Reviewerly/exHarmony huggingface dataset:
An Optimization Algorithm Based on Fuzzy Measure, Fuzzy Integral, Rough Sets and Its Application in Equipment Argument Decision

Fuzzy measures can describe the integrate importance of one or more attributes, and fuzzy integral is an integration operator based on fuzzy measures. So, a optimization algorithm based on fuzzy measure, fuzzy integral, rough sets is studied, what's more, it's application in equipment argument decision under information uncertainty are studied. Firstly, the chart of the decision process based on fuzzy measures, fuzzy integrals and Rough Sets is designed. Secondly, determine the decision attribute, and the comprehensive operational efficiency, life cycle cost (LCC), venture evaluation and review technique (VERT), and manufacture cycle are chosen as decision attributes, also, the model of these chosen decision attributes are build. What's more, the comprehensive operational efficiency model is optimized by fuzzy analytic hierarchy process (FAHP). Thirdly, reduce the decision attribute, and the redundant decision attributes are eliminated. Fourthly, determine the importance of decision attribute, and the importance of decision attribute is modeled by fuzzy measure, so the interrelation of decision attribute is solved. Fifthly, calculate the Shapley value, before calculating fuzzy measure, the Shapley value of decision attributes needs to be determined. Sixthly, the fuzzy measure is calculated by Marichal entropy algorithm. Seventhly, each decision attribute is integrated to get the final decision by the Choquet fuzzy integral. Finally, the simulation is performed, also, this method is compared with another method, the results show that the advanced method is reasonable and effective.
Passive Oppositional Differential Evolution for Global Optimization

This paper presents an algorithm named PODE which is a significant improvement of a previous algorithm with the same name by the first author of this paper. Its key idea is to introduce a set of opposite vectors, not included in the population, but used in the mutation step to gain diversity. Several techniques are also introduced mainly to enhance global exploration capability, which include use of quasi-opposition, introduction of global iterations and local iterations, and the unique design of mutation and crossover operators which are different between global and local iterations. The obtained algorithm is free of parameters. Numerical results on an extensive set of functions show that this algorithm has excellent robustness and has better overall performance than classic DE, ODE, and old PODE.
Multi-secret Sharing Scheme Using Modular Inverse for Compartmented Access Structure

Secret sharing scheme is a cryptographic primitive or a method for increasing the security of crucial information or data. It is used to share a secret among a set of participants, such that specific sets of participants can uniquely reconstruct the secret by pooling their shares. In this paper, we have proposed a new multi-secret sharing scheme for compartment access structure. In this access structure, the set of participants is partitioned into different compartments. The secret can be obtained only if the threshold number of participants from each of the compartments reconstruct their compartment secret, and participate in recovering the actual secret. The proposed scheme uses Shamir's scheme first to retrieve partial secrets and combines them to form the requested secret. The scheme can also verify whether the retrieved secret is valid or not. Security analysis of the scheme is carried out and showed that the scheme resists both the insider as well as outsider attacks. Our proposed scheme is simple and easy to understand as we have used only the modular inverse concept.
Question-Answering System Based on the Knowledge Graph of Traditional Chinese Medicine

With the development of artificial intelligence, the emergence of the QA system meets the search needs of people in the mass information age. The traditional question-answering system mostly matches the questions with fixed templates, and the dataset of questions and answers often rely on human-designed features, which is time-consuming and with low accuracy. To address this dilemma, the current prevailing technology of Knowledge Graph provides a new way, helping to build a domain-specific intelligent question answering system. In this paper, we combine the Knowledge Graph with the question and answer system to analyze the relationship of diseases, prescriptions, and Chinese herbal medicines, and finally implement a smart question answering system for the Traditional Chinese Medicine.
Long-term geochemical assessment of groundwater in a hardrock aquifer system

