Reviewerly/exHarmony huggingface dataset:
From Non-determinism to Goroutines: A Fair Implementation of Curry in Go

The declarative programming language Curry amalgamates demand-driven evaluation from functional programming with non-determinism from logic programming. In contrast to Prolog, the search strategy for non-deterministic computations is not fixed so that complete or parallel strategies are reasonable for Curry. In particular, a desirable option is a fair strategy which frees the programmer from considering the influence of the search strategy to the success of a computation. In this paper we describe an implementation with this property. Based on recent developments on operational models for functional logic programming, we present a new implementation which transforms Curry programs in several transformation steps into Go programs. By exploiting lightweight threads in the form of goroutines, we obtain a complete and fair implementation which automatically uses multi-processing to speed up non-deterministic computations. This has the effect that, in some cases, non-deterministic algorithms are more efficiently evaluated than deterministic ones.
A three-dimensional reconstruction method for the inner surface of a deep hole using a rotating mechanism

An inner surface 3D reconstruction device structure is proposed in this paper to reconstruct the complex curved inner surface of a deep hole. First, the optical paths of the camera and the structured light transmitter are converged on the deep hole inner surface through plane mirrors to form a multi-line structured light projection system (MSLPS). The parameters of the MSLPS are then calibrated before the measurement. Second, the MSLPS rotates during the measurement process to obtain the point cloud collections of the inner surface and the rotation angles at different measurement positions are recorded. The point clouds contain the 3D surface information of the measured areas. Finally, the entire surface shape is reconstructed by the proposed reconstruction algorithm, and the feature parameters of the deep hole inner surface can be calculated by the reconstructed point cloud. The development process of the system was demonstrated in the experiment. The proposed system is validated through the reconstruction experiment of different areas in a deep hole.
Spherical triangle mesh representation and multi-channel residual graph convolution network-based blind omnidirectional image quality assessment

Compared with traditional 2D imaging, omnidirectional imaging techniques can provide users with 360&deg;&times;180&deg; immersive visual experience, which also make the objective quality assessment of omnidirectional images more challenging. In this work, a spherical triangle mesh representation and multi-channel residual graph convolution network (denoted as Multi-RES-GCN) based blind omnidirectional image quality assessment (IQA) is proposed. The proposed method includes two important stages: omnidirectional image’s spherical triangle mesh generation and optimization, and quality predictor based on Multi-RES-GCN. In the first stage, the spherical representation of omnidirectional image is used (called as spherical image), a new scheme of spherical triangle mesh generation and optimization is proposed, which can reasonably sample pixels on the spherical image and optimize the sampled points to generate more accurate triangular meshes. In the second stage, the spherical image is divided into six view regions, and the triangle mesh nodes are classified into the view regions according to their positions, and then input to the quality predictor. The quality predictor is composed of Multi-Res-GCN and Estimator. Multi-Res-GCN can model nodes and the dependency relationship between nodes. Estimator is designed to regress the features extracted by Multi-Res-GCN to the weights and quality score of each view region, and the final quality score of omnidirectional image is predicated by calculating the weighted summation of these quality scores. Experimental results demonstrate that the proposed method outperforms other state-of-the-art IQA metrics on two omnidirectional IQA databases.
Reasoning over Bayesian Networks using Semantic Artificial Neural Networks

Representation of application domains, related concepts and their dependencies is often achieved using Bayesian Networks. In Bayesian Networks nodes represent random variables and arcs represent their dependencies. Since inference over Bayesian Networks is a complex task in this work a novel approach for representing and reasoning over Bayesian Networks using Semantically labeled Neural Networks is proposed and evaluated. Using Semantic Neural Networks combines advantages of Neural Networks such as wide adoption and highly optimized implementations while preserving the interpretability of Bayesian Networks which is an important requirement, especially in medical applications. In addition the proposed approach is evaluated over medical datasets with positive results
Comparative Study of Electricity Production by Photovoltaic Panels with Mirrors for Different Inclinations

This paper presents an analysis of a system consisting of a PV collector augmented with two reflectors to obtain more electrical energy. The model provides the possibility to predict the solar energy received by the panel in the day. To enhance the amount of solar radiation on the fixed photovoltaic Panel we daily changed the tilt angle of the two reflectors to get them in an optimal position which depends on the angle of elevation of the sun, the azimuth angle for typical days throughout the year. In this work, we calculated the electrical efficiency of the photovoltaic panel in two cases, without and with reflectors in the optimal position. The study is made on the site of Tetouan (longitude = − 5°, latitude = 35.25°) for a daily variation (from sunset to sunrise) relatively for the days 21 March 21 Mai 21 June 21 September and 21 December, (the spring and autumn equinox and summer and winter solstices), days considered clear sky type.
Noise influence on DeepDensity: convolutional neural network for local fringe density map estimation

