Reviewerly/exHarmony huggingface dataset:
Meaning of Interrogative Sentence Final Endings ‘-nunka/(u)nka’, ‘-na’, and ‘-(u)lkka’ and Discourse Function of Interrogative Sentences in Academic Texts

The goal of this research was to show meaning of Korean interrogative sentence final endings ‘-nunka/(u)nka’, ‘-na’, and ‘-(u)lkka’ and discourse function of interrogative sentences with these endings in academic texts. In order to accomplish this goal, this paper analyzed their usage in academic texts which were extracted from Modern Korean Written Corpus of the 21st Century Sejong Project Corpus. These academic texts are composed of about 1 million words. The findings of data analysis showed that ‘-nunka/(u)nka’ and ‘-na’ were used for unmarked question while ‘-(u)lkka’ was used for asking hearer’s presumption in academic texts. A few of ‘-nunka/(u)nka’, ‘-na’, and ‘-(u)lkka’ in academic texts were used for self-question as in spoken Korean. In the aspect of discourse function, interrogative sentences with ‘-nunka/(u)nka’, ‘-na’, or ‘-(u)lkka’ were used for attracting readers’ attention and improving their text acceptability in common. In detail, they were used for suggesting topics in forms of raising questions, inducing active involvement of readers, and suggesting objectified questions in academic texts.
The Phonological and Non- phonological Motivations of the Combination Order of Loanword Abbreviation: focusing on New Words ‘eylloski’ and ‘pholamphey’ in Korean

The purpose of this study is to investigate the phonological and non-phonological motivations of the combination order of loanword abbreviation in Korean. We focus on two new words ‘eylloski’ and ‘pholamphey’, which are coined by combining the initial syllables of LG Twins, Lotte Giants and Kia Tigers among Korean baseball teams, and Porsche, Lamborghini and Ferrari among automobile brands, respectively. The origin and combination order of the two words have not been known exactly. In this study, an analysis within the framework of Optimality Theory reveals that phonological processes such as /l/ initial law, vowel harmony, and obligatory contour principle are closely related to the combination order. However, it turns out that the phonological motivations alone cannot provide successful explanation and non-phonological factors are necessary. This study represents that successful prediction and explanation are possible by combining phonological and non-phonological constraints with Maximum Entropy Grammar (Maxent; Hayes & Wilson, 2008). In addition, the grammar not only predicts the possibilities of candidates but also implies the speaker’s linguistic intuition.
Metaheuristic Optimized Voting Ensemble for Recognizing Daily and Sports Activities

This research analyzes the effectiveness of several methods for categorizing human actions captured by inertial and magnetic sensor units worn on the chest, arms, and legs. Each device has tri-axial sensors, including a gyroscope, accelerometer, and magnetometer. Voting ensemble classification models, where votes are weighted and optimized with a new optimization technique, are offered as a means to actualize this classification problem. The optimization technique is a combination of the sine cosine and particle swarm optimization algorithms, and the ensemble model is made up of three classifiers: support vector machines, decision trees, and multilayer perceptron. The classifiers are checked for accuracy using three distinct cross-validation strategies. Classifiers' proper differentiation rates and computational costs are compared to help you choose the best one for your needs. When it comes to body location, sensor devices worn on the legs provide the most valuable data. From a comparison of the various sensor modalities, we can deduce that magnetometers, followed by accelerometers and gyroscopes, provide the best classification results when only a single sensor type is employed. Furthermore, the study contrasts three machine learning models—support vector machines, decision trees, and multilayer perceptron —with respect to their usability, controllability, and classifier performance. Results reveal that the suggested method performs well in categorizing both typical daily activities and athletic endeavors.
On Smart Mobility and Data Stream Mining

Smart Mobility is a concept within Smart Cities, which are “cities in which ICT is merged with traditional infrastructures, coordinated and integrated using new digital technologies” [9]. According to the United Nations 2/3 of world population is expected to live in cities by 2050. Therefore, mobility is a keystone for providing citizens access to services. In this context, Smart Mobility allows to make the most from a multi-modal transportation planning [10], while providing urban managers and planners data intensive decision making support tools.
Neural Network Based Single-Carrier Frequency Domain Equalization

