Reviewerly/exHarmony huggingface dataset:
A Study on the Advancement of Department of Fisheries in Solomon Islands National University

This study aims to evaluate the project for establishment and capacity building of fishery studies in Solomon Islands National University (SINU) granted by KOICA, and to suggest the follow-up project to increase the project sustainability. Department of Fisheries (DOF), which was newly established in the project, was running all curriculum as planned, and the results of the student’ lecture evaluation were “very satisfied”. However, DOF was too reliant on unstable finances of SINU and Ministry of Education and Human Resources Development (MEHRD), and if the budget was not supported in a timely manner, problems were encountered not only in the laboratory but also the department operation including facility maintenance. Therefore, it is necessary to establish an independent finance system through diversification of financial sources for the stable operation and sustainable development of DOF. First, it is necessary to reorganize the Institute of Maritime Studies (IMS) into the Institute of Fisheries and Maritime Technology (IFMT) under the DOF, and integrate the certificate course of DOF with the curriculum of IFMT. In addition, it is necessary new project to renovate IFMT as a commissioned education institute for fishermen not only in Solomon Islands but also in Pacific countries. It is possible that budget of IFMT is secured through 1) operating fishermen training programs funded by Ministry of Fisheries and Marine Resources (MFMR) in Solomon Islands and Pacific countries, 2) development of new projects funded by the deep-sea fishing countries in EEZ of Solomon Islands to support facility maintanance, provision of equipment and dispatch of lectureres, 3) development compulsory training programs for fishermen to renew a domestic fishing license. In order to realize this strategies, SINU should estalish a close cooperative system with MEHRD and MFMR and DOF and IFMT should strive to ensure that all budgets are used transparently and efficiently.
Cluster-Based Distribution Alignment For Generalizable Person Re-Identification

Person re-identification (ReID) aims to identify a person of interest across surveillance cameras. Although supervised ReID methods have made a great success, most of them suffer a large drop in performance on unseen domains. The key idea to solve this problem lies in the distribution alignment of pedestrian images. In this paper, we design a generalizable person ReID framework which is trained on the source domain but still generalizes well on unseen domains. To achieve this goal, our framework adaptively searches and aligns the underlying distributions among images through cluster-based Batch Normalization (BatchNorm). Specifically, the framework iteratively clusters images based on the style features extracted from middle layers of the networks. The clustering labels are then utilized to guide the cluster-based BatchNorm, which standardizes features in the same cluster within a batch. Extensive experiments on several ReID benchmarks demonstrate the effectiveness of our approach.
Strategies of Naive Software Reverse Engineering: A Qualitative Analysis

Considerable amount of research into the cognitive processes of code reading and understanding has been conducted. However, few studies have considered code comprehension with a specific focus on protected code. This research effort aims to contribute to filling this gap by describing strategies of software reverse engineering, the process of making sense of protected software code. Our study presented two Java programs, one available in clear code and one in obfuscated code. Participants worked on two tasks for each of the programs that required them to understand the program logic and how the code produces program behaviour. By means of a codebook, similarities and discrepancies between participants’ strategies are outlined in this paper.
Facial Emotional Recognition Using Legion Kernel Convolutional Neural Networks

Facial expressions can be verbal, and nonverbal, which play an important part of communication. We can convey a message to another person by means of an expression. Automatic recognition of facial expressions through machines can be developed using natural human interfaces. Although, we know that humans can recognize the expression of a person quickly by looking at the person but recognition of expression using a machine is difficult. This paper presents an overview of automatic expression recognition including the expressions of persons who are mentally ill, retarded, etc. It highlights the components of a system and provides research challenges. By definition, the appearance of a face is the facial component in outward appearance acknowledgment. This paper also helps to recognize the emotions of mentally ill people including children. The primary research works finishes up six substance, face distinguished by photographs, settled by Open Source Computer Vision Library (OpenCV), and facial dataset, settled by Kaggle's Fer2013, the handling preparing and testing the facial dataset, settled by the model Convolutional Neural Network (CNN).
New Trends in Scientific and Technological revolution (STR) and Transformation of Science and Education Systems in the Paradigm of Sustainable Development

