Reviewerly/exHarmony huggingface dataset:
A Discrete Particle Swarm Optimization Algorithm for Solving TSP under Dynamic Topology

This paper studied the solution of the traveling salesman problem (TSP), a discrete particle swarm optimization algorithm (DPSO) model for solving this problem under dynamic topology was established. As to the discreteness and order of the TSP solution, a new coding method was designed, which included the time series connection between cities, the mapping relationship between particles and actual problems was established. And aiming at the premature convergence of the algorithm, a dynamic topology strategy based on particle mass clustering was designed to adjust the flight space of the particles. By introducing the gradient learning coefficient, the convergence speed of the algorithm and the probability of obtaining the optimal solution were improved. The simulation results showed that the proposed algorithm model can be applied to the optimization of TSP in discrete space.
No-reference Image Quality Assessment Based on a Multi-feature Extraction Network

Deep convolutional neural network (DCNN) has achieved high performance on computer vision. However, it's hard to directly apply to image quality assessment due to lack of enough subjective scores. In this paper, we tackle this problem by exploiting high-level semantic and low-level structural features of the pre-trained VGG16 model for no-reference image quality assessment. We first divide overlapping 224x224 patches to get a fixed input size, then use VGG16 to extract the patch features. We use a three fully-connected layers network to get each patch quality score and average the patch scores to predict the whole image quality score. Experimental results on the benchmark LIVE-II database show our methods are comparable with start-of-art algorithms and simpler than other DCNN feature-based methods.
Certainty factor and dempster-shafer method analysis for early detection of final year student depression

Abstract Academic loads in writing a thesis cause difficulties to students to complete their studies. This problem can be solved with the application of an expert system with early detection of depressive final-year students that compares Certainty Factor method and Dempster-Shafer method. Certainty Factor method used to measure facts certainty or rules in defining the level of expert confidence, while the Dempster-Shafer method combines uncertainty that has characteristics according to the way of thinking of experts but has a mathematical basis. The purpose of comparing these methods is to get the most appropriate and best method for early detection of depression. The methodology used in this study is a comparative test between two methods, with a mechanism (1) Collecting data related to the depression of final year students; (2) Data acquisition and weighting of confidence values; (3) Calculate formulations from both methods; (4) Perform a comparative test to obtain conclusions. Certainty Factor method is the most appropriate and the best model in early detection of depression, because Certainty Factor method revealed that the more symptoms are given, the higher the probability of detection of depression.
The operating regularity of the stepper motor of thermal inkjet printers and its application in the China national accreditation service 2017ZO146 altered documents identification proficiency test

With the continuous innovation of inkjet printing technology, the methods of using inkjet printers for falsification have also constantly evolved, leading to increasing difficulties in identifying printing alterations made by the same inkjet printer. This paper mainly studies the operating regularity of the stepper motor of inkjet printers to determine the operating mechanism of inkjet printers and thus to identify whether documents had been tampered with. To detect the operating track of the stepper motor, 154 documents printed by 22 different brands and models of thermal inkjet printers were studied according to the periodic morphological characteristics of ink marks and the track of the stepper motor during different printing processes. As a result: ① the maximum gauge of 22 printheads was found to be different and ② the different distribution of ink marks were mainly affected by the direction and speed of the stepper motor. The track of the stepper motor was able to be determined by the periodic morphological characteristics of the ink marks combined with the maximum gauges, and could be used to judge how many times a document was printed. The observed regularities were evaluated by the CNAS 2017ZO146 test, a national proficiency test in China. In conclusion, using the summarized operating regularities of the stepper motor, it is possible to identify printing alterations made by the same thermal inkjet printer.
The Development and Effectiveness Verification of the Academic Coaching Program to Improve Self-directedness of University Students

The purpose of this study was to develop an academic coaching program for college students and to verify its effectiveness.In order to achieve this goal, the components of the academic coaching program were first derived through literature analysis and the Delphi survey.As a result, 5 areas, 9 topics and 24 activities were confirmed, and so the program was developed.To verify the effectiveness of the developed program, an eight-session program was conducted and a self-directed learning ability test and an in-depth interview was conducted for 51 university students.The paired t-test results showed significant effects in all sub-areas of college students' self-directed learning ability, except for persistence.In addition, qualitative analysis showed that participating students gained help in self-understanding and setting goals for college life, as well as acquired effective learning strategies.They also learned how to manage time more efficiently and experienced empathy and support through the exchange of academic issues.Finally, the implications of this study were discussed in regards to the results of the program's content and effectiveness verification.
A Pattern Adherence Analysis to a Blockchain Web Application