The task of equalization on the receiver side of a wireless communication system is typically accomplished with model-based estimation methods. However, the utilization of data-driven approaches, e.g., neural networks (NNs), for equalization is in focus of current research. In this work, we investigate two different NNs for single-carrier frequency domain equalization. We elaborate on how existing model knowledge can be incorporated into NNs, we introduce a data normalization scheme required for the regarded NNs, and we compare these data-driven methods with model-based approaches concerning performance and complexity.
Using Explainable Artificial Intelligence for Data Based Detection of Complications in Records of Patient Treatments

We analyze data of 18,000 patients for identifying models that are able to detect complications in the data of surgeries and other medical treatments. High quality detection models are found using data available for those patients, for whom general data as well as risk factors are available. For identifying these detection models we use explainable artificial intelligence, namely symbolic regression by genetic programming with three different levels of model complexity with respect to model size and complexity of functions used as building blocks for the identified models.
Query Regrouping Problem on Tree Structure for GPUs Accelerated Platform

The paper presents an ongoing study to maximize query performance for tree-like structures on the GPUs platform. We formalized the problem with an assignment problem for minimizing the number of global memory accesses. We also conduct experiments to identify the benefits of query performance optimization.
Learning How to Detect Salient Objects in Nighttime Scenes

The detection of salient objects in nighttime scene settings is an essential research issue in computer vision. None of the known approaches can accurately anticipate salient objects in the nighttime scenes. Due to the lack of visible light, spatial visual information cannot be accurately perceived by traditional and deep network models. This paper proposed a Mountain Basin Network (MBNet) to identify salient objects for distinguishing the pixel-level saliency of low-light images. To improve the objects localizations and pixel classification performances, the proposed model incorporated a High-Low Feature Aggregation Module (HLFA) to synchronize the information from a high-level branch (named Bal-Net) and a low-level branch (called Mol-Net) to fuse the global and local context, and a Hierarchical Supervision Module (HSM) was embedded to aid in obtaining accurate salient objects, particularly the small ones. In addition, a multi-supervised integration technique was explored to optimize the structure and borders of salient objects. In the meantime, to facilitate more investigation into nighttime scenes and assessment of visual saliency models, we created a new nighttime dataset consisting of thirteen categories and a total of one thousand low-light images. Our experimental results demonstrated that the suggested MBNet model outperforms seven current state-of-the-art methods for salient object detection in nighttime scenes.
Object Sub-Categorization and Common Framework&lt;br /&gt; Method using Iterative AdaBoost for Rapid Detection of Multiple Objects

Object detection and tracking in real time has numerous applications and benefits in various fields like survey, crime detection etc. The idea of gaining useful information from real time scenes on the roads is called as Traffic Scene Perception (TSP). TSP actually consists of three subtasks namely, detecting things of interest, recognizing the discovered objects and tracking of the moving objects. Normally the results obtained could be of value in object recognition and tracking, however the detection of a particular object of interest is of higher value in any real time scenario. The prevalent systems focus on developing unique detectors for each of the above-mentioned subtasks and they work upon utilizing different features. This obviously is time consuming and involves multiple redundant operations. Hence in this paper a common framework using the enhanced AdaBoost algorithm is proposed which will examine all dense characteristics only once thereby increasing the detection speed substantially. An object sub-categorization strategy is proposed to capture the intra-class variance of objects in order to boost generalisation performance even more. We use three detection applications to demonstrate the efficiency of the proposed framework: traffic sign detection, car detection, and bike detection. On numerous benchmark data sets, the proposed framework delivers competitive performance using state-of-the-art techniques.
Nigerian intellectual property protection for small and medium-sized enterprise (SME) fashion designers in the digital economy

