Reviewerly/exHarmony huggingface dataset:
Image Aligning and Stitching Based on Multilayer Mesh Deformation

This paper aims to solve the problem of strong disparity in image stitching. By studying current mesh deformation methods based on single grid, we propose an image stitching framework based on multilayer mesh deformation for aligning regions in different layer. With development of image depth perception and semantic segmentation technology, we can get layering maps of images or photos expediently. We introduce images representation with layers and get layer corresponding by using depth or disparity information for large parallax scenarios. Registration of each layer is carried out independently. To ensure the integrity of layer synthesis results, we apply deformation with translation and scaling compensation between different layers before blending. The experiment demonstrates that our method can adequately utilize the prior information in layering maps to decouple 2D transformation between different layers, finally achieve outstanding aligning performance in all layers and naturalness in complete stitching result.
Interactive STEM Talk and Workshop Outreach Programme-By Students, for Students: A Malaysian Context

Malaysia has not been able to achieve the 60:40 ratio of STEM stream to arts stream students as formulated by the Malaysian Planning Committee of Higher Education in the year 1967. An interactive STEM talk and workshop outreach programme was conducted by a team of university students and benefited many secondary school students throughout Malaysia. Surveys were done based on the programme and it was found that Malaysian students lack the opportunity to participate in such workshops. Based on the survey results, participants of the workshop had a positive learning experience during the Interactive STEM Talk and Workshop, and it managed to emphasize on the importance of STEM education.
Zero-Shot Image Classification Algorithm Based on Particle Swarm Optimization Fusion Feature

Aiming at the limitation of describing the semantic attributes of target classification, this paper proposes an adaptive weighted fusion feature based zero-sampling image classification algorithm. Firstly, the fusion weights are initialized randomly. Meantime, the semantic vector features and semantic attributes of the text are fused by neural network. Then, particle swarm optimization algorithm is used to optimize the weight of feature fusion. Finally, the features of weighted fusion are regarded as the transfer knowledge of the classification of zero-sampling images. The experimental results show that the classification algorithm based on adaptive weighted fusion for the zero-sampling image has an accuracy rate of 88.9% on the Animals with Attributes (AWA) data set, which illustrates the effectiveness. What's more, the proposed algorithm also improves the stability of the classification model for the zero-sampling image compared with the fusion feature.
Social Engineering Using Social Networking Sites

Social engineering is a process that cyber criminals use to psychologically manipulate an unsuspecting person into divulging sensitive details. Social engineering is an extremely powerful tool that can be deployed to exploit the human's element of security and inherently psychological manipulation. According to a security industry survey, social engineering tops the list of the 10 most popular hacking methods. The first step in any social engineering attack is to collect information about the attacker's target. A social networking site is an online platform that allows users to create a public profile and interact with other users on the website. They are also a hotbed for social engineering cyber attacks to personal information; they are a relevant source of wealth of personal and organisational information that can be found within these social environments. This article defines social engineering and some techniques of social engineering attacks based on social networking sites, drawbacks on a users' privacy, and arising implications.
Detection and Recognition of Abnormal Behavior based on Multi-level Residual Network

The ability of real-time detection and recognition of abnormal behavior in video monitoring system is a key problem in intelligent monitoring system. This paper proposes a network framework based on multi-level residual network to detect and recognize abnormal human behavior from video. The framework of multi-level residual network includes human body detection module and posture recognition module. Based on the former, this paper proposes the detection residual network (d-Res) to adopt multi-scale target detection strategy to ensure the detection speed and detection effect of human body. The latter is used to extract spatial features of abnormal behaviors, and the recognition residual network (r-Res) based on transfer learning is used to extract deep features of images, so as to classify abnormal behaviors efficiently. Experiments are carried out on UTI dataset to evaluate the performance of the proposed algorithm. The results show that the proposed method is effective in detecting and recognizing abnormal behaviors in real-world scenes.
Intelligent Vehicle’s Control for Tracking Target Path

Intelligent vehicle includes perception, planning, control, decision making and interaction. Path planning and vehicle control act the vehicle directly, and its stability, safety and efficiency play an important role in intelligent driving systems. This article researches optimal path planning and vehicle tracking control capabilities in specific scenario, by the motion description of the vehicle kinematics model, establishing the out-parking map and creating reference path, combining with the model predictive control optimization algorithm and collaborating with sensors and devices which supported by a development project. By analyzing test data, all parts of designed can achieve the expected function, and the overall performance is good.
Multi-granularity Position-Aware Convolutional Memory Network for Aspect-Based Sentiment Analysis