Groundwater is one of the most vital natural resource supporting the survival of human civilization.Lowering of groundwater levels accompanied by deteriorating groundwater quality worldwide has created a serious concern about sustainability of water supply in the 21 st century.Rapid urbanization and unplanned management of day-to-day activities has led to the release of harmful substances to groundwater resources depleting the qualitative aspect of groundwater.In this study, the concentration of 13 groundwater-quality parameters for the confined aquifer of 14 blocks in the study area located in South India were analyzed critically using premonsoon and post-monsoon groundwater-quality data for 34 years.Both statistical and graphical methods were employed to analyze the spatial and temporal variability in the concentration of groundwater-quality parameters.Two groundwater quality diagrams (Piper diagram and Schoeller diagram) were prepared for the geochemical classification of groundwater of the aquifer.Groundwater quality was also analyzed for irrigation suitability.The results indicated statistically significant long-term variation in the concentration of pH, F -, Ca 2+ , Mg 2+ and K + .Also, a majority of the groundwater-quality parameters' concentration was found to be spatially significant.Piper diagram revealed that groundwater in the study area is mainly of Na-Cl -and Ca-Mg-SO 4 2-types with Na + , and Cl -and HCO 3 -as dominant cation and anions, respectively.It was found that the concentration of Total Dissolved Solids and Total Hardness in the confined aquifer exceed their maximum permissible limits for drinking water.The US Salinity Laboratory diagram revealed high salinity in the groundwater with low sodium hazard.In terms of magnesium hazard, groundwater of the entire area is unsuitable for irrigation.
Toward New Evaluation Metrics for Relational Learning

Statistical relational learning (SRL) is a field of machine learning that enables effective and robust reasoning about relational data structures. Several conventional data mining methods have been adapted for direct application to relational data representation. Probabilistic relational models extend Bayesian networks to a relational data mining context. To use this model, it is first necessary to build it: The structure and parameters of a probabilistic relational model must be set manually or learned from a relational observational dataset. Learning the structure remains the most complicated issue as it is a NP-hard problem. Existing approaches for probabilistic relational models structure learning are inspired from classical methods of learning the structure of Bayesian networks. The evaluation of learning approaches requires testing datasets and evaluation measurements. Processes to randomly generate the model and the data are already established in the relational context. However, metrics to evaluate a probabilistic relational model structure learning algorithm are not yet proposed. In fact, only the classical Recall and Precision have been used as evaluation metrics. In this work, we discuss why these metrics are not appropriate in this context, and we propose an adaptation of them to fit probabilistic relational models representation.
Context-Based Blockchain Platform Definition and Analysis Methodology

This paper presents a Context-Based Blockchain Platform Definition and Analysis Methodology (MetaBlockX) to analyse and identify the best blockchain platform to specific context. Nowadays the number of existing platforms is enormous, in this way, assessing and identifying which best suits to context and service needs is a great challenge. In addition, each use case has its specifics, which can vary from prophecy in a certain programming language or need to keep data private, or even response time and delay to process the data. In this way a holistic and well-documented analysis is required to identify such a blockchain development platform that is proposed at this paper.
Innovative Authentication Method for IoT Devices

This work provides an innovative method for a very important cryptographic function used in securing the communications in an IoT network: authenticating a device based on the identity obtained from a unique physical characteristic. An authentication procedure requires a secret key that is usually protected by an anti-tamper mechanism. IoT devices can use tamper detection circuits to protect secret data involved in cryptographic functions as: encryption, decryption, authentication, key exchange, etc. These functions are implemented in electronic security circuits. The tamper detection circuit is independently powered by a battery in order to permanently protect the secret data, even if the equipment is not supplied with power. This paper presents a solution to obtain a unique key from the probing process of the conductive mesh that protects the electronic security circuit.
A Study on Machine Learning-Based Image Identification Towards Assitive Automation of Commentary on Animation Characters

This study aims to assist visually impaired people as well as animation novices by focusing on problems that arise at the time of viewing animation videos and images. We focus on the following problems: (1) difficulty of understanding behaviors and situations, (2) difficulty of discriminating animation characters, and (3) confusion caused by animation characters with similarities. We use deep neural networks to identify animation characters as preliminary verification by training a customized convolutional neural network (CNN) from scratch on a small class of data based on the original database of animation characters. The results show that some combinations of characters are difficult to discriminate in cross validation. To resolve this problem, we performed transfer learning based on the CNN variants pre-trained on the natural image database ImageNet. We confirmed that the learning proceeded steadily with a gradual learning curve, resulting in high accuracy. The results indicate that the bottleneck features of the CNN variants pre-trained on ImageNet are effective in identifying animation characters. Furthermore, we verified the operation speed of the inference of our trained CNN on a microcomputer board with a machine learning accelerator Intel Movidius and confirmed that the speed is sufficient in real-time execution.
Green Computing with Geo-Distributed Heterogeneous Data Centers