Convolutional neural network based, fast and accurate local fringe density map estimation by DeepDensity significantly enhances full-field optical measurement techniques, e.g., holographic microscopy. The numerical capabilities of the proposed algorithmic solution in the case of the presence of noise were studied.
Evaluation of Ensemble Algorithms and Deep Learning Transformers in Medical Sentiment Prediction

Social media continuously produces digital information that can be used to improve service quality.In this aspect sentiment prediction, automated analysis of written user reviews, is an important research area from service quality point of view.Online sentiment prediction is a rich research area from e-business perspective.However, identification of sentiment from medical service user reviews is particularly researched less frequently.From Turkish language point of view, the medical informatics literature needs more research to design automated medical sentiment systems.Automated sentiment analysis systems particularly make use of Machine Learning (ML) algorithm in tandem with Natural Language Processing (NLP) methods to address written user reviews.In this work, ensemble learning approaches are compared with newly developed deep learning variations, Bidirectional Encoder Representations from Transformers (BERT), to investigate medical sentiments.As the obtained results are evaluated, it is observed that newly proposed transformer models are perfectly successful to identify sentiment of Turkish medical reviews.
SE_EDNet: A Robust Manipulated Faces Detection Algorithm

Face manipulation techniques have raised concern over potential threats, which demand effective images forensic methods. Various approaches have been proposed, but when detecting higher-quality manipulated faces, the performance of previous method is not good enough. To prevent the abuse of these techniques and improve the detection ability, this paper proposes a new algorithm named Squeeze-Excitation Euclidean Distance Network (SE_EDNet) to detect manipulated faces, which is suitable for Deepfakes and GANs detection. SE_EDNet use Euclidean distance to describe similaity of vectors, which gives higher weights to important areas than traditional self-attention mechanism. Further, we take frequency into account and extract residuals information, which are obtained by a second-order filter. Then residuals are combined with original images as the input features for the network. Comparison experiment shows SE_EDNet performs better than existing algorithms. Extensive robustness experiments on Celeb-DF and DFFD demonstrate that proposed algorithm is robust against attacking on AUC scores and Recalls.
A Mechatronic System to Distribute Tennis Balls Using Pneumatics

Electrical engineering and mechanical engineering lie at the nexus of the burgeoning field of mechatronics. Today, this field is transforming the manufacturing, healthcare and aerospace landscape by integrating the principles of mechanics, computer science and telecommunication. In this project, we harness mechatronics to develop a mechanical system for uniformly distributing tennis balls. This system is powered by a pneumatics actuator, ultrasonic sensors, microcontrollers and a motor. The design process consisted of three phases which include ideation, making the electrical simulation of the control system and the development of a CAD model to study the dynamics of the mechanical system.
Reverse Engineering Through Automata Learning

We suggest a method for constructing a system that needs to satisfy some given formal specification \(\varphi \). In our setting, a black box system satisfying \(\varphi \) is given; we can only interface with the system through experiments and cannot duplicate it or modify its design. Instead of developing a system satisfying \(\varphi \) from scratch, either manually or through algorithmic synthesis, we use a reverse engineering method based on a combination of Angluin’s automata learning algorithm and model checking.
Blockchain Based Global Financial Service Platform

Recently AI technologies, especially Deep Neural Network (DNN), have been widely used in the financial industry, such as stock price and movement prediction. In order to develop an AI-based solution for the global financial market, daily-based DNN model training for each stock is required to need collaboration among a large scale of entities. However, it is usually challenging due to data privacy, the cost of AI computing, and the lack of motivation to share information. This research proposes a novel Blockchain based platform, which utilizes the decentralized network, federated learning, and master-node to tackle these issues. The decentralized computing framework of federated learning, along with transfer learning, is applied to meet the data privacy requirements. Furthermore, the proposed federated learning platform with collaborative training is built on a decentralized AI computing cloud, which is highly affordable compared to centralized AI clouds. The master-node of Blockchain technology is further employed to enable the scalable global financial service, and effective rewards are applied to incentivize information sharing as well. We have applied the proposed Blockchain based platform to the stock prediction global service, which demonstrates the platform is practical and useful.
Leveraging Neural Networks in Malaria Control

