Reviewerly/exHarmony huggingface dataset:
Building an Operational Solution Assistant System for Foreign SMEs in ROK

Foreign Direct Investment (FDI) is an important resource that helps accelerate the development of the country’s economy, add substantial funding to growth and facilitate technology transfer. Republic of Korea (ROK) is one of the world’s developed countries with dynamic economy, advanced science and technology. In recent years, the Korean government has continuously formulated tax policies, policies to support the business economy and import policies to support foreign businesses in Korea. The Pangyo Valley Creative Economy Valley is being groomed as a global startup hub in Asia. Small and medium enterprises (SMEs) in foreign countries are increasingly interested and eager to seek investment opportunities in the Korean market. Nonetheless, for these companies, language barriers and cultural and institutional differences make it more difficult and time-consuming to learn about the Korean market (such as investment trends, laws, visa policies, taxes and business establishment issues in Korea, etc.). In this study, we explored the process of searching information and seeking investment opportunities and built a business consulting and support application in the first stages of starting a business in ROK to increase effectiveness and save time, which is also an innovative business practice in Use-case ROK. We designed our Virtual Assistant system that can crawl and analyze data on foreign investments in ROK from open data resource websites (data.co.kr) and used analytic and aggregation techniques to explore trends in investments of foreign enterprises. We also researched the process of searching information and seeking investment opportunities for SMEs when investing in ROK, government support policies, laws and taxes as well as a number of other related issues. We built datasets and used Natural Language Processing (NLP) together with Natural Language Understanding (NLU) algorithms to build chatbot applications. Friendly framework for new developers to add and build up the dataset of AI Assistant is built by providing input intent data function, input Entity data function, input utterance data function as well as training and test function. In addition, we built a web-app connected to the server to visualize all the results of research so that SMEs owners can easily use and look for information on investments. Based on the research results, we can make recommendations to SMEs in keeping with the changing investment trends in ROK.
Hierarchical Deep Hashing for Fast Large Scale Image Retrieval

Fast image retrieval is of great importance in many computer vision tasks and especially practical applications. Deep hashing, the state-of-the-art fast image retrieval scheme, introduces deep learning to learn the hash functions and generate binary hash codes, and outperforms the other image retrieval methods in terms of accuracy. However, all the existing deep hashing methods could only generate one level hash codes and require a linear traversal of all the hash codes to figure out the closest one when a new query arrives, which is very time-consuming and even intractable for large scale applications. In this work, we propose a Hierarchical Deep Hashing(HDHash) scheme to speed up the state-of-the-art deep hashing methods. More specifically, hierarchical deep hash codes of multiple levels can be generated and indexed with tree structures rather than linear ones, and pruning irrelevant branches can sharply decrease the retrieval time. To our best knowledge, this is the first work to introduce hierarchical indexed deep hashing for fast large scale image retrieval. Extensive experimental results on three benchmark datasets demonstrate that the proposed HDHash scheme achieves better or comparable accuracy with significantly improved efficiency and reduced memory as compared to state-of-the-art fast image retrieval schemes.
Classification of Image Dataset using Convolutional Neural Network

The present work proposes a methodology for classifying images accurately, using Convolutional Neural Network and Back-Propagation. In Convolutional Neural Network layers are arranged in a sequence to extract features and then perform classification in single structure. Applying an apparent architecture and vital preprocessing, our result analysis achieved 85.36% accuracy on ASIRRA dataset. Execution of our technique with unique structure is practically analogous to the state-of-the-art obtained by previous strategies without depending on hand-crafted feature extractors.
KYC, Client Onboarding: Leveraging Blockchain Technology

Know-your-customer (KYC) and on-boarding processes pose numerous problems; both to banks and their corporate customers. The regulators for financial institutions, on the other hand, require that these processes meet ongoing compliance requirements. Presently, each institution maintains their own copy of customer data and performs verification via centralized data providers.This process is time-consuming, complex, costly, prone to duplication and redundancy; the result is delays in on-boarding a new client. Consequently, the experience can be a make or break for the ongoing relationship the customer has with the organisation. Key variables like client loyalty, experience, referrals, and profitability depends on the company’s customer on-boarding process. On-boarding done the wrong way will drive clients away.In this paper, I’ll explore some of the financial institutions using block-chain technologies to enhance the client on-boarding experience. For instance, 10 of R3’s consortium member banks developed a proof-of-concept for a KYC registry that intended to allow the management of both individuals and entities identities by the identity owners.
Transformative Process of Redefining Language Curriculum Post Pandemic Covid-19 in Higher Education

