Reviewerly/exHarmony huggingface dataset:
Multispectral Bayer Color Image Encryption

A first approach for Bayer color image encryption and decryption is proposed. The original image is downsampled into three basic color components using multispectral property, then each color component is encrypted by elliptic curve cryptography followed by generalized Arnold transformation. In the first stage of encryption, on each color component separate keys of ECC are employed, and the next stage of encryption considers keys from independent parameters of the coefficient matrix of Arnold transform. The two steps of encoding apply disjoint keys for each color component in both stages, which gives a higher level of security and robustness. Simulation analysis and experimental results are performed on several test images to show the strength of the proposed technique, and a comparison is established with other proposed models.
A Novel DP-TBD Algorithm for Tracking Slowly Maneuvering Targets Using ViSAR Image Sequences

We propose a novel algorithm, namely particle filter based dynamic programming track before detect (PF-DP-TBD), to track multiple slowly maneuvering targets using video Synthetic Aperture Radar (ViSAR) image sequences. In the proposed PF-DP-TBD algorithm, an independent particle filter (PF) is adopted around each candidate target. Each PF consists of a number of particles, and evolves according to the dynamic model of targets. A number of predicted states in each PF are selected as the predicted states at each time based on the posterior probabilities. The searching region during the dynamic programming (DP) operation is expanded adaptively. Experimental results show that the proposed algorithm can effectively improve the detection and tracking performance of dynamic programming track before detect (DP-TBD).
RECONSTRUCTION OF PENAL SANCTIONS OF ONLINE PROSTITUTION PERPETRATOR BASED ON JUSTICE VALUE

14Feb 2020 RECONSTRUCTION OF PENAL SANCTIONS OF ONLINE PROSTITUTION PERPETRATOR BASED ON JUSTICE VALUE Ratih Mega Puspa Sari , Gunarto and Eko Soponyono Doctorate Student of Faculty of Law Sultan Agung Islamic University Semarang, Indonesia. Faculty of Law Sultan Agung Islamic University Semarang, Indonesia.
An Automatic Crisis Information Recognition Model Based on BP Neural Network

With the popularity of social networks, crisis information has brought huge harm to social stability and people's lives. The identification of crisis information in social networks plays a crucial role in avoiding the occurrence of crisis events and reducing the harm caused by crisis information. Thus, this paper proposes a crisis information recognition model based on Back Propagation ( BP ) neural network to identify crisis information in social networks. First, we analyze the crisis information and find that the content characteristics, user characteristics and propagation characteristics have a great impact on the identification of crisis information. Thus, we extract the key characteristics of crisis information. Secondly, we label the crisis information. Combined with the extracted crisis information characteristics, we construct a feature tag library for crisis information identification. Then, we use BP neural network to train the feature tag library to obtain the evaluation mechanism of crisis information. When analyzing the suspected crisis information, we use the evaluation mechanism to identify the crisis information. Finally, we evaluate the performance of our recognition model. The experimental results show that the proposed model can effectively identify 97.5% of crisis information.
Investigation on Education in Ideaistic and Governmental Institutions of Higher Learning Based on Informatization Under the New Medium Surroundings

The continuous evolution of information technology has promoted the application and popularization of network medium. At present, China is under the influence of new medium surroundings, which has brought about great social changes. The new medium surroundings puts forward new requirements for education in ideaistic and governmental institutions of higher learning, which promotes the great evolution of education in ideaistic and governmental institutions of higher learning to some extent. Traditional education in ideaistic and political and teaching methods have been unable to adapt to the specific requirements of the new medium surroundings, can not meet the needs of the contemporary society. Therefore, ideaistic and political institutions of higher learning must change teaching methods, update teaching ideas, and strive to build a scientific education and teaching mechanism. The purpose of this paper is to promote the evolution of education in ideaistic and governmental institutions of higher learning and improve the timeliness of education in ideaistic and governmental institutions of higher learning by studying education in ideaistic and political in new medium surroundings on the basis of information technology. On the basis of information technology, this paper firstly summarizes the new medium surroundings and education in ideaistic and governmental institutions of higher learning, and then analyzes the problems existing in the education in ideaistic and governmental institutions of higher learning by means of data query algorithm, and puts forward specific strategies conducive to the evolution of education in ideaistic and governmental institutions of higher learning on this basis. The experiments in this paper show that there are many problems in the education in ideaistic and political in Chinese universities under the new medium surroundings.
Learning Contextual Representation with Convolution Bank and Multi-head Self-attention for Speech Emphasis Detection