Cloud computing in data centers, as an alternative to computing on local machines, has become increasingly popular over the past decade. The need to reduce latency and improve bandwidth for customers has led cloud service providers to scale their data centers across the globe. Such geo-distributed data centers can be physically closer to various groups of target customers, enabling improved performance for their applications. But geo-distributed data centers can be an expensive proposition and require significant investment and justification in terms of return on investment (ROI). In this paper, we present a framework to leverage heterogeneous geo-distributed data centers to reduce electricity costs for cloud computing service providers. Our framework performs intelligent workload management across geo-distributed data centers to minimize the overall energy costs, while considering heterogeneity in data center compute capability, cooling power, workload co-location interference, time-of-use (TOU) electricity pricing, green renewable energy, net metering, and peak demand pricing distribution. Our experimental results indicate that our best technique can achieve an average of 61% cost reduction compared to the state-of-the-art.
An Interference Suppression Based Bearing-only Multiple Target Tracking for Passive Sonar

In this paper, an interference suppression based bearing-only multiple target tracking (IS-BOT) method for passive sonar is proposed. In the IS-BOT method, the multiple target tracking (MTT) issue is considered as a series of parallel single target tracking issues in the presence of interferers. For every target of interest (TOI), there is an interference suppression (IS) module, which is outside the tracking module, being employed to eliminate the interference radiated from the other targets. The tracking module is responsible for smoothing the bearing traces and its predictive function is employed in traces crossed condition. Furthermore, a closed loop feedback structure is developed, which acts as an information bridge, to render the information shareable between IS and tracking modules in real time. Finally, the sea trial results show that the IS-BOT method can achieve precise real-time MTT.
KKG-512: A New Approach for Kryptos Key Generation of Size 512 Bits Using Plaintext

As we know that cryptography is a technique to transmit the data or message in a secure manner so that only authorized user is allowed to read the message. To do this, there are two ways, first one is symmetric encryption and second is asymmetric encryption or public key encryption. In cryptography, encryption and decryption always need some secret information, referred to key. Some algorithm uses the same key for encryption and decryption known as secret key or symmetric encryption while some uses a pair of key, one for encryption and other for decryption. So the key must be kept secret so that unauthorized user cannot decrypt the message. There are three important terms that are related to cryptography, the first one is key generation process, second is encryption process, and third is decryption process. Many algorithms have been proposed for the above three parameters. Some algorithms have good result in encryption and decryption process but very few algorithms are available for the secrecy of the key and we know that the strength of any cryptographic technique is fully dependent on the key. So the objective of this paper is to develop a new approach known as Kryptos key generation to provide security to the key at generation time. The term Kryptos is a Greek word whose meaning is hidden that means this key generation process is only known to sender. Modern cryptography concerns itself with the four objectives: confidentiality, integrity, non-repudiation, and authentication. The proposed algorithm fulfills above four objectives in an efficient manner. The proposed technique when well implemented and used will reduce the attacks on key generation. It will also ensure that key generation will be securely integrated as well as authenticated. This work will contain proposed technique as well as the implementation of test data.
Enhanced Signal-Dependent Rank-Ordered Mean-Based Audio Noise Removal

This study focuses on restoring audio signals corrupted by impulse noise added to them in a random manner, either when generated or when recorded, transferred or processed. More specifically, we investigate the application of a variant of the well-known Signal-Dependent Rank-Ordered Mean (SDROM) method using simulated addition of impulse noise to clean signals. Our study describes an automatic exploration of the threshold space used in this method and selection of optimal thresholds based on global minima for the mean energy of the error in noise identification. Experimental results showed that the method performs well for noise levels up to 10% of the signal.
An Image Mapping Approach for Quick Dissimilarity Detection of Binary Images

In this paper we present an approach for the quick detection of dissimilar and similar binary images. The approach conforms to the Probabilistic Matching Model for Binary Images (PMMBI) that ascertained that detecting dissimilarity between binary images can be done quickly by randomly examining a few points between two images. The approach is based on exploiting the 15 binary image mapping variations that are possible when mapping pixels between binary images and inspecting their tuple size. We call this approach the Dissimilar Detection via Mapping (DDM). We present tests with real images.
Synthesization of High-Utility Patterns in Parallel Computing

High utility pattern mining (HUPM) has become a key issue in knowledge discovery since it provides retailers and managers with useful information for making decisions efficiently. However, previous studies most focused on mining the high-utility patterns (HUPs) from a single database. In this paper, we present a framework to incorporate the weighted model for parallel synthesis of the discovered HUPs from various databases. The pre-large concept was also used as a buffer here in order to provide more prospective HUPs, thus providing higher accuracy of the synthesized patterns. From our experiments, the developed model exceeds existing works, in particular the designed model has increased precision and recall on knowledge synthesization compared to the previous works.
Single-Image Super-Resolution via Multiple Matrix-Valued Kernel Regression

