Reviewerly/exHarmony huggingface dataset:
CPU Performance Prediction Using Various Regression Algorithms

Regression Algorithms are used widely for the purpose of predicting the value of a target variable in a dataset. In this work a dataset for Central Processing Unit(CPU) component of personal computers is compiled. Various regression algorithms have been used to predict the performance of the aforementioned component and the accuracy for the various regression algorithms are compared.
Train Service Planning Prototype Model For Thailand Hi-Speed Train

Due to the efficiency of the transportation system is very important for a developing country's economy. Good management transportation services will help agencies provide sustainability services and for users satisfied. This research has two purposes: (1) to study the usage of large data to design hispeed train service and (2) to create a prototype model for hispeed train service from the master country to get the most benefit from applying for service in Thailand. The Country that has been brought to study for modeling is Japan. The data used in this research will consist of all hi-speed rail service information that is currently available and demographic. These data process by using big data technology and machine learning to help study. After studying we selected 2 suitable models to be used for creating prototype models: multiple linear regression and regression tree. The model predictive performance shows that the multiple linear regression model, which uses the mean square error to test the comparison between the baseline. This model presented in this research is significantly more accurate than baseline with confidence at 95% and from cross-validation in the training dataset. The best predictions have found that the data group consists of demographic data and service information. This model can accurately predict the amount of transportation per day. The findings from this study may directly benefit Thailand's hi-speed train planners in their effort to develop an optimal train schedule and reduce the future train fare in Thailand.
Asset Management System on Somsri.io

Somsri.io is developed for a school management system to analyze data for planning and connecting parents through modern channels in the platform of web application which is developed by using Ruby on Rails, Angular JS, and jQuery to improve school management. The problem of the Somsri.io system is caused by the lack of the Assets Management System (AMS) which is an important module for supporting a school management system. The lack of AMS often has problems which consist of returned assets delay time and redundancy requested assets. The AMS requires for development to solve these problems. So the AMS is developed on MVC pattern and consists of asset request, asset repair, asset history, and supplier service to increase the efficiency of school management for Somsri.io. The experiment found that the AMS increase performance in asset management.
Open-Ended Automatic Programming Through Combinatorial Evolution

Combinatorial evolution – the creation of new things through the combination of existing things – can be a powerful way to evolve rather than design technical objects such as electronic circuits. Intriguingly, this seems to be an ongoing and thus open-ended process creating novelty with increasing complexity. Here, we employ combinatorial evolution in software development. While current approaches such as genetic programming are efficient in solving particular problems, they all converge towards a solution and do not create anything new anymore afterwards. Combinatorial evolution of complex systems such as languages and technology are considered open-ended. Therefore, open-ended automatic programming might be possible through combinatorial evolution. We implemented a computer program simulating combinatorial evolution of code blocks stored in a database to make them available for combining. Automatic programming in the sense of algorithm-based code generation is achieved by evaluating regular expressions. We found that reserved keywords of a programming language are suitable for defining the basic code blocks at the beginning of the simulation. We also found that placeholders can be used to combine code blocks and that code complexity can be described in terms of the importance to the programming language. As in a previous combinatorial evolution simulation of electronic circuits, complexity increased from simple keywords and special characters to more complex variable declarations, class definitions, methods, and classes containing methods and variable declarations. Combinatorial evolution, therefore, seems to be a promising approach for open-ended automatic programming.
The Quantum Mechanical Rotation Operators of Spins 5/2 to 7/2

With the creation of logic gates and algorithms for quantum computers and entering our lives, it is predicted that great developments will take place in this area and important efforts are made.Spin rotation processors are quantum mechanical rotation processors and have no classic counterparts.The rotation operators of spin 1/2 are well known and can be found in related textbooks.But rotation operators of other spins greater than 1/2 can be found numerically by evaluating the series expansions of exponential operator obtained from Schrödinger equation, by evaluation of Wigner-d formula or by recently established expressions in polynomial forms discussed in the text.In a previous paper, rotation operators for spins 1/2 to 2 were published.In this work, explicit symbolic expressions of x, y and z components of rotation operators for spin 5/2, 3 and 7/2 are worked via exponential operator for each element of related spin operators and utilizing simple linear curve fitting process.The procedures gave out exact expressions of each element of the rotation operators.
The Higher-quality High-level Semantics based on Improved Visual Model for VQA