Aspect-based sentiment analysis (ABSA) is a fine-grained sentiment analysis problem, which has received more and more attention in recent years. Convolutional Neural Networks and their variants have shown potentialities for tackling the problem recently. Building upon this line of research, we propose a novel architecture named Multi-Granularity Position-Aware Convolutional Memory Network (MP-CMN) for ABSA in this paper. MP-CMN utilizes multiple convolutional layers to extract features of different granularities to build the convolutional memories, and then incorporates aspect information and position information into convolutional memory network via attention mechanism. To make the mechanism of our model clear, we also make some visualization and case studies. Experiment results on standard SemEval 2014 datasets demonstrate the effectiveness of the proposed model.
Improvement in Phishing Websites Detection Using Meta Classifiers

In the era of the ever-growing number of smart devices, fraudulent practices through Phishing Websites have become an increasingly severe threat to modern computers and internet security. These websites are designed to steal the personal information from the user and spread over the internet without the knowledge of the user using the system. These websites give a false impression of genuinity to the user by mirroring the real trusted web pages which then leads to the loss of important credentials of the user. So, Detection of such fraudulent websites is an essence and the need of the hour. In this paper, various classifiers have been considered and were found that ensemble classifiers predict to utmost efficiency. The idea behind was whether a combined classifier model performs better than a single classifier model leading to a better efficiency and accuracy. In this paper, for experimentation, three Meta Classifiers, namely, AdaBoostM1, Stacking, and Bagging have been taken into consideration for performance comparison. It is found that Meta Classifier built by combining of simple classifier(s) outperform the simple classifier’s performance.
A Study of Developing a Web-based Management System of Document Annotation for Teaching and Learning

In education, the annotation can promote learning effects in the learning process. Several annotation systems have been equipped with this function for learners' learning, such as math problem-solving, writing, physics training, and Chinese calligraphy skills. However, some are limited within the learning devices or the operating system due to possible dilemma existed with the system. To improve this, this study tries to develop a web-based system of document annotation using the technology of responsive web design for education. It supports an automatic document type conversion, offered with several types of annotation both for school- and class-based managements. Teachers can employ this system for their teaching activities, such as peer corrections with comments, annotation sharing, and problem-solving, from basic keyword annotations to multiple perspectives. An evaluation was also conducted to assess the system's appropriateness before making it for a real use. The related feedbacks show the usefulness and friendliness for users to use it in educational activities of learning and teaching.
Dynamic Multi-population Artificial Bee Colony Algorithm

As a relatively new paradigm of bio-inspired optimization techniques, artificial bee colony (ABC) algorithm has attracted much attention for its simplicity and effectiveness. However, the performance of ABC is not satisfactory when solving some complex optimization problems. To improve its performance, we propose a novel ABC variant by designing a dynamic multi-population scheme (DMPS). In DMPS, the population is divided into several subpopulations, and the size of subpopulation is adjusted dynamically by checking the quality of the global best solution. Moreover, we design two novel solution search equations to maximize the effectiveness of DMPS, in which the local best solution of each subpopulation and the global best solution of the whole population are utilized simultaneously. In the experiments, 32 widely used benchmark functions are used, and four well-established ABC variants are involved in the comparison. The comparative results show that our approach performs better on the majority of benchmark functions.
Advancements in optical character recognition for Gurmukhi script

In this paper, we survey an optical character recognition (OCR) literature on one of the most popular Indic script Gurmukhi. Before focusing on the aspects of OCR works on Gurmukhi, the peculiarities of the Gurmukhi script are outlined. For the sake of clarity, the handwritten, printed, and online character recognition for Gurmukhi script are considered. After proper summarization of all the publications related to this topic, a detailed analysis of the methodologies involved along with scrutiny of the results was performed.
Track Initiation with Orthogonal EDS Statistics

Track initiation plays a significant role in targets tracking as determining which measurements of target are. It seems to be a difficult task to detect target from a very noisy background. Targets usually move as straight-line trajectories. So based on the character of the straight line, we find a statistics to illustrate equal difference segments (EDS), and this paper proposes a track initiation method with orthogonal EDS statistics to detect targets in the environment with clutter points. The performances of the method proposed are analyzed theoretically and simulations are designed to verify the analysis. It is proved the track initiation method is better than traditional methods like Rule-Based method, Logic-Based method, Hough Transform technique and modified Hough Transform technique.
A Metaheuristic Approach for Classification Rule Discovery: AntMiner