Nigerian fashion entrepreneurs are finding the market increasingly appealing due to the growing viability of small and medium-size enterprises (SMEs) in the fashion industry, particularly online retail fashion stores, and the easy accessibility of the Internet and digital media. However, with intellectual capital being the hallmark of the fashion industry, the nearly constant violation of intellectual property (IP) rights is a threat to the fashion sector’s continued existence and profitability in Nigeria. Fashion businesses are thwarted by an antiquated IP regime and the conflicting decisions of the courts on infringement cases which continue to frustrate the marketing of fashion brands on social media. This study used a descriptive and analytical approach, relying on both primary and secondary data, to analyse and assess the laws available for the protection of fashion designers’ intellectual works. The study also considers the various developments in fashion IP protection in more advanced countries, such as the United States and in the European Union, and makes practical recommendations to support the growth of IP law, fashion legislation and the Nigerian fashion industry in the digital economy.
129. Predicting carcass cut yields in cattle from digital images using artificial intelligence

Deep Learning (DL) is a successful tool for many image classification problems but has yet to be applied to carcass images. The aim of this study was to train DL models to predict carcass cut yields and compare predictions to more standard machine learning (ML) methods. Three approaches were undertaken to predict the grouped carcass cut yields of Grilling cuts and Roasting cuts from a large dataset of 54,598 and 69,246 animals respectively. The approaches taken were: (1) animal phenotypic data used as features for a range of ML algorithms; (2) carcass images used to train Convolutional Neural Networks; and (3) carcass dimensions measured directly from the images, combined with associated phenotypic data and used as feature data for ML algorithms. Results showed that DL models can be trained to predict carcass cuts yields but an approach that uses carcass dimensions in ML algorithms performs slightly better in absolute terms.
Strategy in practice: applying blockchain to strategic implementation

Strategy development and implementation is the key to an organisation's success in today's competitive marketplace. Many organisations develop excellent strategy but are unable to implement this strategy in order to succeed. Strategy implementation happens through projects and having a project management method that provides certainty and agility will help an organisation become more competitive in implementing strategy. This paper attempts to develop a hybrid project management method that attempts to assist organisations improve strategy implementation. In addition, this study applies blockchain techniques to manage strategic implementation and project delivery, as a result providing a near real-time tracking of a company's strategy and strategic outcomes.
Undergraduate capstone projects in information technology course: bridging the gap between theory and practical skills

This study was conducted in the School of Computing at a public university in Malaysia to identify the current issues in the existing undergraduate capstone project and to identify ways to improve the existing capstone project framework to make it more effective. Data was collected through in-depth, semi-structured interviews of faculty members and students who had recently completed the capstone project and focus group discussions. The findings reveal issues that could be categorised into: 1) lack of orientation; 2) role ambiguity between the supervisor and the lecturer; 3) misalignment between technical skills, project requirement, and support; 4) students' prior educational background. Formal orientation, clear role allotment for lecturers and supervisors, more projects for each major subject, project-based experiences during courses, and inculcation of soft skills among students were some of the suggestions identified through data. The implications for various stakeholders and the limitations of the study have been discussed.
CONVERSATIONAL AI AND ARTIFICIAL NEURAL NETWORKS

Augmented intelligence is revolutionizing the waybusinesses function on a day-to-day basis. ChatOps are one suchexample of how simple queries and activities, such as customerengagementorsalesoperations,can be handled without the involvement of a human. Chat Ops is the synthes is of client queries and an instantaneousinformationalexchangeinstrumentthatfacilitatesdevelopmentofsoftwareandoperationalmethodsinits transmission and execution. These are low-cost, computer-assistedprogramsthathelpincustomerserviceandanymodel of people management. ChatOps offer support at clients’convenience,irrespective of location hence an appeal to amultitude of people. Such a tool is reconstructing the way data isperceived,preventing it sover load and distilling information down to the most needed and practical elements. ChatOps inherentlymeans Conversational AI which uses NLP and other algorithmsthat compliment it and implements its functionalities. The bot istrained by having various intents as inputs, which provide the standard for the automated response. Alarge corpus of user input she lps the AI toget better at predictions and pattern matching. The more number of such inputs, the better trained machine model availabletouse.The intentslieonalargespectrum that could start off as simple chit chat to complicated instructions,while maintaininganinteractiveandcontinuousconversationflow.
INTERVAL–VALUED FUZZY GRAPH OF SEMI-GROUP