Visual question answering is a new research direction that combines computer vision and natural language processing. Some recent research uses image interpretation to reason about answers. However, the accuracy of the answers is not satisfactory due to insufficient understanding of image content. This paper proposes an improved VQA method which applies higher-quality image high-level semantics based an improved visual model, thereby optimizing the performance of answer prediction. Firstly, we use the improved Faster-RCNN model to extract more precise attended objects from images. Then, the corresponding visual features are used to obtain high-level semantic knowledge (refined image captions and attended high-level attributes) in images. Finally, the reasoning module uses the joint features of two semantic knowledge and questions to predict the final answers. We conducted extensive experiments on the public datasets and the results of experiments were analyzed to illustrate effectiveness of the method.
Building Correct Taxonomies with a Well-Founded Graph Grammar

Taxonomies play a central role in conceptual domain modeling having a direct impact in areas such as knowledge representation, ontology engineering, software engineering, as well as in knowledge organization in information sciences. Despite their key role, there is in the literature little guidance on how to build high-quality taxonomies, with notable exceptions such as the OntoClean methodology, and the ontology-driven conceptual modeling language OntoUML. These techniques take into account the ontological meta-properties of types to establish well-founded rules for forming taxonomic structures. In this paper, we show how to leverage on the formal rules underlying these techniques to build taxonomies which are correct by construction. We define a set of correctness-preserving operations to systematically introduce types and subtyping relations into taxonomic structures. To validate our proposal, we formalize these operations as a graph grammar. Moreover, to demonstrate our claim of correctness by construction, we use automatic verification techniques over the grammar language to show that: (i) all taxonomies produced by the grammar rules are correct; and (ii) the rules can generate all correct taxonomies.
Cooperative Parameter Tracking on the Unit Sphere Using Distributed Adapt-Then-Combine Particle Filters and Parallel Transport

This paper introduces a new distributed Adapt-then-Combine (ATC) diffusion algorithm for cooperative tracking of an un-known state vector that evolves on the unit hypersphere. The adapt step is implemented for a general nonlinear observation model and a dynamic state model defined on the hypersphere using a marginal particle filter (PF). The combine step in turn uses parallel transport to build Gaussian parametric approximations on a common tangent space to the spherical manifold. Performance results are compared to those of competing linear diffusion Extended Kalman Filters and non-cooperative PFs.
Decomposing tourists’ sentiment from raw NL text to assess customer satisfaction

The importance of the Word of Mouth is growing day by day in many topics. This phenomenon is evident in everyday life, e.g., the rise of influencers and social media managers. If more people positively debate specific products, then even more people are encouraged to buy them and vice versa. This effect is directly affected by the relationship between the potential customer and the reviewer. Moreover, considering the negative reporting bias is evident in how the Word of Mouth analysis is of absolute interest in many fields. We propose an algorithm to extract the sentiment from a natural language text corpus. The combined approach of Neural Networks, with high predictive power but more challenging interpretation, with more simple but informative models, allows us to quantify a sentiment with a numeric value and to predict if a sentence has a positive (negative) sentiment. The assessment of an objective quantity improves the interpretation of the results in many fields. For example, it is possible to identify crucial specific sectors that require intervention, improving the company's services whilst finding the strengths of the company himself (useful for advertising campaigns). Moreover, considering that the time information is usually available in textual data with a web origin, to analyze trends on macro/micro topics. After showing how to properly reduce the dimensionality of the textual data with a data-cleaning phase, we show how to combine: WordEmbedding, K-Means clustering, SentiWordNet, and the Threshold-based Naïve Bayes classifier. We apply this method to Booking.com and TripAdvisor.com data, analyzing the sentiment of people who discuss a particular issue, providing an example of customer satisfaction.
Data Model For Bigdata System For Multimedia

Big Data systems are characterized by the processing of data that is created in real time, has a different complexity, is variable and diverse and, most importantly, is huge. They require the application of innovative technologies, tools and methods in order to extract new and useful knowledge from them. Effective data acquisition and processing will play a key role in the global and local economy, social policy and large corporations. Big Data is a new challenge and information opportunities. The article presents a project of a system for multimedia data analysis with the use of artificial intelligence.
Adaptive IWD-based algorithm for deployment of business processes into cloud federations

