer-to-peer fashion and provides the means to make path-based payments between two users. The widespread use of credit networks raises serious privacy concerns because the network topology and available credit value of the links are public to all users. In this paper, we present PEAR, a private and fast routing scheme for credit networks. PEAR relies on a centralized payment path provider to collect information from users and serve users' transaction requests. To prevent the untrusted payment path provider from retrieving sensitive information, we utilize trusted execution environments (TEEs) like Intel SGX to perform routing operations and provide integrity and confidentiality protection during path computation. However, researches show that Intel SGX is suffering from side-channel attacks and may leak transaction information. Therefore, we design an oblivious routing algorithm with the help of Path ORAM to hide transaction patterns. We implemented PEAR and showed that it can protect users' privacy in credit networks with low routing overhead.
Bayesian Fusion of Unlabeled Vision and RF Data for Aerial Tracking of Ground Targets

This paper presents a method for target localization and tracking in clutter using Bayesian fusion of vision and Radio Frequency (RF) sensors used aboard a small Unmanned Aircraft System (sUAS). Sensor fusion is used to ensure tracking robustness and reliability in case of camera occlusion or RF signal interference. Camera data is processed using an off-the-shelf algorithm that detects possible objects of interest in a given image frame, and the true RF emitting target needs to be identified from among these if it is present. These data sources, as well as the unknown motion of the target, lead to a heavily non-linear non-Gaussian target state uncertainties, which are not amenable to typical data association methods for tracking. A probabilistic model is thus first rigorously developed to relate conditional dependencies between target movements, RF data and visual object detections. A modified particle filter is then developed to simultaneously reason over target states and RF emitter association hypothesis labels for visual object detections. Truth model simulations are presented to compare and validate the effectiveness of the RF + visual data fusion filter.
ISC : Steganographer Detection Based on Internal Spectral Clustering

Steganographer detection aims to find guilty users who hide secret information in images on social work. In existing work, the distance between users is calculated according to the distribution of all features extracted from corresponding users, then the user who deviates most from others is defined as the guilty user. The challenge is that when the embedding rate of stego images is low, the difference between users is reduced, and it is difficult to locate the guilty users. Aiming at the distribution difference between stego images and cover images within users, a steganographer detection algorithm based on internal spectral clustering (ISC) is proposed in this paper. ISC uses graph-based spectral clustering method to divide the user’s spread image into two clusters, and then feeds the result into the discriminant function to locate the guilty users. The simulation results illustrate that the proposed ISC method is superior to the state-of-the-art method both in effectiveness and efficiency.
Optimization of Polyethylene Glycol Concentration as an Agglutination Potentiator for Examination of Blood Types of Dry Blood Sample

Abstract Blood type is classifying blood based on the presence of antigens on the surface of the erythrocyte membrane. In criminal cases, dry blood spots are often used as samples in determining blood types an the examination with the elution absorption method but this method requires a long and complicated time. polyethylene glycol (PEG) has potentiator properties in the formation of agglutination. The purpose was to determine the optimum concentration of PEG of 1%, 2%, 3%, 4%, 5% as an agglutination potentiator. The experimental research method is blood sample dried on wool yarn overnight. the analysis results obtained on the elution absorption method and without PEG as a control is grade agglutination +2, whereas with the addition PEG 5%, 4%, 3%, 2%, 1% obtained grade agglutination + 4, + 4, + 3, + 3, + 3 results. The results statistic showed that there was quality of the results of agglutination grade were different at each additional concentration of PEG. At the addition of 4% PEG gave optimal results. So that the PEG modification method can be used as a agglutination potentiator in the determination of blood groups of dried blood spots.
Escaping Saddle Points of Empirical Risk Privately and Scalably via DP-Trust Region Method

It has been shown recently that many non-convex objective/loss functions in machine learning are known to be strict saddle. This means that finding a second-order stationary point (i.e., approximate local minimum) and thus escaping saddle points are sufficient for such functions to obtain a classifier with good generalization performance. Existing algorithms for escaping saddle points, however, all fail to take into consideration a critical issue in their designs, that is, the protection of sensitive information in the training set. Models learned by such algorithms can often implicitly memorize the details of sensitive information, and thus offer opportunities for malicious parties to infer it from the learned models. In this paper, we investigate the problem of privately escaping saddle points and finding a second-order stationary point of the empirical risk of non-convex loss function. Previous result on this problem is mainly of theoretical importance and has several issues (e.g., high sample complexity and non-scalable) which hinder its applicability, especially, in big data. To deal with these issues, we propose in this paper a new method called Differentially Private Trust Region, and show that it outputs a second-order stationary point with high probability and less sample complexity, compared to the existing one. Moreover, we also provide a stochastic version of our method (along with some theoretical guarantees) to make it faster and more scalable. Experiments on benchmark datasets suggest that our methods are indeed more efficient and practical than the previous one.
Numerical Simulation of Mechanical Properties of Asphalt Mixture Bridge Deck Pavement Based on Virtual Reality

Numerical simulation of the actual bridge deck overlay of asphalt mixture performance, interface unit is thin, leading to the final simulated pavement displacement value is too large, do not meet the actual mechanics for this problem is proposed based on virtual reality overlay of asphalt mixture performance numerical simulation deck. The bridge deck pavement is described by the method based on continuum mechanics, the mechanical values are generated, the ductile damage rule is used to judge the output mechanical values, the mechanical performance model is constructed, and the virtual reality technology is used to realize the numerical simulation and complete the research and analysis. The asphalt mixture with certain parameters is selected to make the bridge deck pavement components, and the experimental environment is set up. Two traditional numerical simulation methods and the researched numerical simulation methods are used to carry out the experiments. The results show that: the bridge deck paving obtained by the researched numerical simulation method displacement value closest to the actual value of displacement, numerical simulation results more accurate.