In this transformational era of modern computer technology, information and knowledge is enhancing day by day and becoming a challenging task to manage, classify and mining the structured as well as unstructured data. Classification of discovered rules is an important activity responsible for future predictions, extracting knowledge and patterns in the domain of data-mining. This paper deals with study of implementing nature-inspired (Ant Colony based technique) algorithm in classification rule generation in the field of data mining. The antMiner metaheuristic algorithm is applied on 12 standard datasets and simulation study is conducted(along with non-parametric statistical tests) which shows that there is significantly increase in accuracy measurement by c-antMiner2,c-antMinerpb over JRIP and PART algorithms.
The Development of Data Integration Tool with Linked Data: Case Study: Bandung Education Data

Education data is data about entities in education, such as students, education staff, and educational institution. Recently, education data has drawback of being able to be reused by other software, which results in data have to be migrated manually every time there is a change in other databases that also use education data. To enable data to be synchronized without having to migrate data manually, it is necessary to integrate and convert data into data that can be connected with other data in other software. Data structure that can be used by other software and enable data to be integrated is Linked Data. From this problem, this paper aims to build a tool to make data integrated and linked with other data in other software to automate migration. The tool will receive data input that will be converted into Linked Data. The resulted Linked Data then will be stored in RDF Store and can be fetched by other software through Web API or SPARQL Query Endpoint. In addition, users can explore the resulted Linked Data through RDF Subject Page. Based on the results of evaluations carried out using basic education data and PPDB 2018 data from Bandung State Junior High School 45, the built tool can produce integrated data that also can be used by other software. Thus, it can be concluded that developing tool to integrate and convert data can be a solution to make data integrated and can be used by other software.
Thought-Based Authenticated Key Exchange

Identity authentication techniques based on password-authenticated key exchange (PAKE) protocols rely on weak secrets shared between users and host systems. In PAKE, a symmetric key is derived from the shared secret, used to mutually authenticate communicating parties, and then used to establish a secure channel for subsequent communications. A common source of PAKE weak secrets are password and passphrase strings. Though easily recalled by a user, these inputs typically require keyboard entry, limiting their utility in achieving universal access. This paper describes authentication techniques based on weak secrets derived from knowledge extracted from biometric sensors and brain-actuated control systems. The derived secrets are converted into a format suitable for use by a PAKE protocol. When combined with other authentication factors, PAKE protocols can be extended to provide strong, two-factor identity authentication that is easy to use by persons living in assistive environments.
Web crawler for fault diagnosis of CNC machine tools

In the era of soaring network information resources, as the foundation and an important part of big data, the role of web crawlers is particularly important [1]. In the field of CNC machine tool diagnosis, the data is widely distributed and the data exists in different forms, but with the deepening of industrial automation, digitization and intelligence [2], enterprises urgently need to realize fault diagnosis and troubleshooting of CNC machine tools. Therefore, it is of great practical significance to collect these data and form a knowledge base of CNC machine tool fault diagnosis [3]. This paper introduces the difficulties faced by CNC machine tool fault diagnosis data collection and countermeasures, the basic concepts of web crawlers, distributed crawlers and machine learning-based web crawlers, as well as their respective architectures and crawling strategies.
The Generation of Random Numbers Using the Quantum Tunnel Effect in Transistors

This paper proposes a random number generator based on the quantum tunnel effect that takes place in transistors. The main system is divided into two basic components: the entropy source and the postprocessing block. The quantum tunnel effect is subjected to a random character, which means that electrons have a probability to pass through the potential barrier of the transistor. Theoretically, the measurable output current of the transistor should have a random variance in a predetermined interval, that results into a random sequence. Afterwards, the postprocessing block extracts the bias from the generated array. It is implemented through a hash function which employs the Toeplitz matrix, resulting into a true random sequence. This paper accounts the preliminary stages of development of an original random number generator. The focus was the physical phenomena, the tunnel effect in transistors, and the study of its probability with respect to the device parameters. In terms of postprocessing the main regard was choosing the adequate extraction method for the quantum entropy source. The novelty of the paper is represented by using the tunnel FET as an entropy source and finding a suitable postprocessing model for a higher generation rate.
Conversation Partner Grouping Based on Speech Contents