Single-image super-resolution focuses on learning a mapping to recover high-resolution images from given low-resolution images with the help of a set of paired images. Matrix-valued operators serve as an efficient mapping to super-resolve low-resolution images. However, most existed matrix-valued based super-resolution algorithms limit matrix-valued operators as linear mappings. Multiple matrix-valued operators based algorithm is introduced for improving the performance of matrix-value operators in single-image super-resolution. Taking advantages of the non-linear style of multiple matrix-valued operators, we have more accurate super-resolved images. The experimental results show the efficiency and effectiveness of the reported multiple matrix-valued operator learning based super-resolution algorithm.
Design of Prototype Model of Robot for Cleaning

Open space cleaning is one of the important routine works in any place. In this work, a prototype model of robot for cleaning is developed for open spaces. The frame, body, and parts of prototype model of robot are created and assembled using CAD software. The specifications of the parts required to construct the real-life model are mentioned in various sections and subsections of this work. It is designed fully automated and able to sense the waste and remove it with the help of its cleaning mechanism. The basic advantage of this prototype model of robot is that it will be cost-effective with no human enrollment.
Semantic Similarities in Natural Language Requirements

Semantic similarity information supports requirements tracing and helps to reveal important requirements quality defects such as redundancies and inconsistencies. Previous work has applied semantic similarity algorithms to requirements, however, we do not know enough about the performance of machine learning and deep learning models in that context. Therefore, in this work we create the largest dataset for analyzing the similarity of requirements so far through the use of Amazon Mechanical Turk, a crowd-sourcing marketplace for micro-tasks. Based on this dataset, we investigate and compare different types of algorithms for estimating semantic similarities of requirements, covering both relatively simple bag-of-words and machine learning models. In our experiments, a model which relies on averaging trained word and character embeddings as well as an approach based on character sequence occurrences and overlaps achieve the best performances on our requirements dataset.
Fast and Secured Visual Content Hiding in Lossy Compressed Images and Video Streams

This paper describes a reversible visual data hiding scheme. From general Red-Green-Blue image or video footage, a meaningless appearing version of it is created with an optional encrypted set of keys. The encrypted image or video frame appears as random cues, and it is not visually recognisable by general viewers. They can be saved as lossy compressed JPEG image files or lossy compressed MP4 video files. These can be transferred over the public domains such as DropBox, Pixabay, YouTube, Dailymotion, Vimeo, or other public sharing servers. These providers allow anyone broadcast (upstream) images or video feeds, and the public domains can view (downstream) these easily. The main concern is the privacy of the uploaded contents. YouTube, for instance, is relatively simple to set up a free account, and the cost is almost zero (no dedicated server, no maintenance and no storage needed). However, before allowing to download, the images/videos usually are lossy compressed. The quality is reduced for smaller storage and faster transferring rate; although, they may be `visually lossless`. At the receiving end, the image or video frame can be decrypted, and we can achieve its perceivable version (close to the original up-streaming data). This technique allows general users to take advantages of free public image/video hosting services. This novel visual content hiding method gives them a worry-free tool to upload sensitive or insensitive contents without bothering about the leaks of the data (even the hosts can not visualise them correctly without a key). In this paper, the detail of the proposed method is discussed. Results from lossless and various lossy images/videos sequences are examined.
DATA PAPERS AS A FORM OF SECONDARY SCIENTIFIC INFORMATION SOURCES

Data papers are a form of metadata publications in peer-reviewed scientific journals and are considered as secondary sources of scientific and technical information (STI). The specificity of data finding and collecting in the field of grey literature (GL) and, in particular, GL devoted to STI is characterized. The sources of data concerning the input flow of documents (scientific and technical reports and dissertations) to the Federal GL Information System are described. The data is open for a potential reuse once properly obtained from the System.
ISQNL

Databases serve as the forefront for most systems today. Structured query language (SQL) is used to access and manipulate the data stored in a relational database. However, most end users have limited knowledge of SQL and thus face difficulties in accessing such systems. In this paper we describe a novel system (ISQNL) to convert a query provided in Natural Language (English) to an SQL query. By applying several natural language processing techniques ISQNL achieves this conversion without the need for any elaborate schema specific training/modification during setup and is robust enough to handle dynamically changing database states or database schema. ISQNL has demonstrated remarkable accuracy in SQL query synthesis when tested on large sets of natural language input. This paper discusses the methodology and key challenges involved in building ISQNL.