Blockchain is an emerging technology that is attracting a lot of attention from both academia and industry. Blockchain when used as a decentralized technology could be utilized in various useful applications, such as healthcare, logistics, supply chain management, education, among others. However, there is the integration factor between technologies and environments, which have their own characteristics and challenges. A pattern is a solution with reusable fetures for a problem that commonly occurs in a given context during software design phases. Usually, when adopting a design pattern, there are trade-offs among quality attributes. In this context, using patterns can promote a better use for blockchain in traditional application and system development, and disseminate best practices. The aim of this paper is to analyze a traditional web application that uses blockchain features from the viewpoint of some patterns, specifically patterns that describe the blockchain integration with the outside world. As a result, we found that applying blockchain patterns can benefit systems, especially those that require integration with other systems, such as legacy or existing.
A robust approach to detect gas bubbles through images analysis

Bubble detection is a challenging problem in automatic process control in the power and energy industry, medical and pharmaceutical industry and many other fields. Computer vision methods applications for bubble detection and measurement is the principal step of robust bubbles monitoring systems de velopment. In various applications the input image may include a diverse and image background, especially in different environments. This paper presents a new and effective bubble detection approach. The main steps of this proposed approach are as follows: image preprocessing, background subtraction, and contour detection. The graph cut algorithm is used for image segmentation. The Haar wavelet transform is applied to collect bubble component points. The developed approach is evaluated based on the real data set.
Implementation of Building Information Modelling (BIM) Revit to meet the needs of the construction industry

Abstract This research aims to know the students’ Revit operating ability on beginner level competency in extracurricular activities, in order to fulfill the needs of the Indonesia construction industry. This research was conducted by 30-second grade students of DPIB Vocational High School 2 Garut. The research method was class action research, conducted in three cycles. In the first cycle, the percentage of students learning achievement in mastering Revit was only reached Architecture 85%, Annotate 57%, and View 43%. In second cycle, students achievement increased to Architecture 90%, Structure 79%, Annotate 58%, Massing &amp; Site 88%, and View 43%. While in the third cycle, the percentage significantly increased to Architecture 90%, Structure 82%, Annotate 87%, Masing &amp; Site 88%, and View 86%. The average score of Revit mastery in the first cycle reached 60 (not competent category). In the second cycle, the result of Revit’s average scores increased to 77 (competent quite category). And in the third cycle, the average score significantly increases to 88 (included competent category). The conclusion of this research showed the Revit implementation on beginner level competency in Vocational High School 2 Garut had fulfilled the needs of the Indonesia construction industry.
Data and Location Privacy Issues in IoT Applications

The Internet of Things (IoT) is an innovative interconnected framework where all objects can interact with each other and with other people. It enhances the quality of life, business growth and efficiency in multiple domains. However, the heterogeneity of the "Things" that can be associated in such conditions makes interoperability among them a difficult issue. Moreover, the data exchanged between IoT system components are normally not protected. This leads to users losing their privacy, hence, making it difficult to share and reuse data for purposes other than what they were originally set up for. In this paper, we address these challenges in the context of IoT applications considering user's data and location privacy as not to be shared between these `Things'. We first describe two use case scenarios of IoT users in healthcare applications. The first scenario describes an attacker capturing the data traveling from/to the server. The second scenario describes the case of a server acting as a malicious party (i.e., an attacker). The two scenarios highlight data and location privacy issues of IoT users. Based on the use-case scenarios, the paper presents a generic framework for IoT data and location privacy, including a description of entities and interactions among them. The paper then analyzes potential privacy threats in this framework, in order to identify a set of general privacy requirements, with an emphasis on data and location privacy. These requirements will provide guidance to future solutions for secure IoT communication and/or risk assessment.
Efficient Detection of EMVI in Rectal Cancer Via Richer Context Information and Feature Fusion