Conversation analysis plays an important role in social psychology, interpersonal relationship management, and human-care computing. However, few of existing studies considers speech contents for effective conversation partner grouping (abbr. CPG). In this paper, we propose a new framework for conversation partner grouping based on speech contents. Under the proposed framework, we propose two novel algorithms for effective CPG, called CPG-LDA and CPG-LSI, respectively. Both of them use voice recognition tools to convert audio-based speech data into text-based speech contents, and then apply topic modeling and k-means algorithms for CPG. However, the former is based on LDA topic modeling, while the latter is LSI. The experiments show that both CPG-LDA and CPG-LSI have good performance for GPC. More impressively, the proposed CPG-LSI algorithm archives up to 95.83% recognition rate in the experiments.
Prediction of national examination question using C4.5 algorithm

Abstract The National Examination in 2018 is no longer the only determinant of student graduation from their school, however the score of the national examination from 2015 has decreased. One of the reasons is that students found difficulties to predict the questions that probably appeared on the exam sheet. This research aimed to help students predict the questions that probably appeared, so that the average value hopefully will be increased. The prediction of the questions that probably appeared on the exam had been done by processing data of the questions used for national examinations several years ago using C4.5 Algorithm. C4.5 Algorithm is a Data Mining technique that can classify predictive questions and it is able to form a prediction of questions on the exam. The result showed that the prediction of questions that probably appeared on exams by using Algorithm C4.5 had good performance and its accuracy is 70%.
Continuum in the art_nature_technology affluence

In the context of computational art there is an approximation between machines and nature, which promote new possibilities of aesthetic experience from the juxtaposition of art, nature and technology. The presented text has artistic purpose involving evolutionary algorithms characterized by their autonomous and autopoietic behaviors, with emergent and unpredictable results. Considering these questions, we describe below the results of collaborative research with a transdisciplinary team, formed by the areas of arts, science and technology that make us reflect on the concept of life and our relationship with natural and machinic lives.
Tooled approach for formal verification of components interactions modeled in SysML

Software systems are becoming more complex and their implementation requires more rigorous modeling approaches, for this reason the OMG (Object Management Group) has implemented the SysML standard to model complex systems. Sequence diagram is one of the fundamental diagrams of SysML because it allows behavioral specification of systems. However, SysML still has a lack of formal semantics following his semi-formal definition, which makes it impossible to directly apply the simulation and verification methods to these diagrams. The model transformation community offers several solutions to transform the SysML specification into formal methods in order to bridge the gap between them, this community is divided into two principal’s axes, the first ones working on the formalization of structural diagrams, and the others have worked on behavioral diagrams. Our work contributes to behavioral modeling and aims to combine all the highlights of the other approaches in a single framework for formal verification of SDs, using TAN and Uppaal model checker. The proposed approach has been tested through a case study of an interaction between ATM and Bank to prove their reliability.
Joint Enhancement and Restoration of Underwater Images Using Reflectance and Illumination Components

Underwater images are usually suffering from visibility issues like low brightness and low contrast. This is mainly resulted from attenuation of light in the imaging medium by absorption and scattering. In this paper, a joint restoration and enhancement algorithm for improving the quality of underwater images is proposed. Here degraded underwater image is decomposed to reflectance and illumination components. Red channel prior based restoration is applied to the illumination component. The reflectance component is enhanced by Contrast Limited Adaptive Histogram Equalization (CLAHE) and color correction. The contrast and color fidelity improved underwater images is computed by multi-scale wavelet based image fusion of restored and enhanced images. Comparative analysis of the proposed technique is done with the existing techniques both quantitatively and qualitatively. A non-reference metric (underwater image quality measure (UIQM)) is used for evaluating the performance of the proposed method quantitatively. The experimental results reveals that the proposed algorithm effectively enhances the quality of underwater images.
Emerging Perspectives on Using Avatar-Based Management Techniques for Internet User Investigations

This chapter shows that statistics collection methods are the same for various types of websites. Often, a simple “counter” is used for both unique visitors to the site and the total number of hits to the site from unique and previously registered users. Speaking of a “digital” or “smart” economy, authors distinguish different categories (levels) of development: analysis, content of business intelligence, large data warehouses. Business analytics can be divided into a number of parts: modeling and analysis of system dynamics, expert systems and databases; knowledge and technology; geographic information (geo location); system analysis and design. The various methods and forms of information (statistical models) used to identify non-trivial patterns and propose solutions are often associated today with the concept of data mining. Intelligent data analysis involves the use of knowledge from a complex of data (databases). According to experts, data mining is one of the elements that is part of the process (database management system), which includes the analysis and cleaning of data.