Reviewerly/exHarmony huggingface dataset:
A Hybrid Machine Learning Framework for Prediction of Software Effort at the Initial Phase of Software Development

In the era of software application, the prediction of the effort of software plays an essential role in the success of project software. The inconsistent, inaccurate, and unreliable prediction of software leads to failure. As the requirement and specification changes as per the software needs, accurate prediction of effort is a difficult task for developing software. This prediction of effort must be calculated accurately to avoid unpredicted results. At the early stages of development, these inaccurate, unreliability, and uncertainty are the drawback of previously developed models. The main aim of the study is to overcome the drawbacks and develop a model for the prediction of the effort of software. A combination of regression analysis and genetic algorithm has been used to develop the model. The model is trained and validated using the ISBSG dataset. The proposed model is compared for performance with a few baseline models. The results show that the proposed model outperforms most of the baseline models against different performance metrics.
Discussion on the Optimization of Web of Things Supply Chain of Agricultural Products and Information Sharing Based on RFID

Abstract The emergence of Web of things technology has completely refreshed people’s understanding of the relationship between objects and wireless networks. After people realize the use value of the Web of things, people’s skilled use of the Web of things helps people to come up with a combination of the Web of things and the SC. This paper mainly introduces the basic framework of the Web of things and puts forward the optimization scheme and information sharing model of the SC of the Web of things for agricultural products based on RFID.
Optimized Implementation of K_means Clustering Algorithm on Reconfigurable System

Clustering analysis has a very broad application prospects for all walks of life in the era of big data and cloud computing, and there are increasingly high requirements for the optimization and implementation of clustering algorithm. In this paper, we implement a K-means optimization algorithm of clustering algorithm under the background of reconfigurable system application, and provide an effective hardware acceleration solution for machine learning algorithm of large data.
The enforcement of laws regulating digital cultural content: a proposal

Law is becoming increasingly digital in nature, and the State is increasingly using digital technology for the purposes of physical legal enforcement. This paper argues that this poses unique issues for legal regulation of cultural content, changing the relationship between the individual and the State. The paper focuses on laws in the UK and China to demonstrate how these issues have arisen, and what the potential consequences of this change could be. The paper culminates in arguing that legislatures and courts should be required to explicitly consider how digital technology influences, in a given law or case, the enforcement of law.
Multi Objective Association Rule Mining with Soft Computing Approach

Due to widespread use of Internet nowadays, there is a tremendous increase in amount of data especially from the social media platforms. Further, the extraction of useful patterns is required from these huge data stored in datasets. Thus the soft computing is gaining importance due to the incorporation of the Artificial Intelligence and the Machine Learning. In this paper Multi Objective Association Rule Mining is employed with medical datasets. The results are implemented with various multi objective measures like confidence, support, etc. Various level of datasets are employed to authenticate the results of the proposed method of association rule mining. The datasets considered are numerical in nature.
Task-Driven Regional Saliency Analysis Based on a Global–Local Feature Assembly Network in Complex Optical Remote Sensing Scenes

Saliency analysis is an essential task in computer vision and aims to generate distinguishing foreground features from background features. However, due to complex structure distributions in large-scale optical remote sensing scenes, generating effective feature descriptions for regional saliency analysis is challenging. Therefore, in this study, we proposed a novel global-local feature assembly method called GLFA-Net based on a convolution neural network (CNN) that can adaptively learn an effective feature representation for regional saliency analysis to achieve the region-of-interest (ROI) (e.g., aircraft carrier, airport, and urban area) extraction from complex optical remote sensing images. In addition, we also collected these complex optical remote sensing scene images from Google Earth and DOTA data sets to demonstrate the effectiveness of the proposed method. Finally, experimentation shows that the proposed regional saliency analysis method can produce better ROI extraction performance than other methods, reaching a 0.057 mean absolute error (MAE), a 0.703 Kappa coefficient, and a 0.735 F1-score.
Explicitly Defined Sampling Categories for ESA on a Bipartite Graph