Education and science play a key role in the vision and development of a fair, peaceful and sustainable society (global, continental, regional and national). Education is one of the crucial preconditions for the implementation of the UN global strategy “Transforming our world: the 2030 Agenda for Sustainable Development” (“Agenda 2030”). Agenda 2030 sets practical challenges for governments to transform the education system (Education for Sustainable Development). Developments in the field of artificial intelligence (AI), robotics and intelligent systems are developing rapidly right now, purely now bring benefits (and, at the same time – probable danger and harm), directly now – significantly determine our existence – and will soon determine even more. When universities argue that 4IR should be given strong and close attention, it means that they can no longer rely only on traditional forms of human thinking and imagination, but also need thinking that depends on algorithmic computations of machines or forms of technology. In this sense, 4IR has changed not only what we do and how we do it, but also who we have become. The mission of the universities of the XXI century is to form a developed personality, a person of high cultural level, who can act in a situation characterized by significant environmental risks. Educational institutions must be transformed and improved as institutions that must create and strengthen a safe, non-violent, inclusive and effective learning environment for all members of society. Which, in the end, will help to achieve success in cooperation at all levels – both in education and science, and in society in general.
Preliminary Investigation into Machine-Learned 2D Automated Video Surveillance Systems

The purpose of this study is to explore the possibility of using 2D human pose estimation algorithms in automated video surveillance systems. The proposed concept boils down to develop an intelligent application that can handle the incoming results and even prevent the occurrence of critical events in the monitored areas. The so-called critical events represent the acts of vandalism, crime, burglary, theft, fights, etc. The core aspect of the described solution is the use of conventional 2D imaging devices, i.e. CCTV cameras, IP webcams or even mobile phone cameras and an AI (artificial intelligence) neural network trained on a set of images. That makes the proposed solution relatively cheap, universal and easy to install across a wide range of target environments without the need for additional sensors. In the paper the authors highlights the architecture of the system, presents details of the video data acquisition stage followed by a description of the 2D human pose estimation toolkit. It is then complemented by summarizing the data extraction stage from pre-recorded clips for machine learning.
Refractive Light-Field Features for Curved Transparent Objects in Structure From Motion

Curved refractive objects are common in the human environment, and have a complex visual appearance that can cause robotic vision algorithms to fail. Light-field cameras allow us to address this challenge by capturing the view-dependent appearance of such objects in a single exposure. We propose a novel image feature for light fields that detects and describes the patterns of light refracted through curved transparent objects. We derive characteristic points based on these features allowing them to be used in place of conventional 2D features. Using our features, we demonstrate improved structure-from-motion performance in challenging scenes containing refractive objects, including quantitative evaluations that show improved camera pose estimates and 3D reconstructions. Additionally, our methods converge 15-35% more frequently than the state-of-the-art. Our method is a critical step towards allowing robots to operate around refractive objects, with applications in manufacturing, quality assurance, pick-and-place, and domestic robots working with acrylic, glass and other transparent materials.
Enhancing the E-business Through Generalized Association Rule Mining from the Web-Based Data

Present market focuses on “Know your customer” and relies on advertisement and user’s navigation habits. The data collected from Internet and various Web sites is known as Web-based data, i.e., collection of Web pages. The Web pages available on Internet are in form of different levels and make multilevel taxonomy. Generalized multilevel association rule mining considers the items or predicates mined are at different levels of taxonomy. The generalized association rule mining can help to decide the plans for enhancing the business as well as it will also help to target the promotional offers and advertisement for customers with personal care. In this paper, it has been shown how generalized multilevel association rule mining is integrated to the knowledge discovery process, its potential applications, and techniques. Furthermore, an integrated approach has been presented showing how multilevel association rule mining can contribute to e-business via its great potential. Finally, generalized association rule mining algorithms have been presented for discussing the process of enhancing the e-business.
A Blockchain Based Online Voting System: An Indian Scenario

The conventional paper based voting system has many limitations including poor security and privacy. Hence, the internet based online voting system has been evolved as an alternate to the paper-based voting system in the recent years. This paper proposes a novel architecture for online voting system which uses block chain to record electoral data. It describes the various steps like block creation and block sealing for creating adjustable block chain for the polling process. It uses private block chain which is accessible to authorized agencies including government bodies (i.e., Election commission). Moreover, it uses AADHAAR information to verify the voter details and uses SHA hashing algorithms to protect the voting data stored in the block. The proposed system is implemented using PHP and JSON. The proposed method has improved the security and manifestation of electronic voting process significantly compared to the existing systems.
Effects of career maturity, major satisfaction, and self-control on job stress of college students

