Reviewerly/exHarmony huggingface dataset:
Application of steel buoyancy tank in assisting shipment of large-scale non-floating prefabricated caisson

The principles, methods, steps, key points and design of steel buoyancy tanks in assisting shipment of large-scale non-floating prefabricated caisson in hydraulic engineering are described in details in the paper. A set of application methods are summarized through actual application case in Hong Kong ‘Central-Wan Chai Bypass-North Point Reclamation Project’. Steel buoyancy tanks can be reused, thereby effectively improving the construction efficiency, ensuring the lifting security, ensuring the installation quality, and solving the problem that shipment of large-scale non-floating prefabricated caissons can depend on the single method of huge lifting vessels only. Reference can be provided for lifting prefabricated caissons in hydraulic engineering under complex working conditions.
Ford Vehicle Classification Based on Extreme Learning Machine Optimized by Bat Algorithm

The application of automobile identification in life is more and more extensive, so research on related technologies is receiving widespread attention. This article focuses on research on Ford vehicle identification, the theoretical method of identification is proposed and its effectiveness is verified in experiments. We first obtain the side-view image of the Ford car. Secondly, we use gray level co-occurrence to extract the feature of Ford car. Third, we use extreme learning machine as the classifier. Finally, we use bat algorithm to optimize the algorithm, and employ 10-fold cross-validation to ensure the validity of the data. The results of the research indicate that in the same kind of research, the method we employ has the highest accuracy (84.92 ± 0.64%).
Applying distance maintaining calculation method on multi-rotor UAV swarm

Over the past few years, multi-rotor drones have been widely used for the both military and civilian purposes. In this paper, we are taking steps for finding a method to calculate and keep the distance in order to make the ranges of photos of every UAVs connected according to the lens and flight height. Finally, we succeeded to develop the distance maintaining calculation method via implementing angel of view formula.
A Visual Decision Making Support System for the Diabetes Prevention

Our research is based on visualization of association rules for the diabetes prevention. In particular, we are interested in Gestational diabetes, which represents glucose intolerance that results in an increase in blood sugar levels in a pregnant woman. Treatment avoids complications for both mother and child. Data mining aims to extract as much relevant information as possible from a large amount of data. It is done automatically, or by exploring the data using interactive visualization tools. We propose a Visual Decision Making Support System to help experts in diabetes prevention. The main objective was to help experts study the impact of some criteria on the prevention of gestational diabetes in pregnant women. The article presents the application of our Visual4AR approach [1], which jointly uses 2D colored matrices and the Cellular Automaton CASI (Cellular Automaton for Symbolic Induction) for the diabetes prevention.
Finding an Area of Acceptable Oscillability in a Space of Uncertain Parameters of a Control System

The paper is dedicated to a problem of finding an area of values in a space of uncertain parameters of a control system, which provides acceptable oscillability degree for the system. Uncertain parameters may be included in each coefficient of system's characteristic polynomial except two lower ones. The problem is proposed to be solved by a method based on estimating intervals of characteristic polynomial coefficients, which provide acceptable oscillability degree. On a base of interval values of characteristic polynomial coefficients and their dependencies on uncertain parameters of a system, a system of double inequalities can be derived. Solution of the system is the area of values of uncertain parameters allowing to keep acceptable control quality, particularly- acceptable oscillability degree, despite interval parametric uncertainty.
Implementation of CMAC Neural Network Control Systems in Residue Number System

The method and algorithm of cerebellar model arithmetic computer (CMAC) training of the many variables modular function is offered. The submitted method allows using CMAC neural network to implement proportional-integral-derivative controllers, functioning in residue number system. The hardware realization of CMAC on the basis of lookup tables is considered. The simulation results demonstrate the effectiveness of the proposed CMAC neural network controller in residue number system. Application of the proposed CMAC neural network allows reducing of hardware costs while maintaining high reliability and performance of controller.
REMO: A Fast Region-and Morphology-based Scene Text Detector

Scene text detection remains a challenging problem as natural scene images are often displayed with complex issues. These include uneven lighting, blurring, varying text sizes and fonts, perspective distortion and oriented text. In recent years, there has been a growing focus on oriented text detection to address the prevalent issue of perspective distortion in natural scene text. However, most of these proposed methods are computationally expensive and slow. Therefore, they are not applicable to real-time detection, which is a crucial requirement for many practical applications-such as in visual navigation for autonomous vehicles. In this paper, we present a novel morphology-and machine learning-based method, the Region Extracting Morphological Operation (REMO) natural scene text detector. The proposed method is not only capable of detecting perspective-distorted and oriented text effectively, but is also robust and extremely fast. REMO's detection speed is comparable to other methods with state-of-the-art speeds. It is capable of processing up to 46.5 FPS while running on a mid-tier NVIDIA GTX 1060 GPU.
An Empirical Evaluation on Word Embeddings Across Reading Comprehension