In speech interaction scenarios, speech emphasis plays an important role in conveying the underlying intention of the speaker. For better understanding of user intention and further enhancing user experience, techniques are employed to automatically detect emphasis from the user's input speech in human-computer interaction systems. However, even for state-of-the-art approaches, challenges still exist: 1) the various vocal characteristics and expressions of spoken language; 2) the long-range temporal dependencies in the speech utterance. Inspired by human perception mechanism, in this paper, we propose a novel attention-based emphasis detection architecture to address the above challenges. In the proposed approach, convolution bank is utilized to extract informative patterns of different dependency scope and learn various expressions of emphasis, and multi-head self-attention mechanism is utilized to detect local prominence in speech with the consideration of global contextual dependencies. Experimental results have shown the superior performance of the proposed approach, with 2.62% to 3.54% improvement on F1-measure compared with state-of-the-art approaches.
Type II approximate Bayes perspective to multiple hypothesis tracking

Multiple hypothesis tracking (MHT) is a computational procedure for recursively estimating multi-object configurations and states from measurements with association uncertainties, noise, false alarms and less than one probability of detection. From a probabilistic modelling perspective, the complete multi-object tracking (MT) model is intractable to perform statistical inference as the multi-object and measurement association configurations constitute infinite sets. In this article, we provide explicit formulae to demonstrate that MHT is a type II maximum a posteriori (MAP) approximate Bayes inference procedure over the complete MT model. In particular, we introduce a MT model that captures all typical uncertainties and show that the joint density of the global model hypotheses and the other variables involved is well defined. This model allows us to define the MT problem mathematically and contrast MHT and sequential Bayesian filtering. We argue that the computational procedures constituting an MHT algorithm such as model hypothesis pruning can be treated as a second stage of approximation for finding near-optimal solutions to the MAP problem given a computational budget.
Auto Fill Security Solution Using Biometric Authentication for Fake Profile Detection in OSNs

This chapter discusses a model that allows the user to access social networking sites through login using smart phone-based biometric authentication. Currently, social networking websites permit the user to access their page through login and some sites provide auto fill system to login into users account through browser by permit. The browser saves the password in password protected space and automatically auto fills the password to access the account by user. This facility is not highly reliable due to the auto fill system for laptop users. When someone uses the laptop of others and visits any website, the auto fill system opens the content with saved password. Secondly, elderly people have problems logging into today's websites. To remember the password for every account is very difficult for elderly people. This chapter describes a model for security and authenticity. Authors used a hybrid model with android as the application with fingerprint authentication and chrome extension as the auto fill process for user access.
Experimental Investigation for Finding Defects on Epoxy-Coated Cantilever Beam Using Optical Method

This research paper is concentrated mainly on finding the defects in epoxy-coated cantilever beam surface using an optical method. The work primarily focuses on the application of the non-destructive method on a material which is non-reflective in nature and stainless steel material is chosen for the study. Identification of defects either a void or an inclusion in the epoxy resin coating applied on the cantilever beam can be identified using the grating lines on the specimen when it is kept under the illumination of He–Ne laser light and the application of lateral load on it.
Analysis of operation and maintenance cost flood handling system of Badung River

Abstract All of Badung River with a wide width of 10-40 meters is a typical urban river that has very important meaning to the flood handling system in the Denpasar City area. The results showed that the buildings on the Badung River included 2 trash racks, 1 rubber dam, 2 movable weirs and a 42 km long river wall with an average height of 3-5 m. The flood handling system has been carried out with control of the existing discharge through motion weirs and rubber weirs. While reducing waste that can cause flooding is done by building garbage filters. From the results of the analysis the operating and maintenance costs indicate the cost of routine inspection of 57,772,800 IDR/year, routine search fees of 27,936,000 IDR/year, additional eviction fees of 100,550,400 IDR/year, monitoring and evaluation costs 27,360,000 IDR/year, flood material costs 7,240,000 IDR/year, routine operations and maintenance of 3,306,067,750 IDR/years, the need for periodic fees every 5 years is 2,200,000,000 IDR and the cost of dredging sediments and river stone repairs amounting to 1,113,566,000 IDR every five years.
A Metrics Tracking Program for Promoting High-Quality Software Development