The purpose of this study was to investigate the effects of career maturity major satisfaction, self-control on university students' job stress. Data collection was conducted from November 2018 to February, 2019 in 236 students who understood the purpose of the study and agreed to participate in the study. The collected data were analyzed by frequency, mean, standard deviation, Pearson correlation analysis, and multiple regression. The lower the career maturity (β=-.258), job stress was the higher. Also, the lower major satisfaction (β=-.377), the higher the job stress, and the lower the self-control (β= -.275), the job stress was the higher. Through these results, education programs that link students from their childhood to college students are needed to improve their career maturity, satisfaction with majors and self-control(r=-.410, p<.001).
Image Enhancement and Improvement Algorithm Based on Esrgan Singal Frame Remote Sensing Image

Abstract Traditional spline interpolation algorithm for reconstruction of visual effects are not good, based on Super Resolution against Network (Super - Resolution Generative Adversarial Network, SRGAN) edge character to deal with such problems as imperfect, using a generated based on the enhanced Super Resolution against Network to improve the Resolution of ordinary optical remote sensing images, the first Network generated by high Resolution data sets to train (G network), then lower Resolution test data model test, The test results and real results are put into the discrimination network (D network) to get the adversarial loss, and then the generated network is modified according to the adversarial loss. The superiority of the network is improved by introducing dense residuals to SRGAN, modifying the judgment object of the discriminator to be relatively real, and using the eigenvalue before activation to improve the perceived loss. The desert, farmland, forest and mountain data were tested on AID data set, and the algorithm in this paper could obtain the recomposition of the real image more closely. Compared with SRResNet and SRGAN algorithms, PSNR improved by about 4.0db and SSIM improved by about 0.14. This method improves the feature comprehensiveness by increasing the network fineness degree, and USES the modified perception loss to get the brightness closer to the real image, which is beneficial to improve the quality of single frame remote sensing image.
The Determination of the Equivalence of Causal Theories

In order to characterize causal relation, different scholars have studied it from multiple perspectives. In the field of logic, the causal theory proposed by Alexander elaborated the causal relationship from the perspective of nonmonotonic reasoning. The Horn causal theory and the concept of equivalence relationship are defined in his article, but the author did not give the determination method of equivalence relationship. In order to make up for this deficiency, this paper introduces the concept of bisimulation to describe the equivalence relation between Horn causal theories and proves the consistency between bisimulation relation and the equivalence of models. Then the corresponding algorithm is proposed to determine the equivalence. An example is also given to illustrate the application of this method in determining equivalence relationship.
Low resolution image area classifier based on Convolutional Neural Network

Deep learning techniques are widely implemented in computer vision applications. The Convolutional Neural Networks (CNN) is a deep learning class that is the most effective in categorizing the statistical characteristics of images. It is often a challenging task to classify the frequency level region in various low-resolution image. In this research, we proposed the CNN for classification of gradient profile priors by learning on several gradient characteristics such as horizontal gradient acceleration, vertical gradient acceleration, the Relational Gradient Direction and Edge Sketch Image. This technique is used multiple building blocks to designed features through backpropagation with automatic and adaptive spatial hierarchies learning. The performance comparison was improved in classification of the frequency level area in various low-resolution image input that was illustrated in the experimental results which evaluate with several predictive and conventional classification techniques.
Vision-Based Chess Detection for a Robotic Companion

Many countries are facing the aging of their population, and many elders are willing to stay at home instead of nursing homes. The elders could utilize the robots to cook, monitor the condition of health, even give them emotional companionship. Therefore, a robot that brings new solutions to the elderly care problem by accompanying elders playing chess is proposed in this paper. The robot consists of vision system, chess engine and manipulator, and the vision system is mainly discussed in this paper. The EfficientDet with anchor boxes set by k-means clustering algorithm is applied to build the vision system, which had 79.0% of mAP@0.5 in the test set. In the end, we discuss the directions of improvement.
Preview Generation for Mathematical Interactive Educational Resources in Netpad

