Reviewerly/exHarmony huggingface dataset:
A statistical view to study the aphorisms in Nahj al-Balaghah

Abstract Besides the Holly Quran, Nahj al-Balagha is the main source of literature in Arabian nations, especially for Shia as one of the main branches of the Muslims. Along with literary brilliance, the text of this book covers the broad topics. This research deals with the application of the statistical text and data analysis to extract knowledge from the aphorisms in Nahj al-Balaghah. First, we classify these aphorisms in seven topics. Then, the count of the aphorisms in each category is computed. Finally, the counts of the aphorisms of the categories are compared using the chi-square test and the cluster analysis.
Control of a tethered Quadrotor using a quaternion feedback

Abstract In Japan, superannuation of infrastructures poses a problem and the application of unmanned aerial vehicles (UAVs) such as a Quadrotor etc. is being actively studied as one of ways of inspection. Such a Quadrotor needs any position control independent of GPS, when being used for the inspection of tunnels and of interior of building. In previous research, Ouchi et al. proposed a method for controlling the position of a Quadrotor using a tether. In addition, they derived the dynamical model of the Quadrotor considering the influence of a tether, and conducted some experiments on the attitude control. The objective of this study is to realize angle and angular velocity control in the tethered Quadrotor by applying quaternion feedback control proposed by Fresk et al. In the quaternion feedback controller, the angular velocity obtained from the gyro sensor is directly available in the controller design, so that there is a possibility that more stable attitude control can be performed. After giving the overview of the tethered Quadrotor and describing Quaternion feedback control, this paper gives some experiments to verify that it is more stable with an actual machine, compared to the conventional method.
Expert System for Pregnant Mothers Treatment and Early Disease Detection for Infants and Toddlers Based on Android (Kasih Ibu)

Mothers and children are family members who need priority in the context of the implementation of health services. Therefore, efforts to improve maternal and child health are of particular concern. Assessment of maternal health status and performance of health services is important to monitor (Indonesian Ministry of Health, 2014). On the other hand, complications in the health of pregnant women, infants and toddlers are still common, so more effort is needed in handling them. Based on the 2012 Indonesian Demographic and Health Survey (IDHS), the maternal mortality rate in Indonesia is still high, amount 359 / 100,000 live births. Maternal, infant and child mortality rates are still high, one of which is caused by a lack of attention to health when pregnant or improper handling when symptoms of certain diseases arise. In this study, an expert system application was built to help mothers during pregnancy, suppressing MMR (Maternal Mortality Rate,) by making mothers understand their condition and understanding various complications of pregnancy. In addition, this application can help early detect symptoms of diseases for infants and toddlers so that it can help users to prepare everything related to disease management. This application is called "KASIH IBU" and is built on a mobile basis. The mobile base was chosen because currently mobile devices have been widely used by the community, especially mothers.
Deep convolutional neural network-based feature extraction for steganalysis of content-adaptive JPEG steganography

Deep learning-based steganalysis techniques have significantly progressed in recent years. Compared with typical steganalysis methods using rich model features and classifiers, deep learning-based steganalysis methods usually train a deep convolutional neural network (CNN) as the steganography detector. One advantage of a deep CNN is the strong capability for discriminative feature learning. The input feature map of the fully connected layer is the steganalysis feature learned using the deep CNN. We can, therefore, extract the steganalysis feature based on the trained deep CNN. Compared with steganalysis features constructed by hand, the extraction of steganalysis features using a deep CNN can take advantage of the strong feature-learning capability of such a network. Therefore, two types of typical steganalysis frameworks are first compared. Then, a deep CNN for steganalysis feature learning is constructed, and the setting of the image preprocessing layer is discussed. Next, the detection performances of the different learned steganalysis features are compared. Finally, the detailed extraction process of the proposed learned steganalysis feature is described. The experiment results show that the proposed steganalysis method, combining learned and handcrafted steganalysis features, can significantly improve the detection performance for content-adaptive JPEG steganography.
Fire Incident Emergency Response Plan using Hybrid Fuzzy Dijkstra