The world of education has experienced very rapid development and devised a prioritized strategy in a disruptive era. This development is marked by the emergence of adaptive literacy in education. In this vein, it involved three important literacies, namely data literacy, technology literacy, and human literacy. Arguably, the development of education was also accompanied by the use of these three literacies in response to the Covid-19 and post-pandemic era. In this case, education undergoes an extensive transformation process to fulfil the learning process, especially learning English at the university level. Technically, this process involves a series of online learning activities and the use of appropriate technology to achieve the provision of skills, leadership, and social concerns. The reasons are related to communication skills, both oral and written, that enable the graduates to meet international professional standards by integrating literacy excellence. In this background, the research aims to describe the transformative process of the refining language curriculum that excels in the industry. This research uses mix-method. Besides, the respondents involve active BINUS University students who are contributively exposed by the industrial and entrepreneurial world. Data will be analyzed qualitatively. The preliminary result describes a process of redefining the curriculum of English learning that encompassed the utilization of technology and essential aspects of technology selection is more in line with the literacy era in the industrial revolution 4.0. Furthermore, this research will lead to curriculum alteration that is matched with the future industry needs eventually.
Extracting Action Hierarchies from Action Labels and their Use in Deep Action Recognition

Human activity recognition is a fundamental and challenging task in computer vision. Its solution can support multiple and diverse applications in areas including but not limited to smart homes, surveillance, daily living assistance, Human-Robot Collaboration (HRC), etc. In realistic conditions, the complexity of human activities ranges from simple coarse actions, such as siting or standing up, to more complex activities that consist of multiple actions with subtle variations in appearance and motion patterns. A large variety of existing datasets target specific action classes, with some of them being coarse and others being fine-grained. In all of them, a description of the action and its complexity is manifested in the action label sentence. As the action/activity complexity increases, so is the label sentence size and the amount of action-related semantic information contained in this description. In this paper, we propose an approach to exploit the information content of these action labels to formulate a coarse-to-fine action hierarchy based on linguistic label associations, and investigate the potential benefits and drawbacks. Moreover, in a series of quantitative and qualitative experiments, we show that the exploitation of this hierarchical organization of action classes in different levels of granularity improves the learning speed and overall performance of a range of baseline and mid-range deep architectures for human action recognition (HAR).
What We Know About Bug Bounty Programs - An Exploratory Systematic Mapping Study

This paper presents a systematic mapping study of the research on crowdsourced security vulnerability discovery. The aim is to identify aspects of bug bounty program (BBP) research that relate to product owners, the bug-hunting crowd or vulnerability markets. Based on 72 examined papers, we conclude that research has mainly been focused on the organisation of BBPs from the product owner perspective, but that aspects such as mechanisms of the white vulnerability market and incentives for bug hunting have also been addressed. With the increasing importance of cyber security, BBPs need more attention in order to be understood better. In particular, datasets from more diverse types of companies (e.g. safety-critical systems) should be added, as empirical studies are generally based on convenience sampled public data sets. Also, there is a need for more in-depth, qualitative studies in order to understand what drives bug hunters and product owners towards finding constructive ways of working together.
An exploration of the application of computer music production software in music composition

The gradual implementation of computer music production software within the current stage of music composition and traditional composition methods for integration is important for the creation of excellent musical works with unique styles and novel ideas. Based on the original composition process, we actively promote the organic integration of music production software and music composition by learning to use computer music production software and drawing on outstanding foreign works. We are constantly discovering inspirations that emerge from music creation, focusing on self-imposed requirements and quality control of music quality, and reducing the cost of music production in the traditional model. Take an objective, rational, and positive approach to computer music production. This paper takes the role of computer music production software in music composition as the research object, and combines with the excellent works at home and abroad to explore and analyze the practical problems in the application process.
Integration of design smells and role-stereotypes classification dataset

Design smells are recurring patterns of poorly designed (fragments of) software systems that may hinder maintainability. Role-stereotypes indicate generic responsibilities that classes play in system design. Although the concepts of role-stereotypes and design smells are widely divergent, both are significant contributors to the design and maintenance of software systems. To improve software design and maintainability, there is a need to understand the relationship between design smells and role stereotypes. This paper presents a fine-grained dataset of systematically integrated design smells detection and role-stereotypes classification data. The dataset was created from a collection of twelve (12) real-life open-source Java projects mined from GitHub. The dataset consists of 18 design smells columns and 2,513 Java classes (rows) classified into six (6) role-stereotypes taxonomy. We also clustered the dataset into ten (10) different clusters using an unsupervised learning algorithm. Those clusters are useful for understanding the groups of design smells that often co-occur in a particular role-stereotype category. The dataset is significant for understanding the non-innate relationship between design smells and role-stereotypes.
Assessing the Qualities of Synthetic Visual Data Production