It is vital to automatically detect the Extramural Vascular Invasion (EMVI) in rectal cancer before surgery, which facilitates to guide the patient's treatment planning. Nevertheless, there are few studies about EMVI detection through magnetic resonance imaging (MRI). Moreover, since EMVI has three main characteristics: highly-variable appearances, relatively-small sizes and similar shapes with surrounding tissues, current deep learning based methods can not be directly used. In this paper, we propose a novel and efficient EMVI detection framework, which gives rise to three main contributions. Firstly, we introduce a self-attention module to capture dependencies ranging from local to global. Secondly, we design a parallel atrous convolution (PAC) block and a global pyramid pooling (GPP) module to encode richer context information at multiple scales. Thirdly, we fuse the whole-scene and local-region information together to improve the feature representation ability. Experimental results show that our framework can significantly improve the detection accuracy and outperform other state-of-the-art methods.
A Shallow Introduction to Deep Neural Networks

Deep learning is one of the two branches of artificial intelligence that merged to give rise to the field of deep neural evolution. The other one is evolutionary computation introduced in the previous chapter. Deep learning, the most active research area in machine learning, is a powerful family of computational models that learns and processes data using multiple levels of abstractions. Over the last years, deep learning methods have shown amazing performances in a diverse field of applications. This chapter familiarizes the readers with the major classes of deep neural networks that are frequently used, namely CNN (Convolutional Neural Network), RNN (Recurrent Neural Network), DBN (Deep Belief Network), Deep autoencoder, GAN (Generative Adversarial Network) and Deep Recursive Network. For each class of networks, we introduced the architecture, type of layers, processing units, learning algorithms and other relevant information. This chapter aims to provide the readers with necessary background information in deep learning for understanding the contemporary research in deep neural evolution presented in the subsequent chapters of the book.
Decentralized LPV-MPC controller with heuristic load balancing for a private cloud hosted application

Web-services are increasingly being deployed on cloud platforms that utilize virtual machines (VMs) to share physical resources among many subscribers. This allows a service provider to leverage features such as on-demand resource provisioning with a pay-as-you-go pricing model. An important consideration for the provider of a web-service is the response time experienced by the end-user. This work relies on control theory for performance guarantees of web-servers hosted on the cloud. The objective is to instantiate the optimal number of VMs to keep the response time, below a certain threshold. Firstly, a linear parameter-varying (LPV) model is developed for each of the hosted server. Secondly, a decentralized LPV-MPC controller with workload prediction, is designed to ensure performance under varying workload. The incoming workload distribution across different servers is performed based on a heuristic algorithm consisting of three modes: regular, surge, and energy optimization. The results are validated on an experimental test bed.
What could be the future of hydrothermal processing wet biomass in Germany by 2030? A semi-quantitative system analysis

The hydrothermal conversion of wet biomass into carbon-rich products is credited with a high potential. But in Germany corresponding large scale facilities have not been established yet. In order to investigate why this is the case, we have identified key factors for the development of hydrothermal processes (HTP) in Germany in previous works. Based on this, this study presents three scenarios of HTP development in Germany by 2030 that represent different combinations of key development factors considering high probability and relevance of occurrence as well as risks in case of factors non-occurrence. Using fuzzy cognitive mapping, connections between the factors are modelled. Further, the system is analysed on its reaction to the scenarios, so that important impacts can be identified. A punctual result is, that for the scenario including most relevant key factors, a normative and economic stabilization of the system is observable. This is above all reasoned in the assumed supporting legal framework. Thus, this path is the most suitable for a successful HTP development in Germany according to this analysis.
Automatic Perpendicular Parking Trajectory Planning and Following for Vehicle

This paper considers the perpendicular parking problem of front wheel steering vehicles. a collision-free reverse perpendicular parking in one maneuver is presented. At any initial position and orientation of the vehicle, a reference parking trajectory can be established by building relationships between the widths of the parking aisle and the parking place as well as the initial position of the vehicle. Furthermore, the B-spline algorithm is used to optimized the reference trajectory, and the continuity of trajectory curvature is realized. Then, a trajectory following scheme based on Model Predictive Control (MPC) algorithm is proposed for intelligent vehicles, and the trajectory tracker based on MPC algorithm is built. The feasibility of this method is verified through simulations, and the results indicate that the designed MPC controller has excellent robustness and can minimize the following error.
Automated Development of DNN Based Spoken Language Systems Using Evolutionary Algorithms

Spoken language processing is one of the research areas that has contributed significantly to the recent revival in neural network research. For example, speech recognition has been at the forefront of deep learning research, inventing various novel models. Their dramatic performance improvements compared to previous state-of-the-art implementations have resulted in spoken language systems being deployed in a wide range of applications today. However, these systems require intensive tuning of their network designs and the training setups in order to achieve maximal performance. The laborious effort by human experts is becoming a prominent obstacle in system development. In this chapter, we first explain the basic concepts and the neural network-based implementations of spoken language processing systems. Several types of neural network models will be described. We then introduce our effort to automate the tuning of the system meta-parameters using evolutionary algorithms.
Optical Quality Control for Adaptive Polishing Processes