Shortest pathfinding is an algorithm that can be used for the nearest path search solution, in this study the use of the shortest pathfinding method in the best path search for firefighting teams travels to the location of the fire incident. Case studies were conducted in a city in the United States called Naperville, the data taken in the form of locations from the fire department, several important points in the city and the latest data location of the fire incident. The shortest pathfinding algorithm used is hybrid fuzzy Dijkstra, the algorithm is a development of the Dijkstra algorithm, where the algorithm can use more than one consideration parameter for determining the best path, the dataset and various parameters are grouped based on fuzzy algorithms and then processed using Dijkstra algorithm. The use of fuzzy algorithms that group data based on data ranges can speed up the search process at each level of the graph matrix because some data that is almost the same will be grouped into one data range. Based on the research conducted, it can be seen that the method was successful in determining the best path to the location of the fire incident, based on several other route choices.
Kinetics: A Mathematical Model for an On-Screen Gamepad Controllable by Finger-Tilting

With the rise of smartphones and mobile games, a new interaction model is in demand to allow rapid and fine-grained control of a player character. This paper presents an on-screen gamepad with novel touchpoint analytics that enables a user to input both direction and velocity by changing only the angle of the finger, while otherwise maintaining its position on the touchscreen. The unique feature of this gamepad, called Kinetics, is a mathematical model for measuring an intentional finger motion and acceleration from a continuous sequence of touch events, obtained when a user changes thumb angle, by suppressing the effects of ambiguous and noisy activities. Kinetics allows a user to play a mobile game almost as if there were an invisible control stick on the screen. This paper describes a practical system implementation and empirical evaluation studies.
A vision on hybrid AI for military applications

Application of different Artificial Intelligence technologies is increasing over the past couple of years. At a high conceptual level, we can divide these technologies in two different categories: symbolic and sub-symbolic. The term “Hybrid AI” denotes the combination of symbolic and sub-symbolic AI. By combining both semantic reasoning and data-driven machine learning both human specified and data derived knowledge can be combined in one system. In this paper we explore the concept of Hybrid AI by the hand of architectural patterns from literature. The added value of the architectural patterns is that they provide a way to discuss the different elements in the processing pipeline. They stimulate discussion what the input and output of the different processing blocks are, and how they work together. When applying the available design patterns to real military imaging applications, we noticed that we needed more detail in the different blocks to specify the type of data or algorithms that are applied. In future work we will investigate how components such as online learning can be presented in this design pattern framework. We identified the need to further develop this approach with a more intertwined interaction between the reasoning and the data-driven part of the pipelines, and use more world knowledge, domain knowledge and relations between objects in the reasoning part. Improvements are also needed for online learning, where the knowledge of the system performance will be used to ask the users relevant information.
Non-Gaussian Entanglement and Wigner Function

A measure of non-Gaussian entanglement in continuous variable (CV) systems based on the volume of the negative part of the Wigner function is proposed.We analyze comparatively this quantity with a numerical evaluation of the negativity of the partial transpose (NPT) considering a system of Bell states formed in the coherent state basis (quasi-Bell states).
Oracle separations between quantum and non-interactive zero-knowledge classes

We study the relationship between problems solvable by quantum algorithms in polynomial time and those for which zero-knowledge proofs exist. In prior work, Aaronson [1] showed an oracle separation between BQP and SZK, i.e. an oracle A such that SZKA⊈BQPA. In this paper we give a simple extension of Aaronson's result to non-interactive zero-knowledge proofs with perfect security. This class, NIPZK, is the most restrictive zero-knowledge class. We show that even for this class we can construct an A with NIPZKA⊈BQPA. In building up this result, we first review Aaronson's proof for SZK and show a very simple proof for a new intermediate result, a separation between BQP and NISZK.
Accelerated Method for Calculating the Algebraic Immunity of S-Boxes

The accelerated method for calculating the algebraic immunity of nonlinear knots for replacing symmetric ciphers by Jean-Charles Faugere is considered. It is based on the search for the annihilation function to the Zhegalkin polynomial constructed from the original nonlinear replacement node. An analysis of the known algorithm speed for calculating algebraic immunity is given. The details of the implementation of the algorithm are discussed and a description is given of the accelerated algorithm for computing algebraic immunity, optimized for computing time and for using RAM resources.
A multi-authority approach to various predicate encryption types

Abstract We propose a generic construction for fully secure decentralized multiauthority predicate encryption. In such multiauthority predicate encryption scheme, ciphertexts are associated with one or more predicates from various authorities and only if a user has a set of decryption keys that evaluates all predicates to true , the user is able to recover the message. In our decentralized system, anyone can create a new authority and issue decryption keys for their own predicates. We introduce the concept of a multi-authority admissible pair encoding scheme and, based on these encodings, we give a generic conversion algorithm that allows us to easily combine various predicate encryption schemes into a multi-authority predicate encryption variant. The resulting encryption schemes are proven fully secure under standard subgroup decision assumptions in the random oracle model. Finally, by instantiating several concrete multi-authority admissible pair encoding schemes and applying our conversion algorithm, we are able to create a variety of novel multi-authority predicate encryption schemes.
Novel design of an optimized synergetic control for dual stator induction motor