This paper presents a study of extensions of the Explicit Semantic Analysis (ESA) used for text representation. The standard ESA algorithm leads to allocation of blocks of words in (O |V <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> | × n) time on average, where n is the size of the words in text corpora being the subject of analysis and |V <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> | stands for the size of the vocabulary. Proposed extensions have been based on the selection of training data for ESA and employs for that purpose the category structure of Wikipedia called CESA. The paper proposes the metrics for evaluation of the quality and test the performance of the methods in the function of the training data size. We also study the influence of these methods on the quality of the representation. We established that the total number of queries in case of training is (O |D ⊆ V <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> | × n). Furthermore, the CESA method leads to allocation of blocks of words in O (|V <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> | × |V <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> | × n) time on average, and O (|V <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> | ×|V <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> | × n) time on worse case.
Effective Data Generation for E-banking Transactions Using Cycle-Consistent Adversarial Networks

Abstract In the anti-fraud research, the small amount of fraudulent transactions leads to extremely class imbalanced data. This problem becomes a bottleneck of fraud detection. In this paper, we apply the Cycle-Consistent Adversarial Networks (CycleGAN) to generate data for the minority class. Based on real e-banking transaction data, generators and discriminators are designed to generate synthetic data that meets the characteristics of e-banking transactions. Synthetic samples and real samples are mixed into the training of fraud detection model, and multiple metrics are used to verify the effect. Experimental results show that the synthetic data generated by CycleGAN can effectively improve the performance of the fraud detection model.
The Exploration and Practice on the Cultivation of Design and R&amp;D Capability of Computer Students under the Model of School Enterprise Cooperation

Based on the strategic plan, “Made in China 2025”, for transforming China's manufacturing, the integration of informatization and industrialization has entered the stage of comprehensive penetration. The demand for computer students has been increasing year by year. Software project design and research and development ability is the core competitiveness of computer students' employment. How to improve students' design and research capability has become the key to computer undergraduate teaching. This paper proposes to build a platform for industry-university-research cooperation through the use of enterprises as technology demanders, colleges and universities as technology suppliers, and programming courses as a carrier to achieve different degrees of integration for different grades of students. Practice results show that this method can effectively improve the quality of application-oriented talents to meet the needs of enterprises.
Toward Intelligent Solution to Identify Learner Attitude from Source Code

Technology became widely used in all teaching processes. In some advanced applications, teaching and learning are operated in virtual environments allowing consequently developing unlimited new paradigms. Programming and software engineering practical works are generally executed in Integrated Development Environments. This last contain mainly students’ source code. Analyzing source code is usually processed to verify syntax correctness or to verify solution relevance to problem issues. Student personality dimension is an important factor that teacher undertake during teaching mission. This last can serve in understanding student nature and adapting teaching pedagogy to his personality. This paper aims to explain the idea of gathering insights from student source code. We explain how we can use mainly Machine Learning and Natural language processing to obtain a kind of source code understanding. As a first step, which is the purpose of this paper, we have built an intelligent solution architecture that shows how to collect source code rich data and go through analysis technics to finally use machine learning model to learn about this domain.
Object-Centric Street Scene Synthesis with Generative Adversarial Networks

We present a new method to synthesise street scene images from instance information using a conditional Generative Adversarial Network (GAN). These conditional GANs made a variety of applications possible, but remain limited to lower resolutions. The aim of this paper is twofold, synthesise realistic street scenes with a focus on objects and increase the resolution using a divide-and-conquer principle. The application for these images is to allow a performance improvement to an object detection network by augmenting its training data. The idea is that our network generates realistic enough objects, from their instance information, in order to be used by the object detection network. This allows for cheaper data augmentation since gathering training pairs is fairly expensive. Higher resolution images are possible using our proposed network type, which follows a divide-and-conquer principle. First, all the objects in the scene are separately generated from a class specific network. Then, the background is generated by a background specific network dividing the complex scene synthesis task into simpler tasks.
Obstacle Avoidance with Sensors Using Soft Computing Techniques