A literature review was conducted using journal articles and conference proceedings to examine emerging research practices, and applications of synthetic visual data over the past 5 years. The current research examined articles related to research trends in artificial intelligence training intended to improve computer vision and object detection. Search strings were developed and used to retrieve research articles from the ACM and IEEE databases. The resulting articles were examined for trends, general practices, disciplines where the greatest efforts have been made, advances, and relevant production processes. The research reveals that visual synthetic data encompasses filtering, augmentation, and object domain randomization techniques. Further, all of the research that included an evaluation of synthetic visual data suggest that there are noteworthy performance improvements in accuracy. Additionally, producing realistic synthetic data reduces the current limitations related to labeling, image quality, paucity of relevant data, and privacy issues.
A Hylogger-Itrax core-scanner comparison for multi-scale high-resolution petrophysical characterisation workflow

Recent advances in core scanning technologies allow for fast and non-destructive chemical and mineral profiling of rock samples for mineral services and oil and gas exploration. The aim of these automatic core scan methods is to obtain valuable information for profiling drill core cuttings with minimum sample preparation at relatively high speed. In the last decade, a core logging system using an automated infrared-based hyperspectral line-profiling system, Hylogger, has progressed to become an effective standard for the Australian mineral exploration industry. Its results are used to rapidly obtain mineralogical information allowing the characterisation of different geological formations in near real-time. The interpretation of Hylogger data can be challenging for certain complex mineral mixtures. Here we solve this issue by augmenting the Hylogger interpretation with elemental analysis using the Itrax core scanner equipped with an X-ray fluorescence (XRF) spectrometer. The Itrax core scanner produces high-resolution elemental data of major, minor and trace elements in one dimension. We analyse and compare the Hylogger and Itrax data, with each dataset independently cross-checked using X-ray diffraction (XRD) and thin-section petrology and propose a workflow harvesting the mutual strengths of each method. The recommended workflow consists of rapid screening using Hylogger and XRF analysis, providing new insights into the mineralogy based on comparative multiscale element-mineral analysis. The workflow is tested on four different types of volcanic rock samples, where infrared spectra of individual minerals overlap. We tested tuffaceous ash, basaltic, dolerite, and basaltic-andesitic rocks. Our study shows that embedding Itrax core scanner data into the workflow provides a solution to the challenges of interpreting Hylogger data in complex mineral samples. The proposed workflow provides a total system for multiscale, high-resolution petrophysical analyses and rock property modelling.
toqito -- Theory of quantum information toolkit: A Python package for studying quantum information

toqito is an open source library for studying various objects in quantum information, namely: states, channels, and measurements.toqito focuses on providing numerical tools to study problems pertaining to: entanglement theory, nonlocal games, matrix analysis, and other aspects of quantum information that are often associated with computer science.
Industrial Application of Synthetic Irradiance: Case Study of Solar Yield

Industrial activity concerned with the profitability and safety of investments can be supported and promoted by research through the creation of new mathematical modeling approaches, and the quantification and mitigation of uncertainties. In recent years there has been increasing interest in the adoption of probabilistic approaches to assess sources of uncertainty in solar energy systems to estimate their feasibility, considering yield estimates, investments, operation and maintenance costs, and solar resource. In this context, the synthetic solar irradiance data set approach emerges as a promising tool to emulate the variability inherent to the solar resource in confident designs and feasibility analyses of these systems. Chapter 5 deals with the requirements of the industry with respect to synthetic solar data, and how such requirements are currently addressed during the main stages of development of solar projects. We recap methods for benchmarking the success of generated synthetic irradiance, reviewing statistical indicators for that purpose. We discuss and compare the use of single annual and multiple synthetic annual data sets of solar irradiance in the first stages of solar projects, and present their uses in a case study application in a Concentrating Solar Power (CSP) plant with a similar configuration to a well-known operational Parabolic Trough (PT) plant located in Spain.
Cardiac MRI Segmentation and Analysis in Fuzzy Domain