In this paper we build a neural network model to predict prevalence of malaria for a given geographic location and year. We report on our experience of building the most suitable neural network architecture for this problem. We show that both utilizing dropout and Adam optimizer in the network training process is very effective and can lead to a precise model without overfitting issues. Incorporating rainfall data leads to a significant improvement in the precision of the model, highlighting the fact that this is an important factor in the spread of malaria. We then utilize the selected best neural network to predict the outcome of eradicating malaria at given locations. This can help to decide where to use limited resources, like vaccines or insecticides, for the largest possible impact in malaria control.
Applied Artificial Neural Networks and Genetic Algorithms in Simulation Strategy for Trajectory in Collaborative Robotic

This work proposes a solution for collaborative robotics, presenting some precautions with this new perspective of robotics and norm, trajectory planning and observed singularities. Also, it compares techniques for solving three degrees-of-freedom (DOF) robotic manipulator inverse kinematics based on genetic algorithms (GAs) and artificial neural networks (ANNs). In addition, a decision tree was included to increase the arm motion's safety when an object appears in its trajectory, simulating an environment in which collaborative robots work side by side with human beings. Fifth-order polynomials were also compared in trajectory planning, and the analysis showed that the fifth-order polynomial presented a trajectory solution. According to the results obtained from ANN and GA performance, the efficacy of the proposed methodology was demonstrated. The proposed research study demonstrates unique potential, especially in initial 3D tests, providing robust results with a time-sufficient solution. To conclude this scientific investigation, conclusions and possible future developments of this research are summarized.
Http Relay Binding (webHttpRelayBinding) and Crud Contract

The Broker Architecture became popular involving client and server. Representational State Transfer(REST) architecture is the architecture of World Wide Web. REST uses HTTP protocol based on Servlet and ASMX technology is replaced by WCF web service technology. SOAP and REST are two kinds of WCF web services. REST is lightweight compared to SOAP and hence emerged as the popular technology for building distributed applications in the cloud. In this paper, conducted by exposing a HTTP endpoint address, HTTP relay binding (webHttpRelayBinding) and CRUD contract through interface. The interface is decorated using WebGet and WebInvoke attributes. WCF configuration file created using XML tags for use with REST web service.
New Approach for Single Sign-on Improvement using Load Distribution Method

Single sign-on (SSO) is an essential tool for company and organizations to secure access the website or application by using username and password. It is used instead of local database, Lightweight Directory Access Protocol (LDAP) and Active Directory (AD). The number of new sessions on single sign-on is considered on the large organization. The large organizations have many websites and applications that need the secure access. The new sessions and concurrent sessions support are one of the key factors for determining the efficiency of single sign-on. In general, single sign-on can support only hundred scales of the new sessions and thousand scales of the concurrent sessions. In this research, we propose in-memory data store method for single sign-on session storing and load distribution method to improve single sign-on performance. The result showed that in-memory data store and load distribution methods could increase the new sessions support and concurrent sessions support up to ten thousand scales and hundred thousand scales, respectively.
Passive User Identification and Authentication with Smartphone Sensor Data

A unique digital identity, user ID, is essential for everyday online activities in the Internet era. These user IDs represent a user in a digital environment using stored credentials on a system called authentication system. It is possible to capture unique patterns of user movements from smartphone sensor data. This paper presents a framework for passive user identification and authentication using onboard sensors of an Android smartphone. Using this framework, we propose a data preprocessing scheme that uses the absolute difference of consecutive repeated measurements of 7 onboard sensors. We developed 5 models for user identification and authentication using various machine learning and deep learning methods. The experimental results and performance assessment conclude that the Sequential Neural Network (SNN) model performs best with 98.24% accuracy for authenticating users (binary classification) and 84.41% accuracy during multi-class classification for user identification. Furthermore, all the models developed for this research, namely MLP, SNN, CNN, SVM, and Bi-LSTM, provide 100% precision during binary classification for passive user authentication. Our models were trained on 556,746 sensor data samples, each having 20 features. These features include the proximity sensor data, light sensor data, triaxial measurements from accelerometers, gravity sensors, gyroscopes, magnetometers, and rotational vector sensors. We study the possible influence of absolute difference samples for user identification and authentication.
Ordinal notations and induction

Abstract In order to prove that the simplification process for arithmetic eventually reaches a simple proof, it is necessary to measure the complexity of proofs in a more sophisticated way than for the cut-elimination theorem. There, a pair of numbers suffices, and the proof proceeds by double induction on this measure. This chapter develops the system of ordinal notations up to ε0 which serve as this more sophisticated measure for proofs in arithmetic. Ordinal notations are presented as purely combinatorial system of symbols, so that from the outset there is no doubt about the constructive legitimacy of the associated principles of reasoning. The main properties of this notation system are presented, and it is shown that ordinal notations are well-ordered according to its associated less-than relation. The basics of the theory of set-theoretic ordinals is developed in the second half of the chapter, so that the reader can compare the infinitary, set-theoretic development of ordinals up to ε0 to the system of finitary ordinal notations. Finally, Paris-Kirby Hydra game and Goodstein sequences are presented as applications of induction up to ε0.
Effects of Infrastructures on Environmental Quality Contingent on Trade Openness and Governance Dynamics in Africa