Word embeddings which is real-valued word representation able to capture lexical semantics plays a crucial role in machine reading comprehension tasks because the first step is the embedding of the question and the passage. One of the frequently-used models is Word2Vec, but several fashionable competitors have been proposed in recent years, including GloVe and Fasttext. However the question which word embedding model really performs best and is most suitable across different reading comprehension tasks remains unanswered to this date. In this paper we performed the first extrinsic empirical evaluation of three word embeddings across four types of tasks: Multiple Choice, Cloze, Answer Extraction and Conversation. The experiments showed that GloVe and Fasttext have their own strengths in different type of tasks: The accuracy of Multiple Choice task improves significantly when leveraging GloVe, Fasttext is a bit more suitable for Answer Extraction task, and GloVe performs similarly with Fasttext in Cloze task and Conversation task. Finally, we find that Word2Vec has been demoded compared with GloVe and Fasttext in all tasks.
Combining Free Collaborative and Controlled Annotation in Social Media

Social media are usually made of flows of contents (e.g. texts, images, products), which are annotated by content producers and/or consumers. The resulting annotations form a sort of index that is often used for content access. However, free collaborative annotation is known to produce low-quality indexes. In this paper, we present an original approach to overcome this weakness by combining free annotation based on tags with categorization relying on a controlled vocabulary. We propose tools that support the creation and maintenance over time of controlled indexes, so that these operations are manageable for human annotators. Our work is based on a corpus of blogs, in which posts are associated with both tags and categories, but we assume that the proposed approach can be adapted to other types of social media.
Preconditioning the support vector machine algorithm to suit margin and outlier priors of Gaussian data

The Support Vector Machine (SVM) Algorithm is one of the most popular classification method in machine learning and statistics. However, in the presence of outliers, the classifier may be adversely affected. In this paper, we experiment on the hinge loss function of the unconstrained SVM Algorithm to suit prior information about nonlinearly separable sets of Gaussian data. First, we determine if an altered hinge loss function x ↦ max(0, α − x) with several positive values of α will be significantly better in classification compared when α = 1. Then, taking an inspiration from Huber’s least informative distribution model to desensitize regression from outliers, we smoothen the hinge loss function to promote insensitivity of the classification to outliers. Using statistical analysis, we determine that at some level of significance, there is a considerable improvement in classification with respect to the number of misclassified data.
Improving Histopathology Classification using Learnable Preprocessing

A deep learning classifier trained on a source dataset often performs poorly on a target dataset, even for the same classification task, due to the differences in the distributions of the two datasets. In histopathology, the problem of dataset bias is even more severe due to the differences in specific tissue preparation and imaging set ups across patient cohorts. With the objective of improving the generalization across datasets, we propose a set of learnable preprocessing operations - an approach that has not been extensively explored - for a supervised deep learning framework that can be trained separately or together with the rest of the neural network. Through preprocessing, the data from a target domain is transformed before being fed to a classification module trained on the source domain to increase the overlap of the former's distribution with that of the latter. Through an extensive set of experiments on histopathology and face datasets, we show the particular and general utility of the proposed preprocessing operations for domain adaptation and compare it to previous approaches.
Abductive reasoning: let’s Find Out some models

Abstract We present preliminary results on modelling structure of solutions to a task involving abductive reasoning. Research data were gathered using our new tool—Find Out, which has been designed in order to account empirically for abduction relatively close to everyday reasoning processes, with the necessary level of procedure standardization. The tool enables to capture abduction as a compound form of reasoning, from both product and process perspective. Find Out is set up as a game that requires playing the role of an investigator seeking an explanation of what has happened in the presented enigmatic situation. The task consists of three time-limited stages that require different reasoning activities (forming and evaluating of hypotheses, devising final explanations). In the paper, we present quantitative models of structures of solutions to the task proposed by the subjects in the form of situational graphs. We present also some ideas concerning qualitative characteristics of these solutions.
Implementation of Discretionary Security Policy in the Distributed Systems Based on the Secret Sharing Scheme