Purpose The concept of business process (BP) as a service is a new solution in enterprises for the purpose of using specific BPs. BPs represent combinations of software services that must be properly executed by the resources provided by a company’s information technology infrastructure. As the policy requirements are different in each enterprise, processes are constantly evolving and demanding new resources in terms of computation and storage. To support more agility and flexibility, it is common today for enterprises to outsource their processes to clouds and, more recently, to cloud federation environment. Ensuring the optimal allocation of cloud resources to process service during the execution of workflows in accordance with user policy requirements is a major concern. Given the diversity of resources available in a cloud federation environment and the ongoing process changes required based on policies, reallocating cloud resources for service processing may lead to high computational costs and increased overheads in communication costs. Design/methodology/approach This paper presents a new adaptive resource allocation approach that uses a novel algorithm extending the natural-based intelligent water drops (IWD) algorithm that optimizes the resource allocation of workflows on the cloud federation which can estimate and optimize final deployment costs. The proposed algorithm is implemented and embedded within the WokflowSim simulation toolkit and tested in different simulated cloud environments with different workflow models. Findings The algorithm showed noticeable enhancements over the classical workflow deployment algorithms taking into account the challenges of data transfer. This paper made a comparison between the proposed IWD-based workflow deployment (IWFD) algorithm with other proposed algorithms. IWFD presented considerable improvements in the makespan, cost and data transfer in most situations in the cloud federation environment. Originality/value An extension for WorkflowSim to support the implementation of BPs in a federation cloud space regarding BP policy. Optimize workflow execution performance in Federated clouds by means of IWFD algorithm.
The Application of Digital Signature Technology in PKI

This thesis introduces the digital signature technology involved in PKI. First, we introduces two important concepts of public key cryptosystem and trapdoor function related to digital signature. Then we explains the conditions that the digital signature scheme should meet, gives the basic idea and mathematical description of the digital signature scheme, and analyzes the two classic digital signature algorithms, RSA and DSA, and introduces the methods for these two digital signature algorithms. Finally, we use some attacks to further prove the universality of the mathematical description of digital signature schemes, and summarize several commonly used digital signature schemes.
Air-defense Decision-making Model of Single Ship CGF Based on the Theory of Fuzzy Mathematics and Behavior Tree

In view of the urgent and complex demand of the surface ship's air-defense, this paper presents a CGF air-defense decision-making method based on the theory of fuzzy mathematics and the technology of behavior tree of single ships. According to the characteristics of different nodes in the behavior tree, the CGF behavior tree model framework is constructed according to the logical sequence of single ship air-defense action flow in the actual battlefield. Aiming at the requirement of intelligence of conditional nodes, the judgment algorithm constituted by the theory of fuzzy mathematics is applied to fill the conditional nodes of behavior tree, and verified by simulation experiment, so as to construct the CGF decision model of single ship's air-defense with intelligence.
Multi Dimensional Construction and Implementation of “ACCESS” Course Online Classroom Teaching Exploration and Practice

Based on the major teaching and research projects and teaching resources in the special period of the epidemic, combined with the intelligent teaching tools of rain class and Tencent classroom, the online access course teaching scheme was constructed and implemented in multi dimensions. This paper first introduces the use method of course teaching resources and platform, and then expounds the construction and implementation process of each module teaching system of "before class, in class and after class" in online classroom. Finally, the assessment results of the course are evaluated to verify the substantial equivalence between online classroom and offline classroom teaching. The implementation of the online course not only promotes the students' mastery and application of access database knowledge, but also relieves the difficulties in the period of school delay due to the epidemic situation. It explores a better way and mode for online teaching, which plays a strong role in promoting the teaching effect, and also provides some experience and reference for the implementation of other online courses.
Motion Sickness Detection and Prevention

Autonomous vehicles are expected to push towards the evolution of mobility environment in the near future, by decreasing traffic accidents, traffic congestion and vehicle fuel consumption. One of the main limitations they face is motion sickness (MS) and it seems capable of putting in risk fully automated vehicles wide impact, as well as their acceptance by the public [1, 2].
VisLab: Crowdsourcing Visualization Experiments in the Wild

When creating a visualization to understand and communicate data, we face different design choices. Even though past empirical research provides foundational knowledge for visualization design, practitioners still rely on their hunches to deal with intricate trade-offs in the wild. On the other hand, researchers lack the time and resources to rigorously explore the growing design space through controlled experiments. In this work, we aim to address this two-fold problem by crowdsourcing visualization experiments. We developed VisLab, an online platform in which anyone can design and deploy experiments to evaluate their visualizations. To alleviate the complexity of experiment design and analysis, our platform provides scaffold templates and analytic dashboards. To motivate broad participation in the experiments, the platform enables anonymous participation and provides personalized performance feedback. We present use case scenarios that demonstrate the usability and usefulness of the platform in addressing the different needs of practitioners, researchers, and educators.
Specific Topics Oriented Image Annotation and Retrieval