With the development of network technology, some online educational resource platforms have emerged, and the number of resources on these platforms is increasing. How to preview resources is becoming increasingly crucial for users to understand the content of resources and retrieve resources efficiently. NetPad is a dynamic geometric resource platform, the resources on the platform have distinctive characteristics. We need to interact with it to understand the content of interactive dynamic resources, but the cost of performing this manual interactive process is relatively high. In this paper, we propose a method for generating picture preview of mathematical interactive educational resources. Firstly, we analyze the JSON file that stores interactive resource information, obtain interactive element information, and generate an operation sequence. Secondly, we execute the operation sequence using script software to simulate the interaction process between the user and the resource, and save the screenshots in the process to generate picture preview of interactive educational resources. Experimental results indicate that the method can effectively simulate the interactive process, and the generated picture preview is of great help to the user to understand the content of interactive educational resources.
EAK-Schema and Its Epistemic Actions

This chapter constitutes the core of this research and is structured as follows: Sect. 5.1 will describe each of the central notions and afterwards combine them in a diagram that will show how these concepts converge into the EAK-SchemaEAK-Schema that represents the informational attitudesInformational attitude of the agent, leading to Explicit Aware KnowledgeExplicit Aware Knowledge (EAK). Then, Sect. 5.2 will describe five different epistemic actionsEpistemic action. Each action will move informationInformation from one zone of the diagram to another, or, in other words, each action will reflect an informational attitudeInformational attitude of the agent and transform the informationInformation she has access to. Finally, Sect. 5.3 will enumerate some advantages of the EAK-SchemaEAK-Schema both regarding EpistemologyEpistemology and Logic.
Implementation of Adaptive Self-Organizing Fuzzy Wavelet Neural Network: Modeling AC Servo System

In this paper, an adaptive self-organizing fuzzy wavelet neural network (ASOFWNN) controller is investigated for AC servo system, aims to deal with nonlinear problem and time-varying disturbance. In order to acquire better performance, an improved self-organizing wavelet neural network (SOWNN) is proposed as the consequent part fuzzy rule in the training process. Innoviate with adaptive learning, adaptive gradient algorithm (AGA) is used to optimize the parameters in SOFWNN. Furthermore, the Lyapunov approach is implemented to verify the stability of the system. Finally, simulation and experimental results proved that compared with fuzzy wavelet neural network (FWNN), novel ASOFWNN shows fast convergence rate and more precise modeling performance.
Motivational component of educational activity in the context of a new educational network paradigm

The creation of a digital environment is currently a priority in the state policy concerning the educational sphere. This is due to the digitalization of social life in all its manifestations. Today, researchers from various scientific fields are searching for ways to create an adequate model of digital learning that can form the conditions for successful educational activities. This article examines the role of the motivational component in the learning process in the digital educational environment. The change in the educational paradigm, which has arisen as a result of the influence of digitalization of human activity, determines the dominant characteristics of the student’s personality, leading to success in the implementation of educational and work activities. It is established that there is a need to study not only the means and material base of digital learning, but also its essential foundations, including the role of competencies and personal qualities of students. The article describes the competencies of the student that are important for effective learning activities. The role of the teacher in the development of motivation for learning is considered. The directions of pedagogical activity that are significant for the formation of motivation for learning are determined. The analysis of the conditions of digitalization of education has shown that the successful formation of the motivational component of training is possible with a combination of traditional and innovative pedagogical methods.
Visual Communication with UAS: Estimating Parameters for Gestural Transmission of Task Descriptions

Visual communication, and especially that based on gestures, can be an alternative to radio-based command and control links to small unmanned aerial vehicles (UAVs). In order to allow this natural form of communication to be an equivalent substitute to a conventional data link with regard to the efficient transfer of task data, the human-UAV-interaction must consist of as few gestural parameters as possible to keep the mental load for the user low, but at the same time provide enough information for the UAV to estimate the users intention. For this purpose, an experimental study under laboratory conditions with N = 20 German-speaking participants has been conducted to generate a dataset with 120 verbal formulations for six common UAV related tasks. The collected data has been evaluated regarding the distribution of sentences, words and parts of speech. Results indicate that users prefer short task descriptions prioritizing verbs and nouns (84.15%) with a mean number of 2.55 (SD = 1.57) words per sentence. All tasks considered were described with an average of 1.73 sentences (SD = 1.00). A slot filling approach is presented to map parameters in user intents.
Doctoral Colloquium-Designing VR Environments for Distance Learning

Designing useful, usable and efficient VR environments for distance learning requires an evaluation of available technical and methodological tools. This Doctoral Colloquium Paper describes an approach to formalize the design of collaborative VR environments specifically dedicated to distance learning.