There has been substantial focus on software metrics over the last few decades. However, many activities within software engineering are often qualitative and are not consonant with automated approaches. Consequently, there are few tools to measure software development quality or to assess teamwork contribution. This paper uses ideas from the Goal-Questions-Metrics (GQM) paradigm to propose a set of metrics to track product and process quality throughout the software development process. The proposed metrics program consists of a set of quality metrics and associated standards that will encourage software development teams to produce high-quality products. We also propose a framework for a tool that implements the metrics tracking program and demonstrate its utility by developing an Eclipse plugin based on the proposed quality metrics.
Drifting in Four Epistemic Traditions

The previous chapter mapped some of the most important characteristics in which constructive design research differs from professional practices and conditions for design. Our focus was on the implications of a shift from design practice to a knowledge-based discipline. We paid attention specifically to how this shift has been interpreted in constructive design research, and how this shift changes the outcomes of design. We saw a wide variation on perspectives from those focusing on how artifacts carry knowledge to those authors who want to turn design into a science. Although constructive design research focuses on artifacts and cannot exist without them, it differs radically from design practice in one respect, which is the context in which claims are justified. In design practice, the context of justification are the design world and the market. In constructive design research, the context of justification is knowledge and design research community.
Use of a Neurofilter to Extract a Periodic Signal from Noise

An increase in the requirements for airborne radars necessitates continuous improvement of methods for processing radar information. The relevance of the development of new approaches and breakthrough technologies is due to a contradiction between the promising characteristics of the radar and the extreme capabilities of the well-known technical solutions for processing radar information. The neuroprocessor model considered in the article is built on analog circuits. The development goal is to achieve high performance hardware recognition of signals by trained neuro-like devices. The developed analog neuro-filter is designed to solve the problems of recognizing periodic signals under conditions of high-intensity interference in the megahertz range. The experiments showed the possibility of separating signals from interference with a speed of several tens of microseconds, which was achieved through parallel processing of information and the use of analog logic in control circuits. An analog neuro filter can be a competitive alternative to digital signal recognition systems in compact equipment.
Privacy Protection of Class Association Rules produced by medical datasets

The enormous development in Information and Communications technology had increased the requirement for digital data to be used for medical purpose and to be saved and shared securely. This expansion of technology is proved to be very much useful for analysis and extracting various patterns from medical datasets. Classification technique followed by association rule mining is used for pattern extraction. This technique results in the if-then form of rules, which is simple to understand by end users. It is necessary to maintain privacy of individuals in medical dataset, as privacy is a major concern. This paper puts an effort on generation of class association rules and applying anonymization technique on it, to prevent its disclosure from the non-legitimate users.
Architecture for Interactive Internet of Things to Estimate Optimal Mean Temperature with Multiphase in Crop Insurance Payout System

The Proposed Architecture investigates the Interactive Internet of Things (IOT) Technique in Estimating the optimal mean temperature for five agricultural locations in Tamilnadu. The experimented data arises from the climatic data, sensor data, historic data and satellite data that are collected through IoT data Sources and monitored by runtime monitoring services. Those data are analyzed and processed by an Automated Mean value of Multiphase of Temperature. It is done with the Time series Analysis to find the optimal Mean temperature for multiple phases (fifteen to thirty days seasonal period of Daily Temperature). This research paper proposes the model MWTCBKNN (Multiphase Weighted Temperature Class based K-nearest neighbor) for effective classification of temperature zones based on optimal mean temperature and the mean rise in temperature with respect to threshold factors. The PAYRULE (Payout Rule) model is also designed for the prediction of crop insurance payout value based on MWTCBKNN classified data for five districts of Tamilnadu. For the proposed model MWTCBKNN-PAYRULE, the results are adequate as it is assumed that optimal mean temperature captured for specific region has done by IOT Data Sources with Time series analysis.
The Good, the Bad and the (Not So) Ugly of Out-of-Band Authentication with eID Cards and Push Notifications

Everyday life is permeated by new technologies allowing people to perform almost any kind of operation from their smart devices. Although this is amazing from a convenience perspective, it may result in several security issues concerning the need for authenticating users in a proper and secure way. Electronic identity cards (also called eID cards) play a very important role in this regard, due to the high level of assurance they provide in identification and authentication processes. However, authentication solutions relying on them are still uncommon and suffer from many usability limitations. In this paper, we thus present the design and implementation of a novel passwordless, multi-factor authentication protocol based on eID cards. To reduce known usability issues while keeping a high level of security, our protocol leverages push notifications and mobile devices equipped with NFC, which can be used to interact with eID cards. In addition, we evaluate the security of the protocol through a formal security analysis and a risk analysis, whose results emphasize the acceptable level of security.
A Detail-maintained Tone Mapping Algorithm Based on Weighted Least Squares Filter