Robotics design is an exciting design for engineer, researcher, and inter disciplinary field of study. The purpose of this robot is to introduce the fundamentals of robotics and control of smart robot. It should also be useful for engineer and industrial persons. In this research paper, we compare micro-controller-based program and virtual instrumentation program control. So, we implement the virtual instrument control program for this robot to make decisions like vision, task planning, and obstacle avoidance. An autonomous robot, capable of performing different tasks for a human who is physically disabled, has been designed and developed. To avoid accident with unpredicted obstacles, the autonomous robot uses ultrasonic sensor and finds range for recognition and mapping. The obstacle avoidance approach used for this system is described. Since this approach depends deeply on the concert of the ultrasonic sensors, these sensors and the consequence of their restrictions on the obstacle avoidance algorithm and techniques are discussed in detail.
A web service recommendation algorithm based on BaisSVD

With the development of network technology and network services, the number of Web services will also be exploded, so the magnitude of Web services that can provide similar functions will gradually be increased. Quality of service (QoS) is widely used to describe and evaluate the non functional properties of Web services, and has been successfully applied to service recommendation. However, most of the current Web Service recommendations are still limited to traditional collaborative filtering, without considering the bias information of users and web services themselves. In this thesis, firstly, the author introduces the current situation of web services recommendation, then analyzes the existing problems and the characteristics of the data sets. Finally, the author applies the BiasSVD to predict QoS, and then recommends to users. By comparing the experimental results with the traditional collaborative filtering algorithm, the feasibility and superiority of the algorithm in Web services recommendation scenarios are obtained.
Developing a methodology to investigate the correlation between seafarer students’ perception of authenticity in assessment and their achievement

Past research shows seafarer students perceive traditional assessment methods used in maritime education and training (MET) institutes as disengaging, making them adopt surface-learning approaches towards acquiring essential knowledge and skills required for the workplace. Instead of developing skills that may be transferred to shipboard tasks, disengaged seafarer students focus only on achieving the minimum score required to attain a ‘pass’ grade for their certification. In this paper, a quantitative research methodology is developed to investigate seafarer students’ perception of authenticity to workplace tasks in decontextualised traditional assessments versus authentic assessments conducted in real world contexts. The methodology will further correlate the perceptions to student achievement (measured using assessment scores) in the assessment tasks. The paper describes the research design and identifies the ethical issues. Procedures to address validity and reliability of the research are also established. Future research aims to implement the methodology to conduct research in a selected educational institute and a unit of competence. However, this paper acknowledges that the methodology should be replicated in other educational institutes for different units of competence to compare and generalise findings.
Variational Bayesian with Embedded Laplace Approximation for AR Model with Outliers

Abstract The student-t distributed innovation is robust to Auto regressive (AR) observations with outliers. However, the characteristics of student-t distribution lead to unclosed solution while using Variational Bayesian (VB) to estimate the model parameters. This paper proposed to embed the Laplace approximation in VB framework to get closed form solution. Markov Chain Monte Carlo (MCMC) is a typical method to unclosed form solutions, while it is known that MCMC has large computation costs. The degree of freedom parameter (DOF) changes greatly before and after the outlier points has been deleted, so the estimation can be improved. Experimental results show that, more accurate estimation coefficients estimation are obtained using the proposed methods compared with using the Gaussian innovation, and more accurate estimation is obtained after the additive outlier is deleted.
Parallel-Based Techniques for Managing and Analyzing the Performance on Semantic Graph

In recent years, data are generated rapidly that advanced the evolving of the linked data. Modern data are globally distributed over the semantically linked graphs. The nature of the distributed data over the semantic graph raised new demands on further investigation on improving performance on the semantic graphs. In this work, we analyzed the time latency as an important factor to be further investigated and improved. We evaluated the parallel computing on these distributed data in order to better utilize the parallelism approaches. A federation framework based on a multi-threaded environment supporting federated SPARQL query was introduced. In our experiments, we show the achievability and effectiveness of our model on a set of real-world quires through real-world Linked Open Data cloud. Significant performance improvement has noticed. Further, we highlight short-comings that could open an avenue in the research of federated queries. Keywords: Semantic web; distributed query processing; query federation; linked data; join methods.
Morphological Enhancement for Remote Sensing Image Based on GAN Structuring Elements