In this paper, right ventricle is extracted from the cardiac magnetic resonance imaging (cMRI) by applying fuzzy technology. Segmentation of the RV is done in three steps. In the first step, RV is located manually. After that, fuzzy logic is applied to the computed geometrical value of cardiac magnetic resonance imaging. The sequence of erosion and dilation morphological operation is applied for proper segmentation in the last step. Validation of the proposed work is done on end-diastole images of 25 different patients. The accuracy of the segmented region is validated by computing the Jaccard index and Hausdorff distance. Average Jaccard index and Hausdorff distance value of 0.85 and 10.80 is achieved which is better than results computed with existing techniques.
Redirected Tilting: Eliciting Postural Changes with a Rotational Self-Motion Illusion

In this paper, we propose a potential cybersickness mitigation technique, redirected tilting, and conduct an exploratory virtual reality (VR) study to determine whether it is possible to visually induce head tilt during virtual locomotion. Redirected tilting involves rotating the virtual environment (VE) towards the turning direction around the VR headset's roll axis, which we hypothesize could lead users to tilt their bodies in the same direction and maintain balance during curved paths. Unlike previous techniques, like field of view (FOV) restriction or rest frames, this method could potentially reduce cybersickness by manipulating egocentric self-motion rather than modifying visual characteristics of the VE. As an initial exploration, we conducted a within-subjects study with 30 participants to evaluate the effect of redirected tilting on postural behavior. The results showed that the proposed technique was successful in eliciting head tilt, although the magnitude of rotation was not as large as we had expected. We conclude that further investigation is needed to understand the mechanics, ideal parameters, and applications of redirected tilting.
Intelligent Training Scheme for Online Judge System Designed Based on Problem Classification Using Similarity of Abastract Syntax Tree of Source Codes

In this paper, an intelligent training model is designed for the OJ (Online Judge) system. In the traditional OJ system, students practice on a problem-by-problem basis, and get a score for completing a problem. In the intelligent training mode, all the problems are divided into categories according to knowledge points, and students practice on a category-by-category basis, and get a score for completing a category. Each student has to complete a different number of problems in the same category (the system will intelligently determine whether the student has met the passing conditions), thus achieving the goal of personalized training in a hierarchical manner. In order to reduce the teachers' workload and improve the classification accuracy, the similarity matrix is constructed based on the similarity of the abstract syntax tree (AST) of the problems' source code, and the problems are clustered by the K-Means algorithm, and good results are obtained.
Security Features in Hadoop—A Survey

Extensive usage of Information and Communication Technology applications including online banking, ecommerce, retail, social media and smart phone apps etc. are responsible for creation of extremely large amounts of digital data every hour which is termed as Big Data. To store and process Big Data, an open source framework is needed and the framework is known as Apache Hadoop. Initially it was developed for internal use at Yahoo with limited security features. Later Hadoop was made open source and distributed under Apache License. Once Hadoop was made open source, so many developers contributed to Hadoop development. In this process many authentication, authorization services were developed and distributed as part of Hadoop framework. In this paper, the security features of Hadoop are discussed, their limitations and also highlight the future scope of work with respect to Hadoop Security.
Naturalistic audio-visual volumetric sequences dataset of sounding actions for six degree-of-freedom interaction

As audio-visual systems increasingly bring immersive and interactive capabilities into our work and leisure activities, so the need for naturalistic test material grows. New volumetric datasets have captured high-quality 3D video, but accompanying audio is often neglected, making it hard to test an integrated bimodal experience. Designed to cover diverse sound types and features, the presented volumetric dataset was constructed from audio and video studio recordings of scenes to yield forty short action sequences. Potential uses in technical and scientific tests are discussed.
Tractable Reasoning Using Logic Programs with Intensional Concepts

Recent developments triggered by initiatives such as the Semantic Web, Linked Open Data, the Web of Things, and geographic information systems resulted in the wide and increasing availability of machine-processable data and knowledge in the form of data streams and knowledge bases. Applications building on such knowledge require reasoning with modal and intensional concepts, such as time, space, and obligations, that are defeasible. E.g., in the presence of data streams, conclusions may have to be revised due to newly arriving information. The current literature features a variety of domain-specific formalisms that allow for defeasible reasoning using specific intensional concepts. However, many of these formalisms are computationally intractable and limited to one of the mentioned application domains. In this paper, we define a general method for obtaining defeasible inferences over intensional concepts, and we study conditions under which these inferences are computable in polynomial time.