In this article the combination of secret sharing schemes and the requirement of discretionary security policy is considered. Secret sharing schemes of Shamir and Blakley are investigated. Conditions for parameters of schemes the providing forbidden information channels are received. Ways for concealment of the forbidden channels are suggested. Three modifications of the Shamir's scheme and two modifications of the Blakley's scheme are suggested. Transition from polynoms to exponential functions for formation the parts of a secret is carried out. The problem of masking the presence of the forbidden information channels is solved. Several approaches with the complete and partial concealment are suggested.
Research on Registration Algorithm Based on Image Mutual Information

Digital image registration technology is a process of image registration for one or more images obtained from different sensors, different shooting environments and different shooting angles in the same scene. The quality of registration directly affects the quality of subsequent image processing. The multi-camera image fusion can greatly improve the imaging effect of the picture. In this paper, based on two images taken at the same time in parallel coordinate system, an image registration method based on mutual information (gray distribution matrix of left and right images) has been proposed, which realized the left-right image alignment in pixel level. It effectively solved the problem of fusion shadow and faults caused by parallax between left and right images after registration. The experimental results showed that this method has a good registration effect for both the far and near scenes.
Security Analysis of JPEG Image Encryption Algorithm Based on DCT Coefficients Shuffling and Decomposing

In this paper, security of a JPEG image encryption algorithm has been analyzed. The algorithm shuffles DCT quantization coefficients in defined block and decomposes DC coefficient to change the number of nonzero DCT quantization coefficients in each 8 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">*</sup> 8 block. Chosen plain-text attack could be employed to recover the defined block shuffle. Changing the number of nonzero DCT quantization coefficients in each block still leaks plain-text image information. Numbers of nonzero coefficients counting attack is adopted to get mosaic appearance image of the original plain-text.
Generalized Approach for Analysing Quantum Key Distribution Experiments

In this initiative, a generalized approach towards step by step synthesis of Quantum Key Distribution (QKD) experiments is presented. Schematic diagram of the optical setup of any QKD protocol is easily available in the literature whereas step by step synthesis of the circuit needs further attention. For practical implementation, this understanding is necessary. In the current effort, we describe a disciplined methodology to synthesize the optical experimental setup of QKD protocols. This approach can be extended towards any optical experiment. The beam splitter and phase retarders are described in terms of annihilation and creation operators. We represent the polarization of photon in the Fock state basis. We consider two QKD protocols; Passive BB84 with coherent light (Progress in Informatics, 2011) and Reference Frame Independent 6–4 QKD (RFI-QKD) ( https://arxiv.org/abs/1905.09197 ) to test the methodology. We observe that this disciplined methodology can successfully describe the experiments. This can be exploited to build a convenient synthesis tool for modelling any optical arrangement for security analysis.
Echo hiding method for video file message security

Abstract Sending secret message need an algorithm that can protect message to receiver without knowing by other and keep the originality of message. Previous research, encoding secret message in video inserted in frame while audio secret message inserted in bit audio. it makes it easier for someone to detect the presence of a message. In this paper, try to encode the secret message at video file to encode in audio. Audio separated from video in order to encode secret message in audio. Echo Hiding method used in encode secret message to audio process. Testing use Bit Error Rate (BER) method to analysis modification in audio and Normalized Correlation (NC) analysis authentic the secret message. Based on the experiment, the results obtained were NC values (0.98-1) and BER values (0-3.12). based on the results obtained by the method we can insert messages better than the previous method with the addition of noise elimination in audio.
Location and Fusion Algorithm of High-Rise Building Rescue Drill Scene Based on Binocular Vision

In the emergency rescue exercise of high-rise buildings, mastering the accurate position of the participants is an important means for coaches to arrange tactics, evaluate the efficiency of rescue aid, evaluate the effect and ensure the safety of the participants. Video location is a more accurate positioning method, using personnel detection, personnel tracking can lock the position of personnel in the monitoring, but once occlusive, personnel can not be detected, will cause the loss of personnel identity information, another problem is that the current technology is difficult to stably identify the identity of personnel through signs. Therefore, this paper studies the fusion algorithm based on the characteristics that the most widely used WiFi fingerprint location can provide rough position information and personnel identity information. The detection with identity information is obtained by matching the personnel information provided by the WiFi fingerprint location system with the detected personnel in the video. At the same time, the location result of WiFi fingerprint can provide reference position when occlusive for a long time. Aiming at the characteristics of fixed number of participants and fixed identity information in emergency rescue exercise, this paper proposes a personnel tracking algorithm based on appearance and motion characteristics. This algorithm reduces the incidence of identity exchange problem when the personnel are very close, and records the representation information of the participants for a long time, which can make the personnel can be rerecognized after a long period of disappearance, and avoid the problem of matching error caused by multiple matching of WiFi fingerprint information and video location information.
Development of an android document management