High dynamic range (HDR) images require tone mapping to be displayed properly on lower dynamic range devices. This paper presents a detail-maintained tone mapping algorithm based on weighted least squares (WLS) filter, which is an improvement of the ATT algorithm. Our new algorithm can overcome the shortcomings of the ATT algorithm, which may lose the details of the HDR picture and cause color distortion for some colorful pictures simultaneously. We firstly decompose the input HDR image into a base layer and a detail layer by the WLS filter. Then for the base layer, its dynamic range is compressed based on histogram using a sensitivity model of the human visual system (HVS). And for the detail layer, S curve is applied to depress the large deviations away from zero and enhance the low ones. The color balance correction method is adopted to suppress color distortion. Experiments and comparisons with state-of-art tone mapping algorithms demonstrate that our proposed algorithm not only effectively compresses the input image dynamic range, but also reserves the image details well. Simultaneously, the tone mapping images of our algorithm also keep the color, contrast and overall brightness perfectly.
Correction to: On the Links of Vertices in Simplicial d-Complexes Embeddable in the Euclidean 2d-Space

In the following we refer to the original paper [1]. The main reason for writing this correction is the incorrect statement.
Prospects and Limitations of the Use of Blockchain-Options for the Supply of Electricity

The article raises the problem of integrating derivative financial instruments and decentralized networks. Within the framework of the solution of the indicated problem, the authors analyzed blockchain-option schemes. The purpose of the study is also to develop recommendations on the formation of models of blockchain-options in the electricity market. The contribution of the chapter is a proposition of a methodology, which has been developed for assessing the risks of using a platform for holding blockchain-options and then identifying the most likely risk scenarios. It was found that the expediency of options in decentralized models is mainly determined by the possibility of coordinating the interaction of market participants, rather than purely financial provisions. Moreover, the authors proved the possibility of integration in the following models: call option model, where the underlying asset is the energy platform token and put option model, where the underlying asset is the energy platform token. The most promising model was put option, which consists of the following: when the option is sold by the consumer, the supplier gets the right to supply energy in the future at a price higher than the market price, the cost of paying the premium is compensated by the release of additional tokens. This model creates the most favorable conditions for suppliers. Thus, based on the results of the study, it was possible to identify the characteristics of the most promising model of blockchain-options for the electricity market.
Semiparametric regression using empirical likelihood with shape information

The empirical likelihood is a popular tool in statistics and many other fields, including regression analysis. It has the advantage of robustness against model specification and can incorporate side information to improve the estimation accuracy. There is vast literature on empirical likelihood incorporating various side information, mostly in the form of moment constraint(s). Here we study this method under two types of side information: symmetry and unimode of the underlying distribution. To our knowledge, incorporating such shape information formally via empirical likelihood has not been seen and is the goal of our study. Basic properties of the method are investigated, and extensive simulation studies are conducted to evaluate its performance and compared with the cases without such information. We found that the symmetry information can improve the variance of the estimator, while the unimode information has no such effect.
Cloud-type Classification of Ground-Based Images using Deep Learning

Precise cloud detection and classification is a crucial element for various meteorological applications, such as solar irradiance forecast or cloud cover estimation. In this paper, we present an automatic method for cloud-type classification from ground-based images using improved pretrained deep neural network. In the past, there were used algorithms using mainly low-level features. Our approach is to use artificial neural network (ANN), namely convolutional neural network (CNN) because these type of networks use both types of image features: the first couple convolutional layers learn filters for finding lines, dots, curves, etc. while the later layers learn to recognize common objects and shapes. Therefore, with this approach we are able to reach high precision of cloud-type classification with overall accuracy of 85%.
Size Prediction for Online Clothing Shopping with Heterogeneous Information

Online shopping provides great convenience to customers, however, it also brings problems such as size fitting due to no immediately try-out. Without trying out, deciding the size of the product is one of the issues that affect customers' shopping experience, which also leads to high product return rates to businesses. Products' size fitness prediction and size recommendation are thus essential for online shopping. In this paper, we studied the size prediction problem and proposed a method to improve the predication accuracy. We capture the subtle semantics of customer reviews by using Bert model to learn the latent representations. Deep features are learned from the review data, combining with customers' body features and the product's information to predict the product's fitness for a customer. The experiments on the real online clothing retailer dataset validate the effectiveness of the proposed method.