The aim of this study is to merge the interval valued (int-valued) fuzzy graphs with fuzzy graphs of semi groups. We introduce the interval-valued fuzzy graph of semigroup (IVFGS) with some suitable examples and we study some of the properties of IVFGS and isomorphism of IVFGS
Chest Tumor Identification in Mammograms by Selected Features Employing SVM

Cancer is a disease in which cells continue to multiply in an inappropriate manner. The cells divide at an uncontrollable rate, and they may move to other parts of the body or into nearby tissue. One of the most well-known reasons of higher mortality in women is breast cancer. Breast cancer must be detected and treated early if the death rate from the disease is to be reduced. Malignant tumors are not benign tumors. They grow in large size, but they do not disseminate to other parts of the body. Cancerous tumors are malignant tumors. After infecting and destroying neighboring tissue, they move to other parts of the body. Because tumors are exceptionally bright (high intensity) in relation to the surrounding tissue, they can be seen on mammograms. There are a variety of breast cancer diagnostic methods available today, with digital mammography being the most reliable method for detecting early breast cancer. Early detection can help reduce fatality rates. The contributions in this chapter are focused at establishing new and useful features for tissue pattern categorization from mammograms. Radiologists employ computer-aided diagnosis (CAD) to assist them in the interpretation of mammograms, and it is commonly used as a second opinion. The number of CAD therapy choices is growing, and a cure is becoming more likely. The suggested technique will evaluate whether a mammogram-detected breast tumor is benign or malignant. The primary purpose of this research is to refine and introduce a unique feature extraction and selection method for creating a CAD model that can differentiate between cancerous and noncancerous tumors. All the preliminary stages of a diagnostic system are finished here, including noise reduction and region of interest extraction. The system will start by boosting image intensity with image preprocessing techniques, making it easier to find and classify features. The region of interest is then obtained using segmentation (ROIs). The data is segmented using the fuzzy C-means clustering technique. For feature extraction, we use both geometry and texture features, which return the mathematical values of the segmented image’s geometry and texture attributes. Concavity, concave points, fractal dimension, and other geometric and statistical data, as well as textural features, including correlation, autocorrelation, contrast, and others, will be retrieved. Only the most important features will be chosen after the features have been extracted, as not all of them will assist the classifier to improve its performance. For classification, the support vector machine approach is utilized, with all retrieved characteristics included. Existing classification algorithms like as Hippocrates-mst, binary logical classifier, C4.5, RBFN, PNN, ANN, and naive Bayes are outperformed by the proposed method.
A Novel Optimum Clustering Method Using Variant of NOA

Cluster analysis, one of the most prominent data analysis concepts in data mining field, is constantly applied in data analysis. Escaping local optima and obtaining optimal clusters are still challenging tasks in the knowledge extraction process. This work mainly focuses on the design and development of a novel optimum clustering method. The NOA (northern bald ibis optimization algorithm), a recently developed optimization algorithm, was designed by mimicking the energy-saving flying pattern of Northern Ibis birds. Initially, the optimization performance of standard NOA is enhanced by including chaotic space transformation search (CSTS). The enhanced algorithm is termed as CNOA. The CNOA improves the capability of two prominent phases of optimization algorithm, that is, exploration and exploitation, in the search space and successfully avoids trapping into local optima. Later, CNOA is adopted in the design of a novel optimum clustering method. The optimization performance of the improved NOA is tested on seven unimodal and six multimodal benchmark functions. Numerical complexity of proposed optimum clustering is tested on ten UCI clustering problems. Descriptive statistics are considered in ranking improved NOA algorithm and proposed advanced clustering method. Based on the experimental simulations and analysis and comparisons with other popular methods, the superiority of proposed clustering method is confirmed.
Korean Scientific-Technical National Sovereignty Strategy in the Hegemonic War between the U.S. and China