We propose an image-based method to automatically estimate the surface roughness of a polishing process carried out by a numerically controlled machine tool. Given a single photograph of the workpiece, we incorporate techniques from differentiable rendering to infer the object's roughness parameters, resulting in several advantages over existing approaches: since the method fully accounts for global light transport effects, the estimation can occur under general, known lighting conditions and workpiece geometries. This allows deployment of our approach for in-situ measurements by simply equipping the machine tool with a standard digital camera capturing photos of the workpiece. We investigate the feasibility and effectiveness of our novel method in a prototype application considering polished brass plates. Our results demonstrate a promising direction for surface parameter measurement in less restricted polishing process environments.
Research on Color Image Watermarking Algorithm Based on Quaternion Fourier Transform

Abstract This paper mainly proposes a color image digital watermarking algorithm based on quaternion Fourier transform (QFT). We can describe color image as a quaternion matrix, then we can use QFT to get its frequency domain information. By using Fourier transform, we can embed the watermarking information into the frequency domain information. By using inverse QFT, we can get a color image that contain watermarking information. All of the steps can be reversible, so the watermarking extraction process is opposite to the embedding process. The advantage of processing color image by quaternion theory is that three color channels of the image can be treated as a whole.
Research on the Main Problems and Countermeasures in the Construction of University Library Informatization

Abstract University library informationization plays an important role in developing reading habits and knowledge literacy of teachers and students in universities. However, most college libraries in China still have problems such as not paying enough attention to informatization construction and low informatization construction. This paper adheres to the problem-oriented, and on the basis of analyzing the problems, puts forward countermeasures and suggestions to improve the level of informationization construction of university libraries.
Prosthetic hand with 2-dimensional motion based EOG signal control

Abstract A prosthetic hand is an artificial device that resembles a human hand which can help the human with a physical disability. Previously, the development of a prosthetic hand is designed in various method, from passive to bionic. Electrooculography (EOG) is a technique for measuring potential differences between the front (positive pole formed by the cornea) and the back (negative pole formed by the retina) of the eyeball which can be used to detect eye movements. The purpose of this study is to design a prosthetic hand with two degrees (2D) of freedom using EOG based control. This system consists of electrodes, EOG amplifier, Bluetooth transmitter-receiver, servo motors, and hand prosthetics. In this study, the system will recognize the eye movements, namely front, right, left, up, and down. The system will recognize the motion based on a threshold value. In the hardware implementation, the system was composed of five electrode sensors which installed around the eye, instrumentation amplifier, high pass filter, low pass filter, noninverting amplifier, summing amplifier, a notch filter circuit, and Arduino UNO microcontroller. In EOG data acquisition, this study involved ten healthy subjects. After the evaluation with five trial for each motion, the error for each eye movement is 0%, 0%, 36%, 4% and 16% for right, left, top, bottom, and front, respectively. This study provided an alternative method to control a prosthetics hand with good performance.
Progressive Abdominal Segmentation with Adaptively Hard Region Prediction and Feature Enhancement

Abdominal multi-organ segmentation achieves much attention in recent medical image analysis. In this paper, we propose a novel progressive framework to promote the segmentation accuracy of abdominal organs with various shapes and small sizes. The entire framework consists of three parts: 1) a Global Segmentation Module extracting the pixel-wise global feature representation; 2) a Localization Module adaptively discovering the top-n hard local regions and effective both in training and testing phase; 3) an Enhancement Module enhancing the features of hard local regions and aggregating with the global features to refine the final representation. Specifically, we pre-define 512 region proposals on the cross-sectional view of the CT image to generate pseudo labels which can supervise Localization Module. In the training phase, we calculate the segmentation error of each region proposal and select the eight ones with the lowest Dice scores as the hard regions. Once these hard regions are determined, their center coordinates are adopted as the pseudo labels to train the Localization Network by using Manhattan Distance Loss. For inference, the entire model directly accomplishes the hard region localization and feature enhancement to promote pixel-wise accuracy. Without bells and whistles, extensive experimental results demonstrate that the proposed method outperforms its counterparts.