Abstract A morphological image enhancement method based on the GAN (General Adaptive Neighborhood) structuring elements is proposed for some problems such as low overall contrast, noise and inter-target edge of some remote sensing images. Firstly, a pixel to be processed is selected as a seed point, and an adaptive neighborhood is defined according to the image pixel characteristic and the neighborhood constraint relation to obtain the self-adaptive structuring elements set. A corresponding self-adaptive morphological operation based on the self-adaptive structuring elements set is constructed with reference to the classical morphological operation definition, and the self-adaptive alternative filter is constructed by using the adaptive morphological operation combination so as to realize the enhancement of the remote sensing image. The experimental results show that the method can preserve the accurate positioning of the objects edge while enhancing the image, avoid the loss of information, has better noise removing performance. Compared with the classical morphological filter operator, the single-scale Retinex algorithm and bi-histogram equalization (BHE) algorithm, the contrast ratio and the signal-to-noise ratio of the enhanced image are improved.
A Haze Removal Method Based on Additional Depth Information and Image Fusion

To address the problem of image degradation in foggy days, we propose a haze removal method based on additional depth information and image fusion. With recent advances in depth-sensing technology, it has been realized that sensing devices can produce depth images in which the depth value are quite accurate. We adopt the depth estimation dataset of Karlsruhe Institute of Technology and Toyota Technological Institute (KITTI) which contains images collected from different real-world environments. The additional information includes the LiDaR scanning points and original depth images which can be used to estimate the optical depth of each point in the scene. In this paper, we investigate how to use additional depth information to remove haze for a single image. Our method focuses on LiDaR depth imaging, image fusion, and the atmospheric scattering model. We use LiDaR scanning points as input and then deduce a rough depth image with prominent features. The rough depth image is then combined with original depth image to improve reliability of depth estimation by image fusion. Using the atmospheric scattering model, we can remove haze for a single image. Experimental results show that our proposed approach provides better performance of dehazing under different fog conditions and holding the details of remote sensing images than current research methods.
Developing Evaluation System that Scientifically Presents the Rotation Ability of the Top as a Score: “Koma Scouter”

Using by the compact disc are able to easily make an original top. This CD top will support in the learning of the gyroscopic and moment of inertia. Therefore, sometimes it become to a theme of experience course of Science Museum. We using on this CD tops, has developed a “Koma Scouter” as a system to connect the original tops making and edutainment. This system first shooting top of arranging the color nut on the CD in USB camera. Next, the system presents to user the score of rotation ability of the tops taken by image analysis. By presenting the rotation score top the user own, the user can recognize performance of the top that own it. In this paper, we describe a method for recognizing the color nuts, which are arranged in the CD, and a method of evaluating the rotation score of the CD top. As a result of using this system in scientific events, Kids of about 40% has reworked the top.
Speeding Up On-Line Route Scheduling for an Autonomous Robot Through Pre-Built Paths

SUMMARY Today, robots can be found helping humans with their daily tasks. Some tasks require the robot to visit a set of locations in the environment efficiently, like in the Traveling Salesman Problem. As indoor environments are maze-like areas, feasible paths connecting locations must be computed beforehand, so they can be combined during the scheduling, which can be impracticable for real-time applications. This work presents an on-line Route Scheduling supported by a Fast Path Planning Method able to adjust pre-built paths. Experiments were carried out with virtual and real robots to evaluate time and quality of tours.
Biometric-based medical watermarking system for verifying privacy and source authentication

Two of the most requirements in e-health care system is the ensuring the authenticity of the source from which the data is received and the privacy of medical record of the patient must be preserved. Medical record must be accessed and modified just by the authentic source. Any changing in the medical record may lead to misdiagnosis. Consequently, affect the life of the patient. So, the privacy of medical record and the authenticity of the sender source must be satisfied. This paper studied the ability of watermarking technique for satisfying the above two requirements. In our paper a watermarking system based on the biometric is presented, where the iris code of the sender is encoded to be as a key used for authorize the sender. Also, the privacy of patient record is preserved by encrypt it. The activities of the embedding, extracting and verification procedures is illustrated in details in the sections of the paper. Experimental results showed that the suggested system satisfied the two security requirements.