Purpose This paper aims to present a contribution to improve the performance of vector control scheme of double star induction motor drive (DSIM) by using an optimized synergetic control approach. The main advantage of synergetic control is that it supports all parametric and nonparametric uncertainties, which is not the case in several control strategies. Design/methodology/approach The suggested controller is developed based on the synergistic control theory and the particle swarm optimization (PSO) algorithm which allow to obtain the optimal parameter of suggested controller to improve the performance of control system. Findings To show the benefits of proposed controller, a comparative simulation results between conventional PI controller, sliding mode controller and suggested controller were carried out. Originality/value The obtained simulation results illustrate clearly that synergetic controller ensures a rapid response, asymptotic stability of the closed-loop system in the all range operating condition and system robustness in presence of parameter variation in all range of operating conditions.
HR-SQL: Extending SQL with hypothetical reasoning and improved recursion for current database systems

In this work we present a formalization, backed with a contrasted implementation, of a relational database language (called HR-SQL) that extends SQL in two aspects. On the one hand, including non-linear and mutual recursion. On the other hand, including hypothetical relations and queries. Regarding expressiveness, HR-SQL allows a novel form of hypothetical reasoning, completely integrated with recursive definitions. In addition, aggregate functions are added. Regarding formalization, the extended language is founded on a stratified fixpoint semantics based on logic programming techniques. We include results of the existence of such fixpoints. An algorithm that transforms a database containing hypothetical definitions into an equivalent one without hypothesis and its correctness proof are presented. Regarding the implementation, we introduce here a system that incorporates the aforementioned benefits and enhances former implementations in several areas. The current HR-SQL system is targeted to several state-of-the-art relational database systems, and could be used with any SQL-based system with minor modifications in the implementation.
An Efficient Storage Service Method for Multidimensional Meteorological Data in Cloud Environment

Meteorological data have many features, such as a large amount of information, multidimensionality, and high precision. Cloud computing technology has been applied to the storage and service of meteorological big data. Although the constant evolution of big data storage technology is improving meteorological data storage and access, storage and service efficiency is still far from meeting big data requirements. Traditional methods have been used for the storage and service of meteorological data, and some problems still persist, such as a lack of unified storage structure and poor scalability. In this study, an efficient storage and service method for multidimensional meteorological data is designed based on NoSQL big data storage technology and the multidimensional characteristics of meteorological data. In the data storage process, multidimensional block compression technology and data structures are applied to store and transmit meteorological data. In service, heterogeneous NoSQL common components are designed to improve the heterogeneity of the NoSQL database. The research results show that the proposed method has good storage transmission efficiency and versatility and can effectively improve the efficiency of meteorological data storage and service in meteorological applications.
Sentiment Analysis for Product Recommendation System Using Enhanced Stochastic Learning Algorithm

E-Commerce has been known as a rapidly growing commercial enterprise, and even though on line purchasing has no longer accompanied those identical boom patterns within the beyond, it's miles now being diagnosed for its capability. Sentiment evaluation is one of the current research subjects in the subject of textual content mining. Opinions and sentiments mining from natural language are very difficult task. Sentiment analysis is the best solution. This gives important information for decision making in various domains. Various sentiment detection methods are available which affect the quality of result. In this project we are finding the sentiments of people related to the services of E-shopping websites. The sentiments include reviews, ratings and emoticons. The main goal is to recommend the products to users which are posted in E-shopping website and analyzing which one is the best. For this we use hybrid learning algorithm which analyze various feedbacks related to the services. Text mining algorithm is used to find scores of each word. Then sentiments are classified as negative, positive and neutral. It has been observed that the pre-processing of the data is greatly affecting the quality of detected sentiments. Finally analysis takes place based on classification. To find out fake review in the website can be analyzed. This device will discover fake critiques made via posting fake remarks about a product via figuring out the MAC deal with in conjunction with assessment posting styles. User will login to the device using his consumer identification and password and could view various merchandise and will give assessment approximately the product. To discover the evaluation is fake or authentic, system will find out the MAC address of the consumer if the machine observes fake assessment send by way of the identical MAC Address many a times it'll inform the admin to do away with that overview from the device. This gadget uses information mining technique. This machine allows the user to find out accurate overview of the product.
A Data Fusion Algorithm and Simulation Based on TQMM