Abstract Many previous researches shown the percentage of population using mobile technologies was increasing constantly, included in Indonesia. This growth apparently also supported by Android penetration as an Operating System and the easier of internet access. Mobile phone has become the main device in daily life, included in business activities. People increasingly need a mobile phone as gadget for storing various documents. This research is designed to address the document management application in Android. Application help to organize, retrieve and modify document contents. With this Android application, users can use it to store documents and can combine several documents as desired. This application works based on HTML format file. Therefore, the original files are converted to html format. While the conversion process cannot be done directly, but the original file is converted to pdf and then converted to html. All the conversion process is done in the server and application retrieve the result as HTML file for displaying in the mobile phone screen. During the converting process, it is found that the number of concurrent users is not relevant with the server respond time. The server respond time is determined by document size.
A dynamic weighted algorithm for multiple radar fusion in an active protection system

The threat detection system based on short-range radars is an essential part of the active protection system (APS) of armored vehicles. The multi-radar data fusion problem is one of the crucial issues in the APS. Firstly, a general algorithm for multi-radar coordinates transformation is given. Then, based on the weighted fusion model and the trajectory characteristics of targets in the APS, a real-time dynamic weighting factor derivation algorithm is proposed. The algorithm is simulated in a dual-radar threat tracking and ballistic prediction scenario. The results prove the correctness and effectiveness of the algorithm.
Application of Principal Component Analysis for Assessment the Behavior of Knowledge Management

Most of the knowledge is first published in various types of journals in the form of articles. Better manage the knowledge in the form of articles will greatly benefit the development of economics, and society. It is difficult to assess the management of knowledge. Principal component analysis has been used to the knowledge management with the economic data, population data, educational data, research data for two developing countries and two developed countries. It is found that only the first two factors at the most are needed to replace the original variables. Larger fluctuations in the proportion of the loadings for different types of journals to the total loadings are found in the developing countries compared to those in the developed countries. And the largest loading for the journal is almost the same as for the population. The expressions between the factor and the standard formal variable obtained by the principal component analysis can be used to further study the effects of knowledge management on the development of society and economics. the GDP is found have a strong positive relationship with the population, education expenditure, researchers in R&D, the Industrial design applications, the Trademark applications, the Gross enrolment ratio, the Teachers in tertiary education, the Scientific and technical journal articles, different types of journals.
Hydrothermal Alteration Associated with Vein-Type Sulphide Mineralization at Lappadata Prospect, South Sulawesi, Indonesia: A Preliminary Study

Abstract In Lappadata area, Bone Regency, South Sulawesi Province, Indonesia, indication of vein-type sulphide mineralization is occurred. This paper describes a recent study of the prospect which focused on hydrothermal alteration associated with the mineralization. In this study, alteration and mineralization samples have been collected selectively and representatively from the field and then analyzed in laboratory using petrography, ore microscopy, and X-ray diffraction methods. The study resulted that host rocks of the mineralization are porphyritic basalt and limestone members of the Early Eocene to Late Oligocene Salo Kalupang Formation. Hydrothermal alteration mineral assemblages include quartz, chlorite, muscovite, sericite, calcite, actinolite, epidote, illite, kaolinite, and pyrite, which generally zoned from distal-propylitic mainly characterized by chlorite, calcite and epidote to proximal-argillic characterized by sericite and illite. The mineralization occurred in quartz vein and disseminated with identified hypogene ores of sphalerite, chalcopyrite, galena, and pyrite as well as supergene ores of covellite, chalcocite, and malachite. Study of the hydrothermal alteration stability temperatures suggested that the mineralization was formed in temperature range of about 200 to 300°C, from a near-neutral pH hydrothermal fluid. The whole characteristics of hydrothermal alteration, ore and gangue minerals, mineralization types, as well as range of formation temperature and composition of the responsible hydrothermal fluid indicated that the mineralization in Lappadata prospect is an epithermal type.
Design of Blockchain Application Model to Automobile Industry

Blockchain is a key technology for ensuring data dependability and integrity. Many application domains, including automobile industry, require trust of data and consider adopting the technology. In this paper, we identify the applicable areas in the automobile industry domain and classify the data generated in the domain. We also summarize the announced existing blockchain systems in automobile industry. Through this, we identify the issues to be considered in the design of blockchain system in automobile industry. Then, we propose AutoBlond which is an automobile lifetime data management system based on blockchain. We discuss many detail design issues in the proposed system and compare it with existing systems.