The objective of this study is to evaluate: (i) the effects of infrastructures on CO2 emission and (ii) how trade openness and governance contribute to mitigating these effects. The results from the system GMM methodology for 36 African countries between the 2003-2019 period show that infrastructural development exacerbates CO2 emission in Africa. This result is robust across different types of infrastructural development indexes. When the indirect effect regressions are carried out by interacting governance and trade openness with the different infrastructural development variables, the following results are obtained. Firstly, infrastructural development interacts with governance producing a positive net effect, up to a governance threshold estimate of 0.532 when the positive net effect is nullified. Secondly, infrastructures interact with trade openness producing a negative net effect up to a trade openness threshold of 78.066914 (% of GDP) when the negative net effect is nullified. Positive and negative synergy effects are also apparent. Practical policy implications are discussed based on the results obtained.
Proper Choice of a Machine Learning Algorithm for Breast Cancer Prediction

Breast cancer is the most common form of invasive cancer and after lung cancer, it is the second leading cause of cancer death in women. Many statistical models have been used to predict the malignancy of the tumor. Therefore due to the violation of the proportional hazard assumption, a statistical model may fail to predict breast cancer accurately. In the current epoch, machine learning algorithms play a decisive role in predicting the malignancy of a tumor with high accuracy. The primary purpose of this paper is to compare the performance of eleven different machine learning classification techniques for breast cancer prediction. Wisconsin Diagnostic Breast Cancer dataset is utilized to compare these established algorithms based on the k-fold Cross-Validation accuracy score. Additionally, three different feature selection methods have been incorporated to reduce the number of features on the dataset. After the reduction of the features, the same methods are applied again to compare performance based on their accuracy score. It is found that all the algorithms perform very well with more than 93% accuracy score; among these Logistic Regression, Support Vector Classification and Multilayer Perceptron get an accuracy score of over 98%. It is also observed that even after a drastic reduction in the number of features, the result remains satisfactory, and the accuracy score is more than 90% for all the applied algorithms.
A Study on Command and Control Through Cyber Protection Function Analysis

Cyber threats can bypass existing cyber-protection systems and are rapidly developing by exploiting new technologies such as artificial intelligence. In order to respond to such cyber threats, it is important to improve the ability to detect unknown cyber threats by correlating heterogeneous cyber protection systems. In this paper, to enhance cyber-attack response capabilities, we proposed command and control that enables rapid decision-making and response before the attack objectives are achieved, using Lockheed Martin's cyber kill chain and MITRE ATT&amp;CK to analyze the purpose and intention of the attacker.
Digital Image Encryption Scheme Based on Chaos and Fractal

With the rapid development of Internet technology, images are increasingly used as a means of user information exchange. Therefore, ensuring the security of image information has become the focus of attention. Chaos and fractal has the characteristics of self-similarity, pseudo-randomness, and high complexity of image structure, and it's an important subject in ensuring information security. This paper proposes an image encryption algorithm based on chaotic and fractal. The algorithm uses the Julia set constructed by the Hilbert curve to form the encryption key, and the image is diffused horizontally and vertically. Experimental results show that this algorithm has a large key space, high sensitivity to the initial value of the key, and can resist attacks. The research on the image encryption method proposed in this paper helps to improve the effect of image encryption.
Genetic improvement of data for maths functions

Genetic Improvement (GI) can be used to give better quality software and to create new functionality.
Flood Forecasting with Bayesian Approach

A wireless sensor network is made up of low-cost, energy-autonomous devices capable of monitoring physical or environmental conditions (temperature, humidity, noise, vibration, pressure, movement, pollution, etc.), performing specific calculations, and collaborate to transmit their data over wireless links to a recipient. With climate change, the occurrence of heavy rains becomes a danger that often leads to flooding. Heavy rains are characterized by their magnitude, duration, severity, and extent, controlled by sensor networks. This work will present a distributed decision support system that can be deployed to assist decision-makers in flood mitigation operations, namely a real-time flood prediction warning system featuring IBM web services. This system is based on the Bayesian approach, allowing decision-makers to take the necessary actions before the disaster occurs. A regression model is used to infer whether an alert is true or false.