Asynchronous data fusion is more practical than synchronous data fusion, the model of track-to-track fusion in this case has been established and the concept of Track Quality with Multiple Model (TQMM) was put forward, furthermore a data fusion algorithm is proposed, in which the TQMM is used to assign weights, to improve tracking precision in asynchronous multi-sensor data fusion system. The simulation results show that the algorithm has a better tracking performance compared with original algorithms.
Design and Implementation of Medical Dispute Governance Based on Blockchain

Electronic medical record is the core component of hospital information system, and it is also an important evidence in solving social security and governance such as medical disputes, medical insurance cash and fraud. The most advanced digital certificates and other technologies have effectively solved a series of important security issues such as “identity camouflage” and “man-in-the-middle attacks” and have been granted certain legal effects. However, due to their dependence on trusted third-party organizations, there are facts. Technical loopholes and serious trust crises. Blockchain technology is considered to be the best solution for current data security due to its decentralized distributed storage, non-tamperable, consensus computing and other characteristics, and has received widespread attention. Based on the alliance blockchain architecture, the research project supported by this paper selects the health service and management organization of Nanshan District of Shenzhen as the social programming practice scenario; builds the alliance blockchain network based on Fabric super account, writes and deploys intelligent contracts, and collects the core of electronic medical records. The data item (desensitization) generates a HASH digest, and stores the HASH digest on the alliance blockchain network; the user can select the network node to download the corresponding HASH digest, and perform the two-way comparison to complete the electronic medical record security credit, thereby realizing Key governance of medical disputes. The experimental results show that the system can effectively complete the protection and acceptance of electronic medical records, and has a good social security and governance application prospects.
Pectoral Muscle and Breast Density Segmentation Using Modified Region Growing and K-Means Clustering Algorithm

Breast Cancer is the most habitually detected neoplasm amid women in India and it is one of the principal reasons for cancer decreases in females. In order to visualize the breast cancer, radiologists prefer to use mammogram. It consists of many artifacts, which negatively influences the detection of breast cancer. Presence of pectoral muscles makes abnormality detection a cumbersome task. The recognition of glandular tissue in mammograms is imperative in evaluating asymmetry between left and right breasts and in guesstimating the radiation risk connected with screening. Thus, the proposed technique focuses on breast part extraction, muscle part removal, enhancement of mammogram, and segmentation of mammogram images into regions conforming to different densities. The anticipated method has been verified on Mini-MIAS database mammogram images with ground truth offered by expert radiologists. The results show that the proposed technique is efficient in removing pectoral muscles and segmenting different mammographic densities.
Blur parameter locus curve and its applications

Conventionally, the point spread function (PSF) is understood as a characteristic function of any optical system. It captures the information about the amount of blur present along all the directions for a point in the scene. However, the dependence of blur on the PSF is in the form of convolution for any object other than a point source present in the scene and hence their relationship is less explicit. The authors propose a blur parameter locus curve (BPLC) as a system representation which has a one to one relationship with blur. BPLC simply is a chart of blur amounts in all directions of a given PSF with respect to the selected measurement function. They further characterise the PSF by decomposing the variation of BPLC across all directions based on the study performed for different possible forms of the blur kernels. Such decomposition provides powerful tools for various analysis. As PSF can be anisotropic, the computation of BPLC becomes an essential intermediate step to obtain the scale map as at the same scale, blur is different in different directions. Furthermore, they demonstrate the use of BPLC to obtain other system characteristics function such as PSF.
On the Logical Reconstruction of Conductive Arguments

In a book published in 1971, Challenge and Response: Justification in Ethics, Carl Wellman introduces the notion of “conductionConduction” to refer to a particular type of reasoning.
Short-term Photovoltaic Output Prediction Method Based on Similar Day Selection with Grey Relational Theory

With the rapid growth of Photovoltaic (PV) power capacity, how to improve the accuracy of PV output prediction becomes an intensive problem. This paper proposes a prediction model to reduce the error of prediction by combining gray correlation theory to select similar days with BP neural network. Firstly, the factors of weather between sample days selected as similar days and forecast days are calculated to find three similar days which have the highest relevance. After that, different weights are assigned to historical data of similar days according to the degree of relevance from large to small and back propagation (BP) neural network is combined to predict the output of PV. Finally, based on the actual measurement data of a photovoltaic power station in Northeast China, this paper validates the positive effects of this model on MATLAB simulation platform. Results show that selecting similar days by grey correlation and combining BP neural network can effectively improve the prediction accuracy of short-term PV output.