The development of the Internet and the popularization of portable cameras, bring big challenges to the organization, retrieval and management of images. This paper chooses multiple topics (e.g. haze, waterlogging, flowers and maple leaves) that people pay more attention to in daily life as research objects, collects each topic's images with text descriptions from the Internet and extracts text and visual features respectively. At first, this paper processes the text information, which mainly involves the keyword extraction and the semantic extension of the keywords. Secondly, this paper proposes the improved algorithm which weights the image blocks adaptively with partial color information entropy, and extracts LBP texture and Gray-Level Co-occurrence Matrix. Finally, based on the image annotation, this paper proposes an image organization and retrieval framework of combination of image and text information, to improve user experience through a finer presentation.
Feature selection based on niching particle swarm optimization for omics data classification

Classification of omics data suffers from the high error rate due to their high dimensional and small sample size characteristics. To overcome the problem, this paper proposes an ensemble feature selection for omics data classification based on constrained niching binary particle swarm optimization (PSO). Particularly, optimal feature subsets in terms of best classification accuracy are identified by the binary PSO. The proposed method introduces constraint on the particle encoding to constrain the number of selected features, and niching technique from multimodal optimization is imposed to enable the algorithm to obtain multiple diverse feature subsets in a single run. Afterward, multiple base classifiers built on the obtained feature subsets are combined into a stronger classifier which is applied to classify the omics data. Experimental results on real-world omics datasets demonstrate that the proposed feature selection method can stably select compact feature subsets and obtain promising classification performance.
MossWater: A Living Media Interface for Encouraging Office Workers’ Daily Water Intake

Tracking drinking behaviors and digitally presenting the information can motivate people's daily water intake. In this paper, we present MossWater, a living interface designed to visualize the daily water intake and so as to motivate people to drink water more frequently and proactively. The system utilizes living interface as an unobtrusive ambient display to evoke empathy. Caring and watering the moss is designed as a metaphor for caring for people's health by regularly drinking, which invokes people's feeling of empathy to motivate their healthier behaviors. This paper also presents the results of a user study exploring the usability of MossWater and users' feedbacks to this living interface. Our system is expected to promote the welfare of both human and nature in the long-term.
Design and Development of a Holonomic Mobile Robot for Material Handling and Transportation Tasks

Modern intelligent manufacturing systems are dynamic environments with the ability to respond and adapt to various internal and external changes that can occur during the manufacturing process. By default, they imply efficient, reliable, and flexible material handling and transportation system, which can be effectively realized by using intelligent mobile robots. In order to achieve locomotion of an intelligent mobile robot that will minimize the usage of space within the manufacturing environment, we propose the development of a new holonomic mobile robot DOMINO (Deep learning-based Omnidirectional Mobile robot with INtelligentcOntrol). 3D model of holonomic mobile robot prototype is developed in CAD software package SolidWorks, and designed parts are produced with additive manufacturing technology. Single board computer Raspberry Pi 4 and microcontroller board Arduino Mega 2560 are used for motion control of the holonomic mobile robot, while control actions are determined by the defined kinematic model of the omnidirectional wheeled mobile robot. The experimental verification shows that the holonomic mobile robot is capable of following a predetermined path while successfully avoiding obstacles within a laboratory model of a manufacturing environment.
Group - oriented Location Privacy Protection for Mobile Users

In order to achieve privacy protection without reducing the availability of data, it is required to diminish the location data error between the location data before and after privacy protection while maintaining the characteristics of groups of user's location. Aiming at the above requirement, we realize the group - oriented location privacy based on the k-anonymity algorithm and the grid method in crowdsensing task assignment. Firstly, the initial grid size is determined based on the overall density of users in the target area, which is to be divided into grid units. The number of users in each grid unit is calculated, whose relationship with the anonymous parameter k affects the next operation to grid units. Secondly, the grid mergence is conducted on those selected by the heuristic search based on the heuristic search of the clustering result. Then, the grid division is carried out on the grids which need to be divided by means of equilibrium segmentation based on geographic midline. Finally, the anonymous region is created for the grid area satisfying the k-anonymity requirement, achieving the k-anonymity location privacy protection. Experiments show that our method can minimize the location deviation while keep the privacy protection intensity, improving the service quality in crowdsensing.