After end of cold war between U.S and Soviet Union, hegemony of the U.S. has been consisted about 50 years. This situation has been changed rapidly after china’s opening and reformation. China has begun to have strong power in areas of the economics, politics, deplomacy, international trade, international monetary and finance, military and so on. This China’s arise has caused to collide with the U.S. hegemony. This war has been in progress in the most advanced scientific-technical areas including ICT, A.I, Bio, Semi-conduct, Atomic, 5G, Space technology. Because Korea is situated at hegemonic war between the U.S. and China geopolitically and geoeconomically, Korea is in a crisis to have to consider its’ national existence, quality of people life and perrmanence of next generations. Best strategy to overcome this crisis is to create national wealhty and power. To achieve these goals it’s crucial to create, develope the most advanced scientific-technology that any nations or enterprises could not follow or immitate.
ROLE OF RASHTRIYA UCHCHTAR SHIKSHA ABHIYAN (RUSA) IN THE DEVELOPMENT OF HIGHER EDUCATION IN ASSAM: SOME REFLECTIONS

In India scarcity of funds has always been a major problem that negatively affects higher education. For the first time, The government of India launched a scheme called Rashtriya Uchchtar Shiksha Abhiyan (RUSA) to provide strategic and outcome-dependent funding to the state higher education institutions. In Assam too a large number of higher education institutions were covered under the scheme. To know the effectiveness of the scheme a study was conducted among the beneficiary institutions from kamrup metro, kamrup rural and Nalbari districts of Assam. the researcher decided to use the descriptive survey method to serve the purpose. The study attempts to make sherds of evidence on the role of RUSA in developing higher education in the state.
Function of Elementary School Geographic Textbooks in Japanese colonial era from the Perspective of the Pacific War: Focusing on Geography Textbooks used during the 3rd Joseon Education Imperial Order (Joseonkyoyukryung)

본 연구의 목적은 일제강점기 태평양전쟁에 관한 우리나라 학습자의 인식을 변화시키기 위해 지리 교과서가 일제의 도구로서 어떻게 기능했는지 밝히는 것이다. 이를 위해 우선 일제강점기 우리나라의 학교 교육 제도에 대한 검토를 바탕으로 분석 대상 지리 교과서가 도출되었고, 다음으로 지리 교과서의 비교 및 분석을 통해 인식 변화와 관련된 요소들이 밝혀진 후, 마지막으로 요소 간의 관계성을 바탕으로 당시 지리 교과서의 기능이 고찰되었다. 본 연구를 통해 밝혀진 점은 다음과 같다. 첫 번째로 당시 지리 교과서에는 극단적이고 일제 중심적인 방식을 통해 국토를 가치 있는 대상으로 정의하려는 시도가 나타났다. 두 번째로 당시 지리 교과서에는 태평양전쟁 발발 이후 국민의 지속적인 전쟁 지원을 얻기 위한 선전도구로서의 시도가 발견되었다. 세 번째로 첫 번째 분석 결과에서 밝혀진 국토를 가치 있는 대상으로 미화하려는 시도는 두 번째 분석 결과에 해당하는 태평양전쟁 선전도구로서의 지리 교과서가 가지는 기능을 강화하기 위한 전제 조건으로 작용했다. The purpose of this study was to reveal how geography textbooks functioned as a tool of Japanese imperialism to change Korean learners" perceptions of the Pacific War in the Japanese colonial era. To this end, geography textbooks to be analyzed were first derived after reviewing the school education system of Japanese colonial era in Korea. Factors related to changes in perception were then revealed through comparison and analysis of geography textbooks. Finally, the function of geography textbooks at that time was considered based on the relationship between elements. Findings of this study are as follows. First, in the geography textbook at the time, an attempt was made to define the land as a valuable object through an extreme and Japanese-centered method. Second, in the geography textbook at the time, an attempt was found as a propaganda tool to obtain continuous war support from people after the outbreak of the Pacific War. Third, attempts to beautify the land revealed in the first analysis result as a valuable object serve as a prerequisite for strengthening the function of geography textbooks as a propaganda tool for the Pacific War, which corresponds to the second analysis result.